Merge pull request #17 from jrzaurin/precision_recall

Precision recall

README.md

@@ -40,7 +40,9 @@ final output neuron or neurons, depending on whether we are performing a
 binary classification or regression, or a multi-class classification. The
 components within the faded-pink rectangles are concatenated.
 
-In math terms, and following the notation in the [paper](https://arxiv.org/abs/1606.07792), Architecture 1 can be formulated as:
+In math terms, and following the notation in the
+[paper](https://arxiv.org/abs/1606.07792), Architecture 1 can be formulated
+as:
 
 <p align="center">
   <img width="500" src="docs/figures/architecture_1_math.png">
@@ -130,7 +132,7 @@ from sklearn.model_selection import train_test_split
 
 from pytorch_widedeep.preprocessing import WidePreprocessor, DensePreprocessor
 from pytorch_widedeep.models import Wide, DeepDense, WideDeep
-from pytorch_widedeep.metrics import BinaryAccuracy
+from pytorch_widedeep.metrics import Accuracy
 
 # these next 4 lines are not directly related to pytorch-widedeep. I assume
 # you have downloaded the dataset and place it in a dir called data/adult/
@@ -178,7 +180,7 @@ deepdense = DeepDense(
 
 # build, compile and fit
 model = WideDeep(wide=wide, deepdense=deepdense)
-model.compile(method="binary", metrics=[BinaryAccuracy])
+model.compile(method="binary", metrics=[Accuracy])
 model.fit(
     X_wide=X_wide,
     X_deep=X_deep,

VERSION

@@ -1 +1 @@
-0.4.1
+0.4.2

code_style.sh

@@ -1,5 +1,5 @@
 # sort imports
-isort --recursive . pytorch_widedeep tests examples setup.py
+isort . pytorch_widedeep tests examples setup.py
 # Black code style
 black . pytorch_widedeep tests examples setup.py
 # flake8 standards

docs/examples.rst

@@ -0,0 +1,13 @@
+pytorch-widedeep Examples
+*****************************
+
+This section provides links to example notebooks that may be helpful to better
+understand the functionalities withing ``pytorch-widedeep`` and how to use
+them to address different problems
+
+* `Preprocessors and Utils <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/01_Preprocessors_and_utils.ipynb>`__
+* `Model Components <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/02_Model_Components.ipynb>`__
+* `Binary Classification with default parameters <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/03_Binary_Classification_with_Defaults.ipynb>`__
+* `Binary Classification with varying parameters <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/04_Binary_Classification_Varying_Parameters.ipynb>`__
+* `Regression with Images and Text <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/05_Regression_with_Images_and_Text.ipynb>`__
+* `Warm up routines <https://github.com/jrzaurin/pytorch-widedeep/blob/master/examples/06_WarmUp_Model_Components.ipynb>`__

docs/index.rst

@@ -19,6 +19,7 @@ Documentation
     Preprocessing <preprocessing>
     Model Components <model_components>
     Wide and Deep Models <wide_deep/index>
+    Examples <examples>
 
 
 Introduction

docs/quick_start.rst

@@ -30,7 +30,7 @@ Prepare the wide and deep columns
 
     from pytorch_widedeep.preprocessing import WidePreprocessor, DensePreprocessor
     from pytorch_widedeep.models import Wide, DeepDense, WideDeep
-    from pytorch_widedeep.metrics import BinaryAccuracy
+    from pytorch_widedeep.metrics import Accuracy
 
     # prepare wide, crossed, embedding and continuous columns
     wide_cols = [
@@ -83,7 +83,7 @@ Build, compile, fit and predict
 
     # build, compile and fit
     model = WideDeep(wide=wide, deepdense=deepdense)
-    model.compile(method="binary", metrics=[BinaryAccuracy])
+    model.compile(method="binary", metrics=[Accuracy])
     model.fit(
         X_wide=X_wide,
         X_deep=X_deep,

docs/wide_deep/metrics.rst

@@ -1,12 +1,27 @@
 Metrics
 =======
 
-.. autoclass:: pytorch_widedeep.metrics.BinaryAccuracy
+.. autoclass:: pytorch_widedeep.metrics.Accuracy
 	:members:
 	:undoc-members:
 	:show-inheritance:
 
-.. autoclass:: pytorch_widedeep.metrics.CategoricalAccuracy
+.. autoclass:: pytorch_widedeep.metrics.Precision
+	:members:
+	:undoc-members:
+	:show-inheritance:
+
+.. autoclass:: pytorch_widedeep.metrics.Recall
+	:members:
+	:undoc-members:
+	:show-inheritance:
+
+.. autoclass:: pytorch_widedeep.metrics.FBetaScore
+	:members:
+	:undoc-members:
+	:show-inheritance:
+
+.. autoclass:: pytorch_widedeep.metrics.F1Score
 	:members:
 	:undoc-members:
 	:show-inheritance:

examples/02_Model_Components.ipynb

@@ -170,11 +170,11 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[-0.0000, -1.0061, -0.0000, -0.9828, -0.0000, -0.0000, -0.9944, -1.0133],\n",
-       "        [-0.0000, -0.9996,  0.0000, -1.0374,  0.0000, -0.0000, -1.0313, -0.0000],\n",
-       "        [-0.8576, -1.0017, -0.0000, -0.9881, -0.0000,  0.0000, -0.0000, -0.0000],\n",
-       "        [ 3.9816,  0.0000,  0.0000,  0.0000,  3.7309,  1.1728,  0.0000, -1.1160],\n",
-       "        [-1.1339, -0.9925, -0.0000, -0.0000, -0.0000,  0.0000, -0.9638,  0.0000]],\n",
+       "tensor([[-0.0000, -0.9949,  3.8273,  0.0000, -1.3889, -2.9641,  0.0000, -0.0000],\n",
+       "        [ 3.9123, -0.0000, -0.0000,  1.9555, -1.3561,  1.7069, -0.0000,  0.9275],\n",
+       "        [-0.0000, -0.0000,  0.0000, -0.0000,  0.0000, -1.6489, -0.0000, -1.4985],\n",
+       "        [-1.2736,  0.0000, -1.2819,  2.1232,  0.0000,  2.2767, -0.0000,  3.5354],\n",
+       "        [-0.1726, -0.0000, -1.3275, -0.0000, -1.3703,  0.0000, -0.0000, -1.4637]],\n",
        "       grad_fn=<MulBackward0>)"
       ]
      },
@@ -484,10 +484,10 @@
     {
      "data": {
       "text/plain": [
-       "tensor([[-1.4630e-04, -6.1540e-04, -2.4541e-04,  2.7543e-01,  1.2993e-01,\n",
-       "         -1.6553e-03,  6.7002e-02,  2.3974e-01],\n",
-       "        [-9.9619e-04, -1.9412e-03,  1.2113e-01,  1.0122e-01,  2.9080e-01,\n",
-       "         -2.0852e-03, -1.8016e-04,  2.7996e-02]], grad_fn=<LeakyReluBackward1>)"
+       "tensor([[-2.2825e-03, -8.3100e-04, -8.8423e-04, -1.1084e-04,  8.8529e-02,\n",
+       "         -5.1577e-04,  2.8343e-01, -1.7071e-03],\n",
+       "        [-1.8486e-03, -8.5602e-04, -1.8552e-03,  3.6481e-01,  9.0812e-02,\n",
+       "         -9.6603e-04,  3.9017e-01, -2.6355e-03]], grad_fn=<LeakyReluBackward1>)"
       ]
      },
      "execution_count": 18,

examples/03_Binary_Classification_with_Defaults.ipynb

@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,12 +21,12 @@
     "\n",
     "from pytorch_widedeep.preprocessing import WidePreprocessor, DensePreprocessor\n",
     "from pytorch_widedeep.models import Wide, DeepDense, WideDeep\n",
-    "from pytorch_widedeep.metrics import BinaryAccuracy"
+    "from pytorch_widedeep.metrics import Accuracy, Precision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -185,7 +185,7 @@
        "4              30  United-States  <=50K  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +356,7 @@
        "4              30  United-States             0  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -394,7 +394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -489,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -527,7 +527,7 @@
        ")"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -560,16 +560,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "model.compile(method='binary', metrics=[BinaryAccuracy])"
+    "model.compile(method='binary', metrics=[Accuracy, Precision])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -591,16 +591,16 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "epoch 1: 100%|██████████| 153/153 [00:02<00:00, 64.79it/s, loss=0.435, metrics={'acc': 0.7901}]\n",
-      "valid: 100%|██████████| 39/39 [00:00<00:00, 124.97it/s, loss=0.358, metrics={'acc': 0.799}]\n",
-      "epoch 2: 100%|██████████| 153/153 [00:02<00:00, 71.36it/s, loss=0.352, metrics={'acc': 0.8352}]\n",
-      "valid: 100%|██████████| 39/39 [00:00<00:00, 124.33it/s, loss=0.349, metrics={'acc': 0.8358}]\n",
-      "epoch 3: 100%|██████████| 153/153 [00:02<00:00, 72.24it/s, loss=0.345, metrics={'acc': 0.8383}]\n",
-      "valid: 100%|██████████| 39/39 [00:00<00:00, 121.07it/s, loss=0.345, metrics={'acc': 0.8389}]\n",
-      "epoch 4: 100%|██████████| 153/153 [00:02<00:00, 70.39it/s, loss=0.341, metrics={'acc': 0.8404}]\n",
-      "valid: 100%|██████████| 39/39 [00:00<00:00, 123.29it/s, loss=0.343, metrics={'acc': 0.8406}]\n",
-      "epoch 5: 100%|██████████| 153/153 [00:02<00:00, 71.14it/s, loss=0.339, metrics={'acc': 0.8423}]\n",
-      "valid: 100%|██████████| 39/39 [00:00<00:00, 121.12it/s, loss=0.342, metrics={'acc': 0.8426}]\n"
+      "epoch 1: 100%|██████████| 153/153 [00:01<00:00, 102.41it/s, loss=0.585, metrics={'acc': 0.7512, 'prec': 0.1818}]\n",
+      "valid: 100%|██████████| 39/39 [00:00<00:00, 98.78it/s, loss=0.513, metrics={'acc': 0.754, 'prec': 0.2429}] \n",
+      "epoch 2: 100%|██████████| 153/153 [00:01<00:00, 117.30it/s, loss=0.481, metrics={'acc': 0.782, 'prec': 0.8287}] \n",
+      "valid: 100%|██████████| 39/39 [00:00<00:00, 106.49it/s, loss=0.454, metrics={'acc': 0.7866, 'prec': 0.8245}]\n",
+      "epoch 3: 100%|██████████| 153/153 [00:01<00:00, 124.78it/s, loss=0.44, metrics={'acc': 0.8055, 'prec': 0.781}] \n",
+      "valid: 100%|██████████| 39/39 [00:00<00:00, 115.36it/s, loss=0.425, metrics={'acc': 0.8077, 'prec': 0.7818}]\n",
+      "epoch 4: 100%|██████████| 153/153 [00:01<00:00, 125.01it/s, loss=0.418, metrics={'acc': 0.814, 'prec': 0.7661}] \n",
+      "valid: 100%|██████████| 39/39 [00:00<00:00, 114.92it/s, loss=0.408, metrics={'acc': 0.8149, 'prec': 0.7671}]\n",
+      "epoch 5: 100%|██████████| 153/153 [00:01<00:00, 116.57it/s, loss=0.404, metrics={'acc': 0.819, 'prec': 0.7527}]\n",
+      "valid: 100%|██████████| 39/39 [00:00<00:00, 108.89it/s, loss=0.397, metrics={'acc': 0.8203, 'prec': 0.7547}]\n"
      ]
     }
    ],