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,