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Add model comparison to notebook test

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Ricardo Montañana Gómez
2020-05-17 16:17:57 +02:00
parent ad51d0b36b
commit d997bc3460
8 changed files with 461 additions and 93 deletions
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README.md
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@@ -8,7 +8,7 @@ Oblique Tree classifier based on SVM nodes
## Jupyter
[![badge](https://img.shields.io/badge/binder-test%20notebook-579ACA.svg?logo=data:image/png;base64,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)](https://notebooks.gesis.org/user/ricardo.montanana@alu.uclm.es/notebooks/test.ipynb)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/Doctorado-ML/STree/master?filepath=test.ipynb)
## Command line

3
main.py
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@@ -1,6 +1,7 @@
from trees.Stree import Stree
from sklearn.datasets import make_classification
from trees.Stree import Stree
random_state = 1
X, y = make_classification(n_samples=1500, n_features=3, n_informative=3,
n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,

337
test.ipynb
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@@ -6,13 +6,14 @@
"metadata": {},
"outputs": [],
"source": [
"import datetime, time\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
"from trees.Stree import Stree\n",
"import time"
"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, GradientBoostingClassifier\n",
"from trees.Stree import Stree"
]
},
{
@@ -35,66 +36,23 @@
{
"output_type": "stream",
"name": "stdout",
"text": "*Original Fraud: 0.173% 492\n*Original Valid: 99.827% 284315\nX.shape (284807, 28) y.shape (284807, 1)\n-Generated Fraud: 0.173% 492\n-Generated Valid: 99.827% 284315\n"
"text": "2020-05-17 16:15:24\n"
}
],
"source": [
"def load_creditcard(n_examples=0):\n",
" df = pd.read_csv('data/creditcard.csv')\n",
" print(\"*Original Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
" print(\"*Original Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
" y = np.expand_dims(df.Class.values, axis=1)\n",
" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\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",
" if n_examples > 0:\n",
" X = X[:n_examples, :]\n",
" y = y[:n_examples, :]\n",
" else:\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(\"-Generated Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
" print(\"-Generated Valid: {0:.3f}% {1}\".format(len(y[y == 0])*100/X.shape[0], len(y[y == 0])))\n",
" return X, y\n",
"\n",
"random_state = 1\n",
"\n",
"\n",
"# Datasets\n",
"\n",
"#X, y = make_classification(n_samples=1500, n_features=3, n_informative=3, \n",
"# n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,\n",
"# class_sep=1.5, flip_y=0,weights=[0.5,0.5], random_state=random_state)\n",
"\n",
"#X, y = load_wine(return_X_y=True)\n",
"#X, y = load_iris(return_X_y=True)\n",
"#y[y==2]=0\n",
"\n",
"X, y = load_creditcard()"
"print(datetime.date.today(), time.strftime(\"%H:%M:%S\"))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=0 belief=0.999638 counts=(array([0, 1]), array([284242, 103]))\nroot - Down - Up\nroot - Down - Up - Down\nroot - Down - Up - Down - Down, <cgaf> - Leaf class=0 belief=0.857143 counts=(array([0, 1]), array([18, 3]))\nroot - Down - Up - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Down - Up - Up\nroot - Down - Up - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Down - Up - Up - Up, <cgaf> - Leaf class=1 belief=0.862069 counts=(array([0, 1]), array([ 16, 100]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down\nroot - Up - Down - Down - Down, <cgaf> - Leaf class=0 belief=0.920000 counts=(array([0, 1]), array([23, 2]))\nroot - Up - Down - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Up, <cgaf> - Leaf class=1 belief=0.948980 counts=(array([0, 1]), array([ 15, 279]))\n\n41.5053 secs\n"
}
],
"outputs": [],
"source": [
"t = time.time()\n",
"clf = Stree(C=.01, random_state=random_state, use_predictions=False)\n",
"clf.fit(X, y)\n",
"print(clf)\n",
"print(f\"{time.time() - t:.4f} secs\")"
"# Load Dataset\n",
"df = pd.read_csv('data/creditcard.csv')\n",
"df.shape\n",
"random_state = 2020"
]
},
{
@@ -105,22 +63,24 @@
{
"output_type": "stream",
"name": "stdout",
"text": "Accuracy: 0.999512\n0.2389 secs\n"
"text": "Fraud: 0.173% 492\nValid: 99.827% 284,315\n"
}
],
"source": [
"t = time.time()\n",
"clf.score(X, y)\n",
"print(f\"{time.time() - t:.4f} secs\")"
"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()))"
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# outcomes without optimization executing predict_proba. 87 seconds\n",
"(284807, 2)\n",
"87.5212 secs"
"# Normalize Amount\n",
"from sklearn.preprocessing import RobustScaler\n",
"values = RobustScaler().fit_transform(df.Amount.values.reshape(-1, 1))\n",
"df['Amount_Scaled'] = values"
]
},
{
@@ -131,47 +91,214 @@
{
"output_type": "stream",
"name": "stdout",
"text": "0.9991397683343457\n13.6326 secs\n"
"text": "X shape: (284807, 29)\ny shape: (284807,)\n"
}
],
"source": [
"t = time.time()\n",
"clf2 = LinearSVC(C=.01, random_state=random_state)\n",
"clf2.fit(X, y)\n",
"print(clf2.score(X, y))\n",
"print(f\"{time.time() - t:.4f} secs\")"
"# Remove unneeded features\n",
"y = df.Class.values\n",
"X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
"print(f\"X shape: {X.shape}\\ny shape: {y.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Divide dataset\n",
"train_size = .7\n",
"Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=train_size, shuffle=True, random_state=random_state, stratify=y)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# Linear Tree\n",
"linear_tree = tree.DecisionTreeClassifier(random_state=random_state)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# Random Forest\n",
"random_forest = RandomForestClassifier(random_state=random_state, n_jobs=-1, n_estimators=100)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Stree\n",
"stree = Stree(random_state=random_state, C=.01)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# AdaBoost\n",
"adaboost = AdaBoostClassifier(random_state=random_state)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# Gradient Boosting\n",
"gradient = GradientBoostingClassifier(random_state=random_state)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def try_model(name, model):\n",
" print(f\"************************** {name} **********************\")\n",
" now = time.time()\n",
" model.fit(Xtrain, ytrain)\n",
" spent = time.time() - now\n",
" print(f\"Train Model {name} took: {spent:.4} seconds\")\n",
" predict = model.predict(Xtrain)\n",
" predictt = model.predict(Xtest)\n",
" print(f\"=========== {name} - Train {Xtrain.shape[0]:,} samples =============\",)\n",
" print(classification_report(ytrain, predict, digits=6))\n",
" print(f\"=========== {name} - Test {Xtest.shape[0]:,} samples =============\")\n",
" print(classification_report(ytest, predictt, digits=6))\n",
" print(\"Confusion Matrix in Train\")\n",
" print(confusion_matrix(ytrain, predict))\n",
" print(\"Confusion Matrix in Test\")\n",
" print(confusion_matrix(ytest, predictt))\n",
" return f1_score(ytest, predictt), spent"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1.0\n18.8308 secs\n"
"text": "************************** Linear Tree **********************\nTrain Model Linear Tree took: 14.13 seconds\n=========== Linear Tree - 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\n accuracy 1.000000 199364\n macro avg 1.000000 1.000000 1.000000 199364\nweighted avg 1.000000 1.000000 1.000000 199364\n\n=========== Linear Tree - Test 85,443 samples =============\n precision recall f1-score support\n\n 0 0.999578 0.999613 0.999596 85295\n 1 0.772414 0.756757 0.764505 148\n\n accuracy 0.999192 85443\n macro avg 0.885996 0.878185 0.882050 85443\nweighted avg 0.999184 0.999192 0.999188 85443\n\nConfusion Matrix in Train\n[[199020 0]\n [ 0 344]]\nConfusion Matrix in Test\n[[85262 33]\n [ 36 112]]\n************************** Random Forest **********************\n"
},
{
"output_type": "error",
"ename": "KeyboardInterrupt",
"evalue": "",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-29ecc2c3d67b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0moutcomes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_spent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtry_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0moutcomes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_spent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mf1\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mbest_f1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-14-b6f4fa54c657>\u001b[0m in \u001b[0;36mtry_model\u001b[0;34m(name, model)\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"************************** {name} **********************\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mnow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mspent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mnow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Train Model {name} took: {spent:.4} seconds\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Code/pyblique/venv/lib/python3.7/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 382\u001b[0m n_samples_bootstrap=n_samples_bootstrap)\n\u001b[0;32m--> 383\u001b[0;31m for i, t in enumerate(trees))\n\u001b[0m\u001b[1;32m 384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 385\u001b[0m \u001b[0;31m# Collect newly grown trees\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Code/pyblique/venv/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1016\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1017\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1018\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1019\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/Code/pyblique/venv/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 907\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 908\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 909\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 910\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 911\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/multiprocessing/pool.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 647\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_event\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 649\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 650\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 550\u001b[0m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_flag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m \u001b[0msignaled\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_cond\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 553\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msignaled\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"t = time.time()\n",
"clf3 = DecisionTreeClassifier(random_state=random_state)\n",
"clf3.fit(X, y)\n",
"print(clf3.score(X, y))\n",
"print(f\"{time.time() - t:.4f} secs\")"
"# Train & Test models\n",
"models = {\n",
" 'Linear Tree':linear_tree, 'Random Forest': random_forest, 'Stree (SVM Tree)': stree, \n",
" 'AdaBoost model': adaboost, 'Gradient Boost.': gradient\n",
"}\n",
"\n",
"best_f1 = 0\n",
"outcomes = []\n",
"for name, model in models.items():\n",
" f1, time_spent = try_model(name, model)\n",
" outcomes.append((name, f1, time_spent))\n",
" if f1 > best_f1:\n",
" best_model = name\n",
" best_time = time_spent\n",
" best_f1 = f1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "************************************************************************************************************************************\n"
},
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'best_model' is not defined",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-71789a67ee66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"*\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m132\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"*\"\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0;36m132\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime_spent\u001b[0m \u001b[0;32min\u001b[0m \u001b[0moutcomes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Model: {name}\\t Time: {time_spent:6.2f} seconds\\t f1: {f1:.4}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'best_model' is not defined"
]
}
],
"source": [
"print(\"*\"*132)\n",
"print(f\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\")\n",
"print(\"*\"*132)\n",
"for name, f1, time_spent in outcomes:\n",
" print(f\"Model: {name}\\t Time: {time_spent:6.2f} seconds\\t f1: {f1:.4}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from sklearn.utils.estimator_checks import check_estimator\n",
"clf = Stree()\n",
"check_estimator(clf)"
"************************************************************************************************************************************\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\t 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: 0.8467\n",
"Model: AdaBoost model\t Time: 73.83 seconds\t f1: 0.7509\n",
"Model: Gradient Boost.\t Time: 388.69 seconds\t f1: 0.5259\n",
"Model: Neural Network\t Time: 25.47 seconds\t f1: 0.8328"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"************************************************************************************************************************************\n",
"*The best f1 model is Random Forest, with a f1 score: 0.8791 in 1513.23 seconds with 0.7 samples in train dataset\n",
"************************************************************************************************************************************\n",
"Model: Linear Tree\t Time: 25.18 seconds\t f1: 0.7645\n",
"Model: Random Forest\t Time: 1513.23 seconds\t f1: 0.8791"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
@@ -184,12 +311,56 @@
"pygments_lexer": "ipython3",
"version": "3.7.6-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39",
"display_name": "Python 3.7.6 64-bit ('general': venv)"
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
},
"varInspector": {
"cols": {
"lenName": 16,
"lenType": 16,
"lenVar": 40
},
"kernels_config": {
"python": {
"delete_cmd_postfix": "",
"delete_cmd_prefix": "del ",
"library": "var_list.py",
"varRefreshCmd": "print(var_dic_list())"
},
"r": {
"delete_cmd_postfix": ") ",
"delete_cmd_prefix": "rm(",
"library": "var_list.r",
"varRefreshCmd": "cat(var_dic_list()) "
}
},
"position": {
"height": "392px",
"left": "1518px",
"right": "20px",
"top": "40px",
"width": "392px"
},
"types_to_exclude": [
"module",
"function",
"builtin_function_or_method",
"instance",
"_Feature"
],
"window_display": true
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}

195
test2.ipynb Normal file
View File

@@ -0,0 +1,195 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.datasets import make_classification, load_iris, load_wine\n",
"from trees.Stree import Stree\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"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": 3,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "*Original Fraud: 0.173% 492\n*Original Valid: 99.827% 284315\nX.shape (284807, 28) y.shape (284807, 1)\n-Generated Fraud: 0.173% 492\n-Generated Valid: 99.827% 284315\n"
}
],
"source": [
"def load_creditcard(n_examples=0):\n",
" df = pd.read_csv('data/creditcard.csv')\n",
" print(\"*Original Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
" print(\"*Original Valid: {0:.3f}% {1}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))\n",
" y = np.expand_dims(df.Class.values, axis=1)\n",
" X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\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",
" if n_examples > 0:\n",
" X = X[:n_examples, :]\n",
" y = y[:n_examples, :]\n",
" else:\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(\"-Generated Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
" print(\"-Generated Valid: {0:.3f}% {1}\".format(len(y[y == 0])*100/X.shape[0], len(y[y == 0])))\n",
" return X, y\n",
"\n",
"random_state = 1\n",
"\n",
"\n",
"# Datasets\n",
"\n",
"#X, y = make_classification(n_samples=1500, n_features=3, n_informative=3, \n",
"# n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,\n",
"# class_sep=1.5, flip_y=0,weights=[0.5,0.5], random_state=random_state)\n",
"\n",
"#X, y = load_wine(return_X_y=True)\n",
"#X, y = load_iris(return_X_y=True)\n",
"#y[y==2]=0\n",
"\n",
"X, y = load_creditcard()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=0 belief=0.999638 counts=(array([0, 1]), array([284242, 103]))\nroot - Down - Up\nroot - Down - Up - Down\nroot - Down - Up - Down - Down, <cgaf> - Leaf class=0 belief=0.857143 counts=(array([0, 1]), array([18, 3]))\nroot - Down - Up - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Down - Up - Up\nroot - Down - Up - Up - Down, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Down - Up - Up - Up, <cgaf> - Leaf class=1 belief=0.862069 counts=(array([0, 1]), array([ 16, 100]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down\nroot - Up - Down - Down - Down, <cgaf> - Leaf class=0 belief=0.920000 counts=(array([0, 1]), array([23, 2]))\nroot - Up - Down - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Up, <cgaf> - Leaf class=1 belief=0.948980 counts=(array([0, 1]), array([ 15, 279]))\n\n41.5053 secs\n"
}
],
"source": [
"t = time.time()\n",
"clf = Stree(C=.01, random_state=random_state, use_predictions=False)\n",
"clf.fit(X, y)\n",
"print(clf)\n",
"print(f\"{time.time() - t:.4f} secs\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Accuracy: 0.999512\n0.2389 secs\n"
}
],
"source": [
"t = time.time()\n",
"clf.score(X, y)\n",
"print(f\"{time.time() - t:.4f} secs\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# outcomes without optimization executing predict_proba. 87 seconds\n",
"(284807, 2)\n",
"87.5212 secs"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "0.9991397683343457\n13.6326 secs\n"
}
],
"source": [
"t = time.time()\n",
"clf2 = LinearSVC(C=.01, random_state=random_state)\n",
"clf2.fit(X, y)\n",
"print(clf2.score(X, y))\n",
"print(f\"{time.time() - t:.4f} secs\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "1.0\n18.8308 secs\n"
}
],
"source": [
"t = time.time()\n",
"clf3 = DecisionTreeClassifier(random_state=random_state)\n",
"clf3.fit(X, y)\n",
"print(clf3.score(X, y))\n",
"print(f\"{time.time() - t:.4f} secs\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from sklearn.utils.estimator_checks import check_estimator\n",
"clf = Stree()\n",
"check_estimator(clf)"
]
}
],
"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": "3.7.6-final"
},
"orig_nbformat": 2,
"kernelspec": {
"name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39",
"display_name": "Python 3.7.6 64-bit ('general': venv)"
}
},
"nbformat": 4,
"nbformat_minor": 2
}

5
tests/Snode_test.py
View File

@@ -1,9 +1,8 @@
import os
import unittest
from sklearn.datasets import make_classification
import os
import numpy as np
import csv
from sklearn.datasets import make_classification
from trees.Stree import Stree, Snode

6
tests/Stree_test.py
View File

@@ -1,9 +1,9 @@
import csv
import os
import unittest
from sklearn.datasets import make_classification
import os
import numpy as np
import csv
from sklearn.datasets import make_classification
from trees.Stree import Stree, Snode

1
trees/Snode.py
View File

@@ -7,6 +7,7 @@ Node of the Stree (binary tree)
'''
import os
import numpy as np
from sklearn.svm import LinearSVC

5
trees/Stree.py
View File

@@ -8,10 +8,11 @@ Build an oblique tree classifier based on SVM Trees
Uses LinearSVC
'''
import numpy as np
import typing
from sklearn.svm import LinearSVC
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import LinearSVC
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from trees.Snode import Snode