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ZebinYang
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skip grid search cv in build leaf if param_dict is empty; version 0.2.4
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+14
-50
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2 files changed

+14
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‎examples/demo.ipynb

+13-49
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@@ -5,8 +5,8 @@
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"execution_count": 1,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-12-27T11:58:43.079438Z",
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"start_time": "2021-12-27T11:58:41.821923Z"
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"end_time": "2021-12-27T12:22:23.721251Z",
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"start_time": "2021-12-27T12:22:22.456359Z"
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}
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},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-12-27T12:07:47.437564Z",
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"start_time": "2021-12-27T12:07:47.428224Z"
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"end_time": "2021-12-27T12:22:23.745780Z",
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"start_time": "2021-12-27T12:22:23.722805Z"
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}
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},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-12-27T12:10:02.283742Z",
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"start_time": "2021-12-27T12:10:02.097688Z"
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"end_time": "2021-12-27T12:22:28.522606Z",
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"start_time": "2021-12-27T12:22:28.338619Z"
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}
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},
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"outputs": [],
@@ -209,6 +209,8 @@
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}
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],
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"source": [
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"# here we use LogisticRegressor in sklearn\n",
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"# reg_lambda corresponds to parameter \"C\", which is inverse of regularization strength.\n",
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"clf = GLMTreeClassifier(max_depth=3, min_samples_leaf=50, reg_lambda=np.logspace(-5, 5, 10).tolist(),\n",
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" n_split_grid=20, n_screen_grid=5, n_feature_search=10)\n",
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"clf.fit(train_x, train_y)\n",
@@ -217,57 +219,19 @@
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"roc_auc_score(train_y, pred_train.ravel()), roc_auc_score(test_y, pred_test.ravel())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-12-27T12:11:03.435992Z",
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"start_time": "2021-12-27T12:10:29.328703Z"
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}
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},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "__init__() got an unexpected keyword argument 'alpha'",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/tmp/ipykernel_37837/3864539870.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m clf = GLMTreeClassifier(max_depth=1, min_samples_leaf=50, reg_lambda=[0],\n\u001b[1;32m 2\u001b[0m n_split_grid=20, n_screen_grid=5, n_feature_search=10)\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mpred_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mpred_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_x\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/simtree/mobtree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_leaf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 294\u001b[0m node_id = self.add_node(parent_id, is_left, is_leaf, depth,\n\u001b[0;32m--> 295\u001b[0;31m None, None, impurity, sample_indice)\n\u001b[0m\u001b[1;32m 296\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 297\u001b[0m node_id = self.add_node(parent_id, is_left, is_leaf, depth,\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/simtree/mobtree.py\u001b[0m in \u001b[0;36madd_node\u001b[0;34m(self, parent_id, is_left, is_leaf, depth, feature, threshold, impurity, sample_indice)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0mn_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_indice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_leaf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0mpredict_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbest_impurity\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_leaf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_indice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 234\u001b[0m node = {\"node_id\": node_id, \"parent_id\": parent_id, \"depth\": depth, \"feature\": feature, \"impurity\": best_impurity,\n\u001b[1;32m 235\u001b[0m \u001b[0;34m\"n_samples\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_left\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mis_left\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"is_leaf\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mis_leaf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"value\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_indice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/anaconda3/envs/py37/lib/python3.7/site-packages/simtree/glmtree.py\u001b[0m in \u001b[0;36mbuild_leaf\u001b[0;34m(self, sample_indice)\u001b[0m\n\u001b[1;32m 96\u001b[0m cv=5, random_state=self.random_state)\n\u001b[1;32m 97\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 98\u001b[0;31m \u001b[0mbest_estimator\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLogisticRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreg_lambda\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecompute\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 99\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 100\u001b[0m \u001b[0mmx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0msample_indice\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'alpha'"
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]
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}
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],
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"source": [
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"clf = GLMTreeClassifier(max_depth=1, min_samples_leaf=50, reg_lambda=[0],\n",
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" n_split_grid=20, n_screen_grid=5, n_feature_search=10)\n",
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"clf.fit(train_x, train_y)\n",
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"pred_train = clf.predict_proba(train_x)[:, 1]\n",
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"pred_test = clf.predict_proba(test_x)[:, 1]\n",
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"roc_auc_score(train_y, pred_train.ravel()), roc_auc_score(test_y, pred_test.ravel())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2021-12-27T12:11:03.437503Z",
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"start_time": "2021-12-27T12:11:03.437482Z"
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"start_time": "2021-12-27T12:27:37.691Z"
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}
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},
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"outputs": [],
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"source": [
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"clf = SIMTreeClassifier(max_depth=1, min_samples_leaf=50, knot_num=30,\n",
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" n_split_grid=20, n_screen_grid=5, n_feature_search=10,\n",
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" reg_lambda=[0],\n",
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" reg_gamma=[1e-3, 1e-5, 1e-7])\n",
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"clf = GLMTreeClassifier(max_depth=1, min_samples_leaf=50, reg_lambda=[1e4],\n",
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" n_split_grid=20, n_screen_grid=5, n_feature_search=10)\n",
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"clf.fit(train_x, train_y)\n",
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"clf.plot_tree()\n",
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"pred_train = clf.predict_proba(train_x)[:, 1]\n",
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"pred_test = clf.predict_proba(test_x)[:, 1]\n",
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"roc_auc_score(train_y, pred_train.ravel()), roc_auc_score(test_y, pred_test.ravel())"

‎simtree/glmtree.py

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@@ -95,7 +95,7 @@ def build_leaf(self, sample_indice):
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best_estimator = LogisticRegressionCV(Cs=self.reg_lambda, penalty="l1", solver="liblinear", scoring="roc_auc",
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cv=5, random_state=self.random_state)
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else:
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best_estimator = LogisticRegression(C=self.reg_lambda[0], random_state=self.random_state)
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best_estimator = LogisticRegression(C=self.reg_lambda[0], penalty="l1", solver="liblinear", random_state=self.random_state)
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mx = self.x[sample_indice].mean(0)
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sx = self.x[sample_indice].std(0) + self.EPSILON

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