diff --git a/.github/workflows/examples.yml b/.github/workflows/examples.yml index cf39388e..a4b91251 100644 --- a/.github/workflows/examples.yml +++ b/.github/workflows/examples.yml @@ -22,13 +22,14 @@ jobs: python-version: "3.10" - name: Install dependencies run: | - python -m venv ./venv - source venv/bin/activate python -m pip install --upgrade pip pip install -e .[test] - - name: Test examples + - name: Convert example notebooks to scripts + run: | + cd docs/source/examples + python convert_notebooks.py + cd ../../.. + - name: Test example scripts run: | - source venv/bin/activate - ipython kernel install --name "venv" --user cd tests/examples pytest \ No newline at end of file diff --git a/.github/workflows/format.yml b/.github/workflows/format.yml index e4b54e42..99f0d2e4 100644 --- a/.github/workflows/format.yml +++ b/.github/workflows/format.yml @@ -41,10 +41,10 @@ jobs: uses: psf/black@stable with: options: "--check --verbose" - src: "./src/menelaus" + src: "./menelaus" version: "22.3.0" - name: Security check with bandit run: | # exits with code 0 if there are no errors, otherwise complains - bandit -q -r ./src/ \ No newline at end of file + bandit -q -r ./menelaus/ \ No newline at end of file diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 7775ed54..d8f21654 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,7 +1,7 @@ # This workflow will install Python dependencies, run tests and lint with a single version of Python # For more information see: https://help.github.com/actions/language-and-framework-guides/using-python-with-github-actions -name: tests | coverage +name: tests | coverage 100% on: push: @@ -29,5 +29,5 @@ jobs: pip install -e .[dev] - name: Unit and coverage tests run: | - pytest tests/menelaus --cov=src/ --cov-report term + pytest tests/menelaus --cov=menelaus/ --cov-report term coverage report -m --fail-under=100 diff --git a/.github/workflows/update-changelog.yaml b/.github/workflows/update-changelog.yaml deleted file mode 100644 index 61177857..00000000 --- a/.github/workflows/update-changelog.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# As described on https://stefanzweifel.io/posts/2021/11/13/introducing-the-changelog-updater-action - -name: 'Update Changelog' - -on: - release: - types: [released] - -jobs: - update: - runs-on: ubuntu-latest - - steps: - - name: Checkout code - uses: actions/checkout@v2 - with: - ref: main - - - name: Update Changelog - uses: stefanzweifel/changelog-updater-action@v1 - with: - release-notes: ${{ github.event.release.body }} - latest-version: ${{ github.event.release.name }} - - - name: Commit updated CHANGELOG - uses: stefanzweifel/git-auto-commit-action@v4 - with: - branch: main - commit_message: Update CHANGELOG - file_pattern: CHANGELOG.md \ No newline at end of file diff --git a/.gitignore b/.gitignore index 84de4b0b..734db991 100644 --- a/.gitignore +++ b/.gitignore @@ -4,6 +4,7 @@ *.egg-info build/ .pytest_cache +.python-version .vscode docs/build/* dist @@ -13,5 +14,11 @@ _build *.coverage *.DS_Store .idea/ -*.png + +# Images + +examples/*.png +menelaus/*.png +tests/*.png + *.tox* diff --git a/CHANGELOG.md b/CHANGELOG.md index ff3d12fd..d3284808 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,4 +6,25 @@ Notable changes to Menelaus will be documented here. - Initial public release. - Published to pypi. -- Published to readthedocs.io. \ No newline at end of file +- Published to readthedocs.io. + +## v0.1.2 - July 11, 2022 + +- Updated the documentation +- Added example jupyter notebooks to ReadTheDocs +- Switched to sphinx-bibtext for citations +- Formatting and language tweaks. +- Added StreamingDetector and BatchDetector abstract base classes. +- Re-factored kdq-tree to use new abstract base classes: the separate classes KdqTreeStreaming and KdqTreeBatch now exist. +- kdq-tree can now consume dataframes. +- Added new git workflows and improved old ones. + +## v0.2.0 - December 7, 2022 + +- Updates to documentation. +- Updated the arguments for detector `update` and `set_reference` methods. +- Added validation to the detector `update` and `set_reference` methods. +- Added the `datasets` module, which contains or generates example datasets. +- Added implementation of Margin Density Drift Detection (MD3) semi-supervised detector. +- Added implementation of Nearest Neighbor-based Density Variation Identification (NN-DVI) data drift detector. +- Added Ensemble wrapper that allows combining two or more drift detectors. \ No newline at end of file diff --git a/MANIFEST.in b/MANIFEST.in index 68da468d..f32e93e1 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -6,4 +6,4 @@ include README.md # graft tests # graft examples # graft docs -graft src \ No newline at end of file +graft menelaus \ No newline at end of file diff --git a/README.md b/README.md index 6317e625..c88ec409 100644 --- a/README.md +++ b/README.md @@ -33,11 +33,7 @@ algorithms are typically used when it is more important to process large volumes of information simultaneously, where the speed of results after receiving data is of less concern. -In The Odyssey, Menelaus seeks a prophecy known by the shapeshifter -Proteus. Menelaus holds Proteus down as he takes the form of a lion, a -serpent, water, and so on. Eventually, Proteus relents, and Menelaus -gains the answers he sought. Accordingly, this library provides tools -for \"holding\" data as it shifts. +Menelaus is named for the Odyssean hero that defeated the shapeshifting Proteus. # Detector List @@ -47,15 +43,18 @@ Menelaus implements the following drift detectors. |------------------|---------------------------------------------------------------|--------------|-----------|-------| | Change detection | Cumulative Sum Test | CUSUM | x | | | Change detection | Page-Hinkley | PH | x | | -| Concept drift | ADaptive WINdowing | ADWIN | x | | +| Change detection | ADaptive WINdowing | ADWIN | x | | | Concept drift | Drift Detection Method | DDM | x | | | Concept drift | Early Drift Detection Method | EDDM | x | | | Concept drift | Linear Four Rates | LFR | x | | | Concept drift | Statistical Test of Equal Proportions to Detect concept drift | STEPD | x | | +| Concept drift | Margin Density Drift Detection Method | MD3 | x | | | Data drift | Confidence Distribution Batch Detection | CDBD | | x | | Data drift | Hellinger Distance Drift Detection Method | HDDDM | | x | | Data drift | kdq-Tree Detection Method | kdq-Tree | x | x | | Data drift | PCA-Based Change Detection | PCA-CD | x | | +| Ensemble | Streaming Ensemble | - | x | +| Ensemble | Batch Ensemble | - | | x | The three main types of detector are described below. More details, including @@ -67,11 +66,15 @@ documentation on [ReadTheDocs](https://menelaus.readthedocs.io/en/latest/). pre-defined range. - Concept drift detectors monitor the performance characteristics of a given model, trying to identify shifts in the joint distribution of - the data\'s feature values and their labels. + the data\'s feature values and their labels. Note that change detectors + can also be applied in this context. - Data drift detectors monitor the distribution of the features; in that sense, they are model-agnostic. Such changes in distribution might be to single variables or to the joint distribution of all the features. +- Ensembles are groups of detectors, where each watches the same data, and + drift is determined by combining their output. Menelaus implements a + framework for wrapping detectors this way. The detectors may be applied in two settings, as described in the Background section: @@ -87,8 +90,6 @@ then maintains a count of the number of samples from the given dataset that fall into each section of that partition. More details are given in the respective module. -A flowchart breaking down these contexts can be found on the ReadTheDocs page under "Choosing a Detector." - # Installation Create a virtual environment as desired, then: @@ -99,7 +100,7 @@ pip install menelaus # to allow editing, running tests, generating docs, etc. # First, clone the git repo, then: -cd ./menelaus/ +cd ./menelaus_clone_folder/ pip install -e .[dev] ``` @@ -107,34 +108,58 @@ Menelaus should work with Python 3.8 or higher. # Getting Started -Each detector implements the API defined by `menelaus.drift_detector`: -they have an `update` method which allows new data to be passed, a -`drift_state` attribute which tells the user whether drift has been -detected, and a `reset` method (generally called automatically by -`update`) which clears the `drift_state` along with (usually) some other -attributes specific to the detector class. +Each detector implements the API defined by `menelaus.detector`: +notably, they have an `update` method which allows new data to be passed, and a `drift_state` attribute which tells the user whether drift has been +detected, along with (usually) other attributes specific to the detector class. Generally, the workflow for using a detector, given some data, is as follows: ```python -import pandas as pd -from menelaus.concept_drift import ADWIN -df = pd.read_csv('example.csv') -detector = ADWIN() +from menelaus.concept_drift import ADWINAccuracy +from menelaus.data_drift import KdqTreeStreaming +from menelaus.datasets import fetch_rainfall_data +from menelaus.ensemble import StreamingEnsemble, SimpleMajorityElection + + +# has feature columns, and a binary response 'rain' +df = fetch_rainfall_data() + + +# use a concept drift detector (response-only) +detector = ADWINAccuracy() +for i, row in df.iterrows(): + detector.update(X=None, y_true=row['rain'], y_pred=0) + assert detector.drift_state != "drift", f"Drift detected in row {i}" + + +# use data drift detector (features-only) +detector = KdqTreeStreaming(window_size=5) for i, row in df.iterrows(): - detector.update(row['y_predicted'], row['y_true']) - if detector.drift_state is not None: - print("Drift has occurred!") + detector.update(X=df.loc[[i], df.columns != 'rain'], y_true=None, y_pred=None) + assert detector.drift_state != "drift", f"Drift detected in row {i}" + + +# use ensemble detector (detectors + voting function) +ensemble = StreamingEnsemble( + { + 'a': ADWINAccuracy(), + 'k': KdqTreeStreaming(window_size=5) + }, + SimpleMajorityElection() +) + +for i, row in df.iterrows(): + ensemble.update(X=df.loc[[i], df.columns != 'rain'], y_true=row['rain'], y_pred=0) + assert ensemble.drift_state != "drift", f"Drift detected in row {i}" ``` -For this example, because ADWIN is a concept drift detector, it requires -both a predicted value (`y_predicted`) and a true value (`y_true`), at -each update step. Note that this requirement is not true for the -detectors in other modules. More detailed examples, including code for -visualizating drift locations, may be found in the ``examples`` directory, as -stand-alone python scripts. The examples along with output can also be viewed on -the RTD website. +As a concept drift detector, ADWIN requires both a true value (`y_true`) and a +predicted value (`y_predicted`) at each update step. The data drift detector +KdqTreeStreaming only requires the feature values at each step (`X`). More +detailed examples, including code for visualizating drift locations, may be +found in the ``examples`` directory, as stand-alone python scripts. The examples +along with output can also be viewed on the RTD website. # Contributing Install the library using the `[dev]` option, as above. @@ -154,6 +179,15 @@ Install the library using the `[dev]` option, as above. sphinx-build . ../build ``` + If the example notebooks for the docs need to be updated, the corresponding + python scripts in the `examples` directory should also be regenerated via: + ```python + cd docs/source/examples + python convert_notebooks.py + ``` + Note that this will require the installation of `jupyter` and `nbconvert`, + which can be added to installation via `pip install -e ".[dev, test]"`. + - **Formatting**: This project uses `black`, `bandit`, and `flake8` for code formatting and @@ -162,8 +196,8 @@ Install the library using the `[dev]` option, as above. following from the root directory: ```python flake8 # linting - bandit -r ./src # security checks - black ./src/menelaus # formatting + bandit -r ./menelaus # security checks + black ./menelaus # formatting ``` # Copyright diff --git a/bin/Dockerfile b/bin/Dockerfile deleted file mode 100755 index ee7313d9..00000000 --- a/bin/Dockerfile +++ /dev/null @@ -1,4 +0,0 @@ -FROM python:3 -COPY requirements.txt . -COPY setup.py . -RUN pip install --upgrade pip && pip install -r requirements.txt \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 7739152d..1f97db8f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,15 +14,15 @@ import sys from inspect import getsourcefile -sys.path.insert(0, os.path.abspath("../src/menelaus")) +sys.path.insert(0, os.path.abspath("../menelaus")) # -- Project information ----------------------------------------------------- project = "menelaus" copyright = "©2022 The MITRE Corporation. ALL RIGHTS RESERVED" -author = "Leigh Nicholl, Thomas Schill, India Lindsay, Anmol Srivastava, Kodie P McNamara, Austin Downing" -release = "0.1.0" +author = "Leigh Nicholl, Thomas Schill, India Lindsay, Anmol Srivastava, Kodie P McNamara, Shashank Jarmale" +# release = "0.1.2" # -- General configuration --------------------------------------------------- @@ -43,6 +43,8 @@ autodoc_default_options = { "members": True, + "inherited-members": True, + "member-order": "groupwise", "undoc-members": False, "private-members": False, "special-members": "__init__", @@ -107,7 +109,7 @@ def run_apidoc(_): os.chdir("..") print("cwd", os.getcwd(), "\n") print("contents", os.listdir()) - src_dir = os.path.join("../src/menelaus") + src_dir = os.path.join("../menelaus") template_dir = os.path.join("source", "templates") main(["-M", "--templatedir", template_dir, "-f", "-o", "source", src_dir]) diff --git a/docs/source/examples/change_detection/change_detection_examples.ipynb b/docs/source/examples/change_detection/change_detection_examples.ipynb index c36c0ab8..126e8761 100644 --- a/docs/source/examples/change_detection/change_detection_examples.ipynb +++ b/docs/source/examples/change_detection/change_detection_examples.ipynb @@ -19,13 +19,18 @@ "conditional distributions P(y|var1) and P(y|var2). The drift occurs from index \n", "1000 to 1250, and affects 66% of the sample.\n", "\n", + "Rainfall is a real source of weather data. We use the first 1000 samples, where\n", + "no drift has been injected; but many features are cyclical, and haven't been\n", + "corrected, so change does occur.\n", + "\n", "These change detectors can be applied to any given single variable; below, \n", - "they are applied to var2." + "they are applied to `var2` and the `max_sustained_wind_speed` columns in the \n", + "respective datasets." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -35,46 +40,62 @@ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from menelaus.change_detection import PageHinkley, CUSUM" + "from menelaus.change_detection import ADWIN, CUSUM, PageHinkley\n", + "from menelaus.datasets import fetch_circle_data, fetch_rainfall_data" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "## Import Data ##\n", "\n", "# read in Circle dataset\n", - "df = pd.read_csv(\n", - " os.path.join(\n", - " \"..\", \"..\", \"..\", \"..\", \"src\", \"menelaus\", \"datasets\", \"dataCircleGSev3Sp3Train.csv\"\n", - " ),\n", - " usecols=[0, 1, 2],\n", - " names=[\"var1\", \"var2\", \"y\"],\n", - ")\n", - "drift_start, drift_end = 1000, 1250" + "df = fetch_circle_data()\n", + "drift_start, drift_end = 1000, 1250\n", + "\n", + "rainfall_df = fetch_rainfall_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cumulative Sum (CUSUM) Test" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Page-Hinkley (PH) Test Example" + "- This monitors a moving average of var2, starting from an initial estimate of mean\n", + "and standard deviation.\n", + "\n", + "- It will only alarm if 50 or more samples have been observed since\n", + "initialization/drift.\n", + "\n", + "- This will alarm if var2 passes a critical value controlled by delta and\n", + "threshold in either direction, positive or negative.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "## Setup ##\n", - "\n", - "# Set up one-directional PH test: this will only alarm if the mean of the\n", - "# monitored variable decreases, and only after seeing 30 or more samples.\n", - "ph = PageHinkley(delta=0.01, threshold=15, direction=\"negative\", burn_in=30)\n", + "cusum = CUSUM(\n", + " target=np.mean(df.loc[:drift_start, \"var2\"]), # mean / std of 'Var 2' pre-drift\n", + " sd_hat=np.std(df.loc[:drift_start, \"var2\"]),\n", + " burn_in=50,\n", + " delta=0.005,\n", + " threshold=40,\n", + " direction=None,\n", + ")\n", "\n", "# setup DF to record results\n", "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\"])\n", @@ -82,15 +103,38 @@ "# iterate through data; feed each sample to the detector, in turn\n", "for i in range(len(df)):\n", " obs = df[\"var2\"][i]\n", - " ph.update(next_obs=obs, obs_id=i)\n", - " status.loc[i] = [i, obs, ph.drift_state]" + " cusum.update(obs)\n", + " status.loc[i] = [i, obs, cusum.drift_state]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "## Plotting ##\n", "\n", @@ -100,7 +144,7 @@ "plt.grid(False, axis=\"x\")\n", "plt.xticks(fontsize=16)\n", "plt.yticks(fontsize=16)\n", - "plt.title(\"PH Test Results\", fontsize=22)\n", + "plt.title(\"CUSUM Test Results\", fontsize=22)\n", "plt.ylabel(\"Value\", fontsize=18)\n", "plt.xlabel(\"Index\", fontsize=18)\n", "ylims = [-0.05, 1.1]\n", @@ -115,52 +159,36 @@ " label=\"Drift Detected\",\n", " color=\"red\",\n", ")\n", - "plt.legend()\n", - "\n" + "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Page-Hinkley alarms shortly after the drift induction window closes, and then makes\n", - "several apparently erroneous alarms afterwards. The parameters may not be\n", - "well-chosen for the new regime.\n", - "Change detection algorithms come out of process control, so a priori\n", - "characterization of the bounds of the process, not performed here, would not\n", - "be unreasonable.\n" + "CUSUM alarms several times within the drift induction window, roughly halfway\n", + "through. After the alarm is reset, change is detected a few more times,\n", + "including an apparently erroneous detection after the drift induction window\n", + "is passed. The current threshold settings may then be too sensitive for the\n", + "new regime.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ + "\n", "plt.show()\n", - "# plt.savefig(\"example_Page-Hinkley_detections.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cumulative Sum (CUSUM) Test Example" + "# plt.savefig(\"example_CUSUM_detections.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "- This monitors a moving average of var2, starting from an initial estimate of mean\n", - "and standard deviation.\n", - "\n", - "- It will only alarm if 50 or more samples have been observed since\n", - "initialization/drift.\n", - "\n", - "- This will alarm if var2 passes a critical value controlled by delta and\n", - "threshold in either direction, positive or negative.\n", - "\n" + "## Page-Hinkley (PH) Test" ] }, { @@ -170,14 +198,10 @@ "outputs": [], "source": [ "## Setup ##\n", - "cusum = CUSUM(\n", - " target=np.mean(df.loc[:drift_start, \"var2\"]), # mean / std of 'Var 2' pre-drift\n", - " sd_hat=np.std(df.loc[:drift_start, \"var2\"]),\n", - " burn_in=50,\n", - " delta=0.005,\n", - " threshold=40,\n", - " direction=None,\n", - ")\n", + "\n", + "# Set up one-directional PH test: this will only alarm if the mean of the\n", + "# monitored variable decreases, and only after seeing 30 or more samples.\n", + "ph = PageHinkley(delta=0.01, threshold=15, direction=\"negative\", burn_in=30)\n", "\n", "# setup DF to record results\n", "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\"])\n", @@ -185,8 +209,8 @@ "# iterate through data; feed each sample to the detector, in turn\n", "for i in range(len(df)):\n", " obs = df[\"var2\"][i]\n", - " cusum.update(obs)\n", - " status.loc[i] = [i, obs, cusum.drift_state]" + " ph.update(X=obs)\n", + " status.loc[i] = [i, obs, ph.drift_state]" ] }, { @@ -203,7 +227,7 @@ "plt.grid(False, axis=\"x\")\n", "plt.xticks(fontsize=16)\n", "plt.yticks(fontsize=16)\n", - "plt.title(\"CUSUM Test Results\", fontsize=22)\n", + "plt.title(\"PH Test Results\", fontsize=22)\n", "plt.ylabel(\"Value\", fontsize=18)\n", "plt.xlabel(\"Index\", fontsize=18)\n", "ylims = [-0.05, 1.1]\n", @@ -219,29 +243,129 @@ " color=\"red\",\n", ")\n", "plt.legend()\n", - "\n" + "plt.show()\n", + "# plt.savefig(\"example_Page-Hinkley_detections.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "CUSUM alarms several times within the drift induction window, roughly halfway\n", - "through. After the alarm is reset, change is detected a few more times,\n", - "including an apparently erroneous detection after the drift induction window\n", - "is passed. The current threshold settings may then be too sensitive for the\n", - "new regime.\n" + "Page-Hinkley alarms shortly after the drift induction window closes, and then makes\n", + "several apparently erroneous alarms afterwards. The parameters may not be\n", + "well-chosen for the new regime.\n", + "Change detection algorithms come out of process control, so a priori\n", + "characterization of the bounds of the process, not performed here, would not\n", + "be unreasonable.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ADaptive WINdowing (ADWIN)\n", + "\n", + "ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ + "## Setup ##\n", "\n", + "adwin = ADWIN()\n", + "\n", + "# setup DF to record results\n", + "status = pd.DataFrame(columns=[\"index\", \"actual value\", \"drift_detected\", \"ADWIN mean\"])\n", + "df2 = rainfall_df.loc[:1000, 'max_sustained_wind_speed']\n", + "rec_list = []\n", + "\n", + "# iterate through data; feed each sample to the detector, in turn\n", + "for i in range(len(df2)):\n", + " obs = df2[i]\n", + " adwin.update(X=obs)\n", + " status.loc[i] = [i, obs, adwin.drift_state, adwin.mean()]\n", + "\n", + " #monitor the size of ADWIN's window as it changes\n", + " if adwin.drift_state == \"drift\":\n", + " retrain_start = adwin.retraining_recs[0]\n", + " retrain_end = adwin.retraining_recs[1]\n", + " rec_list.append([retrain_start, retrain_end])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "## Plotting ##\n", + "\n", + "# plot the monitored variable and the status of the detector\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"actual value\", data=status, label=\"max_sustained_wind_speed\", alpha=.5)\n", + "plt.plot(\"index\", \"ADWIN mean\", data=status, color='blue', linewidth=3)\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"ADWIN Results\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-2, 6]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.6 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.8 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"][::-1],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"black\",\n", + " label=\"New Observation Windows\",\n", + ")\n", + "\n", + "plt.legend()\n", "plt.show()\n", - "# plt.savefig(\"example_CUSUM_detections.png\")" + "# plt.savefig(\"example_ADWIN.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ADWIN monitors the running average of the `max_sustained_wind_speed` column and, once that mean begins to change enough around index 600, shrinks its observation window (in black) to only include more recent samples. This process repeats as further changes are detected. We can see that the size of the observation window shrinks and grows as the incoming data changes." ] } ], @@ -252,7 +376,15 @@ "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", "version": "3.10.2" }, "orig_nbformat": 4, diff --git a/docs/source/examples/concept_drift/concept_drift_examples.ipynb b/docs/source/examples/concept_drift/concept_drift_examples.ipynb index 91448729..bc0999de 100644 --- a/docs/source/examples/concept_drift/concept_drift_examples.ipynb +++ b/docs/source/examples/concept_drift/concept_drift_examples.ipynb @@ -1,947 +1,1129 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Concept Drift Detector Examples" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The examples in this notebook show how to set up, run, and produce output from detectors in the \n", - "concept_drift module. The parameters aren't necessarily tuned for best \n", - "performance for the input data, just notional.\n", - "\n", - "Circle is a synthetic data source, where drift occurs in both var1, var2, and the \n", - "conditional distributions P(y|var1) and P(y|var2). The drift occurs from index \n", - "1000 to 1250, and affects 66% of the sample.\n", - "\n", - "These detectors are generally to be applied to the true class and predicted class \n", - "from a particular model. ADWIN is an exception in that it could also be used to \n", - "monitor an arbitrary real-valued feature. So, each of the summary plots displays \n", - "the running accuracy of the classifier alongside the drift detector's output.\n", - "\n", - "They also track the indices of portions of the incoming data stream which are \n", - "more similar to each other -- i.e., data that seems to be part of the same \n", - "concept, which could be used to retrain a model.\n", - "\n", - "NOTE: The LinearFourRates example has a relatively long runtime, roughly 5 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Imports ##\n", - "\n", - "import os\n", - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from sklearn.naive_bayes import GaussianNB\n", - "from sklearn.linear_model import SGDClassifier\n", - "from menelaus.concept_drift import LinearFourRates, ADWIN, DDM, EDDM, STEPD" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Import Data ##\n", - "\n", - "# read in Circle dataset\n", - "df = pd.read_csv(\n", - " os.path.join(\n", - " \"..\", \"..\", \"..\", \"..\", \"src\", \"menelaus\", \"datasets\", \"dataCircleGSev3Sp3Train.csv\"\n", - " ),\n", - " usecols=[0, 1, 2],\n", - " names=[\"var1\", \"var2\", \"y\"],\n", - ")\n", - "drift_start, drift_end = 1000, 1250\n", - "training_size = 500" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear Four Rates (LFR) Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Linear Four Rates monitors the four cells of the confusion matrix (TPR, FPR, TNR, FNR) and alarms when one of these becomes different enough from earlier performance." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Setup ##\n", - "\n", - "# Set up classifier: train on first training_size rows\n", - "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", - "y_train = df.loc[0:training_size, \"y\"]\n", - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)\n", - "\n", - "# Set up LFR detector to detect at significance of .001. 5000 Monte Carlo\n", - "# simulations will be run every 10 samples to detect drift.\n", - "lfr = LinearFourRates(\n", - " time_decay_factor=0.6,\n", - " warning_level=0.01,\n", - " detect_level=0.001,\n", - " num_mc=5000,\n", - " burn_in=10,\n", - " subsample=10,\n", - ")\n", - "\n", - "# Set up DF to store results.\n", - "status = pd.DataFrame(columns=[\"index\", \"y\", \"y_pred\", \"drift_detected\", \"accuracy\"])\n", - "correct = 0" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Run LFR ##\n", - "\n", - "np.random.seed(123) # set seed for this example\n", - "\n", - "# Run LFR and retrain.\n", - "\n", - "rec_list = []\n", - "n = 1\n", - "for i in range(training_size, len(df)):\n", - " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", - " y_pred = int(clf.predict(X_test))\n", - " y_true = int(df.loc[[i], \"y\"])\n", - "\n", - " # increment accuracy\n", - " if y_pred == y_true:\n", - " correct += 1\n", - " accuracy = correct / n\n", - "\n", - " lfr.update(y_pred, y_true)\n", - " status.loc[i] = [i, y_true, y_pred, lfr.drift_state, accuracy]\n", - "\n", - " # If drift is detected, examine the retraining recommendations and retrain.\n", - " if lfr.drift_state == \"drift\":\n", - "\n", - " retrain_start = lfr.retraining_recs[0] + training_size\n", - " retrain_end = lfr.retraining_recs[1] + training_size\n", - " if (\n", - " retrain_start == retrain_end\n", - " ): # minimum retraining window in case of sudden drift\n", - " retrain_start = max(0, retrain_start - 300)\n", - " rec_list.append([retrain_start, retrain_end])\n", - "\n", - " # If retraining is not desired, omit the next four lines.\n", - " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", - " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", - " clf = GaussianNB()\n", - " clf.fit(X_train, y_train)\n", - "\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Plotting ##\n", - "\n", - "plt.figure(figsize=(20, 5))\n", - "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"LFR Results: Accuracy\", fontsize=22)\n", - "plt.ylabel(\"Value\", fontsize=18)\n", - "plt.xlabel(\"Index\", fontsize=18)\n", - "ylims = [-0.05, 1.1]\n", - "plt.ylim(ylims)\n", - "\n", - "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", - "\n", - "# Draw red lines that indicate where drift was detected\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Drift Detected\",\n", - " color=\"red\",\n", - ")\n", - "\n", - "# Draw orange lines that indicate where warnings of drift were provided\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Warning\",\n", - " color=\"orange\",\n", - " alpha=0.3,\n", - ")\n", - "\n", - "# Create a list of lines that indicate the retraining windows.\n", - "# Space them evenly, vertically.\n", - "rec_list = pd.DataFrame(rec_list)\n", - "rec_list[\"y_val\"] = np.linspace(\n", - " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", - " stop=0.2 * ylims[1],\n", - " num=len(rec_list),\n", - ")\n", - "\n", - "# Draw green lines that indicate where retraining occurred\n", - "plt.hlines(\n", - " y=rec_list[\"y_val\"],\n", - " xmin=rec_list[0],\n", - " xmax=rec_list[1],\n", - " color=\"green\",\n", - " label=\"Retraining Windows\",\n", - ")\n", - "\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "One of the four rates immediately passes outside its threshold when drift is\n", - "induced. The same occurs shortly after leaving the drift region. The\n", - "recommended retraining data includes most of the drift induction window and\n", - "the data after regime change.\n", - "\n", - "The classifier's accuracy decreases again later, which causes the detector to\n", - "enter a \"warning\" state. Note that the retraining recommendations *begin* with\n", - "the index corresponding to the warning state, and end where drift is detected.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ADaptive WINdowing (ADWIN) Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "ADWIN can be used to monitor the average of a given real-valued feature. In this case, we use it to monitor the accuracy of a classifier. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Setup ##\n", - "\n", - "# Set up classifier: train on first training_size rows\n", - "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", - "y_train = df.loc[0:training_size, \"y\"]\n", - "\n", - "np.random.seed(123)\n", - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)\n", - "\n", - "adwin = ADWIN()\n", - "\n", - "# Set up DF to record results.\n", - "status = pd.DataFrame(\n", - " columns=[\"index\", \"results\", \"accuracy\", \"adwin mean\", \"drift_detected\"]\n", - ")\n", - "correct = 0\n", - "rec_list = []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run ADWIN\n", - "n = 1\n", - "for i in range(training_size, len(df)):\n", - "\n", - " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", - " y_pred = int(clf.predict(X_test))\n", - " y_true = int(df.loc[[i], \"y\"])\n", - "\n", - " # increment accuracy\n", - " if y_pred == y_true:\n", - " correct += 1\n", - " accuracy = correct / n\n", - "\n", - " adwin.update(int(y_true == y_pred))\n", - " status.loc[i] = [\n", - " i,\n", - " int(y_true == y_pred),\n", - " accuracy,\n", - " adwin.mean(),\n", - " adwin.drift_state,\n", - " ]\n", - "\n", - " # If drift is detected, examine the window and retrain.\n", - " if adwin.drift_state == \"drift\":\n", - " retrain_start = adwin.retraining_recs[0] + training_size\n", - " retrain_end = adwin.retraining_recs[1] + training_size\n", - " rec_list.append([retrain_start, retrain_end])\n", - "\n", - " # The retraining recommendations produced here correspond to the samples\n", - " # which belong to ADWIN's new, smaller window, after drift is detected.\n", - " # If retraining is not desired, omit the next four lines.\n", - " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", - " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", - " clf = GaussianNB()\n", - " clf.fit(X_train, y_train)\n", - "\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Plotting ##\n", - "\n", - "plt.figure(figsize=(20, 6))\n", - "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"ADWIN Results: Accuracy\", fontsize=22)\n", - "plt.ylabel(\"Value\", fontsize=18)\n", - "plt.xlabel(\"Index\", fontsize=18)\n", - "ylims = [0, 1.1]\n", - "plt.ylim(ylims)\n", - "\n", - "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", - "\n", - "# Draw red lines that indicate where drift was detected\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Drift Detected\",\n", - " color=\"red\",\n", - ")\n", - "\n", - "# Create a list of lines that indicate the retraining windows.\n", - "# Space them evenly, vertically.\n", - "rec_list = pd.DataFrame(rec_list)\n", - "rec_list[\"y_val\"] = np.linspace(\n", - " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", - " stop=0.2 * ylims[1],\n", - " num=len(rec_list),\n", - ")\n", - "\n", - "# Draw green lines that indicate where retraining occurred\n", - "plt.hlines(\n", - " y=rec_list[\"y_val\"],\n", - " xmin=rec_list[0],\n", - " xmax=rec_list[1],\n", - " color=\"green\",\n", - " label=\"Retraining Windows\",\n", - ")\n", - "\n", - "plt.legend(loc='lower right')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "After drift is induced, the accuracy decreases enough for ADWIN to shrink its\n", - "window and alarm; subsequent windows also include data from the old regime,\n", - "so drift continues to be detected until the window shrinks enough to be\n", - "comprised mostly by the new regime.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.show()\n", - "# plt.savefig(\"example_ADWIN.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Drift Detection Method (DDM) Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DDM can enter either a \"drift\" or \"warning\" state, depending on how close a classifier's error rate has approached to those respective thresholds, defined by the warning_scale and drift_scale parameters." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Setup ##\n", - "\n", - "np.random.seed(123)\n", - "# setup classifier: train on first training_size rows\n", - "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", - "y_train = df.loc[0:training_size, \"y\"]\n", - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "These parameter values are chosen somewhat arbitrarily.\n", - "At least 100 samples must be seen before DDM tests for drift (the n_threshold\n", - "parameter); the other two define the warning and drift regions. The minimum\n", - "error rate (and its standard deviation) are found during a stable regime; the\n", - "warning_scale and drift_scale roughly correspond to how many standard standard\n", - "deviations away the current estimate must be in order for the detector to\n", - "alarm.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ddm = DDM(n_threshold=100, warning_scale=7, drift_scale=10)\n", - "\n", - "# setup DF to store results\n", - "status = pd.DataFrame(columns=[\"index\", \"y\", \"y_pred\", \"drift_detected\", \"accuracy\"])\n", - "correct = 0\n", - "rec_list = []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run DDM and retrain\n", - "n = 1\n", - "for i in range(training_size, len(df)):\n", - "\n", - " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", - " y_pred = int(clf.predict(X_test))\n", - " y_true = int(df.loc[[i], \"y\"])\n", - "\n", - " # increment accuracy\n", - " if y_pred == y_true:\n", - " correct += 1\n", - " accuracy = correct / n\n", - "\n", - " ddm.update(y_pred, y_true)\n", - " status.loc[i] = [i, y_true, y_pred, ddm.drift_state, accuracy]\n", - "\n", - " # If drift is detected, examine the window and retrain.\n", - " if ddm.drift_state == \"drift\":\n", - " retrain_start = ddm.retraining_recs[0] + training_size\n", - " retrain_end = ddm.retraining_recs[1] + training_size\n", - " if (\n", - " retrain_start == retrain_end\n", - " ): # minimum retraining window in case of sudden drift\n", - " retrain_start = max(0, retrain_start - 300)\n", - " rec_list.append([retrain_start, retrain_end])\n", - "\n", - " # If retraining is not desired, omit the next four lines.\n", - " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", - " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", - " clf = GaussianNB()\n", - " clf.fit(X_train, y_train)\n", - "\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Plotting ##\n", - "\n", - "plt.figure(figsize=(20, 6))\n", - "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"DDM Results: Accuracy\", fontsize=22)\n", - "plt.ylabel(\"Value\", fontsize=18)\n", - "plt.xlabel(\"Index\", fontsize=18)\n", - "ylims = [-0.05, 1.1]\n", - "plt.ylim(ylims)\n", - "\n", - "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", - "\n", - "# Draw red lines that indicate where drift was detected\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Drift Detected\",\n", - " color=\"red\",\n", - " linewidth=3,\n", - ")\n", - "\n", - "# Draw orange lines that indicate where warnings of drift were provided\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Warning\",\n", - " color=\"orange\",\n", - " alpha=0.3,\n", - ")\n", - "\n", - "# Create a list of lines that indicate the retraining windows.\n", - "# Space them evenly, vertically.\n", - "rec_list = pd.DataFrame(rec_list)\n", - "rec_list[\"y_val\"] = np.linspace(\n", - " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", - " stop=0.2 * ylims[1],\n", - " num=len(rec_list),\n", - ")\n", - "\n", - "# Draw green lines that indicate where retraining occurred\n", - "plt.hlines(\n", - " y=rec_list[\"y_val\"],\n", - " xmin=rec_list[0],\n", - " xmax=rec_list[1],\n", - " color=\"green\",\n", - " label=\"Retraining Windows\",\n", - ")\n", - "\n", - "plt.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "DDM initially alarms during the drift induction window, triggering retraining.\n", - "The subsequent dip in accuracy is large enough to put the detector in the\n", - "\"warning\" state, but not large enough for \"drift\" to be identified.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plt.show()\n", - "# plt.savefig(\"example_DDM.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Early Drift Detection Method (EDDM) Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "EDDM monitors the distance between two errors of a classifier - i.e., the number of samples between errors - rather than monitoring the error rate itself. Similar to DDM, it uses separate thresholds for \"warning\" and \"drift.\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Setup ##\n", - "\n", - "np.random.seed(123)\n", - "# setup classifier: train on first 500 rows\n", - "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", - "y_train = df.loc[0:training_size, \"y\"]\n", - "clf = GaussianNB()\n", - "clf.fit(X_train, y_train)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "- n_threshold specifies the number of new samples which must be observed before\n", - "tests for drift are run.\n", - "\n", - "- The warning_thresh and drift_thresh values roughly correspond to the ratio of\n", - "the 95th percentile for the current distance distribution vs. the 95th percentile\n", - "for the \"best\" distance distribution observed so far. So, lower values correspond to less conservative monitoring - the current\n", - "distance between errors is allowed to be a smaller fraction of the \"best\"\n", - "distance.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "eddm = EDDM(n_threshold=30, warning_thresh=0.7, drift_thresh=0.5)\n", - "\n", - "# setup DF to store results\n", - "status = pd.DataFrame(columns=[\"index\", \"y\", \"y_pred\", \"drift_detected\", \"accuracy\"])\n", - "correct = 0\n", - "rec_list = []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run EDDM and retrain\n", - "n = 1\n", - "for i in range(training_size, len(df)):\n", - "\n", - " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", - " y_pred = int(clf.predict(X_test))\n", - " y_true = int(df.loc[[i], \"y\"])\n", - "\n", - " # increment accuracy\n", - " if y_pred == y_true:\n", - " correct += 1\n", - " accuracy = correct / n\n", - "\n", - " eddm.update(y_pred, y_true)\n", - " status.loc[i] = [i, y_true, y_pred, eddm.drift_state, accuracy]\n", - "\n", - " # If drift is detected, examine the window and retrain.\n", - " if eddm.drift_state == \"drift\":\n", - " retrain_start = eddm.retraining_recs[0] + training_size\n", - " retrain_end = eddm.retraining_recs[1] + training_size\n", - " if (\n", - " retrain_start == retrain_end\n", - " ): # minimum retraining window in case of sudden drift\n", - " retrain_start = max(0, retrain_start - 300)\n", - " rec_list.append([retrain_start, retrain_end])\n", - "\n", - " # If retraining is not desired, omit the next four lines.\n", - " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", - " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", - " clf = GaussianNB()\n", - " clf.fit(X_train, y_train)\n", - "\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Plotting ##\n", - "\n", - "plt.figure(figsize=(20, 6))\n", - "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"EDDM Results: Accuracy\", fontsize=22)\n", - "plt.ylabel(\"Value\", fontsize=18)\n", - "plt.xlabel(\"Index\", fontsize=18)\n", - "ylims = [-0.05, 1.1]\n", - "plt.ylim(ylims)\n", - "\n", - "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", - "\n", - "# Draw orange lines that indicate where warnings of drift were provided\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Warning\",\n", - " color=\"orange\",\n", - " alpha=0.3,\n", - ")\n", - "\n", - "# Draw red lines that indicate where drift was detected\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Drift Detected\",\n", - " color=\"red\",\n", - ")\n", - "\n", - "# Create a list of lines that indicate the retraining windows.\n", - "# Space them evenly, vertically.\n", - "rec_list = pd.DataFrame(rec_list)\n", - "rec_list[\"y_val\"] = np.linspace(\n", - " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", - " stop=0.2 * ylims[1],\n", - " num=len(rec_list),\n", - ")\n", - "\n", - "# Draw green lines that indicate where retraining occurred\n", - "plt.hlines(\n", - " y=rec_list[\"y_val\"],\n", - " xmin=rec_list[0],\n", - " xmax=rec_list[1],\n", - " color=\"green\",\n", - " label=\"Retraining Windows\",\n", - " alpha=0.3,\n", - ")\n", - "\n", - "plt.legend()\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "EDDM enters a drift state shortly after the drift induction window, triggering\n", - "retraining. The later increase in the error rate causes the detector to enter\n", - "the warning state, but is not large enough to be identified as drift with this\n", - "threshold setting.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.show()\n", - "# plt.savefig(\"example_EDDM.png\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Statistical Test of Equal Proportions to Detect Concept Drift (STEPD) Example" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "STEPD is a detector specifically intended for online classifiers, where each new sample is used to update the parameters of the classifier. STEPD monitors the accuracy in two windows, \"recent\" and \"past,\" and compares those in order to detect drift in classifier accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Setup ##\n", - "\n", - "np.random.seed(123)\n", - "df_ex = df\n", - "train_ix = [0, training_size]\n", - "\n", - "\n", - "# For the purposes of this example, our online classifier is an SGDClassifier\n", - "# with a constant learning rate, since this is available in the pre-existing\n", - "# package dependency sklearn.\n", - "clf = SGDClassifier(learning_rate=\"constant\", shuffle=False, eta0=0.2)\n", - "clf.fit(\n", - " df_ex.loc[train_ix[0] : train_ix[1], [\"var1\", \"var2\"]].values,\n", - " df_ex.loc[train_ix[0] : train_ix[1], \"y\"].values,\n", - ")\n", - "\n", - "\n", - "stepd = STEPD(window_size=100)\n", - "\n", - "# setup DF to store results\n", - "status = pd.DataFrame(columns=[\"index\", \"y\", \"y_pred\", \"drift_detected\", \"accuracy\"])\n", - "correct = 0\n", - "rec_list = []" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# run STEPD and retrain\n", - "n = 1\n", - "for i, row in df_ex.iloc[training_size:].iterrows():\n", - " y_pred = clf.predict(np.array(row[[\"var1\", \"var2\"]]).reshape(1, -1))\n", - " y_true = row[\"y\"]\n", - "\n", - " if y_pred == y_true:\n", - " correct += 1\n", - " accuracy = correct / n\n", - "\n", - " stepd.update(y_pred, y_true)\n", - " status.loc[i] = [i, y_true, y_pred, stepd.drift_state, accuracy]\n", - " # train_ix[1] = train_ix[1] + 1\n", - "\n", - " if stepd.drift_state == \"drift\":\n", - " rec_list.append(stepd.retraining_recs)\n", - " # retrain the classifier using STEPD's recommendations\n", - " train_ix = stepd.retraining_recs\n", - " train_ix[0] = train_ix[0] + training_size # adjust for starting index\n", - " train_ix[1] = train_ix[1] + training_size\n", - " clf.fit(\n", - " df_ex.loc[train_ix[0] : train_ix[1], [\"var1\", \"var2\"]].values,\n", - " df_ex.loc[train_ix[0] : train_ix[1], \"y\"].values,\n", - " )\n", - "\n", - " else:\n", - " # update the classifier with the newest sample\n", - " clf.partial_fit(row[[\"var1\", \"var2\"]].values.reshape(1, -1), [row[\"y\"]])\n", - "\n", - " n += 1" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "## Plotting ##\n", - "\n", - "plt.figure(figsize=(20, 6))\n", - "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Accuracy\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"STEPD Results: Accuracy\", fontsize=22)\n", - "plt.ylabel(\"Value\", fontsize=18)\n", - "plt.xlabel(\"Index\", fontsize=18)\n", - "ylims = [-0.05, 1.1]\n", - "plt.ylim(ylims)\n", - "\n", - "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", - "\n", - "# Draw orange lines that indicate where warnings of drift were provided\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Warning\",\n", - " color=\"orange\",\n", - " alpha=0.3,\n", - ")\n", - "\n", - "# Draw red lines that indicate where drift was detected\n", - "plt.vlines(\n", - " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", - " ymin=ylims[0],\n", - " ymax=ylims[1],\n", - " label=\"Drift Detected\",\n", - " color=\"red\",\n", - " linewidth=3,\n", - ")\n", - "\n", - "# Create a list of lines that indicate the retraining windows.\n", - "# Space them evenly, vertically.\n", - "rec_list = pd.DataFrame(rec_list)\n", - "rec_list[\"y_val\"] = np.linspace(\n", - " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", - " stop=0.2 * ylims[1],\n", - " num=len(rec_list),\n", - ")\n", - "\n", - "# Draw green lines that indicate where retraining occurred\n", - "plt.hlines(\n", - " y=rec_list[\"y_val\"],\n", - " xmin=rec_list[0],\n", - " xmax=rec_list[1],\n", - " color=\"green\",\n", - " label=\"Retraining Windows\",\n", - ")\n", - "\n", - "plt.legend(loc='upper left')\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "STEPD identifies drift quite early in the drift induction window, triggering\n", - "retraining on a relatively small amount of data; after this, the online\n", - "classifier updates sufficiently that its accuracy is roughly flat over the\n", - "remaining data, albeit with a big enough change to trigger more warnings.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "plt.show()\n", - "# plt.savefig(\"example_STEPD.png\")" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Concept Drift Detector Examples" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The examples in this notebook show how to set up, run, and produce output from detectors in the \n", + "concept_drift module. The parameters aren't necessarily tuned for best \n", + "performance for the input data, just notional.\n", + "\n", + "Circle is a synthetic data source, where drift occurs in both var1, var2, and the \n", + "conditional distributions P(y|var1) and P(y|var2). The drift occurs from index \n", + "1000 to 1250, and affects 66% of the sample.\n", + "\n", + "Rainfall is a real data source into which concept drift has been injected. This\n", + "set contains approximately 18,000 samples, and the data has been standardized.\n", + "Drift starts from index 12,000 and continues through the rest of the dataset.\n", + "In this example, we take the first 10,000 samples of the dataset for training\n", + "an initial classifier, and then use the remaining samples for testing.\n", + "\n", + "These detectors are generally to be applied to the true class and predicted class \n", + "from a particular model. For the sake of example, a simple running accuracy of the \n", + "classifier is presented for each detector as a measure of its performance, \n", + "rather than e.g. the prequential error.\n", + "\n", + "They also track the indices of portions of the incoming data stream which are \n", + "more similar to each other -- i.e., data that seems to be part of the same \n", + "concept, which could be used to retrain a model.\n", + "\n", + "NOTE: The LinearFourRates example has a relatively long runtime, roughly 5 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Imports ##\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.naive_bayes import GaussianNB\n", + "from sklearn.linear_model import SGDClassifier\n", + "from sklearn import svm\n", + "from sklearn.base import clone\n", + "from menelaus.concept_drift import LinearFourRates, ADWINAccuracy, DDM, EDDM, STEPD, MD3\n", + "from menelaus.datasets import fetch_circle_data, fetch_rainfall_data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Import Data ##\n", + "\n", + "# read in Circle dataset\n", + "df = fetch_circle_data()\n", + "drift_start, drift_end = 1000, 1250\n", + "training_size = 500\n", + "\n", + "# read in Rainfall dataset\n", + "rainfall_df = fetch_rainfall_data()\n", + "rainfall_drift_start, rainfall_drift_end = 12000, 18158\n", + "rainfall_training_size = 10000\n", + "rainfall_columns = [\"temperature\", \"dew_point\", \"sea_level_pressure\", \"visibility\", \"average_wind_speed\", \"max_sustained_wind_speed\", \"minimum_temperature\", \"maximum_temperature\", \"rain\"]\n", + "rainfall_features = [\"temperature\", \"dew_point\", \"sea_level_pressure\", \"visibility\", \"average_wind_speed\", \"max_sustained_wind_speed\", \"minimum_temperature\", \"maximum_temperature\"]\n", + "rainfall_df[rainfall_features] = rainfall_df[rainfall_features].astype(float)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Four Rates (LFR)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Linear Four Rates monitors the four cells of the confusion matrix (TPR, FPR, TNR, FNR) and alarms when one of these becomes different enough from earlier performance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "# Set up classifier: train on first training_size rows\n", + "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", + "y_train = df.loc[0:training_size, \"y\"]\n", + "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n", + "y_true = df.loc[training_size:len(df), \"y\"]\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)\n", + "\n", + "acc_orig = np.cumsum(clf.predict(X_test) == y_true)\n", + "acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig))\n", + "\n", + "# Set up LFR detector to detect at significance of .001. 5000 Monte Carlo\n", + "# simulations will be run every 10 samples to detect drift.\n", + "lfr = LinearFourRates(\n", + " time_decay_factor=0.6,\n", + " warning_level=0.01,\n", + " detect_level=0.001,\n", + " num_mc=5000,\n", + " burn_in=10,\n", + " subsample=10,\n", + ")\n", + "\n", + "# Set up DF to store results.\n", + "status = pd.DataFrame(columns=[\"index\", \"y_true\", \"y_pred\", \"drift_detected\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Run LFR ##\n", + "\n", + "np.random.seed(123) # set seed for this example\n", + "\n", + "# Run LFR and retrain.\n", + "\n", + "rec_list = []\n", + "for i in range(training_size, len(df)):\n", + " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", + " y_pred = int(clf.predict(X_test))\n", + " y_true = int(df.loc[[i], \"y\"])\n", + "\n", + " lfr.update(y_true, y_pred)\n", + " status.loc[i] = [i, y_true, y_pred, lfr.drift_state]\n", + "\n", + " # If drift is detected, examine the retraining recommendations and retrain.\n", + " if lfr.drift_state == \"drift\":\n", + "\n", + " retrain_start = lfr.retraining_recs[0] + training_size\n", + " retrain_end = lfr.retraining_recs[1] + training_size\n", + " if (\n", + " retrain_start == retrain_end\n", + " ): # minimum retraining window in case of sudden drift\n", + " retrain_start = max(0, retrain_start - 300)\n", + " rec_list.append([retrain_start, retrain_end])\n", + "\n", + " # If retraining is not desired, omit the next four lines.\n", + " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", + " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", + " clf = GaussianNB()\n", + " clf.fit(X_train, y_train)\n", + "\n", + "status['original_accuracy'] = acc_orig\n", + "status['accuracy'] = np.cumsum(status.y_true == status.y_pred)\n", + "status['accuracy'] = status['accuracy'] / np.arange(1, 1 + len(status))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 5))\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n", + "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"LFR Results: Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-0.05, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + ")\n", + "\n", + "# Draw orange lines that indicate where warnings of drift were provided\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Warning\",\n", + " color=\"orange\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"green\",\n", + " label=\"Retraining Windows\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.show()\n", + "# plt.savefig(\"example_LFR.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "One of the four rates immediately passes outside its threshold when drift is\n", + "induced. The same occurs shortly after leaving the drift region. The\n", + "recommended retraining data includes most of the drift induction window and\n", + "the data after regime change.\n", + "\n", + "The classifier's accuracy decreases again later, which causes the detector to\n", + "enter a \"warning\" state. Note that the retraining recommendations *begin* with\n", + "the index corresponding to the warning state, and end where drift is detected.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ADaptive WINdowing (ADWIN)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case.\n", + "\n", + "It can be used to monitor the accuracy of a classifier by checking `y_true == y_pred` at each time step. So, for convenience, `concept_drift.ADWINAccuracy`, takes `y_true` and `y_pred` as arugments, as shown below. `change_detection.ADWIN` can be used more generally, as shown in the change detection examples." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "# Set up classifier: train on first training_size rows\n", + "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", + "y_train = df.loc[0:training_size, \"y\"]\n", + "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n", + "y_true = df.loc[training_size:len(df), \"y\"]\n", + "\n", + "np.random.seed(123)\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# get running accuracy from the original classifier to compare performance\n", + "acc_orig = np.cumsum(clf.predict(X_test) == y_true)\n", + "acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig))\n", + "\n", + "adwin = ADWINAccuracy()\n", + "\n", + "# Set up DF to record results.\n", + "status = pd.DataFrame(\n", + " columns=[\"index\", \"y_true\", \"y_pred\", \"adwin mean\", \"drift_detected\"]\n", + ")\n", + "rec_list = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run ADWIN\n", + "for i in range(training_size, len(df)):\n", + "\n", + " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", + " y_pred = int(clf.predict(X_test))\n", + " y_true = int(df.loc[[i], \"y\"])\n", + "\n", + " adwin.update(y_true, y_pred)\n", + " status.loc[i] = [\n", + " i, y_true, y_pred, adwin.mean(), adwin.drift_state,\n", + " ]\n", + "\n", + " # If drift is detected, examine the window and retrain.\n", + " if adwin.drift_state == \"drift\":\n", + " retrain_start = adwin.retraining_recs[0] + training_size\n", + " retrain_end = adwin.retraining_recs[1] + training_size\n", + " rec_list.append([retrain_start, retrain_end])\n", + "\n", + " # The retraining recommendations produced here correspond to the samples\n", + " # which belong to ADWIN's new, smaller window, after drift is detected.\n", + " # If retraining is not desired, omit the next four lines.\n", + " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", + " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", + " clf = GaussianNB()\n", + " clf.fit(X_train, y_train)\n", + "\n", + "status['original_accuracy'] = acc_orig\n", + "status['accuracy'] = np.cumsum(status.y_true == status.y_pred)\n", + "status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n", + "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"ADWIN Results: Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [0, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"green\",\n", + " label=\"Retraining Windows\",\n", + ")\n", + "\n", + "plt.legend(loc='lower right')\n", + "plt.show()\n", + "# plt.savefig(\"example_ADWINAccuracy.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "After drift is induced, the accuracy decreases enough for ADWIN to shrink its\n", + "window and alarm; subsequent windows also include data from the old regime, so\n", + "drift continues to be detected until the window shrinks enough to be comprised\n", + "mostly by the new regime. We can see that, by retraining on the new window of\n", + "data reported by ADWIN, the accuracy of the classifier is improved over time." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Drift Detection Method (DDM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DDM can enter either a \"drift\" or \"warning\" state, depending on how close a classifier's error rate has approached to those respective thresholds, defined by the warning_scale and drift_scale parameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "np.random.seed(123)\n", + "# setup classifier: train on first training_size rows\n", + "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", + "y_train = df.loc[0:training_size, \"y\"]\n", + "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n", + "y_true = df.loc[training_size:len(df), \"y\"]\n", + "\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)\n", + "\n", + "acc_orig = np.cumsum(clf.predict(X_test) == y_true)\n", + "acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "These parameter values are chosen somewhat arbitrarily.\n", + "At least 100 samples must be seen before DDM tests for drift (the n_threshold\n", + "parameter); the other two define the warning and drift regions. The minimum\n", + "error rate (and its standard deviation) are found during a stable regime; the\n", + "warning_scale and drift_scale roughly correspond to how many standard standard\n", + "deviations away the current estimate must be in order for the detector to\n", + "alarm.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "ddm = DDM(n_threshold=100, warning_scale=7, drift_scale=10)\n", + "\n", + "# setup DF to store results\n", + "status = pd.DataFrame(columns=[\"index\", \"y_true\", \"y_pred\", \"drift_detected\"])\n", + "rec_list = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run DDM and retrain\n", + "for i in range(training_size, len(df)):\n", + "\n", + " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", + " y_pred = int(clf.predict(X_test))\n", + " y_true = int(df.loc[[i], \"y\"])\n", + "\n", + " ddm.update(y_true, y_pred)\n", + " status.loc[i] = [i, y_true, y_pred, ddm.drift_state]\n", + "\n", + " # If drift is detected, examine the window and retrain.\n", + " if ddm.drift_state == \"drift\":\n", + " retrain_start = ddm.retraining_recs[0] + training_size\n", + " retrain_end = ddm.retraining_recs[1] + training_size\n", + " if (\n", + " retrain_start == retrain_end\n", + " ): # minimum retraining window in case of sudden drift\n", + " retrain_start = max(0, retrain_start - 300)\n", + " rec_list.append([retrain_start, retrain_end])\n", + "\n", + " # If retraining is not desired, omit the next four lines.\n", + " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", + " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", + " clf = GaussianNB()\n", + " clf.fit(X_train, y_train)\n", + "\n", + "status['original_accuracy'] = acc_orig\n", + "status['accuracy'] = np.cumsum(status.y_true == status.y_pred)\n", + "status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0]))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n", + "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"DDM Results: Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-0.05, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + " linewidth=3,\n", + ")\n", + "\n", + "# Draw orange lines that indicate where warnings of drift were provided\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Warning\",\n", + " color=\"orange\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"green\",\n", + " label=\"Retraining Windows\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.show()\n", + "# plt.savefig(\"example_DDM.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "DDM initially alarms during the drift induction window, triggering retraining.\n", + "The subsequent dip in accuracy is large enough to put the detector in the\n", + "\"warning\" state, but not large enough for \"drift\" to be identified.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Early Drift Detection Method (EDDM)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "EDDM monitors the distance between two errors of a classifier - i.e., the number of samples between errors - rather than monitoring the error rate itself. Similar to DDM, it uses separate thresholds for \"warning\" and \"drift.\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "np.random.seed(123)\n", + "# setup classifier: train on first 500 rows\n", + "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", + "y_train = df.loc[0:training_size, \"y\"]\n", + "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n", + "y_true = df.loc[training_size:len(df), \"y\"]\n", + "\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)\n", + "\n", + "acc_orig = np.cumsum(clf.predict(X_test) == y_true)\n", + "acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "- n_threshold specifies the number of new samples which must be observed before\n", + "tests for drift are run.\n", + "\n", + "- The warning_thresh and drift_thresh values roughly correspond to the ratio of\n", + "the 95th percentile for the current distance distribution vs. the 95th percentile\n", + "for the \"best\" distance distribution observed so far. So, lower values correspond to less conservative monitoring - the current\n", + "distance between errors is allowed to be a smaller fraction of the \"best\"\n", + "distance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "eddm = EDDM(n_threshold=30, warning_thresh=0.7, drift_thresh=0.5)\n", + "\n", + "# setup DF to store results\n", + "status = pd.DataFrame(columns=[\"index\", \"y_true\", \"y_pred\", \"drift_detected\"])\n", + "rec_list = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run EDDM and retrain\n", + "for i in range(training_size, len(df)):\n", + "\n", + " X_test = df.loc[[i], [\"var1\", \"var2\"]]\n", + " y_pred = int(clf.predict(X_test))\n", + " y_true = int(df.loc[[i], \"y\"])\n", + "\n", + " eddm.update(y_true, y_pred)\n", + " status.loc[i] = [i, y_true, y_pred, eddm.drift_state]\n", + "\n", + " # If drift is detected, examine the window and retrain.\n", + " if eddm.drift_state == \"drift\":\n", + " retrain_start = eddm.retraining_recs[0] + training_size\n", + " retrain_end = eddm.retraining_recs[1] + training_size\n", + " if (\n", + " retrain_start == retrain_end\n", + " ): # minimum retraining window in case of sudden drift\n", + " retrain_start = max(0, retrain_start - 300)\n", + " rec_list.append([retrain_start, retrain_end])\n", + "\n", + " # If retraining is not desired, omit the next four lines.\n", + " X_train = df.loc[retrain_start:retrain_end, [\"var1\", \"var2\"]]\n", + " y_train = df.loc[retrain_start:retrain_end, \"y\"]\n", + " clf = GaussianNB()\n", + " clf.fit(X_train, y_train)\n", + "\n", + "status['original_accuracy'] = acc_orig\n", + "status['accuracy'] = np.cumsum(status.y_true == status.y_pred)\n", + "status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n", + "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"EDDM Results: Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-0.05, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw orange lines that indicate where warnings of drift were provided\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Warning\",\n", + " color=\"orange\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"green\",\n", + " label=\"Retraining Windows\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.show()\n", + "# plt.savefig(\"example_EDDM.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "EDDM enters a drift state shortly after the drift induction window, triggering\n", + "retraining. The later increase in the error rate causes the detector to enter\n", + "the warning state, but is not large enough to be identified as drift with this\n", + "threshold setting.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Statistical Test of Equal Proportions to Detect Concept Drift (STEPD)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "STEPD is a detector specifically intended for online classifiers, where each new sample is used to update the parameters of the classifier. STEPD monitors the accuracy in two windows, \"recent\" and \"past,\" and compares those in order to detect drift in classifier accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "np.random.seed(123)\n", + "# setup classifier: train on first 500 rows\n", + "X_train = df.loc[0:training_size, [\"var1\", \"var2\"]]\n", + "y_train = df.loc[0:training_size, \"y\"]\n", + "X_test = df.loc[training_size:len(df), [\"var1\", \"var2\"]]\n", + "y_true = df.loc[training_size:len(df), \"y\"]\n", + "\n", + "clf = GaussianNB()\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Calculate the accuracy the classifier would have had without any retraining \n", + "# recommendations from STEPD.\n", + "\n", + "df_test = df.loc[training_size:, :]\n", + "\n", + "y_pred = []\n", + "for i in range(len(df_test)):\n", + " X_curr = df.loc[i:i, [\"var1\", \"var2\"]]\n", + " y_curr = df.loc[i:i, \"y\"]\n", + " y_pred.append(clf.predict(X_curr))\n", + " clf.partial_fit(X_curr, y_curr)\n", + "y_pred = np.array(y_pred).reshape(len(y_pred))\n", + "\n", + "acc_orig = np.cumsum(y_pred == y_true)\n", + "acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig))\n", + "acc_orig = acc_orig.reset_index()['y']\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "#reset the classifier for loop with detection\n", + "np.random.seed(123)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "stepd = STEPD(window_size=250)\n", + "\n", + "# setup DF to store results\n", + "status = pd.DataFrame(columns=[\"index\", \"y_true\", \"y_pred\", \"drift_detected\"])\n", + "rec_list = []\n", + "\n", + "for i in range(len(df_test)):\n", + " X_curr = df.loc[i:i, [\"var1\", \"var2\"]]\n", + " y_curr = df.loc[i:i, \"y\"]\n", + " y_pred = clf.predict(X_curr)[0]\n", + "\n", + " stepd.update(y_true=y_curr.values[0], y_pred=y_pred)\n", + " status.loc[i] = [i, y_curr.values[0], y_pred, stepd.drift_state]\n", + "\n", + " # If drift is detected, examine the window and retrain.\n", + " if stepd.drift_state == \"drift\":\n", + " #account for the training_size offset\n", + " recs = [x + training_size for x in stepd.retraining_recs]\n", + " rec_list.append(recs)\n", + "\n", + " # If retraining is not desired, omit the next four lines.\n", + " X_train = df.loc[recs[0]:recs[1], [\"var1\", \"var2\"]]\n", + " y_train = df.loc[recs[0]:recs[1], \"y\"]\n", + " clf = GaussianNB()\n", + " clf.fit(X_train, y_train)\n", + " else:\n", + " clf.partial_fit(X_curr, y_curr.values)\n", + "\n", + "status['original_accuracy'] = acc_orig\n", + "status['accuracy'] = np.cumsum(status.y_true == status.y_pred)\n", + "status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0]))\n", + "status['index'] = range(training_size, len(status) + training_size)\n", + "\n", + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color='red')\n", + "plt.scatter(\"index\", \"accuracy\", data=status, label=\"Retrain Accuracy\", color='green')\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"STEPD Results: Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-0.05, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(1000, 1250, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw orange lines that indicate where warnings of drift were provided\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Warning\",\n", + " color=\"orange\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + " linewidth=3,\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "rec_list = pd.DataFrame(rec_list)\n", + "rec_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(rec_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=rec_list[\"y_val\"],\n", + " xmin=rec_list[0],\n", + " xmax=rec_list[1],\n", + " color=\"green\",\n", + " label=\"Retraining Windows\",\n", + ")\n", + "\n", + "plt.legend(loc='lower left')\n", + "plt.show()\n", + "# plt.savefig(\"example_STEPD.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With a window roughly the same size as the drift induction window, STEPD doesn't\n", + "enter a warning state until roughly 400 samples after the drift induction\n", + "window. \n", + "\n", + "Because the online classifier is updating at each step, its performance is\n", + "relatively flat until roughly 400 samples after the drift induction window.\n", + "STEPD enters a warning state after this degradation, at which point the\n", + "classifier is retrained on samples buffered by STEPD during the time in the\n", + "warning region. Retraining the classifier on this sample, then performing\n", + "subsequent updates, slightly improves performance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Margin Density Drift Detection (MD3) Method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "MD3 is a detector intended for semi-supervised and online classifier contexts. A cumulative margin density statistic (unsupervised) is tracked, representing the number of samples that fall into the uncertainty region, or margin, of the classifier. When margin density increases or decreases beyond a certain threshold, a drift warning is issued. When a warning is issued, a set of labeled samples is requested. If prediction accuracy by the model on these samples is lower than on the initial labeled reference dataset, drift is confirmed. If not, drift is ruled out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Setup ##\n", + "\n", + "# Set up classifier: train on first training_size rows\n", + "training_data = rainfall_df.loc[0:rainfall_training_size, rainfall_columns]\n", + "X_train = rainfall_df.loc[0:rainfall_training_size, rainfall_features]\n", + "y_train = rainfall_df.loc[0:rainfall_training_size, \"rain\"]\n", + "\n", + "np.random.seed(123)\n", + "clf = svm.SVC(kernel='linear')\n", + "clf.fit(X_train, y_train.values.ravel())\n", + "retrain_clf = clone(clf)\n", + "retrain_clf.fit(X_train, y_train.values.ravel())\n", + "oracle_labels = 1000\n", + "\n", + "# Initialize detector\n", + "md3 = MD3(clf=clf, sensitivity=1.5, oracle_data_length_required=oracle_labels)\n", + "md3.set_reference(X=training_data, target_name=\"rain\")\n", + "\n", + "# Set up DF to record results.\n", + "status = pd.DataFrame(\n", + " columns=[\"index\", \"y_true\", \"margin_density\", \"original_accuracy\", \"retrain_accuracy\", \"drift_detected\"]\n", + ")\n", + "correct_orig, correct_retrain = 0, 0\n", + "n = 1\n", + "rec_list = []\n", + "oracle_list = []" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# run MD3 and track results for both original model and retrained model\n", + "for i in range(rainfall_training_size, len(rainfall_df)):\n", + "\n", + " X_test = rainfall_df.loc[[i], rainfall_features]\n", + " y_pred_orig = int(clf.predict(X_test))\n", + " y_pred_retrain = int(retrain_clf.predict(X_test))\n", + " y_true = int(rainfall_df.loc[[i], \"rain\"])\n", + " \n", + " # increment accuracy\n", + " if y_pred_orig == y_true:\n", + " correct_orig += 1\n", + " if y_pred_retrain == y_true:\n", + " correct_retrain += 1\n", + " accuracy_orig = correct_orig / n\n", + " accuracy_retrain = correct_retrain / n\n", + "\n", + " # call give_oracle_label if detector is currently waiting for oracle data\n", + " if md3.waiting_for_oracle == True:\n", + " oracle_label = rainfall_df.loc[[i], rainfall_columns]\n", + " md3.give_oracle_label(oracle_label)\n", + " if md3.waiting_for_oracle == False:\n", + " retrain_clf.fit(md3.reference_batch_features, md3.reference_batch_target.values.ravel())\n", + " status.loc[i] = [\n", + " i,\n", + " y_true,\n", + " None,\n", + " accuracy_orig,\n", + " accuracy_retrain,\n", + " md3.drift_state,\n", + " ]\n", + "\n", + " # call update otherwise\n", + " else:\n", + " md3.update(X_test)\n", + " status.loc[i] = [\n", + " i,\n", + " y_true,\n", + " md3.curr_margin_density,\n", + " accuracy_orig,\n", + " accuracy_retrain,\n", + " md3.drift_state,\n", + " ]\n", + " \n", + " # If there was a drift warning, track the window of the labeled\n", + " # oracle data used\n", + " if md3.drift_state == \"warning\":\n", + " oracle_start = i + 1\n", + " oracle_end = i + md3.oracle_data_length_required\n", + " \n", + " oracle_list.append([oracle_start, oracle_end])\n", + "\n", + " n += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "## Plotting ##\n", + "\n", + "plt.figure(figsize=(20, 6))\n", + "plt.scatter(\"index\", \"margin_density\", data=status, label=\"Margin Density\")\n", + "plt.scatter(\"index\", \"original_accuracy\", data=status, label=\"Original Accuracy\", color=\"red\")\n", + "plt.scatter(\"index\", \"retrain_accuracy\", data=status, label=\"Retrain Accuracy\", color=\"green\")\n", + "plt.grid(False, axis=\"x\")\n", + "plt.xticks(fontsize=16)\n", + "plt.yticks(fontsize=16)\n", + "plt.title(\"MD3 Results: Margin Density and Accuracy\", fontsize=22)\n", + "plt.ylabel(\"Value\", fontsize=18)\n", + "plt.xlabel(\"Index\", fontsize=18)\n", + "ylims = [-0.05, 1.1]\n", + "plt.ylim(ylims)\n", + "\n", + "plt.axvspan(rainfall_drift_start, rainfall_drift_end, alpha=0.5, label=\"Drift Induction Window\")\n", + "\n", + "# Draw red lines that indicate where drift was detected\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"drift\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Drift Detected\",\n", + " color=\"red\",\n", + ")\n", + "\n", + "# Draw orange lines that indicate where warnings of drift were provided\n", + "plt.vlines(\n", + " x=status.loc[status[\"drift_detected\"] == \"warning\"][\"index\"],\n", + " ymin=ylims[0],\n", + " ymax=ylims[1],\n", + " label=\"Warning\",\n", + " color=\"orange\",\n", + " alpha=0.3,\n", + ")\n", + "\n", + "# Create a list of lines that indicate the retraining windows.\n", + "# Space them evenly, vertically.\n", + "oracle_list = pd.DataFrame(oracle_list)\n", + "oracle_list[\"y_val\"] = np.linspace(\n", + " start=0.05 * (ylims[1] - ylims[0]) + ylims[0],\n", + " stop=0.2 * ylims[1],\n", + " num=len(oracle_list),\n", + ")\n", + "\n", + "# Draw green lines that indicate where retraining occurred\n", + "plt.hlines(\n", + " y=oracle_list[\"y_val\"],\n", + " xmin=oracle_list[0],\n", + " xmax=oracle_list[1],\n", + " color=\"green\",\n", + " label=\"Labeled Oracle Data\",\n", + ")\n", + "\n", + "plt.legend()\n", + "plt.show()\n", + "# plt.savefig(\"example_MD3.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After drift is induced, the margin density decreases enough for MD3 to emit a warning. From there, the predictive accuracy of the classifier is tested, and this has already decreased sufficiently for the detector to alarm. Then, a new reference batch is set and the detector continues tracking the margin density statistic until the next warning. It can be seen from the plot that retraining at drift results in better accuracy moving forward compared with a model that is kept static." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.2 ('molten_env')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cebda30e5482c3c83cb9c7c7d8ef1c1d1f67dcf93284b9e88f69b68f560a7bf6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/source/examples/convert_notebooks.py b/docs/source/examples/convert_notebooks.py new file mode 100644 index 00000000..2f2dd829 --- /dev/null +++ b/docs/source/examples/convert_notebooks.py @@ -0,0 +1,23 @@ +""" +This utility script converts all of the notebooks in the /docs/source/examples +directory into python scripts in the /examples/ directory, to avoid having to +modify the same code in both locations. + +This script should be run from the /docs/source/examples directory. +""" + +import os +from menelaus.utils._locate import find_git_root + +root_dir = find_git_root() +walk_dir = os.path.join(root_dir, "docs", "source", "examples") +out_dir = os.path.join(root_dir, "examples") + +for subdir, dirs, files in os.walk(walk_dir): + for file in files: + if os.path.splitext(file)[1] == ".ipynb": + in_file = os.path.join(subdir, file) + command = f"jupyter nbconvert --to python {in_file} --output-dir {out_dir}" + print(f"nbconvert {in_file} \n\t to dir {out_dir}") + os.system(command) + print("\n") diff --git a/docs/source/examples/data_drift/data_drift_examples.ipynb b/docs/source/examples/data_drift/data_drift_examples.ipynb index 9ebd7b9e..a94f4e3f 100644 --- a/docs/source/examples/data_drift/data_drift_examples.ipynb +++ b/docs/source/examples/data_drift/data_drift_examples.ipynb @@ -30,7 +30,6 @@ "source": [ "## Imports ##\n", "\n", - "import os\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", @@ -40,7 +39,8 @@ "from menelaus.data_drift.hdddm import HDDDM\n", "from menelaus.data_drift import PCACD\n", "from menelaus.data_drift import KdqTreeStreaming, KdqTreeBatch\n", - "from menelaus.datasets import make_example_batch_data" + "from menelaus.data_drift import NNDVI\n", + "from menelaus.datasets import make_example_batch_data, fetch_circle_data" ] }, { @@ -55,20 +55,14 @@ "example_data = make_example_batch_data()\n", "\n", "# Import PCA-CD and KDQ-Tree Data (circle dataset)\n", - "circle_data = pd.read_csv(\n", - " os.path.join(\n", - " \"..\", \"..\", \"..\", \"..\", \"src\", \"menelaus\", \"datasets\", \"dataCircleGSev3Sp3Train.csv\"\n", - " ),\n", - " usecols=[0, 1, 2],\n", - " names=[\"var1\", \"var2\", \"y\"],\n", - ")" + "circle_data = fetch_circle_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Confidence Distribution Batch Detection (CDBD) Example" + "## Confidence Distribution Batch Detection (CDBD)" ] }, { @@ -78,7 +72,7 @@ "This section details how to setup, run, and produce plots for CDBD. This script\n", "monitors the feature \"confidence\", simulated confidence scores output by a\n", "classifier. Drift occurs in 2018 and persists through 2021. See\n", - "src/menelaus/tools/artifacts/README_example_data.txt for more info.\n", + "`make_example_batch_data` for more info.\n", "\n", "CDBD must be setup and run with batches of data containing 1 variable. \n", "\n", @@ -196,7 +190,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Hellinger Distance Drift Detection Method (HDDDM) Example" + "## Hellinger Distance Drift Detection Method (HDDDM)" ] }, { @@ -205,8 +199,8 @@ "source": [ "This section details how to setup, run, and produce plots for HDDDM, using both\n", "numeric and categorical data. Drift occurs in 2009, 2012, 2015, 2018, and 2021.\n", - "Drift in 2018 persists through 2021. See\n", - "src/menelaus/tools/artifacts/README_example_data.txt for more info.\n", + "Drift in 2018 persists through 2021. See `make_example_batch_data` for more info.\n", + "\n", "It additionally contains an example of using a custom divergence function. \n", "\n", "HDDDM must be setup and run with batches of data. \n", @@ -319,7 +313,6 @@ "metadata": {}, "outputs": [], "source": [ - "\n", "## Plot Heatmap ##\n", "sns.set_style(\"whitegrid\")\n", "sns.set(rc={\"figure.figsize\": (15, 8)})\n", @@ -383,7 +376,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## PCA-Based Change Detection (PCA-CD) Example" + "## PCA-Based Change Detection (PCA-CD)" ] }, { @@ -467,8 +460,7 @@ " color=\"red\",\n", ")\n", "\n", - "plt.legend()\n", - "\n" + "plt.legend()" ] }, { @@ -492,7 +484,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## KDQ-Tree Detection Method Example" + "## KDQ-Tree Detection Method (Streaming Setting)" ] }, { @@ -568,7 +560,7 @@ "\n", "plt.legend()\n", "plt.show()\n", - "# plt.savefig(\"example_streaming_kdqtree_feature_stream.png\")\n" + "# plt.savefig(\"example_streaming_kdqtree_feature_stream.png\")" ] }, { @@ -621,7 +613,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## KDQ-Tree Detection Method Example (Batch Setting)" + "## KDQ-Tree Detection Method (Batch Setting)" ] }, { @@ -634,7 +626,7 @@ "\n", "Drift in the example dataset occurs in 2009, 2012, 2015, 2018, and 2021. Drift\n", "in 2018 persists through 2021. See\n", - "src/menelaus/tools/artifacts/README_example_data.txt for more details.\n", + "`make_example_batch_data` for more details.\n", "\n", "This example takes roughly a minute to run." ] @@ -746,7 +738,7 @@ " )\n", " fig.update_traces(root_color=\"lightgrey\")\n", " fig.show()\n", - " # fig.write_html(f\"example_kdqtree_treemap_{year}.html\")\n" + " # fig.write_html(f\"example_kdqtree_treemap_{year}.html\")" ] }, { @@ -758,6 +750,7 @@ "the items that were modified in a given year.\n", "\n", "For reference, the detailed descriptions of drift induction:\n", + "\n", "- Drift 1: change the mean & var of item B in 2009, means will revert for 2010 on\n", "\n", "- Drift 2: change the variance of item c and d in 2012 by replacing some with the mean\n", @@ -769,6 +762,72 @@ "\n", "- Drift 5: change mean and var just for a year of J in 2021\n" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Nearest-Neighbor Density Variation Identification (NN-DVI)\n", + "\n", + "This example shows up how to set up, run, and produce output from the NN-DVI\n", + "detector, which is implemented to run in the batch setting. NN-DVI uses k-nearest \n", + "neighbors to partition the data, then uses measures defined with the adjacency \n", + "matrix for the reference and test data to determine whether drift has occurred." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = make_example_batch_data()\n", + "drift_years = data.groupby(\"year\")[\"drift\"].apply(lambda x: x.unique()[0]).reset_index()\n", + "drift_years.loc[drift_years[\"year\"].isin([2010, 2013, 2016]), \"drift\"] = True\n", + "\n", + "data_grouped = data.groupby('year')\n", + "\n", + "# Note that a small subsample is used, to decrease the runtime for the purpose\n", + "# of example.\n", + "batches = {year: (group.sample(frac=.1)\n", + "\t\t\t\t\t\t.drop(['year', 'cat', 'confidence', 'drift'], axis=1)\n", + "\t\t\t\t\t\t.values)\n", + "\t\t for year, group in data_grouped}\n", + "\n", + "status = pd.DataFrame(columns=[\"year\", \"drift\"])\n", + "\n", + "det = NNDVI(k_nn=2, sampling_times=50)\n", + "det.set_reference(batches.pop(2007))\n", + "\n", + "for year, batch in batches.items():\n", + "\tdet.update(batch)\n", + "\tstatus = pd.concat([status, pd.DataFrame({\"year\":[year], \"drift\":[det.drift_state]})], \n", + "\t\t\t\t\t\taxis=0, \n", + " ignore_index=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using this small subsample of data runs quickly, but yields inaccurate results \n", + "for the test data. A run with the full data takes much longer, but gives the \n", + "correct result in 9 of 13 years." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "(\n", + " status.merge(drift_years, how=\"left\", on=\"year\", suffixes=[\"_nndvi\", \"_true\"])\n", + " .replace({True: \"drift\", False: None})\n", + " # .to_csv(\"example_nndvi_drift_comparison.csv\", index=False)\n", + ")" + ] } ], "metadata": { diff --git a/docs/source/examples/ensembles/ensemble_examples.ipynb b/docs/source/examples/ensembles/ensemble_examples.ipynb new file mode 100644 index 00000000..b6ae5ce3 --- /dev/null +++ b/docs/source/examples/ensembles/ensemble_examples.ipynb @@ -0,0 +1,214 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ensemble Drift Detector Examples\n", + "\n", + "This notebook contains examples on how to build and use ensemble detectors using the individual algorithms in the `menelaus` suite. These examples also include instructions on specifying evaluation schemes and setting custom subsets of data per constituent detector. \n", + "\n", + "Most parameterizations and initalizations therein may not result in optimal performance or detection, and are provided just for demonstration.\n", + "\n", + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "from menelaus.concept_drift import STEPD\n", + "from menelaus.change_detection import ADWIN\n", + "from menelaus.datasets import make_example_batch_data, fetch_rainfall_data\n", + "from menelaus.data_drift import HDDDM, KdqTreeBatch, KdqTreeStreaming\n", + "from menelaus.ensemble import BatchEnsemble, StreamingEnsemble\n", + "from menelaus.ensemble import SimpleMajorityElection, MinimumApprovalElection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Import Data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "example_data = make_example_batch_data()\n", + "rainfall_data = fetch_rainfall_data()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Batch Ensemble\n", + "\n", + "The simplest use of an ensemble is to combine three data-drift-only detectors with few additional settings. In this case we can combine three instances of batch detectors (`KdqTreeBatch`, `HDDDM`), all operating on the same data columns, with a very basic evaluation scheme (*i.e.* a simple majority of detectors alarming, causes the ensemble to alarm)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize set of detectors with desired parameterizations\n", + "detectors = {\n", + " 'k1': KdqTreeBatch(bootstrap_samples=500),\n", + " 'k2': KdqTreeBatch(bootstrap_samples=475),\n", + " 'h1': HDDDM()\n", + "}\n", + "\n", + "# choose an election scheme\n", + "election = SimpleMajorityElection()\n", + "\n", + "# initialize an ensemble object\n", + "ensemble = BatchEnsemble(detectors, election)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that `BatchEnsemble` and `StreamingEnsemble` are instances of `BatchDetector` and `StreamingDetector` themselves (respectively). As such, they are used in the same syntactic way and possess similar properties." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make dataset smaller\n", + "df_example_data = example_data[example_data.year < 2010]\n", + "\n", + "# split dataset into 1 dataset for each 'batch' (year)\n", + "df_into_batches = [x for _,x in df_example_data.groupby('year')]\n", + "df_into_batches = [x[['a', 'b', 'c']] for x in df_into_batches]\n", + "\n", + "# batch detectors -- and ensembles -- need an initial reference batch\n", + "ensemble.set_reference(df_into_batches[0])\n", + "print(f\"Batch #{0} | Ensemble reference set\")\n", + "\n", + "for i, batch in enumerate(df_into_batches[1:]):\n", + " ensemble.update(batch)\n", + " print(f\"Batch #{i+1} | Ensemble overall drift state: {ensemble.drift_state}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Streaming Ensemble\n", + "\n", + "Using an ensemble of streaming detectors can involve additional features. This example uses both data and concept drift detectors (`KdqTreeStreaming`, `STEPD`), custom subsets of data for different detectors, as well as a different election scheme that will alarm if a custom, minimum number of detectors \"approve\" or alarm for drift." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# initialize set of detectors with desired parameterizations\n", + "detectors = {\n", + " 'k1': KdqTreeStreaming(window_size=200, bootstrap_samples=250),\n", + " 'k2': KdqTreeStreaming(window_size=225, bootstrap_samples=200),\n", + " 's1': STEPD(window_size=50),\n", + " 'a': ADWIN(delta=.001)\n", + "\n", + "}\n", + "\n", + "# Functions that select the part of 'X' each detector needs - keys must match!\n", + "# Note that the default behavior, without a selector specified, is to use all columns.\n", + "# For detectors that only operate on y_true and y_pred, like STEPD, specifying\n", + "# a selector isn't necessary. But for detectors like ADWIN, which only monitor \n", + "# a single variable within X, the selector *must* be specified!\n", + "column_selectors = {\n", + " 'k1': lambda x: x[['temperature', 'visibility', 'dew_point']],\n", + " 'k2': lambda x: x[['temperature', 'visibility', 'average_wind_speed']],\n", + " 'a': lambda x: x[['visibility']]\n", + "}\n", + "\n", + "# choose an election scheme\n", + "election = MinimumApprovalElection(approvals_needed=1)\n", + "\n", + "# initialize an ensemble object\n", + "stream_ensemble = StreamingEnsemble(detectors, election, column_selectors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When mixing concept and data drift detectors, it's especially important to pass data explicitly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# make data smaller\n", + "df_stream = rainfall_data[0:1000]\n", + "\n", + "# random \"predicted\" outcomes -- in case a concept drift detector needs them\n", + "y_preds = np.random.randint(low=0, high=2, size=1000)\n", + "\n", + "# use ensemble\n", + "for i, row in df_stream.iterrows():\n", + " stream_ensemble.update(\n", + " X=df_stream.loc[[i]],\n", + " y_true=row['rain'],\n", + " y_pred=y_preds[i]\n", + " )\n", + " if stream_ensemble.drift_state is not None:\n", + " print(f\"Example #{i} | Ensemble drift state: {stream_ensemble.drift_state}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.10.2 ('molten_env')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.2" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cebda30e5482c3c83cb9c7c7d8ef1c1d1f67dcf93284b9e88f69b68f560a7bf6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/source/examples/partitioners/partitioner_plots_example.ipynb b/docs/source/examples/partitioners/partitioner_plots_example.ipynb index d18ed2ee..2ea6cc13 100644 --- a/docs/source/examples/partitioners/partitioner_plots_example.ipynb +++ b/docs/source/examples/partitioners/partitioner_plots_example.ipynb @@ -40,7 +40,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Basic Plot Example" + "## Basic Plot" ] }, { @@ -110,7 +110,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Modifications Example" + "## Modifications" ] }, { @@ -225,7 +225,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Alternatives Example" + "## Alternatives" ] }, { @@ -298,10 +298,21 @@ } ], "metadata": { + "kernelspec": { + "display_name": "Python 3.10.2 ('molten_env')", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python" + "name": "python", + "version": "3.10.2" }, - "orig_nbformat": 4 + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "cebda30e5482c3c83cb9c7c7d8ef1c1d1f67dcf93284b9e88f69b68f560a7bf6" + } + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/source/index.rst b/docs/source/index.rst index fa69a66e..5104b052 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -22,8 +22,9 @@ Package structure examples/change_detection/change_detection_examples.ipynb examples/concept_drift/concept_drift_examples.ipynb examples/data_drift/data_drift_examples.ipynb + examples/ensembles/ensemble_examples.ipynb examples/partitioners/partitioner_plots_example.ipynb - + references Indices and tables diff --git a/docs/source/refs.bib b/docs/source/refs.bib index 43491638..9927a01b 100644 --- a/docs/source/refs.bib +++ b/docs/source/refs.bib @@ -1,3 +1,25 @@ + @inproceedings{macielelectionreference, + author = {Maciel, Bruno Iran Ferreira and Santos, Silas and Barros, Roberto}, + year = {2015}, + month = {11}, + pages = {}, + title = {A Lightweight Concept Drift Detection Ensemble}, + doi = {10.1109/ICTAI.2015.151}, + booktitle={2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI)}, + organization={IEEE} + } + +@article{duelectionreference, + author = {Du, Lei and Song, Qinbao and Zhu, Lei and Zhu, Xiaoyan}, + year = {2014}, + month = {05}, + pages = {0}, + title = {A Selective Detector Ensemble for Concept Drift Detection}, + volume = {0}, + journal = {The Computer Journal}, + doi = {10.1093/comjnl/bxu050} +} + @article{hinkley1971inference, title={Inference about the change-point from cumulative sum tests}, author={Hinkley, David V}, @@ -99,4 +121,30 @@ @inproceedings{qahtan2015pca booktitle={Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining}, pages={935--944}, year={2015} +} + +@article{sethi2017reliable, + title={On the reliable detection of concept drift from streaming unlabeled data}, + author={Sethi, Tegjyot Singh and Kantardzic, Mehmed}, + journal={Expert Systems with Applications}, + volume={82}, + pages={77--99}, + year={2017}, + publisher={Elsevier} +} + +@misc{minku2010, + title={Artificial Concept Drift Data Sets}, + author={Minku, Leandro L}, + year={2010}, + howpublished="\url{https://www.cs.bham.ac.uk/\textasciitilde minkull/open-source.html}", + note={Online; accessed 19-July-2022}, +} + +@misc{souza2020, + title={Challenges in Benchmarking Stream Learning Algorithms with Real-World Data}, + author={Souza, Vinicius M. A. and dos Reis, Denis M. and Maletzke, Andre G. and Batistia, Gustavo E. A. P. A.}, + year={2020}, + howpublished="\url{https://arxiv.org/abs/2005.00113}", + note={Online; accessed 20-July-2022}, } \ No newline at end of file diff --git a/examples/batch/CDBD_Example.ipynb b/examples/batch/CDBD_Example.ipynb deleted file mode 100644 index 50962114..00000000 --- a/examples/batch/CDBD_Example.ipynb +++ /dev/null @@ -1,160 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Confidence Distribution Batch Detection (CDBD) Example\n", - "\n", - "This file details how to setup, run, and produce plots for CDBD. This script\n", - "monitors the feature \"confidence\", simulated confidence scores output by a\n", - "classifier. Drift occurs in 2018 and persists through 2021. See\n", - "src/menelaus/tools/artifacts/README_example_data.txt for more info.\n", - "It additionally contains an example of using a custom divergence function. \n", - "\n", - "CDBD must be setup and run with batches of data containing 1 variable. \n", - "\n", - "Plots include: \n", - "- A line plot visualizing test statistics for detection of drift" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from menelaus.data_drift.cdbd import CDBD\n", - "from menelaus.datasets import make_example_batch_data\n", - "\n", - "\n", - "## Setup ##\n", - "\n", - "# Import data\n", - "data = make_example_batch_data()\n", - "\n", - "# Set up reference and test batches, using 2007 as reference year\n", - "reference = pd.DataFrame(data[data.year == 2007].loc[:, \"confidence\"])\n", - "all_test = data[data.year != 2007]\n", - "\n", - "# Setup CDBD\n", - "np.random.seed(1)\n", - "cdbd = CDBD(subsets=8)\n", - "\n", - "# Store drift for test statistic plot\n", - "years = all_test.year.unique()\n", - "detected_drift = []\n", - "\n", - "# Run CDBD\n", - "cdbd.set_reference(reference)\n", - "for year, subset_data in all_test.groupby(\"year\"):\n", - " cdbd.update(pd.DataFrame(subset_data.loc[:, \"confidence\"]))\n", - " detected_drift.append(cdbd.drift_state)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "## Plot Line Graph ##\n", - "\n", - "\n", - "kl_divergence = [\n", - " ep - th for ep, th in zip(cdbd.epsilon_values.values(), cdbd.thresholds.values())\n", - "]\n", - "\n", - "plot_data = pd.DataFrame(\n", - " {\"Year\": years, \"KL Divergence\": kl_divergence, \"Detected Drift\": detected_drift}\n", - ")\n", - "sns.set_style(\"white\")\n", - "plt.figure(figsize=(20, 6))\n", - "plt.plot(\"Year\", \"KL Divergence\", data=plot_data, label=\"KL Divergence\", marker=\".\")\n", - "plt.grid(False, axis=\"x\")\n", - "plt.xticks(years, fontsize=16)\n", - "plt.yticks(fontsize=16)\n", - "plt.title(\"CDBD Test Statistics\", fontsize=22)\n", - "plt.ylabel(\"KL Divergence\", fontsize=18)\n", - "plt.xlabel(\"Year\", fontsize=18)\n", - "plt.ylim([min(kl_divergence) - 0.02, max(kl_divergence) + 0.02])\n", - "for _, t in enumerate(plot_data.loc[plot_data[\"Detected Drift\"] == \"drift\"][\"Year\"]):\n", - " plt.axvspan(\n", - " t - 0.2, t + 0.2, alpha=0.5, color=\"red\", label=(\"Drift Detected\" if _ == 0 else None)\n", - " )\n", - "plt.legend()\n", - "plt.axhline(y=0, color=\"orange\", linestyle=\"dashed\")\n", - "\n", - "# plt.show()\n", - "plt.savefig(\"example_CDBD_test_statistics.png\")\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "\n", - "\n", - "### Custom Divergence Metric ### \n", - "\n", - "# Define divergence function\n", - "def distance_metric(reference_histogram, test_histogram):\n", - "\n", - " # Convert inputs to appropriate datatype \n", - " ref = np.array(reference_histogram[0])\n", - " test = np.array(test_histogram[0])\n", - "\n", - " return np.sqrt(np.sum(np.square(ref-test)))\n", - "\n", - "# Test self-defined divergence metric \n", - "cdbd = CDBD(\n", - " divergence=distance_metric,\n", - " detect_batch=1,\n", - " statistic=\"stdev\",\n", - " significance=0.05,\n", - " subsets=5,\n", - ")\n", - "\n", - "cdbd.set_reference(reference)\n", - "cdbd.update(pd.DataFrame(data[data.year == 2008].loc[:, \"confidence\"]))" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.10.5 ('venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "e333d0cd20283ee96f449771faa0e76b10d6c7d969d83355cc002b346ae878c6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/batch/cdbd_example.py b/examples/batch/cdbd_example.py deleted file mode 100644 index 11a3e006..00000000 --- a/examples/batch/cdbd_example.py +++ /dev/null @@ -1,103 +0,0 @@ -""" - -Confidence Distribution Batch Detection (CDBD) Example - -This file details how to setup, run, and produce plots for CDBD. This script -monitors the feature "confidence", simulated confidence scores output by a -classifier. Drift occurs in 2018 and persists through 2021. See -src/menelaus/tools/artifacts/README_example_data.txt for more info. -It additionally contains an example of using a custom divergence function. - -CDBD must be setup and run with batches of data containing 1 variable. - -Plots include: -- A line plot visualizing test statistics for detection of drift - -""" - -import os -import numpy as np -import pandas as pd -import seaborn as sns -import matplotlib.pyplot as plt - -from menelaus.data_drift.cdbd import CDBD -from menelaus.datasets import make_example_batch_data - -## Setup ## - -# Import data -data = make_example_batch_data() - -# Set up reference and test batches, using 2007 as reference year -reference = pd.DataFrame(data[data.year == 2007].loc[:, "confidence"]) -all_test = data[data.year != 2007] - -# Setup CDBD -np.random.seed(1) -cdbd = CDBD(subsets=8) - -# Store drift for test statistic plot -years = all_test.year.unique() -detected_drift = [] - -# Run CDBD -cdbd.set_reference(reference) -for year, subset_data in all_test.groupby("year"): - cdbd.update(pd.DataFrame(subset_data.loc[:, "confidence"])) - detected_drift.append(cdbd.drift_state) - - -## Plot Line Graph ## - - -kl_divergence = [ - ep - th for ep, th in zip(cdbd.epsilon_values.values(), cdbd.thresholds.values()) -] - -plot_data = pd.DataFrame( - {"Year": years, "KL Divergence": kl_divergence, "Detected Drift": detected_drift} -) -sns.set_style("white") -plt.figure(figsize=(20, 6)) -plt.plot("Year", "KL Divergence", data=plot_data, label="KL Divergence", marker=".") -plt.grid(False, axis="x") -plt.xticks(years, fontsize=16) -plt.yticks(fontsize=16) -plt.title("CDBD Test Statistics", fontsize=22) -plt.ylabel("KL Divergence", fontsize=18) -plt.xlabel("Year", fontsize=18) -plt.ylim([min(kl_divergence) - 0.02, max(kl_divergence) + 0.02]) -for _, t in enumerate(plot_data.loc[plot_data["Detected Drift"] == "drift"]["Year"]): - plt.axvspan( - t - 0.2, t + 0.2, alpha=0.5, color="red", label=("Drift Detected" if _ == 0 else None) - ) -plt.legend() -plt.axhline(y=0, color="orange", linestyle="dashed") - -# plt.show() -plt.savefig("example_CDBD_test_statistics.png") - - -### Custom Divergence Metric ### - -# Define divergence function -def distance_metric(reference_histogram, test_histogram): - - # Convert inputs to appropriate datatype - ref = np.array(reference_histogram[0]) - test = np.array(test_histogram[0]) - - return np.sqrt(np.sum(np.square(ref-test))) - -# Test self-defined divergence metric -cdbd = CDBD( - divergence=distance_metric, - detect_batch=1, - statistic="stdev", - significance=0.05, - subsets=5, -) - -cdbd.set_reference(reference) -cdbd.update(pd.DataFrame(data[data.year == 2008].loc[:, "confidence"])) diff --git a/examples/batch/hdddm_example.py b/examples/batch/hdddm_example.py deleted file mode 100644 index 640d0fb4..00000000 --- a/examples/batch/hdddm_example.py +++ /dev/null @@ -1,157 +0,0 @@ -""" - -Hellinger Distance Drift Detection Method (HDDDM) Example - -This file details how to setup, run, and produce plots for HDDDM, using both -numeric and categorical data. Drift occurs in 2009, 2012, 2015, 2018, and 2021. -Drift in 2018 persists through 2021. See -src/menelaus/tools/artifacts/README_example_data.txt for more info. -It additionally contains an example of using a custom divergence function. - -HDDDM must be setup and run with batches of data. - -Plots include: -- A line plot visualizing test statistics for detection of drift - -- A heatmap visualizing "where" drift is occuring, showing features for each - year's test batch with the greatest Hellinger distance from the reference - batch. - -""" - -import matplotlib.pyplot as plt -import numpy as np -import os -import pandas as pd -import seaborn as sns - -from menelaus.data_drift.hdddm import HDDDM -from menelaus.datasets import make_example_batch_data - - -## Setup ## - -# Import data -df = make_example_batch_data() - -# Set up reference and test batches, using 2007 as reference year -# -2 indexing removes columns "drift" and "confidence" -reference = df[df.year == 2007].iloc[:, 1:-2] -all_test = df[df.year != 2007] - -# Setup HDDDM -np.random.seed(1) -hdddm = HDDDM(subsets=8) - -# Store epsilons per feature for heatmap -years = all_test.year.unique() -heatmap_data = pd.DataFrame(columns = years) - -# Store drift for test statistic plot -detected_drift = [] - -# Run HDDDM -hdddm.set_reference(reference) -for year, subset_data in df[df.year != 2007].groupby("year"): - hdddm.update(subset_data.iloc[:, 1:-2]) - heatmap_data[year] = hdddm.feature_epsilons - detected_drift.append(hdddm.drift_state) - - -## Plot Line Graph ## - -# Plot Info: -# HDDDM identifies drifts in 2009, 2010, 2012, 2019, 2021. These drifts involve -# a change in mean or variance. Drift in 2010 is likely identified as the -# distribution returns to state prior to 2009 drift. Drift in 2015, a change in -# correlation, is undetected. Drift in 2018 is detected one year late. - -h_distances = [ - ep - th for ep, th in zip(hdddm.epsilon_values.values(), hdddm.thresholds.values()) -] - -# Plot Hellinger Distance against Year, along with detected drift -plot_data = pd.DataFrame( - {"Year": years, "Hellinger Distance": h_distances, "Detected Drift": detected_drift} -) -sns.set_style("white") -plt.figure(figsize=(20, 6)) -plt.plot( - "Year", "Hellinger Distance", data=plot_data, label="Hellinger Distance", marker="." -) -plt.grid(False, axis="x") -plt.xticks(years, fontsize=16) -plt.yticks(fontsize=16) -plt.title("HDDDM Test Statistics", fontsize=22) -plt.ylabel("Hellinger Distance", fontsize=18) -plt.xlabel("Year", fontsize=18) -plt.ylim([min(h_distances) - 0.02, max(h_distances) + 0.02]) -for _, t in enumerate(plot_data.loc[plot_data["Detected Drift"] == "drift"]["Year"]): - plt.axvspan( - t - 0.2, t + 0.2, alpha=0.5, color="red", label=("Drift Detected" if _ == 0 else None) - ) -plt.legend() -plt.axhline(y=0, color="orange", linestyle="dashed") - -# plt.show() -plt.savefig("example_HDDDM_test_statistics.png") - - -## Plot Heatmap ## - -# Plot Info: -# Drift in feature B is detected in 2009 and 2010 (as it reverts to normal). -# Drift in feature D is detected in 2012 and 2013 (as it reverts to normal). -# Drift in feature H is detected in 2019. Drift in feature J is detected in 2021. -# The undetected drift occurs in 2015 in the correlations between features E and F. - -sns.set_style("whitegrid") -sns.set(rc={"figure.figsize": (15, 8)}) - -# Setup plot - -# Setup plot -grid_kws = {"height_ratios": (0.9, 0.05), "hspace": 0.3} -f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws) -coloring = sns.cubehelix_palette(start=0.8, rot=-0.5, as_cmap=True) -ax = sns.heatmap( - heatmap_data, - ax=ax, - cmap=coloring, - xticklabels=heatmap_data.columns, - yticklabels=heatmap_data.index, - linewidths=0.5, - cbar_ax=cbar_ax, - cbar_kws={"orientation": "horizontal"}, -) -ax.set_title('HDDDM Feature Heatmap') -ax.set(xlabel="Years", ylabel="Features") -ax.collections[0].colorbar.set_label("Difference in Hellinger Distance") -ax.set_yticklabels(ax.get_yticklabels(), rotation=0) - -# plt.show() -plt.savefig("example_HDDDM_feature_heatmap.png") - - -### Custom Divergence Metric ### - -# Define divergence function -def distance_metric(reference_histogram, test_histogram): - - # Convert inputs to appropriate datatype - ref = np.array(reference_histogram[0]) - test = np.array(test_histogram[0]) - - return np.sqrt(np.sum(np.square(ref-test))) - -# Test self-defined divergence metric -hdddm = HDDDM( - divergence=distance_metric, - detect_batch=1, - statistic="stdev", - significance=0.05, - subsets=5, -) - -hdddm.set_reference(reference) -hdddm.update(df[df.year == 2008].iloc[:, 1:-2]) diff --git a/examples/batch/kdqtree_batch_example.py b/examples/batch/kdqtree_batch_example.py deleted file mode 100644 index f5e3f6f5..00000000 --- a/examples/batch/kdqtree_batch_example.py +++ /dev/null @@ -1,103 +0,0 @@ -""" - -kdq-Tree Example (batch setting) ----------------------------- - -This example shows up how to set up, run, and produce output from the kdq-Tree -detector, specifically in the batch data setting. The parameters aren't -necessarily tuned for best performance, just notional. - -Drift in the example dataset occurs in 2009, 2012, 2015, 2018, and 2021. Drift -in 2018 persists through 2021. See -src/menelaus/tools/artifacts/README_example_data.txt for more details. - -This example takes roughly a minute to run. - -""" - -import os -import pandas as pd -import numpy as np -import plotly.express as px - -from menelaus.data_drift.kdq_tree import KdqTreeBatch -from menelaus.datasets import make_example_batch_data - -# kdq-Tree does use bootstrapping to define its critical thresholds, so setting -# the seed is important to reproduce exact behavior. -np.random.seed(123) - -# Import data -df = make_example_batch_data() - -# Capture the column which tells us when drift truly occurred -drift_years = df.groupby("year")["drift"].apply(lambda x: x.unique()[0]).reset_index() - -# Because the drift in 2009, 2012, and 2016 is intermittent - it reverts -# back to the prior distribution in the subsequent year - we should also detect -# drift in 2010, 2013, and 2016. So: -drift_years.loc[drift_years["year"].isin([2010, 2013, 2016]), "drift"] = True - -df.drop(columns=["cat", "confidence", "drift"], inplace=True) - - -plot_data = {} -status = pd.DataFrame(columns=["year", "drift"]) -det = KdqTreeBatch() - -# Set up reference batch, using 2007 as reference year -det.set_reference(df[df.year == 2007].drop(columns=['year'])) - -# Batch the data by year and run kdqTree -for group, sub_df in df[df.year != 2007].groupby("year"): - det.update(sub_df.drop(columns=["year"])) - status = pd.concat( - [status, pd.DataFrame({"year": [group], "drift": [det.drift_state]})], - axis=0, - ignore_index=True, - ) - if det.drift_state is not None: - # capture the visualization data - plot_data[group] = det.to_plotly_dataframe() - - # option to specify reference batch to be any year - # det.set_reference(df[df.year == XXXX].values) - -# Print out the true drift status, and that according to the detector. -# The detector successfully identifies drift in every year but 2018; -# that's the first year where persistent drift, from 2018-2021, was induced. -# The detector picks it up in 2019, the second year of persistent drift. -( - status.merge(drift_years, how="left", on="year", suffixes=["_kdqTree", "_true"]) - .replace({True: "drift", False: None}) - .to_csv("example_kdqtree_drift_comparison.csv", index=False) -) - -# If we save off the to_plotly_dataframe at each drift detection, we can display -# the Kulldorff Spatial Scan Statistic (KSS) for each. Higher values of KSS -# indicate that a given region of the data space has greater divergence between -# the reference and test data. -for year, df_plot in plot_data.items(): - fig = px.treemap( - data_frame=df_plot, - names="name", - ids="idx", - parents="parent_idx", - color="kss", - color_continuous_scale="blues", - title=year, - ) - fig.update_traces(root_color="lightgrey") - # fig.show() - fig.write_html(f"example_kdqtree_treemap_{year}.html") - -# We can see that the regions of greatest drift do line up with at least one of -# the items that were modified in a given year. - -# For reference, the detailed descriptions of drift induction: -# Drift 1: change the mean & var of item B in 2009, means will revert for 2010 on -# Drift 2: change the variance of item c and d in 2012 by replacing some with the mean -# keep same mean as other years, revert by 2013 -# Drift 3: change the correlation of item e and f in 2015 (go from correlation of 0 to correlation of 0.5) -# Drift 4: change mean and var of H and persist it from 2018 on -# Drift 5: change mean and var just for a year of J in 2021 diff --git a/examples/change_detection_examples.py b/examples/change_detection_examples.py new file mode 100644 index 00000000..6d85577e --- /dev/null +++ b/examples/change_detection_examples.py @@ -0,0 +1,264 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Change Detection Examples + +# These examples show how to set up, run, and produce output from detectors in the +# change_detection module. The parameters aren't necessarily tuned for best +# performance for the input data, just notional. +# +# Circle is a synthetic data source, where drift occurs in both var1, var2, and the +# conditional distributions P(y|var1) and P(y|var2). The drift occurs from index +# 1000 to 1250, and affects 66% of the sample. +# +# Rainfall is a real source of weather data. We use the first 1000 samples, where +# no drift has been injected; but many features are cyclical, and haven't been +# corrected, so change does occur. +# +# These change detectors can be applied to any given single variable; below, +# they are applied to `var2` and the `max_sustained_wind_speed` columns in the +# respective datasets. + +# In[1]: + + +## Imports ## + +import os +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from menelaus.change_detection import ADWIN, CUSUM, PageHinkley +from menelaus.datasets import fetch_circle_data, fetch_rainfall_data + + +# In[2]: + + +## Import Data ## + +# read in Circle dataset +df = fetch_circle_data() +drift_start, drift_end = 1000, 1250 + +rainfall_df = fetch_rainfall_data() + + +# ## Cumulative Sum (CUSUM) Test + +# - This monitors a moving average of var2, starting from an initial estimate of mean +# and standard deviation. +# +# - It will only alarm if 50 or more samples have been observed since +# initialization/drift. +# +# - This will alarm if var2 passes a critical value controlled by delta and +# threshold in either direction, positive or negative. +# +# + +# In[5]: + + +## Setup ## +cusum = CUSUM( + target=np.mean(df.loc[:drift_start, "var2"]), # mean / std of 'Var 2' pre-drift + sd_hat=np.std(df.loc[:drift_start, "var2"]), + burn_in=50, + delta=0.005, + threshold=40, + direction=None, +) + +# setup DF to record results +status = pd.DataFrame(columns=["index", "actual value", "drift_detected"]) + +# iterate through data; feed each sample to the detector, in turn +for i in range(len(df)): + obs = df["var2"][i] + cusum.update(obs) + status.loc[i] = [i, obs, cusum.drift_state] + + +# In[6]: + + +## Plotting ## + +# plot the monitored variable and the status of the detector +plt.figure(figsize=(20, 6)) +plt.scatter("index", "actual value", data=status, label="Var 2") +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("CUSUM Test Results", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") + +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) +plt.legend() + + +# CUSUM alarms several times within the drift induction window, roughly halfway +# through. After the alarm is reset, change is detected a few more times, +# including an apparently erroneous detection after the drift induction window +# is passed. The current threshold settings may then be too sensitive for the +# new regime. +# + +# In[7]: + + +plt.show() +# plt.savefig("example_CUSUM_detections.png") + + +# ## Page-Hinkley (PH) Test + +# In[ ]: + + +## Setup ## + +# Set up one-directional PH test: this will only alarm if the mean of the +# monitored variable decreases, and only after seeing 30 or more samples. +ph = PageHinkley(delta=0.01, threshold=15, direction="negative", burn_in=30) + +# setup DF to record results +status = pd.DataFrame(columns=["index", "actual value", "drift_detected"]) + +# iterate through data; feed each sample to the detector, in turn +for i in range(len(df)): + obs = df["var2"][i] + ph.update(X=obs) + status.loc[i] = [i, obs, ph.drift_state] + + +# In[ ]: + + +## Plotting ## + +# plot the monitored variable and the status of the detector +plt.figure(figsize=(20, 6)) +plt.scatter("index", "actual value", data=status, label="Var 2") +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("PH Test Results", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") + +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) +plt.legend() +plt.show() +# plt.savefig("example_Page-Hinkley_detections.png") + + +# Page-Hinkley alarms shortly after the drift induction window closes, and then makes +# several apparently erroneous alarms afterwards. The parameters may not be +# well-chosen for the new regime. +# Change detection algorithms come out of process control, so a priori +# characterization of the bounds of the process, not performed here, would not +# be unreasonable. +# + +# ## ADaptive WINdowing (ADWIN) +# +# ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case. + +# In[3]: + + +## Setup ## + +adwin = ADWIN() + +# setup DF to record results +status = pd.DataFrame(columns=["index", "actual value", "drift_detected", "ADWIN mean"]) +df2 = rainfall_df.loc[:1000, 'max_sustained_wind_speed'] +rec_list = [] + +# iterate through data; feed each sample to the detector, in turn +for i in range(len(df2)): + obs = df2[i] + adwin.update(X=obs) + status.loc[i] = [i, obs, adwin.drift_state, adwin.mean()] + + #monitor the size of ADWIN's window as it changes + if adwin.drift_state == "drift": + retrain_start = adwin.retraining_recs[0] + retrain_end = adwin.retraining_recs[1] + rec_list.append([retrain_start, retrain_end]) + + +# In[4]: + + +## Plotting ## + +# plot the monitored variable and the status of the detector +plt.figure(figsize=(20, 6)) +plt.scatter("index", "actual value", data=status, label="max_sustained_wind_speed", alpha=.5) +plt.plot("index", "ADWIN mean", data=status, color='blue', linewidth=3) +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("ADWIN Results", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-2, 6] +plt.ylim(ylims) + +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.6 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.8 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"][::-1], + xmin=rec_list[0], + xmax=rec_list[1], + color="black", + label="New Observation Windows", +) + +plt.legend() +plt.show() +# plt.savefig("example_ADWIN.png") + + +# ADWIN monitors the running average of the `max_sustained_wind_speed` column and, once that mean begins to change enough around index 600, shrinks its observation window (in black) to only include more recent samples. This process repeats as further changes are detected. We can see that the size of the observation window shrinks and grows as the incoming data changes. diff --git a/examples/concept_drift_examples.py b/examples/concept_drift_examples.py new file mode 100644 index 00000000..878d0e05 --- /dev/null +++ b/examples/concept_drift_examples.py @@ -0,0 +1,931 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Concept Drift Detector Examples + +# The examples in this notebook show how to set up, run, and produce output from detectors in the +# concept_drift module. The parameters aren't necessarily tuned for best +# performance for the input data, just notional. +# +# Circle is a synthetic data source, where drift occurs in both var1, var2, and the +# conditional distributions P(y|var1) and P(y|var2). The drift occurs from index +# 1000 to 1250, and affects 66% of the sample. +# +# Rainfall is a real data source into which concept drift has been injected. This +# set contains approximately 18,000 samples, and the data has been standardized. +# Drift starts from index 12,000 and continues through the rest of the dataset. +# In this example, we take the first 10,000 samples of the dataset for training +# an initial classifier, and then use the remaining samples for testing. +# +# These detectors are generally to be applied to the true class and predicted class +# from a particular model. For the sake of example, a simple running accuracy of the +# classifier is presented for each detector as a measure of its performance, +# rather than e.g. the prequential error. +# +# They also track the indices of portions of the incoming data stream which are +# more similar to each other -- i.e., data that seems to be part of the same +# concept, which could be used to retrain a model. +# +# NOTE: The LinearFourRates example has a relatively long runtime, roughly 5 minutes. + +# In[ ]: + + +## Imports ## + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +from sklearn.naive_bayes import GaussianNB +from sklearn.linear_model import SGDClassifier +from sklearn import svm +from sklearn.base import clone +from menelaus.concept_drift import LinearFourRates, ADWINAccuracy, DDM, EDDM, STEPD, MD3 +from menelaus.datasets import fetch_circle_data, fetch_rainfall_data + + +# In[ ]: + + +## Import Data ## + +# read in Circle dataset +df = fetch_circle_data() +drift_start, drift_end = 1000, 1250 +training_size = 500 + +# read in Rainfall dataset +rainfall_df = fetch_rainfall_data() +rainfall_drift_start, rainfall_drift_end = 12000, 18158 +rainfall_training_size = 10000 +rainfall_columns = ["temperature", "dew_point", "sea_level_pressure", "visibility", "average_wind_speed", "max_sustained_wind_speed", "minimum_temperature", "maximum_temperature", "rain"] +rainfall_features = ["temperature", "dew_point", "sea_level_pressure", "visibility", "average_wind_speed", "max_sustained_wind_speed", "minimum_temperature", "maximum_temperature"] +rainfall_df[rainfall_features] = rainfall_df[rainfall_features].astype(float) + + +# ## Linear Four Rates (LFR) + +# Linear Four Rates monitors the four cells of the confusion matrix (TPR, FPR, TNR, FNR) and alarms when one of these becomes different enough from earlier performance. + +# In[ ]: + + +## Setup ## + +# Set up classifier: train on first training_size rows +X_train = df.loc[0:training_size, ["var1", "var2"]] +y_train = df.loc[0:training_size, "y"] +X_test = df.loc[training_size:len(df), ["var1", "var2"]] +y_true = df.loc[training_size:len(df), "y"] +clf = GaussianNB() +clf.fit(X_train, y_train) + +acc_orig = np.cumsum(clf.predict(X_test) == y_true) +acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig)) + +# Set up LFR detector to detect at significance of .001. 5000 Monte Carlo +# simulations will be run every 10 samples to detect drift. +lfr = LinearFourRates( + time_decay_factor=0.6, + warning_level=0.01, + detect_level=0.001, + num_mc=5000, + burn_in=10, + subsample=10, +) + +# Set up DF to store results. +status = pd.DataFrame(columns=["index", "y_true", "y_pred", "drift_detected"]) + + +# In[ ]: + + +## Run LFR ## + +np.random.seed(123) # set seed for this example + +# Run LFR and retrain. + +rec_list = [] +for i in range(training_size, len(df)): + X_test = df.loc[[i], ["var1", "var2"]] + y_pred = int(clf.predict(X_test)) + y_true = int(df.loc[[i], "y"]) + + lfr.update(y_true, y_pred) + status.loc[i] = [i, y_true, y_pred, lfr.drift_state] + + # If drift is detected, examine the retraining recommendations and retrain. + if lfr.drift_state == "drift": + + retrain_start = lfr.retraining_recs[0] + training_size + retrain_end = lfr.retraining_recs[1] + training_size + if ( + retrain_start == retrain_end + ): # minimum retraining window in case of sudden drift + retrain_start = max(0, retrain_start - 300) + rec_list.append([retrain_start, retrain_end]) + + # If retraining is not desired, omit the next four lines. + X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] + y_train = df.loc[retrain_start:retrain_end, "y"] + clf = GaussianNB() + clf.fit(X_train, y_train) + +status['original_accuracy'] = acc_orig +status['accuracy'] = np.cumsum(status.y_true == status.y_pred) +status['accuracy'] = status['accuracy'] / np.arange(1, 1 + len(status)) + + +# In[ ]: + + +## Plotting ## + +plt.figure(figsize=(20, 5)) +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color='red') +plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color='green') +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("LFR Results: Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +# Draw orange lines that indicate where warnings of drift were provided +plt.vlines( + x=status.loc[status["drift_detected"] == "warning"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Warning", + color="orange", + alpha=0.3, +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"], + xmin=rec_list[0], + xmax=rec_list[1], + color="green", + label="Retraining Windows", +) + +plt.legend() +plt.show() +# plt.savefig("example_LFR.png") + + +# +# One of the four rates immediately passes outside its threshold when drift is +# induced. The same occurs shortly after leaving the drift region. The +# recommended retraining data includes most of the drift induction window and +# the data after regime change. +# +# The classifier's accuracy decreases again later, which causes the detector to +# enter a "warning" state. Note that the retraining recommendations *begin* with +# the index corresponding to the warning state, and end where drift is detected. +# + +# ## ADaptive WINdowing (ADWIN) + +# ADWIN is a change detection algorithm that can be used to monitor a real-valued number. ADWIN maintains a window of the data stream, which grows to the right as new elements are received. When the mean of the feature in one of the subwindows is different enough, ADWIN drops older elements in its window until this ceases to be the case. +# +# It can be used to monitor the accuracy of a classifier by checking `y_true == y_pred` at each time step. So, for convenience, `concept_drift.ADWINAccuracy`, takes `y_true` and `y_pred` as arugments, as shown below. `change_detection.ADWIN` can be used more generally, as shown in the change detection examples. + +# In[ ]: + + +## Setup ## + +# Set up classifier: train on first training_size rows +X_train = df.loc[0:training_size, ["var1", "var2"]] +y_train = df.loc[0:training_size, "y"] +X_test = df.loc[training_size:len(df), ["var1", "var2"]] +y_true = df.loc[training_size:len(df), "y"] + +np.random.seed(123) +clf = GaussianNB() +clf.fit(X_train, y_train) + +# get running accuracy from the original classifier to compare performance +acc_orig = np.cumsum(clf.predict(X_test) == y_true) +acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig)) + +adwin = ADWINAccuracy() + +# Set up DF to record results. +status = pd.DataFrame( + columns=["index", "y_true", "y_pred", "adwin mean", "drift_detected"] +) +rec_list = [] + + +# In[ ]: + + +# run ADWIN +for i in range(training_size, len(df)): + + X_test = df.loc[[i], ["var1", "var2"]] + y_pred = int(clf.predict(X_test)) + y_true = int(df.loc[[i], "y"]) + + adwin.update(y_true, y_pred) + status.loc[i] = [ + i, y_true, y_pred, adwin.mean(), adwin.drift_state, + ] + + # If drift is detected, examine the window and retrain. + if adwin.drift_state == "drift": + retrain_start = adwin.retraining_recs[0] + training_size + retrain_end = adwin.retraining_recs[1] + training_size + rec_list.append([retrain_start, retrain_end]) + + # The retraining recommendations produced here correspond to the samples + # which belong to ADWIN's new, smaller window, after drift is detected. + # If retraining is not desired, omit the next four lines. + X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] + y_train = df.loc[retrain_start:retrain_end, "y"] + clf = GaussianNB() + clf.fit(X_train, y_train) + +status['original_accuracy'] = acc_orig +status['accuracy'] = np.cumsum(status.y_true == status.y_pred) +status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0])) + + +# In[ ]: + + +## Plotting ## + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color='red') +plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color='green') +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("ADWIN Results: Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [0, 1.1] +plt.ylim(ylims) + +plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"], + xmin=rec_list[0], + xmax=rec_list[1], + color="green", + label="Retraining Windows", +) + +plt.legend(loc='lower right') +plt.show() +# plt.savefig("example_ADWINAccuracy.png") + + +# +# After drift is induced, the accuracy decreases enough for ADWIN to shrink its +# window and alarm; subsequent windows also include data from the old regime, so +# drift continues to be detected until the window shrinks enough to be comprised +# mostly by the new regime. We can see that, by retraining on the new window of +# data reported by ADWIN, the accuracy of the classifier is improved over time. + +# ## Drift Detection Method (DDM) + +# DDM can enter either a "drift" or "warning" state, depending on how close a classifier's error rate has approached to those respective thresholds, defined by the warning_scale and drift_scale parameters. + +# In[ ]: + + +## Setup ## + +np.random.seed(123) +# setup classifier: train on first training_size rows +X_train = df.loc[0:training_size, ["var1", "var2"]] +y_train = df.loc[0:training_size, "y"] +X_test = df.loc[training_size:len(df), ["var1", "var2"]] +y_true = df.loc[training_size:len(df), "y"] + +clf = GaussianNB() +clf.fit(X_train, y_train) + +acc_orig = np.cumsum(clf.predict(X_test) == y_true) +acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig)) + + +# +# These parameter values are chosen somewhat arbitrarily. +# At least 100 samples must be seen before DDM tests for drift (the n_threshold +# parameter); the other two define the warning and drift regions. The minimum +# error rate (and its standard deviation) are found during a stable regime; the +# warning_scale and drift_scale roughly correspond to how many standard standard +# deviations away the current estimate must be in order for the detector to +# alarm. +# + +# In[ ]: + + +ddm = DDM(n_threshold=100, warning_scale=7, drift_scale=10) + +# setup DF to store results +status = pd.DataFrame(columns=["index", "y_true", "y_pred", "drift_detected"]) +rec_list = [] + + +# In[ ]: + + +# run DDM and retrain +for i in range(training_size, len(df)): + + X_test = df.loc[[i], ["var1", "var2"]] + y_pred = int(clf.predict(X_test)) + y_true = int(df.loc[[i], "y"]) + + ddm.update(y_true, y_pred) + status.loc[i] = [i, y_true, y_pred, ddm.drift_state] + + # If drift is detected, examine the window and retrain. + if ddm.drift_state == "drift": + retrain_start = ddm.retraining_recs[0] + training_size + retrain_end = ddm.retraining_recs[1] + training_size + if ( + retrain_start == retrain_end + ): # minimum retraining window in case of sudden drift + retrain_start = max(0, retrain_start - 300) + rec_list.append([retrain_start, retrain_end]) + + # If retraining is not desired, omit the next four lines. + X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] + y_train = df.loc[retrain_start:retrain_end, "y"] + clf = GaussianNB() + clf.fit(X_train, y_train) + +status['original_accuracy'] = acc_orig +status['accuracy'] = np.cumsum(status.y_true == status.y_pred) +status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0])) + + +# In[ ]: + + +## Plotting ## + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color='red') +plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color='green') +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("DDM Results: Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", + linewidth=3, +) + +# Draw orange lines that indicate where warnings of drift were provided +plt.vlines( + x=status.loc[status["drift_detected"] == "warning"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Warning", + color="orange", + alpha=0.3, +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"], + xmin=rec_list[0], + xmax=rec_list[1], + color="green", + label="Retraining Windows", +) + +plt.legend() +plt.show() +# plt.savefig("example_DDM.png") + + +# +# DDM initially alarms during the drift induction window, triggering retraining. +# The subsequent dip in accuracy is large enough to put the detector in the +# "warning" state, but not large enough for "drift" to be identified. +# +# + +# ## Early Drift Detection Method (EDDM) + +# EDDM monitors the distance between two errors of a classifier - i.e., the number of samples between errors - rather than monitoring the error rate itself. Similar to DDM, it uses separate thresholds for "warning" and "drift." + +# In[ ]: + + +## Setup ## + +np.random.seed(123) +# setup classifier: train on first 500 rows +X_train = df.loc[0:training_size, ["var1", "var2"]] +y_train = df.loc[0:training_size, "y"] +X_test = df.loc[training_size:len(df), ["var1", "var2"]] +y_true = df.loc[training_size:len(df), "y"] + +clf = GaussianNB() +clf.fit(X_train, y_train) + +acc_orig = np.cumsum(clf.predict(X_test) == y_true) +acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig)) + + +# - n_threshold specifies the number of new samples which must be observed before +# tests for drift are run. +# +# - The warning_thresh and drift_thresh values roughly correspond to the ratio of +# the 95th percentile for the current distance distribution vs. the 95th percentile +# for the "best" distance distribution observed so far. So, lower values correspond to less conservative monitoring - the current +# distance between errors is allowed to be a smaller fraction of the "best" +# distance. +# + +# In[ ]: + + +eddm = EDDM(n_threshold=30, warning_thresh=0.7, drift_thresh=0.5) + +# setup DF to store results +status = pd.DataFrame(columns=["index", "y_true", "y_pred", "drift_detected"]) +rec_list = [] + + +# In[ ]: + + +# run EDDM and retrain +for i in range(training_size, len(df)): + + X_test = df.loc[[i], ["var1", "var2"]] + y_pred = int(clf.predict(X_test)) + y_true = int(df.loc[[i], "y"]) + + eddm.update(y_true, y_pred) + status.loc[i] = [i, y_true, y_pred, eddm.drift_state] + + # If drift is detected, examine the window and retrain. + if eddm.drift_state == "drift": + retrain_start = eddm.retraining_recs[0] + training_size + retrain_end = eddm.retraining_recs[1] + training_size + if ( + retrain_start == retrain_end + ): # minimum retraining window in case of sudden drift + retrain_start = max(0, retrain_start - 300) + rec_list.append([retrain_start, retrain_end]) + + # If retraining is not desired, omit the next four lines. + X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] + y_train = df.loc[retrain_start:retrain_end, "y"] + clf = GaussianNB() + clf.fit(X_train, y_train) + +status['original_accuracy'] = acc_orig +status['accuracy'] = np.cumsum(status.y_true == status.y_pred) +status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0])) + + +# In[ ]: + + +## Plotting ## + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color='red') +plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color='green') +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("EDDM Results: Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") + +# Draw orange lines that indicate where warnings of drift were provided +plt.vlines( + x=status.loc[status["drift_detected"] == "warning"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Warning", + color="orange", + alpha=0.3, +) + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"], + xmin=rec_list[0], + xmax=rec_list[1], + color="green", + label="Retraining Windows", + alpha=0.3, +) + +plt.legend() +plt.show() +# plt.savefig("example_EDDM.png") + + +# EDDM enters a drift state shortly after the drift induction window, triggering +# retraining. The later increase in the error rate causes the detector to enter +# the warning state, but is not large enough to be identified as drift with this +# threshold setting. +# + +# ## Statistical Test of Equal Proportions to Detect Concept Drift (STEPD) + +# STEPD is a detector specifically intended for online classifiers, where each new sample is used to update the parameters of the classifier. STEPD monitors the accuracy in two windows, "recent" and "past," and compares those in order to detect drift in classifier accuracy. + +# In[ ]: + + +## Setup ## + +np.random.seed(123) +# setup classifier: train on first 500 rows +X_train = df.loc[0:training_size, ["var1", "var2"]] +y_train = df.loc[0:training_size, "y"] +X_test = df.loc[training_size:len(df), ["var1", "var2"]] +y_true = df.loc[training_size:len(df), "y"] + +clf = GaussianNB() +clf.fit(X_train, y_train) + +# Calculate the accuracy the classifier would have had without any retraining +# recommendations from STEPD. + +df_test = df.loc[training_size:, :] + +y_pred = [] +for i in range(len(df_test)): + X_curr = df.loc[i:i, ["var1", "var2"]] + y_curr = df.loc[i:i, "y"] + y_pred.append(clf.predict(X_curr)) + clf.partial_fit(X_curr, y_curr) +y_pred = np.array(y_pred).reshape(len(y_pred)) + +acc_orig = np.cumsum(y_pred == y_true) +acc_orig = acc_orig / np.arange(1, 1 + len(acc_orig)) +acc_orig = acc_orig.reset_index()['y'] + + +# In[ ]: + + +#reset the classifier for loop with detection +np.random.seed(123) +clf.fit(X_train, y_train) + +stepd = STEPD(window_size=250) + +# setup DF to store results +status = pd.DataFrame(columns=["index", "y_true", "y_pred", "drift_detected"]) +rec_list = [] + +for i in range(len(df_test)): + X_curr = df.loc[i:i, ["var1", "var2"]] + y_curr = df.loc[i:i, "y"] + y_pred = clf.predict(X_curr)[0] + + stepd.update(y_true=y_curr.values[0], y_pred=y_pred) + status.loc[i] = [i, y_curr.values[0], y_pred, stepd.drift_state] + + # If drift is detected, examine the window and retrain. + if stepd.drift_state == "drift": + #account for the training_size offset + recs = [x + training_size for x in stepd.retraining_recs] + rec_list.append(recs) + + # If retraining is not desired, omit the next four lines. + X_train = df.loc[recs[0]:recs[1], ["var1", "var2"]] + y_train = df.loc[recs[0]:recs[1], "y"] + clf = GaussianNB() + clf.fit(X_train, y_train) + else: + clf.partial_fit(X_curr, y_curr.values) + +status['original_accuracy'] = acc_orig +status['accuracy'] = np.cumsum(status.y_true == status.y_pred) +status['accuracy'] = status['accuracy'] / np.cumsum(np.repeat(1, status.shape[0])) +status['index'] = range(training_size, len(status) + training_size) + +## Plotting ## + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color='red') +plt.scatter("index", "accuracy", data=status, label="Retrain Accuracy", color='green') +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("STEPD Results: Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") + +# Draw orange lines that indicate where warnings of drift were provided +plt.vlines( + x=status.loc[status["drift_detected"] == "warning"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Warning", + color="orange", + alpha=0.3, +) + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", + linewidth=3, +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +rec_list = pd.DataFrame(rec_list) +rec_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(rec_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=rec_list["y_val"], + xmin=rec_list[0], + xmax=rec_list[1], + color="green", + label="Retraining Windows", +) + +plt.legend(loc='lower left') +plt.show() +# plt.savefig("example_STEPD.png") + + +# With a window roughly the same size as the drift induction window, STEPD doesn't +# enter a warning state until roughly 400 samples after the drift induction +# window. +# +# Because the online classifier is updating at each step, its performance is +# relatively flat until roughly 400 samples after the drift induction window. +# STEPD enters a warning state after this degradation, at which point the +# classifier is retrained on samples buffered by STEPD during the time in the +# warning region. Retraining the classifier on this sample, then performing +# subsequent updates, slightly improves performance. + +# ## Margin Density Drift Detection (MD3) Method + +# MD3 is a detector intended for semi-supervised and online classifier contexts. A cumulative margin density statistic (unsupervised) is tracked, representing the number of samples that fall into the uncertainty region, or margin, of the classifier. When margin density increases or decreases beyond a certain threshold, a drift warning is issued. When a warning is issued, a set of labeled samples is requested. If prediction accuracy by the model on these samples is lower than on the initial labeled reference dataset, drift is confirmed. If not, drift is ruled out. + +# In[ ]: + + +## Setup ## + +# Set up classifier: train on first training_size rows +training_data = rainfall_df.loc[0:rainfall_training_size, rainfall_columns] +X_train = rainfall_df.loc[0:rainfall_training_size, rainfall_features] +y_train = rainfall_df.loc[0:rainfall_training_size, "rain"] + +np.random.seed(123) +clf = svm.SVC(kernel='linear') +clf.fit(X_train, y_train.values.ravel()) +retrain_clf = clone(clf) +retrain_clf.fit(X_train, y_train.values.ravel()) +oracle_labels = 1000 + +# Initialize detector +md3 = MD3(clf=clf, sensitivity=1.5, oracle_data_length_required=oracle_labels) +md3.set_reference(X=training_data, target_name="rain") + +# Set up DF to record results. +status = pd.DataFrame( + columns=["index", "y_true", "margin_density", "original_accuracy", "retrain_accuracy", "drift_detected"] +) +correct_orig, correct_retrain = 0, 0 +n = 1 +rec_list = [] +oracle_list = [] + + +# In[ ]: + + +# run MD3 and track results for both original model and retrained model +for i in range(rainfall_training_size, len(rainfall_df)): + + X_test = rainfall_df.loc[[i], rainfall_features] + y_pred_orig = int(clf.predict(X_test)) + y_pred_retrain = int(retrain_clf.predict(X_test)) + y_true = int(rainfall_df.loc[[i], "rain"]) + + # increment accuracy + if y_pred_orig == y_true: + correct_orig += 1 + if y_pred_retrain == y_true: + correct_retrain += 1 + accuracy_orig = correct_orig / n + accuracy_retrain = correct_retrain / n + + # call give_oracle_label if detector is currently waiting for oracle data + if md3.waiting_for_oracle == True: + oracle_label = rainfall_df.loc[[i], rainfall_columns] + md3.give_oracle_label(oracle_label) + if md3.waiting_for_oracle == False: + retrain_clf.fit(md3.reference_batch_features, md3.reference_batch_target.values.ravel()) + status.loc[i] = [ + i, + y_true, + None, + accuracy_orig, + accuracy_retrain, + md3.drift_state, + ] + + # call update otherwise + else: + md3.update(X_test) + status.loc[i] = [ + i, + y_true, + md3.curr_margin_density, + accuracy_orig, + accuracy_retrain, + md3.drift_state, + ] + + # If there was a drift warning, track the window of the labeled + # oracle data used + if md3.drift_state == "warning": + oracle_start = i + 1 + oracle_end = i + md3.oracle_data_length_required + + oracle_list.append([oracle_start, oracle_end]) + + n += 1 + + +# In[ ]: + + +## Plotting ## + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "margin_density", data=status, label="Margin Density") +plt.scatter("index", "original_accuracy", data=status, label="Original Accuracy", color="red") +plt.scatter("index", "retrain_accuracy", data=status, label="Retrain Accuracy", color="green") +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("MD3 Results: Margin Density and Accuracy", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.1] +plt.ylim(ylims) + +plt.axvspan(rainfall_drift_start, rainfall_drift_end, alpha=0.5, label="Drift Induction Window") + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +# Draw orange lines that indicate where warnings of drift were provided +plt.vlines( + x=status.loc[status["drift_detected"] == "warning"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Warning", + color="orange", + alpha=0.3, +) + +# Create a list of lines that indicate the retraining windows. +# Space them evenly, vertically. +oracle_list = pd.DataFrame(oracle_list) +oracle_list["y_val"] = np.linspace( + start=0.05 * (ylims[1] - ylims[0]) + ylims[0], + stop=0.2 * ylims[1], + num=len(oracle_list), +) + +# Draw green lines that indicate where retraining occurred +plt.hlines( + y=oracle_list["y_val"], + xmin=oracle_list[0], + xmax=oracle_list[1], + color="green", + label="Labeled Oracle Data", +) + +plt.legend() +plt.show() +# plt.savefig("example_MD3.png") + + +# After drift is induced, the margin density decreases enough for MD3 to emit a warning. From there, the predictive accuracy of the classifier is tested, and this has already decreased sufficiently for the detector to alarm. Then, a new reference batch is set and the detector continues tracking the margin density statistic until the next warning. It can be seen from the plot that retraining at drift results in better accuracy moving forward compared with a model that is kept static. diff --git a/examples/data_drift_examples.py b/examples/data_drift_examples.py new file mode 100644 index 00000000..9dd7a9a3 --- /dev/null +++ b/examples/data_drift_examples.py @@ -0,0 +1,659 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Data Drift Detector Examples + +# This notebook contains examples that show how to set up, run, and produce output from detectors in the +# data_drift module. The parameters aren't necessarily tuned for best +# performance for the input data, just notional. These detectors are generally +# applied to the whole feature set for a given data source. +# +# The example data for PCA-CD and KDQ-Tree, Circle, is a synthetic data source, where drift +# occurs in both var1, var2, and the conditional distributions P(y|var1) and +# P(y|var2). The drift occurs from index 1000 to 1250, and affects 66% of the +# sample. + +# In[ ]: + + +## Imports ## + +import numpy as np +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import plotly.express as px +from menelaus.data_drift.cdbd import CDBD +from menelaus.data_drift.hdddm import HDDDM +from menelaus.data_drift import PCACD +from menelaus.data_drift import KdqTreeStreaming, KdqTreeBatch +from menelaus.data_drift import NNDVI +from menelaus.datasets import make_example_batch_data, fetch_circle_data + + +# In[ ]: + + +## Import Data ## + +# Import CDBD and HDDDM Data +example_data = make_example_batch_data() + +# Import PCA-CD and KDQ-Tree Data (circle dataset) +circle_data = fetch_circle_data() + + +# ## Confidence Distribution Batch Detection (CDBD) + +# This section details how to setup, run, and produce plots for CDBD. This script +# monitors the feature "confidence", simulated confidence scores output by a +# classifier. Drift occurs in 2018 and persists through 2021. See +# `make_example_batch_data` for more info. +# +# CDBD must be setup and run with batches of data containing 1 variable. +# +# Plots include: +# +# - A line plot visualizing test statistics for detection of drift + +# In[ ]: + + +## Setup ## + +# Set up reference and test batches, using 2007 as reference year +reference = pd.DataFrame(example_data[example_data.year == 2007].loc[:, "confidence"]) +all_test = example_data[example_data.year != 2007] + +# Run CDBD +cdbd = CDBD(subsets=8) +cdbd.set_reference(reference) + +# Store drift for test statistic plot +detected_drift = [] +for year, subset_data in all_test.groupby("year"): + cdbd.update(pd.DataFrame(subset_data.loc[:, "confidence"])) + detected_drift.append(cdbd.drift_state) + + +# Drift occurs in 2018 and persists through end of dataset. CDBD identifies drift +# occurring in 2019, one year late. It alerts to a false alarm in 2012. +# +# + +# In[ ]: + + +## Plot Line Graph ## +# Calculate divergences for all years in dataset +years = list(example_data.year.value_counts().index[1:]) +kl_divergence = [ + ep - th for ep, th in zip(cdbd.epsilon_values.values(), cdbd.thresholds.values()) +] + +# Remove potential infs that arise because of small confidence scores +kl_divergence = [ + x if np.isnan(x) == False and np.isinf(x) == False else 0 for x in kl_divergence +] + +# Plot KL Divergence against Year, along with detected drift +plot_data = pd.DataFrame( + {"Year": years, "KL Divergence": kl_divergence, "Detected Drift": detected_drift} +) +sns.set_style("white") +plt.figure(figsize=(20, 6)) +plt.plot("Year", "KL Divergence", data=plot_data, label="KL Divergence", marker=".") +plt.grid(False, axis="x") +plt.xticks(years, fontsize=16) +plt.yticks(fontsize=16) +plt.title("CDBD Test Statistics", fontsize=22) +plt.ylabel("KL Divergence", fontsize=18) +plt.xlabel("Year", fontsize=18) +plt.ylim([min(kl_divergence) - 0.02, max(kl_divergence) + 0.02]) +for _, t in enumerate(plot_data.loc[plot_data["Detected Drift"] == "drift"]["Year"]): + plt.axvspan( + t - 0.2, t + 0.2, alpha=0.5, color="red", label=("Drift Detected" if _ == 0 else None) + ) +plt.legend() +plt.axhline(y=0, color="orange", linestyle="dashed") + +plt.show() +# plt.savefig("example_CDBD_test_statistics.png") + + +# In[ ]: + + +### Custom Divergence Metric ### + +# Define divergence function +def distance_metric(reference_histogram, test_histogram): + + # Convert inputs to appropriate datatype + ref = np.array(reference_histogram[0]) + test = np.array(test_histogram[0]) + + return np.sqrt(np.sum(np.square(ref-test))) + +# Test self-defined divergence metric +cdbd = CDBD( + divergence=distance_metric, + detect_batch=1, + statistic="stdev", + significance=0.05, + subsets=5, +) + +cdbd.set_reference(reference) +cdbd.update(pd.DataFrame(example_data[example_data.year == 2008].loc[:, "confidence"])) + + +# ## Hellinger Distance Drift Detection Method (HDDDM) + +# This section details how to setup, run, and produce plots for HDDDM, using both +# numeric and categorical data. Drift occurs in 2009, 2012, 2015, 2018, and 2021. +# Drift in 2018 persists through 2021. See `make_example_batch_data` for more info. +# +# It additionally contains an example of using a custom divergence function. +# +# HDDDM must be setup and run with batches of data. +# +# Plots include: +# +# - A line plot visualizing test statistics for detection of drift +# +# - A heatmap visualizing "where" drift is occuring, showing features for each +# year's test batch with the greatest Hellinger distance from the reference +# batch. + +# In[ ]: + + +## Setup ## + +# Set up reference and test batches, using 2007 as reference year +# -2 indexing removes columns "drift" and "confidence" +reference = example_data[example_data.year == 2007].iloc[:, 1:-2] +all_test = example_data[example_data.year != 2007] + +# Setup HDDDM +np.random.seed(1) +hdddm = HDDDM(subsets=8) + +# Store epsilons per feature for heatmap +years = all_test.year.unique() +heatmap_data = pd.DataFrame(columns = years) + +# Store drift for test statistic plot +detected_drift = [] + +# Run HDDDM +hdddm.set_reference(reference) +for year, subset_data in example_data[example_data.year != 2007].groupby("year"): + hdddm.update(subset_data.iloc[:, 1:-2]) + heatmap_data[year] = hdddm.feature_epsilons + detected_drift.append(hdddm.drift_state) + + +# HDDDM identifies drifts in 2009, 2010, 2012, 2019, 2021. These drifts involve +# a change in mean or variance. Drift in 2010 is likely identified as the +# distribution returns to state prior to 2009 drift. Drift in 2015, a change in +# correlation, is undetected. Drift in 2018 is detected one year late. +# + +# In[ ]: + + +## Plot Line Graph ## +h_distances = [ + ep - th for ep, th in zip(hdddm.epsilon_values.values(), hdddm.thresholds.values()) +] + +# Plot Hellinger Distance against Year, along with detected drift +plot_data = pd.DataFrame( + {"Year": years, "Hellinger Distance": h_distances, "Detected Drift": detected_drift} +) +sns.set_style("white") +plt.figure(figsize=(20, 6)) +plt.plot( + "Year", "Hellinger Distance", data=plot_data, label="Hellinger Distance", marker="." +) +plt.grid(False, axis="x") +plt.xticks(years, fontsize=16) +plt.yticks(fontsize=16) +plt.title("HDDDM Test Statistics", fontsize=22) +plt.ylabel("Hellinger Distance", fontsize=18) +plt.xlabel("Year", fontsize=18) +plt.ylim([min(h_distances) - 0.02, max(h_distances) + 0.02]) +for _, t in enumerate(plot_data.loc[plot_data["Detected Drift"] == "drift"]["Year"]): + plt.axvspan( + t - 0.2, t + 0.2, alpha=0.5, color="red", label=("Drift Detected" if _ == 0 else None) + ) +plt.legend() +plt.axhline(y=0, color="orange", linestyle="dashed") + +plt.show() +# plt.savefig("example_HDDDM_test_statistics.png") + + +# - Drift in feature B is detected in 2009 and 2010 (as it reverts to normal). +# +# - Drift in feature D is detected in 2012 and 2013 (as it reverts to normal). +# +# - Drift in feature H is detected in 2019. Drift in feature J is detected in 2021. +# +# - The undetected drift occurs in 2015 in the correlations between features E and F. +# + +# In[ ]: + + +## Plot Heatmap ## +sns.set_style("whitegrid") +sns.set(rc={"figure.figsize": (15, 8)}) + +# Setup plot + +# Setup plot +grid_kws = {"height_ratios": (0.9, 0.05), "hspace": 0.3} +f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws) +coloring = sns.cubehelix_palette(start=0.8, rot=-0.5, as_cmap=True) +ax = sns.heatmap( + heatmap_data, + ax=ax, + cmap=coloring, + xticklabels=heatmap_data.columns, + yticklabels=heatmap_data.index, + linewidths=0.5, + cbar_ax=cbar_ax, + cbar_kws={"orientation": "horizontal"}, +) +ax.set_title('HDDDM Feature Heatmap') +ax.set(xlabel="Years", ylabel="Features") +ax.collections[0].colorbar.set_label("Difference in Hellinger Distance") +ax.set_yticklabels(ax.get_yticklabels(), rotation=0) + +plt.show() +# plt.savefig("example_HDDDM_feature_heatmap.png") + + +# In[ ]: + + +### Custom Divergence Metric ### + +# Define divergence function +def distance_metric(reference_histogram, test_histogram): + + # Convert inputs to appropriate datatype + ref = np.array(reference_histogram[0]) + test = np.array(test_histogram[0]) + + return np.sqrt(np.sum(np.square(ref-test))) + +# Test self-defined divergence metric +hdddm = HDDDM( + divergence=distance_metric, + detect_batch=1, + statistic="stdev", + significance=0.05, + subsets=5, +) + +hdddm.set_reference(reference) +hdddm.update(example_data[example_data.year == 2008].iloc[:, 1:-2]) + + +# ## PCA-Based Change Detection (PCA-CD) + +# PCA-CD is a drift detector that transforms the passed data into its principal components, then watches the transformed data for signs of drift by monitoring the KL-divergence via the Page-Hinkley algorithm. + +# In[ ]: + + +## Setup ## + +pca_cd = PCACD(window_size=50, divergence_metric="intersection") + +# set up dataframe to record results +status = pd.DataFrame(columns=["index", "var1", "var2", "drift_detected"]) + +# Put together a dataframe of several features, each of which abruptly changes +# at index 1000. +np.random.seed(1) +size = 1000 +data = pd.concat( + [ + pd.DataFrame( + [ + np.random.normal(1, 10, size), + np.random.uniform(1, 2, size), + np.random.normal(0, 1, size), + ] + ).T, + pd.DataFrame( + [ + np.random.normal(9, 10, size), + np.random.normal(1, 3, size), + np.random.gamma(20, 30, size), + ] + ).T, + ] +) + +# Update the drift detector with each new sample +for i in range(len(circle_data)): + pca_cd.update(data.iloc[[i]]) + status.loc[i] = [i, data.iloc[i, 0], data.iloc[i, 1], pca_cd.drift_state] + + +# In[ ]: + + +## Plotting ## + +# Plot the features and the drift +plt.figure(figsize=(20, 6)) +plt.scatter(status.index, status.var2, label="Var 2") +plt.scatter(status.index, status.var1, label="Var 1", alpha=0.5) +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("PCA-CD Test Results", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = min(status.var1.min(), status.var2.min()), max( + status.var1.max(), status.var1.max() +) +plt.ylim(ylims) + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +plt.legend() + + +# PCA-CD detects this very abrupt drift within a few samples of its induction. +# + +# In[ ]: + + +plt.show() +# plt.savefig("example_PCA_CD.png") + + +# ## KDQ-Tree Detection Method (Streaming Setting) + +# KdqTree monitors incoming features by constructing a tree which partitions the feature-space, and then monitoring a divergence statistic that is defined over that partition. It watches data within a sliding window of a particular size. When that window is full, it builds the reference tree. As the window moves forward, point-by-point, the data in that new window is compared against the reference tree to detect drift. + +# In[ ]: + + +## Setup ## + +# kdqTree does use bootstrapping to define its critical thresholds, so setting +# the seed is important to reproduce exact behavior. +np.random.seed(1) + +# Note that the default input_type for KDQTree is "stream". +# The window size, corresponding to the portion of the stream which KDQTree +# monitors, must be specified. +det = KdqTreeStreaming(window_size=500, alpha=0.05, bootstrap_samples=500, count_ubound=50) + +# setup DF to record results +status = pd.DataFrame(columns=["index", "var1", "var2", "drift_detected"]) + +# iterate through X data and run detector +data = circle_data[["var1", "var2"]] + + +# In[ ]: + + +## Plotting ## + +plot_data = {} +for i in range(len(circle_data)): + det.update(data.iloc[[i]]) + status.loc[i] = [i, data.iloc[i, 0], data.iloc[i, 1], det.drift_state] + if det.drift_state is not None: + # capture the visualization data + plot_data[i] = det.to_plotly_dataframe() + +plt.figure(figsize=(20, 6)) +plt.scatter("index", "var2", data=status, label="var2") +plt.scatter("index", "var1", data=status, label="var1", alpha=0.5) +plt.grid(False, axis="x") +plt.xticks(fontsize=16) +plt.yticks(fontsize=16) +plt.title("KDQ Tree Test Results", fontsize=22) +plt.ylabel("Value", fontsize=18) +plt.xlabel("Index", fontsize=18) +ylims = [-0.05, 1.05] +plt.ylim(ylims) + +drift_start, drift_end = 1000, 1250 +plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") + +# Draw red lines that indicate where drift was detected +plt.vlines( + x=status.loc[status["drift_detected"] == "drift"]["index"], + ymin=ylims[0], + ymax=ylims[1], + label="Drift Detected", + color="red", +) + +plt.legend() +plt.show() +# plt.savefig("example_streaming_kdqtree_feature_stream.png") + + +# Given a window_size of 500, with only the two input features, KdqTree detects +# a change after about half of the data within its window is in the new regime. +# + +# If we save off the to_plotly_dataframe at each drift detection, we can display +# the Kulldorff Spatial Scan Statistic (KSS) for each. Higher values of KSS +# indicate that a given region of the data space has greater divergence between +# the reference and test data. +# +# Note that the structure of the particular tree depends on the reference data +# and the order of the columns within the dataframe! +# +# Since this data only contains two features, the tree is relatively +# shallow. + +# In[ ]: + + +## Kulldorff Spatial Scan Statistic (KSS) ## +for title, df_plot in plot_data.items(): + fig = px.treemap( + data_frame=df_plot, + names="name", + ids="idx", + parents="parent_idx", + color="kss", + color_continuous_scale="blues", + title=f"Index {title}", + ) + fig.update_traces(root_color="lightgrey") + fig.show() + # fig.write_html(f"example_streaming_kdqtree_treemap_{title}.html") + + +# ## KDQ-Tree Detection Method (Batch Setting) + +# This example shows up how to set up, run, and produce output from the kdq-Tree +# detector, specifically in the batch data setting. The parameters aren't +# necessarily tuned for best performance, just notional. +# +# Drift in the example dataset occurs in 2009, 2012, 2015, 2018, and 2021. Drift +# in 2018 persists through 2021. See +# `make_example_batch_data` for more details. +# +# This example takes roughly a minute to run. + +# In[ ]: + + +## Setup ## + +# kdq-Tree does use bootstrapping to define its critical thresholds, so setting +# the seed is important to reproduce exact behavior. +np.random.seed(123) + +df = example_data + +# Capture the column which tells us when drift truly occurred +drift_years = df.groupby("year")["drift"].apply(lambda x: x.unique()[0]).reset_index() + +# Because the drift in 2009, 2012, and 2016 is intermittent - it reverts +# back to the prior distribution in the subsequent year - we should also detect +# drift in 2010, 2013, and 2016. So: +drift_years.loc[drift_years["year"].isin([2010, 2013, 2016]), "drift"] = True + + +df.drop(columns=["cat", "confidence", "drift"], inplace=True) + + +plot_data = {} +status = pd.DataFrame(columns=["year", "drift"]) +det = KdqTreeBatch() + +# Set up reference batch, using 2007 as reference year +det.set_reference(df[df.year == 2007].drop(columns=['year'])) + + +# In[ ]: + + +# Batch the data by year and run kdqTree +for group, sub_df in df[df.year != 2007].groupby("year"): + det.update(sub_df.drop(columns=["year"])) + status = pd.concat( + [status, pd.DataFrame({"year": [group], "drift": [det.drift_state]})], + axis=0, + ignore_index=True, + ) + if det.drift_state is not None: + # capture the visualization data + plot_data[group] = det.to_plotly_dataframe() + + # option to specify reference batch to be any year + #det.set_reference(df[df.year == XXXX]) + + +# Print out the true drift status, and that according to the detector. +# The detector successfully identifies drift in every year but 2018; +# that's the first year where persistent drift, from 2018-2021, was induced. +# The detector picks it up in 2019, the second year of persistent drift. + +# In[ ]: + + +( + status.merge(drift_years, how="left", on="year", suffixes=["_kdqTree", "_true"]) + .replace({True: "drift", False: None}) + # .to_csv("example_kdqtree_drift_comparison.csv", index=False) +) + + +# If we save off the to_plotly_dataframe at each drift detection, we can display +# the Kulldorff Spatial Scan Statistic (KSS) for each. Higher values of KSS +# indicate that a given region of the data space has greater divergence between +# the reference and test data. + +# In[ ]: + + +for year, df_plot in plot_data.items(): + fig = px.treemap( + data_frame=df_plot, + names="name", + ids="idx", + parents="parent_idx", + color="kss", + color_continuous_scale="blues", + title=year, + ) + fig.update_traces(root_color="lightgrey") + fig.show() + # fig.write_html(f"example_kdqtree_treemap_{year}.html") + + +# +# We can see that the regions of greatest drift do line up with at least one of +# the items that were modified in a given year. +# +# For reference, the detailed descriptions of drift induction: +# +# - Drift 1: change the mean & var of item B in 2009, means will revert for 2010 on +# +# - Drift 2: change the variance of item c and d in 2012 by replacing some with the mean +# keep same mean as other years, revert by 2013 +# +# - Drift 3: change the correlation of item e and f in 2015 (go from correlation of 0 to correlation of 0.5) +# +# - Drift 4: change mean and var of H and persist it from 2018 on +# +# - Drift 5: change mean and var just for a year of J in 2021 +# + +# +# # Nearest-Neighbor Density Variation Identification (NN-DVI) +# +# This example shows up how to set up, run, and produce output from the NN-DVI +# detector, which is implemented to run in the batch setting. NN-DVI uses k-nearest +# neighbors to partition the data, then uses measures defined with the adjacency +# matrix for the reference and test data to determine whether drift has occurred. + +# In[ ]: + + +data = make_example_batch_data() +drift_years = data.groupby("year")["drift"].apply(lambda x: x.unique()[0]).reset_index() +drift_years.loc[drift_years["year"].isin([2010, 2013, 2016]), "drift"] = True + +data_grouped = data.groupby('year') + +# Note that a small subsample is used, to decrease the runtime for the purpose +# of example. +batches = {year: (group.sample(frac=.1) + .drop(['year', 'cat', 'confidence', 'drift'], axis=1) + .values) + for year, group in data_grouped} + +status = pd.DataFrame(columns=["year", "drift"]) + +det = NNDVI(k_nn=2, sampling_times=50) +det.set_reference(batches.pop(2007)) + +for year, batch in batches.items(): + det.update(batch) + status = pd.concat([status, pd.DataFrame({"year":[year], "drift":[det.drift_state]})], + axis=0, + ignore_index=True) + + +# Using this small subsample of data runs quickly, but yields inaccurate results +# for the test data. A run with the full data takes much longer, but gives the +# correct result in 9 of 13 years. + +# In[ ]: + + +( + status.merge(drift_years, how="left", on="year", suffixes=["_nndvi", "_true"]) + .replace({True: "drift", False: None}) + # .to_csv("example_nndvi_drift_comparison.csv", index=False) +) + diff --git a/examples/ensemble_examples.py b/examples/ensemble_examples.py new file mode 100644 index 00000000..ad825421 --- /dev/null +++ b/examples/ensemble_examples.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Ensemble Drift Detector Examples +# +# This notebook contains examples on how to build and use ensemble detectors using the individual algorithms in the `menelaus` suite. These examples also include instructions on specifying evaluation schemes and setting custom subsets of data per constituent detector. +# +# Most parameterizations and initalizations therein may not result in optimal performance or detection, and are provided just for demonstration. +# +# ## Imports + +# In[ ]: + + +import numpy as np + +from menelaus.concept_drift import STEPD +from menelaus.change_detection import ADWIN +from menelaus.datasets import make_example_batch_data, fetch_rainfall_data +from menelaus.data_drift import HDDDM, KdqTreeBatch, KdqTreeStreaming +from menelaus.ensemble import BatchEnsemble, StreamingEnsemble +from menelaus.ensemble import SimpleMajorityElection, MinimumApprovalElection + + +# ## Import Data + +# In[ ]: + + +example_data = make_example_batch_data() +rainfall_data = fetch_rainfall_data() + + +# ## Batch Ensemble +# +# The simplest use of an ensemble is to combine three data-drift-only detectors with few additional settings. In this case we can combine three instances of batch detectors (`KdqTreeBatch`, `HDDDM`), all operating on the same data columns, with a very basic evaluation scheme (*i.e.* a simple majority of detectors alarming, causes the ensemble to alarm). + +# In[ ]: + + +# initialize set of detectors with desired parameterizations +detectors = { + 'k1': KdqTreeBatch(bootstrap_samples=500), + 'k2': KdqTreeBatch(bootstrap_samples=475), + 'h1': HDDDM() +} + +# choose an election scheme +election = SimpleMajorityElection() + +# initialize an ensemble object +ensemble = BatchEnsemble(detectors, election) + + +# Note that `BatchEnsemble` and `StreamingEnsemble` are instances of `BatchDetector` and `StreamingDetector` themselves (respectively). As such, they are used in the same syntactic way and possess similar properties. + +# In[ ]: + + +# make dataset smaller +df_example_data = example_data[example_data.year < 2010] + +# split dataset into 1 dataset for each 'batch' (year) +df_into_batches = [x for _,x in df_example_data.groupby('year')] +df_into_batches = [x[['a', 'b', 'c']] for x in df_into_batches] + +# batch detectors -- and ensembles -- need an initial reference batch +ensemble.set_reference(df_into_batches[0]) +print(f"Batch #{0} | Ensemble reference set") + +for i, batch in enumerate(df_into_batches[1:]): + ensemble.update(batch) + print(f"Batch #{i+1} | Ensemble overall drift state: {ensemble.drift_state}") + + +# ## Streaming Ensemble +# +# Using an ensemble of streaming detectors can involve additional features. This example uses both data and concept drift detectors (`KdqTreeStreaming`, `STEPD`), custom subsets of data for different detectors, as well as a different election scheme that will alarm if a custom, minimum number of detectors "approve" or alarm for drift. + +# In[ ]: + + +# initialize set of detectors with desired parameterizations +detectors = { + 'k1': KdqTreeStreaming(window_size=200, bootstrap_samples=250), + 'k2': KdqTreeStreaming(window_size=225, bootstrap_samples=200), + 's1': STEPD(window_size=50), + 'a': ADWIN(delta=.001) + +} + +# Functions that select the part of 'X' each detector needs - keys must match! +# Note that the default behavior, without a selector specified, is to use all columns. +# For detectors that only operate on y_true and y_pred, like STEPD, specifying +# a selector isn't necessary. But for detectors like ADWIN, which only monitor +# a single variable within X, the selector *must* be specified! +column_selectors = { + 'k1': lambda x: x[['temperature', 'visibility', 'dew_point']], + 'k2': lambda x: x[['temperature', 'visibility', 'average_wind_speed']], + 'a': lambda x: x[['visibility']] +} + +# choose an election scheme +election = MinimumApprovalElection(approvals_needed=1) + +# initialize an ensemble object +stream_ensemble = StreamingEnsemble(detectors, election, column_selectors) + + +# When mixing concept and data drift detectors, it's especially important to pass data explicitly. + +# In[ ]: + + +# make data smaller +df_stream = rainfall_data[0:1000] + +# random "predicted" outcomes -- in case a concept drift detector needs them +y_preds = np.random.randint(low=0, high=2, size=1000) + +# use ensemble +for i, row in df_stream.iterrows(): + stream_ensemble.update( + X=df_stream.loc[[i]], + y_true=row['rain'], + y_pred=y_preds[i] + ) + if stream_ensemble.drift_state is not None: + print(f"Example #{i} | Ensemble drift state: {stream_ensemble.drift_state}") + + +# In[ ]: + + + + diff --git a/examples/batch/partitioner_plots_example.py b/examples/partitioner_plots_example.py similarity index 65% rename from examples/batch/partitioner_plots_example.py rename to examples/partitioner_plots_example.py index fa658208..8226f743 100644 --- a/examples/batch/partitioner_plots_example.py +++ b/examples/partitioner_plots_example.py @@ -1,30 +1,33 @@ -""" +#!/usr/bin/env python +# coding: utf-8 -Partitioner Plot Example -------------------------- +# # Partitioner Plot Example -This example shows how to visualize the tree-based partitioning performed by -KDQTreePartitioner. +# These examples show how to visualize the tree-based partitioning performed by +# KDQTreePartitioner. +# +# KDQTreePartitioner takes input data and partitions the data into a kdq-Tree. New +# data can be passed subsequently and counted according to that same reference +# partition. The two datasets can then be compared. +# +# This partitioner is used within the library within ``data_drift.kdq_tree``, +# where the reference data is used to build the partition, and the test data is +# then divided by that partition, in order to compare the two distributions. -KDQTreePartitioner takes input data and partitions the data into a kdq-Tree. New -data can be passed subsequently and counted according to that same reference -partition. The two datasets can then be compared. +# In[ ]: -This partitioner is used within the library within ``data_drift.kdq_tree``, -where the reference data is used to build the partition, and the test data is -then divided by that partition, in order to compare the two distributions. -""" +## Imports ## import numpy as np import plotly.express as px - from menelaus.partitioners.KDQTreePartitioner import KDQTreePartitioner, KDQTreeNode -################################################################################ -################################ Basic Plot #################################### -################################################################################ +# ## Basic Plot + +# In[ ]: + # Create some data and build the tree. data = np.random.randint(0, 10, (20, 3)) @@ -35,12 +38,9 @@ # Grab the output for plotly. df_plot = kp.to_plotly_dataframe(tree_id1="build") -# This uses only cell_count, which depends only one tree, and doesn't -# compare them. -# Note that it doesn't include any special statistics, just the count. -# df_plot -df_plot.to_csv("example_partitioner_plots_basic.csv", index=False) +# In[ ]: + # Note that plotly's textinfo value could be used to add alternative values, # if desired. @@ -52,8 +52,12 @@ values="cell_count", ) fig.update_traces(root_color="lightgrey") -# fig.show() -fig.write_html(f"example_partitioner_plots_basic_plot.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_basic_plot.html") + + +# In[ ]: + ############## Filter by depth ############## kp = KDQTreePartitioner(count_ubound=25) @@ -72,13 +76,14 @@ values="cell_count", ) fig.update_traces(root_color="lightgrey") -# fig.show() -fig.write_html(f"example_partitioner_plots_basic_plot_depth.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_basic_plot_depth.html") -################################################################################ -################################# Modifications ################################ -################################################################################ +# ## Modifications + +# In[ ]: + # Count differences between builds can be accessed. # This can be used to modify the display. @@ -88,8 +93,10 @@ _ = kp.build(df) _ = kp.fill(df2, "fill1") df_plot = kp.to_plotly_dataframe("build", "fill1") -# df_plot -df_plot.to_csv("example_partitioner_plots_modifications.csv", index=False) + + +# In[ ]: + fig = px.treemap( data_frame=df_plot, @@ -99,8 +106,11 @@ color="count_diff", ) fig.update_traces(root_color="lightgrey") -# fig.show() -fig.write_html(f"example_partitioner_plots_modifications1_count.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_modifications1_count.html") + + +# In[ ]: ############## Display additional information ############## @@ -114,8 +124,11 @@ fig.update_traces( root_color="lightgrey", textinfo="label+current path" ) # see textinfo in https://plotly.com/python/reference/treemap/ -# fig.show() -fig.write_html(f"example_partitioner_plots_modifications2_path.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_modifications2_path.html") + + +# In[ ]: ##### Access the plot and color using the Kulldorff Spatial Scan Statistic (KSS) ##### @@ -130,8 +143,11 @@ color_continuous_scale="blues", ) fig.update_traces(root_color="lightgrey") -# fig.show() -fig.write_html(f"example_partitioner_plots_modifications3_kss.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_modifications3_kss.html") + + +# In[ ]: ############# Outline the cells according to the direction of change in counts ############# @@ -155,13 +171,15 @@ }, root_color="lightgrey", ) -# fig.show() -fig.write_html(f"example_partitioner_plots_modifications4_outline.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_modifications4_outline.html") + + +# ## Alternatives + +# In[ ]: -################################################################################ -################################# Alternatives ################################# -################################################################################ # For the most part, sunburst and icicle plots take the same arguments and # behave, though they're not as space-efficient visually. @@ -177,6 +195,10 @@ df_plot.loc[df_plot.count_diff == 0, "count_dir"] = "lightgrey" df_plot.loc[df_plot.count_diff > 0, "count_dir"] = "green" + +# In[ ]: + + ############# Sunburst Plot ############# fig = px.sunburst( data_frame=df_plot, @@ -190,8 +212,11 @@ insidetextfont={"color": df_plot.count_dir}, root_color="lightgrey", ) -# fig.show() -fig.write_html(f"example_partitioner_plots_alternatives_sunburst.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_alternatives_sunburst.html") + + +# In[ ]: ############# Icicle Plot ############# @@ -207,5 +232,6 @@ insidetextfont={"color": df_plot.count_dir}, root_color="lightgrey", ) -# fig.show() -fig.write_html(f"example_partitioner_plots_alternatives_icicle.html") +fig.show() +# fig.write_html(f"example_partitioner_lots_alternatives_icicle.html") + diff --git a/examples/streaming/change_detection_example.py b/examples/streaming/change_detection_example.py deleted file mode 100644 index 7d48520c..00000000 --- a/examples/streaming/change_detection_example.py +++ /dev/null @@ -1,143 +0,0 @@ -""" - -Change Detection Examples - 1. Page-Hinkley (PH) - 2. Cumulative Sum Test (CUSUM) -------------------------- - -These examples show how to set up, run, and produce output from detectors in the -change_detection module. The parameters aren't necessarily tuned for best -performance for the input data, just notional. - -Circle is a synthetic data source, where drift occurs in both var1, var2, and the -conditional distributions P(y|var1) and P(y|var2). The drift occurs from index -1000 to 1250, and affects 66% of the sample. - -These change detectors can be applied to any given single variable; below, -they are applied to var2. - -""" - -import os -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from sklearn.naive_bayes import GaussianNB -from menelaus.change_detection import PageHinkley, CUSUM - - -# read in Circle dataset -data_path = os.path.join("..", "..", "src", "menelaus", "datasets", "dataCircleGSev3Sp3Train.csv") -df = pd.read_csv(data_path, usecols=[0, 1, 2], names=["var1", "var2", "y"]) - -drift_start, drift_end = 1000, 1250 - -################################################################################ -############################ Page-Hinkley ###################################### -################################################################################ -# Set up one-directional PH test: this will only alarm if the mean of the -# monitored variable decreases, and only after seeing 30 or more samples. -ph = PageHinkley(delta=0.01, threshold=15, direction="negative", burn_in=30) - -# setup DF to record results -status = pd.DataFrame(columns=["index", "actual value", "drift_detected"]) - -# iterate through data; feed each sample to the detector, in turn -for i in range(len(df)): - obs = df["var2"][i] - ph.update(next_obs=obs, obs_id=i) - status.loc[i] = [i, obs, ph.drift_state] - - -# plot the monitored variable and the status of the detector -plt.figure(figsize=(20, 6)) -plt.scatter("index", "actual value", data=status, label="Var 2") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("PH Test Results", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") - -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) -plt.legend() - -# Page-Hinkley alarms shortly after the drift induction window closes, and then -# several apparently erroneous alarms afterwards. The parameters may not be -# well-chosen for the new regime. -# Change detection algorithms come out of process control, so a priori -# characterization of the bounds of the process, not performed here, would not -# be unreasonable. -# plt.show() -plt.savefig("example_Page-Hinkley_detections.png") - - -################################################################################ -############################### CUSUM ########################################## -################################################################################ -# Monitors a moving average of var2, starting from an initial estimate of mean -# and standard deviation. -# Will only alarm if 50 or more samples have been observed since -# initialization/drift. -# This will alarm if var2 passes a critical value controlled by delta and -# threshold in either direction, positive or negative. -cusum = CUSUM( - target=np.mean(df.loc[:drift_start, "var2"]), # mean / std of 'Var 2' pre-drift - sd_hat=np.std(df.loc[:drift_start, "var2"]), - burn_in=50, - delta=0.005, - threshold=40, - direction=None, -) - -# setup DF to record results -status = pd.DataFrame(columns=["index", "actual value", "drift_detected"]) - -# iterate through data; feed each sample to the detector, in turn -for i in range(len(df)): - obs = df["var2"][i] - cusum.update(obs) - status.loc[i] = [i, obs, cusum.drift_state] - - -# plot the monitored variable and the status of the detector -plt.figure(figsize=(20, 6)) -plt.scatter("index", "actual value", data=status, label="Var 2") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("CUSUM Test Results", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") - -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) -plt.legend() - -# CUSUM alarms several times within the drift induction window, roughly halfway -# through. After the alarm is reset, change is detected a few more times, -# including an apparently erroneous detection after the drift induction window -# is passed. The current threshold settings may then be too sensitive for the -# new regime. - -# plt.show() -plt.savefig("example_CUSUM_detections.png") diff --git a/examples/streaming/concept_drift_example.py b/examples/streaming/concept_drift_example.py deleted file mode 100644 index 604aad99..00000000 --- a/examples/streaming/concept_drift_example.py +++ /dev/null @@ -1,667 +0,0 @@ -""" - -Concept Drift Examples - 1. Linear Four Rates (LFR) - 2. ADaptive WINdowing (ADWIN) - 3. Drift Detection Method (DDM) - 4. Early Drift Detection Method (EDDM) - 5. Statistical Test of Equal Proportions to Detect Concept Drift (STEPD) -------------------------- - -These examples show how to set up, run, and produce output from detectors in the -concept_drift module. The parameters aren't necessarily tuned for best -performance for the input data, just notional. - -Circle is a synthetic data source, where drift occurs in both var1, var2, and the -conditional distributions P(y|var1) and P(y|var2). The drift occurs from index -1000 to 1250, and affects 66% of the sample. - -These detectors are generally to be applied to the true class and predicted class -from a particular model. ADWIN is an exception in that it could also be used to -monitor an arbitrary real-valued feature. So, each of the summary plots displays -the running accuracy of the classifier alongside the drift detector's output. - -They also track the indices of portions of the incoming data stream which are -more similar to each other -- i.e., data that seems to be part of the same -concept, which could be used to retrain a model. - -NOTE: The LinearFourRates example has a relatively long runtime, roughly 5 minutes. - -""" - - -import os -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -from sklearn.naive_bayes import GaussianNB -from sklearn.linear_model import SGDClassifier -from menelaus.concept_drift import LinearFourRates, ADWIN, DDM, EDDM, STEPD - - -# read in Circle dataset -data_path = os.path.join("..", "..", "src", "menelaus", "datasets", "dataCircleGSev3Sp3Train.csv") -df = pd.read_csv(data_path, usecols=[0, 1, 2], names=["var1", "var2", "y"]) - -drift_start, drift_end = 1000, 1250 -training_size = 500 - -################################################################################ -############################# Linear Four Rates ################################ -################################################################################ -# Linear Four Rates monitors the four cells of the confusion matrix (TPR, FPR, -# TNR, FNR) and alarms when one of these becomes different enough from earlier -# performance. - -# Set up classifier: train on first training_size rows -X_train = df.loc[0:training_size, ["var1", "var2"]] -y_train = df.loc[0:training_size, "y"] -clf = GaussianNB() -clf.fit(X_train, y_train) - -# Set up LFR detector to detect at significance of .001. 5000 Monte Carlo -# simulations will be run every 10 samples to detect drift. -lfr = LinearFourRates( - time_decay_factor=0.6, - warning_level=0.01, - detect_level=0.001, - num_mc=1, # XXX - need to lower for pipeline testing - Anmol - burn_in=10, - subsample=10, -) - -# Set up DF to store results. -status = pd.DataFrame(columns=["index", "y", "y_pred", "drift_detected", "accuracy"]) -correct = 0 - -np.random.seed(123) # set seed for this example - -# Run LFR and retrain. - -rec_list = [] -n = 1 -for i in range(training_size, len(df)): - X_test = df.loc[[i], ["var1", "var2"]] - y_pred = int(clf.predict(X_test)) - y_true = int(df.loc[[i], "y"]) - - # increment accuracy - if y_pred == y_true: - correct += 1 - accuracy = correct / n - - lfr.update(y_pred, y_true) - status.loc[i] = [i, y_true, y_pred, lfr.drift_state, accuracy] - - # If drift is detected, examine the retraining recommendations and retrain. - if lfr.drift_state == "drift": - - retrain_start = lfr.retraining_recs[0] + training_size - retrain_end = lfr.retraining_recs[1] + training_size - if ( - retrain_start == retrain_end - ): # minimum retraining window in case of sudden drift - retrain_start = max(0, retrain_start - 300) - rec_list.append([retrain_start, retrain_end]) - - # If retraining is not desired, omit the next four lines. - X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] - y_train = df.loc[retrain_start:retrain_end, "y"] - clf = GaussianNB() - clf.fit(X_train, y_train) - - n += 1 - -plt.figure(figsize=(20, 5)) -plt.scatter("index", "accuracy", data=status, label="Accuracy") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("LFR Results: Accuracy", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) - -# Draw orange lines that indicate where warnings of drift were provided -plt.vlines( - x=status.loc[status["drift_detected"] == "warning"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Warning", - color="orange", - alpha=0.3, -) - -# Create a list of lines that indicate the retraining windows. -# Space them evenly, vertically. -rec_list = pd.DataFrame(rec_list) -rec_list["y_val"] = np.linspace( - start=0.05 * (ylims[1] - ylims[0]) + ylims[0], - stop=0.2 * ylims[1], - num=len(rec_list), -) - -# Draw green lines that indicate where retraining occurred -plt.hlines( - y=rec_list["y_val"], - xmin=rec_list[0], - xmax=rec_list[1], - color="green", - label="Retraining Windows", -) - -plt.legend() -# plt.show() - -# One of the four rates immediately passes outside its threshold when drift is -# induced. The same occurs shortly after leaving the drift region. The -# recommended retraining data includes most of the drift induction window and -# the data after regime change. -# -# The classifier's accuracy decreases again later, which causes the detector to -# enter a "warning" state. Note that the retraining recommendations *begin* with -# the index corresponding to the warning state, and end where drift is detected. -# - -plt.savefig("example_LFR.png") - - -################################################################################ -################################### ADWIN ###################################### -################################################################################ -# ADWIN can be used to monitor the average of a given real-valued feature. -# In this case, we use it to monitor the accuracy of a classifier. -# ADWIN maintains a window of the data stream, which grows to the right as new -# elements are received. -# When the mean of the feature in one of the subwindows is different enough, -# ADWIN drops older elements in its window until this ceases to be the case. - -# Set up classifier: train on first training_size rows -X_train = df.loc[0:training_size, ["var1", "var2"]] -y_train = df.loc[0:training_size, "y"] - -np.random.seed(123) -clf = GaussianNB() -clf.fit(X_train, y_train) - -adwin = ADWIN() - -# Set up DF to record results. -status = pd.DataFrame( - columns=["index", "results", "accuracy", "adwin mean", "drift_detected"] -) -correct = 0 -rec_list = [] - -# run ADWIN -n = 1 -for i in range(training_size, len(df)): - - X_test = df.loc[[i], ["var1", "var2"]] - y_pred = int(clf.predict(X_test)) - y_true = int(df.loc[[i], "y"]) - - # increment accuracy - if y_pred == y_true: - correct += 1 - accuracy = correct / n - - adwin.update(int(y_true == y_pred)) - status.loc[i] = [ - i, - int(y_true == y_pred), - accuracy, - adwin.mean(), - adwin.drift_state, - ] - - # If drift is detected, examine the window and retrain. - if adwin.drift_state == "drift": - retrain_start = adwin.retraining_recs[0] + training_size - retrain_end = adwin.retraining_recs[1] + training_size - rec_list.append([retrain_start, retrain_end]) - - # The retraining recommendations produced here correspond to the samples - # which belong to ADWIN's new, smaller window, after drift is detected. - # If retraining is not desired, omit the next four lines. - X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] - y_train = df.loc[retrain_start:retrain_end, "y"] - clf = GaussianNB() - clf.fit(X_train, y_train) - - n += 1 - -plt.figure(figsize=(20, 6)) -plt.scatter("index", "accuracy", data=status, label="Accuracy") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("ADWIN Results: Accuracy", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [0, 1.1] -plt.ylim(ylims) - -plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) - -# Create a list of lines that indicate the retraining windows. -# Space them evenly, vertically. -rec_list = pd.DataFrame(rec_list) -rec_list["y_val"] = np.linspace( - start=0.05 * (ylims[1] - ylims[0]) + ylims[0], - stop=0.2 * ylims[1], - num=len(rec_list), -) - -# Draw green lines that indicate where retraining occurred -plt.hlines( - y=rec_list["y_val"], - xmin=rec_list[0], - xmax=rec_list[1], - color="green", - label="Retraining Windows", -) - -plt.legend(loc='lower right') - -# After drift is induced, the accuracy decreases enough for ADWIN to shrink its -# window and alarm; subsequent windows also include data from the old regime, -# so drift continues to be detected until the window shrinks enough to be -# comprised mostly by the new regime. - -# plt.show() -plt.savefig("example_ADWIN.png") - - -################################################################################ -#################################### DDM ####################################### -################################################################################ -# DDM can enter either a "drift" or "warning" state, depending on how close -# a classifier's error rate has approached to those respective thresholds, -# defined by the warning_scale and drift_scale parameters. - -np.random.seed(123) -# setup classifier: train on first training_size rows -X_train = df.loc[0:training_size, ["var1", "var2"]] -y_train = df.loc[0:training_size, "y"] -clf = GaussianNB() -clf.fit(X_train, y_train) - -# These parameter values are chosen somewhat arbitrarily. -# At least 100 samples must be seen before DDM tests for drift (the n_threshold -# parameter); the other two define the warning and drift regions. The minimum -# error rate (and its standard deviation) are found during a stable regime; the -# warning_scale and drift_scale roughly correspond to how many standard standard -# deviations away the current estimate must be in order for the detector to -# alarm. -ddm = DDM(n_threshold=100, warning_scale=7, drift_scale=10) - -# setup DF to store results -status = pd.DataFrame(columns=["index", "y", "y_pred", "drift_detected", "accuracy"]) -correct = 0 -rec_list = [] - -# run DDM and retrain -n = 1 -for i in range(training_size, len(df)): - - X_test = df.loc[[i], ["var1", "var2"]] - y_pred = int(clf.predict(X_test)) - y_true = int(df.loc[[i], "y"]) - - # increment accuracy - if y_pred == y_true: - correct += 1 - accuracy = correct / n - - ddm.update(y_pred, y_true) - status.loc[i] = [i, y_true, y_pred, ddm.drift_state, accuracy] - - # If drift is detected, examine the window and retrain. - if ddm.drift_state == "drift": - retrain_start = ddm.retraining_recs[0] + training_size - retrain_end = ddm.retraining_recs[1] + training_size - if ( - retrain_start == retrain_end - ): # minimum retraining window in case of sudden drift - retrain_start = max(0, retrain_start - 300) - rec_list.append([retrain_start, retrain_end]) - - # If retraining is not desired, omit the next four lines. - X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] - y_train = df.loc[retrain_start:retrain_end, "y"] - clf = GaussianNB() - clf.fit(X_train, y_train) - - n += 1 - - -plt.figure(figsize=(20, 6)) -plt.scatter("index", "accuracy", data=status, label="Accuracy") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("DDM Results: Accuracy", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", - linewidth=3, -) - -# Draw orange lines that indicate where warnings of drift were provided -plt.vlines( - x=status.loc[status["drift_detected"] == "warning"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Warning", - color="orange", - alpha=0.3, -) - -# Create a list of lines that indicate the retraining windows. -# Space them evenly, vertically. -rec_list = pd.DataFrame(rec_list) -rec_list["y_val"] = np.linspace( - start=0.05 * (ylims[1] - ylims[0]) + ylims[0], - stop=0.2 * ylims[1], - num=len(rec_list), -) - -# Draw green lines that indicate where retraining occurred -plt.hlines( - y=rec_list["y_val"], - xmin=rec_list[0], - xmax=rec_list[1], - color="green", - label="Retraining Windows", -) - -plt.legend() - -# DDM initially alarms during the drift induction window, triggering retraining. -# The subsequent dip in accuracy is large enough to put the detector in the -# "warning" state, but not large enough for "drift" to be identified. - -# plt.show() -plt.savefig("example_DDM.png") - - -################################################################################ -################################### EDDM ####################################### -################################################################################ -# EDDM monitors the distance between two errors of a classifier - i.e., the -# number of samples between errors - rather than monitoring the error rate -# itself. Similar to DDM, it uses separate thresholds for "warning" and "drift." - -np.random.seed(123) -# setup classifier: train on first 500 rows -X_train = df.loc[0:training_size, ["var1", "var2"]] -y_train = df.loc[0:training_size, "y"] -clf = GaussianNB() -clf.fit(X_train, y_train) - -# n_threshold specifies the number of new samples which must be observed before -# tests for drift are run. -# The warning_thresh and drift_thresh values roughly correspond to the ratio of -# the 95th percentile for the current distance distribution vs. the 95th percentile -# for the "best" distance distribution observed so far. -# So, lower values correspond to less conservative monitoring - the current -# distance between errors is allowed to be a smaller fraction of the "best" -# distance. -eddm = EDDM(n_threshold=30, warning_thresh=0.7, drift_thresh=0.5) - -# setup DF to store results -status = pd.DataFrame(columns=["index", "y", "y_pred", "drift_detected", "accuracy"]) -correct = 0 -rec_list = [] - -# run EDDM and retrain -n = 1 -for i in range(training_size, len(df)): - - X_test = df.loc[[i], ["var1", "var2"]] - y_pred = int(clf.predict(X_test)) - y_true = int(df.loc[[i], "y"]) - - # increment accuracy - if y_pred == y_true: - correct += 1 - accuracy = correct / n - - eddm.update(y_pred, y_true) - status.loc[i] = [i, y_true, y_pred, eddm.drift_state, accuracy] - - # If drift is detected, examine the window and retrain. - if eddm.drift_state == "drift": - retrain_start = eddm.retraining_recs[0] + training_size - retrain_end = eddm.retraining_recs[1] + training_size - if ( - retrain_start == retrain_end - ): # minimum retraining window in case of sudden drift - retrain_start = max(0, retrain_start - 300) - rec_list.append([retrain_start, retrain_end]) - - # If retraining is not desired, omit the next four lines. - X_train = df.loc[retrain_start:retrain_end, ["var1", "var2"]] - y_train = df.loc[retrain_start:retrain_end, "y"] - clf = GaussianNB() - clf.fit(X_train, y_train) - - n += 1 - -plt.figure(figsize=(20, 6)) -plt.scatter("index", "accuracy", data=status, label="Accuracy") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("EDDM Results: Accuracy", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") - -# Draw orange lines that indicate where warnings of drift were provided -plt.vlines( - x=status.loc[status["drift_detected"] == "warning"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Warning", - color="orange", - alpha=0.3, -) - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) - -# Create a list of lines that indicate the retraining windows. -# Space them evenly, vertically. -rec_list = pd.DataFrame(rec_list) -rec_list["y_val"] = np.linspace( - start=0.05 * (ylims[1] - ylims[0]) + ylims[0], - stop=0.2 * ylims[1], - num=len(rec_list), -) - -# Draw green lines that indicate where retraining occurred -plt.hlines( - y=rec_list["y_val"], - xmin=rec_list[0], - xmax=rec_list[1], - color="green", - label="Retraining Windows", - alpha=0.3, -) - -plt.legend() - -# EDDM enters a drift state shortly after the drift induction window, triggering -# retraining. The later increase in the error rate causes the detector to enter -# the warning state, but is not large enough to be identified as drift with this -# threshold setting. - -# plt.show() -plt.savefig("example_EDDM.png") - - -################################################################################ -################################### STEPD ###################################### -################################################################################ -# STEPD is a detector specifically intended for online classifiers, where each -# new sample is used to update the parameters of the classifier. STEPD monitors -# the accuracy in two windows, "recent" and "past," and compares those in order -# to detect drift in classifier accuracy. - -np.random.seed(123) -df_ex = df -train_ix = [0, training_size] - - -# For the purposes of this example, our online classifier is an SGDClassifier -# with a constant learning rate, since this is available in the pre-existing -# package dependency sklearn. -clf = SGDClassifier(learning_rate="constant", shuffle=False, eta0=0.2) -clf.fit( - df_ex.loc[train_ix[0] : train_ix[1], ["var1", "var2"]].values, - df_ex.loc[train_ix[0] : train_ix[1], "y"].values, -) - - -stepd = STEPD(window_size=100) - -# setup DF to store results -status = pd.DataFrame(columns=["index", "y", "y_pred", "drift_detected", "accuracy"]) -correct = 0 -rec_list = [] - -# run STEPD and retrain -n = 1 -for i, row in df_ex.iloc[training_size:].iterrows(): - y_pred = clf.predict(np.array(row[["var1", "var2"]]).reshape(1, -1)) - y_true = row["y"] - - if y_pred == y_true: - correct += 1 - accuracy = correct / n - - stepd.update(y_pred, y_true) - status.loc[i] = [i, y_true, y_pred, stepd.drift_state, accuracy] - # train_ix[1] = train_ix[1] + 1 - - if stepd.drift_state == "drift": - rec_list.append(stepd.retraining_recs) - # retrain the classifier using STEPD's recommendations - train_ix = stepd.retraining_recs - train_ix[0] = train_ix[0] + training_size # adjust for starting index - train_ix[1] = train_ix[1] + training_size - clf.fit( - df_ex.loc[train_ix[0] : train_ix[1], ["var1", "var2"]].values, - df_ex.loc[train_ix[0] : train_ix[1], "y"].values, - ) - - else: - # update the classifier with the newest sample - clf.partial_fit(row[["var1", "var2"]].values.reshape(1, -1), [row["y"]]) - - n += 1 - - -plt.figure(figsize=(20, 6)) -plt.scatter("index", "accuracy", data=status, label="Accuracy") -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("STEPD Results: Accuracy", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.1] -plt.ylim(ylims) - -plt.axvspan(1000, 1250, alpha=0.5, label="Drift Induction Window") - -# Draw orange lines that indicate where warnings of drift were provided -plt.vlines( - x=status.loc[status["drift_detected"] == "warning"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Warning", - color="orange", - alpha=0.3, -) - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", - linewidth=3, -) - -# Create a list of lines that indicate the retraining windows. -# Space them evenly, vertically. -rec_list = pd.DataFrame(rec_list) -rec_list["y_val"] = np.linspace( - start=0.05 * (ylims[1] - ylims[0]) + ylims[0], - stop=0.2 * ylims[1], - num=len(rec_list), -) - -# Draw green lines that indicate where retraining occurred -plt.hlines( - y=rec_list["y_val"], - xmin=rec_list[0], - xmax=rec_list[1], - color="green", - label="Retraining Windows", -) - -plt.legend(loc='upper left') - -# STEPD identifies drift quite early in the drift induction window, triggering -# retraining on a relatively small amount of data; after this, the online -# classifier updates sufficiently that its accuracy is roughly flat over the -# remaining data, albeit with a big enough change to trigger more warnings. - -# plt.show() -plt.savefig("example_STEPD.png") diff --git a/examples/streaming/data_drift_example.py b/examples/streaming/data_drift_example.py deleted file mode 100644 index b658f5a2..00000000 --- a/examples/streaming/data_drift_example.py +++ /dev/null @@ -1,194 +0,0 @@ -""" - -Data Drift Examples - 1. PCA-Based Change Detection (PCA-CD) - 2. KDQ-Tree Detection Method -------------------------- - -These examples show how to set up, run, and produce output from detectors in the -data_drift module. The parameters aren't necessarily tuned for best -performance for the input data, just notional. These detectors are generally -applied to the whole feature set for a given data source. - -The example data for kdqTree, Circle, is a synthetic data source, where drift -occurs in both var1, var2, and the conditional distributions P(y|var1) and -P(y|var2). The drift occurs from index 1000 to 1250, and affects 66% of the -sample. - -NOTE: This example takes roughly 30 seconds to run. - -""" - -import os -import pandas as pd -import numpy as np -import matplotlib.pyplot as plt -import plotly.express as px -from menelaus.data_drift import PCACD, KdqTreeStreaming - - -# read in Circle dataset -data_path = os.path.join('..', '..', 'src', 'menelaus', 'datasets', 'dataCircleGSev3Sp3Train.csv') -df = pd.read_csv(data_path, usecols=[0, 1, 2], names=["var1", "var2", "y"]) - -################################################################################ -################################## PCA_CD ###################################### -################################################################################ -# PCA-CD is a drift detector that transforms the passed data into its -# principal components, then watches the transformed data for signs of drift -# by monitoring the KL-divergence via the Page-Hinkley algorithm. - -pca_cd = PCACD(window_size=50, divergence_metric="intersection") - -# set up dataframe to record results -status = pd.DataFrame(columns=["index", "var1", "var2", "drift_detected"]) - -# Put together a dataframe of several features, each of which abruptly changes -# at index 1000. -np.random.seed(1) -size = 1000 -data = pd.concat( - [ - pd.DataFrame( - [ - np.random.normal(1, 10, size), - np.random.uniform(1, 2, size), - np.random.normal(0, 1, size), - ] - ).T, - pd.DataFrame( - [ - np.random.normal(9, 10, size), - np.random.normal(1, 3, size), - np.random.gamma(20, 30, size), - ] - ).T, - ] -) - -# Update the drift detector with each new sample -for i in range(len(df)): - pca_cd.update(data.iloc[[i]]) - status.loc[i] = [i, data.iloc[i, 0], data.iloc[i, 1], pca_cd.drift_state] - - -# Plot the features and the drift -plt.figure(figsize=(20, 6)) -plt.scatter(status.index, status.var2, label="Var 2") -plt.scatter(status.index, status.var1, label="Var 1", alpha=0.5) -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("PCA-CD Test Results", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = min(status.var1.min(), status.var2.min()), max( - status.var1.max(), status.var1.max() -) -plt.ylim(ylims) - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) - -plt.legend() - -# PCA-CD detects this very abrupt drift within a few samples of its induction. -# plt.show() -plt.savefig("example_PCA_CD.png") - - -################################################################################ -################################# KDQ-Tree ##################################### -################################################################################ -# KdqTree monitors incoming features by constructing a tree which partitions the -# feature-space, and then monitoring a divergence statistic that is defined over -# that partition. It watches data within a sliding window of a particular size. -# When that window is full, it builds the reference tree. As the window moves -# forward, point-by-point, the data in that new window is compared against the -# reference tree to detect drift. - - -# kdqTree does use bootstrapping to define its critical thresholds, so setting -# the seed is important to reproduce exact behavior. -np.random.seed(1) - -# Note that the default input_type for KDQTree is "stream". -# The window size, corresponding to the portion of the stream which KDQTree -# monitors, must be specified. -det = KdqTreeStreaming(window_size=500, alpha=0.05, bootstrap_samples=500, count_ubound=50) - -# setup DF to record results -status = pd.DataFrame(columns=["index", "var1", "var2", "drift_detected"]) - -# iterate through X data and run detector -data = df[["var1", "var2"]] - -plot_data = {} -for i in range(len(df)): - det.update(data.iloc[[i]]) - status.loc[i] = [i, data.iloc[i, 0], data.iloc[i, 1], det.drift_state] - if det.drift_state is not None: - # capture the visualization data - plot_data[i] = det.to_plotly_dataframe() - -plt.figure(figsize=(20, 6)) -plt.scatter("index", "var2", data=status, label="var2") -plt.scatter("index", "var1", data=status, label="var1", alpha=0.5) -plt.grid(False, axis="x") -plt.xticks(fontsize=16) -plt.yticks(fontsize=16) -plt.title("KDQ Tree Test Results", fontsize=22) -plt.ylabel("Value", fontsize=18) -plt.xlabel("Index", fontsize=18) -ylims = [-0.05, 1.05] -plt.ylim(ylims) - -drift_start, drift_end = 1000, 1250 -plt.axvspan(drift_start, drift_end, alpha=0.5, label="Drift Induction Window") - -# Draw red lines that indicate where drift was detected -plt.vlines( - x=status.loc[status["drift_detected"] == "drift"]["index"], - ymin=ylims[0], - ymax=ylims[1], - label="Drift Detected", - color="red", -) - -plt.legend() - - -# Given a window_size of 500, with only the two input features, KdqTree detects -# a change after about half of the data within its window is in the new regime. -# plt.show() -plt.savefig("example_streaming_kdqtree_feature_stream.png") - -# If we save off the to_plotly_dataframe at each drift detection, we can display -# the Kulldorff Spatial Scan Statistic (KSS) for each. Higher values of KSS -# indicate that a given region of the data space has greater divergence between -# the reference and test data. -# -# Note that the structure of the particular tree depends on the reference data -# and the order of the columns within the dataframe! - -# Since this data only contains two features, the tree is relatively -# shallow. -for title, df_plot in plot_data.items(): - fig = px.treemap( - data_frame=df_plot, - names="name", - ids="idx", - parents="parent_idx", - color="kss", - color_continuous_scale="blues", - title=f"Index {title}", - ) - fig.update_traces(root_color="lightgrey") - # fig.show() - fig.write_html(f"example_streaming_kdqtree_treemap_{title}.html") diff --git a/src/menelaus/__init__.py b/menelaus/__init__.py similarity index 100% rename from src/menelaus/__init__.py rename to menelaus/__init__.py diff --git a/src/menelaus/change_detection/__init__.py b/menelaus/change_detection/__init__.py similarity index 90% rename from src/menelaus/change_detection/__init__.py rename to menelaus/change_detection/__init__.py index cdf054d2..7b3a133c 100644 --- a/src/menelaus/change_detection/__init__.py +++ b/menelaus/change_detection/__init__.py @@ -6,5 +6,6 @@ either upward or downward changes in a sequence. """ -from menelaus.change_detection.page_hinkley import PageHinkley +from menelaus.change_detection.adwin import ADWIN from menelaus.change_detection.cusum import CUSUM +from menelaus.change_detection.page_hinkley import PageHinkley diff --git a/src/menelaus/concept_drift/adwin.py b/menelaus/change_detection/adwin.py similarity index 94% rename from src/menelaus/concept_drift/adwin.py rename to menelaus/change_detection/adwin.py index 028c8468..856eb49e 100644 --- a/src/menelaus/concept_drift/adwin.py +++ b/menelaus/change_detection/adwin.py @@ -1,3 +1,5 @@ +# most of these get called within loops (or an outer loop on the detector), +# so this is more efficient from numpy import ( power, log, @@ -7,15 +9,13 @@ zeros, ) -# most of these get called within loops (or an outer loop on the detector), -# so this is more efficient -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class ADWIN(DriftDetector): - """ADWIN (ADaptive WINdowing) is a drift detection algorithm which uses a +class ADWIN(StreamingDetector): + """ADWIN (ADaptive WINdowing) is a change detection algorithm which uses a sliding window to estimate the running mean and variance of a given - statistic. + real-valued number. As each sample is added, ADWIN stores a running estimate (mean and variance) for a given statistic, calculated over a sliding window which will grow to @@ -35,19 +35,8 @@ class ADWIN(DriftDetector): window is stored in ``self.retraining_recs``. Ref. :cite:t:`bifet2007learning` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. - """ - input_type = "stream" - def __init__( self, delta=0.002, @@ -104,20 +93,30 @@ def __init__( self._window_size = 0 self._retraining_recs = [None, None] - def update(self, new_value): + def update(self, X, y_true=None, y_pred=None): """Update the detector with a new sample. Args: - new_value: next sample in the stream of data being fed to ADWIN + X: one row of features from input data. + y_true: one true label from input data. Not used by ADWIN. + y_pred: one predicted label from input data. Not used by ADWIN. """ + if self.drift_state is not None: # note that the other attributes should *not* be initialized after drift self.reset() - super().update() + X, _, _ = super()._validate_input(X, None, None) + if len(X.shape) > 1 and X.shape[1] != 1: + raise ValueError("ADWIN should only be used to monitor 1 variable.") + super().update(X, None, None) + + # the array should have a single element after validation. + X = X[0][0] + # add new sample to the head of the window self._window_size += 1 - self._add_sample(new_value) + self._add_sample(X) self._shrink_window() def reset(self): @@ -136,7 +135,7 @@ def retraining_recs(self): """Recommended indices for retraining. If drift is detected, set to ``[beginning of ADWIN's new window, end of ADWIN's new window]``. If these are e.g. the 5th and 13th sample that ADWIN has been updated - with, the values with be ``[4, 12]``. + with, the values will be ``[4, 12]``. Returns: list: the current retraining recommendations @@ -208,7 +207,7 @@ def _compress_buckets(self): break else: # similarly, if the first bucket isn't full, neither are any others - break # this line will be noted as uncovered due to https://github.com/nedbat/coveragepy/issues/772 + break curr_bucket_row = curr_bucket_row.next_bucket list_position += 1 @@ -219,7 +218,7 @@ def _shrink_window(self): size and set the ``drift_state`` to ``"drift"``. """ if ( - self.total_updates % self.new_sample_thresh == 0 + self.total_samples % self.new_sample_thresh == 0 and self._window_size > self.window_size_thresh ): # either we reduced the window and must restart to check the new @@ -271,8 +270,8 @@ def _shrink_window(self): if self._window_size > 0: n_elements0 -= self._remove_last() self._retraining_recs = ( - self.total_updates - self._window_size, - self.total_updates - 1, + self.total_samples - self._window_size, + self.total_samples - 1, ) exit_shrink = True break @@ -380,7 +379,7 @@ class _BucketRowList: parameter from Bifet 2006. At each update step, if the ``BucketRows`` are at overflow, their oldest buckets will be moved into the next largest ``BucketRow`` by ``ADWIN._compress_buckets``. So, the tail of the ``bucket_row_list`` will be the - oldest elements. + estimates corresponding to the oldest elements. Note that each ``BucketRow`` only stores estimates related to 2^i elements in each bucket: so, each position in ``BucketRowList.head``'s arrays corresponds to diff --git a/src/menelaus/change_detection/cusum.py b/menelaus/change_detection/cusum.py similarity index 75% rename from src/menelaus/change_detection/cusum.py rename to menelaus/change_detection/cusum.py index 4b4d73d5..67374b27 100644 --- a/src/menelaus/change_detection/cusum.py +++ b/menelaus/change_detection/cusum.py @@ -1,23 +1,15 @@ import numpy as np -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class CUSUM(DriftDetector): +class CUSUM(StreamingDetector): """CUSUM is a method from the field of statistical process control. This detector tests for changes in the mean of a time series by calculating a moving average over recent observations. CUSUM can be used for tracking a single model performance metric, or could be applied to the mean of a feature variable of interest. - Ref. :cite:t:`hinkley1971inference` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. + Ref. :cite:t:`page1954continuous` """ input_type = "stream" @@ -75,11 +67,13 @@ def reset(self): self._upper_bound = [0] self._lower_bound = [0] - def update(self, next_obs): + def update(self, X, y_true=None, y_pred=None): """Update the detector with a new sample. Args: - next_obs: The value of the new sample. + X (numpy.ndarray): one row of features from input data. + y_true (numpy.ndarray): one true label from input data. Not used in CUSUM. + y_pred (numpy.ndarray): one predicted label from input data. Not used in CUSUM. """ # if the last run resulted in drift, reset everything and use last 30 # obs to estimate stats @@ -88,23 +82,26 @@ def update(self, next_obs): self.sd_hat = np.std(self._stream[-self.burn_in :]) self.reset() - super().update() - self._stream.append(next_obs) + X, _, _ = super()._validate_input(X, None, None) + if len(X.shape) > 1 and X.shape[1] != 1: + raise ValueError("CUSUM should only be used to monitor 1 variable.") + super().update(X, None, None) + self._stream.append(X) # cannot compute s_h/s_l so set to 0 - if (self.target is None) & (self.updates_since_reset < self.burn_in): + if (self.target is None) & (self.samples_since_reset < self.burn_in): s_h = 0 s_l = 0 self._upper_bound.append(s_h) self._lower_bound.append(s_l) # derive mean and sd from first n points if they are not specified - if (self.target is None) & (self.updates_since_reset == self.burn_in): + if (self.target is None) & (self.samples_since_reset == self.burn_in): self.target = np.mean(self._stream) self.sd_hat = np.std(self._stream) # if sd = 0 then no variance in stream and no drift -- raise error - if (self.sd_hat == 0) & (self.updates_since_reset > self.burn_in): + if (self.sd_hat == 0) & (self.samples_since_reset > self.burn_in): raise ValueError( """Standard deviation is 0. Confirm imput is a time series with more than 1 unique value.""" @@ -114,31 +111,31 @@ def update(self, next_obs): if self.target is not None: s_h = max( 0, - self._upper_bound[self.updates_since_reset - 1] - + (self._stream[self.updates_since_reset - 1] - self.target) + self._upper_bound[self.samples_since_reset - 1] + + (self._stream[self.samples_since_reset - 1] - self.target) / self.sd_hat - self.delta, ) s_l = max( 0, - self._lower_bound[self.updates_since_reset - 1] + self._lower_bound[self.samples_since_reset - 1] - self.delta - - (self._stream[self.updates_since_reset - 1] - self.target) + - (self._stream[self.samples_since_reset - 1] - self.target) / self.sd_hat, ) self._upper_bound.append(s_h) self._lower_bound.append(s_l) # check alarm if past burn in - if self.updates_since_reset > self.burn_in: + if self.samples_since_reset > self.burn_in: if self.direction is None: - if (self._upper_bound[self.updates_since_reset] > self.threshold) | ( - self._lower_bound[self.updates_since_reset] > self.threshold + if (self._upper_bound[self.samples_since_reset] > self.threshold) | ( + self._lower_bound[self.samples_since_reset] > self.threshold ): self.drift_state = "drift" elif self.direction == "positive": - if self._upper_bound[self.updates_since_reset] > self.threshold: + if self._upper_bound[self.samples_since_reset] > self.threshold: self.drift_state = "drift" elif self.direction == "negative": - if self._lower_bound[self.updates_since_reset] > self.threshold: + if self._lower_bound[self.samples_since_reset] > self.threshold: self.drift_state = "drift" diff --git a/src/menelaus/change_detection/page_hinkley.py b/menelaus/change_detection/page_hinkley.py similarity index 82% rename from src/menelaus/change_detection/page_hinkley.py rename to menelaus/change_detection/page_hinkley.py index 90cb87b7..9f0a9b3d 100644 --- a/src/menelaus/change_detection/page_hinkley.py +++ b/menelaus/change_detection/page_hinkley.py @@ -1,8 +1,8 @@ import pandas as pd -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class PageHinkley(DriftDetector): +class PageHinkley(StreamingDetector): """Page-Hinkley is a univariate change detection algorithm, designed to detect changes in a sequential Gaussian signal. Both the running mean and the running Page Hinkley (PH) statistic are incremented with each @@ -21,15 +21,7 @@ class PageHinkley(DriftDetector): If the threshold is too small, PH may result in many false alarms. If too large, the PH test will be more robust, but may miss true drift. - Ref. :cite:t:`page1954continuous` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"``, or ``None``. + Ref. :cite:t:`hinkley1971inference` """ input_type = "stream" @@ -70,7 +62,6 @@ def __init__(self, delta=0.01, threshold=20, burn_in=30, direction="positive"): # currently, if these need to be made available, they are through the # to_dataframe method - self._ids = [] self._change_scores = [] self._page_hinkley_values = [] self._page_hinkley_differences = [] @@ -80,19 +71,24 @@ def __init__(self, delta=0.01, threshold=20, burn_in=30, direction="positive"): self._mins = [] self._means = [] - def update(self, next_obs, *args, obs_id=None, **kwargs): + def update(self, X, y_true=None, y_pred=None): """Update the detector with a new sample. Args: - next_obs: The value of the new sample. - obs_id: Index of new sample to store in dataframe. Defaults to ``None``. + X: one row of features from input data. + y_true: one true label from input data. Not used by Page-Hinkley. + y_pred: one predicted label from input data. Not used by Page-Hinkley. """ if self.drift_state == "drift": self.reset() - super().update() - self._mean = self._mean + (next_obs - self._mean) / self.updates_since_reset - self._sum = self._sum + next_obs - self._mean - self.delta + X, _, _ = super()._validate_input(X, None, None) + if len(X.shape) > 1 and X.shape[1] != 1: + raise ValueError("Page-Hinkley should only be used to monitor 1 variable.") + super().update(X, None, None) + + self._mean = self._mean + (X - self._mean) / self.samples_since_reset + self._sum = self._sum + X - self._mean - self.delta theta = self.threshold * self._mean if self._sum < self._min: @@ -108,11 +104,10 @@ def update(self, next_obs, *args, obs_id=None, **kwargs): drift_check = ph_difference > theta - if drift_check and self.updates_since_reset > self.burn_in: + if drift_check and self.samples_since_reset > self.burn_in: self.drift_state = "drift" - self._ids.append(obs_id) - self._change_scores.append(next_obs) + self._change_scores.append(X) self._page_hinkley_values.append(self._sum) self._page_hinkley_differences.append(ph_difference) self._drift_detected.append(drift_check) @@ -132,7 +127,6 @@ def reset(self): self._sum = 0 self._mean = 0 - self._ids = [] self._change_scores = [] self._page_hinkley_values = [] self._page_hinkley_differences = [] @@ -147,7 +141,6 @@ def to_dataframe(self): """Returns a dataframe storing current statistics""" return pd.DataFrame( { - "ids": self._ids, "change_scores": self._change_scores, "page_hinkley_values": self._page_hinkley_values, "page_hinkley_differences": self._page_hinkley_differences, diff --git a/src/menelaus/concept_drift/__init__.py b/menelaus/concept_drift/__init__.py similarity index 90% rename from src/menelaus/concept_drift/__init__.py rename to menelaus/concept_drift/__init__.py index c10a50cb..a84879f7 100644 --- a/src/menelaus/concept_drift/__init__.py +++ b/menelaus/concept_drift/__init__.py @@ -12,8 +12,9 @@ multiple metrics simultaneously, such as true positive, false positive, true negative, and false negative rates. """ -from menelaus.concept_drift.adwin import ADWIN +from menelaus.concept_drift.adwin_accuracy import ADWINAccuracy from menelaus.concept_drift.ddm import DDM from menelaus.concept_drift.eddm import EDDM from menelaus.concept_drift.lfr import LinearFourRates from menelaus.concept_drift.stepd import STEPD +from menelaus.concept_drift.md3 import MD3 diff --git a/menelaus/concept_drift/adwin_accuracy.py b/menelaus/concept_drift/adwin_accuracy.py new file mode 100644 index 00000000..6ca25670 --- /dev/null +++ b/menelaus/concept_drift/adwin_accuracy.py @@ -0,0 +1,92 @@ +from menelaus.change_detection.adwin import ADWIN + + +class ADWINAccuracy(ADWIN): + """ADWIN (ADaptive WINdowing) is a change detection algorithm which uses a + sliding window to estimate the running mean and variance of a given + real-valued number. It can be applied as a concept drift detector by + monitoring a performance metric for a given classifier. ADWINAccuracy + specifically expects ``y_true``, ``y_pred``, and uses that input to monitor + the running accuracy of a classifier. To use ADWIN to monitor other values, + see ``change_detection.ADWIN``. + + As each sample is added, ADWIN stores a running estimate (mean and variance) + for a given statistic, calculated over a sliding window which will grow to + the right until drift is detected. The condition for drift is defined over + pairs of subwindows at certain cutpoints within the current window. If, for + any such pair, the difference between the running estimates of the statistic + is over a certain threshold (controlled by delta), we identify drift, and + remove the oldest elements of the window until all differences are again + below the threshold. + + The running estimates in each subwindow are maintained by storing summaries + of the elements in "buckets," which, in this implementation, are themselves + stored in the ``bucket_row_list`` attribute, whose total size scales with the + ``max_buckets`` parameter. + + When drift occurs, the index of the element at the beginning of ADWIN's new + window is stored in ``self.retraining_recs``. + + Ref. :cite:t:`bifet2007learning` + """ + + def __init__( + self, + delta=0.002, + max_buckets=5, + new_sample_thresh=32, + window_size_thresh=10, + subwindow_size_thresh=5, + conservative_bound=False, + ): + """ + + Args: + delta (float, optional): confidence value on on 0 to 1. ADWIN will + incorrectly detect drift with at most probability ``delta``, and + correctly detect drift with at least probability ``1 - delta``. + Defaults to 0.002. + max_buckets (int, optional): the maximum number of buckets to + maintain in each BucketRow. Corresponds to the "M" parameter in + Bifet 2006. Defaults to 5. + new_sample_thresh (int, optional): the drift detection procedure + will run every ``new_sample_thresh samples``, not in between. + Defaults to 32. + window_size_thresh (int, optional): the minimum number of samples in + the window required to check for drift. Defaults to 10. + subwindow_size_thresh (int, optional): the minimum number of samples + in each subwindow reqired to check it for drift. Defaults to 5. + conservative_bound (bool, optional): whether to assume a 'large + enough' sample when constructing drift cutoff. Defaults to ``False``. + + Raises: + ValueError: If ``ADWIN.delta`` is not on the range 0 to 1. + """ + # TODO - um, shouldn't this use the init parameters + super().__init__( + delta=0.002, + max_buckets=5, + new_sample_thresh=32, + window_size_thresh=10, + subwindow_size_thresh=5, + conservative_bound=False, + ) + + def update(self, y_true, y_pred, X=None): + """Update the detector with a new sample. + + Args: + y_true: one true label from input data. + y_pred: one predicted label from input data. + X: next sample in the stream of data. Not used for this + accuracy-based ADWIN. See ``change_detection.ADWIN`` for that + application. + """ + + # This class is here to avoid asking the user to provide such a direct + # function of (y_true, y_pred) in the X argument, which is unintuitive. + _, y_true, y_pred = super()._validate_input(None, y_true, y_pred) + new_value = int(y_true == y_pred) + # the arrays should have a single element after validation. + y_true, y_pred = y_true[0], y_pred[0] + super().update(new_value, y_true=None, y_pred=None) diff --git a/src/menelaus/concept_drift/ddm.py b/menelaus/concept_drift/ddm.py similarity index 77% rename from src/menelaus/concept_drift/ddm.py rename to menelaus/concept_drift/ddm.py index af570395..5b7dec75 100644 --- a/src/menelaus/concept_drift/ddm.py +++ b/menelaus/concept_drift/ddm.py @@ -1,8 +1,8 @@ import numpy as np -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class DDM(DriftDetector): +class DDM(StreamingDetector): """DDM is a drift detection algorithm which uses a binary classifier's error rate, which is binomially distributed. The minimum probability of an error and its standard deviation (``p_min``, ``s_min``) are found during training. If the @@ -18,21 +18,9 @@ class DDM(DriftDetector): detector's state is set to ``"drift"``. The index of the first sample which triggered a warning/drift state - (relative to ``self.updates_since_reset``) is stored in ``self.retraining_recs``. + (relative to ``self.samples_since_reset``) is stored in ``self.retraining_recs``. Ref. :cite:t:`gama2004learning` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"``, ``"warning"``, or ``None``. - n_threshold: the minimum number of samples required to test whether - drift has occurred - warning_scale: defines the threshold over which to enter the warning state. - drift_scale: defines the threshold over which to enter the drift state. """ input_type = "stream" @@ -69,33 +57,43 @@ def reset(self): self._error_std_min = float("inf") self._initialize_retraining_recs() - def update(self, y_pred, y_true): + # XXX - Order of y_true, y_pred, X differs from abstractmethod signature + # for update(). This is done for convenience, so users can call e.g. + # DDM.update(1,1) without misinterpretation, but exposes them to a + # potential issue where LFR.update(X, y, y) would assign arguments + # incorrectly. + def update(self, y_true, y_pred, X=None): """Update the detector with a new sample. Args: - y_pred: predicted class - y_true: actual class + y_true: one true label from input data. + y_pred: one predicted label from input data. + X: one row of features from input data. Not used in DDM. """ + if self.drift_state == "drift": self.reset() - super().update() + _, y_true, y_pred = super()._validate_input(None, y_true, y_pred) + super().update(None, y_true, y_pred) + # the arrays should have a single element after validation. + y_true, y_pred = y_true[0], y_pred[0] classifier_result = int(y_pred != y_true) # with each sample, update estimate of error and its std, along with minimums error_rate_prev = self._error_rate self._error_rate = ( self._error_rate - + (classifier_result - self._error_rate) / self.updates_since_reset + + (classifier_result - self._error_rate) / self.samples_since_reset ) self._error_std = self._error_std + (classifier_result - self._error_rate) * ( classifier_result - error_rate_prev ) - self._error_std = np.sqrt(self._error_std / self.updates_since_reset) + self._error_std = np.sqrt(self._error_std / self.samples_since_reset) # it's unclear whether the 'burn-in' period should be updating the # minimums - seems like a bad idea though. - if self.updates_since_reset < self.n_threshold: + if self.samples_since_reset < self.n_threshold: return if ( @@ -130,12 +128,12 @@ def _increment_retraining_recs(self): drift/warning region. """ if self.drift_state == "warning" and self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 if self.drift_state == "drift": - self._retraining_recs[1] = self.total_updates - 1 + self._retraining_recs[1] = self.total_samples - 1 if self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 @property def retraining_recs(self): diff --git a/src/menelaus/concept_drift/eddm.py b/menelaus/concept_drift/eddm.py similarity index 76% rename from src/menelaus/concept_drift/eddm.py rename to menelaus/concept_drift/eddm.py index 37c62a16..998022e8 100644 --- a/src/menelaus/concept_drift/eddm.py +++ b/menelaus/concept_drift/eddm.py @@ -1,8 +1,8 @@ import numpy as np -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class EDDM(DriftDetector): +class EDDM(StreamingDetector): """EDDM is a drift detection algorithm for binary classifiers which uses the distance between two classification errors. The running average distance between two errors (``dist_i``) and its standard deviation (``s_i``) are @@ -22,29 +22,22 @@ class EDDM(DriftDetector): distance for "large" samples. The index of the first sample which triggered a warning/drift state - (relative to ``self.updates_since_reset``) is stored in ``self.retraining_recs``. + (relative to ``self.samples_since_reset``) is stored in ``self.retraining_recs``. Ref. :cite:t:`baena2006early` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"``, ``"warning"``, or ``None``. """ input_type = "stream" def __init__(self, n_threshold=30, warning_thresh=0.95, drift_thresh=0.9): """ - :param n_threshold: the minimum number of samples required to test - whether drift has occurred - :param warning_thresh: defines the threshold over which to enter the - warning state. - :param drift_thresh: defines the threshold over which to enter the - drift state. + Args: + n_threshold (int, optional): the minimum number of samples required + to test whether drift has occurred. Defaults to 30. + warning_thresh (float, optional): defines the threshold over which + to enter the warning state. Defaults to 0.95. + drift_thresh (float, optional): defines the threshold over which to + enter the drift state. Defaults to 0.9. """ super().__init__() self.warning_thresh = warning_thresh @@ -73,17 +66,26 @@ def reset(self): self._test_statistic = None self._initialize_retraining_recs() - def update(self, y_pred, y_true): + # XXX - Order of y_true, y_pred, X differs from abstractmethod signature + # for update(). This is done for convenience, so users can call e.g. + # EDDM.update(1,1) without misinterpretation, but exposes them to a + # potential issue where LFR.update(X, y, y) would assign arguments + # incorrectly. + def update(self, y_true, y_pred, X=None): """Update the detector with a new sample. Args: - y_pred: predicted class - y_true: actual class + y_true: one true label from input data. + y_pred: one predicted label from input data. + X: one row of features from input data. Not used in EDDM. """ if self.drift_state == "drift": self.reset() - super().update() + _, y_true, y_pred = super()._validate_input(None, y_true, y_pred) + super().update(None, y_true, y_pred) + # the arrays should have a single element after validation. + y_true, y_pred = y_true[0], y_pred[0] classifier_result = int(y_pred == y_true) # found a new error, so update @@ -92,7 +94,7 @@ def update(self, y_pred, y_true): # calculate the distance between two errors self._index_error_last = self._index_error_curr self._index_error_curr = ( - self.updates_since_reset - 1 + self.samples_since_reset - 1 ) # n is count, not index! dist = self._index_error_curr - self._index_error_last @@ -142,12 +144,12 @@ def _increment_retraining_recs(self): drift/warning region. """ if self.drift_state == "warning" and self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 if self.drift_state == "drift": - self._retraining_recs[1] = self.total_updates - 1 + self._retraining_recs[1] = self.total_samples - 1 if self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 @property def retraining_recs(self): diff --git a/src/menelaus/concept_drift/lfr.py b/menelaus/concept_drift/lfr.py similarity index 87% rename from src/menelaus/concept_drift/lfr.py rename to menelaus/concept_drift/lfr.py index 476d9dbd..7b72e081 100644 --- a/src/menelaus/concept_drift/lfr.py +++ b/menelaus/concept_drift/lfr.py @@ -1,10 +1,10 @@ import pandas as pd import numpy as np from joblib import Parallel, delayed -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class LinearFourRates(DriftDetector): +class LinearFourRates(StreamingDetector): """Linear Four Rates detects drift in a learner's true positive rate (TPR), true negative rate (TNR), negative predictive value (NPV), and positive predictive value (PPV) over time. It relies on the assumption that @@ -24,14 +24,6 @@ class LinearFourRates(DriftDetector): has been simulated before, the bounds are re-used. Ref. :cite:t:`wang2015concept` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"``, ``"warning"``, or ``None``. """ input_type = "stream" @@ -46,6 +38,7 @@ def __init__( subsample=1, rates_tracked=["tpr", "tnr", "ppv", "npv"], parallelize=False, + round_val=4, ): """ Args: @@ -76,6 +69,10 @@ def __init__( algorithm will be parallelized or not. Advantageous for large datasets, but will slow down runtime for fewer data due to overhead of threading. Defaults to False. + round_val: number of decimal points the estimate rate is rounded to + when stored in bounds dictionary. The greater the ``round_val``, the + more precise the bounds dictionary will be, and the longer the + runtime. (Default value = 4) """ super().__init__() self.time_decay_factor = time_decay_factor @@ -86,6 +83,7 @@ def __init__( self.subsample = subsample self.rates_tracked = rates_tracked self.parallelize = parallelize + self.round_val = round_val self.all_drift_states = [] self._warning_states = { 0: {"tpr": False, "tnr": False, "ppv": False, "npv": False} @@ -121,7 +119,12 @@ def reset(self): } self._initialize_retraining_recs() - def update(self, y_pred, y_true, *args, round_val=4, **kwargs): + # XXX - Order of y_true, y_pred, X differs from abstractmethod signature + # for update(). This is done for convenience, so users can call e.g. + # LFR.update(1,1) without misinterpretation, but exposes them to a + # potential issue where LFR.update(X, y, y) would assign arguments + # incorrectly. + def update(self, y_true, y_pred, X=None): """Update detector with a new observation: #. Updates confusion matrix (``self._confusion``) with new predictions @@ -135,17 +138,19 @@ def update(self, y_pred, y_true, *args, round_val=4, **kwargs): ``"warning"`` or ``"drift"`` Args: - y_pred: predicted class - y_true: actual class - round_val: number of decimal points the estimate rate is rounded to - when stored in bounds dictionary. The greater the ``round_val``, the - more precise the bounds dictionary will be, and the longer the - runtime. (Default value = 4) + y_true: one true label from input data. + y_pred: one predicted label from input data. + X: one row of features from input data. Not used in LFR. """ + if self.drift_state == "drift": self.reset() - super().update() + _, y_true, y_pred = super()._validate_input(None, y_true, y_pred) + super().update(None, y_true, y_pred) + # the arrays should have a single element after validation. + y_true, y_pred = y_true[0], y_pred[0] + y_p = 1 * y_pred y_t = 1 * y_true @@ -156,21 +161,21 @@ def update(self, y_pred, y_true, *args, round_val=4, **kwargs): # init next index for test stats self._r_stat.update( { - self.updates_since_reset: self._r_stat[ - self.updates_since_reset - 1 + self.samples_since_reset: self._r_stat[ + self.samples_since_reset - 1 ].copy() } ) self._p_table.update( { - self.updates_since_reset: self._p_table[ - self.updates_since_reset - 1 + self.samples_since_reset: self._p_table[ + self.samples_since_reset - 1 ].copy() } ) self._warning_states.update( { - self.updates_since_reset: { + self.samples_since_reset: { "tpr": False, "tnr": False, "ppv": False, @@ -180,7 +185,7 @@ def update(self, y_pred, y_true, *args, round_val=4, **kwargs): ) self._alarm_states.update( { - self.updates_since_reset: { + self.samples_since_reset: { "tpr": False, "tnr": False, "ppv": False, @@ -192,25 +197,25 @@ def update(self, y_pred, y_true, *args, round_val=4, **kwargs): def _calculate_rate_bounds(rate): if new_rates[rate] != old_rates[rate]: new_r_stat = self.time_decay_factor * self._r_stat[ - self.updates_since_reset + self.samples_since_reset ][rate] + (1 - self.time_decay_factor) * (y_t == y_p) else: - new_r_stat = self._r_stat[self.updates_since_reset - 1][rate] + new_r_stat = self._r_stat[self.samples_since_reset - 1][rate] - self._p_table[self.updates_since_reset][rate] = new_rates[rate] - self._r_stat[self.updates_since_reset][rate] = new_r_stat + self._p_table[self.samples_since_reset][rate] = new_rates[rate] + self._r_stat[self.samples_since_reset][rate] = new_r_stat self._denominators[rate + "_N"] = self._get_four_denominators( self._confusion )[rate + "_N"] - if (self.updates_since_reset > self.burn_in) & ( - self.updates_since_reset % self.subsample == 0 + if (self.samples_since_reset > self.burn_in) & ( + self.samples_since_reset % self.subsample == 0 ): est_rate = new_rates[rate] curr_denom = self._denominators[rate + "_N"] - r_est_rate = round(est_rate, round_val) - r_curr_denom = round(curr_denom, round_val) + r_est_rate = round(est_rate, self.round_val) + r_curr_denom = round(curr_denom, self.round_val) bound_dict = self._update_bounds_dict( est_rate, curr_denom, r_est_rate, r_curr_denom @@ -221,10 +226,10 @@ def _calculate_rate_bounds(rate): lb_detect = bound_dict["lb_detect"] ub_detect = bound_dict["ub_detect"] - self._warning_states[self.updates_since_reset][rate] = ( + self._warning_states[self.samples_since_reset][rate] = ( new_r_stat < lb_warn ) | (new_r_stat > ub_warn) - self._alarm_states[self.updates_since_reset][rate] = ( + self._alarm_states[self.samples_since_reset][rate] = ( new_r_stat < lb_detect ) | (new_r_stat > ub_detect) @@ -236,10 +241,10 @@ def _calculate_rate_bounds(rate): for rate in self.rates_tracked: _calculate_rate_bounds(rate) - if any(self._alarm_states[self.updates_since_reset].values()): + if any(self._alarm_states[self.samples_since_reset].values()): self.all_drift_states.append("drift") self.drift_state = "drift" - elif any(self._warning_states[self.updates_since_reset].values()): + elif any(self._warning_states[self.samples_since_reset].values()): self.all_drift_states.append("warning") self.drift_state = "warning" else: @@ -258,12 +263,12 @@ def _increment_retraining_recs(self): drift/warning region. """ if self.drift_state == "warning" and self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 if self.drift_state == "drift": - self._retraining_recs[1] = self.total_updates - 1 + self._retraining_recs[1] = self.total_samples - 1 if self._retraining_recs[0] is None: - self._retraining_recs[0] = self.total_updates - 1 + self._retraining_recs[0] = self.total_samples - 1 @staticmethod def _get_four_rates(confusion): diff --git a/menelaus/concept_drift/md3.py b/menelaus/concept_drift/md3.py new file mode 100644 index 00000000..59d2988b --- /dev/null +++ b/menelaus/concept_drift/md3.py @@ -0,0 +1,324 @@ +import copy +import numpy as np +import pandas as pd +from sklearn.model_selection import KFold +from sklearn.metrics import accuracy_score +from sklearn.base import clone +from menelaus.detector import DriftDetector + + +class MD3(DriftDetector): + """The Margin Density Drift Detection (MD3) method is a drift detection + algorithm that alarms based on the cumulative tracking of the number of + samples in the margin, the uncertainty region of a classifier. Tracking + samples that fall in the margin is an unsupervised task, as no true + labels are required. However, this can lead to more common false alarms. + + To counter this, MD3 has an initial drift warning step based on Margin + Density, and then confirms or rules out drift based on accuracy of + predictions on a labeled dataset that is accumulated from the "Oracle", + or directly from the data stream. + + Margin Density (MD): "The expected number of data samples that fall within + a robust classifier's (one that distributes importance weights among its + features) region of uncertainty, i.e. its margin." :cite:t:`sethi2017reliable` + + Because the MD metric is essentially the total number of samples that fall + with the margin divided by the total number of samples in the set, + its value is in the range of [0, 1]. + + Ref. :cite:t:`sethi2017reliable` + """ + + input_type = "stream" + + def calculate_margin_inclusion_signal(self, sample, clf): + """ + Calculate the value of the margin inclusion signal for an incoming sample that + the detector is being updated with. Uses the classifier passed in for this + margin calculation. If the sample lies in the margin of the classifier, then + a value of 1 is returned for the margin inclusion signal. Otherwise, a value + of 0 is returned. + + Args: + sample (numpy.array): feature values/sample data for the new incoming sample + clf (sklearn.svm.SVC): the classifier for which we are calculating margin + inclusion signal. + """ + + w = np.array(clf.coef_[0]) + intercept = np.array(clf.intercept_) + b = intercept[0] / w[1] + + mis = np.abs(np.dot(w, sample) + b) + + if mis <= 1: + return 1 + else: + return 0 + + def __init__( + self, + clf, + margin_calculation_function=calculate_margin_inclusion_signal, + sensitivity=2, + k=10, + oracle_data_length_required=None, + ): + """ + Args: + clf (classifier): the classifier for which we are tracking drift. If + classifier is not of type sklearn.svm.svc, a ``margin_calculation_function`` + must be passed in for appropriate margin signal tracking. + margin_calculation_function (function): the appropriate margin signal + function for the classifier. Takes in two arguments: (1) an incoming + sample of size 1 as a numpy array and (2) the classifier for this + detector. Should return 1 if the sample falls in the margin of the + classifier, 0 if not. Defaults to the + ``calculate_margin_inclusion_signal`` function, which is designed + specifically for an sklearn.svm.SVC classifier. + sensitivity (float): the sensitivity at which a change in margin density + will be detected. Change is signaled when the margin density at a + time t, given by MD_t, deviates by more than ``sensitivity`` + standard deviations from the reference margin density value MD_Ref. + A larger value can be set if frequent signaling is not desired. + Alternatively, a lower value could be used for applications + where small changes could be harmful, if undetected. Defaults to 2. + k (int): the number of folds that will be used in k-fold cross validation + when measuring the distribution statistics of the reference batch + of data. Defaults to 10. + oracle_data_length_required (int): the number of samples that will need to + be collected by the oracle when drift is suspected, for the purpose of + either confirming or ruling out drift, and then retraining the classifier + if drift is confirmed. Defaults to the length of the reference distribution + (this is set in the set_reference method). + """ + + super().__init__() + self.classifier = clf + self.margin_calculation_function = margin_calculation_function + self.sensitivity = sensitivity + self.k = k + self.oracle_data_length_required = oracle_data_length_required + self.oracle_data = None + self.waiting_for_oracle = False + + def set_reference(self, X, y_true=None, y_pred=None, target_name=None): + """ + Initialize detector with a reference batch. Reference batch must be + manually set and updated by user using this method. Reference batch + is not automatically updated after a drift is detected. + + Args: + reference_batch (pandas.DataFrame): initial baseline dataset + y_true (numpy.array): true labels of dataset - not used in MD3 + y_pred (numpy.array): predicted labels of dataset - not used in MD3 + target_name (string): name of the column in the reference_batch + dataframe which is the target variable + """ + + self.reference_batch_features = copy.deepcopy( + X.loc[:, X.columns != target_name] + ) + self.reference_batch_target = copy.deepcopy(X.loc[:, X.columns == target_name]) + + self.reference_distribution = self.calculate_distribution_statistics(X) + + if self.oracle_data_length_required is None: + self.oracle_data_length_required = self.reference_distribution["len"] + + self.forgetting_factor = ( + self.reference_distribution["len"] - 1 + ) / self.reference_distribution["len"] + self.curr_margin_density = self.reference_distribution["md"] + + def calculate_distribution_statistics(self, data): + """ + Calculate the following five statistics for the data distribution + passed in: + + 1. Length/Number of Samples (len) + + 2. Margin Density (md) + + 3. Standard Deviation of Margin Density (md_std) + + 4. Accuracy (acc) + + 5. Standard Deivation of Accuracy (acc_std) + + Args: + data (DataFrame): batch of data to calculate distribution + statistics for + """ + + duplicate_classifier = clone(self.classifier) + + # prepare the cross-validation procedure + margin_densities = [] + accuracies = [] + cv = KFold(n_splits=self.k, random_state=42, shuffle=True) + + # perform k-fold cross validation to acquire distribution margin density and acuracy values + for train_index, test_index in cv.split(self.reference_batch_features): + X_train, X_test = ( + self.reference_batch_features.iloc[train_index], + self.reference_batch_features.iloc[test_index], + ) + y_train, y_test = ( + self.reference_batch_target.iloc[train_index], + self.reference_batch_target.iloc[test_index], + ) + + duplicate_classifier.fit(X_train, y_train.values.ravel()) + + # record margin inclusion signals for all samples in this test band + signal_func_values = [] + for i in range(len(X_test)): + sample_np_array = X_test.iloc[i].to_numpy() + margin_inclusion_signal = self.margin_calculation_function( + self, sample_np_array, duplicate_classifier + ) + signal_func_values.append(margin_inclusion_signal) + + # record margin density over this test band + margin_density = sum(signal_func_values) / len(signal_func_values) + margin_densities.append(margin_density) + + # record accuracy of prediction over this test band + y_pred = duplicate_classifier.predict(X_test) + accuracy = accuracy_score(y_test, y_pred) + accuracies.append(accuracy) + + # record performance - margin density + md = np.mean(margin_densities) + md_std = np.std(margin_densities) + + # record performance - accuracy + acc = np.mean(accuracies) + acc_std = np.std(accuracies) + + # return reference distribution statistics + return { + "len": len(data), + "md": md, + "md_std": md_std, + "acc": acc, + "acc_std": acc_std, + } + + def update(self, X, y_true=None, y_pred=None): + """ + Update the detector with a new sample. + + Args: + X (DataFrame): feature values/sample data for the new incoming sample + y_true (numpy.array): true label of new sample - not used in MD3 + y_pred (numpy.array): predicted label of new sample - not used in MD3 + """ + + if self.waiting_for_oracle == True: + raise ValueError( + """give_oracle_label method must be called to provide detector with a + labeled sample to confirm or rule out drift.""" + ) + + if len(X) != 1: + raise ValueError( + """This method is only available for data inputs in the form of + a Pandas DataFrame with exactly 1 record.""" + ) + + if self.drift_state == "drift": + self.reset() + + super().update(X, y_true, y_pred) + + sample_np_array = X.to_numpy()[0] + margin_inclusion_signal = self.margin_calculation_function( + self, sample_np_array, self.classifier + ) + self.curr_margin_density = ( + self.forgetting_factor * self.curr_margin_density + + (1 - self.forgetting_factor) * margin_inclusion_signal + ) + + warning_level = np.abs( + self.curr_margin_density - self.reference_distribution["md"] + ) + warning_threshold = self.sensitivity * self.reference_distribution["md_std"] + + if warning_level > warning_threshold: + self.drift_state = "warning" + self.waiting_for_oracle = True + + def give_oracle_label(self, labeled_sample): + """ + Provide the detector with a labeled sample to confirm or rule out drift. Once a + certain number of samples is accumulated, drift can be confirmed or ruled out. If + drift is confirmed, retraining will be initiated using these samples, and the + reference distribution will be updated accordingly. + + Args: + labeled_sample (DataFrame): labeled data sample + """ + + if self.waiting_for_oracle != True: + raise ValueError( + """give_oracle_label method can be called only when a drift warning has + been issued and drift needs to be confirmed or ruled out.""" + ) + + if len(labeled_sample) != 1: + raise ValueError( + """This method is only available for data inputs in the form of + a Pandas DataFrame with exactly 1 record.""" + ) + + labeled_columns = list(labeled_sample.columns) + feature_columns = list(self.reference_batch_features.columns) + target_column = list(self.reference_batch_target.columns) + reference_columns = feature_columns + target_column + if len(labeled_columns) != len(reference_columns) or set( + labeled_columns + ) != set(reference_columns): + raise ValueError( + """give_oracle_label method can be called only with a sample containing + the same number and names of columns as the original reference distribution.""" + ) + + self.drift_state = None + + if self.oracle_data is None: + self.oracle_data = labeled_sample + else: + self.oracle_data = pd.concat( + [self.oracle_data, labeled_sample], ignore_index=True + ) + + if len(self.oracle_data) == self.oracle_data_length_required: + X_test, y_test = ( + self.oracle_data[feature_columns], + self.oracle_data[target_column], + ) + y_pred = self.classifier.predict(X_test) + acc_labeled_samples = accuracy_score(y_test, y_pred) + + drift_level = self.reference_distribution["acc"] - acc_labeled_samples + drift_threshold = self.sensitivity * self.reference_distribution["acc_std"] + + if drift_level > drift_threshold: + self.drift_state = "drift" + + # update reference distribution + self.set_reference(self.oracle_data, target_name=target_column[0]) + self.oracle_data = None + self.waiting_for_oracle = False + + def reset(self): + """ + Initialize the detector's drift state and other relevant attributes. + Intended for use after ``drift_state == 'drift'``. + """ + super().reset() + self.curr_margin_density = self.reference_distribution["md"] diff --git a/src/menelaus/concept_drift/stepd.py b/menelaus/concept_drift/stepd.py similarity index 84% rename from src/menelaus/concept_drift/stepd.py rename to menelaus/concept_drift/stepd.py index b1728ed0..ea83e6a4 100644 --- a/src/menelaus/concept_drift/stepd.py +++ b/menelaus/concept_drift/stepd.py @@ -1,9 +1,9 @@ import numpy as np import scipy.stats -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector -class STEPD(DriftDetector): +class STEPD(StreamingDetector): """STEPD is a drift detection algorithm based on a binary classifier's accuracy, intended for an online classifier. @@ -20,7 +20,7 @@ class STEPD(DriftDetector): * and P(T) < ``alpha_drift``, the detector's state is set to ``"drift"``. The index of the first sample which triggered a warning/drift state - (relative to ``self.updates_since_reset``) is stored in ``self._retraining_recs``, + (relative to ``self.samples_since_reset``) is stored in ``self._retraining_recs``, for retraining the classifier when drift occurs. STEPD is intended for use with an online classifier, which is trained on @@ -32,14 +32,6 @@ class STEPD(DriftDetector): set. Ref. :cite:t:`nishida2007detecting` - - Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"``, ``"warning"``, or ``None``. """ input_type = "stream" @@ -75,17 +67,21 @@ def reset(self): self._test_p = None self._initialize_retraining_recs() - def update(self, y_pred, y_true): + def update(self, y_true, y_pred, X=None): """Update the detector with a new sample. Args: - y_pred: predicted class - y_true: actual class + y_true: one true label from input data + y_pred: one predicted label from input data + X: one row of features from input data. Not used by STEPD. """ if self.drift_state == "drift": self.reset() - super().update() + _, y_true, y_pred = super()._validate_input(None, y_true, y_pred) + super().update(None, y_true, y_pred) + # the arrays should have a single element after validation. + y_true, y_pred = y_true[0], y_pred[0] classifier_result = int(y_pred == y_true) self._s += classifier_result @@ -98,7 +94,7 @@ def update(self, y_pred, y_true): self._r += self._window[0] self._window = self._window[1:] - if self.updates_since_reset >= 2 * self.window_size: + if self.samples_since_reset >= 2 * self.window_size: recent_accuracy = self.recent_accuracy() past_accuracy = self.past_accuracy() overall_accuracy = self.overall_accuracy() @@ -106,14 +102,14 @@ def update(self, y_pred, y_true): np.absolute(past_accuracy - recent_accuracy) - 0.5 * ( - (1 / (self.updates_since_reset - self.window_size)) + (1 / (self.samples_since_reset - self.window_size)) + (1 / self.window_size) ) ) / np.sqrt( overall_accuracy * (1 - overall_accuracy) * ( - (1 / (self.updates_since_reset - self.window_size)) + (1 / (self.samples_since_reset - self.window_size)) + (1 / self.window_size) ) ) @@ -152,10 +148,10 @@ def past_accuracy(self): has seen before its current window, but after the last time the detector was reset """ - if (self.updates_since_reset - len(self._window)) == 0: + if (self.samples_since_reset - len(self._window)) == 0: out = 0 else: - out = self._r / (self.updates_since_reset - len(self._window)) + out = self._r / (self.samples_since_reset - len(self._window)) return out def overall_accuracy(self): @@ -164,10 +160,10 @@ def overall_accuracy(self): float: the accuracy of the classifier among the samples the detector has seen since the detector was last reset """ - if self.updates_since_reset == 0: + if self.samples_since_reset == 0: out = 0 else: - out = (self._r + self._s) / (self.updates_since_reset) + out = (self._r + self._s) / (self.samples_since_reset) return out def _initialize_retraining_recs(self): @@ -180,8 +176,8 @@ def _increment_retraining_recs(self): """ if self._retraining_recs[0] is None: self._retraining_recs[0], self._retraining_recs[1] = ( - self.total_updates - 1, - self.total_updates - 1, + self.total_samples - 1, + self.total_samples - 1, ) else: self._retraining_recs[1] += 1 diff --git a/src/menelaus/data_drift/__init__.py b/menelaus/data_drift/__init__.py similarity index 85% rename from src/menelaus/data_drift/__init__.py rename to menelaus/data_drift/__init__.py index 099411b7..c14608c8 100644 --- a/src/menelaus/data_drift/__init__.py +++ b/menelaus/data_drift/__init__.py @@ -16,3 +16,6 @@ from menelaus.data_drift.hdddm import HDDDM from menelaus.data_drift.kdq_tree import KdqTreeStreaming, KdqTreeBatch from menelaus.data_drift.pca_cd import PCACD +from menelaus.data_drift.nndvi import NNDVI +from menelaus.data_drift.cdbd import CDBD +from menelaus.data_drift.histogram_density_method import HistogramDensityMethod diff --git a/src/menelaus/data_drift/cdbd.py b/menelaus/data_drift/cdbd.py similarity index 76% rename from src/menelaus/data_drift/cdbd.py rename to menelaus/data_drift/cdbd.py index d46f9d25..19748936 100644 --- a/src/menelaus/data_drift/cdbd.py +++ b/menelaus/data_drift/cdbd.py @@ -48,16 +48,6 @@ class CDBD(HistogramDensityMethod): Ref. :cite:t:`lindstrom2013drift` Attributes: - total_updates (int): total number of batches the drift detector has - been updated with. If detect_batch = 1, attr refers to total number of - batches + 1 to account for additional update call due to the initial - splitting of the reference batch. - updates_since_reset (int): number of batches since the last drift - detection. If detect_batch = 1, attr refers to total number of batches - + 1 to account for additional update call due to the initial splitting - of the reference batch. - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. Epsilon (list): stores Epsilon values since the last drift detection. reference_n (int): number of samples in reference batch. total_epsilon (int): stores running sum of Epsilon values until @@ -112,16 +102,16 @@ def __init__( See class docstrings for more information on this modification. Defaults to 1. - * if detect_batch = 1 - HDDDM can detect drift on the first test - batch passed to the update method. BE AWARE, total samples - and samples since reset will be number of batches passed to HDM + * if ``detect_batch == 1`` - CDBD can detect drift on the first + test batch passed to the update method. Total samples and + samples since reset will be number of batches passed to HDM plus 1, due to splitting of reference batch - * if detect_batch = 2 - HDDDM can detect drift on the second test - batch passed to the update method. + * if ``detect_batch == 2`` - CDBD can detect drift on the second + test batch passed to the update method. - * if detect_batch = 3 - HDDDM can detect drift on the third test - batch passed to the update method. + * if ``detect_batch == 3`` - CDBD can detect drift on the third + test batch passed to the update method. statistic (str): statistical method used to compute adaptive threshold. Defaults to ``"tstat"`` @@ -161,43 +151,35 @@ def __init__( subsets=subsets, ) - def set_reference(self, reference_batch): + def set_reference(self, X, y_true=None, y_pred=None): """ - Initialize detector with a reference batch. After drift, reference batch is - automatically set to most recent test batch. Option for user to specify - alternative reference batch using this method. + Initialize detector with a reference batch. After drift, reference batch + is automatically set to most recent test batch. Option for user to + specify alternative reference batch using this method. Args: - reference_batch (DataFrame): initial baseline dataset + X (pandas.DataFrame): initial baseline dataset + y_true (numpy.array): true labels for dataset - not used by CDBD + y_pred (numpy.array): predicted labels for dataset - not used by CDBD """ - - super().set_reference(reference_batch) - # Ensure only being used with 1 variable in reference - if self._num_features != 1: - raise ValueError("CDBD should only be used to monitor 1 variable") + if len(X.shape) > 1 and X.shape[1] != 1: + raise ValueError("CDBD should only be used to monitor 1 variable.") + super().set_reference(X, None, None) - def update(self, test_batch): + def update(self, X, y_true=None, y_pred=None): """ Update the detector with a new test batch. If drift is detected, new reference batch becomes most recent test batch. If drift is not detected, reference batch is updated to include most recent test batch. Args: - test_batch (DataFrame): next batch of data to detect drift on. + X (DataFrame): next batch of data to detect drift on. + y_true (numpy.ndarray): true labels of next batch - not used in CDBD + y_pred (numpy.ndarray): predicted labels of next batch - not used in CDBD """ # Ensure only being used with 1 variable in test - if test_batch.shape[1] != 1: - raise ValueError("CDBD should only be used to monitor 1 variable") - - super().update(test_batch) - - def reset(self): - """ - Initialize relevant attributes to original values, to ensure information - only stored from updates_since_reset (lambda) onwards. Intended for use - after ``drift_state == 'drift'``. - """ - # This is here to make sphinx behave. - super().reset() + if len(X.shape) > 1 and X.shape[1] != 1: + raise ValueError("CDBD should only be used to monitor 1 variable.") + super().update(X, None, None) diff --git a/menelaus/data_drift/hdddm.py b/menelaus/data_drift/hdddm.py new file mode 100644 index 00000000..a453acef --- /dev/null +++ b/menelaus/data_drift/hdddm.py @@ -0,0 +1,216 @@ +from menelaus.data_drift.histogram_density_method import HistogramDensityMethod + + +class HDDDM(HistogramDensityMethod): + + """HDDDM is a batch-based, unsupervised drift detection algorithm that + detects changes in feature distributions. It uses the Hellinger distance + metric to compare test and reference batches and is capable of detecting + gradual or abrupt changes in data. + + This method relies upon three statistics: + + * Hellinger distance: the sum of the normalized, squared differences in + frequency counts for each bin between reference and test datasets, + averaged across all features. + + * Epsilon: the differences in Hellinger distances between sets of + reference and test batches. + + * Beta: the adaptive threshold adapted at each time stamp. It is based + on the mean of Epsilon plus the scaled standard deviation of Epsilon. + The scale applied to the standard deviation is determined by the + ``statistic`` parameter. It is either the number of standard + deviations deemed significant (``"stdev"``) or the t-statistic + (``"tstat"``). + + HDDDM operates by: + + #. Estimating density functions of reference and test data using + histograms. The number of bins in each histogram equals the square + root of the length of reference window. Bins are aligned by computing + the minimum and maximum value for each feature from both test and + reference window. + + #. Computing the distance between reference and test distributions. The + Hellinger distance is first calculated between each feature in the + reference and test batches. Then, the final Hellinger statistic used + is the average of each feature's distance. + + #. Computing Epsilon. + + #. Computing the adaptive threshold Beta. + + #. Comparing current Epsilon to Beta. If Epsilon > Beta, drift is + detected. The new reference batch is now the test batch on which + drift was detected. All statistics are reset. If Epsilon <= Beta, + drift is not detected. The reference batch is updated to include this + most recent test batch. All statistics are maintained. + + Two key modifications were added to Ditzler and Polikar's presentation of + HDDDM: + + * To answer the research question of "where is drift occuring?", it + stores the distance values and Epsilon values for each feature. These + statistics can be used to identify and visualize the features + containing the most significant drifts. + + * The Hellinger distance values are calculated for each feature in + the test batch. These values can be accessed when drift occurs + using the ``self.feature_info`` dictionary. + + * The Epsilon values for each feature are stored, for each set of + reference and test batches. For each feature, these values + represent the difference in Hellinger distances within the test + and reference batch at time *t*, to the Hellinger distances within + the test and reference batch at time *t-1*. These can be acccessed + with each update call using the ``self.feature_epsilons`` + variable. They also can be accessed when drift occurs using the + ``self.feature_info`` dictionary. + + * The original algorithm cannot detect drift until it is updated with + the third test batch after either a) initilization or b) reset upon + drift, because the threshold for drift detection is defined from the + *difference* Epsilon. To have sufficient values to define this + threshold, then, three batches are needed. The ``detect_batch`` + parameter can be set such that bootstrapping is used to define this + threshold earlier than the third test batch. + + * if ``detect_batch == 3``, HDDDM will operate as described in + :cite:t:`ditzler2011hellinger`. + + * if ``detect_batch == 2``, HDDDM will detect drift on the second + test batch. On the second test batch, HDDDM uses bootstrapped + samples from the reference batch to estimate the mean and standard + deviation of Epsilon; this is used to calculate the necessary + threshold. On the third test batch, this value is removed from all + proceeding calculations. + + * if ``detect_batch == 1``, HDDDM will detect drift on the first + test batch. The initial reference batch is split randomly into two + halves. The first half will serve as the original reference batch. + The second half will serve as a proxy for the first test batch, + allowing us to calculate the distance statistic. When HDDDM is + updated with the first actual test batch, HDDDM will perform the + method for bootstrapping Epsilon, as described in the above bullet + for ``detect_batch == 2``. This will allow a Beta threshold to be + calculated using the first test batch, allowing for detection of + drift on this batch. + + Ref. :cite:t:`ditzler2011hellinger` + + Attributes: + Epsilon (list): stores Epsilon values since the last drift detection. + reference_n (int): number of samples in reference batch. + total_epsilon (int): stores running sum of Epsilon values until drift is + detected, initialized to 0. + bins (int): number of bins in histograms, equivalent to square root of + number of samples in reference batch. + num_feat (int): number of features in reference batch. + lambda (int): batch number on which last drift was detected. + distances (dict): For each batch seen (key), stores the Hellinger + distance between test and reference batch (value). Useful for + visualizing drift detection statistics. + epsilon_values (dict): For each batch seen (key), stores the Epsilon + value between the current and previous test and reference batches + (value). Useful for visualizing drift detection statistics. Does not + store the bootstrapped estimate of Epsilon, if used. + thresholds (dict): For each batch seen (key), stores the Beta thresholds + between test and reference batch (value). Useful for visualizing drift + detection statistics. + """ + + input_type = "batch" + + def __init__( + self, + detect_batch=1, + divergence="H", + statistic="tstat", + significance=0.05, + subsets=5, + ): + + """ + Args: + divergence (str): divergence measure used to compute distance + between histograms. Default is "H". + + * "H" - Hellinger distance, original use is for HDDDM + + * "KL" - Kullback-Leibler Divergence, original use is for CDBD + + * User can pass in custom divergence function. Input is two + two-dimensional arrays containing univariate histogram + estimates of density, one for reference, one for test. It + must return the distance value between histograms. To be + a valid distance metric, it must satisfy the following + properties: non-negativity, identity, symmetry, + and triangle inequality, e.g. that in + examples/hdddm_example.py. + + detect_batch (int): the test batch on which drift will be detected. + See class docstrings for more information on this modification. + Defaults to 1. + + * if ``detect_batch == 1`` - HDDDM can detect drift on the first + test batch passed to the update method. Total batches and + batches since reset will be number of batches passed to HDM + plus 1, due to splitting of reference batch + + * if ``detect_batch == 2`` - HDDDM can detect drift on the + second test batch passed to the update method. + + * if ``detect_batch == 3`` - HDDDM can detect drift on the third + test batch passed to the update method. + + statistic (str): statistical method used to compute adaptive + threshold. Defaults to ``"tstat"``. + + * ``"tstat"`` - t-statistic with desired significance level and + degrees of freedom = 2 for hypothesis testing on two + populations. + + * ``"stdev"`` - uses number of standard deviations deemed + significant to compute threhsold. + + significance (float): statistical significance used to identify + adaptive threshold. Defaults to 0.05. + + * if statistic = ``"tstat"`` - statistical significance of + t-statistic, e.g. .05 for 95% significance level. + + * if statistic = ``"stdev"`` - number of standard deviations of + change around the mean accepted. + + subsets (int): the number of subsets of reference data to take to + compute initial estimate of Epsilon. + + * if too small - initial Epsilon value will be too small. + Increases risk of missing drift. + + * if too high - intial Epsilon value will be too large. + Increases risk of false alarms. + + """ + + super().__init__( + divergence=divergence, + detect_batch=detect_batch, + statistic=statistic, + significance=significance, + subsets=subsets, + ) + + def update(self, X, y_true=None, y_pred=None): + """ + Update the detector with a new test batch. If drift is detected, new + reference batch becomes most recent test batch. If drift is not + detected, reference batch is updated to include most recent test batch. + + Args: + X (DataFrame): next batch of data to detect drift on. + y_true (numpy.ndarray): true labels of next batch - not used in HDDDM + y_pred (numpy.ndarray): predicted labels of next batch - not used in HDDDM + """ + super().update(X, y_true, y_pred) diff --git a/src/menelaus/data_drift/histogram_density_method.py b/menelaus/data_drift/histogram_density_method.py similarity index 68% rename from src/menelaus/data_drift/histogram_density_method.py rename to menelaus/data_drift/histogram_density_method.py index a4789dcb..4a3236d9 100644 --- a/src/menelaus/data_drift/histogram_density_method.py +++ b/menelaus/data_drift/histogram_density_method.py @@ -1,11 +1,12 @@ +import copy import numpy as np import pandas as pd import scipy.stats -from menelaus.drift_detector import DriftDetector +from menelaus.detector import BatchDetector from scipy.spatial.distance import jensenshannon -class HistogramDensityMethod(DriftDetector): +class HistogramDensityMethod(BatchDetector): """ The Histogram Density Method (HDM) is the base class for both HDDDM and CDBD. HDDDM differs from CDBD by relying upon the Hellinger distance measure @@ -19,29 +20,29 @@ class HistogramDensityMethod(DriftDetector): normalized, squared differences in frequency counts for each bin between reference and test datasets, averaged across all features. - * KL divergence (default if called via CDBD): Jensen-Shannon distances, a - symmetric and bounded measure based upon Kullback-Leibler - Divergence + * KL divergence (default if called via CDBD): Jensen-Shannon + distances, a symmetric and bounded measure based upon + Kullback-Leibler Divergence * Optional user-defined distance metric - * Epsilon: the differences in Hellinger distances between sets - of reference and test batches. + * Epsilon: the differences in Hellinger distances between sets of + reference and test batches. - * Beta: the adaptive threshold adapted at each time stamp. It is - based on the mean of Epsilon plus the scaled standard - deviation of Epsilon. The scale applied to the standard deviation is - determined by the ``statistic`` parameter. It is either the number of - standard deviations deemed significant (``"stdev"``) or the t-statistic + * Beta: the adaptive threshold adapted at each time stamp. It is based + on the mean of Epsilon plus the scaled standard deviation of Epsilon. + The scale applied to the standard deviation is determined by the + ``statistic`` parameter. It is either the number of standard + deviations deemed significant (``"stdev"``) or the t-statistic (``"tstat"``). HDM operates by: #. Estimating density functions of reference and test data using histograms. The number of bins in each histogram equals the square - root of the length of reference window. Bins are aligned - by computing the minimum and maximum value for each feature from both - test and reference window. + root of the length of reference window. Bins are aligned by computing + the minimum and maximum value for each feature from both test and + reference window. #. Computing the distance between reference and test distributions. In HDDDM, the Hellinger distance is first calculated between each @@ -60,13 +61,12 @@ class HistogramDensityMethod(DriftDetector): drift is not detected. The reference batch is updated to include this most recent test batch. All statistics are maintained. - Two key modifications, present in this parent method, were added to Ditzler - and Polikar's presentation of HDDDM: + Two key modifications were added to Ditzler and Polikar's presentation: - * For HDDDM, to answer the research question of "Where is drift - occuring?", it stores the distance values and Epsilon values for each - feature. These statistics can be used to identify and visualize the - features containing the most significant drifts. + * For HDDDM, to answer the question of "where is drift occuring?", it + stores the distance values and Epsilon values for each feature. These + statistics can be used to identify and visualize the features + containing the most significant drifts. * The Hellinger distance values are calculated for each feature in the test batch. These values can be accessed when drift occurs @@ -75,75 +75,55 @@ class HistogramDensityMethod(DriftDetector): * The Epsilon values for each feature are stored, for each set of reference and test batches. For each feature, these values represent the difference in Hellinger distances within the test - and reference batch at time t, to the Hellinger distances within - the test and reference batch at time t-1. These can be acccessed + and reference batch at time *t*, to the Hellinger distances within + the test and reference batch at time *t-1*. These can be acccessed with each update call using the self.feature_epsilons variable. They also can be accessed when drift occurs using the self.feature_info dictionary. * The original algorithm cannot detect drift until it is updated with - the third test batch after initial setup or third test batch after - each drift detection. When HDM is updated with the first test batch - (T=1), the distance is calculated between test and reference batch. - When updated with the second test batch (T=2), HDM calculates the - distance and Epsilon, the difference in distances. When HDM is updated - with the third test batch (T=3), the distance and Epsilon are - calculated. The adaptive threshold Beta is computed using Epsilon - values from lambda -> T-1 (lambda is either T=0 or the test batch - number on which drift was last detected). Beta needs at least one - prior value of Epsilon to be calculated. Now that Beta can be - calculated, HDM can detect drift on the 3rd test batch. In order to - allow for more immediate detection of drift, we added the following - options, specified through the parameter "detect_batch": - - * if ``detect_batch`` = 3, HDM will operate as described above. - - * if ``detect_batch`` = 2, HDM will detect drift on the second test - batch. On the second test batch only, HDM uses a bootstrapped - estimate of an initial Epsilon value, to serve as a proxy for the - first value of Epsilon occurring at T-1. This initial estimate of - Epsilon is used to calculate Beta, allowing for drift to be - detected on the second test batch. On the third test batch, this - value is removed from all Epsilon and Beta calculations. + the third test batch after either a) initilization or b) reset upon + drift, because the threshold for drift detection is defined from the + *difference* Epsilon. To have sufficient values to define this + threshold, then, three batches are needed. The ``detect_batch`` + parameter can be set such that bootstrapping is used to define this + threshold earlier than the third test batch. + + * if ``detect_batch == 3``, HDM will operate as described in + :cite:t:`ditzler2011hellinger`. + + * if ``detect_batch == 2``, HDM will detect drift on the second test + batch. On the second test batch, HDM uses bootstrapped samples + from the reference batch to estimate the mean and standard + deviation of Epsilon; this is used to calculate the necessary + threshold. On the third test batch, this value is removed from all + proceeding * if ``detect_batch`` = 1, HDM will detect drift on the first test batch. The initial reference batch is split randomly into two - halves. The first halve will serve as the original reference - batch. The second half will serve as a proxy for the first test - batch, allowing us to calculate the distance statistic. - When HDM is updated with the first actual test batch, HDM will - perform the method for bootstrapping Epsilon, as described in the - above bullet for ``detect_batch`` = 2. This will allow a Beta - threshold to be calculated using the first test batch, allowing - for detection of drift on this batch. BE AWARE, total samples - and samples since reset will be number of batches passed to HDM - plus 1, due to splitting of reference batch + halves. The first half will serve as the original reference batch. + The second half will serve as a proxy for the first test batch, + allowing us to calculate the distance statistic. When HDM is + updated with the first actual test batch, HDM will perform the + method for bootstrapping Epsilon, as described in the above bullet + for ``detect_batch`` = 2. This will allow a Beta threshold to be + calculated using the first test batch, allowing for detection of + drift on this batch. Ref. :cite:t:`lindstrom2013drift` and :cite:t:`ditzler2011hellinger` Attributes: - total_updates (int): total number of batches the drift detector has - been updated with. If detect_batch = 1, attr refers to total - number of batches + 1 to account for additional update call - due to the initial splitting of the reference batch. - updates_since_reset (int): number of batches since the last drift - detection. If detect_batch = 1, attr refers to total - number of batches + 1 to account for additional update call - due to the initial splitting of the reference batch. - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. Epsilon (list): stores Epsilon values since the last drift detection. - reference_n (int): number of samples in reference batch . + reference_n (int): number of samples in reference batch. total_epsilon (int): stores running sum of Epsilon values until drift is detected, initialized to 0. distances (dict): For each batch seen (key), stores the distance between test and reference batch (value). Useful for visualizing drift detection statistics. epsilon_values (dict):For each batch seen (key), stores the Epsilon - value between the current and previous test and reference - batches (value). Useful for visualizing drift detection - statistics. Does not store the bootstrapped estimate of Epsilon, - if used. + value between the current and previous test and reference batches + (value). Useful for visualizing drift detection statistics. Does not + store the bootstrapped estimate of Epsilon, if used. thresholds (dict): For each batch seen (key), stores the Beta thresholds between test and reference batch (value). Useful for visualizing drift detection statistics. @@ -162,8 +142,8 @@ def __init__( """ Args: - divergence (str or function): divergence measure used to compute distance - between histograms. Default is "H". + divergence (str or function): divergence measure used to compute + distance between histograms. Default is "H". * "H" - Hellinger distance, original use is for HDDDM @@ -171,25 +151,25 @@ def __init__( * User can pass in custom divergence function. Input is two two-dimensional arrays containing univariate histogram - estimates of density, one for reference, one for test. It - must return the distance value between histograms. To be - a valid distance metric, it must satisfy the following - properties: non-negativity, identity, symmetry, - and triangle inequality, e.g. that in - examples/cdbd_example.py or examples/hdddm_example.py. + estimates of density, one for reference, one for test. It must + return the distance value between histograms. To be a valid + distance metric, it must satisfy the following properties: + non-negativity, identity, symmetry, and triangle inequality, + e.g. that in examples/cdbd_example.py or + examples/hdddm_example.py. detect_batch (int): the test batch on which drift will be detected. See class docstrings for more information on this modification. Defaults to 1. - * if detect_batch = 1 - HDM can detect drift on the first test - batch passed to the update method + * if ``detect_batch == 1`` - HDM can detect drift on the first + test batch passed to the update method - * if detect_batch = 2 - HDM can detect drift on the second test - batch passed to the update method + * if ``detect_batch == 2`` - HDM can detect drift on the second + test batch passed to the update method - * if detect_batch = 3 - HDM can detect drift on the third test - batch passed to the update method + * if ``detect_batch == 3`` - HDM can detect drift on the third + test batch passed to the update method statistic (str): statistical method used to compute adaptive threshold. Defaults to ``"tstat"``. @@ -241,22 +221,26 @@ def __init__( self.epsilon_values = {} self.thresholds = {} - def set_reference(self, reference_batch): + def set_reference(self, X, y_true=None, y_pred=None): """ - Initialize detector with a reference batch. After drift, reference batch is - automatically set to most recent test batch. Option for user to specify - alternative reference batch using this method. + Initialize detector with a reference batch. After drift, reference batch + is automatically set to most recent test batch. Option for user to + specify alternative reference batch using this method. Args: - reference_batch (DataFrame): initial baseline dataset + X (pandas.DataFrame): initial baseline dataset + y_true (numpy.array): true labels for dataset - not used by HDM + y_pred (numpy.array): predicted labels for dataset - not used by HDM """ - + X, _, _ = super()._validate_input(X, None, None) + X = pd.DataFrame( + X, columns=self._input_cols + ) # TODO: subsequent operations expect dataframes, not numpy arrays # Initialize attributes - self.reference = reference_batch - self._num_features = self.reference.shape[1] + self.reference = copy.deepcopy(X) self.reset() - def update(self, test_batch): + def update(self, X, y_true=None, y_pred=None): """ Update the detector with a new test batch. If drift is detected, new @@ -264,49 +248,56 @@ def update(self, test_batch): detected, reference batch is updated to include most recent test batch. Args: - test_batch (DataFrame): next batch of data to detect drift on. + X (DataFrame): next batch of data to detect drift on. + y_true (numpy.ndarray): true labels of next batch - not used in HDM + y_pred (numpy.ndarray): predicted labels of next batch - not used in HDM """ if self._drift_state == "drift": self.reset() - super().update() - test_n = test_batch.shape[0] + X, _, _ = super()._validate_input(X, None, None) + X = pd.DataFrame( + X, columns=self._input_cols + ) # TODO: subsequent operations expect dataframes, not numpy arrays + + super().update(X, None, None) + test_n = X.shape[0] # Estimate reference and test histograms mins = [] maxes = [] - for f in range(self._num_features): + for f in range(self._input_col_dim): reference_variable = self.reference.iloc[:, f] - test_variable = test_batch.iloc[:, f] + test_variable = X.iloc[:, f] mins.append(np.concatenate((reference_variable, test_variable)).min()) maxes.append(np.concatenate((reference_variable, test_variable)).max()) self._reference_density = self._build_histograms(self.reference, mins, maxes) - test_density = self._build_histograms(test_batch, mins, maxes) + test_density = self._build_histograms(X, mins, maxes) # Divergence metric total_distance = 0 feature_distances = [] - for f in range(self._num_features): + for f in range(self._input_col_dim): f_distance = self.distance_function( self._reference_density[f], test_density[f] ) total_distance += f_distance feature_distances.append(f_distance) - self.current_distance = (1 / self._num_features) * total_distance - self.distances[self.total_updates] = self.current_distance + self.current_distance = (1 / self._input_col_dim) * total_distance + self.distances[self.total_batches] = self.current_distance # For each feature, calculate Epsilon, difference in distances - if self.total_updates > 1: + if self.total_batches > 1: self.feature_epsilons = [ a_i - b_i for a_i, b_i in zip(feature_distances, self._prev_feature_distances) ] # Compute Epsilon and Beta - if self.updates_since_reset >= 2: + if self.batches_since_reset >= 2: - if self.updates_since_reset == 2 and self.detect_batch != 3: + if self.batches_since_reset == 2 and self.detect_batch != 3: initial_epsilon = self._estimate_initial_epsilon( self.reference, self.subsets, mins, maxes ) @@ -314,20 +305,20 @@ def update(self, test_batch): current_epsilon = abs(self.current_distance - self._prev_distance) * 1.0 self.epsilon.append(current_epsilon) - self.epsilon_values[self.total_updates] = current_epsilon + self.epsilon_values[self.total_batches] = current_epsilon - condition1 = bool(self.updates_since_reset >= 2 and self.detect_batch != 3) - condition2 = bool(self.updates_since_reset >= 3 and self.detect_batch == 3) + condition1 = bool(self.batches_since_reset >= 2 and self.detect_batch != 3) + condition2 = bool(self.batches_since_reset >= 3 and self.detect_batch == 3) if condition1 or condition2: self.beta = self._adaptive_threshold(self.statistic, test_n) - self.thresholds[self.total_updates] = self.beta + self.thresholds[self.total_batches] = self.beta # Detect drift if current_epsilon > self.beta: # Feature information - if self._num_features > 1: + if self._input_col_dim > 1: self.feature_info = { "Epsilons": self.feature_epsilons, @@ -338,13 +329,13 @@ def update(self, test_batch): } self._drift_state = "drift" - self.reference = test_batch - self._lambda = self.total_updates + self.reference = X + self._lambda = self.total_batches if self._drift_state != "drift": self._prev_distance = self.current_distance self._prev_feature_distances = feature_distances - self.reference = pd.concat([self.reference, test_batch]) + self.reference = pd.concat([self.reference, X]) self.reference_n = self.reference.shape[0] # number of bins for histogram, from reference batch self._bins = int(np.floor(np.sqrt(self.reference_n))) @@ -352,7 +343,7 @@ def update(self, test_batch): def reset(self): """ Initialize relevant attributes to original values, to ensure information - only stored from updates_since_reset (lambda) onwards. Intended for use + only stored from batches_since_reset (lambda) onwards. Intended for use after ``drift_state == 'drift'``. """ @@ -401,7 +392,7 @@ def _build_histograms(self, dataset, min_values, max_values): bins=self._bins, range=(min_values[f], max_values[f]), )[0] - for f in range(self._num_features) + for f in range(self._input_col_dim) ] return histograms @@ -411,8 +402,8 @@ def _hellinger_distance(self, reference_density, test_density): Computes Hellinger distance between reference and test histograms Args: - reference_density (list): Univariate output of _build_histograms from reference - batch. + reference_density (list): Univariate output of _build_histograms + from reference batch. test_density (list): Univariate tput of _build_histograms from test batch. @@ -446,22 +437,22 @@ def _adaptive_threshold(self, stat, test_n): Adaptive threshold Beta. """ - if self.updates_since_reset == 3 and self.detect_batch != 3: + if self.batches_since_reset == 3 and self.detect_batch != 3: self.total_epsilon -= self.epsilon[0] self.epsilon = self.epsilon[1:] # update scale for denominator (t - lambda - 1), accounting for our initial Epsilon estimate - if self.updates_since_reset == 2 and self.detect_batch != 3: + if self.batches_since_reset == 2 and self.detect_batch != 3: d_scale = 1 else: - d_scale = self.total_updates - self._lambda - 1 + d_scale = self.total_batches - self._lambda - 1 - # Increment running mean of epsilon from updates_since_reset (lambda) -> t-1 + # Increment running mean of epsilon from batches_since_reset (lambda) -> t-1 self.total_epsilon += self.epsilon[-2] # was -2 before total samples change... epsilon_hat = (1 / d_scale) * self.total_epsilon - # Compute standard deviation for updates_since_reset (lambda) -> t-1 + # Compute standard deviation for batches_since_reset (lambda) -> t-1 total_stdev = sum( (self.epsilon[i] - epsilon_hat) ** 2 for i in range(len(self.epsilon) - 1) ) @@ -481,8 +472,8 @@ def _adaptive_threshold(self, stat, test_n): def _estimate_initial_epsilon( self, reference, num_subsets, histogram_mins, histogram_maxes ): - """Computes a bootstrapped initial estimate of Epsilon on 2nd test batch, allowing HDM - to detect drift on the 2nd batch. + """Computes a bootstrapped initial estimate of Epsilon on 2nd test + batch, allowing HDM to detect drift on the 2nd batch. 1. Subsets reference data with replacement 2. Computes distance between each subset. @@ -523,7 +514,7 @@ def _estimate_initial_epsilon( # Divergence metric total_distance = 0 - for f in range(self._num_features): + for f in range(self._input_col_dim): f_distance = self.distance_function(subset1[f], subset2[f]) total_distance += f_distance distances.append(total_distance) @@ -548,9 +539,10 @@ def _KL_divergence(self, reference_density, test_density): histograms. JS is a bounded, symmetric form of KL divergence. Args: - reference_density (list): Univariate output of _build_histograms from reference - batch. - test_density (list): Univariate output of _build_histograms from test batch. + reference_density (list): Univariate output of _build_histograms + from reference batch. + test_density (list): Univariate output of _build_histograms from + test batch. Returns: JS divergence between univariate reference and test density diff --git a/src/menelaus/data_drift/kdq_tree.py b/menelaus/data_drift/kdq_tree.py similarity index 84% rename from src/menelaus/data_drift/kdq_tree.py rename to menelaus/data_drift/kdq_tree.py index 1e7b5f83..50c8fbb7 100644 --- a/src/menelaus/data_drift/kdq_tree.py +++ b/menelaus/data_drift/kdq_tree.py @@ -1,10 +1,11 @@ +import copy import numpy as np from numpy import unique import pandas as pd from pandas import DataFrame import scipy.stats -from menelaus.drift_detector import StreamingDetector, BatchDetector +from menelaus.detector import StreamingDetector, BatchDetector from menelaus.partitioners.KDQTreePartitioner import KDQTreePartitioner @@ -58,10 +59,6 @@ class KdqTreeDetector: data. Ref. :cite:t:`dasu2006information` - - Attributes: - drift_state (str): detector's current drift state, can - take ``str`` values ``"drift"`` or ``None``. """ def __init__( @@ -102,41 +99,6 @@ def reset(self): self._test_data_size = 0 self._kdqtree = None self._critical_dist = None - self.input_cols = None - - def _prepare_data(self, data): - """ - Validates and processes new data to be used in update sequence, - adjusting saved feature names where needed. - - Args: - data (pandas.DataFrame or numpy.ndarray): new data for update - - Returns - pandas.DataFrame or numpy.ndarray: validated data for update - """ - if isinstance(data, pd.DataFrame): - if self.input_cols is None: - # The first update with a dataframe will constrain subsequent - # input. This will also fire if set_reference has been used with - # a dataframe. - self.input_cols = data.columns - elif self.input_cols is not None: - if not data.columns.equals(self.input_cols): - raise ValueError( - "Columns of new data must match with columns of reference data." - ) - ary = data.values - elif isinstance(data, np.ndarray): - # This allows starting with a dataframe, then later passing bare - # numpy arrays. For now, assume users are not miscreants. - ary = data - else: - raise ValueError( - """This method is only available for data inputs in the form of - a Pandas DataFrame or a Numpy Array.""" - ) - return ary def _evaluate_kdqtree(self, ary, input_type): """ @@ -227,20 +189,21 @@ def to_plotly_dataframe( Returns: pd.DataFrame: A dataframe where each row corresponds to a node, and each column contains some information: + * ``name``: a label corresponding to which feature this split is on * ``idx``: a unique ID for the node, to pass - ``plotly.express.treemap``'s id argument + ``plotly.express.treemap``'s id argument * ``parent_idx``: the ID of the node's parent * ``cell_count``: how many samples are in this node in the - reference tree. + reference tree. * ``depth``: how deep the node is in the tree * ``count_diff``: if ``tree_id2`` is specified, the change in - counts from the reference tree. + counts from the reference tree. * ``kss``: the Kulldorff Spatial Scan Statistic for this node, - defined as the Kullback-Leibler divergence for this node - between the reference and test trees, using the individual - node and all other nodes combined as the bins for the - distributions. + defined as the Kullback-Leibler divergence for this node + between the reference and test trees, using the individual + node and all other nodes combined as the bins for the + distributions. """ if input_cols is not None: return self._kdqtree.to_plotly_dataframe( @@ -248,7 +211,7 @@ def to_plotly_dataframe( ) else: return self._kdqtree.to_plotly_dataframe( - tree_id1, tree_id2, max_depth, self.input_cols + tree_id1, tree_id2, max_depth, self._input_cols ) def _get_critical_kld(self, ref_counts, sample_size): @@ -331,14 +294,6 @@ class KdqTreeStreaming(KdqTreeDetector, StreamingDetector): output structured for use with ``plotly.express.treemap``. Ref. :cite:t:`dasu2006information` - - Attributes: - total_samples (int): total number of samples the drift - detector has been udpated with - samples_since_reset (int): number of samples since last - drift detection - drift_state (str): detector's current drift state, can - take ``str`` values e.g. ``"drift"`` or ``None`` """ def __init__( @@ -395,7 +350,7 @@ def reset(self): KdqTreeDetector.reset(self) self._drift_counter = 0 # samples consecutively in the drift region - def update(self, data): + def update(self, X, y_true=None, y_pred=None): """ Update the detector with a new sample point. Constructs the reference data's kdqtree; then, when sufficient samples have been received, puts @@ -407,16 +362,18 @@ def update(self, data): reference windows will be constructed once sufficient samples are received. Args: - data (pandas.DataFrame or numpy array): If just reset/initialized, + X (pandas.DataFrame or numpy array): If just reset/initialized, the reference data. Otherwise, a new sample to put into the test window. + y_true (numpy.ndarray): true labels of input data - not used in KdqTree + y_pred (numpy.ndarray): predicted labels of input data - not used in KdqTree """ - ary = self._prepare_data(data) - if self.drift_state == "drift": self.reset() - StreamingDetector.update(self) + X, _, _ = super()._validate_input(X, None, None) + StreamingDetector.update(self, X, None, None) + ary = copy.deepcopy(X) KdqTreeDetector._evaluate_kdqtree(self, ary, "stream") @@ -464,14 +421,6 @@ class KdqTreeBatch(KdqTreeDetector, BatchDetector): output structured for use with ``plotly.express.treemap``. Ref. :cite:t:`dasu2006information` - - Attributes: - total_samples (int): total number of samples the drift - detector has been udpated with - samples_since_reset (int): number of samples since last - drift detection - drift_state (str): detector's current drift state, can - take ``str`` values e.g. ``"drift"`` or ``None`` """ def __init__( @@ -512,28 +461,22 @@ def reset(self): BatchDetector.reset(self) KdqTreeDetector.reset(self) - def set_reference(self, data): + def set_reference(self, X, y_true=None, y_pred=None): """ Initialize detector with a reference batch. The user may specify an alternate reference batch than the one maintained by kdq-Tree. This will reset the detector. Args: - data (pandas.DataFrame or numpy array): baseline dataset + X (pandas.DataFrame or numpy.array): baseline dataset + y_true (numpy.array): actual labels of dataset - not used in KdqTree + y_pred (numpy.array): predicted labels of dataset - not used in KdqTree """ - if isinstance(data, pd.DataFrame): - # XXX - notice how inner_set calling KLD requires us to continue - # branching on input_type, which is not ideal - Anmol Srivastava - self._inner_set_reference(data.values, input_type="batch") - self.input_cols = data.columns - elif isinstance(data, np.ndarray): - self._inner_set_reference(data, input_type="batch") - else: - raise ValueError( - "This method is only available for data inputs in the form of a Pandas DataFrame or a Numpy Array." - ) + X, _, _ = super()._validate_input(X, None, None) + ary = copy.deepcopy(X) + self._inner_set_reference(ary, input_type="batch") - def update(self, data): + def update(self, X, y_true=None, y_pred=None): """ Update the detector with a new batch. Constructs the reference data's kdqtree; then, when sufficient samples have been received, puts @@ -546,18 +489,19 @@ def update(self, data): batches to ``update``. Args: - data (pandas.DataFrame or numpy array): If just reset/initialized, + X (pandas.DataFrame or numpy array): If just reset/initialized, the reference data. Otherwise, a new batch of data to be compared to the reference window. + y_true (numpy.ndarray): true labels of input data - not used in KdqTree + y_pred (numpy.ndarray): predicted labels of input data - not used in KdqTree """ - ary = self._prepare_data(data) if self.drift_state == "drift": + # Note that set_reference resets the detector. self.set_reference(self.ref_data) - BatchDetector.update(self) - KdqTreeDetector._evaluate_kdqtree(self, ary, "batch") + X, _, _ = super()._validate_input(X, None, None) + BatchDetector.update(self, X, None, None) + ary = copy.deepcopy(X) - # if _evaluate_kdqtree resulted in drift for batch data, do a redundant check - if self.drift_state == "drift" and isinstance(data, pd.DataFrame): - self.input_cols = data.columns + KdqTreeDetector._evaluate_kdqtree(self, ary, "batch") diff --git a/menelaus/data_drift/nndvi.py b/menelaus/data_drift/nndvi.py new file mode 100644 index 00000000..3866b55c --- /dev/null +++ b/menelaus/data_drift/nndvi.py @@ -0,0 +1,132 @@ +import numpy as np +from scipy.stats import norm +import warnings + +from menelaus.detector import BatchDetector +from menelaus.partitioners import NNSpacePartitioner + + +class NNDVI(BatchDetector): + """ + This class encodes the Nearest Neigbors Density Variation + Identification (NN-DVI) drift detection algorithm, introduced + in Liu et al. (2018). Note that this implementation is intended + for batch datasets, rather than the streaming context. + + Broadly, NN-DVI combines a reference and test data batch, creates + a normalized version of the subsequent adjacency matrix (after a + k-NN search), and then analyzes distance changes in the reference + and test sections of the combined adjacency matrix. Those changes + are compared against a threshold distance value, which is found + by randomly sampling new reference and test sections, then fitting + a Gaussian distribution to distance changes for those trials. + + Attributes: + total_samples (int): number of batches the drift detector has ever + been updated with. + samples_since_reset (int): number of batches since the last drift detection. + drift_state (str): detector's current drift state. Can take values + ``"drift"``, ``"warning"``, or ``None``. + k_nn (int): the 'k' in k-Nearest-Neighbor (k-NN) search + reference_batch (numpy.array): initial batch of data + sampling_times (int): number of times to perform sampling for threshold estimation + alpha (float): significance level for detecting drift + """ + + def __init__(self, k_nn: int = 30, sampling_times: int = 500, alpha: float = 0.01): + """ + Attributes: + k_nn (int, optional): the 'k' in k-Nearest-Neighbor (k-NN) search. Default 30. + sampling_times (int, optional): number of times to perform sampling for threshold + estimation. Default 500. + alpha (float, optional): significance level for detecting drift. Default 0.01. + """ + super().__init__() + self.k_nn = k_nn + self.sampling_times = sampling_times + self.alpha = alpha + + def update(self, X: np.array, y_true=None, y_pred=None): + """ + Update the detector with a new test batch. If drift is detected, new + reference batch becomes most recent test batch. + + Args: + X (numpy.array): next batch of data to detect drift on. + y_true (numpy.array): true labels, not used in NN-DVI + y_pred (numpy.array): predicted labels, not used in NN-DVI + """ + if self._drift_state == "drift": + self.reset() + + X, _, _ = super()._validate_input(X, None, None) + + super().update(X=X, y_true=None, y_pred=None) + test_batch = np.array(X) + + nnsp = NNSpacePartitioner(self.k_nn) + nnsp.build(self.reference_batch, test_batch) + M_nnps = nnsp.nnps_matrix + v_ref, v_test = nnsp.v1, nnsp.v2 + d_act = NNSpacePartitioner.compute_nnps_distance(M_nnps, v_ref, v_test) + + theta_drift = self._compute_drift_threshold( + M_nnps, v_ref, v_test, self.sampling_times, self.alpha + ) + if d_act > theta_drift: + self._drift_state = "drift" + self.set_reference(test_batch) + + def set_reference(self, X, y_true=None, y_pred=None): + """ + Set the detector's reference batch to an updated value; typically + used in ``update``. + + Attributes: + X (numpy.array): updated reference batch + y_true (numpy.array): true labels, not used in NNDVI + y_pred (numpy.array): predicted labels, not used in NNDVI + """ + X, _, _ = super()._validate_input(X, None, None) + self.reference_batch = X + + def reset(self): + """ + Initialize relevant attributes to original values, to ensure information + only stored from samples_since_reset onwards. Intended for use + after ``drift_state == 'drift'``. + """ + super().reset() + + @staticmethod + def _compute_drift_threshold(M_nnps, v_ref, v_test, sampling_times, alpha): + """ + Compute critical value for drift detection, by repeatedly sampling + NNPS distance calculations and estimating a normal distribution from + the same. Takes an NNPS matrix, indices of reference/test sections in + the same, number of times to sample, and a significance level for which + to determine the critical value. Intended for use within ``update`` after + the data has been partitioned. + + Attributes: + M_nnps (numpy.array): NNPS matrix that results from ``NNSpacePartitioner.build`` + v_ref (numpy.array): 1D array of same length as ``M_nnps``, with a one-hot encoding + of reference data point locations in ``M_nnps``. + v_test (numpy.array): 1D array of same length as ``M_nnps``, with a one-hot + encoding of test data point locations in ``M_nnps``. + sampling_times (int): number of times to perform sampling for threshold estimation + alpha (float): significance level for detecting drift + """ + # TODO - Would like to parallelize this - Anmol + d_shuffle = [] + for _ in range(sampling_times): + v1_shuffle = np.random.permutation(v_ref) + v2_shuffle = 1 - v1_shuffle + + d_i_shuffle = NNSpacePartitioner.compute_nnps_distance( + M_nnps, v1_shuffle, v2_shuffle + ) + d_shuffle.append(d_i_shuffle) + mu, std = norm.fit(d_shuffle) + drift_threshold = norm.ppf(1 - alpha, mu, std) + return drift_threshold diff --git a/src/menelaus/data_drift/pca_cd.py b/menelaus/data_drift/pca_cd.py similarity index 93% rename from src/menelaus/data_drift/pca_cd.py rename to menelaus/data_drift/pca_cd.py index eae3c5ae..24c79a50 100644 --- a/src/menelaus/data_drift/pca_cd.py +++ b/menelaus/data_drift/pca_cd.py @@ -4,11 +4,11 @@ from sklearn.decomposition import PCA from sklearn.preprocessing import StandardScaler from sklearn.neighbors import KernelDensity -from menelaus.drift_detector import DriftDetector +from menelaus.detector import StreamingDetector from menelaus.change_detection.page_hinkley import PageHinkley -class PCACD(DriftDetector): +class PCACD(StreamingDetector): """Principal Component Analysis Change Detection (PCA-CD) is a drift detection algorithm which checks for change in the distribution of the given data using one of several divergence metrics calculated on the data's @@ -27,12 +27,6 @@ class PCACD(DriftDetector): Ref. :cite:t:`qahtan2015pca` Attributes: - total_updates (int): number of samples the drift detector has ever - been updated with - updates_since_reset (int): number of samples since the last time the - drift detector was reset - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. step (int): how frequently (by number of samples), to detect drift. This is either 100 samples or ``sample_period * window_size``, whichever is smaller. @@ -42,7 +36,7 @@ class PCACD(DriftDetector): the specified ``ev_threshold`` parameter. """ - input_type = "batch" + input_type = "streaming" def __init__( self, @@ -115,12 +109,16 @@ def __init__( self._density_reference = {} self._change_score = [0] - def update(self, next_obs): + def update(self, X, y_true=None, y_pred=None): """Update the detector with a new observation. Args: - next_obs: next observation, as a ``pandas`` ``Series`` + X (numpy.ndarray): next observation + y_true (numpy.ndarray): true label of observation - not used in PCACD + y_pred (numpy.ndarray): predicted label of observation - not used in PCACD """ + X, _, _ = super()._validate_input(X, None, None) + super().update(X, None, None) if self._build_reference_and_test: if self.drift_state is not None: @@ -138,13 +136,11 @@ def update(self, next_obs): elif len(self._reference_window) < self.window_size: self._reference_window = pd.concat( - [self._reference_window, pd.DataFrame(next_obs)] + [self._reference_window, pd.DataFrame(X)] ) elif len(self._test_window) < self.window_size: - self._test_window = pd.concat( - [self._test_window, pd.DataFrame(next_obs)] - ) + self._test_window = pd.concat([self._test_window, pd.DataFrame(X)]) if len(self._test_window) == self.window_size: self._build_reference_and_test = False @@ -204,7 +200,7 @@ def update(self, next_obs): # Add new obs to test window if self.online_scaling is True: - next_obs = pd.DataFrame(self._reference_scaler.transform(next_obs)) + next_obs = pd.DataFrame(self._reference_scaler.transform(X)) self._test_window = pd.concat([self._test_window.iloc[1:, :], next_obs]) # Project new observation onto PCs @@ -227,8 +223,9 @@ def update(self, next_obs): ) # Compute change score - if (self.total_updates % self.step) == 0 and self.total_updates != 0: - + if (((self.total_samples - 1) % self.step) == 0) and ( + (self.total_samples - 1) != 0 + ): # Compute density distribution for test data self._density_test = {} for i in range(self.num_pcs): @@ -271,16 +268,12 @@ def update(self, next_obs): change_score = max(change_scores) self._change_score.append(change_score) - self._drift_detection_monitor.update( - next_obs=change_score, obs_id=next_obs.index.values[0] - ) + self._drift_detection_monitor.update(X=change_score) if self._drift_detection_monitor.drift_state is not None: self._build_reference_and_test = True self.drift_state = "drift" - super().update() - def reset(self): """Initialize the detector's drift state and other relevant attributes. Intended for use after ``drift_state == 'drift'``. diff --git a/src/menelaus/datasets/__init__.py b/menelaus/datasets/__init__.py similarity index 62% rename from src/menelaus/datasets/__init__.py rename to menelaus/datasets/__init__.py index 8814fae9..c1feb33f 100644 --- a/src/menelaus/datasets/__init__.py +++ b/menelaus/datasets/__init__.py @@ -1,5 +1,7 @@ """ This module contains functions and classes to generated data for testing detectors. """ from menelaus.datasets.make_example_data import make_example_batch_data +from menelaus.datasets.make_example_data import fetch_circle_data +from menelaus.datasets.make_example_data import fetch_rainfall_data # from menelaus.datasets.generator import DataGenerator diff --git a/src/menelaus/datasets/dataCircleGSev3Sp3Train.csv b/menelaus/datasets/dataCircleGSev3Sp3Train.csv similarity index 100% rename from src/menelaus/datasets/dataCircleGSev3Sp3Train.csv rename to menelaus/datasets/dataCircleGSev3Sp3Train.csv diff --git a/src/menelaus/datasets/generator.py b/menelaus/datasets/generator.py similarity index 100% rename from src/menelaus/datasets/generator.py rename to menelaus/datasets/generator.py diff --git a/menelaus/datasets/make_example_data.py b/menelaus/datasets/make_example_data.py new file mode 100644 index 00000000..5be5c02f --- /dev/null +++ b/menelaus/datasets/make_example_data.py @@ -0,0 +1,163 @@ +""" Functions to generate example data according to a fixed scheme. """ + +import os +import pandas as pd +import numpy as np + + +def make_example_batch_data(): + """ + This function returns a dataframe containing synthetic batch data for use + with the repo's examples. The dataframe's columns are ``"year", "a", "b", ... "j", "cat", "confidence", "drift"``. + + * ``year`` covers 2007-2021, with 20,000 observations each. + + * Features ``"b", "e", "f"`` are normally distributed. + + * Features ``"a", "c", "d", "g", "h", "i", "j"`` have a gamma distribution. + + * The ``"cat"`` feature contains categorical variables ranging from 1-7, + sampled with varying probability. + + * ``"confidence"`` contains values on ``[0, 0.6]`` through 2018, then values + on ``[0.4, 1]``. + + Drift occurs as follows: + + * Change the mean of column ``"b"`` in 2009. Reverts to original distribution + in 2010. + + * Change the variance of columns ``"c"`` and ``"d"`` in 2012 by replacing + some samples with the mean. Reverts to original distribution in 2013. + + * Increase the correlation of columns ``"e"`` and ``"f"`` in 2015 (0 correlation + to 0.5 correlation). + + * Change the mean and variance of column ``"h"`` in 2019, and maintain this + new distribution going forward. Change the range of the "confidence" + column going forward. + + * Change the mean and variance of column ``"j"`` in 2021. + + Returns: + pd.DataFrame: A dataframe containing a synthetic batch dataset. + """ + np.random.seed(123) + year_size = 20000 + df = pd.DataFrame() + df["year"] = year_size * list(range(2007, 2022)) + df.sort_values(by="year", inplace=True) + df.reset_index(inplace=True) + sample_size = df.shape[0] + + df["a"] = np.random.gamma(shape=8, size=sample_size) * 1000 + df["b"] = np.random.normal(loc=200, scale=10, size=sample_size) + df["c"] = np.random.gamma(shape=7, size=sample_size) * 1000 + df["d"] = np.random.gamma(shape=10, size=sample_size) * 10000 + df[["e", "f"]] = np.random.multivariate_normal( + mean=(0, 0), cov=np.array([[2, 0], [0, 2]]), size=sample_size + ) + df["g"] = np.random.gamma(shape=11, size=sample_size) * 10000 + df["h"] = np.random.gamma(shape=12, size=sample_size) * 1000 + df["i"] = np.random.gamma(shape=9, size=sample_size) * 1000 + df["j"] = np.random.gamma(shape=10, size=sample_size) * 100 + df["cat"] = np.random.choice( + range(7), size=sample_size, p=(0.3, 0.3, 0.2, 0.1, 0.05, 0.04, 0.01) + ) + df["confidence"] = np.random.uniform(low=0, high=0.6, size=sample_size) + + ###################################################################### + # Drift 1: change the mean of B in 2009, means will revert for 2010 on + df.loc[df.year == 2009, "b"] = np.random.normal(size=year_size, loc=500, scale=10) + + ###################################################################### + # Drift 2: change the variance of c and d in 2012 by replacing some with the mean + # keep same mean as other years, revert by 2013 + mu_c = df["c"].mean() + mu_d = df["d"].mean() + + # subtle change, every 10 obs + df.loc[(df.year == 2012) & (df.index % 10 == 0), "c"] = mu_c + np.random.normal( + loc=0, scale=10, size=year_size // 10 + ) + + # bigger change, every other obs + df.loc[(df.year == 2012) & (df.index % 2 == 0), "d"] = mu_d + np.random.normal( + loc=0, scale=10, size=year_size // 2 + ) + + ###################################################################### + # Drift 3: change the correlation of e and f in 2015 (go from correlation of 0 to correlation of 0.5) + df.loc[df.year == 2015, ["e", "f"]] = np.random.multivariate_normal( + mean=(0, 0), cov=np.array([[2, 1], [1, 2]]), size=year_size + ) + + ###################################################################### + # Drift 4: change mean and var of H and persist it from 2018 on, change range of confidence scores + df.loc[df.year > 2018, "h"] = ( + np.random.gamma(shape=1, scale=1, size=3 * year_size) * 1000 + ) + df.loc[df.year > 2018, "confidence"] = np.random.uniform( + low=0.4, high=1, size=3 * year_size + ) + + ###################################################################### + # Drift 5: change mean and var just for a year of J in 2021 + df.loc[df.year == 2021, "j"] = np.random.gamma(shape=10, size=year_size) * 10 + + df["drift"] = df["year"].isin([2009, 2012, 2015, 2018, 2021]) + df.drop("index", axis=1, inplace=True) + return df + + +def fetch_circle_data(): + """Retrieve the Circle data from the datasets directory. Circle is synthetic + data containing drift due to both a change in the feature distribution and a + change in the conditional target distribution. Drift occurs from index + 1000-1250 and affects 66% of the data points. + + Ref. :cite:t:`minku2010` + + Returns: + pd.DataFrame: A dataframe containing the Circle dataset. + """ + data_path = os.path.join(os.path.dirname(__file__), "dataCircleGSev3Sp3Train.csv") + return pd.read_csv(data_path, usecols=[0, 1, 2], names=["var1", "var2", "y"]) + + +def fetch_rainfall_data(): + """Retrieve the Rainfall data from the datasets directory. National Oceanic + and Atmospheric Administration (NOAA) rainfall data contains weather + measurements collected over a 50 year period at a site location in + Bellevue, Nebraska. It contains eight features: temperature, dew point, + sea-level pressure, visibility, average wind speed, max sustained wind-speed, + minimum temperature, and maximum temperature. The dependent variable is rain. + Concept and data drift starts in index 12,000 and persists through the rest + of the dataset. + + Ref. :cite:t:`souza2020` + + Returns: + pd.DataFrame: A dataframe containing the Rainfall dataset. + + """ + data_path = os.path.join(os.path.dirname(__file__), "rainfall_data.csv") + df = pd.read_csv( + data_path, + usecols=[1, 2, 3, 4, 5, 6, 7, 8, 9], + names=[ + "index", + "temperature", + "dew_point", + "sea_level_pressure", + "visibility", + "average_wind_speed", + "max_sustained_wind_speed", + "minimum_temperature", + "maximum_temperature", + "rain", + ], + ) + df = df.iloc[1:, :].reset_index(drop=True) + df = df.apply(pd.to_numeric) + return df diff --git a/menelaus/datasets/rainfall_data.csv b/menelaus/datasets/rainfall_data.csv new file mode 100644 index 00000000..fc5dcae8 --- /dev/null +++ b/menelaus/datasets/rainfall_data.csv @@ -0,0 +1,18160 @@ +,temperature,dew point,sea-level pressure,visibility,average wind speed,max sustained 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@abstractmethod + def update(self, X, y_true, y_pred): + """ + Update detector with new sample (data point). + + Args: + X (numpy.ndarray): if applicable, one row of features from input data + y_true (numpy.ndarray): if applicable, one true label from input data + y_pred (numpy.ndarray): if applicable, one predicted label from input data + """ + self.total_samples += 1 + self.samples_since_reset += 1 + + @abstractmethod + def reset(self, *args, **kwargs): + """ + Initialize the detector's drift state and other relevant attributes. + Intended for use after ``drift_state == "drift"``. + """ + self.samples_since_reset = 0 + self.drift_state = None + + def _validate_X(self, X): + """Validate that the input only contains one observation, and that its + dimensions/column names match earlier input. If there is no + earlier input, store the dimension/column names. + + Args: + X (array-like or numeric): One row from input features. + + Raises: + ValueError: if a dataframe has ever been passed, raised if X's + column names don't match + ValueError: if an array has ever been passed, raised if X's number + of columns don't match + ValueError: raised if X contains more than one observation after coercion + """ + if isinstance(X, DataFrame): + # The first update with a dataframe will constrain subsequent input. + if self._input_cols is None: + self._input_cols = X.columns + self._input_col_dim = len(self._input_cols) + elif self._input_cols is not None: + if not X.columns.equals(self._input_cols): + raise ValueError( + "Columns of new data must match with columns of prior data." + ) + ary = X.values + else: + ary = copy.copy(X) + ary = np.array(ary) + if len(ary.shape) <= 1: + # only one sample should be passed, so coerce column vectors (e.g. pd.Series) to rows + ary = ary.reshape(1, -1) + if self._input_col_dim is None: + # This allows starting with a dataframe, then later passing bare + # numpy arrays. For now, assume users are not miscreants. + self._input_col_dim = ary.shape[1] + elif self._input_col_dim is not None: + if ary.shape[1] != self._input_col_dim: + raise ValueError( + "Column-dimension of new data must match prior data." + ) + + if ary.shape[0] != 1: + raise ValueError( + "Input for streaming detectors should contain only one observation." + ) + return ary + + def _validate_y(self, y): + """Validate that input contains only one observation. + + Args: + y (numeric): the current value for `y_true` or `y_pred`, given to + `update`. + + Raises: + ValueError: raised if more than one observation is passed. + """ + ary = np.array(y).ravel() + if ary.shape != (1,): + raise ValueError( + "Input for streaming detectors should contain only one observation." + ) + return ary + + def _validate_input(self, X, y_true, y_pred): + """Helper method for `update`. Validates whether the input is appropriate + for a streaming detector. Errors will be raised if the input is more + than one observation, or if X's dimensions don't match prior input. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, one true label from input data + y_pred (numpy.ndarray): if applicable, one predicted label from input data + """ + if X is not None: + X = self._validate_X(X) + if y_true is not None: + y_true = self._validate_y(y_true) + if y_pred is not None: + y_pred = self._validate_y(y_pred) + return X, y_true, y_pred + + @property + def total_samples(self): + """Total number of samples the drift detector has been updated with. + + Returns: + int + """ + return self._total_samples + + @total_samples.setter + def total_samples(self, value): + self._total_samples = value + + @property + def samples_since_reset(self): + """Number of samples since last drift detection. + + Returns: + int + """ + return self._samples_since_reset + + @samples_since_reset.setter + def samples_since_reset(self, value): + self._samples_since_reset = value + + @property + def drift_state(self): + """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``.""" + return self._drift_state + + @drift_state.setter + def drift_state(self, value): + """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. + + Args: + value (str): ``"drift"``, ``"warning"``, or ``None`` + + Raises: + ValueError: raised if disallowed value is given + """ + if value not in ("drift", "warning", None): + raise ValueError("tbd") + else: + self._drift_state = value + + +class BatchDetector(ABC): + """ + Abstract base class for all batch data-based detectors. + Minimally implements abstract methods common to all batch + based detection algorithms. + """ + + def __init__(self, *args, **kwargs): + self._total_batches = 0 + self._batches_since_reset = 0 + self._drift_state = None + self._input_cols = None + self._input_col_dim = None + + @abstractmethod + def update(self, X, y_true, y_pred): + """ + Update detector with new batch of data + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + self.total_batches += 1 + self.batches_since_reset += 1 + + @abstractmethod + def set_reference(self, X, y_true, y_pred): + """ + Initialize detector with a reference batch. + + Args: + X (pandas.DataFrame or numpy.array): baseline dataset + y_true (numpy.array): actual labels of dataset + y_pred (numpy.array): predicted labels of dataset + """ + raise NotImplementedError + + @abstractmethod + def reset(self, *args, **kwargs): + """ + Initialize the detector's drift state and other relevant attributes. + Intended for use after ``drift_state == 'drift'``. + """ + self.batches_since_reset = 0 + self.drift_state = None + + def _validate_X(self, X): + """Validate that the input only contains one observation, and that its + dimensions/column names match earlier input. If there is no + earlier input, store the dimension/column names. + + Args: + X (array-like or numeric): Input features. + + Raises: + ValueError: if a dataframe has ever been passed, raised if X's + column names don't match + ValueError: if an array has ever been passed, raised if X's number + of columns don't match + ValueError: if only one sample has been passed + """ + if isinstance(X, DataFrame): + # The first update with a dataframe will constrain subsequent input. + if self._input_cols is None: + self._input_cols = X.columns + self._input_col_dim = len(self._input_cols) + elif self._input_cols is not None: + if not X.columns.equals(self._input_cols): + raise ValueError( + "Columns of new data must match with columns of prior data." + ) + ary = X.values + else: + ary = copy.copy(X) + ary = np.array(ary) + if len(ary.shape) <= 1: + # Batch size of 1 will break downstream - don't allow it. + # Attempts to coerce a row vector into a column vector. + ary = ary.reshape(-1, 1) + if self._input_col_dim is None: + # This allows starting with a dataframe, then later passing bare + # numpy arrays. For now, assume users are not miscreants. + self._input_col_dim = ary.shape[1] + elif self._input_col_dim is not None: + if ary.shape[1] != self._input_col_dim: + raise ValueError( + "Column-dimension of new data must match prior data." + ) + if ary.shape[0] <= 1: + raise ValueError( + "Input for batch detectors should contain more than one observation." + ) + return ary + + def _validate_y(self, y): + """Validate that input contains only one column. + + Args: + y (numeric): the current value for `y_true` or `y_pred`, given to + `update`. + + Raises: + ValueError: if an array has been passed that has more than one column + """ + ary = np.array(y) + if len(ary.shape) <= 1: + ary = ary.reshape(1, -1) + if ary.shape[0] == 1: + raise ValueError( + "Input for batch detectors should contain more than one obsevation." + ) + if ary.shape[1] != 1: + raise ValueError("y input for detectors should contain only one column.") + return ary + + def _validate_input(self, X, y_true, y_pred): + """Helper method for `update` and `set_reference`. Validates whether the + input is appropriate for a batch detector. Errors will be raised if X's + dimensions don't match prior input, or a y input has more than one + column. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + if X is not None: + X = self._validate_X(X) + if y_true is not None: + y_true = self._validate_y(y_true) + if y_pred is not None: + y_pred = self._validate_y(y_pred) + return X, y_true, y_pred + + @property + def total_batches(self): + """Total number of batches the drift detector has been updated with. + + Returns: + int + """ + return self._total_batches + + @total_batches.setter + def total_batches(self, value): + self._total_batches = value + + @property + def batches_since_reset(self): + """Number of batches since last drift detection. + + Returns: + int + """ + return self._batches_since_reset + + @batches_since_reset.setter + def batches_since_reset(self, value): + self._batches_since_reset = value + + @property + def drift_state(self): + """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``.""" + return self._drift_state + + @drift_state.setter + def drift_state(self, value): + """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. + + Args: + value (str): ``"drift"``, ``"warning"``, or ``None`` + + Raises: + ValueError: raised if disallowed value is given + """ + if value not in ("drift", "warning", None): + raise ValueError("tbd") + else: + self._drift_state = value + + +class DriftDetector(ABC): + """ + This class is deprecated in 0.2.0+. + + Base class for Menelaus drift detectors. + + A DriftDetector object implements the ``update`` and ``reset`` methods and + calls the ``super`` methods to initialize and update the attributes below. + + Generally, a DriftDetector is instantiated, then repeatedly passed new data + via ``update``. At each ``update`` step, its ``drift_state`` will reflect + whether drift has been detected or almost been detected. After the + detector's state is set to ``"drift"``, ``update`` calls ``reset`` to + re-initialize the relevant attributes. + + A "batch" detector will compare a new dataset, passed via ``update``, to a + reference dataset, usually the original reference dataset. A "stream" + detector compares only one new sample at a time, also passed via ``update``. + """ + + def __init__(self, *args, **kwargs): + super().__init__() + self._total_updates = 0 + self._updates_since_reset = 0 + self._drift_state = None + self._input_type = None + + @abstractmethod + def update(self, X, y_true, y_pred): + """ + Update the detector with a new sample or batch. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + self.total_updates += 1 + self.updates_since_reset += 1 + + @abstractmethod + def reset(self, *args, **kwargs): + """Initialize the detector's drift state and other relevant attributes. + Intended for use after ``drift_state == 'drift'``.""" + self.updates_since_reset = ( + 0 # number of elements the detector has been updated with since last reset + ) + self.drift_state = None + + @property + def total_updates(self): + """Number of samples/batches the drift detector has ever been updated + with. + + Returns: + int + """ + return self._total_updates + + @total_updates.setter + def total_updates(self, value): + self._total_updates = value + + @property + def updates_since_reset(self): + """Number of samples/batches since the last time the drift detector was + reset. + + Returns: + int + """ + return self._updates_since_reset + + @updates_since_reset.setter + def updates_since_reset(self, value): + self._updates_since_reset = value + + @property + def drift_state(self): + """Detector's current drift state, with values ``"drift"``, ``"warning"``,or + ``None``. + """ + return self._drift_state + + @drift_state.setter + def drift_state(self, value): + """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. + + Args: + value (str): ``"drift"``, ``"warning"``, or ``None`` + + Raises: + ValueError: raised if disallowed value is given + """ + if value not in ("drift", "warning", None): + raise ValueError( + """DriftDetector._drift_state must be ``"drift"``, + ``"warning"``, or ``None``.""" + ) + else: + self._drift_state = value + + @property + @abstractmethod + def input_type(self): + """The type of input the detector accepts, either ``"batch"``, with multiple + samples in one call to update(), or ``"stream"``, with one sample per call + to update(). + """ + return self._input_type diff --git a/menelaus/ensemble/__init__.py b/menelaus/ensemble/__init__.py new file mode 100644 index 00000000..ea0a5bf1 --- /dev/null +++ b/menelaus/ensemble/__init__.py @@ -0,0 +1,5 @@ +from menelaus.ensemble.election import SimpleMajorityElection, MinimumApprovalElection +from menelaus.ensemble.election import OrderedApprovalElection, ConfirmedElection + +from menelaus.ensemble.ensemble import Ensemble +from menelaus.ensemble.ensemble import BatchEnsemble, StreamingEnsemble diff --git a/menelaus/ensemble/election.py b/menelaus/ensemble/election.py new file mode 100644 index 00000000..0aa76428 --- /dev/null +++ b/menelaus/ensemble/election.py @@ -0,0 +1,201 @@ +from abc import ABC, abstractmethod + + +####################### +# Ensemble Evaluators +####################### + + +class Election(ABC): + """ + Abstract base class for implementations of election schemes + used to evaluate drift state of ensembles, by operating on + the drift states of constituent detectors. + + Constructors for sub-classes may differ, but all ``Election`` + classes are callable classes, where the call takes only the + list of detectors to evaluate. + + The surrounding ``Ensemble`` class will update its drift state + within its ``update`` function, by calling the ``Election`` + it is given at initialization-time, upon its detectors. + """ + + @abstractmethod + def __call__(self, detectors: list): # pragma: no cover + raise NotImplemented + + +class SimpleMajorityElection(Election): + """ + ``Election`` that determines drift for an ensemble, + based on whether a simple majority of the ensemble's + detectors have voted for drift. + + Ref :cite:t:`duelectionreference` + """ + + def __call__(self, detectors: list): + """ + Args: + detectors (list): detector objects to examine + + Returns + str: ``"drift"`` if drift is determined, or ``None`` + """ + simple_majority_threshold = len(detectors) // 2 + alarms = [d for d in detectors if d.drift_state == "drift"] + num_drift = len(alarms) + if num_drift > simple_majority_threshold: + return "drift" + else: + return None + + +class MinimumApprovalElection(Election): + """ + ``Election`` that determines drift based on whether + a minimum number of provided detectors have alarmed. This + threshold can be 1 to the maximum number of detectors. + """ + + def __init__(self, approvals_needed: int = 1): + """ + Args: + approvals_needed (int): minimum approvals to alarm + """ + self.approvals_needed = approvals_needed + + def __call__(self, detectors: list): + """ + Args: + detectors (list): detector objects to examine + + Returns: + str: ``"drift_state"`` if drift is determined, or ``None`` + """ + num_approvals = 0 + for d in detectors: + if d.drift_state == "drift": + num_approvals += 1 + if num_approvals >= self.approvals_needed: + return "drift" + return None + + +class OrderedApprovalElection(Election): + """ + ``Election`` that determines drift based on whether: + + 1) An initial ``a`` count of detectors alarmed for drift. + 2) A subsequent ``c`` count of detectors confirmed drift. + + Hypothethically, the distinction between this and + ``MinimumApprovalElection(a+c)``, is if the detectors were added to + a collection in a meaningful order. As such this voting + scheme iterates over detectors in preserved order of insertion into + the user-defined list, and uses the first ``approvals_needed`` + amount for initial detection, and the next ``confirmations_needed`` + amount for confirmation of drift. + """ + + def __init__(self, approvals_needed: int = 1, confirmations_needed: int = 1): + """ + Args: + approvals_needed (int): Minimum number of detectors that + must alarm for the ensemble to alarm. + confirmations_needed (int): Minimum number of confirmations + needed to alarm, after ``approvals_needed`` alarms have been + observed. + """ + self.approvals_needed = approvals_needed + self.confirmations_needed = confirmations_needed + + def __call__(self, detectors: list): + """ + Args: + detectors (list): detector objects to examine + + Returns: + str: ``"drift_state"`` if drift is determined, or ``None`` + """ + num_approvals = 0 + num_confirmations = 0 + + for d in detectors: + if d.drift_state == "drift": + if num_approvals < self.approvals_needed: + num_approvals += 1 + else: + num_confirmations += 1 + + if ( + num_approvals >= self.approvals_needed + and num_confirmations >= self.confirmations_needed + ): + return "drift" + + return None + + +class ConfirmedElection(Election): + """ + ``Election`` for handling detectors, typically in + streaming setting. In this scheme, when a single + detector alarms, the ``Election`` will wait for a + certain number of samples, until one or more other + detectors also alarm, confirming the drift. + + Derived from the ensemble scheme described in + :cite:t:`macielelectionreference`. + """ + + def __init__(self, sensitivity: int, wait_time: int): + """ + Args: + sensitivity (int): how many combined waiting/new drift alarms + should result in ensemble alarm + wait_time (int): after how many steps of waiting, should each + detector reset its time spent waiting post-drift-alarm + """ + self.sensitivity = sensitivity + self.wait_time = wait_time + self.wait_period_counters = None + + def __call__(self, detectors: list): + """ + Args: + detectors (list): detector objects to examine + + Returns: + str: ``"drift_state"`` if drift is determined, or ``None`` + """ + if self.wait_period_counters is None: + self.wait_period_counters = [0] * len(detectors) + + num_drift = 0 + num_warning = 0 + + states = [d.drift_state for d in detectors] + for i, state in enumerate(states): + if state == "drift" and self.wait_period_counters[i] == 0: + num_drift += 1 + self.wait_period_counters[i] += 1 + elif state == "warning": + num_warning += 1 + elif self.wait_period_counters[i] != 0: + num_drift += 1 + self.wait_period_counters[i] += 1 + + if num_drift >= self.sensitivity: + ret = "drift" + elif num_warning + num_drift >= self.sensitivity: + ret = "warning" + else: + ret = None + + for i, count in enumerate(self.wait_period_counters): + if count > self.wait_time: + self.wait_period_counters[i] = 0 + + return ret diff --git a/menelaus/ensemble/ensemble.py b/menelaus/ensemble/ensemble.py new file mode 100644 index 00000000..029c4c1b --- /dev/null +++ b/menelaus/ensemble/ensemble.py @@ -0,0 +1,184 @@ +from collections import defaultdict + +from menelaus.detector import BatchDetector, StreamingDetector + + +############# +# Ensembles +############# + + +class Ensemble: + """ + Parent class for Ensemble detectors. Does not inherit from any + detector parent class, but has similar functions ``set_reference``, + ``update``, ``reset``. Can also evaluate the results from all + detectors per some voting scheme. + + Any class hoping to implement ensemble functionality should implement + from this. + """ + + def __init__(self, detectors: dict, election, column_selectors: dict = {}): + # XXX - Since rigid type-checking is sort of discouraged in Python + # it makes the most sense to just treat election as (always) + # a function operating on detectors. + self.detectors = detectors.copy() + self.election = election + + def default_column_selector(): + return lambda data: data + + self.column_selectors = defaultdict(default_column_selector) + self.column_selectors.update(column_selectors) + + def update(self, X, y_true=None, y_pred=None): + """ + Update each detector in ensemble with new batch of data. + Calls self.evaluate() at the end, to determine voting result. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + for det_key in self.detectors: + # XXX - Cannot re-define X = constrain(), else external reference is modified + # Need to see why this is happening and where to put e.g. a copy() stmt. + X_selected = self.column_selectors[det_key](X) + self.detectors[det_key].update(X=X_selected, y_true=y_true, y_pred=y_pred) + + det_list = list(self.detectors.values()) + self.drift_state = self.election(det_list) + + def reset(self): + """ + Initialize each detector's drift state and other relevant attributes. + Intended for use after ``drift_state == 'drift'``. + """ + for det_key in self.detectors: + self.detectors[det_key].reset() + + +class StreamingEnsemble(StreamingDetector, Ensemble): + """ + Implements Ensemble class for streaming drift detectors. Inherits + from ``Ensemble`` and ``StreamingDetector`` (i.e., ``StreamingEnsemble`` + IS-A ``StreamingDetector``). As such it has the functions of a regular + detector: ``update``, ``reset``, etc. Internally, these operate not only + on the ensemble's own attributes, but on the set of detectors given to it. + """ + + def __init__(self, detectors: dict, election, column_selectors: dict = {}): + """ + Args: + detectors (dict): Dictionary of detectors in ensemble, where the key + is some unique identifier for a detector, and the value is the + initialized detector object. For instance, ``{'a': ADWIN()}``. + election (str): Initialized ``Election`` object for ensemble to evaluate + drift among constituent detectors. See implemented election schemes + in ``menelaus.ensemble``. + columns_selectors (dict, optional): Functions to use + for each detector. Functions should take data instance X + and return the columns of X that the corresponding detector + should operate on. Should match format of ``detectors`` i.e. + ``{'a': ADWIN()}`` would need an entry ``{'a': function}`` to + use this feature. By default, no column selection + function is applied to any detector, and they will all use + the entirely of the attributes in X. + """ + StreamingDetector.__init__(self) + Ensemble.__init__(self, detectors, election, column_selectors) + + def update(self, X, y_true, y_pred): + """ + Update ensemble itself, and each constituent detector with new data. + Calls ``Ensemble.update`` and ``StreamingDetector.update`` to do so. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + Ensemble.update(self, X=X, y_true=y_true, y_pred=y_pred) + StreamingDetector.update(self, X=X, y_true=y_true, y_pred=y_pred) + + def reset(self): + """ + Reset ensemble itself, and each constituent detector's drift state + and other relevant attributes. Intended for use after ``drift_state == 'drift'``. + Calls ``Ensemble.reset`` and ``StreamingDetector.reset`` to do so. + """ + Ensemble.reset(self) + StreamingDetector.reset(self) + + +class BatchEnsemble(BatchDetector, Ensemble): + """ + Implements ``Ensemble`` class for batch-based drift detectors. Inherits + from ``Ensemble`` and ``BatchDetector`` (i.e., ``BatchEnsemble`` IS-A ``BatchDetector``). + As such it has the functions of a regular detector, ``set_reference``, + ``update``, and ``reset``. These functions will operate not only on the + ensemble's own attributes, but on the set of detectors given to it. + """ + + def __init__(self, detectors: dict, election, column_selectors: dict = {}): + """ + Args: + detectors (dict): Dictionary of detectors in ensemble, where the key + is some unique identifier for a detector, and the value is the + initialized detector object. For instance, ``{'p': PCA_CD()}``. + election (str): Initialized ``Election`` object for ensemble to evaluate + drift among constituent detectors. See implemented election schemes + in ``menelaus.ensemble``. + columns_selectors (dict, optional): Table of functions to use + for each detector. Functions should take data instance X + and return the columns of X that the corresponding detector + should operate on. Should match format of ``detectors`` i.e. + ``{'p': PCA_CD()}`` would need an entry ``{'a': function}`` to + use this feature. By default, no column selection + function is applied to any detector, and they will all use + the entirely of the attributes in X. + """ + BatchDetector.__init__(self) + Ensemble.__init__(self, detectors, election, column_selectors) + + def update(self, X, y_true=None, y_pred=None): + """ + Update ensemble itself, and each constituent detector with new data. + Calls ``Ensemble.update`` and ``BatchDetector.update`` to do so. + + Args: + X (numpy.ndarray): input data + y_true (numpy.ndarray): if applicable, true labels of input data + y_pred (numpy.ndarray): if applicable, predicted labels of input data + """ + Ensemble.update(self, X=X, y_true=y_true, y_pred=y_pred) + BatchDetector.update(self, X=X, y_true=y_true, y_pred=y_pred) + + def reset(self): + """ + Reset ensemble itself, and each constituent detector's drift state + and other relevant attributes. Intended for use after ``drift_state == 'drift'``. + Calls ``Ensemble.reset`` and ``BatchDetector.reset`` to do so. + """ + Ensemble.reset(self) + BatchDetector.reset(self) + + def set_reference(self, X, y_true=None, y_pred=None): + """ + Initialize ensemble itself, and each constituent detector + with a reference batch. Calls ``Ensemble.set_reference`` to do so. + + Args: + X (pandas.DataFrame or numpy.array): baseline dataset + y_true (numpy.array): actual labels of dataset + y_pred (numpy.array): predicted labels of dataset + """ + for det_key in self.detectors: + # XXX - Cannot re-define X = constrain(), else external reference is modified + # Need to see why this is happening and where to put e.g. a copy() stmt. + X_selected = self.column_selectors[det_key](X) + self.detectors[det_key].set_reference( + X=X_selected, y_true=y_true, y_pred=y_pred + ) diff --git a/src/menelaus/partitioners/KDQTreePartitioner.py b/menelaus/partitioners/KDQTreePartitioner.py similarity index 98% rename from src/menelaus/partitioners/KDQTreePartitioner.py rename to menelaus/partitioners/KDQTreePartitioner.py index 44818edf..750717c0 100644 --- a/src/menelaus/partitioners/KDQTreePartitioner.py +++ b/menelaus/partitioners/KDQTreePartitioner.py @@ -92,7 +92,7 @@ def fill(self, data, tree_id, reset=False): KDQTreeNode: root node of KDQ-Tree """ if self.node is None or len(data.shape) <= 1: - return None # this line will be noted as uncovered due to https://github.com/nedbat/coveragepy/issues/772 + return None KDQTreeNode.fill(data, self.node, self.count_ubound, tree_id, reset) return self.node @@ -125,7 +125,7 @@ def kl_distance(self, tree_id1, tree_id2): float: Kullback-Leibler divergence among trees """ if self.leaves == []: - return None # this line will be noted as uncovered due to https://github.com/nedbat/coveragepy/issues/772 + return None counts1 = self.leaf_counts(tree_id1) counts2 = self.leaf_counts(tree_id2) hist1 = KDQTreePartitioner._distn_from_counts(counts1) diff --git a/menelaus/partitioners/NNSpacePartitioner.py b/menelaus/partitioners/NNSpacePartitioner.py new file mode 100644 index 00000000..c83448c8 --- /dev/null +++ b/menelaus/partitioners/NNSpacePartitioner.py @@ -0,0 +1,105 @@ +import numpy as np +from sklearn.neighbors import NearestNeighbors + + +class NNSpacePartitioner: + """ + This class encodes the Nearest Neighbors Space Partitioning + scheme (NNPS) for use in the Nearest Neigbors Density Variation + Identification (NN-DVI) drift detection algorithm, both of which + are introduced in Liu et al. (2018). + + Broadly, NNSP combines two input data samples, finds the adjacency + matrix using Nearest Neighbor search, and transforms the data points + into a set of shared subspaces for estimating density and changes in + density between the two samples, via a distance function. + + Attributes: + k (int): the 'k' in k-Nearest-Neighbor (k-NN) search + D (numpy.array): combined data from two samples + v1 (numpy.array): indices of first sample data within ``D`` + v2 (numpy.array): indices of second sample data within ``D`` + nnps_matrix (numpy.array): NNSP shared-subspace representation of ``D`` + and its adjacency matrix + adjacency_matrix (numpy.array): result of k-NN search on ``D`` + """ + + def __init__(self, k: int): + """ + Args: + k (int): the 'k' in k-NN search, describing size of searched neighborhood + """ + self.k = k + self.D = None + self.v1 = None + self.v2 = None + self.nnps_matrix = None + self.adjacency_matrix = None + + def build(self, sample1: np.array, sample2: np.array): + """ + Builds an NNSP representation matrix given two samples of data. + Internally stores computed union set, adjacency matrix, index + arrays for the two samples, and NNSP representation matrix. + + Args: + sample1 (numpy.array): first (possibly the 'reference') sample set + sample2 (numpy.array): second (possibly the 'test') sample set + """ + data = np.vstack((sample1, sample2)) + D, inverted_indices = np.unique(data, axis=0, return_inverse=True) + self.D = D + v1, v2 = np.array_split(inverted_indices, 2) + v1_onehot = np.zeros(D.shape[0]) + v2_onehot = np.zeros(D.shape[0]) + # XXX - Alternatively, v1_onehot = np.identity(adjacency_matrix.shape[0])[v1] - Anmol + v1_onehot[v1] = 1.0 + v2_onehot[v2] = 1.0 + self.v1 = v1_onehot + self.v2 = v2_onehot + nn = NearestNeighbors(n_neighbors=self.k).fit(D) + # TODO: maybe we can gain performance by performing operations using the returned + # scipy.sparse array, as opposed to converting this way. + M_adj = nn.kneighbors_graph(D).toarray() + self.adjacency_matrix = M_adj + # XXX - NearestNeighbors already adds the self-neighbors + # TODO - check about order preservation + P_nnps = M_adj + weight_array = np.sum(P_nnps, axis=1).astype(int) + Q = np.lcm.reduce(weight_array) + m = Q / weight_array + m = m * np.identity(len(m)) + self.nnps_matrix = np.matmul(m, P_nnps) + + @staticmethod + def compute_nnps_distance(nnps_matrix, v1, v2): + """ + Breaks NNSP reprsentation matrix into NNPS matrices + for two samples using indices, computes difference in + densities of shared subspaces, between samples. + + Args: + nnps_matrix (numpy.array): NNSP representation matrix + v1 (numpy.array): indices of first sample in ``D``, ``nnps_matrix``, + in one-hot encoding format + v2 (numpy.array): indices of second sample in ``D``, ``nnps_matrix``, + in one-hot encoding format + + Returns: + float: distance value between samples, representing difference + in shared subspace densities + """ + M_s1 = np.dot(v1, nnps_matrix) + M_s2 = np.dot(v2, nnps_matrix) + + # These commented lines would only be relevant if there were overlap + # between the two vectors, which there never should be for our use case. + # Otherwise, this is always going to be the number of elements. + # membership = np.sum(np.array([v1, v2]), axis=0) + # membership = membership >= 1 # in case of overlap + # denom = sum(membership) + denom = len(v1) + + d_nnps = np.sum(np.abs(M_s1 - M_s2) / (M_s1 + M_s2)) + d_nnps /= denom + return d_nnps diff --git a/src/menelaus/partitioners/__init__.py b/menelaus/partitioners/__init__.py similarity index 86% rename from src/menelaus/partitioners/__init__.py rename to menelaus/partitioners/__init__.py index f6ec58a8..36be53eb 100644 --- a/src/menelaus/partitioners/__init__.py +++ b/menelaus/partitioners/__init__.py @@ -7,3 +7,4 @@ """ from menelaus.partitioners.KDQTreePartitioner import KDQTreePartitioner, KDQTreeNode +from menelaus.partitioners.NNSpacePartitioner import NNSpacePartitioner diff --git a/menelaus/utils/_locate.py b/menelaus/utils/_locate.py new file mode 100644 index 00000000..2c8e0e24 --- /dev/null +++ b/menelaus/utils/_locate.py @@ -0,0 +1,24 @@ +"""This module for now contains a single function that finds the +root of the git directory, so we can operate out of a known directory on the +various runners (local, github, readthedocs). + +If you are tempted to add more functions to this location, and they aren't +related, they should probably live in a separate script, and potentially in +another module than utils entirely, depending on volume and purpose. +""" +import os + + +def find_git_root(search_dirs=(".git",)): + """Find the root directory for the git repo, so that we don't have to + fool with strange filepaths. + """ + test_dir = os.getcwd() + prev_dir, test_dir = None, os.path.abspath(test_dir) + while prev_dir != test_dir: + if any(os.path.isdir(os.path.join(test_dir, d)) for d in search_dirs): + return test_dir + prev_dir, test_dir = test_dir, os.path.abspath( + os.path.join(test_dir, os.pardir) + ) + return None diff --git a/setup.cfg b/setup.cfg index c6f1d114..64f7b533 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,7 +3,7 @@ [metadata] name = menelaus -version = 0.1.2 +version = 0.2.0 license = Apache 2.0 description = This library implements algorithms for detecting data drift and concept drift for ML and statistics applications. long_description = file: readme_short.rst @@ -19,16 +19,15 @@ classifiers= url = https://github.com/mitre/menelaus [options] -packages = find: -package_dir = - =src +packages = + menelaus include_package_data = True install_requires = joblib numpy >= 1.22.0 # This is due to the call to np.quantile in data_drift.kdq_tree, which uses an updated arg name. The code is otherwise compatible with python >= 3.5. pandas scipy - sklearn + scikit-learn [options.extras_require] test = @@ -54,7 +53,7 @@ dev = pytest pytest-cov seaborn - sphinx + sphinx==5.0.2 #freeze the version with one compatible with a fresh python 3.10.2 install. mainly important to avoid breaking RTD sphinx-autoapi sphinxcontrib.mermaid sphinxcontrib-bibtex @@ -68,7 +67,7 @@ format = flake8 [options.packages.find] -where=src +where=menelaus [flake8] max-line-length = 88 @@ -84,7 +83,7 @@ select=E9,F63,F7,F82 testpaths = tests/menelaus addopts = - --cov=src/ + --cov=menelaus/ --cov-report=term --cov-report=html --doctest-modules diff --git a/src/menelaus/data_drift/hdddm.py b/src/menelaus/data_drift/hdddm.py deleted file mode 100644 index 11034edb..00000000 --- a/src/menelaus/data_drift/hdddm.py +++ /dev/null @@ -1,257 +0,0 @@ -from menelaus.data_drift.histogram_density_method import HistogramDensityMethod - - -class HDDDM(HistogramDensityMethod): - - """HDDDM is a batch-based, unsupervised drift detection algorithm that - detects changes in feature distributions. It uses the Hellinger - distance metric to compare test and reference batches and is capable of - detecting gradual or abrupt changes in data. - - This method relies upon three statistics: - - * Hellinger distance: the sum of the normalized, squared differences in - frequency counts for each bin between reference and test datasets, - averaged across all features. - - * Epsilon: the differences in Hellinger distances between sets - of reference and test batches. - - * Beta: the adaptive threshold adapted at each time stamp. It is - based on the mean of Epsilon plus the scaled standard - deviation of Epsilon. The scale applied to the standard deviation is - determined by the ``statistic`` parameter. It is either the number of - standard deviations deemed significant (``"stdev"``) or the t-statistic - (``"tstat"``). - - HDDDM operates by: - - #. Estimating density functions of reference and test data using - histograms. The number of bins in each histogram equals the square - root of the length of reference window. Bins are aligned - by computing the minimum and maximum value for each feature from both - test and reference window. - - #. Computing the distance between reference and test - distributions. The Hellinger distance is first calculated between - each feature in the reference and test batches. Then, the final - Hellinger statistic used is the average of each feature's distance. - - #. Computing Epsilon. - - #. Computing the adaptive threshold Beta. - - #. Comparing current Epsilon to Beta. If Epsilon > Beta, drift is - detected. The new reference batch is now the test batch on which - drift was detected. All statistics are reset. If Epsilon <= Beta, - drift is not detected. The reference batch is updated to include this - most recent test batch. All statistics are maintained. - - Two key modifications, were added to Ditzler and Polikar's presentation of - HDDDM: - - * To answer the research question of "Where is drift occuring?", it - stores the distance values and Epsilon values for each - feature. These statistics can be used to identify and visualize the - features containing the most significant drifts. - - * The Hellinger distance values are calculated for each feature in - the test batch. These values can be accessed when drift - occurs using the ``self.feature_info`` dictionary. - - * The Epsilon values for each feature are stored, for each set of - reference and test batches. For each feature, these values - represent the difference in Hellinger distances within the test - and reference batch at time t, to the Hellinger distances within - the test and reference batch at time t-1. These can be acccessed - with each update call using the ``self.feature_epsilons`` variable. - They also can be accessed when drift occurs using the - ``self.feature_info`` dictionary. - - * The original algorithm cannot detect drift until it is updated with - the third 3rd test batch after initial setup or 3rd test batch after - each drift detection. When HDDDM is updated with the first test batch - (T=1), the distance is calculated between test and reference batch. - When updated with the second test batch (T=2), HDDDM calculates the - distance and Epsilon, the difference in distances. When HDDDM is - updated with the third test batch (T=3), the distance and Epsilon are - calculated. The adaptive threshold Beta is computed using Epsilon - values from lambda -> T-1 (lambda is either T=0 or the test batch - number on which drift was last detected). Beta needs at least one - prior value of Epsilon to be calculated. Now that Beta can be - calculated, HDDDM can detect drift on the 3rd test batch. In order to - allow for more immediate detection of drift, we added the following - options, specified through the parameter ``detect_batch``: - - * if ``detect_batch`` = 3, HDDDM will operate as described above. - - * if ``detect_batch`` = 2, HDDDM will detect drift on the second test - batch. On the second test batch only, HDDDM uses a bootstrapped - estimate of an initial Epsilon value, to serve as a proxy for the - first value of Epsilon occurring at T-1. This initial estimate of - Epsilon is used to calculate Beta, allowing to allow for drift to - be detected on the second test batch. On the third test batch, - this value is removed from all proceeeding Epsilon and Beta - calculations. - - * if ``detect_batch`` = 1, HDDDM will detect drift on the first test - batch. The initial reference batch is split randomly into two - halves. The first halve will serve as the original reference - batch. The second half will serve as a proxy for the first test - batch, allowing us to calculate the distance statistic. When HDM - is updated with the first actual test batch, HDM will perform the - method for bootstrapping Epsilon, as described in the above bullet - for ``detect_batch`` = 2. This will allow a Beta threshold to be - calculated using the first test batch, allowing for detection of - drift on this batch. - - Ref. :cite:t:`ditzler2011hellinger` - - Attributes: - total_updates (int): total number of batches the drift detector has - been updated with. If detect_batch = 1, attr refers to total - number of batches + 1 to account for additional update call - due to the initial splitting of the reference batch. - updates_since_reset (int): number of batches since the last drift - detection. If detect_batch = 1, attr refers to total - number of batches + 1 to account for additional update call - due to the initial splitting of the reference batch. - drift_state (str): detector's current drift state. Can take values - ``"drift"`` or ``None``. - Epsilon (list): stores Epsilon values since the last drift detection. - reference_n (int): number of samples in reference batch. - total_epsilon (int): stores running sum of Epsilon values until drift is detected, - initialized to 0. - bins (int): number of bins in histograms, equivalent to square root of number of - samples in reference batch. - num_feat (int): number of features in reference batch. - lambda (int): batch number on which last drift was detected. - distances (dict): For each batch seen (key), stores the Hellinger distance between test and - reference batch (value). Useful for visualizing drift detection statistics. - epsilon_values (dict):For each batch seen (key), stores the Epsilon - value between the current and previous test and reference - batches (value). Useful for visualizing drift detection - statistics. Does not store the bootstrapped estimate of Epsilon, - if used. - thresholds (dict): For each batch seen (key), stores the Beta thresholds between test and - reference batch (value). Useful for visualizing drift detection statistics. - """ - - input_type = "batch" - - def __init__( - self, - detect_batch=1, - divergence="H", - statistic="tstat", - significance=0.05, - subsets=5, - ): - - """ - Args: - divergence (str): divergence measure used to compute distance - between histograms. Default is "H". - - * "H" - Hellinger distance, original use is for HDDDM - - * "KL" - Kullback-Leibler Divergence, original use is for CDBD - - * User can pass in custom divergence function. Input is two - two-dimensional arrays containing univariate histogram - estimates of density, one for reference, one for test. It - must return the distance value between histograms. To be - a valid distance metric, it must satisfy the following - properties: non-negativity, identity, symmetry, - and triangle inequality, e.g. that in - examples/hdddm_example.py. - - detect_batch (int): the test batch on which drift will be detected. - See class docstrings for more information on this modification. - Defaults to 1. - - * if detect_batch = 1 - HDDDM can detect drift on the first test - batch passed to the update method. BE AWARE, total samples - and samples since reset will be number of batches passed to HDM - plus 1, due to splitting of reference batch - - * if detect_batch = 2 - HDDDM can detect drift on the second test - batch passed to the update method. - - * if detect_batch = 3 - HDDDM can detect drift on the third test - batch passed to the update method. - - statistic (str): statistical method used to compute adaptive - threshold. Defaults to ``"tstat"``. - - * ``"tstat"`` - t-statistic with desired significance level and - degrees of freedom = 2 for hypothesis testing on two - populations. - - * ``"stdev"`` - uses number of standard deviations deemed - significant to compute threhsold. - - significance (float): statistical significance used to identify - adaptive threshold. Defaults to 0.05. - - * if statistic = ``"tstat"`` - statistical significance of - t-statistic, e.g. .05 for 95% significance level. - - * if statistic = ``"stdev"`` - number of standard deviations of - change around the mean accepted. - - subsets (int): the number of subsets of reference data to take to - compute initial estimate of Epsilon. - - * if too small - initial Epsilon value will be too small. - Increases risk of missing drift. - - * if too high - intial Epsilon value will be too large. - Increases risk of false alarms. - - """ - - super().__init__( - divergence=divergence, - detect_batch=detect_batch, - statistic=statistic, - significance=significance, - subsets=subsets, - ) - - def set_reference(self, reference_batch): - """ - Initialize detector with a reference batch. After drift, reference batch is - automatically set to most recent test batch. Option for user to specify - alternative reference batch using this method. - - Args: - reference_batch (DataFrame): initial baseline dataset - """ - - super().set_reference(reference_batch) - - def update(self, test_batch): - """ - Update the detector with a new test batch. If drift is detected, new - reference batch becomes most recent test batch. If drift is not - detected, reference batch is updated to include most recent test batch. - - Args: - test_batch (DataFrame): next batch of data to detect drift on. - """ - - # Ensure batch datasets have equivalent features - if not self._num_features == test_batch.shape[1]: - raise ValueError("Batch features must match") - - super().update(test_batch) - - def reset(self): - """ - Initialize relevant attributes to original values, to ensure information - only stored from updates_since_reset (lambda) onwards. Intended for use - after ``drift_state == 'drift'``. - """ - # This is here to make sphinx behave. - super().reset() diff --git a/src/menelaus/datasets/README.md b/src/menelaus/datasets/README.md deleted file mode 100644 index cc49c517..00000000 --- a/src/menelaus/datasets/README.md +++ /dev/null @@ -1,30 +0,0 @@ -### Circle Dataset - -circleG dataset. -Synthetic data containing drift due to both a change in the feature distribution and a change in the -conditional target distribution. Drift occurs from index 1000-1250 and affects 66% of the data points. - -Source: -Minku, Leandro L. “Datasets.” Leandro L. Minku's Lab Open Source / Data, https://www.cs.bham.ac.uk/~minkull/open-source.html. - -### Example Dataset - -*Note that this section will eventually be removed, as the documentation will go in the new Python function generating this data.* - -2022-04-15 - -README for example_data.csv. -Generated from make_example_data.R. - -Covers years 2007 through 2021, each year has 20,000 observations -Features B, E, and F are normally distributed; A, C, D, G, H, I, J are gamma. -Feature cat contains categorical variables ranging from 1-7 sampled with varying probability -Feature confidence contains integers ranging from (0,0.6) - -Drift 1 (2009): B changes in mean but not variance -Drift 2 (2012): D changes in just variance (mean stays same) -Drift 3 (2015): E and F become more strongly correlated. Their mean and variance stay the same -Drift 4 (2018 - 2021): H changes in mean and variance, range of Confidence Scores increases to (0.4,1) -Drift 5 (2021): J change mean and variance - -With the except of drift 4, all instances of drift are only for that single year of data. \ No newline at end of file diff --git a/src/menelaus/datasets/make_example_data.py b/src/menelaus/datasets/make_example_data.py deleted file mode 100644 index 3d7c3a92..00000000 --- a/src/menelaus/datasets/make_example_data.py +++ /dev/null @@ -1,88 +0,0 @@ -""" Functions to generate example data according to a fixed scheme. """ - -import pandas as pd -import numpy as np - - -def make_example_batch_data(): - """ - This function returns a dataframe containing synthetic batch data for use - with the repo's examples: - - #. Change the mean of column 'b' in 2009. Reverts to original distribution - in 2010. - #. Change the variance of columns 'c' and 'd' in 2012 by replacing - some samples with the mean. Reverts to original distribution in 2013. - #. Change the correlation of columns 'e' and 'f' in 2015 (0 correlation - to 0.5 correlation). - #. Change the mean and variance of column 'h' in 2018, and maintain this - new distribution going forward. Change the range of the "confidence" - column going forward. - #. Change the mean and variance of column 'j' in 2021. - """ - np.random.seed(123) - year_size = 20000 - df = pd.DataFrame() - df["year"] = year_size * list(range(2007, 2022)) - df.sort_values(by="year", inplace=True) - df.reset_index(inplace=True) - sample_size = df.shape[0] - - df["a"] = np.random.gamma(shape=8, size=sample_size) * 1000 - df["b"] = np.random.normal(loc=200, scale=10, size=sample_size) - df["c"] = np.random.gamma(shape=7, size=sample_size) * 1000 - df["d"] = np.random.gamma(shape=10, size=sample_size) * 10000 - df[["e", "f"]] = np.random.multivariate_normal( - mean=(0, 0), cov=np.array([[2, 0], [0, 2]]), size=sample_size - ) - df["g"] = np.random.gamma(shape=11, size=sample_size) * 10000 - df["h"] = np.random.gamma(shape=12, size=sample_size) * 1000 - df["i"] = np.random.gamma(shape=9, size=sample_size) * 1000 - df["j"] = np.random.gamma(shape=10, size=sample_size) * 100 - df["cat"] = np.random.choice( - range(7), size=sample_size, p=(0.3, 0.3, 0.2, 0.1, 0.05, 0.04, 0.01) - ) - df["confidence"] = np.random.uniform(low=0, high=0.6, size=sample_size) - - ###################################################################### - # Drift 1: change the mean of B in 2009, means will revert for 2010 on - df.loc[df.year == 2009, "b"] = np.random.normal(size=year_size, loc=500, scale=10) - - ###################################################################### - # Drift 2: change the variance of c and d in 2012 by replacing some with the mean - # keep same mean as other years, revert by 2013 - mu_c = df["c"].mean() - mu_d = df["d"].mean() - - # subtle change, every 10 obs - df.loc[(df.year == 2012) & (df.index % 10 == 0), "c"] = mu_c + np.random.normal( - loc=0, scale=10, size=year_size // 10 - ) - - # bigger change, every other obs - df.loc[(df.year == 2012) & (df.index % 2 == 0), "d"] = mu_d + np.random.normal( - loc=0, scale=10, size=year_size // 2 - ) - - ###################################################################### - # Drift 3: change the correlation of e and f in 2015 (go from correlation of 0 to correlation of 0.5) - df.loc[df.year == 2015, ["e", "f"]] = np.random.multivariate_normal( - mean=(0, 0), cov=np.array([[2, 1], [1, 2]]), size=year_size - ) - - ###################################################################### - # Drift 4: change mean and var of H and persist it from 2018 on, change range of confidence scores - df.loc[df.year > 2018, "h"] = ( - np.random.gamma(shape=1, scale=1, size=3 * year_size) * 1000 - ) - df.loc[df.year > 2018, "confidence"] = np.random.uniform( - low=0.4, high=1, size=3 * year_size - ) - - ###################################################################### - # Drift 5: change mean and var just for a year of J in 2021 - df.loc[df.year == 2021, "j"] = np.random.gamma(shape=10, size=year_size) * 10 - - df["drift"] = df["year"].isin([2009, 2012, 2015, 2018, 2021]) - df.drop("index", axis=1, inplace=True) - return df diff --git a/src/menelaus/drift_detector.py b/src/menelaus/drift_detector.py deleted file mode 100644 index 8296f983..00000000 --- a/src/menelaus/drift_detector.py +++ /dev/null @@ -1,195 +0,0 @@ -from abc import ABC, abstractmethod - - -class StreamingDetector(ABC): - """ - Abstract base class for all streaming data-based detectors. - Minimally implements abstract methods common to all stream - based detection algorithms. - - Attributes: - total_samples (int): total number of samples the drift - detector has been updated with - samples_since_reset (int): number of samples since last - drift detection - drift_state (str): detector's current drift state, can - take ``str`` values ``"drift"``, ``"warning"``, or ``None``. - """ - - def __init__(self, *args, **kwargs): - self.total_samples = 0 - self.samples_since_reset = 0 - self._drift_state = None - - @abstractmethod - def update(self, *args, **kwargs): - """Update detector with new sample (data point).""" - self.total_samples += 1 - self.samples_since_reset += 1 - - @abstractmethod - def reset(self, *args, **kwargs): - """ - Initialize the detector's drift state and other relevant attributes. - Intended for use after ``drift_state == "drift"``. - """ - self.samples_since_reset = 0 - self.drift_state = None - - @property - def drift_state(self): - """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``.""" - return self._drift_state - - @drift_state.setter - def drift_state(self, value): - """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. - - Args: - value (str): ``"drift"``, ``"warning"``, or ``None`` - - Raises: - ValueError: raised if disallowed value is given - """ - if value not in ("drift", "warning", None): - raise ValueError("tbd") - else: - self._drift_state = value - - -class BatchDetector(ABC): - """ - Abstract base class for all batch data-based detectors. - Minimally implements abstract methods common to all batch - based detection algorithms. - - Attributes: - total_batches (int): total number of batches the drift - detector has been updated with - batches_since_reset (int): number of batches since last - drift detection - drift_state (str): detector's current drift state, can - take ``str`` values ``"drift"``, ``"warning"``, or ``None``. - """ - - def __init__(self, *args, **kwargs): - self.total_batches = 0 - self.batches_since_reset = 0 - self._drift_state = None - - @abstractmethod - def update(self, *args, **kwargs): - """Update detector with new batch of data""" - self.total_batches += 1 - self.batches_since_reset += 1 - - @abstractmethod - def reset(self, *args, **kwargs): - """ - Initialize the detector's drift state and other relevant attributes. - Intended for use after ``drift_state == 'drift'``. - """ - self.batches_since_reset = 0 - self.drift_state = None - - @property - def drift_state(self): - """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``.""" - return self._drift_state - - @drift_state.setter - def drift_state(self, value): - """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. - - Args: - value (str): ``"drift"``, ``"warning"``, or ``None`` - - Raises: - ValueError: raised if disallowed value is given - """ - if value not in ("drift", "warning", None): - raise ValueError("tbd") - else: - self._drift_state = value - - -####################### -# Deprecated in 0.2.0 -####################### -class DriftDetector(ABC): - """Base class for Menelaus drift detectors. - A DriftDetector object implements the ``update`` and ``reset`` methods and - calls the ``super`` methods to initialize and update the attributes below. - - Generally, a DriftDetector is instantiated, then repeatedly passed new data - via ``update``. At each ``update`` step, its ``drift_state`` will reflect - whether drift has been detected or almost been detected. After the - detector's state is set to ``"drift"``, ``update`` calls ``reset`` to - re-initialize the relevant attributes. - - A "batch" detector will compare a new dataset, passed via ``update``, to a - reference dataset, usually the original reference dataset. A "stream" - detector compares only one new sample at a time, also passed via ``update``. - - Attributes: - total_updates (int): number of samples/batches the drift detector - has ever been updated with - updates_since_reset (int): number of samples/batches since the last - time the drift detector was reset - """ - - def __init__(self, *args, **kwargs): - super().__init__() - self.total_updates = 0 - self.updates_since_reset = 0 - self._drift_state = None - self._input_type = None - - @abstractmethod - def update(self, *args, **kwargs): - """Update the detector with a new sample or batch.""" - self.total_updates += 1 - self.updates_since_reset += 1 - - @abstractmethod - def reset(self, *args, **kwargs): - """Initialize the detector's drift state and other relevant attributes. - Intended for use after ``drift_state == 'drift'``.""" - self.updates_since_reset = ( - 0 # number of elements the detector has been updated with since last reset - ) - self.drift_state = None - - @property - def drift_state(self): - """Detector's current drift state, with values ``"drift"``, ``"warning"``,or - ``None``. - """ - return self._drift_state - - @drift_state.setter - def drift_state(self, value): - """Set detector's drift state to ``"drift"``, ``"warning"``, or ``None``. - - Args: - value (str): ``"drift"``, ``"warning"``, or ``None`` - - Raises: - ValueError: raised if disallowed value is given - """ - if value not in ("drift", "warning", None): - raise ValueError( - """DriftDetector._drift_state must be ``"drift"``, - ``"warning"``, or ``None``.""" - ) - else: - self._drift_state = value - - @property - @abstractmethod - def input_type(self): - """The type of input the detector accepts, either ``"batch"``, with multiple - samples in one call to update(), or ``"stream"``, with one sample per call - to update(). - """ - return self._input_type diff --git a/tests/examples/test_example_notebooks.py b/tests/examples/test_example_notebooks.py deleted file mode 100644 index 9c84b141..00000000 --- a/tests/examples/test_example_notebooks.py +++ /dev/null @@ -1,69 +0,0 @@ -""" -Run .py scripts in examples and ensure they execute without error. - -Taken from github.com/AureumChaos/LEAP/ -""" -import glob -import os -import pathlib - -from nbconvert import NotebookExporter -from nbconvert.preprocessors import ExecutePreprocessor -import nbformat -import pytest -from traitlets.config import Config - - -def run_notebook(path, timeout=120): - """ - Execute a Jupyter Notebook and return any errors that it produces. - :param path: path to the .ipynb file to execute - :param int timeout: number of seconds to let the notebook run before we throw an exception by default. - :return: a tuple (nb, errors) containing the parsed Notebook object and a list of errors, respectively - """ - # We'll use a NotebookExporter from the nbconvert package to load the notebook and re-export it to a temporary file. - # First we want to configure it to execute the notebook before writing it: - c = Config() - c.NotebookExporter.preprocessors = ['nbconvert.preprocessors.ExecutePreprocessor'] - c.ExecutePreprocessor.timeout = timeout - c.ExecutePreprocessor.kernel_name = 'venv' # We assume a kernel named "venv" that lives in our venv - exp = NotebookExporter(config=c) - - # Load the notebook - with open(path, 'r') as nb_file: - body, resources = exp.from_file(nb_file) - - # Parse the notebook string into a notebook object - nb = nbformat.reads(body, nbformat.current_nbformat) - - errors = [output for cell in nb.cells if "outputs" in cell - for output in cell["outputs"] - if output.output_type == "error"] - - return nb, errors - - -def is_hidden_path(path): - """Return true if the file or any of its ancesor directories begins with '.'.""" - head, tail = os.path.split(path) - if tail.startswith('.'): - return True - elif head == '' or tail == '': - return False - else: - return is_hidden_path(head) - - -notebooks = pathlib.Path(__file__, '..', '../..', 'examples').resolve().rglob('*.ipynb') -notebooks = [ p for p in notebooks if not is_hidden_path(p) ] - - -# We give Jupyter tests a separate marker, because they can only run if the 'venv' kernel is configured propertly by the user -@pytest.mark.jupyter -@pytest.mark.parametrize('path', notebooks) -def test_notebook(path): - """Ensure that all of the notebooks in the examples directory run without errors.""" - nb, errors = run_notebook(path) - - # No errors is success - assert errors == [] \ No newline at end of file diff --git a/tests/examples/test_example_scripts.py b/tests/examples/test_example_scripts.py index 0b5f6964..a2312a8a 100644 --- a/tests/examples/test_example_scripts.py +++ b/tests/examples/test_example_scripts.py @@ -1,5 +1,8 @@ """ Run .py scripts in examples and ensure they execute without error. + +Note that the git workflow will have converted these from the most recent +version of the .ipynb files in docs/source/examples. """ import pathlib @@ -9,33 +12,35 @@ import pytest -from menelaus import test_env_var +from menelaus import test_env_var -scripts = pathlib.Path(__file__, '..', '..', '..', 'examples').resolve().rglob('*.py') +scripts = pathlib.Path(__file__, "..", "..", "..", "examples").resolve().rglob("*.py") ############################## # Tests for example scripts ############################## -@pytest.mark.parametrize('script', scripts) +@pytest.mark.parametrize("script", scripts) @pytest.mark.no_cover # Don't measure test coverage for this harness def test_script_execution(script): print(f"Running {script}") # We have to tweak sys.argv to avoid the pytest arguments from being passed along to our scripts sys_orig = sys.argv - sys.argv = [ str(script) ] - os.environ[test_env_var] = 'True' + sys.argv = [str(script)] + os.environ[test_env_var] = "True" try: - runpy.run_path(str(script), run_name='__main__') + runpy.run_path(str(script), run_name="__main__") except SystemExit as e: # Some scripts may explicitly call `sys.exit()`, in which case we'll check the error code - assert(e.code == 0) - + assert e.code == 0 + sys.argv = sys_orig # clean output files appearing in current or parent directory - output_file_pattern = 'example_*.*' - output_files = pathlib.Path(__file__, '..', '..').resolve().rglob(output_file_pattern) + output_file_pattern = "example_*.*" + output_files = ( + pathlib.Path(__file__, "..", "..").resolve().rglob(output_file_pattern) + ) for output_file in output_files: - os.remove(output_file) \ No newline at end of file + os.remove(output_file) diff --git a/tests/menelaus/change_detection/__init__.py b/tests/menelaus/change_detection/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/test_adwin.py b/tests/menelaus/change_detection/test_adwin.py similarity index 93% rename from tests/menelaus/test_adwin.py rename to tests/menelaus/change_detection/test_adwin.py index b4a9522b..1b709fd1 100644 --- a/tests/menelaus/test_adwin.py +++ b/tests/menelaus/change_detection/test_adwin.py @@ -1,6 +1,7 @@ """Methods for checking simple behaviors of ADWIN.""" import pytest -from menelaus.concept_drift.adwin import ADWIN, _BucketRow, _BucketRowList +import numpy as np +from menelaus.change_detection.adwin import ADWIN, _BucketRow, _BucketRowList def test_compression(): @@ -23,11 +24,16 @@ def test_compression(): assert overflow is False -def test_validation(): +def test_delta_validation(): """Validation on delta parameter should raise ValueError""" with pytest.raises(ValueError) as _: _ = ADWIN(delta=25) +def test_univariate_validation(): + det = ADWIN() + input = np.array([[1, 2]]) + with pytest.raises(ValueError) as _: + det.update(input) def test_mean(): """Check that ADWIN.mean returns sane values""" diff --git a/tests/menelaus/test_cusum.py b/tests/menelaus/change_detection/test_cusum.py similarity index 93% rename from tests/menelaus/test_cusum.py rename to tests/menelaus/change_detection/test_cusum.py index 13649bdc..b81fa253 100644 --- a/tests/menelaus/test_cusum.py +++ b/tests/menelaus/change_detection/test_cusum.py @@ -1,5 +1,6 @@ """All methods for testing correctness of CUSUM implementation.""" import numpy as np +import pytest from menelaus.change_detection.cusum import CUSUM @@ -15,7 +16,7 @@ def test_no_drift(): assert cusum.drift_state is None assert len(cusum._stream) == stream_size assert ( - cusum.updates_since_reset == stream_size + cusum.samples_since_reset == stream_size ) # no reset til at least after burn in @@ -98,3 +99,10 @@ def test_negative_drift(): assert cusum.drift_state is None else: pass + + +def test_univariate_validation(): + det = CUSUM() + input = np.array([[1, 2]]) + with pytest.raises(ValueError) as _: + det.update(input) diff --git a/tests/menelaus/test_page_hinkley.py b/tests/menelaus/change_detection/test_page_hinkley.py similarity index 88% rename from tests/menelaus/test_page_hinkley.py rename to tests/menelaus/change_detection/test_page_hinkley.py index 234ea872..a5e2aba6 100644 --- a/tests/menelaus/test_page_hinkley.py +++ b/tests/menelaus/change_detection/test_page_hinkley.py @@ -1,4 +1,6 @@ """All methods for testing correctness of Page Hinkley implementation.""" +import pytest +import numpy as np from menelaus.change_detection.page_hinkley import PageHinkley @@ -9,7 +11,7 @@ def test_build_no_drift(): # constant update monitor.update(1) assert monitor.drift_state is None - assert monitor.updates_since_reset == i + 1 + assert monitor.samples_since_reset == i + 1 def test_build_pos_drift(): @@ -54,3 +56,10 @@ def test_build_neg_drift(): assert ( len(monitor.to_dataframe()) == stream_size ) # hack to test all reset() behavior + + +def test_univariate_validation(): + det = PageHinkley() + input = np.array([[1, 2]]) + with pytest.raises(ValueError) as _: + det.update(input) diff --git a/tests/menelaus/concept_drift/__init__.py b/tests/menelaus/concept_drift/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/concept_drift/test_adwin_accuracy.py b/tests/menelaus/concept_drift/test_adwin_accuracy.py new file mode 100644 index 00000000..f1e88143 --- /dev/null +++ b/tests/menelaus/concept_drift/test_adwin_accuracy.py @@ -0,0 +1,13 @@ +import pytest +from menelaus.concept_drift.adwin_accuracy import ADWINAccuracy + + +def test_aliased_input(): + det = ADWINAccuracy() + + det.update(y_true=1, y_pred=0) + assert det.total_samples == 1 + + # the alias shouldn't take any "feature" input + with pytest.raises(TypeError) as _: + det.update(X="hello!") diff --git a/tests/menelaus/test_ddm.py b/tests/menelaus/concept_drift/test_ddm.py similarity index 97% rename from tests/menelaus/test_ddm.py rename to tests/menelaus/concept_drift/test_ddm.py index fec92b19..f33e57f9 100644 --- a/tests/menelaus/test_ddm.py +++ b/tests/menelaus/concept_drift/test_ddm.py @@ -26,7 +26,7 @@ def test_warning(): # enter the "warning" region for _ in range(n_error_samples): - det.update(1, 0) + det.update(y_true=0, y_pred=1) assert det.drift_state == "warning" # leave the "warning" region diff --git a/tests/menelaus/test_eddm.py b/tests/menelaus/concept_drift/test_eddm.py similarity index 92% rename from tests/menelaus/test_eddm.py rename to tests/menelaus/concept_drift/test_eddm.py index cd930938..0959a7a9 100644 --- a/tests/menelaus/test_eddm.py +++ b/tests/menelaus/concept_drift/test_eddm.py @@ -11,11 +11,11 @@ def test_drift(): det.update(1, 1) for _ in range(n_error_samples): - det.update(1, 0) + det.update(y_true=0, y_pred=1) # drift and reset assert det.drift_state == "drift" - det.update(1, 0) + det.update(y_true=0, y_pred=1) assert det.drift_state is None @@ -29,12 +29,12 @@ def test_warning(): # enter warning region for _ in range(n_error_samples): - det.update(1, 0) + det.update(y_true=0, y_pred=1) assert det.drift_state == "warning" # increase distance between errors, leave warning region det.update(1, 1) - det.update(0, 1) + det.update(y_true=1, y_pred=0) assert det.drift_state is None diff --git a/tests/menelaus/test_lfr.py b/tests/menelaus/concept_drift/test_lfr.py similarity index 88% rename from tests/menelaus/test_lfr.py rename to tests/menelaus/concept_drift/test_lfr.py index e3463742..414a667e 100644 --- a/tests/menelaus/test_lfr.py +++ b/tests/menelaus/concept_drift/test_lfr.py @@ -12,10 +12,10 @@ def test_build_no_drift(): detParallel = LinearFourRates(burn_in=burn_in, num_mc=15, parallelize=True) for _ in range(burn_in + 50): # values that ostensibly should not indicate drift - det.update(y_pred=1, y_true=1) - det.update(y_pred=0, y_true=1) - det.update(y_pred=1, y_true=0) - det.update(y_pred=0, y_true=0) + det.update(y_true=1, y_pred=1) + det.update(y_true=1, y_pred=0) + det.update(y_true=0, y_pred=1) + det.update(y_true=0, y_pred=0) assert det.drift_state is None assert det.retraining_recs == [None, None] @@ -53,30 +53,29 @@ def test_build_with_warning_drift(): # have warning and drift significance levels be spread out for i in range(burn_in + 20): if i < burn_in: - #det.update(1,np.random.binomial(n=1,p=0.6)) - det.update(1,1) + det.update(y_true=1, y_pred=1) assert det.drift_state is None assert det.retraining_recs == [None, None] np.random.seed(123) - detParallel.update(1,1) + detParallel.update(y_true=1, y_pred=1) # x=1, y_true=1 assert detParallel.drift_state is None assert detParallel.retraining_recs == [None, None] elif i == burn_in: # get this to be warning - det.update(1,1) + det.update(y_true=1, y_pred=1) assert det.drift_state == "warning" np.random.seed(123) - detParallel.update(1,1) + detParallel.update(y_true=1, y_pred=1) assert detParallel.drift_state == "warning" elif i == burn_in + 1: # get this to be drift - det.update(1,0) + det.update(y_true=0, y_pred=1) assert det.drift_state == "drift" np.random.seed(123) - detParallel.update(1,0) + detParallel.update(y_true=0, y_pred=1) assert detParallel.drift_state == "drift" else: - det.update(1,1) + det.update(y_true=1, y_pred=1) np.random.seed(123) - detParallel.update(1,1) + detParallel.update(y_true=1, y_pred=1) # need a test that goes straight from no drift to drift (bypassing warning) @@ -90,8 +89,8 @@ def test_build_with_drift(): burn_in=burn_in, num_mc=1, detect_level=0.5, parallelize=True # get it to drift asap ) for i in range(burn_in + 1): - det.update(1, 1) - detParallel.update(1, 1) + det.update(y_true=1, y_pred=1) + detParallel.update(y_true=1, y_pred=1) if i >= burn_in: assert det.drift_state == "drift" assert det._retraining_recs == [burn_in,burn_in] @@ -107,7 +106,7 @@ def test_drift_no_rates_tracked(): burn_in=burn_in, num_mc=1, detect_level=0.5, rates_tracked=[] # get it to drift asap ) for i in range(burn_in + 1): - det.update(1, 1) + det.update(y_true=1, y_pred=1) if i >= burn_in: assert det.drift_state is None assert det._retraining_recs == [None, None] @@ -189,19 +188,19 @@ def test_drift_rate_dependence(): detNPV.update(1,1) assert detNPV.drift_state is None elif i == burn_in + 1: # get this to be drift - detTPR.update(1,0) + detTPR.update(y_true=0, y_pred=1) assert detTPR.drift_state == "warning" np.random.seed(123) - detTNR.update(1,0) + detTNR.update(y_true=0, y_pred=1) assert detTNR.drift_state == "warning" np.random.seed(123) - detPPV.update(1,0) + detPPV.update(y_true=0, y_pred=1) assert detPPV.drift_state == "drift" np.random.seed(123) - detNPV.update(1,0) + detNPV.update(y_true=0, y_pred=1) assert detNPV.drift_state is None else: detTPR.update(1,1) diff --git a/tests/menelaus/concept_drift/test_md3.py b/tests/menelaus/concept_drift/test_md3.py new file mode 100644 index 00000000..8a0f5454 --- /dev/null +++ b/tests/menelaus/concept_drift/test_md3.py @@ -0,0 +1,147 @@ +"""Methods for checking simple behaviors of MD3.""" +import pytest +import numpy as np +import pandas as pd +from sklearn import svm +from menelaus.concept_drift.md3 import MD3 + + +def test_init(): + """Confirm MD3 parameters are initialized as expected""" + clf = svm.SVC(kernel='linear') + det = MD3(clf=clf) + assert det.classifier == clf + assert det.sensitivity == 2 + assert det.k == 10 + assert det.oracle_data_length_required is None + assert det.oracle_data is None + assert det.waiting_for_oracle == False + + +def test_set_reference(): + """Confirm that the set_reference method behaves as expected, + calculating the correct distribution statistics""" + np.random.seed(123) + data = [[1, 2, 0], [1, 1, 0], [5, 6, 1], [6, 7, 1], [3, 4, 1]] + df = pd.DataFrame(data, columns=['var1', 'var2', 'y']) + X_train = df.loc[:, ['var1', 'var2']] + y_train = df.loc[:, 'y'] + clf = svm.SVC(kernel='linear') + clf.fit(X_train, y_train) + det = MD3(clf=clf, k=2) + det.set_reference(df, target_name='y') + assert X_train.equals(det.reference_batch_features) + assert df.loc[:, df.columns == 'y'].equals(det.reference_batch_target) + assert det.reference_distribution["len"] == len(data) + assert det.reference_distribution["md"] == 0.0 + assert det.reference_distribution["md_std"] == 0.0 + assert np.abs(det.reference_distribution["acc"] - 0.833) < 0.1 + assert np.abs(det.reference_distribution["acc_std"] - 0.166) < 0.1 + assert det.oracle_data_length_required == len(data) + assert det.forgetting_factor == (len(data) - 1) / len(data) + + +def test_update(): + """Confirm that the update method, including validation, behaves + as expected""" + np.random.seed(123) + data = [[1, 1, 0], [2, 2, 0], [3, 4, 1], [5, 6, 1], [6, 7, 1]] + df = pd.DataFrame(data, columns=['var1', 'var2', 'y']) + X_train = df.loc[:, ['var1', 'var2']] + y_train = df.loc[:, 'y'] + clf = svm.SVC(kernel='linear') + clf.fit(X_train, y_train) + det = MD3(clf=clf, k=2) + det.set_reference(df, target_name='y') + + data_update1 = [[-4, 8], [3, 4]] + df_update1 = pd.DataFrame(data_update1, columns=['var1', 'var2']) + with pytest.raises(ValueError) as _: + det.update(df_update1) + + data_update2 = [[2.5, 3.5]] + df_update2 = pd.DataFrame(data_update2, columns=['var1', 'var2']) + det.update(df_update2) + assert det.drift_state == "warning" + assert det.waiting_for_oracle == True + + with pytest.raises(ValueError) as _: + det.update(df_update2) + + +def test_give_oracle_label(): + """Confirm that providing a stream of labeled samples to the detector + with give_oracle_label behaves as expected""" + np.random.seed(123) + data = [[1, 1, 0], [3, 4, 1], [1.25, 2.25, 0], [4, 4, 1], [2, 2, 0], [5, 6, 1], [1, 2, 0], [6, 7, 1]] + df = pd.DataFrame(data, columns=['var1', 'var2', 'y']) + X_train = df.loc[:, ['var1', 'var2']] + y_train = df.loc[:, 'y'] + clf = svm.SVC(kernel='linear') + clf.fit(X_train, y_train) + det = MD3(clf=clf, sensitivity=0.5, k=3) + det.set_reference(df, target_name='y') + + labeled_sample = [[1, 2, 0]] + labeled_df = pd.DataFrame(labeled_sample, columns=['var1', 'var2', 'y']) + with pytest.raises(ValueError) as _: + det.give_oracle_label(labeled_df) + + data_update1 = [[-4, 8], [3, 4]] + df_update1 = pd.DataFrame(data_update1, columns=['var1', 'var2']) + with pytest.raises(ValueError) as _: + det.update(df_update1) + + data_update2 = [[3, 4]] + df_update2 = pd.DataFrame(data_update2, columns=['var1', 'var2']) + for _ in range(2): + det.update(df_update2) + assert det.drift_state == "warning" + assert det.waiting_for_oracle == True + + with pytest.raises(ValueError) as _: + det.update(df_update2) + + double_labeled_sample = [[1, 2, 0], [7, 8, 1]] + double_labeled_df = pd.DataFrame(double_labeled_sample, columns=['var1', 'var2', 'y']) + with pytest.raises(ValueError) as _: + det.give_oracle_label(double_labeled_df) + + wrong_labeled_sample = [[1, 2, 0], [7, 8, 1]] + wrong_labeled_df = pd.DataFrame(wrong_labeled_sample, columns=['var1', 'var2', 'target']) + with pytest.raises(ValueError) as _: + det.give_oracle_label(wrong_labeled_df) + + wrong_len_sample = [[1, 0]] + wrong_len_df = pd.DataFrame(wrong_len_sample, columns=['var1', 'target']) + with pytest.raises(ValueError) as _: + det.give_oracle_label(wrong_len_df) + + labeled_sample = [[1, 2, 0]] + labeled_df = pd.DataFrame(labeled_sample, columns=['var1', 'var2', 'y']) + det.give_oracle_label(labeled_df) + assert det.drift_state is None + assert labeled_df.equals(det.oracle_data) + + for i in range(len(data) - 1): + labeled_sample = [[3.5, 4.5, i % 2]] + labeled_df = pd.DataFrame(labeled_sample, columns=['var1', 'var2', 'y']) + det.give_oracle_label(labeled_df) + if i == len(data) - 2: + assert det.drift_state == "drift" + assert det.oracle_data is None + assert det.waiting_for_oracle == False + else: + assert det.drift_state is None + assert len(det.oracle_data) == i + 2 + + data_update = [[1, 2]] + df_update = pd.DataFrame(data_update, columns=['var1', 'var2']) + assert det.drift_state == "drift" + det.update(df_update) + assert det.drift_state is None + + det.reset() + assert det.updates_since_reset == ( 0 ) + assert det.drift_state is None + \ No newline at end of file diff --git a/tests/menelaus/test_stepd.py b/tests/menelaus/concept_drift/test_stepd.py similarity index 100% rename from tests/menelaus/test_stepd.py rename to tests/menelaus/concept_drift/test_stepd.py diff --git a/tests/menelaus/data_drift/__init__.py b/tests/menelaus/data_drift/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/data_drift/test_cdbd.py b/tests/menelaus/data_drift/test_cdbd.py new file mode 100644 index 00000000..b9549714 --- /dev/null +++ b/tests/menelaus/data_drift/test_cdbd.py @@ -0,0 +1,29 @@ +"""All methods for testing CDBD implementation.""" +import numpy as np +import pandas as pd +import pytest +from menelaus.data_drift.cdbd import CDBD + + +def test_batch_validation(): + """Test ability to raise error if updating CDBD with a dataset containing more than 1 variable""" + data1 = np.repeat(1, 100) + reference = pd.DataFrame(data=[data1]).T + test = pd.DataFrame(data=[data1, data1, data1]).T + + # initialize with reference batch + cdbd = CDBD() + cdbd.set_reference(reference) + + # update with test batch + with pytest.raises(ValueError) as _: + _ = cdbd.update(test) + + +def test_init_validation(): + """Test ability to raise error if initializing CDBD with a dataset containing more than 1 variable""" + data1 = np.repeat(1, 100) + reference = pd.DataFrame(data=[data1, data1, data1]).T + with pytest.raises(ValueError) as _: + _ = CDBD(detect_batch=3) + _.set_reference(reference) diff --git a/tests/menelaus/data_drift/test_hdddm.py b/tests/menelaus/data_drift/test_hdddm.py new file mode 100644 index 00000000..1b656bba --- /dev/null +++ b/tests/menelaus/data_drift/test_hdddm.py @@ -0,0 +1,17 @@ +"""All methods for testing HDDDM implementation.""" +import numpy as np +import pandas as pd +import pytest +from menelaus.data_drift.hdddm import HDDDM + + +def test_validation(): + """Test ability to raise error if batches have different number of features""" + data1 = np.repeat(1, 100) + reference = pd.DataFrame(data=[data1, data1, data1]).T + hdddm = HDDDM() + hdddm.set_reference(reference) + + test = pd.DataFrame(data=[data1, data1, data1, data1]).T + with pytest.raises(ValueError) as _: + _ = hdddm.update(test) diff --git a/tests/menelaus/test_histogram_density_method.py b/tests/menelaus/data_drift/test_histogram_density_method.py similarity index 95% rename from tests/menelaus/test_histogram_density_method.py rename to tests/menelaus/data_drift/test_histogram_density_method.py index 68373965..befa44ba 100644 --- a/tests/menelaus/test_histogram_density_method.py +++ b/tests/menelaus/data_drift/test_histogram_density_method.py @@ -82,8 +82,8 @@ def test_detect_batch_1_init(): ) hdm.set_reference(reference) - assert hdm.total_updates == 1 - assert hdm.updates_since_reset == 1 + assert hdm.total_batches == 1 + assert hdm.batches_since_reset == 1 assert hdm.drift_state == None assert hdm._prev_feature_distances != [] assert hdm.reference_n == len(reference) @@ -109,8 +109,8 @@ def test_detect_batch_1_no_drift(): # update with identical test batch hdm.update(reference) - assert hdm.total_updates == 2 - assert hdm.updates_since_reset == 2 + assert hdm.total_batches == 2 + assert hdm.batches_since_reset == 2 assert hdm.drift_state == None assert hdm.reference_n == len(reference) * 2 assert hdm.feature_epsilons != [] @@ -195,8 +195,8 @@ def test_detect_batch_1_reset(): # test reset updating with identical batch to test hdm.update(test) - assert hdm.total_updates == 4 - assert hdm.updates_since_reset == 2 + assert hdm.total_batches == 4 + assert hdm.batches_since_reset == 2 assert len(hdm.epsilon) == 2 # epsilon is reset assert hdm.beta != None assert hdm.drift_state is None @@ -217,8 +217,8 @@ def test_detect_batch_2_init(): ) hdm.set_reference(reference) - assert hdm.total_updates == 0 - assert hdm.updates_since_reset == 0 + assert hdm.total_batches == 0 + assert hdm.batches_since_reset == 0 assert hdm.drift_state == None assert hdm.reference_n == len(reference) @@ -249,7 +249,7 @@ def test_detect_batch_2_no_drift(): hdm.update(reference) assert hdm.drift_state == None - assert hdm.total_updates == 2 + assert hdm.total_batches == 2 assert hdm.feature_epsilons != [] assert len(hdm.epsilon) == 2 assert hdm.beta != None @@ -314,8 +314,8 @@ def test_detect_batch_2_reset(): # test reset updating with identical batch to test batch 2 hdm.update(test) - assert hdm.total_updates == 3 - assert hdm.updates_since_reset == 1 + assert hdm.total_batches == 3 + assert hdm.batches_since_reset == 1 assert len(hdm.epsilon) == 0 # epsilon is reset assert hdm.beta != None assert hdm._drift_state == None @@ -336,8 +336,8 @@ def test_detect_batch_3_init(): ) hdm.set_reference(reference) - assert hdm.total_updates == 0 - assert hdm.updates_since_reset == 0 + assert hdm.total_batches == 0 + assert hdm.batches_since_reset == 0 assert hdm.drift_state == None assert hdm.reference_n == len(reference) @@ -371,7 +371,7 @@ def test_detect_batch_3_no_drift(): hdm.update(reference) assert hdm.drift_state == None - assert hdm.total_updates == 3 + assert hdm.total_batches == 3 assert hdm.feature_epsilons != [] assert len(hdm.epsilon) == 2 assert hdm.beta != None @@ -442,8 +442,8 @@ def test_detect_batch_3_reset(): # test reset updating with identical batch to test batch 3 hdm.update(test) - assert hdm.total_updates == 4 - assert hdm.updates_since_reset == 1 + assert hdm.total_batches == 4 + assert hdm.batches_since_reset == 1 assert len(hdm.epsilon) == 0 # epsilon is reset assert hdm.beta != None assert hdm._drift_state == None diff --git a/tests/menelaus/test_kdq_tree.py b/tests/menelaus/data_drift/test_kdq_tree.py similarity index 92% rename from tests/menelaus/test_kdq_tree.py rename to tests/menelaus/data_drift/test_kdq_tree.py index 57ca135b..fbcacb02 100644 --- a/tests/menelaus/test_kdq_tree.py +++ b/tests/menelaus/data_drift/test_kdq_tree.py @@ -70,28 +70,28 @@ def test_reset_stream(kdq_det_stream): assert det.drift_state is None det.reset() det.update(new_sample) - assert det.input_cols.equals(new_sample.columns) + assert det._input_cols.equals(new_sample.columns) def test_reset_batch(kdq_det_batch): det = copy.copy(kdq_det_batch) - new_sample = pd.DataFrame(np.random.sample((1, NUM_FEATURES))).values + new_sample = pd.DataFrame(np.random.sample((2, NUM_FEATURES))).values det.update(new_sample) assert det.drift_state is None def test_set_reference(kdq_det_batch): det = copy.copy(kdq_det_batch) - new_sample = pd.DataFrame(np.random.sample((1, NUM_FEATURES))).values + new_sample = pd.DataFrame(np.random.sample((2, NUM_FEATURES))).values det.set_reference(new_sample) det.update(new_sample) assert det.drift_state is None - # make sure that input_cols is set appropriately when initialized + # make sure that _input_cols is set appropriately when initialized det = det = KdqTreeBatch(count_ubound=1, bootstrap_samples=10) - new_sample = pd.DataFrame(np.random.sample((1, NUM_FEATURES))) + new_sample = pd.DataFrame(np.random.sample((2, NUM_FEATURES))) det.set_reference(new_sample) - assert det.input_cols.equals(new_sample.columns) + assert det._input_col_dim == new_sample.shape[1] def test_init_validation_stream(kdq_det_stream): diff --git a/tests/menelaus/data_drift/test_nndvi.py b/tests/menelaus/data_drift/test_nndvi.py new file mode 100644 index 00000000..2edae726 --- /dev/null +++ b/tests/menelaus/data_drift/test_nndvi.py @@ -0,0 +1,84 @@ +import numpy as np +import pytest + +from menelaus.data_drift import NNDVI + + +def test_nndvi_init(): + """Test correct default initialization for NN-DVI""" + det = NNDVI() + assert det.k_nn == 30 + assert det.sampling_times == 500 + assert det.alpha == 0.01 + assert det.batches_since_reset == 0 + assert det.drift_state is None + + +def test_nndvi_set_reference(): + """Assert NN-DVI.set_reference works as intended""" + det = NNDVI() + ref = np.random.randint(0, 5, (3, 3)) + det.set_reference(ref) + assert np.array_equal(ref, det.reference_batch) + + +def test_nndvi_update_1(): + """Ensure NN-DVI can update with small random batches""" + det = NNDVI(k_nn=3, sampling_times=10) + det.set_reference(np.random.randint(0, 5, (10, 10))) + det.update(X=np.random.randint(0, 5, (10, 10))) + + +def test_nndvi_update_2(): + """Ensure NN-DVI can update with drift actions triggered""" + det = NNDVI(k_nn=5, sampling_times=10) + # XXX - AS added this method of forcing drift in NNDVI, which + # is otherwise hard to induce drift in, for small data + # examples. More stable alternatives may exist + np.random.seed(123) + det.set_reference(np.random.randint(0, 5, (10, 10))) + det.update(X=np.random.randint(10, 40, (10, 10))) + assert det.drift_state is not None + + +def test_nndvi_update_3(): + """Check NNDVI.update behavior after drift alarm""" + det = NNDVI(k_nn=3) + det.set_reference(np.random.randint(0, 5, (5, 5))) + det._drift_state = "drift" + det.update(X=np.random.randint(0, 5, (5, 5))) + assert det.drift_state is None + + +def test_nndvi_update_4(): + """Check failure when batch shapes don't match""" + det = NNDVI() + det.set_reference(np.random.randint(0, 5, (5, 6))) + with pytest.raises(ValueError): + det.update(np.random.randint(0, 5, (5, 5))) + + +def test_nndvi_reset(): + """Check NNDVI.reset works as intended""" + det = NNDVI() + det.batches_since_reset = 1 + det.drift_state = "drift" + det.reset() + assert det.batches_since_reset == 0 + assert det.drift_state is None + + +def test_nndvi_compute_threshold_1(): + """Check NNDVI._compute_threshold works correctly""" + det = NNDVI() + # XXX - Hardcoded known example added by AS, in the future a + # dynamic way to test this function may be used + np.random.seed(123) + threshold = det._compute_drift_threshold( + M_nnps=np.random.randint(0, 5, (5, 5)), + v_ref=np.random.randint(0, 2, 5), + v_test=np.random.randint(0, 2, 5), + sampling_times=50, + alpha=0.01, + ) + assert threshold >= 0.6 and threshold <= 0.7 diff --git a/tests/menelaus/test_pca_cd.py b/tests/menelaus/data_drift/test_pca_cd.py similarity index 99% rename from tests/menelaus/test_pca_cd.py rename to tests/menelaus/data_drift/test_pca_cd.py index 347b972a..41be2be5 100644 --- a/tests/menelaus/test_pca_cd.py +++ b/tests/menelaus/data_drift/test_pca_cd.py @@ -93,7 +93,7 @@ def test_reset(): det.update(drift.iloc[[i]]) assert det._build_reference_and_test is True assert det._drift_state is None - assert det.updates_since_reset == 1 + assert det.samples_since_reset == 0 def test_kl_no_drift(): diff --git a/tests/menelaus/datasets/__init__.py b/tests/menelaus/datasets/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/datasets/test_example_data.py b/tests/menelaus/datasets/test_example_data.py new file mode 100644 index 00000000..a5898c4c --- /dev/null +++ b/tests/menelaus/datasets/test_example_data.py @@ -0,0 +1,71 @@ +from numpy import float64, int32, int64 +from menelaus.datasets.make_example_data import ( + make_example_batch_data, + fetch_circle_data, + fetch_rainfall_data, +) + + +def test_example_batch_data_1(): + """Ensure generated example data has correct shape""" + df = make_example_batch_data() + assert list(df.columns) == [ + "year", + "a", + "b", + "c", + "d", + "e", + "f", + "g", + "h", + "i", + "j", + "cat", + "confidence", + "drift", + ] + assert list(df.year.unique()) == [ + 2007, + 2008, + 2009, + 2010, + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + ] + df_grouped = df.groupby("year") + dfs = [x for _, x in df_grouped] + for df in dfs: + assert len(df) == 20000 + + +def test_example_batch_data_2(): + """Ensure generated example data has right column types""" + df = make_example_batch_data() + assert df.dtypes["year"] == int64 + for char in "abcdefghij": + assert df.dtypes[char] == float64 + assert (df.dtypes["cat"] == int32) or (df.dtypes["cat"] == int64) + assert df.dtypes["confidence"] == float64 + assert df.dtypes["drift"] == bool + + +def test_circle_data(): + df = fetch_circle_data() + assert all(df.dtypes == float64) + assert df.shape == (2000, 3) + + +def test_rainfall_data(): + df = fetch_rainfall_data() + assert df.select_dtypes("number").shape == df.shape + assert df.shape == (18159, 9) diff --git a/tests/menelaus/partitioners/__init__.py b/tests/menelaus/partitioners/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/test_kdqtree_partitioner.py b/tests/menelaus/partitioners/test_kdqtree_partitioner.py similarity index 100% rename from tests/menelaus/test_kdqtree_partitioner.py rename to tests/menelaus/partitioners/test_kdqtree_partitioner.py diff --git a/tests/menelaus/partitioners/test_nn_space_partitioner.py b/tests/menelaus/partitioners/test_nn_space_partitioner.py new file mode 100644 index 00000000..94139ed5 --- /dev/null +++ b/tests/menelaus/partitioners/test_nn_space_partitioner.py @@ -0,0 +1,40 @@ +import numpy as np + +from menelaus.partitioners import NNSpacePartitioner + +def test_nnsp_init(): + ''' Check correct initialization state for NNSpacePartitioner''' + part = NNSpacePartitioner(30) + assert part.k == 30 + attrs = [part.adjacency_matrix, part.nnps_matrix, part.D, part.v1, part.v2] + for attr in attrs: + assert attr is None + +def test_nnsp_build(): + ''' Check basic correct execution for NNSpacePartitioner.build ''' + part = NNSpacePartitioner(k=5) + part.build(np.random.randint(0,5,(6,6)), np.random.randint(0,5,(6,6))) + assert part.D.shape[0] <= 6*2 + assert part.D.shape[1] == 6 + assert len(part.v1) <= 6*2 + assert len(part.v2) <= 6*2 + assert part.adjacency_matrix.shape[0] <= 6*2 + assert part.nnps_matrix.shape[0] <= 6*2 + +def test_nnsp_compute_nnps_distance_1(): + ''' Check correct computation for NNPS distance function in 0-case ''' + nnps_mat = np.random.randint(0,5,(6,6)) + v1 = np.random.randint(0,2,6) + distance = NNSpacePartitioner.compute_nnps_distance(nnps_mat, v1, v1) + assert distance == 0 + +def test_nnsp_compute_nnps_distance_2(): + ''' Check correct computation for NNPS distance in normal case ''' + # XXX - Hard-coded example added by AS as it becomes complicated to + # test dynamically + np.random.seed(123) + nnps_mat = np.random.randint(0,5,(6,6)) + v1 = np.random.randint(0,2,6) + v2 = 1-v1 + distance = NNSpacePartitioner.compute_nnps_distance(nnps_mat, v1, v2) + assert distance >= 0.29 and distance <= 0.3 \ No newline at end of file diff --git a/tests/menelaus/test_cdbd.py b/tests/menelaus/test_cdbd.py deleted file mode 100644 index 893f3129..00000000 --- a/tests/menelaus/test_cdbd.py +++ /dev/null @@ -1,72 +0,0 @@ -"""All methods for testing CDBD implementation.""" -import numpy as np -import pandas as pd -import pytest -from menelaus.data_drift.cdbd import CDBD - - -def test_batch_validation(): - """Test ability to raise error if updating CDBD with a dataset containing more than 1 variable""" - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1]).T - test = pd.DataFrame(data=[data1, data1, data1]).T - - # initialize with reference batch - cdbd = CDBD() - cdbd.set_reference(reference) - - # update with test batch - with pytest.raises(ValueError) as _: - _ = cdbd.update(test) - - -def test_init_validation(): - """Test ability to raise error if initializing CDBD with a dataset containing more than 1 variable""" - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1, data1, data1]).T - with pytest.raises(ValueError) as _: - _ = CDBD(detect_batch=3) - _.set_reference(reference) - - -def test_drift(): - """Test of modified CDBD test using KL divergence""" - - # np.random.seed(1) - reference = pd.DataFrame(np.random.uniform(0, 1, 100)) - test = pd.DataFrame(np.random.uniform(3, 4, 100)) - - # initialize with reference batch - cdbd = CDBD() - cdbd.set_reference(reference) - - # update with different test batch - cdbd.update(test) - - assert cdbd._drift_state == "drift" - assert len(cdbd.reference) == len(test) - -def test_custom_divmetric(): - """ Tests functionality of user defining custom divergence metric """ - - # Define divergence metric - def distance_metric1(reference_histogram, test_histogram): - - # Convert inputs to appropriate datatype - ref = np.array(reference_histogram[0]) - test = np.array(test_histogram[0]) - - dist = np.sqrt(np.sum(np.square(ref-test))) - - return dist - - # Setup data - reference = pd.DataFrame(data=[np.repeat(1, 100)]).T - test = pd.DataFrame(data=[np.repeat(4, 100)]).T - - # initialize with reference batch - cdbd = CDBD(divergence=distance_metric1) - cdbd.set_reference(reference) - - cdbd.update(test) - assert cdbd.current_distance != 0 diff --git a/tests/menelaus/test_detector.py b/tests/menelaus/test_detector.py new file mode 100644 index 00000000..ab2e30fc --- /dev/null +++ b/tests/menelaus/test_detector.py @@ -0,0 +1,144 @@ +"""Module for checking behavior of DriftDetector.""" +from menelaus.detector import DriftDetector, StreamingDetector, BatchDetector +from menelaus.data_drift import KdqTreeStreaming, KdqTreeBatch +import pytest +import numpy as np +import pandas as pd + +# override this attribute so that it is instantiable +# hacky, but proper child classes doesn't cover the input_type setter. +DriftDetector.__abstractmethods__ = set() +StreamingDetector.__abstractmethods__ = set() +BatchDetector.__abstractmethods__ = set() + + +def test_drift_state_validation_streaming(): + """Ensure ValueError on invalid drift_state for StreamingDetector""" + det = StreamingDetector() + with pytest.raises(ValueError) as _: + det.drift_state = "balloon" + + for state in ["drift", "warning", None]: + det.drift_state = state + assert det.drift_state == state + + +def test_drift_state_validation_batch(): + """Ensure ValueError on invalid drift_state for BatchDetector""" + det = BatchDetector() + with pytest.raises(ValueError) as _: + det.drift_state = "balloon" + + for state in ["drift", "warning", None]: + det.drift_state = state + assert det.drift_state == state + + +def test_drift_state_validation(): + """Confirm that bad drift_state values raise ValueErrors.""" + det = DriftDetector() + with pytest.raises(ValueError) as _: + det.drift_state = "balloon" + + for state in ["drift", "warning", None]: + det.drift_state = state + assert det.drift_state == state + + +def test_streaming_validation_y_one_obs(): + det = StreamingDetector() + with pytest.raises(ValueError) as _: + det._validate_input( + X=None, y_true=np.array([[1], [1]]), y_pred=np.array([[0], [0]]) + ) + + +def test_streaming_validation_X_one_obs(): + det = StreamingDetector() + with pytest.raises(ValueError) as _: + det._validate_input(X=np.array([[5], [4]]), y_true=None, y_pred=None) + + +def test_streaming_validation_X_columns(): + det = KdqTreeStreaming(window_size=2) + input1 = pd.DataFrame({"a": [1], "b": [2]}) + input2 = pd.DataFrame({"c": [1], "d": [2]}) + det.update(input1, y_true=None, y_pred=None) + with pytest.raises(ValueError) as _: + det.update(input2, y_true=None, y_pred=None) + + +def test_streaming_validation_X_dimensions(): + det = KdqTreeStreaming(window_size=2) + input1 = np.array([1, 2, 3]) + input2 = pd.DataFrame(input1.reshape(1, -1), columns=["a", "b", "c"]) + input3 = input2.values + input4 = np.array([1, 2, 3, 4]) + + det.update(input1, y_true=None, y_pred=None) + det.update(input2, y_true=None, y_pred=None) + det.update(input3, y_true=None, y_pred=None) + with pytest.raises(ValueError) as _: + det.update(input4, y_true=None, y_pred=None) + + +def test_batch_validation_y_one_column(): + det = BatchDetector() + det.update(X=None, y_true=1, y_pred=[1]) + with pytest.raises(ValueError) as _: + det._validate_input( + X=None, y_true=np.array([[1, 2], [1, 2]]), y_pred=np.array([[0], [0]]) + ) + + +def test_batch_validation_y_one_obs(): + + # cover normal return + det = BatchDetector() + det._validate_input(X=None, y_true=[[1], [1]], y_pred=[[1], [1]]) + + det = BatchDetector() + with pytest.raises(ValueError) as _: + det._validate_input(X=None, y_true=1, y_pred=[1]) + + +def test_batch_validation_X_columns(): + det = KdqTreeBatch(bootstrap_samples=1) + input1 = pd.DataFrame({"a": [1, 2], "b": [2, 2]}) + input2 = pd.DataFrame({"c": [1, 2], "d": [2, 2]}) + det.update(input1, y_true=None, y_pred=None) + with pytest.raises(ValueError) as _: + det.update(input2, y_true=None, y_pred=None) + + +def test_batch_validation_X_dimensions(): + det = KdqTreeBatch(bootstrap_samples=1) + input1 = np.array(range(6)) + input2 = pd.DataFrame(input1.reshape(2, 3), columns=["a", "b", "c"]) + input3 = input2.values + input4 = np.array([1, 2, 3, 4]) + + det.update(input1, y_true=None, y_pred=None) + det.update(input2, y_true=None, y_pred=None) + det.update(input3, y_true=None, y_pred=None) + with pytest.raises(ValueError) as _: + det.update(input4, y_true=None, y_pred=None) + + +def test_batch_validation_X_one_obs(): + det = BatchDetector() + input1 = pd.DataFrame({"a": [1], "b": [2]}) + with pytest.raises(ValueError) as _: + det._validate_input(input1, y_true=None, y_pred=None) + + +def test_input_type(): + det = DriftDetector() + det._input_type = "stream" + assert det.input_type == "stream" + + +def test_set_reference_unimplemented(): + det = BatchDetector() + with pytest.raises(NotImplementedError) as _: + det.set_reference(None, None, None) diff --git a/tests/menelaus/test_drift_detector.py b/tests/menelaus/test_drift_detector.py deleted file mode 100644 index 7e0c57dd..00000000 --- a/tests/menelaus/test_drift_detector.py +++ /dev/null @@ -1,45 +0,0 @@ -"""Module for checking behavior of DriftDetector.""" -from menelaus.drift_detector import DriftDetector, StreamingDetector, BatchDetector -import pytest - -# override this attribute so that it is instantiable -# hacky, but proper child classes doesn't cover the input_type setter. -DriftDetector.__abstractmethods__ = set() -StreamingDetector.__abstractmethods__ = set() -BatchDetector.__abstractmethods__ = set() - - -def test_validation_streaming(): - '''Ensure ValueError on invalid drift_state for StreamingDetector''' - det = StreamingDetector() - with pytest.raises(ValueError) as _: - det.drift_state = "balloon" - - for state in ["drift", "warning", None]: - det.drift_state = state - assert det.drift_state == state - -def test_validation_batch(): - '''Ensure ValueError on invalid drift_state for BatchDetector''' - det = BatchDetector() - with pytest.raises(ValueError) as _: - det.drift_state = "balloon" - - for state in ["drift", "warning", None]: - det.drift_state = state - assert det.drift_state == state - -def test_validation(): - """Confirm that bad drift_state values raise ValueErrors.""" - det = DriftDetector() - with pytest.raises(ValueError) as _: - det.drift_state = "balloon" - - for state in ["drift", "warning", None]: - det.drift_state = state - assert det.drift_state == state - -def test_input_type(): - det = DriftDetector() - det._input_type = "stream" - assert det.input_type == "stream" diff --git a/tests/menelaus/test_ensemble.py b/tests/menelaus/test_ensemble.py new file mode 100644 index 00000000..ef6acea3 --- /dev/null +++ b/tests/menelaus/test_ensemble.py @@ -0,0 +1,294 @@ +import pandas as pd + +from menelaus.ensemble import Ensemble, StreamingEnsemble, BatchEnsemble +from menelaus.ensemble import ( + SimpleMajorityElection, + MinimumApprovalElection, + OrderedApprovalElection, + ConfirmedElection +) +from menelaus.data_drift import KdqTreeBatch +from menelaus.concept_drift import STEPD +from menelaus.change_detection import ADWIN + + +def test_stream_ensemble_1(): + """Ensure stream ensemble executes with no drift""" + step1 = STEPD(window_size=2) + step2 = STEPD(window_size=2) + step3 = STEPD(window_size=2) + se = StreamingEnsemble( + detectors={"s1": step1, "s2": step2, "s3": step3}, + election=SimpleMajorityElection(), + ) + df = pd.DataFrame({"a": [0, 0], "b": [0, 0], "c": [0, 0]}) + se.update(X=df.iloc[[0]], y_true=0, y_pred=0) + assert se.drift_state == None + + +def test_stream_ensemble_2(): + """Ensure stream ensemble executes when columns specified""" + step1 = STEPD(window_size=2) + step2 = STEPD(window_size=2) + step3 = STEPD(window_size=2) + + column_selectors = { + "s1": lambda X: X[["a"]], + "s2": lambda X: X[["b", "c"]] + } + + se = StreamingEnsemble( + detectors={"s1": step1, "s2": step2, "s3": step3}, + election=SimpleMajorityElection(), + column_selectors=column_selectors + ) + + df = pd.DataFrame({"a": [0,0], "b": [0,0], "c": [0,0]}) + se.update(X=df.iloc[[0]], y_true=0, y_pred=0) + assert se.drift_state == None + + +def test_stream_ensemble_3(): + """Ensure stream ensemble executes with univariate data""" + adwin1 = ADWIN() + adwin2 = ADWIN() + adwin3 = ADWIN() + + column_selectors = { + "a1": lambda X: X[["a"]], + "a2": lambda X: X[["b"]], + "a3": lambda X: X[["c"]] + } + + se = StreamingEnsemble( + detectors={"a1": adwin1, "a2": adwin2, "a3": adwin3}, + election=SimpleMajorityElection(), + column_selectors=column_selectors + ) + + df = pd.DataFrame({"a": [0,0], "b": [0,0], "c": [0,0]}) + se.update(X=df.iloc[[0]], y_true=0, y_pred=0) + assert se.drift_state == None + + +def test_stream_ensemble_reset_1(): + """Ensure reset works in stream ensemble and its detectors""" + step1 = STEPD(window_size=2) + step2 = STEPD(window_size=2) + column_selectors = { + "s1": lambda X: X[["a"]], + "s2": lambda X: X[["b", "c"]] + } + be = StreamingEnsemble( + detectors={"s1": step1, "s2": step2}, + election=SimpleMajorityElection(), + column_selectors=column_selectors + ) + df = pd.DataFrame({"a": [0, 10.0], "b": [0, 11.0], "c": [0, 12.0]}) + be.update(df.loc[1:], 0, 0) + be.drift_state = "drift" + be.detectors["s1"].drift_state = "drift" + be.detectors["s2"].drift_state = "drift" + be.reset() + assert be.samples_since_reset == 0 + assert be.drift_state == None + assert be.total_samples == 1 + for det_key in be.detectors: + assert be.detectors[det_key].samples_since_reset == 0 + assert be.detectors[det_key].drift_state == None + assert be.detectors[det_key].total_samples == 1 + + +def test_batch_ensemble_1(): + """Ensure batch ensemble executes with no drift""" + kdq1 = KdqTreeBatch(bootstrap_samples=1) + kdq2 = KdqTreeBatch(bootstrap_samples=1) + kdq3 = KdqTreeBatch(bootstrap_samples=1) + be = BatchEnsemble( + detectors={"k1": kdq1, "k2": kdq2, "k3": kdq3}, + election=SimpleMajorityElection(), + ) + df = pd.DataFrame( + { + "a": [0, 10.0, 11.0, 12.0], + "b": [0, 11.0, 12.0, 13.0], + "c": [0, 12.0, 13.0, 14.0], + } + ) + be.set_reference(df.loc[:1]) + be.update(df.loc[2:]) + assert be.drift_state == None + + +def test_batch_ensemble_2(): + """Ensure batch ensemble executes when columns specified""" + kdq1 = KdqTreeBatch(bootstrap_samples=1) + kdq2 = KdqTreeBatch(bootstrap_samples=1) + column_selectors = { + "k1": lambda X: X[["a", "b"]], + "k2": lambda X: X[["b", "c"]] + } + be = BatchEnsemble( + detectors={"k1": kdq1, "k2": kdq2}, + election=SimpleMajorityElection(), + # XXX - forcing >1 columns to satisfy KdqTree Batch + column_selectors=column_selectors, + ) + df = pd.DataFrame( + { + "a": [0, 10.0, 11.0, 12.0], + "b": [0, 11.0, 12.0, 13.0], + "c": [0, 12.0, 13.0, 14.0], + } + ) + be.set_reference(df.loc[:1]) + be.update(df.loc[2:]) + assert len(be.detectors["k1"]._input_cols) == 2 + assert len(be.detectors["k2"]._input_cols) == 2 + + +def test_batch_ensemble_reset_1(): + """Ensure reset works in batch ensemble and its detectors""" + kdq1 = KdqTreeBatch(bootstrap_samples=1) + kdq2 = KdqTreeBatch(bootstrap_samples=1) + column_selectors = { + "k1": lambda X: X[["a", "b"]], + "k2": lambda X: X[["b", "c"]] + } + be = BatchEnsemble( + detectors={"k1": kdq1, "k2": kdq2}, + election=SimpleMajorityElection(), + column_selectors=column_selectors, + ) + df = pd.DataFrame( + { + "a": [0, 10.0, 11.0, 12.0], + "b": [0, 11.0, 12.0, 13.0], + "c": [0, 12.0, 13.0, 14.0], + } + ) + be.set_reference(df.loc[:1]) + be.update(df.loc[2:]) + be.drift_state = "drift" + be.detectors["k1"].drift_state = "drift" + be.detectors["k2"].drift_state = "drift" + be.reset() + assert be.batches_since_reset == 0 + assert be.drift_state == None + assert be.total_batches == 1 + for det_key in be.detectors: + assert be.detectors[det_key].batches_since_reset == 0 + assert be.detectors[det_key].drift_state == None + assert be.detectors[det_key].total_batches == 1 + + +def test_eval_simple_majority_1(): + """Ensure simple majority scheme can identify drift""" + det1 = det2 = KdqTreeBatch() + det1.drift_state = det2.drift_state = "drift" + det3 = KdqTreeBatch() + det3.drift_state = None + election = SimpleMajorityElection() + assert election([det1, det2, det3]) == "drift" + + +def test_eval_simple_majority_2(): + """Ensure simple majority scheme does not false alarm""" + det1 = det2 = KdqTreeBatch() + det1.drift_state = det2.drift_state = None + det3 = KdqTreeBatch() + det3.drift_state = "drift" + election = SimpleMajorityElection() + assert election([det1, det2, det3]) == None + + +def test_eval_min_election_1(): + """Ensure minimimum approval scheme can identify drift""" + s1 = s2 = STEPD() + s1.drift_state = s2.drift_state = "drift" + s3 = STEPD() + s3.drift_state = None + election = MinimumApprovalElection(approvals_needed=2) + assert election([s1, s2, s3]) == "drift" + + +def test_eval_min_election_2(): + """Ensure minimimum approval scheme does not false alarm""" + s1 = s2 = STEPD() + s1.drift_state = s2.drift_state = None + s3 = STEPD() + s3.drift_state = "drift" + election = MinimumApprovalElection(approvals_needed=2) + assert election([s1, s2, s3]) == None + + +def test_eval_ordered_election_1(): + """Ensure confirmed approval scheme can identify drift""" + s1 = s2 = STEPD() + s1.drift_state = s2.drift_state = "drift" + s3 = STEPD() + s3.drift_state = None + election = OrderedApprovalElection( + approvals_needed=1, + confirmations_needed=1 + ) + assert election([s1, s2, s3]) == "drift" + + +def test_eval_ordered_election_2(): + """Ensure confirmed approval scheme does not false alarm""" + s1 = s2 = STEPD() + s1.drift_state = "drift" + s3 = STEPD() + s2.drift_state = s3.drift_state = None + election = OrderedApprovalElection( + approvals_needed=2, + confirmations_needed=1 + ) + assert election([s1, s2, s3]) == None + + +def test_confirmed_election_1(): + """Ensure ConfirmedElection can detect drift""" + d1 = ADWIN() + d2 = ADWIN() + d3 = STEPD() + d1.drift_state = "drift" + election = ConfirmedElection(sensitivity=2, wait_time=10) + election([d1,d2,d3]) # call #1 + d2.drift_state = "drift" + assert election([d1, d2, d3]) == "drift" # by call #2, drift + + +def test_confirmed_election_2(): + """Ensure ConfirmedElection can detect warnings""" + d1 = ADWIN() + d2 = ADWIN() + d3 = STEPD() + d1.drift_state = "drift" + election = ConfirmedElection(sensitivity=2, wait_time=10) + election([d1,d2,d3]) # call #1 + d2.drift_state = "warning" + assert election([d1, d2, d3]) == "warning" # by call #2, warning + + +def test_confirmed_election_3(): + """Ensure ConfirmedElection does not false alarm""" + d1 = ADWIN() + d2 = ADWIN() + d3 = STEPD() + d1.drift_state = "drift" + election = ConfirmedElection(sensitivity=2, wait_time=10) + election([d1, d2, d3]) # call 1 + assert election([d1, d2, d3]) == None # no more drift, so call 2 : None + + +def test_confirmed_election_4(): + """Ensure resetting of wait period counters""" + d1 = ADWIN() + d1.drift_state = "drift" + election = ConfirmedElection(sensitivity=1, wait_time=1) + election([d1]) + assert election.wait_period_counters[0] == 1 + election([d1]) # wait period counter for d1 would be 2, so rest to 0 + assert election.wait_period_counters[0] == 0 \ No newline at end of file diff --git a/tests/menelaus/test_example_data.py b/tests/menelaus/test_example_data.py deleted file mode 100644 index 47a24ccc..00000000 --- a/tests/menelaus/test_example_data.py +++ /dev/null @@ -1,29 +0,0 @@ -from numpy import float64, int32, int64 -from menelaus.datasets import make_example_batch_data - -def test_example_batch_data_1(): - '''Ensure generated example data has correct shape''' - df = make_example_batch_data() - assert list(df.columns) == [ - 'year', 'a', 'b', 'c', 'd', 'e', 'f', 'g', - 'h', 'i', 'j', 'cat', 'confidence', 'drift' - ] - assert list(df.year.unique()) == [ - 2007, 2008, 2009, 2010, 2011, - 2012, 2013, 2014, 2015, 2016, - 2017, 2018, 2019, 2020, 2021 - ] - df_grouped = df.groupby('year') - dfs = [x for _,x in df_grouped] - for df in dfs: - assert len(df) == 20000 - -def test_example_batch_data_2(): - '''Ensure generated example data has right column types''' - df = make_example_batch_data() - assert df.dtypes['year'] == int64 - for char in 'abcdefghij': - assert df.dtypes[char] == float64 - assert (df.dtypes['cat'] == int32) or (df.dtypes['cat'] == int64) - assert df.dtypes['confidence'] == float64 - assert df.dtypes['drift'] == bool \ No newline at end of file diff --git a/tests/menelaus/test_hdddm.py b/tests/menelaus/test_hdddm.py deleted file mode 100644 index cd64f383..00000000 --- a/tests/menelaus/test_hdddm.py +++ /dev/null @@ -1,81 +0,0 @@ -"""All methods for testing HDDDM implementation.""" -import numpy as np -import pandas as pd -import pytest -from menelaus.data_drift.hdddm import HDDDM - - -def test_validation(): - """Test ability to raise error if batches have different number of features""" - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1, data1, data1]).T - hdddm = HDDDM() - hdddm.set_reference(reference) - - test = pd.DataFrame(data=[data1, data1, data1, data1]).T - with pytest.raises(ValueError) as _: - _ = hdddm.update(test) - - -def test_no_drift(): - """Tests functionality of update for detect batch = 1 with identical test batch, also tests drift is not detected""" - - # init with first test batch - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1, data1, data1]).T - hdddm = HDDDM() - hdddm.set_reference(reference) - - # update with identical test batch - hdddm.update(reference) - - assert hdddm.drift_state == None - - -def test_detect_drift(): - """Tests functionality of detecting drift for detect batch = 1 with very different batches""" - - # init with first test batch - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1, data1, data1]).T - hdddm = HDDDM() - hdddm.set_reference(reference) - - # update with different test batch - data2 = np.random.randint(2, 10, 150) - test = pd.DataFrame(data=[data2, data2, data2]).T - hdddm.update(test) - - assert hdddm.feature_epsilons != None - assert hdddm._drift_state == "drift" - assert hdddm._lambda == 2 - assert len(hdddm.reference) == len(test) - - - -def test_custom_divmetric(): - """ Tests functionality of user defining custom divergence metric """ - - # Define divergence metric - def distance_metric1(reference_histogram, test_histogram): - - # Convert inputs to appropriate datatype - ref = np.array(reference_histogram[0]) - test = np.array(test_histogram[0]) - - dist = np.sqrt(np.sum(np.square(ref-test))) - - return dist - - # Setup data - data1 = np.repeat(1, 100) - reference = pd.DataFrame(data=[data1, data1, data1]).T - data2 = np.random.randint(2, 10, 150) - test = pd.DataFrame(data=[data2, data2, data2]).T - - # Run HDDDM - hdddm = HDDDM(divergence=distance_metric1) - hdddm.set_reference(reference) - hdddm.update(test) - - assert hdddm.current_distance != 0 diff --git a/tests/menelaus/utils/__init__.py b/tests/menelaus/utils/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/menelaus/utils/test_utils.py b/tests/menelaus/utils/test_utils.py new file mode 100644 index 00000000..df8f7c05 --- /dev/null +++ b/tests/menelaus/utils/test_utils.py @@ -0,0 +1,7 @@ +import os +from menelaus.utils._locate import find_git_root + + +def test_find_root_dir(): + assert find_git_root("garbage_directory_0978697834703245345") is None + assert ".git" in os.listdir(find_git_root()) diff --git a/tox.ini b/tox.ini index 0bceff98..222c458e 100644 --- a/tox.ini +++ b/tox.ini @@ -7,5 +7,5 @@ deps = pytest pytest-cov commands = - pytest tests/menelaus --cov=src/ --cov-report term + pytest tests/menelaus --cov=menelaus/ --cov-report term coverage report -m