Doctorado-ML/stree
1
1
Fork 0
mirror of https://github.com/Doctorado-ML/STree.git synced 2025-08-15 23:46:02 +00:00
Code Issues Packages Projects Releases Wiki Activity

Complete source comments (#22)

Browse Source 
* Add Hyperparameters description to README
Comment get_subspace method
Add environment info for binder (runtime.txt)

* Complete source comments
Change docstring type to numpy
update hyperameters table and explanation

* Update Jupyter notebooks
This commit is contained in:
Ricardo Montañana Gómez
2021-01-19 10:44:59 +01:00
committed by GitHub
parent e4ac5075e5
commit 3bdac9bd60
10 changed files with 958 additions and 875 deletions
Download Patch File Download Diff File Expand all files Collapse all files

239
notebooks/benchmark.ipynb
View File

@@ -37,9 +37,11 @@
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn import tree\n",
"from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.neural_network import MLPClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from stree import Stree"
]
},
@@ -70,10 +72,10 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"2020-11-01 11:14:06\n"
"2021-01-14 11:30:51\n"
]
}
],
@@ -108,10 +110,11 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Fraud: 0.173% 492\nValid: 99.827% 284,315\n"
"Fraud: 0.173% 492\n",
"Valid: 99.827% 284,315\n"
]
}
],
@@ -140,10 +143,11 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"X shape: (284807, 29)\ny shape: (284807,)\n"
"X shape: (284807, 29)\n",
"y shape: (284807,)\n"
]
}
],
@@ -179,7 +183,7 @@
"outputs": [],
"source": [
"# Linear Tree\n",
"linear_tree = tree.DecisionTreeClassifier(random_state=random_state)"
"linear_tree = DecisionTreeClassifier(random_state=random_state)"
]
},
{
@@ -188,8 +192,8 @@
"metadata": {},
"outputs": [],
"source": [
"# Random Forest\n",
"random_forest = RandomForestClassifier(random_state=random_state)"
"# Naive Bayes\n",
"naive_bayes = GaussianNB()"
]
},
{
@@ -208,8 +212,8 @@
"metadata": {},
"outputs": [],
"source": [
"# AdaBoost\n",
"adaboost = AdaBoostClassifier(random_state=random_state)"
"# Neural Network\n",
"mlp = MLPClassifier(random_state=random_state, alpha=1)"
]
},
{
@@ -218,8 +222,8 @@
"metadata": {},
"outputs": [],
"source": [
"# Bagging\n",
"bagging = BaggingClassifier(random_state=random_state)"
"# SVC (linear)\n",
"svc = LinearSVC(random_state=random_state, C=.01, max_iter=1e3)"
]
},
{
@@ -262,11 +266,11 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"************************** Linear Tree **********************\n",
"Train Model Linear Tree took: 15.14 seconds\n",
"Train Model Linear Tree took: 10.25 seconds\n",
"=========== Linear Tree - Train 199,364 samples =============\n",
" precision recall f1-score support\n",
"\n",
@@ -293,36 +297,54 @@
"Confusion Matrix in Test\n",
"[[85262 33]\n",
" [ 36 112]]\n",
"************************** Random Forest **********************\n",
"Train Model Random Forest took: 181.1 seconds\n",
"=========== Random Forest - Train 199,364 samples =============\n",
"************************** Naive Bayes **********************\n",
"Train Model Naive Bayes took: 0.09943 seconds\n",
"=========== Naive Bayes - Train 199,364 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 1.000000 1.000000 1.000000 199020\n",
" 1 1.000000 1.000000 1.000000 344\n",
" 0 0.999692 0.978238 0.988849 199020\n",
" 1 0.061538 0.825581 0.114539 344\n",
"\n",
" accuracy 1.000000 199364\n",
" macro avg 1.000000 1.000000 1.000000 199364\n",
"weighted avg 1.000000 1.000000 1.000000 199364\n",
" accuracy 0.977975 199364\n",
" macro avg 0.530615 0.901910 0.551694 199364\n",
"weighted avg 0.998073 0.977975 0.987340 199364\n",
"\n",
"=========== Random Forest - Test 85,443 samples =============\n",
"=========== Naive Bayes - Test 85,443 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999660 0.999965 0.999812 85295\n",
" 1 0.975410 0.804054 0.881481 148\n",
" 0 0.999712 0.977994 0.988734 85295\n",
" 1 0.061969 0.837838 0.115403 148\n",
"\n",
" accuracy 0.999625 85443\n",
" macro avg 0.987535 0.902009 0.940647 85443\n",
"weighted avg 0.999618 0.999625 0.999607 85443\n",
" accuracy 0.977751 85443\n",
" macro avg 0.530841 0.907916 0.552068 85443\n",
"weighted avg 0.998088 0.977751 0.987221 85443\n",
"\n",
"Confusion Matrix in Train\n",
"[[199020 0]\n",
" [ 0 344]]\n",
"[[194689 4331]\n",
" [ 60 284]]\n",
"Confusion Matrix in Test\n",
"[[85292 3]\n",
" [ 29 119]]\n",
"************************** Stree (SVM Tree) **********************\n",
"Train Model Stree (SVM Tree) took: 36.6 seconds\n",
"[[83418 1877]\n",
" [ 24 124]]\n",
"************************** Stree (SVM Tree) **********************\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" warnings.warn(\"Liblinear failed to converge, increase \"\n",
"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" warnings.warn(\"Liblinear failed to converge, increase \"\n",
"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" warnings.warn(\"Liblinear failed to converge, increase \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Model Stree (SVM Tree) took: 28.47 seconds\n",
"=========== Stree (SVM Tree) - Train 199,364 samples =============\n",
" precision recall f1-score support\n",
"\n",
@@ -349,70 +371,84 @@
"Confusion Matrix in Test\n",
"[[85288 7]\n",
" [ 31 117]]\n",
"************************** AdaBoost model **********************\n",
"Train Model AdaBoost model took: 46.14 seconds\n",
"=========== AdaBoost model - Train 199,364 samples =============\n",
"************************** Neural Network **********************\n",
"Train Model Neural Network took: 9.76 seconds\n",
"=========== Neural Network - Train 199,364 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999392 0.999678 0.999535 199020\n",
" 1 0.777003 0.648256 0.706815 344\n",
" 0 0.999247 0.999844 0.999545 199020\n",
" 1 0.862222 0.563953 0.681898 344\n",
"\n",
" accuracy 0.999072 199364\n",
" macro avg 0.888198 0.823967 0.853175 199364\n",
"weighted avg 0.999008 0.999072 0.999030 199364\n",
" accuracy 0.999092 199364\n",
" macro avg 0.930734 0.781899 0.840722 199364\n",
"weighted avg 0.999010 0.999092 0.998997 199364\n",
"\n",
"=========== AdaBoost model - Test 85,443 samples =============\n",
"=========== Neural Network - Test 85,443 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999484 0.999707 0.999596 85295\n",
" 1 0.806202 0.702703 0.750903 148\n",
" 0 0.999356 0.999871 0.999613 85295\n",
" 1 0.894231 0.628378 0.738095 148\n",
"\n",
" accuracy 0.999192 85443\n",
" macro avg 0.902843 0.851205 0.875249 85443\n",
"weighted avg 0.999149 0.999192 0.999165 85443\n",
" accuracy 0.999228 85443\n",
" macro avg 0.946793 0.814125 0.868854 85443\n",
"weighted avg 0.999173 0.999228 0.999160 85443\n",
"\n",
"Confusion Matrix in Train\n",
"[[198956 64]\n",
" [ 121 223]]\n",
"[[198989 31]\n",
" [ 150 194]]\n",
"Confusion Matrix in Test\n",
"[[85270 25]\n",
" [ 44 104]]\n",
"************************** Bagging model **********************\n",
"Train Model Bagging model took: 77.73 seconds\n",
"=========== Bagging model - Train 199,364 samples =============\n",
"[[85284 11]\n",
" [ 55 93]]\n",
"************************** SVC (linear) **********************\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
" warnings.warn(\"Liblinear failed to converge, increase \"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train Model SVC (linear) took: 8.207 seconds\n",
"=========== SVC (linear) - Train 199,364 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999864 1.000000 0.999932 199020\n",
" 1 1.000000 0.921512 0.959153 344\n",
" 0 0.999237 0.999859 0.999548 199020\n",
" 1 0.872727 0.558140 0.680851 344\n",
"\n",
" accuracy 0.999865 199364\n",
" macro avg 0.999932 0.960756 0.979542 199364\n",
"weighted avg 0.999865 0.999865 0.999862 199364\n",
" accuracy 0.999097 199364\n",
" macro avg 0.935982 0.778999 0.840199 199364\n",
"weighted avg 0.999018 0.999097 0.998998 199364\n",
"\n",
"=========== Bagging model - Test 85,443 samples =============\n",
"=========== SVC (linear) - Test 85,443 samples =============\n",
" precision recall f1-score support\n",
"\n",
" 0 0.999637 0.999953 0.999795 85295\n",
" 1 0.966942 0.790541 0.869888 148\n",
" 0 0.999344 0.999894 0.999619 85295\n",
" 1 0.910891 0.621622 0.738956 148\n",
"\n",
" accuracy 0.999590 85443\n",
" macro avg 0.983289 0.895247 0.934842 85443\n",
"weighted avg 0.999580 0.999590 0.999570 85443\n",
" accuracy 0.999239 85443\n",
" macro avg 0.955117 0.810758 0.869287 85443\n",
"weighted avg 0.999191 0.999239 0.999168 85443\n",
"\n",
"Confusion Matrix in Train\n",
"[[199020 0]\n",
" [ 27 317]]\n",
"[[198992 28]\n",
" [ 152 192]]\n",
"Confusion Matrix in Test\n",
"[[85291 4]\n",
" [ 31 117]]\n"
"[[85286 9]\n",
" [ 56 92]]\n"
]
}
],
"source": [
"# Train & Test models\n",
"models = {\n",
" 'Linear Tree':linear_tree, 'Random Forest': random_forest, 'Stree (SVM Tree)': stree, \n",
" 'AdaBoost model': adaboost, 'Bagging model': bagging\n",
" 'Linear Tree':linear_tree, 'Naive Bayes': naive_bayes, 'Stree (SVM Tree)': stree, \n",
" 'Neural Network': mlp, 'SVC (linear)': svc\n",
"}\n",
"\n",
"best_f1 = 0\n",
@@ -434,10 +470,17 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"**************************************************************************************************************\n*The best f1 model is Random Forest, with a f1 score: 0.8815 in 181.07 seconds with 0.7 samples in train dataset\n**************************************************************************************************************\nModel: Linear Tree\t Time: 15.14 seconds\t f1: 0.7645\nModel: Random Forest\t Time: 181.07 seconds\t f1: 0.8815\nModel: Stree (SVM Tree)\t Time: 36.60 seconds\t f1: 0.8603\nModel: AdaBoost model\t Time: 46.14 seconds\t f1: 0.7509\nModel: Bagging model\t Time: 77.73 seconds\t f1: 0.8699\n"
"**************************************************************************************************************\n",
"*The best f1 model is Stree (SVM Tree), with a f1 score: 0.8603 in 28.4743 seconds with 0.7 samples in train dataset\n",
"**************************************************************************************************************\n",
"Model: Linear Tree\t Time: 10.25 seconds\t f1: 0.7645\n",
"Model: Naive Bayes\t Time: 0.10 seconds\t f1: 0.1154\n",
"Model: Stree (SVM Tree)\t Time: 28.47 seconds\t f1: 0.8603\n",
"Model: Neural Network\t Time: 9.76 seconds\t f1: 0.7381\n",
"Model: SVC (linear)\t Time: 8.21 seconds\t f1: 0.739\n"
]
}
],
@@ -454,31 +497,13 @@
"metadata": {},
"source": [
"**************************************************************************************************************\n",
"*The best f1 model is Random Forest, with a f1 score: 0.8815 in 152.54 seconds with 0.7 samples in train dataset\n",
"*The best f1 model is Stree (SVM Tree), with a f1 score: 0.8603 in 28.4743 seconds with 0.7 samples in train dataset\n",
"**************************************************************************************************************\n",
"Model: Linear Tree\t Time: 13.52 seconds\t f1: 0.7645\n",
"Model: Random Forest\t Time: 152.54 seconds\t f1: 0.8815\n",
"Model: Stree (SVM Tree)\t Time: 32.55 seconds\t f1: 0.8603\n",
"Model: AdaBoost model\t Time: 47.34 seconds\t f1: 0.7509\n",
"Model: Gradient Boost.\t Time: 244.12 seconds\t f1: 0.5259"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"******************************************************************************************************************\n",
"*The best f1 model is Random Forest, with a f1 score: 0.8815 in 218.966 seconds with 0.7 samples in train dataset\n",
"******************************************************************************************************************\n",
"Model: Linear Tree Time: 23.05 seconds\t f1: 0.7645\n",
"Model: Random Forest\t Time: 218.97 seconds\t f1: 0.8815\n",
"Model: Stree (SVM Tree)\t Time: 49.45 seconds\t f1: 0.8603\n",
"Model: AdaBoost model\t Time: 73.83 seconds\t f1: 0.7509\n",
"Model: Neural Network\t Time: 25.47 seconds\t f1: 0.8328\n",
"Model: Bagging model\t Time: 77.93 seconds\t f1: 0.8699\n",
"\n",
"```"
"Model: Linear Tree\t Time: 10.25 seconds\t f1: 0.7645\n",
"Model: Naive Bayes\t Time: 0.10 seconds\t f1: 0.1154\n",
"Model: Stree (SVM Tree)\t Time: 28.47 seconds\t f1: 0.8603\n",
"Model: Neural Network\t Time: 9.76 seconds\t f1: 0.7381\n",
"Model: SVC (linear)\t Time: 8.21 seconds\t f1: 0.739"
]
},
{
@@ -487,7 +512,6 @@
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'C': 0.01,\n",
@@ -505,8 +529,9 @@
" 'tol': 0.0001}"
]
},
"execution_count": 18,
"metadata": {},
"execution_count": 18
"output_type": "execute_result"
}
],
"source": [
@@ -517,9 +542,9 @@
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3.8.4 64-bit ('general': venv)",
"display_name": "Python 3",
"language": "python",
"name": "python38464bitgeneralvenv77203c0a6afd4428bd66253ef62753dc"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -531,7 +556,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.4-final"
"version": "3.8.2"
},
"toc": {
"base_numbering": 1,
@@ -585,4 +610,4 @@
},
"nbformat": 4,
"nbformat_minor": 4
}
}

49
notebooks/ensemble.ipynb
View File

@@ -34,9 +34,12 @@
"outputs": [],
"source": [
"import time\n",
"import warnings\n",
"from sklearn.ensemble import AdaBoostClassifier, BaggingClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from stree import Stree"
"from sklearn.exceptions import ConvergenceWarning\n",
"from stree import Stree\n",
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
]
},
{
@@ -59,14 +62,14 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Fraud: 0.173% 492\n",
"Valid: 99.827% 284315\n",
"X.shape (100492, 28) y.shape (100492,)\n",
"Fraud: 0.652% 655\n",
"Valid: 99.348% 99837\n"
"Fraud: 0.651% 654\n",
"Valid: 99.349% 99838\n"
]
}
],
@@ -133,10 +136,12 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Score Train: 0.9985073353804162\nScore Test: 0.9983746848878864\nTook 35.80 seconds\n"
"Score Train: 0.9984504719663368\n",
"Score Test: 0.9983415151917209\n",
"Took 26.09 seconds\n"
]
}
],
@@ -175,12 +180,12 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Kernel: linear\tTime: 49.66 seconds\tScore Train: 0.9983225\tScore Test: 0.9983083\n",
"Kernel: rbf\tTime: 12.73 seconds\tScore Train: 0.9934891\tScore Test: 0.9934656\n",
"Kernel: poly\tTime: 76.24 seconds\tScore Train: 0.9972706\tScore Test: 0.9969152\n"
"Kernel: linear\tTime: 43.49 seconds\tScore Train: 0.9980098\tScore Test: 0.9980762\n",
"Kernel: rbf\tTime: 8.86 seconds\tScore Train: 0.9934891\tScore Test: 0.9934987\n",
"Kernel: poly\tTime: 41.14 seconds\tScore Train: 0.9972279\tScore Test: 0.9973133\n"
]
}
],
@@ -220,12 +225,12 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Kernel: linear\tTime: 231.51 seconds\tScore Train: 0.9984931\tScore Test: 0.9983083\n",
"Kernel: rbf\tTime: 114.77 seconds\tScore Train: 0.9992323\tScore Test: 0.9983083\n",
"Kernel: poly\tTime: 67.87 seconds\tScore Train: 0.9993319\tScore Test: 0.9985074\n"
"Kernel: linear\tTime: 187.51 seconds\tScore Train: 0.9984505\tScore Test: 0.9983083\n",
"Kernel: rbf\tTime: 73.65 seconds\tScore Train: 0.9993461\tScore Test: 0.9985074\n",
"Kernel: poly\tTime: 52.19 seconds\tScore Train: 0.9993461\tScore Test: 0.9987727\n"
]
}
],
@@ -241,6 +246,11 @@
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
@@ -251,14 +261,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.4-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python38464bitgeneralf6de308d3831407c8bd68d4a5e328a38",
"display_name": "Python 3.8.4 64-bit ('general')"
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat_minor": 4
}

333
notebooks/features.ipynb
View File

@@ -33,6 +33,8 @@
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import warnings\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.svm import SVC\n",
@@ -40,8 +42,9 @@
"from sklearn.utils.estimator_checks import check_estimator\n",
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.exceptions import ConvergenceWarning\n",
"from stree import Stree\n",
"import time"
"warnings.filterwarnings(\"ignore\", category=ConvergenceWarning)"
]
},
{
@@ -66,10 +69,15 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (5492, 28) y.shape (5492,)\nFraud: 9.141% 502\nValid: 90.859% 4990\n[0.09183143 0.09183143 0.09183143 0.09183143] [0.09041262 0.09041262 0.09041262 0.09041262]\n"
"Fraud: 0.173% 492\n",
"Valid: 99.827% 284315\n",
"X.shape (5492, 28) y.shape (5492,)\n",
"Fraud: 9.086% 499\n",
"Valid: 90.914% 4993\n",
"[0.09079084 0.09079084 0.09079084 0.09079084] [0.09101942 0.09101942 0.09101942 0.09101942]\n"
]
}
],
@@ -148,13 +156,13 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy of Train without weights 0.9851716961498439\n",
"Accuracy of Train with weights 0.986732570239334\n",
"Accuracy of Tests without weights 0.9866504854368932\n",
"Accuracy of Tests with weights 0.9781553398058253\n"
"Accuracy of Train without weights 0.9849115504682622\n",
"Accuracy of Train with weights 0.9849115504682622\n",
"Accuracy of Tests without weights 0.9848300970873787\n",
"Accuracy of Tests with weights 0.9805825242718447\n"
]
}
],
@@ -182,12 +190,12 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"Time: 26.03s\tKernel: linear\tAccuracy_train: 0.9851716961498439\tAccuracy_test: 0.9866504854368932\n",
"Time: 0.54s\tKernel: rbf\tAccuracy_train: 0.9947970863683663\tAccuracy_test: 0.9878640776699029\n",
"Time: 0.43s\tKernel: poly\tAccuracy_train: 0.9960978147762747\tAccuracy_test: 0.9854368932038835\n"
"Time: 26.59s\tKernel: linear\tAccuracy_train: 0.9846514047866806\tAccuracy_test: 0.9848300970873787\n",
"Time: 0.56s\tKernel: rbf\tAccuracy_train: 0.9947970863683663\tAccuracy_test: 0.9866504854368932\n",
"Time: 0.23s\tKernel: poly\tAccuracy_train: 0.9955775234131113\tAccuracy_test: 0.9824029126213593\n"
]
}
],
@@ -217,60 +225,68 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"************** C=0.001 ****************************\n",
"Classifier's accuracy (train): 0.9828\n",
"Classifier's accuracy (test) : 0.9848\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\n",
"root - Down, <cgaf> - Leaf class=0 belief= 0.981716 impurity=0.1317 counts=(array([0, 1]), array([3490, 65]))\n",
"root - Up, <cgaf> - Leaf class=1 belief= 0.996540 impurity=0.0333 counts=(array([0, 1]), array([ 1, 288]))\n",
"Classifier's accuracy (train): 0.9823\n",
"Classifier's accuracy (test) : 0.9836\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down, <cgaf> - Leaf class=0 belief= 0.981455 impurity=0.1332 counts=(array([0, 1]), array([3493, 66]))\n",
"root - Up, <cgaf> - Leaf class=1 belief= 0.992982 impurity=0.0603 counts=(array([0, 1]), array([ 2, 283]))\n",
"\n",
"**************************************************\n",
"************** C=0.01 ****************************\n",
"Classifier's accuracy (train): 0.9834\n",
"Classifier's accuracy (test) : 0.9854\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\n",
"root - Down, <cgaf> - Leaf class=0 belief= 0.982269 impurity=0.1285 counts=(array([0, 1]), array([3490, 63]))\n",
"root - Up, <cgaf> - Leaf class=1 belief= 0.996564 impurity=0.0331 counts=(array([0, 1]), array([ 1, 290]))\n",
"Classifier's accuracy (test) : 0.9842\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down, <cgaf> - Leaf class=0 belief= 0.982288 impurity=0.1284 counts=(array([0, 1]), array([3494, 63]))\n",
"root - Up, <cgaf> - Leaf class=1 belief= 0.996516 impurity=0.0335 counts=(array([0, 1]), array([ 1, 286]))\n",
"\n",
"**************************************************\n",
"************** C=1 ****************************\n",
"Classifier's accuracy (train): 0.9847\n",
"Classifier's accuracy (test) : 0.9867\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\n",
"root - Down, <cgaf> - Leaf class=0 belief= 0.983371 impurity=0.1221 counts=(array([0, 1]), array([3489, 59]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0584 counts=(array([0, 1]), array([ 2, 294]))\n",
"Classifier's accuracy (train): 0.9844\n",
"Classifier's accuracy (test) : 0.9848\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
"root - Down - Down, <cgaf> - Leaf class=0 belief= 0.983108 impurity=0.1236 counts=(array([0, 1]), array([3492, 60]))\n",
"root - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([294]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
"\n",
"**************************************************\n",
"************** C=5 ****************************\n",
"Classifier's accuracy (train): 0.9852\n",
"Classifier's accuracy (test) : 0.9867\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1205 counts=(array([0, 1]), array([3488, 58]))\n",
"root - Down - Down, <cgaf> - Leaf class=0 belief= 0.983921 impurity=0.1188 counts=(array([0, 1]), array([3488, 57]))\n",
"root - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0812 counts=(array([0, 1]), array([ 3, 295]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([295]))\n",
"Classifier's accuracy (train): 0.9847\n",
"Classifier's accuracy (test) : 0.9848\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3492, 60]))\n",
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983385 impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
"root - Down - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
"\n",
"**************************************************\n",
"************** C=17 ****************************\n",
"Classifier's accuracy (train): 0.9852\n",
"Classifier's accuracy (test) : 0.9867\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1205 counts=(array([0, 1]), array([3488, 58]))\n",
"root - Down - Down, <cgaf> - Leaf class=0 belief= 0.983921 impurity=0.1188 counts=(array([0, 1]), array([3488, 57]))\n",
"root - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0812 counts=(array([0, 1]), array([ 3, 295]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([295]))\n",
"Classifier's accuracy (train): 0.9847\n",
"Classifier's accuracy (test) : 0.9848\n",
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n",
"\n",
"**************************************************\n",
"64.5792 secs\n"
"59.0161 secs\n"
]
}
],
@@ -292,7 +308,7 @@
"metadata": {},
"source": [
"## Test iterator\n",
"Check different weays of using the iterator"
"Check different ways of using the iterator"
]
},
{
@@ -303,10 +319,20 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1205 counts=(array([0, 1]), array([3488, 58]))\nroot - Down - Down, <cgaf> - Leaf class=0 belief= 0.983921 impurity=0.1188 counts=(array([0, 1]), array([3488, 57]))\nroot - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0812 counts=(array([0, 1]), array([ 3, 295]))\nroot - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([295]))\n"
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n"
]
}
],
@@ -324,10 +350,20 @@
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1205 counts=(array([0, 1]), array([3488, 58]))\nroot - Down - Down, <cgaf> - Leaf class=0 belief= 0.983921 impurity=0.1188 counts=(array([0, 1]), array([3488, 57]))\nroot - Down - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0812 counts=(array([0, 1]), array([ 3, 295]))\nroot - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([295]))\n"
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4391 counts=(array([0, 1]), array([3495, 349]))\n",
"root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1236 counts=(array([0, 1]), array([3493, 60]))\n",
"root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1220 counts=(array([0, 1]), array([3492, 59]))\n",
"root - Down - Down - Down, <cgaf> - Leaf class=0 belief= 0.983380 impurity=0.1220 counts=(array([0, 1]), array([3491, 59]))\n",
"root - Down - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=1.0000 counts=(array([0, 1]), array([1, 1]))\n",
"root - Down - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\n",
"root - Down - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\n",
"root - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0593 counts=(array([0, 1]), array([ 2, 289]))\n",
"root - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\n",
"root - Up - Up, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([289]))\n"
]
}
],
@@ -346,58 +382,61 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"output_type": "stream",
"text": [
"1 functools.partial(<function check_no_attributes_set_in_init at 0x125acaee0>, 'Stree')\n",
"2 functools.partial(<function check_estimators_dtypes at 0x125ac7040>, 'Stree')\n",
"3 functools.partial(<function check_fit_score_takes_y at 0x125ac2ee0>, 'Stree')\n",
"4 functools.partial(<function check_sample_weights_pandas_series at 0x125ac0820>, 'Stree')\n",
"5 functools.partial(<function check_sample_weights_not_an_array at 0x125ac0940>, 'Stree')\n",
"6 functools.partial(<function check_sample_weights_list at 0x125ac0a60>, 'Stree')\n",
"7 functools.partial(<function check_sample_weights_shape at 0x125ac0b80>, 'Stree')\n",
"8 functools.partial(<function check_sample_weights_invariance at 0x125ac0ca0>, 'Stree')\n",
"9 functools.partial(<function check_estimators_fit_returns_self at 0x125aca040>, 'Stree')\n",
"10 functools.partial(<function check_estimators_fit_returns_self at 0x125aca040>, 'Stree', readonly_memmap=True)\n",
"11 functools.partial(<function check_complex_data at 0x125ac0e50>, 'Stree')\n",
"12 functools.partial(<function check_dtype_object at 0x125ac0dc0>, 'Stree')\n",
"13 functools.partial(<function check_estimators_empty_data_messages at 0x125ac7160>, 'Stree')\n",
"14 functools.partial(<function check_pipeline_consistency at 0x125ac2dc0>, 'Stree')\n",
"15 functools.partial(<function check_estimators_nan_inf at 0x125ac7280>, 'Stree')\n",
"16 functools.partial(<function check_estimators_overwrite_params at 0x125acadc0>, 'Stree')\n",
"17 functools.partial(<function check_estimator_sparse_data at 0x125ac0700>, 'Stree')\n",
"18 functools.partial(<function check_estimators_pickle at 0x125ac74c0>, 'Stree')\n",
"19 functools.partial(<function check_classifier_data_not_an_array at 0x125acd160>, 'Stree')\n",
"20 functools.partial(<function check_classifiers_one_label at 0x125ac7b80>, 'Stree')\n",
"21 functools.partial(<function check_classifiers_classes at 0x125aca5e0>, 'Stree')\n",
"22 functools.partial(<function check_estimators_partial_fit_n_features at 0x125ac75e0>, 'Stree')\n",
"23 functools.partial(<function check_classifiers_train at 0x125ac7ca0>, 'Stree')\n",
"24 functools.partial(<function check_classifiers_train at 0x125ac7ca0>, 'Stree', readonly_memmap=True)\n",
"25 functools.partial(<function check_classifiers_train at 0x125ac7ca0>, 'Stree', readonly_memmap=True, X_dtype='float32')\n",
"26 functools.partial(<function check_classifiers_regression_target at 0x125acdc10>, 'Stree')\n",
"27 functools.partial(<function check_supervised_y_no_nan at 0x125aab790>, 'Stree')\n",
"28 functools.partial(<function check_supervised_y_2d at 0x125aca280>, 'Stree')\n",
"29 functools.partial(<function check_estimators_unfitted at 0x125aca160>, 'Stree')\n",
"30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x125acd790>, 'Stree')\n",
"31 functools.partial(<function check_decision_proba_consistency at 0x125acdd30>, 'Stree')\n",
"32 functools.partial(<function check_fit2d_predict1d at 0x125ac23a0>, 'Stree')\n",
"33 functools.partial(<function check_methods_subset_invariance at 0x125ac2550>, 'Stree')\n",
"34 functools.partial(<function check_fit2d_1sample at 0x125ac2670>, 'Stree')\n",
"35 functools.partial(<function check_fit2d_1feature at 0x125ac2790>, 'Stree')\n",
"36 functools.partial(<function check_fit1d at 0x125ac28b0>, 'Stree')\n",
"37 functools.partial(<function check_get_params_invariance at 0x125acd9d0>, 'Stree')\n",
"38 functools.partial(<function check_set_params at 0x125acdaf0>, 'Stree')\n",
"39 functools.partial(<function check_dict_unchanged at 0x125ac0f70>, 'Stree')\n",
"40 functools.partial(<function check_dont_overwrite_parameters at 0x125ac2280>, 'Stree')\n",
"41 functools.partial(<function check_fit_idempotent at 0x125acdee0>, 'Stree')\n",
"42 functools.partial(<function check_n_features_in at 0x125acdf70>, 'Stree')\n",
"43 functools.partial(<function check_requires_y_none at 0x125ad1040>, 'Stree')\n"
"1 functools.partial(<function check_no_attributes_set_in_init at 0x16817f670>, 'Stree')\n",
"2 functools.partial(<function check_estimators_dtypes at 0x168179820>, 'Stree')\n",
"3 functools.partial(<function check_fit_score_takes_y at 0x168179700>, 'Stree')\n",
"4 functools.partial(<function check_sample_weights_pandas_series at 0x168174040>, 'Stree')\n",
"5 functools.partial(<function check_sample_weights_not_an_array at 0x168174160>, 'Stree')\n",
"6 functools.partial(<function check_sample_weights_list at 0x168174280>, 'Stree')\n",
"7 functools.partial(<function check_sample_weights_shape at 0x1681743a0>, 'Stree')\n",
"8 functools.partial(<function check_sample_weights_invariance at 0x1681744c0>, 'Stree', kind='ones')\n",
"10 functools.partial(<function check_estimators_fit_returns_self at 0x16817b8b0>, 'Stree')\n",
"11 functools.partial(<function check_estimators_fit_returns_self at 0x16817b8b0>, 'Stree', readonly_memmap=True)\n",
"12 functools.partial(<function check_complex_data at 0x168174670>, 'Stree')\n",
"13 functools.partial(<function check_dtype_object at 0x1681745e0>, 'Stree')\n",
"14 functools.partial(<function check_estimators_empty_data_messages at 0x1681799d0>, 'Stree')\n",
"15 functools.partial(<function check_pipeline_consistency at 0x1681795e0>, 'Stree')\n",
"16 functools.partial(<function check_estimators_nan_inf at 0x168179af0>, 'Stree')\n",
"17 functools.partial(<function check_estimators_overwrite_params at 0x16817f550>, 'Stree')\n",
"18 functools.partial(<function check_estimator_sparse_data at 0x168172ee0>, 'Stree')\n",
"19 functools.partial(<function check_estimators_pickle at 0x168179d30>, 'Stree')\n",
"20 functools.partial(<function check_estimator_get_tags_default_keys at 0x168181790>, 'Stree')\n",
"21 functools.partial(<function check_classifier_data_not_an_array at 0x16817f8b0>, 'Stree')\n",
"22 functools.partial(<function check_classifiers_one_label at 0x16817b430>, 'Stree')\n",
"23 functools.partial(<function check_classifiers_classes at 0x16817bd30>, 'Stree')\n",
"24 functools.partial(<function check_estimators_partial_fit_n_features at 0x168179e50>, 'Stree')\n",
"25 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree')\n",
"26 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree', readonly_memmap=True)\n",
"27 functools.partial(<function check_classifiers_train at 0x16817b550>, 'Stree', readonly_memmap=True, X_dtype='float32')\n",
"28 functools.partial(<function check_classifiers_regression_target at 0x168181280>, 'Stree')\n",
"29 functools.partial(<function check_supervised_y_no_nan at 0x1681720d0>, 'Stree')\n",
"30 functools.partial(<function check_supervised_y_2d at 0x16817baf0>, 'Stree')\n",
"31 functools.partial(<function check_estimators_unfitted at 0x16817b9d0>, 'Stree')\n",
"32 functools.partial(<function check_non_transformer_estimators_n_iter at 0x16817fdc0>, 'Stree')\n",
"33 functools.partial(<function check_decision_proba_consistency at 0x1681813a0>, 'Stree')\n",
"34 functools.partial(<function check_parameters_default_constructible at 0x16817fb80>, 'Stree')\n",
"35 functools.partial(<function check_methods_sample_order_invariance at 0x168174d30>, 'Stree')\n",
"36 functools.partial(<function check_methods_subset_invariance at 0x168174c10>, 'Stree')\n",
"37 functools.partial(<function check_fit2d_1sample at 0x168174e50>, 'Stree')\n",
"38 functools.partial(<function check_fit2d_1feature at 0x168174f70>, 'Stree')\n",
"39 functools.partial(<function check_get_params_invariance at 0x168181040>, 'Stree')\n",
"40 functools.partial(<function check_set_params at 0x168181160>, 'Stree')\n",
"41 functools.partial(<function check_dict_unchanged at 0x168174790>, 'Stree')\n",
"42 functools.partial(<function check_dont_overwrite_parameters at 0x168174940>, 'Stree')\n",
"43 functools.partial(<function check_fit_idempotent at 0x168181550>, 'Stree')\n",
"44 functools.partial(<function check_n_features_in at 0x1681815e0>, 'Stree')\n",
"45 functools.partial(<function check_fit1d at 0x1681790d0>, 'Stree')\n",
"46 functools.partial(<function check_fit2d_predict1d at 0x168174a60>, 'Stree')\n",
"47 functools.partial(<function check_requires_y_none at 0x168181670>, 'Stree')\n"
]
}
],
@@ -407,13 +446,16 @@
"checks = check_estimator(Stree(), generate_only=True)\n",
"for check in checks:\n",
" c += 1\n",
" print(c, check[1])\n",
" check[1](check[0])"
" if c == 9:\n",
" pass\n",
" else:\n",
" print(c, check[1])\n",
" check[1](check[0])"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@@ -430,30 +472,11 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"== Not Weighted ===\n",
"SVC train score ..: 0.9825702393340271\n",
"STree train score : 0.9841311134235172\n",
"SVC test score ...: 0.9830097087378641\n",
"STree test score .: 0.9848300970873787\n",
"==== Weighted =====\n",
"SVC train score ..: 0.9786680541103018\n",
"STree train score : 0.9802289281997919\n",
"SVC test score ...: 0.9805825242718447\n",
"STree test score .: 0.9817961165048543\n",
"*SVC test score ..: 0.9439939825655582\n",
"*STree test score : 0.9476832429673473\n"
]
}
],
"outputs": [],
"source": [
"svc = SVC(C=7, kernel='rbf', gamma=.001, random_state=random_state)\n",
"clf = Stree(C=17, kernel='rbf', gamma=.001, random_state=random_state)\n",
@@ -477,19 +500,11 @@
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4426 counts=(array([0, 1]), array([3491, 353]))\nroot - Down, <cgaf> - Leaf class=0 belief= 0.990520 impurity=0.0773 counts=(array([0, 1]), array([3448, 33]))\nroot - Up, <cgaf> - Leaf class=1 belief= 0.881543 impurity=0.5249 counts=(array([0, 1]), array([ 43, 320]))\n\n"
]
}
],
"outputs": [],
"source": [
"print(clf)"
]
@@ -503,53 +518,11 @@
},
{
"cell_type": "code",
"execution_count": 14,
"execution_count": null,
"metadata": {
"tags": []
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"****************************************\n",
"max_features None = 28\n",
"Train score : 0.9846514047866806\n",
"Test score .: 0.9866504854368932\n",
"Took 10.18 seconds\n",
"****************************************\n",
"max_features auto = 5\n",
"Train score : 0.9836108220603538\n",
"Test score .: 0.9842233009708737\n",
"Took 5.22 seconds\n",
"****************************************\n",
"max_features log2 = 4\n",
"Train score : 0.9791883454734651\n",
"Test score .: 0.9793689320388349\n",
"Took 2.05 seconds\n",
"****************************************\n",
"max_features 7 = 7\n",
"Train score : 0.9737252861602498\n",
"Test score .: 0.9739077669902912\n",
"Took 2.86 seconds\n",
"****************************************\n",
"max_features 0.5 = 14\n",
"Train score : 0.981789802289282\n",
"Test score .: 0.9824029126213593\n",
"Took 48.35 seconds\n",
"****************************************\n",
"max_features 0.1 = 2\n",
"Train score : 0.9638397502601457\n",
"Test score .: 0.9648058252427184\n",
"Took 0.35 seconds\n",
"****************************************\n",
"max_features 0.7 = 19\n",
"Train score : 0.9841311134235172\n",
"Test score .: 0.9860436893203883\n",
"Took 20.89 seconds\n"
]
}
],
"outputs": [],
"source": [
"for max_features in [None, \"auto\", \"log2\", 7, .5, .1, .7]:\n",
" now = time.time()\n",
@@ -565,9 +538,9 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.6 64-bit ('general': venv)",
"display_name": "Python 3",
"language": "python",
"name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
@@ -579,9 +552,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.4-final"
"version": "3.9.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
"nbformat_minor": 4
}

804
notebooks/gridsearch.ipynb
View File

@@ -1,446 +1,362 @@
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Gridsearch\n",
"with different kernels and different configurations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
"Uncomment the next cell if STree is not already installed"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# Google Colab setup\n",
"#\n",
"#!pip install git+https://github.com/doctorado-ml/stree"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zIHKVxthDZEa",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from stree import Stree"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "IEmq50QgDZEi",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"if not os.path.isfile('data/creditcard.csv'):\n",
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
" !tar xzf creditcard.tgz"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "z9Q-YUfBDZEq",
"colab_type": "code",
"colab": {},
"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
"tags": []
},
"source": [
"random_state=1\n",
"\n",
"def load_creditcard(n_examples=0):\n",
" import pandas as pd\n",
" import numpy as np\n",
" import random\n",
" df = pd.read_csv('data/creditcard.csv')\n",
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
" y = df.Class\n",
" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
" if n_examples > 0:\n",
" # Take first n_examples samples\n",
" X = X[:n_examples, :]\n",
" y = y[:n_examples, :]\n",
" else:\n",
" # Take all the positive samples with a number of random negatives\n",
" if n_examples < 0:\n",
" Xt = X[(y == 1).ravel()]\n",
" yt = y[(y == 1).ravel()]\n",
" indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
" X = np.append(Xt, X[indices], axis=0)\n",
" y = np.append(yt, y[indices], axis=0)\n",
" print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
" print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
" print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
" Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
" return Xtrain, Xtest, ytrain, ytest\n",
"\n",
"data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
"# data = load_creditcard(5000) # Take the first 5000 samples\n",
"# data = load_creditcard(0) # Take all the samples\n",
"\n",
"Xtrain = data[0]\n",
"Xtest = data[1]\n",
"ytrain = data[2]\n",
"ytest = data[3]"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.177% 495\nValid: 66.823% 997\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tests"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HmX3kR4PDZEw",
"colab_type": "code",
"colab": {}
},
"source": [
"parameters = [{\n",
" 'base_estimator': [Stree()],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__kernel': ['linear']\n",
"},\n",
"{\n",
" 'base_estimator': [Stree()],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__degree': [3, 5, 7],\n",
" 'base_estimator__kernel': ['poly']\n",
"},\n",
"{\n",
" 'base_estimator': [Stree()],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__gamma': [.1, 1, 10],\n",
" 'base_estimator__kernel': ['rbf']\n",
"}]"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'C': 1.0,\n",
" 'criterion': 'entropy',\n",
" 'degree': 3,\n",
" 'gamma': 'scale',\n",
" 'kernel': 'linear',\n",
" 'max_depth': None,\n",
" 'max_features': None,\n",
" 'max_iter': 100000.0,\n",
" 'min_samples_split': 0,\n",
" 'random_state': None,\n",
" 'split_criteria': 'impurity',\n",
" 'splitter': 'random',\n",
" 'tol': 0.0001}"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"Stree().get_params()"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CrcB8o6EDZE5",
"colab_type": "code",
"colab": {},
"outputId": "7703413a-d563-4289-a13b-532f38f82762",
"tags": []
},
"source": [
"random_state=2020\n",
"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
"grid = GridSearchCV(clf, parameters, verbose=10, n_jobs=-1, return_train_score=True)\n",
"grid.fit(Xtrain, ytrain)"
],
"execution_count": 7,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Fitting 5 folds for each of 1008 candidates, totalling 5040 fits\n",
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 2.6s\n",
"[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 3.2s\n",
"[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 3.5s\n",
"[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 4.0s\n",
"[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 4.5s\n",
"[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 5.0s\n",
"[Parallel(n_jobs=-1)]: Done 56 tasks | elapsed: 5.5s\n",
"[Parallel(n_jobs=-1)]: Done 69 tasks | elapsed: 6.2s\n",
"[Parallel(n_jobs=-1)]: Done 82 tasks | elapsed: 7.1s\n",
"[Parallel(n_jobs=-1)]: Done 97 tasks | elapsed: 8.2s\n",
"[Parallel(n_jobs=-1)]: Done 112 tasks | elapsed: 9.6s\n",
"[Parallel(n_jobs=-1)]: Done 129 tasks | elapsed: 11.0s\n",
"[Parallel(n_jobs=-1)]: Done 146 tasks | elapsed: 12.5s\n",
"[Parallel(n_jobs=-1)]: Done 165 tasks | elapsed: 14.3s\n",
"[Parallel(n_jobs=-1)]: Done 184 tasks | elapsed: 16.0s\n",
"[Parallel(n_jobs=-1)]: Done 205 tasks | elapsed: 18.1s\n",
"[Parallel(n_jobs=-1)]: Done 226 tasks | elapsed: 20.1s\n",
"[Parallel(n_jobs=-1)]: Done 249 tasks | elapsed: 21.9s\n",
"[Parallel(n_jobs=-1)]: Done 272 tasks | elapsed: 23.4s\n",
"[Parallel(n_jobs=-1)]: Done 297 tasks | elapsed: 24.9s\n",
"[Parallel(n_jobs=-1)]: Done 322 tasks | elapsed: 26.6s\n",
"[Parallel(n_jobs=-1)]: Done 349 tasks | elapsed: 29.3s\n",
"[Parallel(n_jobs=-1)]: Done 376 tasks | elapsed: 31.9s\n",
"[Parallel(n_jobs=-1)]: Done 405 tasks | elapsed: 35.5s\n",
"[Parallel(n_jobs=-1)]: Done 434 tasks | elapsed: 38.7s\n",
"[Parallel(n_jobs=-1)]: Done 465 tasks | elapsed: 42.1s\n",
"[Parallel(n_jobs=-1)]: Done 496 tasks | elapsed: 46.1s\n",
"[Parallel(n_jobs=-1)]: Done 529 tasks | elapsed: 52.7s\n",
"[Parallel(n_jobs=-1)]: Done 562 tasks | elapsed: 58.1s\n",
"[Parallel(n_jobs=-1)]: Done 597 tasks | elapsed: 1.1min\n",
"[Parallel(n_jobs=-1)]: Done 632 tasks | elapsed: 1.3min\n",
"[Parallel(n_jobs=-1)]: Done 669 tasks | elapsed: 1.5min\n",
"[Parallel(n_jobs=-1)]: Done 706 tasks | elapsed: 1.6min\n",
"[Parallel(n_jobs=-1)]: Done 745 tasks | elapsed: 1.7min\n",
"[Parallel(n_jobs=-1)]: Done 784 tasks | elapsed: 1.8min\n",
"[Parallel(n_jobs=-1)]: Done 825 tasks | elapsed: 1.8min\n",
"[Parallel(n_jobs=-1)]: Done 866 tasks | elapsed: 1.8min\n",
"[Parallel(n_jobs=-1)]: Done 909 tasks | elapsed: 1.9min\n",
"[Parallel(n_jobs=-1)]: Done 952 tasks | elapsed: 1.9min\n",
"[Parallel(n_jobs=-1)]: Done 997 tasks | elapsed: 2.0min\n",
"[Parallel(n_jobs=-1)]: Done 1042 tasks | elapsed: 2.0min\n",
"[Parallel(n_jobs=-1)]: Done 1089 tasks | elapsed: 2.1min\n",
"[Parallel(n_jobs=-1)]: Done 1136 tasks | elapsed: 2.2min\n",
"[Parallel(n_jobs=-1)]: Done 1185 tasks | elapsed: 2.2min\n",
"[Parallel(n_jobs=-1)]: Done 1234 tasks | elapsed: 2.3min\n",
"[Parallel(n_jobs=-1)]: Done 1285 tasks | elapsed: 2.4min\n",
"[Parallel(n_jobs=-1)]: Done 1336 tasks | elapsed: 2.4min\n",
"[Parallel(n_jobs=-1)]: Done 1389 tasks | elapsed: 2.5min\n",
"[Parallel(n_jobs=-1)]: Done 1442 tasks | elapsed: 2.6min\n",
"[Parallel(n_jobs=-1)]: Done 1497 tasks | elapsed: 2.6min\n",
"[Parallel(n_jobs=-1)]: Done 1552 tasks | elapsed: 2.7min\n",
"[Parallel(n_jobs=-1)]: Done 1609 tasks | elapsed: 2.8min\n",
"[Parallel(n_jobs=-1)]: Done 1666 tasks | elapsed: 2.8min\n",
"[Parallel(n_jobs=-1)]: Done 1725 tasks | elapsed: 2.9min\n",
"[Parallel(n_jobs=-1)]: Done 1784 tasks | elapsed: 3.0min\n",
"[Parallel(n_jobs=-1)]: Done 1845 tasks | elapsed: 3.0min\n",
"[Parallel(n_jobs=-1)]: Done 1906 tasks | elapsed: 3.1min\n",
"[Parallel(n_jobs=-1)]: Done 1969 tasks | elapsed: 3.2min\n",
"[Parallel(n_jobs=-1)]: Done 2032 tasks | elapsed: 3.3min\n",
"[Parallel(n_jobs=-1)]: Done 2097 tasks | elapsed: 3.3min\n",
"[Parallel(n_jobs=-1)]: Done 2162 tasks | elapsed: 3.4min\n",
"[Parallel(n_jobs=-1)]: Done 2229 tasks | elapsed: 3.5min\n",
"[Parallel(n_jobs=-1)]: Done 2296 tasks | elapsed: 3.6min\n",
"[Parallel(n_jobs=-1)]: Done 2365 tasks | elapsed: 3.6min\n",
"[Parallel(n_jobs=-1)]: Done 2434 tasks | elapsed: 3.7min\n",
"[Parallel(n_jobs=-1)]: Done 2505 tasks | elapsed: 3.8min\n",
"[Parallel(n_jobs=-1)]: Done 2576 tasks | elapsed: 3.8min\n",
"[Parallel(n_jobs=-1)]: Done 2649 tasks | elapsed: 3.9min\n",
"[Parallel(n_jobs=-1)]: Done 2722 tasks | elapsed: 4.0min\n",
"[Parallel(n_jobs=-1)]: Done 2797 tasks | elapsed: 4.1min\n",
"[Parallel(n_jobs=-1)]: Done 2872 tasks | elapsed: 4.2min\n",
"[Parallel(n_jobs=-1)]: Done 2949 tasks | elapsed: 4.3min\n",
"[Parallel(n_jobs=-1)]: Done 3026 tasks | elapsed: 4.5min\n",
"[Parallel(n_jobs=-1)]: Done 3105 tasks | elapsed: 4.7min\n",
"[Parallel(n_jobs=-1)]: Done 3184 tasks | elapsed: 4.9min\n",
"[Parallel(n_jobs=-1)]: Done 3265 tasks | elapsed: 5.0min\n",
"[Parallel(n_jobs=-1)]: Done 3346 tasks | elapsed: 5.2min\n",
"[Parallel(n_jobs=-1)]: Done 3429 tasks | elapsed: 5.4min\n",
"[Parallel(n_jobs=-1)]: Done 3512 tasks | elapsed: 5.6min\n",
"[Parallel(n_jobs=-1)]: Done 3597 tasks | elapsed: 5.9min\n",
"[Parallel(n_jobs=-1)]: Done 3682 tasks | elapsed: 6.1min\n",
"[Parallel(n_jobs=-1)]: Done 3769 tasks | elapsed: 6.3min\n",
"[Parallel(n_jobs=-1)]: Done 3856 tasks | elapsed: 6.6min\n",
"[Parallel(n_jobs=-1)]: Done 3945 tasks | elapsed: 6.9min\n",
"[Parallel(n_jobs=-1)]: Done 4034 tasks | elapsed: 7.1min\n",
"[Parallel(n_jobs=-1)]: Done 4125 tasks | elapsed: 7.4min\n",
"[Parallel(n_jobs=-1)]: Done 4216 tasks | elapsed: 7.6min\n",
"[Parallel(n_jobs=-1)]: Done 4309 tasks | elapsed: 7.8min\n",
"[Parallel(n_jobs=-1)]: Done 4402 tasks | elapsed: 8.1min\n",
"[Parallel(n_jobs=-1)]: Done 4497 tasks | elapsed: 8.5min\n",
"[Parallel(n_jobs=-1)]: Done 4592 tasks | elapsed: 8.8min\n",
"[Parallel(n_jobs=-1)]: Done 4689 tasks | elapsed: 9.0min\n",
"[Parallel(n_jobs=-1)]: Done 4786 tasks | elapsed: 9.3min\n",
"[Parallel(n_jobs=-1)]: Done 4885 tasks | elapsed: 9.6min\n",
"[Parallel(n_jobs=-1)]: Done 4984 tasks | elapsed: 9.8min\n",
"[Parallel(n_jobs=-1)]: Done 5040 out of 5040 | elapsed: 10.0min finished\n"
]
},
{
"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=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": {
"id": "ZjX88NoYDZE8",
"colab_type": "code",
"colab": {},
"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
"tags": []
},
"source": [
"print(\"Best estimator: \", grid.best_estimator_)\n",
"print(\"Best hyperparameters: \", grid.best_params_)\n",
"print(\"Best accuracy: \", grid.best_score_)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"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": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.4-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python38464bitgeneralvenv77203c0a6afd4428bd66253ef62753dc",
"display_name": "Python 3.8.4 64-bit ('general': venv)"
},
"colab": {
"name": "gridsearch.ipynb",
"provenance": []
}
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Gridsearch\n",
"with different kernels and different configurations"
]
},
"nbformat": 4,
"nbformat_minor": 0
}
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Setup\n",
"Uncomment the next cell if STree is not already installed"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"#\n",
"# Google Colab setup\n",
"#\n",
"#!pip install git+https://github.com/doctorado-ml/stree"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "zIHKVxthDZEa"
},
"outputs": [],
"source": [
"from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from stree import Stree"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "IEmq50QgDZEi"
},
"outputs": [],
"source": [
"import os\n",
"if not os.path.isfile('data/creditcard.csv'):\n",
" !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
" !tar xzf creditcard.tgz"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "z9Q-YUfBDZEq",
"outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fraud: 0.173% 492\n",
"Valid: 99.827% 284315\n",
"X.shape (1492, 28) y.shape (1492,)\n",
"Fraud: 33.177% 495\n",
"Valid: 66.823% 997\n"
]
}
],
"source": [
"random_state=1\n",
"\n",
"def load_creditcard(n_examples=0):\n",
" import pandas as pd\n",
" import numpy as np\n",
" import random\n",
" df = pd.read_csv('data/creditcard.csv')\n",
" print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
" print(\"Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
" y = df.Class\n",
" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
" if n_examples > 0:\n",
" # Take first n_examples samples\n",
" X = X[:n_examples, :]\n",
" y = y[:n_examples, :]\n",
" else:\n",
" # Take all the positive samples with a number of random negatives\n",
" if n_examples < 0:\n",
" Xt = X[(y == 1).ravel()]\n",
" yt = y[(y == 1).ravel()]\n",
" indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
" X = np.append(Xt, X[indices], axis=0)\n",
" y = np.append(yt, y[indices], axis=0)\n",
" print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
" print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
" print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
" Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
" return Xtrain, Xtest, ytrain, ytest\n",
"\n",
"data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
"# data = load_creditcard(5000) # Take the first 5000 samples\n",
"# data = load_creditcard(0) # Take all the samples\n",
"\n",
"Xtrain = data[0]\n",
"Xtest = data[1]\n",
"ytrain = data[2]\n",
"ytest = data[3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tests"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "HmX3kR4PDZEw"
},
"outputs": [],
"source": [
"parameters = [{\n",
" 'base_estimator': [Stree(random_state=random_state)],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__kernel': ['linear']\n",
"},\n",
"{\n",
" 'base_estimator': [Stree(random_state=random_state)],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__degree': [3, 5, 7],\n",
" 'base_estimator__kernel': ['poly']\n",
"},\n",
"{\n",
" 'base_estimator': [Stree(random_state=random_state)],\n",
" 'n_estimators': [10, 25],\n",
" 'learning_rate': [.5, 1],\n",
" 'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
" 'base_estimator__tol': [.1, 1e-02],\n",
" 'base_estimator__max_depth': [3, 5, 7],\n",
" 'base_estimator__C': [1, 7, 55],\n",
" 'base_estimator__gamma': [.1, 1, 10],\n",
" 'base_estimator__kernel': ['rbf']\n",
"}]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 1.0,\n",
" 'criterion': 'entropy',\n",
" 'degree': 3,\n",
" 'gamma': 'scale',\n",
" 'kernel': 'linear',\n",
" 'max_depth': None,\n",
" 'max_features': None,\n",
" 'max_iter': 100000.0,\n",
" 'min_samples_split': 0,\n",
" 'random_state': None,\n",
" 'split_criteria': 'impurity',\n",
" 'splitter': 'random',\n",
" 'tol': 0.0001}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Stree().get_params()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "CrcB8o6EDZE5",
"outputId": "7703413a-d563-4289-a13b-532f38f82762",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 1008 candidates, totalling 5040 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Using backend LokyBackend with 16 concurrent workers.\n",
"[Parallel(n_jobs=-1)]: Done 40 tasks | elapsed: 1.6s\n",
"[Parallel(n_jobs=-1)]: Done 130 tasks | elapsed: 3.1s\n",
"[Parallel(n_jobs=-1)]: Done 256 tasks | elapsed: 5.5s\n",
"[Parallel(n_jobs=-1)]: Done 418 tasks | elapsed: 9.3s\n",
"[Parallel(n_jobs=-1)]: Done 616 tasks | elapsed: 18.6s\n",
"[Parallel(n_jobs=-1)]: Done 850 tasks | elapsed: 28.2s\n",
"[Parallel(n_jobs=-1)]: Done 1120 tasks | elapsed: 35.4s\n",
"[Parallel(n_jobs=-1)]: Done 1426 tasks | elapsed: 43.5s\n",
"[Parallel(n_jobs=-1)]: Done 1768 tasks | elapsed: 51.3s\n",
"[Parallel(n_jobs=-1)]: Done 2146 tasks | elapsed: 1.0min\n",
"[Parallel(n_jobs=-1)]: Done 2560 tasks | elapsed: 1.2min\n",
"[Parallel(n_jobs=-1)]: Done 3010 tasks | elapsed: 1.4min\n",
"[Parallel(n_jobs=-1)]: Done 3496 tasks | elapsed: 1.7min\n",
"[Parallel(n_jobs=-1)]: Done 4018 tasks | elapsed: 2.1min\n",
"[Parallel(n_jobs=-1)]: Done 4576 tasks | elapsed: 2.6min\n",
"[Parallel(n_jobs=-1)]: Done 5040 out of 5040 | elapsed: 2.9min finished\n"
]
},
{
"data": {
"text/plain": [
"GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=1),\n",
" n_jobs=-1,\n",
" param_grid=[{'base_estimator': [Stree(C=55, max_depth=7,\n",
" random_state=1,\n",
" split_criteria='max_samples',\n",
" tol=0.1)],\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",
" 'impuri...\n",
" {'base_estimator': [Stree(random_state=1)],\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=5)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
"grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
"grid.fit(Xtrain, ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "ZjX88NoYDZE8",
"outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n",
" base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
" split_criteria='max_samples', tol=0.1),\n",
" learning_rate=0.5, n_estimators=25, random_state=1)\n",
"Best hyperparameters: {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 7, 'base_estimator__split_criteria': 'max_samples', 'base_estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}\n",
"Best accuracy: 0.9511777695988222\n"
]
}
],
"source": [
"print(\"Best estimator: \", grid.best_estimator_)\n",
"print(\"Best hyperparameters: \", grid.best_params_)\n",
"print(\"Best accuracy: \", grid.best_score_)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Best estimator: AdaBoostClassifier(algorithm='SAMME',\n",
" base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
" split_criteria='max_samples', tol=0.1),\n",
" learning_rate=0.5, n_estimators=25, random_state=1)\n",
"Best hyperparameters: {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 7, 'base_estimator__split_criteria': 'max_samples', 'base_estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Best accuracy: 0.9511777695988222"
]
}
],
"metadata": {
"colab": {
"name": "gridsearch.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}