feat: update readme and notebooks

dreamquark-ai · Oct 9, 2020 · 9cb38d2 · 9cb38d2
1 parent cc57d62
commit 9cb38d2
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Showing 7 changed files with 162 additions and 54 deletions.
diff --git a/README.md b/README.md
@@ -51,7 +51,10 @@ TabNet is now scikit-compatible, training a TabNetClassifier or TabNetRegressor
 from pytorch_tabnet.tab_model import TabNetClassifier, TabNetRegressor
 
 clf = TabNetClassifier() #TabNetRegressor()
-clf.fit(X_train, Y_train, X_valid, y_valid)
+clf.fit(
+ X_train, Y_train,
+ eval_set=[(X_valid, y_valid)]
+)
 preds = clf.predict(X_test)
 ```
 
@@ -60,10 +63,37 @@ or for TabNetMultiTaskClassifier :
 ```
 from pytorch_tabnet.multitask import TabNetMultiTaskClassifier
 clf = TabNetMultiTaskClassifier()
-clf.fit(X_train, Y_train, X_valid, y_valid)
+clf.fit(
+ X_train, Y_train,
+ eval_set=[(X_valid, y_valid)]
+)
 preds = clf.predict(X_test)
 ```
 
+### Custom early_stopping_metrics
+
+```
+from pytorch_tabnet.metrics import Metric
+from sklearn.metrics import roc_auc_score
+
+class Gini(Metric):
+ def __init__(self):
+ self._name = "gini"
+ self._maximize = True
+
+ def __call__(self, y_true, y_score):
+ auc = roc_auc_score(y_true, y_score[:, 1])
+ return max(2*auc - 1, 0.)
+
+clf = TabNetClassifier()
+clf.fit(
+ X_train, Y_train,
+ eval_set=[(X_valid, y_valid)],
+ eval_metric=[Gini]
+)
+
+```
+
 # Useful links
 
 - explanatory video : https://youtu.be/ysBaZO8YmX8
@@ -175,13 +205,18 @@ preds = clf.predict(X_test)
 
  Training targets
 
-- X_valid : np.array
+- eval_set: list of tuple 
 
- Validation features for early stopping
+ List of eval tuple set (X, y). 
+ The last one is used for early stopping 
 
-- y_valid : np.array for early stopping
+- eval_name: list of str 
+ List of eval set names. 
+
+- eval_metric : list of str 
+ List of evaluation metrics. 
+ The last metric is used for early stopping.
 
- Validation targets 
 - max_epochs : int (default = 200)
 
  Maximum number of epochs for trainng.
@@ -218,3 +253,6 @@ preds = clf.predict(X_test)
 - drop_last : bool (default=False)
 
  Whether to drop last batch if not complete during training
+
+- callbacks : list of callback function 
+ List of custom callbacks
diff --git a/census_example.ipynb b/census_example.ipynb
@@ -194,14 +194,14 @@
  {
  "cell_type": "code",
  "execution_count": null,
- "metadata": {
- "scrolled": false
- },
+ "metadata": {},
  "outputs": [],
  "source": [
  "clf.fit(\n",
  " X_train=X_train, y_train=y_train,\n",
- " X_valid=X_valid, y_valid=y_valid,\n",
+ " eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
+ " eval_name=['train', 'valid'],\n",
+ " eval_metric=['auc'],\n",
  " max_epochs=max_epochs , patience=20,\n",
  " batch_size=1024, virtual_batch_size=128,\n",
  " num_workers=0,\n",
@@ -217,8 +217,7 @@
  "outputs": [],
  "source": [
  "# plot losses\n",
- "plt.plot(clf.history['train']['loss'])\n",
- "plt.plot(clf.history['valid']['loss'])"
+ "plt.plot(clf.history['loss'])"
  ]
  },
  {
@@ -228,8 +227,8 @@
  "outputs": [],
  "source": [
  "# plot auc\n",
- "plt.plot([-x for x in clf.history['train']['metric']])\n",
- "plt.plot([-x for x in clf.history['valid']['metric']])"
+ "plt.plot(clf.history['train_auc'])\n",
+ "plt.plot(clf.history['valid_auc'])"
  ]
  },
  {
@@ -239,7 +238,7 @@
  "outputs": [],
  "source": [
  "# plot learning rates\n",
- "plt.plot([x for x in clf.history['train']['lr']])"
+ "plt.plot(clf.history['lr'])"
  ]
  },
  {
@@ -421,9 +420,9 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3",
+ "display_name": ".shap",
  "language": "python",
- "name": "python3"
+ "name": ".shap"
  },
  "language_info": {
  "codemirror_mode": {
@@ -435,7 +434,20 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.7.6"
+ "version": "3.6.8"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
  }
  },
  "nbformat": 4,

diff --git a/forest_example.ipynb b/forest_example.ipynb
@@ -237,7 +237,7 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "max_epochs = 1000 if not os.getenv(\"CI\", False) else 2"
+ "max_epochs = 5 if not os.getenv(\"CI\", False) else 2"
  ]
  },
  {
@@ -250,7 +250,8 @@
  "source": [
  "clf.fit(\n",
  " X_train=X_train, y_train=y_train,\n",
- " X_valid=X_valid, y_valid=y_valid,\n",
+ " eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
+ " eval_name=['train', 'valid'],\n",
  " max_epochs=max_epochs, patience=100,\n",
  " batch_size=16384, virtual_batch_size=256\n",
  ") "
@@ -263,8 +264,7 @@
  "outputs": [],
  "source": [
  "# plot losses\n",
- "plt.plot(clf.history['train']['loss'])\n",
- "plt.plot(clf.history['valid']['loss'])"
+ "plt.plot(clf.history['loss'])"
  ]
  },
  {
@@ -273,9 +273,9 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "# plot accuracies\n",
- "plt.plot([-x for x in clf.history['train']['metric']])\n",
- "plt.plot([-x for x in clf.history['valid']['metric']])"
+ "# plot accuracy\n",
+ "plt.plot(clf.history['train_accuracy'])\n",
+ "plt.plot(clf.history['valid_accuracy'])"
  ]
  },
  {
@@ -495,9 +495,9 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3",
+ "display_name": ".shap",
  "language": "python",
- "name": "python3"
+ "name": ".shap"
  },
  "language_info": {
  "codemirror_mode": {
@@ -509,7 +509,20 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.6.8"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
  }
  },
  "nbformat": 4,

diff --git a/multi_regression_example.ipynb b/multi_regression_example.ipynb
@@ -19,7 +19,12 @@
  "\n",
  "import os\n",
  "import wget\n",
- "from pathlib import Path"
+ "from pathlib import Path\n",
+ "\n",
+ "\n",
+ "%load_ext autoreload\n",
+ "\n",
+ "%autoreload 2"
  ]
  },
  {
@@ -188,20 +193,21 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "max_epochs = 1000 if not os.getenv(\"CI\", False) else 2"
+ "max_epochs = 10 if not os.getenv(\"CI\", False) else 2"
  ]
  },
  {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
- "scrolled": true
+ "scrolled": false
  },
  "outputs": [],
  "source": [
  "clf.fit(\n",
  " X_train=X_train, y_train=y_train,\n",
- " X_valid=X_valid, y_valid=y_valid,\n",
+ " eval_set=[(X_train, y_train), (X_valid, y_valid)],\n",
+ " eval_name=['train', 'valid'],\n",
  " max_epochs=max_epochs,\n",
  " patience=50,\n",
  " batch_size=1024, virtual_batch_size=128,\n",
@@ -216,17 +222,12 @@
  "metadata": {},
  "outputs": [],
  "source": [
- "# Deprecated : best model is automatically loaded at end of fit\n",
- "# clf.load_best_model()\n",
- "\n",
  "preds = clf.predict(X_test)\n",
  "\n",
- "y_true = y_test\n",
- "\n",
- "test_score = mean_squared_error(y_pred=preds, y_true=y_true)\n",
+ "test_mse = mean_squared_error(y_pred=preds, y_true=y_test)\n",
  "\n",
  "print(f\"BEST VALID SCORE FOR {dataset_name} : {clf.best_cost}\")\n",
- "print(f\"FINAL TEST SCORE FOR {dataset_name} : {test_score}\")"
+ "print(f\"FINAL TEST SCORE FOR {dataset_name} : {test_mse}\")"
  ]
  },
  {
@@ -296,9 +297,9 @@
  ],
  "metadata": {
  "kernelspec": {
- "display_name": "Python 3",
+ "display_name": ".shap",
  "language": "python",
- "name": "python3"
+ "name": ".shap"
  },
  "language_info": {
  "codemirror_mode": {
@@ -310,7 +311,20 @@
  "name": "python",
  "nbconvert_exporter": "python",
  "pygments_lexer": "ipython3",
- "version": "3.7.5"
+ "version": "3.6.8"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": true,
+ "sideBar": true,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": true,
+ "toc_window_display": false
  }
  },
  "nbformat": 4,