mirror of
https://github.com/Doctorado-ML/STree.git
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Enhance partition (#16)
#15 Create impurity function in Stree (consistent name, same criteria as other splitter parameter) Create test for the new function Update init test Update test splitter parameters Rename old impurity function to partition_impurity close #15 * Complete implementation of splitter_type = impurity with tests Remove max_distance & min_distance splitter types * Fix mistake in computing multiclass node belief Set default criterion for split to entropy instead of gini Set default max_iter to 1e5 instead of 1e3 change up-down criterion to match SVC multiclass Fix impurity method of splitting nodes Update jupyter Notebooks
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@@ -61,7 +61,13 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (100492, 28) y.shape (100492,)\nFraud: 0.644% 647\nValid: 99.356% 99845\n"
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"text": [
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"Fraud: 0.173% 492\n",
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"Valid: 99.827% 284315\n",
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"X.shape (100492, 28) y.shape (100492,)\n",
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"Fraud: 0.652% 655\n",
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"Valid: 99.348% 99837\n"
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Score Train: 0.9985784146480154\nScore Test: 0.9981093273185617\nTook 73.27 seconds\n"
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"text": [
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"Score Train: 0.9985073353804162\nScore Test: 0.9983746848878864\nTook 35.80 seconds\n"
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]
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}
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],
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"source": [
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"now = time.time()\n",
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"clf = Stree(max_depth=3, random_state=random_state)\n",
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"clf = Stree(max_depth=3, random_state=random_state, max_iter=1e3)\n",
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"clf.fit(Xtrain, ytrain)\n",
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"print(\"Score Train: \", clf.score(Xtrain, ytrain))\n",
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"print(\"Score Test: \", clf.score(Xtest, ytest))\n",
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@@ -169,13 +177,17 @@
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Kernel: linear\tTime: 93.78 seconds\tScore Train: 0.9983083\tScore Test: 0.9983083\nKernel: rbf\tTime: 18.32 seconds\tScore Train: 0.9935602\tScore Test: 0.9935651\nKernel: poly\tTime: 69.68 seconds\tScore Train: 0.9973132\tScore Test: 0.9972801\n"
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"text": [
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"Kernel: linear\tTime: 49.66 seconds\tScore Train: 0.9983225\tScore Test: 0.9983083\n",
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"Kernel: rbf\tTime: 12.73 seconds\tScore Train: 0.9934891\tScore Test: 0.9934656\n",
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"Kernel: poly\tTime: 76.24 seconds\tScore Train: 0.9972706\tScore Test: 0.9969152\n"
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]
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}
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],
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"source": [
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"for kernel in ['linear', 'rbf', 'poly']:\n",
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" now = time.time()\n",
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" clf = AdaBoostClassifier(base_estimator=Stree(C=C, kernel=kernel, max_depth=max_depth, random_state=random_state), algorithm=\"SAMME\", n_estimators=n_estimators, random_state=random_state)\n",
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" clf = AdaBoostClassifier(base_estimator=Stree(C=C, kernel=kernel, max_depth=max_depth, random_state=random_state, max_iter=1e3), algorithm=\"SAMME\", n_estimators=n_estimators, random_state=random_state)\n",
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" clf.fit(Xtrain, ytrain)\n",
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" score_train = clf.score(Xtrain, ytrain)\n",
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" score_test = clf.score(Xtest, ytest)\n",
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{
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"output_type": "stream",
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"name": "stdout",
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"text": "Kernel: linear\tTime: 387.06 seconds\tScore Train: 0.9985784\tScore Test: 0.9981093\nKernel: rbf\tTime: 144.00 seconds\tScore Train: 0.9992750\tScore Test: 0.9983415\nKernel: poly\tTime: 101.78 seconds\tScore Train: 0.9992466\tScore Test: 0.9981757\n"
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"text": [
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"Kernel: linear\tTime: 231.51 seconds\tScore Train: 0.9984931\tScore Test: 0.9983083\n",
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"Kernel: rbf\tTime: 114.77 seconds\tScore Train: 0.9992323\tScore Test: 0.9983083\n",
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"Kernel: poly\tTime: 67.87 seconds\tScore Train: 0.9993319\tScore Test: 0.9985074\n"
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]
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}
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],
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"source": [
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"for kernel in ['linear', 'rbf', 'poly']:\n",
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" now = time.time()\n",
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" clf = BaggingClassifier(base_estimator=Stree(C=C, kernel=kernel, max_depth=max_depth, random_state=random_state), n_estimators=n_estimators, random_state=random_state)\n",
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" clf = BaggingClassifier(base_estimator=Stree(C=C, kernel=kernel, max_depth=max_depth, random_state=random_state, max_iter=1e3), n_estimators=n_estimators, random_state=random_state)\n",
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" clf.fit(Xtrain, ytrain)\n",
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" score_train = clf.score(Xtrain, ytrain)\n",
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" score_test = clf.score(Xtest, ytest)\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.6-final"
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"version": "3.8.4-final"
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},
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"orig_nbformat": 2,
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"kernelspec": {
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"name": "python37664bitgeneralvenve3128601eb614c5da59c5055670b6040",
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"display_name": "Python 3.7.6 64-bit ('general': venv)"
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"name": "python38464bitgeneralf6de308d3831407c8bd68d4a5e328a38",
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"display_name": "Python 3.8.4 64-bit ('general')"
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"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 32.976% 492\nValid: 67.024% 1000\n"
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"text": [
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"Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.177% 495\nValid: 66.823% 997\n"
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"colab": {}
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},
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"source": [
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"parameters = {\n",
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"parameters = [{\n",
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" 'base_estimator': [Stree()],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
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" 'base_estimator__max_depth': [3, 5],\n",
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" 'base_estimator__C': [7, 55],\n",
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" 'base_estimator__kernel': ['linear', 'poly', 'rbf']\n",
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"}"
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" 'base_estimator__max_depth': [3, 5, 7],\n",
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__kernel': ['linear']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree()],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
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" 'base_estimator__max_depth': [3, 5, 7],\n",
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__degree': [3, 5, 7],\n",
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" 'base_estimator__kernel': ['poly']\n",
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"},\n",
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"{\n",
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" 'base_estimator': [Stree()],\n",
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" 'n_estimators': [10, 25],\n",
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" 'learning_rate': [.5, 1],\n",
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" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
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" 'base_estimator__tol': [.1, 1e-02],\n",
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" 'base_estimator__max_depth': [3, 5, 7],\n",
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" 'base_estimator__C': [1, 7, 55],\n",
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" 'base_estimator__gamma': [.1, 1, 10],\n",
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" 'base_estimator__kernel': ['rbf']\n",
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"}]"
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],
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"execution_count": 5,
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"outputs": []
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": "{'C': 1.0,\n 'criterion': 'gini',\n 'degree': 3,\n 'gamma': 'scale',\n 'kernel': 'linear',\n 'max_depth': None,\n 'max_features': None,\n 'max_iter': 1000,\n 'min_samples_split': 0,\n 'random_state': None,\n 'split_criteria': 'max_samples',\n 'splitter': 'random',\n 'tol': 0.0001}"
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"text/plain": [
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"{'C': 1.0,\n",
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" 'criterion': 'entropy',\n",
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" 'degree': 3,\n",
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" 'gamma': 'scale',\n",
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" 'kernel': 'linear',\n",
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" 'max_depth': None,\n",
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" 'max_features': None,\n",
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" 'max_iter': 100000.0,\n",
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" 'min_samples_split': 0,\n",
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" 'random_state': None,\n",
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" 'split_criteria': 'impurity',\n",
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" 'splitter': 'random',\n",
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" 'tol': 0.0001}"
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]
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},
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"metadata": {},
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"execution_count": 6
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"output_type": "stream",
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"text": "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 2.0s\n[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 2.4s\n[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 2.7s\n[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 3.3s\n[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.3s\n[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 5.3s\n[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 6.6s\n[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 8.1s\n[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 9.4s\n[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 10.1s\n[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 11.1s\n[Parallel(n_jobs=-1)]: Done 129 tasks | elapsed: 12.3s\n[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 13.6s\n[Parallel(n_jobs=-1)]: Done 165 tasks | elapsed: 14.9s\n[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 16.2s\n[Parallel(n_jobs=-1)]: Done 205 tasks | elapsed: 17.6s\n[Parallel(n_jobs=-1)]: Done 226 tasks | elapsed: 19.1s\n[Parallel(n_jobs=-1)]: Done 249 tasks | elapsed: 21.6s\n[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 25.9s\n[Parallel(n_jobs=-1)]: Done 297 tasks | elapsed: 30.4s\n[Parallel(n_jobs=-1)]: Done 322 tasks | elapsed: 36.7s\n[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 38.1s\n[Parallel(n_jobs=-1)]: Done 376 tasks | elapsed: 39.6s\n[Parallel(n_jobs=-1)]: Done 405 tasks | elapsed: 41.9s\n[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 44.9s\n[Parallel(n_jobs=-1)]: Done 465 tasks | elapsed: 48.2s\n[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed: 49.2s finished\n"
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"text": [
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"Fitting 5 folds for each of 1008 candidates, totalling 5040 fits\n",
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"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
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"[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 2.6s\n",
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"[Parallel(n_jobs=-1)]: Done 4689 tasks | elapsed: 9.0min\n",
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"[Parallel(n_jobs=-1)]: Done 4786 tasks | elapsed: 9.3min\n",
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"[Parallel(n_jobs=-1)]: Done 4885 tasks | elapsed: 9.6min\n",
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"[Parallel(n_jobs=-1)]: Done 4984 tasks | elapsed: 9.8min\n",
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"[Parallel(n_jobs=-1)]: Done 5040 out of 5040 | elapsed: 10.0min finished\n"
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]
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},
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{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": "GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=2020),\n n_jobs=-1,\n param_grid={'base_estimator': [Stree(C=55, max_depth=3, tol=0.01)],\n 'base_estimator__C': [7, 55],\n 'base_estimator__kernel': ['linear', 'poly', 'rbf'],\n 'base_estimator__max_depth': [3, 5],\n 'base_estimator__tol': [0.1, 0.01],\n 'learning_rate': [0.5, 1], 'n_estimators': [10, 25]},\n return_train_score=True, verbose=10)"
|
||||
"text/plain": [
|
||||
"GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=2020),\n",
|
||||
" n_jobs=-1,\n",
|
||||
" param_grid=[{'base_estimator': [Stree(C=7, max_depth=5,\n",
|
||||
" split_criteria='max_samples',\n",
|
||||
" tol=0.01)],\n",
|
||||
" 'base_estimator__C': [1, 7, 55],\n",
|
||||
" 'base_estimator__kernel': ['linear'],\n",
|
||||
" 'base_estimator__max_depth': [3, 5, 7],\n",
|
||||
" 'base_estimator__split_criteria': ['max_samples',\n",
|
||||
" 'impurity'],\n",
|
||||
" 'base_e...\n",
|
||||
" 'learning_rate': [0.5, 1], 'n_estimators': [10, 25]},\n",
|
||||
" {'base_estimator': [Stree()],\n",
|
||||
" 'base_estimator__C': [1, 7, 55],\n",
|
||||
" 'base_estimator__gamma': [0.1, 1, 10],\n",
|
||||
" 'base_estimator__kernel': ['rbf'],\n",
|
||||
" 'base_estimator__max_depth': [3, 5, 7],\n",
|
||||
" 'base_estimator__split_criteria': ['max_samples',\n",
|
||||
" 'impurity'],\n",
|
||||
" 'base_estimator__tol': [0.1, 0.01],\n",
|
||||
" 'learning_rate': [0.5, 1],\n",
|
||||
" 'n_estimators': [10, 25]}],\n",
|
||||
" return_train_score=True, verbose=10)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=2020),\n",
|
||||
" n_jobs=-1,\n",
|
||||
" param_grid={'base_estimator': [Stree(C=55, max_depth=3, tol=0.01)],\n",
|
||||
" 'base_estimator__C': [7, 55],\n",
|
||||
" 'base_estimator__kernel': ['linear', 'poly', 'rbf'],\n",
|
||||
" 'base_estimator__max_depth': [3, 5],\n",
|
||||
" 'base_estimator__tol': [0.1, 0.01],\n",
|
||||
" 'learning_rate': [0.5, 1], 'n_estimators': [10, 25]},\n",
|
||||
" return_train_score=True, verbose=10)"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"metadata": {
|
||||
@@ -214,9 +391,31 @@
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": "Best estimator: AdaBoostClassifier(algorithm='SAMME',\n base_estimator=Stree(C=55, max_depth=3, tol=0.01),\n learning_rate=0.5, n_estimators=25, random_state=2020)\nBest hyperparameters: {'base_estimator': Stree(C=55, max_depth=3, tol=0.01), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 3, 'base_estimator__tol': 0.01, 'learning_rate': 0.5, 'n_estimators': 25}\nBest accuracy: 0.9559440559440558\n"
|
||||
"text": [
|
||||
"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n base_estimator=Stree(C=7, max_depth=5,\n split_criteria='max_samples',\n tol=0.01),\n learning_rate=0.5, n_estimators=25, random_state=2020)\nBest hyperparameters: {'base_estimator': Stree(C=7, max_depth=5, split_criteria='max_samples', tol=0.01), 'base_estimator__C': 7, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 5, 'base_estimator__split_criteria': 'max_samples', 'base_estimator__tol': 0.01, 'learning_rate': 0.5, 'n_estimators': 25}\nBest accuracy: 0.9549825174825175\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n",
|
||||
" base_estimator=Stree(C=55, max_depth=3, tol=0.01),\n",
|
||||
" learning_rate=0.5, n_estimators=25, random_state=2020)\n",
|
||||
"\n",
|
||||
"Best hyperparameters: {'base_estimator': Stree(C=55, max_depth=3, tol=0.01), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 3, 'base_estimator__tol': 0.01, 'learning_rate': 0.5, 'n_estimators': 25}\n",
|
||||
"\n",
|
||||
"Best accuracy: 0.9559440559440558"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"source": [
|
||||
"0.9511547662863451"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
@@ -230,12 +429,12 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.6-final"
|
||||
"version": "3.8.4-final"
|
||||
},
|
||||
"orig_nbformat": 2,
|
||||
"kernelspec": {
|
||||
"name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39",
|
||||
"display_name": "Python 3.7.6 64-bit ('general': venv)"
|
||||
"name": "python38464bitgeneralvenv77203c0a6afd4428bd66253ef62753dc",
|
||||
"display_name": "Python 3.8.4 64-bit ('general': venv)"
|
||||
},
|
||||
"colab": {
|
||||
"name": "gridsearch.ipynb",
|
||||
|
Reference in New Issue
Block a user