diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..ec5750e Binary files /dev/null and b/.DS_Store differ diff --git a/hello.py b/hello.py deleted file mode 100644 index 5b312bc..0000000 --- a/hello.py +++ /dev/null @@ -1,2 +0,0 @@ -def hello_world(): - return "Hello World!" diff --git a/hello_test.py b/hello_test.py deleted file mode 100644 index 708a061..0000000 --- a/hello_test.py +++ /dev/null @@ -1,5 +0,0 @@ -import hello - - -def test_hello(): - assert hello.hello_world() == "Hello World!" diff --git a/src/.DS_Store b/src/.DS_Store new file mode 100644 index 0000000..5008ddf Binary files /dev/null and b/src/.DS_Store differ diff --git a/src/BOv4.ipynb b/src/BOv4.ipynb new file mode 100644 index 0000000..5a8a959 --- /dev/null +++ b/src/BOv4.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":544,"id":"e8bb4295","metadata":{},"outputs":[],"source":["import numpy as np\n","import math\n","import pandas as pd\n","import os\n","import pickle\n","from sklearn import preprocessing\n","import random\n","from scipy.stats import norm\n","import time\n","from sklearn.ensemble import RandomForestRegressor, BaggingRegressor\n","from sklearn.linear_model import LinearRegression\n","import copy\n","from itertools import product\n","import seaborn as sns\n","import matplotlib.pyplot as plt\n","\n","\n","# TO DO: Instructions\n","# from matminer.featurizers.conversions import StrToComposition\n","# from matminer.featurizers.composition import ElementProperty, AtomicOrbitals, BandCenter"]},{"cell_type":"markdown","id":"160e55f5-b540-4717-912a-a1731658149f","metadata":{"id":"160e55f5-b540-4717-912a-a1731658149f"},"source":["# Process Data Functions"]},{"cell_type":"code","execution_count":545,"id":"2726d963-5e53-4841-a1a6-5fbe63e7c1a4","metadata":{"id":"2726d963-5e53-4841-a1a6-5fbe63e7c1a4"},"outputs":[],"source":["# def featurize(df, formula_col='formula', pbar=False, n_jobs=None, n_chunksize=None):\n","# \"\"\"\n","# Extract magpie feature set from formula\n","# \"\"\"\n","# starttime = time.time()\n","# if formula_col not in list(df):\n","# raise KeyError(f\"Data does not contain {formula_col} column\")\n","# print(\"Featurizing dataset...\")\n","\n","# if n_jobs:\n","# stc.set_n_jobs(n_jobs)\n","\n","# stc = StrToComposition()\n","\n","# feat = stc.featurize_dataframe(df, formula_col, ignore_errors=True, pbar=pbar)\n","# element_property = ElementProperty.from_preset(preset_name='magpie')\n","# for f in [element_property, BandCenter(), AtomicOrbitals()]:\n","# if n_jobs:\n","# f.set_n_jobs(n_jobs)\n","# if n_chunksize:\n","# f.set_chunksize(n_chunksize)\n","# feat = f.featurize_dataframe(feat, \"composition\", pbar=pbar, ignore_errors=True)\n","\n","# feat = feat.set_index(feat[formula_col])\n","# print(f\"Featurization time: {round((time.time() - starttime) / 60, 2)} min\")\n","# return feat\n","\n","\n","def get_identical_cols(df):\n"," \"\"\"\n"," Get the columns of a dataframe which have identical values.\n"," \"\"\"\n"," xx = {}\n"," for c in df:\n"," vals1 = list(df[c])\n"," for c2 in df:\n"," vals2 = list(df[c2])\n"," # if the columns contain identical values\n"," if c != c2 and vals1 == vals2:\n"," # if these values have not been seen before\n"," if vals1 not in [list(df[y]) for y in xx]:\n"," xx[c] = [c2]\n"," # if these values have been seen before\n"," else:\n"," y = [yy for yy in xx if list(df[yy]) == vals1][0]\n"," if c2 != y and c2 not in xx[y]:\n"," xx[y].append(c2)\n"," return xx\n","\n","\n","def process_dataset(df, target):\n"," \"\"\"\n"," Clean dataset and extract magpie features if formula is provided\n"," \"\"\"\n"," # drop extreme outlier rows?\n","\n"," # featurize magpie datasets\n"," # if 'formula' in df.columns:\n"," # df = featurize(df)\n","\n"," # remove non-numeric columns\n"," df = df[[c for c in df if pd.api.types.is_numeric_dtype(df[c])]]\n"," # remove columns with not many unique values?\n","\n"," # remove constant columns\n"," df = df.loc[:, (df != df.iloc[0]).any()]\n","\n"," # change all infinite values to nan\n"," df = df.replace([np.inf, -np.inf], np.nan)\n","\n"," # remove rows containing a nan\n"," df = df.dropna()\n","\n"," # remove columns with identical values\n"," remove_cols = [v0 for _, v in get_identical_cols(df).items() for v0 in v]\n"," df = df[[c for c in df if c not in remove_cols]]\n","\n"," # for some datasets, each input feature x could have been evaluated more than once.\n"," # take average\n"," features = set(df.columns)\n"," features.remove(target)\n"," df = df.groupby(list(features))[target].agg(lambda x: x.unique().mean())\n"," df = (df.to_frame()).reset_index()\n"," return df"]},{"cell_type":"markdown","id":"373aadf9-1e35-4193-82ad-11696c70c623","metadata":{"id":"373aadf9-1e35-4193-82ad-11696c70c623"},"source":["# Feature Engineering Functions"]},{"cell_type":"code","execution_count":546,"id":"c8f90cdf-bc5a-4a4e-b3d1-6045bea83fb7","metadata":{"id":"c8f90cdf-bc5a-4a4e-b3d1-6045bea83fb7"},"outputs":[],"source":["def array_is_constant(arr: list) -> np.array:\n"," \"\"\"Check if a 1D array is constant\"\"\"\n"," return np.allclose(arr, np.repeat(arr[0], len(arr)))\n","\n","\n","def add_additional_cols(\n"," df: pd.DataFrame,\n"," ignore: list = [],\n"," nat_log: bool = True,\n"," powers: list = [-4, -3, -2, -1, -0.5, -0.333, -0.25, 0.25, 0.33, 0.5, 2, 3, 4],\n",") -> pd.DataFrame:\n"," \"\"\"\n"," Add additional columns to a dataframe by executing\n"," mathematical operations on existing columns.\n"," \"\"\"\n"," # now raise existing columns to varying powers\n"," new_col_names = []\n"," new_vals_all = np.empty((len(df), 0))\n","\n"," # loop over each column to use for creating additional columns\n"," for c in [cc for cc in df if cc not in ignore]:\n","\n"," # get original column values\n"," vv = df[c].values\n","\n"," # raise existing columns to various powers\n"," for p in powers:\n"," # first assess mathematical viability of different conditions.\n"," # for example, we can't perform (-2)^(1/2).\n"," # if conditions are met, save new column\n"," if any(\n"," [\n"," p > 0 and isinstance(p, int), # pos integer powers\n"," p < 0 and isinstance(p, int) and 0 not in vv, # neg integer powers\n"," p > 0\n"," and not isinstance(p, int)\n"," and np.all(vv >= 0), # pos non-int powers\n"," p < 0\n"," and not isinstance(p, int)\n"," and np.all(vv > 0), # neg non-int powers\n"," ]\n"," ):\n","\n"," new_col_vals = np.float_power(vv, p).reshape((-1, 1))\n"," if (\n"," not array_is_constant(new_col_vals)\n"," and np.isfinite(new_col_vals).all()\n"," ):\n"," new_vals_all = np.hstack((new_vals_all, new_col_vals))\n"," new_col_names.append(f\"{c}**{str(p)}\")\n","\n"," # take natural logs of existing columns\n"," if nat_log:\n"," if not c.startswith(\"ln \") and np.all(vv > 0):\n"," new_col_vals = np.log(vv).reshape((-1, 1))\n"," if (\n"," not array_is_constant(new_col_vals)\n"," and np.isfinite(new_col_vals).all()\n"," ):\n"," new_vals_all = np.hstack((new_vals_all, new_col_vals))\n"," new_col_names.append(f\"{c}**ln\")\n","\n"," # combine new columns with original columns in a single dataframe\n"," new_df = pd.DataFrame(data=new_vals_all, columns=new_col_names, index=df.index)\n"," df = pd.concat([df, new_df], axis=1)\n"," return df\n","\n","\n","def find_top_n_features(df, original_features, target, n):\n"," \"\"\"\n"," Finds the top n features of the dataset according to how well they fit\n"," to a quadratic polynomial.\n","\n"," Note: A diversity criteria has been enforced to prevent the top features\n"," being powers of the same feature when adding additional columns (ie. we are\n"," trying to prevent the case where the top features are: [temp, temp**2, temp**3...])\n"," \"\"\"\n"," # obtain a list of all features\n"," features = set(df.columns)\n"," features.remove(target)\n"," features = list(features)\n","\n"," # create dictionary where the keys are the original features and the values\n"," # are a list of tuples of the form: (r2, features based on original feature)\n"," # eg. {'temp': [(0.9, temp), (0.87, temp**2), (0.95, temp**3)...]}\n"," unsorted_features = {f: [] for f in original_features}\n"," for col in features:\n"," # find how well the feature fits to the target using a quadratic polynomial\n"," fit = np.polyfit(df[col], df[target], 2, full=True)\n"," ssr = fit[1] # sum of squared residuals\n"," sst = np.sum((df[target] - df[target].mean())**2) # total sum of squares\n","\n"," # if ssr is 0, numpy will return a blank list\n"," if len(ssr) == 0:\n"," # if the number of unique values is less than 4, the fit will always\n"," # be perfect. Therefore, set ssr to sst and force r2 to be 0\n"," if len(df[col].unique()) < 4:\n"," ssr = sst\n"," else:\n"," ssr = 0\n"," else:\n"," ssr = ssr[0]\n"," r2 = 1 - ssr / sst\n","\n"," original_col = col.split('**')[0]\n"," unsorted_features[original_col].append((r2, col))\n","\n"," # sort features based on r2 value\n"," # eg. {'temp': [(0.95, temp**3), (0.9, temp), (0.87, temp**2)...]}\n"," sorted_features = {\n"," f: sorted(unsorted_features[f])[::-1] for f in original_features\n"," }\n","\n"," # find top features while enforcing diversity criteria\n"," top_n = []\n"," for i in range(n):\n"," # create mini list of top \"diverse\" features\n"," # eg. [(0.98, temp**3), (0.95, theta**0.5), (0.99, t**3)...]\n"," top_i = []\n"," for original_col in sorted_features:\n"," if i < len(sorted_features[original_col]):\n"," top_i.append(sorted_features[original_col][i])\n","\n"," # sort mini list of top \"diverse\" features\n"," # eg. [(0.99, t**3), (0.98, temp**3), (0.95, theta**0.5)...]\n"," top_i = sorted(top_i)[::-1]\n"," for j in top_i:\n"," top_n.append(j[1])\n"," if len(top_n) >= n:\n"," break\n"," # once n features have been found, break out of loop\n"," if len(top_n) >= n:\n"," break\n","\n"," return top_n"]},{"cell_type":"markdown","id":"2f5ec23b-d139-4228-a317-f1164acb760a","metadata":{"id":"2f5ec23b-d139-4228-a317-f1164acb760a"},"source":["# Predicting Functions"]},{"cell_type":"code","execution_count":547,"id":"c2f4ae6b-d46d-4844-8b14-11ac735fce26","metadata":{"id":"c2f4ae6b-d46d-4844-8b14-11ac735fce26"},"outputs":[],"source":["def RF_pred(X, RF_model):\n"," tree_predictions = []\n"," for j in np.arange(n_est):\n"," tree_predictions.append((RF_model.estimators_[j].predict(np.array([X]))).tolist())\n"," mean = np.mean(np.array(tree_predictions), axis=0)[0]\n","\n","\n"," std = np.std(np.array(tree_predictions), axis=0)[0]\n"," return mean, std"]},{"cell_type":"markdown","id":"95f37ac9-922a-42f0-8ea1-e586b74dd00f","metadata":{"id":"95f37ac9-922a-42f0-8ea1-e586b74dd00f"},"source":["# Acquisition Functions"]},{"cell_type":"code","execution_count":548,"id":"ad7cf7a1-edd0-4a93-b237-97468585f09d","metadata":{"id":"ad7cf7a1-edd0-4a93-b237-97468585f09d"},"outputs":[],"source":["def EI(mean, std, y_best, ratio):\n"," std = ratio * std\n","\n"," z = (y_best - mean)/std\n"," return (y_best - mean) * norm.cdf(z) + std * norm.pdf(z)"]},{"cell_type":"markdown","id":"b764198e-213d-4240-9093-bf042c6b7745","metadata":{"id":"b764198e-213d-4240-9093-bf042c6b7745"},"source":["# Set up Combinations"]},{"cell_type":"code","execution_count":549,"id":"09db2514-2f0c-4341-b4b6-87f10610b77c","metadata":{"id":"09db2514-2f0c-4341-b4b6-87f10610b77c"},"outputs":[],"source":["datasets = ['Crossed barrel_dataset.csv', 'double_perovskites_gap.csv', 'concrete.csv', 'concrete_mod.csv'] # delete datasets you won't use\n","n_features = [3, 8, 20]\n","acq_func = [0.01, 1, 1.5]\n","model = ['Linear', 'RF']"]},{"cell_type":"code","execution_count":550,"id":"c6aacbd9-716d-4877-a750-afbd72c0406c","metadata":{"id":"c6aacbd9-716d-4877-a750-afbd72c0406c"},"outputs":[],"source":["hyperparameter_combinations = list(product(datasets, n_features, acq_func, model)) # find all combination of hyperparameters\n","\n","# put combos into dictionary for easier searching\n","all_combos = []\n","for combo in hyperparameter_combinations:\n"," all_combos.append({\n"," 'dataset': combo[0],\n"," 'n_features': combo[1],\n"," 'acq_func': combo[2],\n"," 'model': combo[3]\n"," })"]},{"cell_type":"markdown","id":"5677fefb-1895-4afc-84e5-c724742064b7","metadata":{"id":"5677fefb-1895-4afc-84e5-c724742064b7"},"source":["# Pick Combo to Look At"]},{"cell_type":"code","execution_count":551,"id":"30888f77-d8fc-4052-a540-37c4b60d0f58","metadata":{"id":"30888f77-d8fc-4052-a540-37c4b60d0f58","outputId":"830343a5-fb11-4085-b2d8-a0167b11907c"},"outputs":[{"data":{"text/plain":["{'dataset': 'Crossed barrel_dataset.csv',\n"," 'n_features': 3,\n"," 'acq_func': 0.01,\n"," 'model': 'Linear'}"]},"execution_count":551,"metadata":{},"output_type":"execute_result"}],"source":["n = 0\n","combo = all_combos[n]\n","combo"]},{"cell_type":"markdown","id":"21fed3c0-922a-44cd-a003-bb3a666baa12","metadata":{"id":"21fed3c0-922a-44cd-a003-bb3a666baa12"},"source":["# Read and Process Dataset"]},{"cell_type":"code","execution_count":552,"id":"780a907f-7ca1-486f-84f5-7429f428eba0","metadata":{"id":"780a907f-7ca1-486f-84f5-7429f428eba0","outputId":"cc3e63ae-9f95-48e7-b039-469705b8fe51"},"outputs":[{"data":{"text/html":["
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nthetarttoughness
0601.50.701.144667
1601.51.051.607561
2601.51.401.144338
3601.70.703.642738
4601.71.053.748405
..................
1795122002.31.051.358975
1796122002.31.403.196306
1797122002.50.7036.104187
1798122002.51.051.313487
1799122002.51.401.069728
\n","

1800 rows × 5 columns

\n","
"],"text/plain":[" n theta r t toughness\n","0 6 0 1.5 0.70 1.144667\n","1 6 0 1.5 1.05 1.607561\n","2 6 0 1.5 1.40 1.144338\n","3 6 0 1.7 0.70 3.642738\n","4 6 0 1.7 1.05 3.748405\n","... .. ... ... ... ...\n","1795 12 200 2.3 1.05 1.358975\n","1796 12 200 2.3 1.40 3.196306\n","1797 12 200 2.5 0.70 36.104187\n","1798 12 200 2.5 1.05 1.313487\n","1799 12 200 2.5 1.40 1.069728\n","\n","[1800 rows x 5 columns]"]},"execution_count":552,"metadata":{},"output_type":"execute_result"}],"source":["# load a dataset\n","raw = pd.read_csv(combo['dataset'])\n","raw"]},{"cell_type":"code","execution_count":553,"id":"d1f5960d-2e3f-411c-8e49-c94eddbfb522","metadata":{"id":"d1f5960d-2e3f-411c-8e49-c94eddbfb522","outputId":"0b48f59b-7678-4b19-e2e9-3e89d5faaad4"},"outputs":[{"data":{"text/plain":["'toughness'"]},"execution_count":553,"metadata":{},"output_type":"execute_result"}],"source":["# define target\n","objective_name = list(raw.columns)[-1]\n","objective_name"]},{"cell_type":"code","execution_count":554,"id":"90fe8f74-3752-4c00-8666-af491fedae6a","metadata":{"id":"90fe8f74-3752-4c00-8666-af491fedae6a","outputId":"01bd090c-6e92-4cb8-e05b-82e5923f7f94"},"outputs":[{"data":{"text/html":["
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trthetantoughness
00.71.5061.135453
10.71.5081.970165
20.71.50102.514642
30.71.50122.581751
40.71.55060.665487
..................
5951.42.5150121.493588
5961.42.5200620.968543
5971.42.5200814.529138
5981.42.5200102.154458
5991.42.5200121.337742
\n","

600 rows × 5 columns

\n","
"],"text/plain":[" t r theta n toughness\n","0 0.7 1.5 0 6 1.135453\n","1 0.7 1.5 0 8 1.970165\n","2 0.7 1.5 0 10 2.514642\n","3 0.7 1.5 0 12 2.581751\n","4 0.7 1.5 50 6 0.665487\n",".. ... ... ... .. ...\n","595 1.4 2.5 150 12 1.493588\n","596 1.4 2.5 200 6 20.968543\n","597 1.4 2.5 200 8 14.529138\n","598 1.4 2.5 200 10 2.154458\n","599 1.4 2.5 200 12 1.337742\n","\n","[600 rows x 5 columns]"]},"execution_count":554,"metadata":{},"output_type":"execute_result"}],"source":["# process dataset\n","processed = process_dataset(raw, objective_name)\n","processed"]},{"cell_type":"code","execution_count":555,"id":"53bd0354-1c53-4b8f-ba01-cab5a4f07de6","metadata":{"id":"53bd0354-1c53-4b8f-ba01-cab5a4f07de6","outputId":"15a6b5a1-a3be-44c5-d350-190764a75b2a"},"outputs":[{"data":{"text/plain":["['t', 'r', 'theta', 'n']"]},"execution_count":555,"metadata":{},"output_type":"execute_result"}],"source":["features = set(processed.columns)\n","features.remove(objective_name)\n","feature_name = list(features)\n","feature_name"]},{"cell_type":"code","execution_count":556,"id":"0937fe11-6d1c-4e04-9d8a-b9995099a74c","metadata":{"id":"0937fe11-6d1c-4e04-9d8a-b9995099a74c","outputId":"11b411fc-5d28-4312-9e87-57276d9d1204"},"outputs":[{"data":{"text/html":["
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trthetantoughness
00.71.506-1.135453
10.71.508-1.970165
20.71.5010-2.514642
30.71.5012-2.581751
40.71.5506-0.665487
..................
5951.42.515012-1.493588
5961.42.52006-20.968543
5971.42.52008-14.529138
5981.42.520010-2.154458
5991.42.520012-1.337742
\n","

600 rows × 5 columns

\n","
"],"text/plain":[" t r theta n toughness\n","0 0.7 1.5 0 6 -1.135453\n","1 0.7 1.5 0 8 -1.970165\n","2 0.7 1.5 0 10 -2.514642\n","3 0.7 1.5 0 12 -2.581751\n","4 0.7 1.5 50 6 -0.665487\n",".. ... ... ... .. ...\n","595 1.4 2.5 150 12 -1.493588\n","596 1.4 2.5 200 6 -20.968543\n","597 1.4 2.5 200 8 -14.529138\n","598 1.4 2.5 200 10 -2.154458\n","599 1.4 2.5 200 12 -1.337742\n","\n","[600 rows x 5 columns]"]},"execution_count":556,"metadata":{},"output_type":"execute_result"}],"source":["# we add negative sign to all of its objective values here\n","# because default BO in the framework below aims for global minimization\n","df = copy.deepcopy(processed)\n","df[objective_name] = -processed[objective_name].values\n","df"]},{"cell_type":"markdown","id":"b430f3f6-6531-462b-a2a9-cf95a28a91c1","metadata":{"id":"b430f3f6-6531-462b-a2a9-cf95a28a91c1"},"source":["# Engineer New Features, Then Select Top n Features"]},{"cell_type":"code","execution_count":557,"id":"3029abb7-1a43-4426-8012-deedeaf7d349","metadata":{"id":"3029abb7-1a43-4426-8012-deedeaf7d349","outputId":"99853349-b65a-4a58-ad38-967277edbf05"},"outputs":[{"data":{"text/plain":["['theta**2', 'r**4', 'n**2']"]},"execution_count":557,"metadata":{},"output_type":"execute_result"}],"source":["df = add_additional_cols(df, ignore=[objective_name])\n","top_n_features = find_top_n_features(df, feature_name, objective_name, combo['n_features'])\n","top_n_features"]},{"cell_type":"markdown","id":"b93ec941-d0bf-4835-a867-0dc9074ca152","metadata":{"id":"b93ec941-d0bf-4835-a867-0dc9074ca152"},"source":["# Limit Number of Rows for Speed"]},{"cell_type":"code","execution_count":558,"id":"72af6006-e990-4456-8882-0311b8d5e0eb","metadata":{"id":"72af6006-e990-4456-8882-0311b8d5e0eb","outputId":"79bb8cc5-2a9f-4118-ab6f-0f0c1da71868"},"outputs":[{"data":{"text/html":["
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indextrthetantoughnesst**-4t**-3t**-2t**-1...n**-0.5n**-0.333n**-0.25n**0.25n**0.33n**0.5n**2n**3n**4n**ln
000.71.506-1.1354534.1649312.9154522.0408161.428571...0.4082480.5506500.6389431.5650851.8063002.44949036.0216.01296.01.791759
130.71.5012-2.5817514.1649312.9154522.0408161.428571...0.2886750.4371520.5372851.8612102.2705433.464102144.01728.020736.02.484907
290.71.51008-1.1768394.1649312.9154522.0408161.428571...0.3535530.5003470.5946041.6817931.9861852.82842764.0512.04096.02.079442
3130.71.51508-15.0972054.1649312.9154522.0408161.428571...0.3535530.5003470.5946041.6817931.9861852.82842764.0512.04096.02.079442
4170.71.52008-19.1573944.1649312.9154522.0408161.428571...0.3535530.5003470.5946041.6817931.9861852.82842764.0512.04096.02.079442
..................................................................
955821.42.5010-1.2699990.2603080.3644310.5102040.714286...0.3162280.4645150.5623411.7782792.1379623.162278100.01000.010000.02.302585
965851.42.5508-1.8928050.2603080.3644310.5102040.714286...0.3535530.5003470.5946041.6817931.9861852.82842764.0512.04096.02.079442
975881.42.51006-21.5956430.2603080.3644310.5102040.714286...0.4082480.5506500.6389431.5650851.8063002.44949036.0216.01296.01.791759
985911.42.510012-1.5509370.2603080.3644310.5102040.714286...0.2886750.4371520.5372851.8612102.2705433.464102144.01728.020736.02.484907
995921.42.51506-18.2177280.2603080.3644310.5102040.714286...0.4082480.5506500.6389431.5650851.8063002.44949036.0216.01296.01.791759
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100 rows × 54 columns

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"],"text/plain":[" index t r theta n toughness t**-4 t**-3 t**-2 \\\n","0 0 0.7 1.5 0 6 -1.135453 4.164931 2.915452 2.040816 \n","1 3 0.7 1.5 0 12 -2.581751 4.164931 2.915452 2.040816 \n","2 9 0.7 1.5 100 8 -1.176839 4.164931 2.915452 2.040816 \n","3 13 0.7 1.5 150 8 -15.097205 4.164931 2.915452 2.040816 \n","4 17 0.7 1.5 200 8 -19.157394 4.164931 2.915452 2.040816 \n",".. ... ... ... ... .. ... ... ... ... \n","95 582 1.4 2.5 0 10 -1.269999 0.260308 0.364431 0.510204 \n","96 585 1.4 2.5 50 8 -1.892805 0.260308 0.364431 0.510204 \n","97 588 1.4 2.5 100 6 -21.595643 0.260308 0.364431 0.510204 \n","98 591 1.4 2.5 100 12 -1.550937 0.260308 0.364431 0.510204 \n","99 592 1.4 2.5 150 6 -18.217728 0.260308 0.364431 0.510204 \n","\n"," t**-1 ... n**-0.5 n**-0.333 n**-0.25 n**0.25 n**0.33 \\\n","0 1.428571 ... 0.408248 0.550650 0.638943 1.565085 1.806300 \n","1 1.428571 ... 0.288675 0.437152 0.537285 1.861210 2.270543 \n","2 1.428571 ... 0.353553 0.500347 0.594604 1.681793 1.986185 \n","3 1.428571 ... 0.353553 0.500347 0.594604 1.681793 1.986185 \n","4 1.428571 ... 0.353553 0.500347 0.594604 1.681793 1.986185 \n",".. ... ... ... ... ... ... ... \n","95 0.714286 ... 0.316228 0.464515 0.562341 1.778279 2.137962 \n","96 0.714286 ... 0.353553 0.500347 0.594604 1.681793 1.986185 \n","97 0.714286 ... 0.408248 0.550650 0.638943 1.565085 1.806300 \n","98 0.714286 ... 0.288675 0.437152 0.537285 1.861210 2.270543 \n","99 0.714286 ... 0.408248 0.550650 0.638943 1.565085 1.806300 \n","\n"," n**0.5 n**2 n**3 n**4 n**ln \n","0 2.449490 36.0 216.0 1296.0 1.791759 \n","1 3.464102 144.0 1728.0 20736.0 2.484907 \n","2 2.828427 64.0 512.0 4096.0 2.079442 \n","3 2.828427 64.0 512.0 4096.0 2.079442 \n","4 2.828427 64.0 512.0 4096.0 2.079442 \n",".. ... ... ... ... ... \n","95 3.162278 100.0 1000.0 10000.0 2.302585 \n","96 2.828427 64.0 512.0 4096.0 2.079442 \n","97 2.449490 36.0 216.0 1296.0 1.791759 \n","98 3.464102 144.0 1728.0 20736.0 2.484907 \n","99 2.449490 36.0 216.0 1296.0 1.791759 \n","\n","[100 rows x 54 columns]"]},"execution_count":558,"metadata":{},"output_type":"execute_result"}],"source":["dataset_limit = 991\n","df = df.sample(dataset_limit, random_state=1).sort_index().reset_index()\n","df"]},{"cell_type":"markdown","id":"f19eed3f-002e-4540-9e30-98b75d439f98","metadata":{"id":"f19eed3f-002e-4540-9e30-98b75d439f98"},"source":["# Split into X and y datasets"]},{"cell_type":"code","execution_count":559,"id":"9172285c-5f8b-4487-9430-dcc44cdd8cce","metadata":{"id":"9172285c-5f8b-4487-9430-dcc44cdd8cce","outputId":"00f6fa69-48eb-42c8-f89c-e557a1bb352e"},"outputs":[{"name":"stdout","output_type":"stream","text":["100\n"]}],"source":["# these are the input feature x and objective value y used in framework\n","X_feature = df[top_n_features].values\n","\n","y = np.array(df[objective_name].values)\n","\n","assert len(df) == len(X_feature) == len(y)\n","\n","# total number of data in set\n","N = len(df)\n","print(N)"]},{"cell_type":"markdown","id":"ac428810-2c4d-48ea-950e-dbeca4a4c843","metadata":{"id":"ac428810-2c4d-48ea-950e-dbeca4a4c843"},"source":["# Specify Parameters of Framework"]},{"cell_type":"code","execution_count":560,"id":"8f8ea549-c2be-49fa-9f9f-afa774424ee6","metadata":{"id":"8f8ea549-c2be-49fa-9f9f-afa774424ee6"},"outputs":[],"source":["# here are some parameters of the framework, feel free to modify for your own purposes\n","\n","# number of ensembles. in the paper n_ensemble = 50.\n","n_ensemble = 5\n","# number of top candidates, currently using top 5% of total dataset size\n","n_top = int(math.ceil(len(y) * 0.05))\n","# the top candidates and their indicies\n","top_indices = list(df.sort_values(objective_name).head(n_top).index)\n","# number of initial experiments\n","n_initial = 10\n","\n","# random seeds used to distinguish between different ensembles\n","# there are 300 of them, but only first n_ensemble are used\n","seed_list = [4295, 8508, 326, 3135, 1549, 2528, 1274, 6545, 5971, 6269, 2422, 4287, 9320, 4932, 951, 4304, 1745, 5956, 7620, 4545, 6003, 9885, 5548, 9477, 30, 8992, 7559, 5034, 9071, 6437, 3389, 9816, 8617, 3712, 3626, 1660, 3309, 2427, 9872, 938, 5156, 7409, 7672, 3411, 3559, 9966, 7331, 8273, 8484, 5127, 2260, 6054, 5205, 311, 6056, 9456, 928, 6424, 7438, 8701, 8634, 4002, 6634, 8102, 8503, 1540, 9254, 7972, 7737, 3410, 4052, 8640, 9659, 8093, 7076, 7268, 2046, 7492, 3103, 3034, 7874, 5438, 4297, 291, 5436, 9021, 3711, 7837, 9188, 2036, 8013, 6188, 3734, 187, 1438, 1061, 674, 777, 7231, 7096, 3360, 4278, 5817, 5514, 3442, 6805, 6750, 8548, 9751, 3526, 9969, 8979, 1526, 1551, 2058, 6325, 1237, 5917, 5821, 9946, 5049, 654, 7750, 5149, 3545, 9165, 2837, 5621, 6501, 595, 3181, 1747, 4405, 4480, 4282, 9262, 6219, 3960, 4999, 1495, 6007, 9642, 3902, 3133, 1085, 3278, 1104, 5939, 7153, 971, 8733, 3785, 9056, 2020, 7249, 5021, 3384, 8740, 4593, 7869, 9941, 8813, 3688, 8139, 6436, 3742, 5503, 1587, 4766, 9846, 9117, 7001, 4853, 9346, 4927, 8480, 5298, 4753, 1151, 9768, 5405, 6196, 5721, 3419, 8090, 8166, 7834, 1480, 1150, 9002, 1134, 2237, 3995, 2029, 5336, 7050, 6857, 8794, 1754, 1184, 3558, 658, 6804, 8750, 5088, 1136, 626, 8462, 5203, 3196, 979, 7419, 1162, 5451, 6492, 1562, 8145, 8937, 8764, 4174, 7639, 8902, 7003, 765, 1554, 6135, 1689, 9530, 1398, 2273, 7925, 5948, 1036, 868, 4617, 1203, 7680, 7, 93, 3128, 5694, 6979, 7136, 8084, 5770, 9301, 1599, 737, 7018, 3774, 9843, 2296, 2287, 9875, 2349, 2469, 8941, 4973, 3798, 54, 2938, 4665, 3942, 3951, 9400, 3094, 2248, 3376, 1926, 5180, 1773, 3681, 1808, 350, 6669, 826, 539, 5313, 6193, 5752, 9370, 2782, 8399, 4881, 3166, 4906, 5829, 4827, 29, 6899, 9012, 6986, 4175, 1035, 8320, 7802, 3777, 6340, 7798, 7705]"]},{"cell_type":"markdown","id":"5ebeddf6-0a9a-4236-9af1-8c2acd91e781","metadata":{"id":"5ebeddf6-0a9a-4236-9af1-8c2acd91e781"},"source":["# Run Framework"]},{"cell_type":"code","execution_count":561,"id":"daaa81f5-36d1-47ba-ab28-9c67ad0c30bd","metadata":{"id":"daaa81f5-36d1-47ba-ab28-9c67ad0c30bd","outputId":"8826439c-7adc-4ca1-c7ae-361b1107079d"},"outputs":[{"name":"stdout","output_type":"stream","text":["initializing seed = 0\n","0\n","num top: 1\n","1\n","num top: 1\n","2\n"]},{"name":"stdout","output_type":"stream","text":["num top: 2\n","3\n","num top: 3\n","4\n","num top: 3\n","5\n","num top: 4\n","6\n","num top: 4\n","7\n","num top: 4\n","8\n","num top: 4\n","9\n","num top: 4\n","10\n","num top: 4\n","11\n","num top: 4\n","12\n","num top: 4\n","13\n","num top: 4\n","14\n","num top: 4\n","15\n","num top: 4\n","16\n","num top: 4\n","17\n","num top: 4\n","18\n","num top: 4\n","19\n","num top: 4\n","20\n","num top: 4\n","21\n","num top: 4\n","22\n","num top: 4\n","23\n","num top: 4\n","24\n","num top: 4\n","25\n","num top: 4\n","26\n","num top: 4\n","27\n","num top: 4\n","28\n","num top: 4\n","29\n","num top: 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4\n","21\n","num top: 4\n","22\n","num top: 4\n","23\n","num top: 4\n","24\n","num top: 4\n","25\n","num top: 4\n","26\n","num top: 4\n","27\n","num top: 4\n","28\n","num top: 4\n","29\n","num top: 4\n","30\n","num top: 4\n","31\n","num top: 4\n","32\n","num top: 4\n","33\n","num top: 4\n","34\n","num top: 4\n","35\n","num top: 4\n","36\n","num top: 4\n","37\n","num top: 4\n","38\n","num top: 4\n","39\n","num top: 4\n","40\n","num top: 4\n","41\n","num top: 4\n","42\n","num top: 4\n","43\n","num top: 4\n","44\n","num top: 4\n","45\n","num top: 4\n","46\n","num top: 4\n","47\n","num top: 4\n","48\n","num top: 5\n","Finished seed\n","initializing seed = 3\n","0\n","num top: 1\n","1\n","num top: 2\n","2\n","num top: 3\n","3\n","num top: 4\n","4\n","num top: 4\n","5\n","num top: 4\n","6\n","num top: 4\n","7\n","num top: 4\n","8\n","num top: 4\n","9\n","num top: 4\n","10\n","num top: 4\n","11\n","num top: 4\n","12\n","num top: 4\n","13\n","num top: 4\n","14\n","num top: 4\n","15\n","num top: 4\n","16\n","num top: 4\n","17\n","num top: 4\n","18\n","num top: 4\n","19\n","num top: 4\n","20\n","num top: 4\n","21\n","num top: 4\n","22\n","num top: 4\n","23\n","num top: 4\n","24\n","num top: 4\n","25\n","num top: 4\n","26\n","num top: 4\n","27\n","num top: 4\n","28\n","num top: 4\n","29\n","num top: 4\n","30\n","num top: 4\n","31\n","num top: 4\n","32\n","num top: 4\n","33\n","num top: 4\n","34\n","num top: 4\n","35\n","num top: 4\n","36\n","num top: 4\n","37\n","num top: 4\n","38\n","num top: 4\n","39\n","num top: 4\n","40\n","num top: 4\n","41\n","num top: 4\n","42\n","num top: 4\n","43\n","num top: 4\n","44\n","num top: 4\n","45\n","num top: 4\n","46\n","num top: 4\n","47\n","num top: 4\n","48\n","num top: 4\n","49\n","num top: 5\n","Finished seed\n","initializing seed = 4\n","0\n","num top: 2\n","1\n","num top: 3\n","2\n","num top: 3\n","3\n","num top: 4\n","4\n","num top: 4\n","5\n","num top: 4\n","6\n","num top: 4\n","7\n","num top: 4\n","8\n","num top: 4\n","9\n","num top: 4\n","10\n","num top: 4\n","11\n","num top: 4\n","12\n","num top: 4\n","13\n","num top: 4\n","14\n","num top: 4\n","15\n","num top: 4\n","16\n","num top: 4\n","17\n","num top: 4\n","18\n","num top: 4\n","19\n","num top: 4\n","20\n","num top: 4\n","21\n","num top: 4\n","22\n","num top: 4\n","23\n","num top: 4\n","24\n","num top: 4\n","25\n","num top: 4\n","26\n","num top: 4\n","27\n","num top: 4\n","28\n","num top: 4\n","29\n","num top: 4\n","30\n","num top: 4\n","31\n","num top: 4\n","32\n","num top: 4\n","33\n","num top: 4\n","34\n","num top: 4\n","35\n","num top: 4\n","36\n","num top: 4\n","37\n","num top: 4\n","38\n","num top: 4\n","39\n","num top: 4\n","40\n","num top: 4\n","41\n","num top: 4\n","42\n","num top: 5\n","Finished seed\n","16.344209909439087\n"]},{"data":{"text/plain":["[[0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 1,\n"," 1,\n"," 2,\n"," 3,\n"," 3,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 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4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 5]]"]},"execution_count":561,"metadata":{},"output_type":"execute_result"}],"source":["# framework\n","\n","\n","# good practice to keep check of time used\n","start_time = time.time()\n","\n","# these will carry results along optimization sequence from all n_ensemble runs\n","index_collection = []\n","X_collection = []\n","y_collection = []\n","TopCount_collection = []\n","\n","#keep track of longest learning cycle\n","longest_learning_cycle = 0\n","\n","\n","for s in seed_list:\n","\n"," if len(index_collection) == n_ensemble:\n"," break\n","\n"," print('initializing seed = ' +str(seed_list.index(s)))\n"," random.seed(s)\n","\n"," indices = list(np.arange(N))\n","# index_learn is the pool of candidates to be examined\n"," index_learn = indices.copy()\n","# index_ is the list of candidates we have already observed\n","# adding in the initial experiments\n"," index_ = random.sample(index_learn, n_initial)\n","\n","# list to store all observed good candidates' input feature X\n"," X_ = []\n","# list to store all observed good candidates' objective value y\n"," y_ = []\n","# number of top candidates found so far\n"," c = 0\n","# list of cumulative number of top candidates found at each learning cycle\n"," TopCount_ = []\n","# add the first n_initial experiments to collection\n"," for i in index_:\n"," X_.append(X_feature[i])\n"," y_.append(y[i])\n"," if i in top_indices:\n"," c += 1\n"," TopCount_.append(c)\n"," index_learn.remove(i)\n","\n","\n","# for each of the the rest of (N - n_initial) learning cycles\n","# this for loop ends when all candidates in pool are observed\n","\n"," lawrence = 0\n"," for i in np.arange(len(index_learn)):\n"," print(lawrence)\n"," lawrence += 1\n"," y_best = np.min(y_)\n","\n"," s_scaler = preprocessing.StandardScaler()\n"," X_train = s_scaler.fit_transform(X_)\n"," y_train = s_scaler.fit_transform([[i] for i in y_])\n"," y_train = np.ravel(y_train)\n","\n"," if combo['model'] == 'RF':\n"," n_est = 50\n"," model = RandomForestRegressor(n_estimators= n_est, n_jobs= -1)\n"," model.fit(X_train, y_train)\n"," else:\n"," n_est = 5\n"," model = BaggingRegressor(LinearRegression(),n_estimators = n_est, n_jobs = -1)\n"," model.fit(X_train, y_train)\n","\n","\n","# by evaluating acquisition function values at candidates remaining in pool\n","# we choose candidate with larger acquisition function value to be observed next\n"," next_index = None\n"," max_ac = -10**10\n"," for j in index_learn:\n"," X_j = X_feature[j]\n"," y_j = y[j]\n","\n"," # TODO: CREATE PREDICTION FUNCTION WITH UNCERTAINTY\n"," ensemble_predictions = []\n"," for k in np.arange(n_est):\n"," ensemble_predictions.append((model.estimators_[k].predict(np.array([X_j]))).tolist())\n"," mean = np.mean(np.array(ensemble_predictions), axis=0)[0]\n"," std = np.std(np.array(ensemble_predictions), axis=0)[0]\n","\n"," # select acquisition function\n"," ac_value = EI(mean, std, y_best, combo['acq_func'])\n","\n"," if max_ac <= ac_value:\n"," max_ac = ac_value\n"," next_index = j\n","\n","\n","\n"," X_.append(X_feature[next_index])\n"," y_.append(y[next_index])\n","\n","\n"," if next_index in top_indices:\n"," c += 1\n"," print('num top: ', c)\n","\n"," TopCount_.append(c)\n","\n"," index_learn.remove(next_index)\n"," index_.append(next_index)\n"," if c == len(top_indices):\n"," break\n","\n"," if len(TopCount_)>longest_learning_cycle:\n"," longest_learning_cycle = len(TopCount_) \n"," \n","\n"," # assert len(index_) == N\n","\n"," index_collection.append(index_)\n"," X_collection.append(X_)\n"," y_collection.append(y_)\n"," TopCount_collection.append(TopCount_)\n","\n","\n","\n","\n","\n","\n","\n","\n"," print('Finished seed')\n","\n","total_time = time.time() - start_time\n","print(total_time)\n","TopCount_collection # PUT INTO DATASET\n","\n","\n","# master = np.array([index_collection, X_collection, y_collection, TopCount_collection, total_time])\n","# # #TODO: name output file\n","# np.save(f'{combo[\"dataset\"]} ({combo[\"n_features\"]}, {combo[\"acq_func\"]}, {combo[\"model\"]})', TopCount_collection)"]},{"cell_type":"markdown","id":"c6cefbca","metadata":{},"source":["# Graphing Results of Run"]},{"cell_type":"code","execution_count":562,"id":"9fc5145b-b6cc-4ba3-a86e-c4851026c6f5","metadata":{"id":"9fc5145b-b6cc-4ba3-a86e-c4851026c6f5"},"outputs":[{"data":{"text/plain":["[[0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 1,\n"," 1,\n"," 2,\n"," 3,\n"," 3,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 5],\n"," 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0,\n"," 0,\n"," 1,\n"," 2,\n"," 3,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 5],\n"," [0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 0,\n"," 1,\n"," 1,\n"," 2,\n"," 3,\n"," 3,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 4,\n"," 5]]"]},"execution_count":562,"metadata":{},"output_type":"execute_result"}],"source":["#TODO use seaborn to output line function with error bars, save the figures and the csv (New CSV for each combo)\n","\n","TopCount_collection\n"]},{"cell_type":"code","execution_count":563,"id":"887bd7b0","metadata":{},"outputs":[],"source":["output_array=[]\n","#Double for-loop magic to add extra values to compensate as TopCount is likely jagged array\n","#This adds the MAXIMUM Number IS EVERYONE OK WITH THIS SOS\n","\n","for i in range(len(TopCount_collection)): \n"," for j in range(longest_learning_cycle):\n"," if j >= len(TopCount_collection[i]):\n"," TopCount_collection[i].append(TopCount_collection[i][j-1])\n","\n","for i in range(len(TopCount_collection)):\n"," output_array.append(np.array(TopCount_collection[i]))\n","\n","#make an output dataframe summarizing the output of each run SOS Relies on n_ensemble == 5\n","\n","output_df = pd.DataFrame(output_array, index=[\"Seed 1\", \"Seed 2\", \"Seed 3\", \"Seed 4\", \"Seed 5\"])\n","output_df = output_df.T\n"]},{"cell_type":"code","execution_count":564,"id":"fcd06afa-8aef-4779-9547-6ef2fbb9bbd8","metadata":{"id":"fcd06afa-8aef-4779-9547-6ef2fbb9bbd8"},"outputs":[{"data":{"text/html":["
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Seed 1Seed 2Seed 3Seed 4Seed 5MeanStd DevLearning Cycle
0000000.00.0000000
1000000.00.0000001
2000000.00.0000002
3000000.00.0000003
4000000.00.0000004
5000000.00.0000005
6010000.20.4472146
7010000.20.4472147
8010010.40.5477238
9010010.40.5477239
10121121.40.54772310
11121231.80.83666011
12232332.60.54772312
13332443.20.83666013
14343443.60.54772314
15444444.00.00000015
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18444444.00.00000018
19444444.00.00000019
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26444444.00.00000026
27444444.00.00000027
28444444.00.00000028
29444444.00.00000029
30444444.00.00000030
31444444.00.00000031
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50444444.00.00000050
51444444.00.00000051
52444454.20.44721452
53444454.20.44721453
54444454.20.44721454
55444454.20.44721455
56454454.40.54772356
57454454.40.54772357
58555454.80.44721458
59555555.00.00000059
\n","
"],"text/plain":[" Seed 1 Seed 2 Seed 3 Seed 4 Seed 5 Mean Std Dev Learning Cycle\n","0 0 0 0 0 0 0.0 0.000000 0\n","1 0 0 0 0 0 0.0 0.000000 1\n","2 0 0 0 0 0 0.0 0.000000 2\n","3 0 0 0 0 0 0.0 0.000000 3\n","4 0 0 0 0 0 0.0 0.000000 4\n","5 0 0 0 0 0 0.0 0.000000 5\n","6 0 1 0 0 0 0.2 0.447214 6\n","7 0 1 0 0 0 0.2 0.447214 7\n","8 0 1 0 0 1 0.4 0.547723 8\n","9 0 1 0 0 1 0.4 0.547723 9\n","10 1 2 1 1 2 1.4 0.547723 10\n","11 1 2 1 2 3 1.8 0.836660 11\n","12 2 3 2 3 3 2.6 0.547723 12\n","13 3 3 2 4 4 3.2 0.836660 13\n","14 3 4 3 4 4 3.6 0.547723 14\n","15 4 4 4 4 4 4.0 0.000000 15\n","16 4 4 4 4 4 4.0 0.000000 16\n","17 4 4 4 4 4 4.0 0.000000 17\n","18 4 4 4 4 4 4.0 0.000000 18\n","19 4 4 4 4 4 4.0 0.000000 19\n","20 4 4 4 4 4 4.0 0.000000 20\n","21 4 4 4 4 4 4.0 0.000000 21\n","22 4 4 4 4 4 4.0 0.000000 22\n","23 4 4 4 4 4 4.0 0.000000 23\n","24 4 4 4 4 4 4.0 0.000000 24\n","25 4 4 4 4 4 4.0 0.000000 25\n","26 4 4 4 4 4 4.0 0.000000 26\n","27 4 4 4 4 4 4.0 0.000000 27\n","28 4 4 4 4 4 4.0 0.000000 28\n","29 4 4 4 4 4 4.0 0.000000 29\n","30 4 4 4 4 4 4.0 0.000000 30\n","31 4 4 4 4 4 4.0 0.000000 31\n","32 4 4 4 4 4 4.0 0.000000 32\n","33 4 4 4 4 4 4.0 0.000000 33\n","34 4 4 4 4 4 4.0 0.000000 34\n","35 4 4 4 4 4 4.0 0.000000 35\n","36 4 4 4 4 4 4.0 0.000000 36\n","37 4 4 4 4 4 4.0 0.000000 37\n","38 4 4 4 4 4 4.0 0.000000 38\n","39 4 4 4 4 4 4.0 0.000000 39\n","40 4 4 4 4 4 4.0 0.000000 40\n","41 4 4 4 4 4 4.0 0.000000 41\n","42 4 4 4 4 4 4.0 0.000000 42\n","43 4 4 4 4 4 4.0 0.000000 43\n","44 4 4 4 4 4 4.0 0.000000 44\n","45 4 4 4 4 4 4.0 0.000000 45\n","46 4 4 4 4 4 4.0 0.000000 46\n","47 4 4 4 4 4 4.0 0.000000 47\n","48 4 4 4 4 4 4.0 0.000000 48\n","49 4 4 4 4 4 4.0 0.000000 49\n","50 4 4 4 4 4 4.0 0.000000 50\n","51 4 4 4 4 4 4.0 0.000000 51\n","52 4 4 4 4 5 4.2 0.447214 52\n","53 4 4 4 4 5 4.2 0.447214 53\n","54 4 4 4 4 5 4.2 0.447214 54\n","55 4 4 4 4 5 4.2 0.447214 55\n","56 4 5 4 4 5 4.4 0.547723 56\n","57 4 5 4 4 5 4.4 0.547723 57\n","58 5 5 5 4 5 4.8 0.447214 58\n","59 5 5 5 5 5 5.0 0.000000 59"]},"execution_count":564,"metadata":{},"output_type":"execute_result"}],"source":["#Get Average and Std Deviation of the run\n","\n","TopCount_mean = output_df.mean(axis= 'columns', skipna=True)\n","TopCount_std_dev = output_df.std(axis= 'columns', skipna=True)\n","\n","output_df['Mean'] = TopCount_mean\n","output_df['Std Dev'] = TopCount_std_dev\n","output_df['Learning Cycle'] = np.arange(0, longest_learning_cycle)\n","\n","output_df"]},{"cell_type":"code","execution_count":569,"id":"29b0cc1b","metadata":{},"outputs":[{"data":{"text/plain":["'Crossed barrel_dataset_3_0.01_Linear'"]},"execution_count":569,"metadata":{},"output_type":"execute_result"}],"source":["# #Output to CSV \n","output_name = f\"{combo['dataset'].split('.')[0]}_{combo['n_features']}_{combo['acq_func']}_{combo['model']}\"\n","output_name\n","\n","output_df.to_csv(f\"{output_name}.csv\")"]},{"cell_type":"code","execution_count":574,"id":"352671e2","metadata":{},"outputs":[{"data":{"image/png":"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","text/plain":["
"]},"metadata":{},"output_type":"display_data"}],"source":["#Make a Nice Graph\n","fig, ax = plt.subplots()\n","\n","x = output_df['Learning Cycle']\n","y = output_df ['Mean']\n","\n","yerr0 = y - output_df['Std Dev']\n","yerr1 = y + output_df['Std Dev']\n","\n","#Plot and fill Std Dev\n","ax.plot(x,y)\n","plt.fill_between(x, yerr0, yerr1, color='C0', alpha=0.5)\n","\n","#Format the figure with axes labels\n","plt.xlabel(\"Learning Cycle\")\n","plt.ylabel(\"Top Samples Identified\")\n","plt.title(f\"{output_name}\")\n","\n","plt.savefig(f\"{output_name}.png\")\n","\n"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.12.1"}},"nbformat":4,"nbformat_minor":5} diff --git a/src/concrete_mod.csv 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mock(self): - print("this is a mock function") diff --git a/src/mock2/__init__.py b/src/mock2/__init__.py deleted file mode 100644 index 9d29bf3..0000000 --- a/src/mock2/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -"""Mock function that gets installed by requirements.txt""" -from mock2._mock2 import Mock2 diff --git a/src/mock2/_mock2.py b/src/mock2/_mock2.py deleted file mode 100644 index f5b7725..0000000 --- a/src/mock2/_mock2.py +++ /dev/null @@ -1,6 +0,0 @@ -class Mock2: - def __init__(self): - pass - - def mock(self): - print("this is a mock function")