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Refactor split_data adding sample_weight

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Ricardo Montañana Gómez
2020-05-29 18:52:23 +02:00
parent ed98054f0d
commit a22ae81b54
5 changed files with 152 additions and 141 deletions
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1
data/.gitignore vendored
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@@ -1 +0,0 @@
*

132
notebooks/gridsearch.ipynb
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@@ -8,7 +8,8 @@
"source": [ "source": [
"from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.ensemble import AdaBoostClassifier\n",
"from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.model_selection import GridSearchCV\n", "from sklearn.svm import LinearSVC\n",
"from sklearn.model_selection import GridSearchCV, train_test_split\n",
"from sklearn.datasets import load_iris\n", "from sklearn.datasets import load_iris\n",
"from stree import Stree" "from stree import Stree"
] ]
@@ -27,12 +28,51 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 3,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "Fraud: 0.244% 196\nValid: 99.755% 80234\nX.shape (1196, 28) y.shape (1196,)\nFraud: 16.472% 197\nValid: 83.528% 999\n"
}
],
"source": [ "source": [
"import pandas as pd\n", "random_state=1\n",
"df = pd.read_csv('data/creditcard.csv')\n", "\n",
"y = df.Class.values\n", "def load_creditcard(n_examples=0):\n",
"X = df.drop(['Class', 'Time', 'Amount'], axis=1).values" " 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(-5000) # Take all true samples + 5000 of the others\n",
"# data = load_creditcard(5000) # Take the first 5000 samples\n",
"data = load_creditcard(-1000) # Take all the samples\n",
"\n",
"Xtrain = data[0]\n",
"Xtest = data[1]\n",
"ytrain = data[2]\n",
"ytest = data[3]"
] ]
}, },
{ {
@@ -43,14 +83,12 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "\n" "text": "root\nroot - Down - Leaf class=1.0 belief=0.976000 counts=(array([0., 1.]), array([ 3, 122]))\nroot - Up - Leaf class=0.0 belief=0.977528 counts=(array([0., 1.]), array([696, 16]))\n\n"
} }
], ],
"source": [ "source": [
"c = Stree(C=17, max_depth=2)\n", "c = Stree(max_depth=2)\n",
"print(c)\n", "c.fit(Xtrain, ytrain)\n",
"c.fit(X, y)\n",
"print(len(list(c)))\n",
"print(c)" "print(c)"
] ]
}, },
@@ -62,7 +100,7 @@
"source": [ "source": [
"#'base_estimator': [DecisionTreeClassifier(max_depth=1), Stree(max_depth=2), Stree(max_depth=3)],\n", "#'base_estimator': [DecisionTreeClassifier(max_depth=1), Stree(max_depth=2), Stree(max_depth=3)],\n",
"parameters = {\n", "parameters = {\n",
" 'base_estimator': [Stree(max_depth=2), Stree(max_depth=3)],\n", " 'base_estimator': [LinearSVC(), Stree(max_depth=2), Stree(max_depth=3)],\n",
" 'n_estimators': [20, 50, 100, 150],\n", " 'n_estimators': [20, 50, 100, 150],\n",
" 'learning_rate': [.5, 1, 1.5] \n", " 'learning_rate': [.5, 1, 1.5] \n",
"}" "}"
@@ -89,41 +127,58 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 2.5s\n[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 2.6s\n[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 2.6s\n[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 2.6s\n[Parallel(n_jobs=-1)]: Batch computation too fast (0.1837s.) Setting batch_size=2.\n[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 2.7s\n[Parallel(n_jobs=-1)]: Done 45 tasks | elapsed: 2.7s\n[Parallel(n_jobs=-1)]: Batch computation too fast (0.0313s.) Setting batch_size=4.\n[Parallel(n_jobs=-1)]: Done 64 tasks | elapsed: 2.7s\n[Parallel(n_jobs=-1)]: Batch computation too fast (0.0302s.) Setting batch_size=8.\n[Parallel(n_jobs=-1)]: Done 92 out of 120 | elapsed: 2.7s remaining: 0.8s\n[Parallel(n_jobs=-1)]: Done 118 out of 120 | elapsed: 2.7s remaining: 0.0s\n[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed: 2.7s finished\n" "text": "(X: numpy.ndarray, y: numpy.ndarray, sample_weight: <built-in function array> = None) -> 'Stree'\n"
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "Stree doesn't support sample_weight.",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-7-1a0e5b8c6bec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAdaBoostClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mgrid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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[0mgrid\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/.virtualenvs/general/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 71\u001b[0m FutureWarning)\n\u001b[1;32m 72\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\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---> 73\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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 74\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/general/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 763\u001b[0m \u001b[0mrefit_start_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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 764\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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--> 765\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\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 766\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 767\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbest_estimator_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/general/lib/python3.7/site-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 442\u001b[0m \u001b[0;31m# Fit\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 443\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\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 444\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 445\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_validate_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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~/.virtualenvs/general/lib/python3.7/site-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 115\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 116\u001b[0m \u001b[0;31m# Check parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 117\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_estimator\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 118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[0;31m# Clear any previous fit results\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/.virtualenvs/general/lib/python3.7/site-packages/sklearn/ensemble/_weight_boosting.py\u001b[0m in \u001b[0;36m_validate_estimator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 459\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mhas_fit_parameter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_estimator_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"sample_weight\"\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 460\u001b[0m raise ValueError(\"%s doesn't support sample_weight.\"\n\u001b[0;32m--> 461\u001b[0;31m % self.base_estimator_.__class__.__name__)\n\u001b[0m\u001b[1;32m 462\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_boost\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0miboost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Stree doesn't support sample_weight."
]
} }
], ],
"source": [ "source": [
"random_state=2020\n", "from inspect import signature\n",
"clf = AdaBoostClassifier(random_state=random_state)\n", "print(signature(c.fit))"
"grid = GridSearchCV(clf, parameters, verbose=10, n_jobs=-1, return_train_score=True)\n",
"grid.fit(X, y)"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [
"from sklearn.utils.validation import _check_sample_weight"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [ "outputs": [
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "AdaBoostClassifier(base_estimator=Stree(max_depth=2), learning_rate=0.5,\n n_estimators=20, random_state=2020)\n" "text": "Fitting 5 folds for each of 36 candidates, totalling 180 fits\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 2 tasks | elapsed: 1.3s\n[Parallel(n_jobs=-1)]: Done 9 tasks | elapsed: 1.3s\n[Parallel(n_jobs=-1)]: Done 16 tasks | elapsed: 1.3s\n[Parallel(n_jobs=-1)]: Batch computation too fast (0.1671s.) Setting batch_size=2.\n[Parallel(n_jobs=-1)]: Done 25 tasks | elapsed: 1.3s\n[Parallel(n_jobs=-1)]: Done 34 tasks | elapsed: 1.4s\n[Parallel(n_jobs=-1)]: Batch computation too fast (0.0413s.) Setting batch_size=4.\n[Parallel(n_jobs=-1)]: Done 50 tasks | elapsed: 1.4s\n[Parallel(n_jobs=-1)]: Batch computation too slow (7.7880s.) Setting batch_size=1.\n[Parallel(n_jobs=-1)]: Done 74 tasks | elapsed: 9.2s\n[Parallel(n_jobs=-1)]: Done 121 tasks | elapsed: 48.9s\n[Parallel(n_jobs=-1)]: Done 140 tasks | elapsed: 1.0min\n[Parallel(n_jobs=-1)]: Done 161 tasks | elapsed: 1.3min\n[Parallel(n_jobs=-1)]: Done 180 out of 180 | elapsed: 1.6min finished\n"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "GridSearchCV(estimator=AdaBoostClassifier(random_state=2020), n_jobs=-1,\n param_grid={'base_estimator': [LinearSVC(), Stree(max_depth=2),\n Stree(max_depth=3)],\n 'learning_rate': [0.5, 1, 1.5],\n 'n_estimators': [20, 50, 100, 150]},\n return_train_score=True, verbose=10)"
},
"metadata": {},
"execution_count": 9
}
],
"source": [
"random_state=2020\n",
"clf = AdaBoostClassifier(random_state=random_state)\n",
"grid = GridSearchCV(clf, parameters, verbose=10, n_jobs=-1, return_train_score=True)\n",
"grid.fit(Xtrain, ytrain)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": "AdaBoostClassifier(base_estimator=Stree(max_depth=2), learning_rate=0.5,\n n_estimators=150, random_state=2020)\n"
} }
], ],
"source": [ "source": [
@@ -131,11 +186,12 @@
] ]
}, },
{ {
"cell_type": "code", "cell_type": "markdown",
"execution_count": null,
"metadata": {}, "metadata": {},
"outputs": [], "source": [
"source": [] "AdaBoostClassifier(base_estimator=Stree(max_depth=3), learning_rate=0.5,\n",
" n_estimators=20, random_state=2020)"
]
} }
], ],
"metadata": { "metadata": {

22
notebooks/test2.ipynb
View File

@@ -48,7 +48,7 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28) y.shape (1492,)\nFraud: 33.043% 493\nValid: 66.957% 999\n" "text": "Fraud: 0.244% 196\nValid: 99.755% 80234\nX.shape (1196, 28) y.shape (1196,)\nFraud: 16.722% 200\nValid: 83.278% 996\n"
} }
], ],
"source": [ "source": [
@@ -103,7 +103,7 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "depth: 1\ndepth: 2\ndepth: 2\n************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9550\nClassifier's accuracy (test) : 0.9598\nroot\nroot - Down, <cgaf> - Leaf class=1 belief=0.983766 counts=(array([0, 1]), array([ 5, 303]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.942935 counts=(array([0, 1]), array([694, 42]))\n\n**************************************************\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 3\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9569\nClassifier's accuracy (test) : 0.9598\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1 belief=0.990196 counts=(array([0, 1]), array([ 3, 303]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([2]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.942935 counts=(array([0, 1]), array([694, 42]))\n\n**************************************************\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 4\ndepth: 3\ndepth: 2\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9684\nClassifier's accuracy (test) : 0.9688\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([310]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([4]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.954608 counts=(array([0, 1]), array([694, 33]))\n\n**************************************************\ndepth: 1\ndepth: 2\ndepth: 2\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9693\nClassifier's accuracy (test) : 0.9710\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([313]))\nroot - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([7]))\nroot - Up, <cgaf> - Leaf class=0 belief=0.955801 counts=(array([0, 1]), array([692, 32]))\n\n**************************************************\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 4\ndepth: 5\ndepth: 6\ndepth: 6\ndepth: 5\ndepth: 4\ndepth: 3\ndepth: 2\ndepth: 3\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9818\nClassifier's accuracy (test) : 0.9554\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([307]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([25]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([5]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.972263 counts=(array([0, 1]), array([666, 19]))\n\n**************************************************\n0.6576 secs\n" "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9797\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down\nroot - Down - Down, <cgaf> - Leaf class=1.0 belief=0.984127 counts=(array([0., 1.]), array([ 2, 124]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([5]))\nroot - Up\nroot - Up - Down, <cgaf> - Leaf class=0.0 belief=0.750000 counts=(array([0., 1.]), array([3, 1]))\nroot - Up - Up\nroot - Up - Up - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Up, <cgaf> - Leaf class=0.0 belief=0.980029 counts=(array([0., 1.]), array([687, 14]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9809\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([124]))\nroot - Up, <cgaf> - Leaf class=0.0 belief=0.977560 counts=(array([0., 1.]), array([697, 16]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9869\nClassifier's accuracy (test) : 0.9749\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([129]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([2]))\nroot - Up, <cgaf> - Leaf class=0.0 belief=0.984419 counts=(array([0., 1.]), array([695, 11]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9869\nClassifier's accuracy (test) : 0.9777\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([129]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([2]))\nroot - Up, <cgaf> - Leaf class=0.0 belief=0.984419 counts=(array([0., 1.]), array([695, 11]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9916\nClassifier's accuracy (test) : 0.9833\nroot\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([131]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([8]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([5]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0.0 belief=0.989855 counts=(array([0., 1.]), array([683, 7]))\n\n**************************************************\n0.2235 secs\n"
} }
], ],
"source": [ "source": [
@@ -144,7 +144,7 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([307]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([25]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([5]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.972263 counts=(array([0, 1]), array([666, 19]))\n" "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([131]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([8]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([5]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0.0 belief=0.989855 counts=(array([0., 1.]), array([683, 7]))\n"
} }
], ],
"source": [ "source": [
@@ -161,7 +161,7 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([307]))\nroot - Down - Up\nroot - Down - Up - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([8]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([25]))\nroot - Up\nroot - Up - Down\nroot - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([3]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([2]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down\nroot - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([5]))\nroot - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([5]))\nroot - Up - Up - Up - Up\nroot - Up - Up - Up - Up - Down\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief=1.000000 counts=(array([1]), array([1]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief=1.000000 counts=(array([0]), array([1]))\nroot - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief=0.972263 counts=(array([0, 1]), array([666, 19]))\n" "text": "root\nroot - Down\nroot - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([131]))\nroot - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([8]))\nroot - Up\nroot - Up - Down, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([1]))\nroot - Up - Up\nroot - Up - Up - Down\nroot - Up - Up - Down - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0.0 belief=1.000000 counts=(array([0.]), array([5]))\nroot - Up - Up - Up\nroot - Up - Up - Up - Down, <pure> - Leaf class=1.0 belief=1.000000 counts=(array([1.]), array([1]))\nroot - Up - Up - Up - Up, <cgaf> - Leaf class=0.0 belief=0.989855 counts=(array([0., 1.]), array([683, 7]))\n"
} }
], ],
"source": [ "source": [
@@ -174,13 +174,7 @@
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 9,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"output_type": "stream",
"name": "stdout",
"text": "depth: 1\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 2\ndepth: 1\ndepth: 1\ndepth: 1\ndepth: 1\n"
}
],
"source": [ "source": [
"# Check if the classifier is a sklearn estimator\n", "# Check if the classifier is a sklearn estimator\n",
"from sklearn.utils.estimator_checks import check_estimator\n", "from sklearn.utils.estimator_checks import check_estimator\n",
@@ -195,7 +189,7 @@
{ {
"output_type": "stream", "output_type": "stream",
"name": "stdout", "name": "stdout",
"text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x12959d320>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x129595440>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 1\ndepth: 1\n3 functools.partial(<function check_fit_score_takes_y at 0x129595320>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12958ed40>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12958ee60>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12958ef80>, 'Stree')\n7 functools.partial(<function check_sample_weights_invariance at 0x1295900e0>, 'Stree')\n8 functools.partial(<function check_estimators_fit_returns_self at 0x129598440>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 2\n9 functools.partial(<function check_estimators_fit_returns_self at 0x129598440>, 'Stree', readonly_memmap=True)\ndepth: 1\ndepth: 2\ndepth: 2\n10 functools.partial(<function check_complex_data at 0x129590290>, 'Stree')\n11 functools.partial(<function check_dtype_object at 0x129590200>, 'Stree')\ndepth: 1\n12 functools.partial(<function check_estimators_empty_data_messages at 0x129595560>, 'Stree')\n13 functools.partial(<function check_pipeline_consistency at 0x129595200>, 'Stree')\ndepth: 1\ndepth: 1\n14 functools.partial(<function check_estimators_nan_inf at 0x129595680>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\n15 functools.partial(<function check_estimators_overwrite_params at 0x12959d200>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 2\n16 functools.partial(<function check_estimator_sparse_data at 0x12958ec20>, 'Stree')\n17 functools.partial(<function check_estimators_pickle at 0x1295958c0>, 'Stree')\ndepth: 1\n18 functools.partial(<function check_classifier_data_not_an_array at 0x12959d560>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\n19 functools.partial(<function check_classifiers_one_label at 0x129595f80>, 'Stree')\n20 functools.partial(<function check_classifiers_classes at 0x1295989e0>, 'Stree')\ndepth: 1\ndepth: 1\ndepth: 1\n21 functools.partial(<function check_estimators_partial_fit_n_features at 0x1295959e0>, 'Stree')\n22 functools.partial(<function check_classifiers_train at 0x1295980e0>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\n23 functools.partial(<function check_classifiers_train at 0x1295980e0>, 'Stree', readonly_memmap=True)\ndepth: 1\ndepth: 2\ndepth: 2\ndepth: 1\ndepth: 2\ndepth: 2\n24 functools.partial(<function check_classifiers_regression_target at 0x12959df80>, 'Stree')\n25 functools.partial(<function check_supervised_y_no_nan at 0x12958cd40>, 'Stree')\n26 functools.partial(<function check_supervised_y_2d at 0x129598680>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\ndepth: 1\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 2\ndepth: 3\ndepth: 3\ndepth: 4\ndepth: 4\ndepth: 5\n27 functools.partial(<function check_estimators_unfitted at 0x129598560>, 'Stree')\n28 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12959db00>, 'Stree')\ndepth: 1\ndepth: 2\ndepth: 3\n29 functools.partial(<function check_decision_proba_consistency at 0x1295a20e0>, 'Stree')\n30 functools.partial(<function check_fit2d_predict1d at 0x1295907a0>, 'Stree')\ndepth: 1\n31 functools.partial(<function check_methods_subset_invariance at 0x129590950>, 'Stree')\ndepth: 1\n32 functools.partial(<function check_fit2d_1sample at 0x129590a70>, 'Stree')\n33 functools.partial(<function check_fit2d_1feature at 0x129590b90>, 'Stree')\ndepth: 1\ndepth: 2\n34 functools.partial(<function check_fit1d at 0x129590cb0>, 'Stree')\n35 functools.partial(<function check_get_params_invariance at 0x12959dd40>, 'Stree')\n36 functools.partial(<function check_set_params at 0x12959de60>, 'Stree')\n37 functools.partial(<function check_dict_unchanged at 0x1295903b0>, 'Stree')\ndepth: 1\n38 functools.partial(<function check_dont_overwrite_parameters at 0x129590680>, 'Stree')\ndepth: 1\n39 functools.partial(<function check_fit_idempotent at 0x1295a2290>, 'Stree')\ndepth: 1\ndepth: 1\n" "text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x12aabb320>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x12aab0440>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x12aab0320>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x12aaaac20>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x12aaaad40>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x12aaaae60>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x12aaaaf80>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x12aaac0e0>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x12aab6440>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x12aab6440>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x12aaac290>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x12aaac200>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x12aab0560>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x12aab0200>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x12aab0680>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x12aabb200>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x12aaaab00>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x12aab08c0>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x12aabb560>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x12aab0f80>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x12aab69e0>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x12aab09e0>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x12aab60e0>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x12aab60e0>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x12aab60e0>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x12aabf050>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x12aaa0c20>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x12aab6680>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x12aab6560>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x12aabbb90>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x12aabf170>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x12aaac7a0>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x12aaac950>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x12aaaca70>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x12aaacb90>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x12aaaccb0>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x12aabbdd0>, 'Stree')\n38 functools.partial(<function check_set_params at 0x12aabbef0>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x12aaac3b0>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x12aaac680>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x12aabf320>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x12aabf3b0>, 'Stree')\n"
} }
], ],
"source": [ "source": [
@@ -211,9 +205,9 @@
], ],
"metadata": { "metadata": {
"kernelspec": { "kernelspec": {
"display_name": "Python 3", "display_name": "Python 3.7.6 64-bit ('general': venv)",
"language": "python", "language": "python",
"name": "python3" "name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39"
}, },
"language_info": { "language_info": {
"codemirror_mode": { "codemirror_mode": {

115
stree/Strees.py
View File

@@ -13,7 +13,8 @@ import os
import numpy as np import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.svm import LinearSVC from sklearn.svm import LinearSVC
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted, _check_sample_weight, check_random_state
class Snode: class Snode:
@@ -102,9 +103,8 @@ class Siterator:
class Stree(BaseEstimator, ClassifierMixin): class Stree(BaseEstimator, ClassifierMixin):
""" """
""" """
__folder = 'data/'
def __init__(self, C: float = 1.0, max_iter: int = 1000, random_state: int = 0, def __init__(self, C: float = 1.0, max_iter: int = 1000, random_state: int = None,
max_depth: int=None, tol: float=1e-4, use_predictions: bool = False): max_depth: int=None, tol: float=1e-4, use_predictions: bool = False):
self.max_iter = max_iter self.max_iter = max_iter
self.C = C self.C = C
@@ -145,25 +145,25 @@ class Stree(BaseEstimator, ClassifierMixin):
return origin[up[:, 0]] if any(up) else None, \ return origin[up[:, 0]] if any(up) else None, \
origin[down[:, 0]] if any(down) else None origin[down[:, 0]] if any(down) else None
def _split_data(self, node: Snode, data: np.ndarray, indices: np.ndarray) -> list: def _distances(self, node: Snode, data: np.ndarray) -> np.array:
if self.use_predictions: if self.use_predictions:
yp = node._clf.predict(data)
down = (yp == 1).reshape(-1, 1)
res = np.expand_dims(node._clf.decision_function(data), 1) res = np.expand_dims(node._clf.decision_function(data), 1)
else: else:
# doesn't work with multiclass as each sample has to do inner product with its own coeficients # doesn't work with multiclass as each sample has to do inner product with its own coeficients
# computes positition of every sample is w.r.t. the hyperplane # computes positition of every sample is w.r.t. the hyperplane
res = self._linear_function(data, node) res = self._linear_function(data, node)
down = res > 0 # data_up, data_down = self._split_array(data, down)
data_up, data_down = self._split_array(data, down) # indices_up, indices_down = self._split_array(indices, down)
indices_up, indices_down = self._split_array(indices, down) # res_up, res_down = self._split_array(res, down)
res_up, res_down = self._split_array(res, down) # weight_up, weight_down = self._split_array(weights, down)
return [data_up, indices_up, data_down, indices_down, res_up, res_down] #return [data_up, indices_up, data_down, indices_down, weight_up, weight_down, res_up, res_down]
return res
def fit(self, X: np.ndarray, y: np.ndarray, weighted_samples: np.array=None, **fitparams: dict) -> 'Stree': def _split_criteria(self, data: np.array) -> np.array:
from sklearn.utils.multiclass import check_classification_targets return data > 0
if fitparams is not None:
self.set_params(**fitparams) def fit(self, X: np.ndarray, y: np.ndarray, sample_weight: np.array = None) -> 'Stree':
# Check parameters are Ok.
if type(y).__name__ == 'np.ndarray': if type(y).__name__ == 'np.ndarray':
y = y.ravel() y = y.ravel()
if self.C < 0: if self.C < 0:
@@ -173,12 +173,15 @@ class Stree(BaseEstimator, ClassifierMixin):
raise ValueError(f"Maximum depth has to be greater than 1... got (max_depth={self.max_depth})") raise ValueError(f"Maximum depth has to be greater than 1... got (max_depth={self.max_depth})")
check_classification_targets(y) check_classification_targets(y)
X, y = check_X_y(X, y) X, y = check_X_y(X, y)
sample_weight = _check_sample_weight(sample_weight, X)
check_classification_targets(y)
# Initialize computed parameters
#self.random_state = check_random_state(self.random_state)
self.classes_ = np.unique(y) self.classes_ = np.unique(y)
self.n_iter_ = self.max_iter self.n_iter_ = self.max_iter
self.depth_ = 0 self.depth_ = 0
check_classification_targets(y)
self.n_features_in_ = X.shape[1] self.n_features_in_ = X.shape[1]
self.tree_ = self.train(X, y, 1, 'root') self.tree_ = self.train(X, y, sample_weight, 1, 'root')
self._build_predictor() self._build_predictor()
return self return self
@@ -195,7 +198,7 @@ class Stree(BaseEstimator, ClassifierMixin):
run_tree(self.tree_) run_tree(self.tree_)
def train(self, X: np.ndarray, y: np.ndarray, depth: int, title: str = 'root') -> Snode: def train(self, X: np.ndarray, y: np.ndarray, sample_weight: np.ndarray, depth: int, title: str) -> Snode:
if depth > self.__max_depth: if depth > self.__max_depth:
return None return None
@@ -203,21 +206,24 @@ class Stree(BaseEstimator, ClassifierMixin):
# only 1 class => pure dataset # only 1 class => pure dataset
return Snode(None, X, y, title + ', <pure>') return Snode(None, X, y, title + ', <pure>')
# Train the model # Train the model
clf = LinearSVC(max_iter=self.max_iter, C=self.C, clf = LinearSVC(max_iter=self.max_iter, random_state=self.random_state,
random_state=self.random_state) C=self.C) #, sample_weight=sample_weight)
clf.fit(X, y) clf.fit(X, y, sample_weight=sample_weight)
tree = Snode(clf, X, y, title) tree = Snode(clf, X, y, title)
self.depth_ = max(depth, self.depth_) self.depth_ = max(depth, self.depth_)
X_U, y_u, X_D, y_d, _, _ = self._split_data(tree, X, y) down = self._split_criteria(self._distances(tree, X))
X_U, X_D = self._split_array(X, down)
y_u, y_d = self._split_array(y, down)
sw_u, sw_d = self._split_array(sample_weight, down)
if X_U is None or X_D is None: if X_U is None or X_D is None:
# didn't part anything # didn't part anything
return Snode(clf, X, y, title + ', <cgaf>') return Snode(clf, X, y, title + ', <cgaf>')
tree.set_up(self.train(X_U, y_u, depth + 1, title + ' - Up')) tree.set_up(self.train(X_U, y_u, sw_u, depth + 1, title + ' - Up'))
tree.set_down(self.train(X_D, y_d, depth + 1, title + ' - Down')) tree.set_down(self.train(X_D, y_d, sw_d, depth + 1, title + ' - Down'))
return tree return tree
def _reorder_results(self, y: np.array, indices: np.array, proba=False) -> np.array: def _reorder_results(self, y: np.array, indices: np.array) -> np.array:
if proba: if y.ndim > 1 and y.shape[1] > 1:
# if predict_proba return np.array of floats # if predict_proba return np.array of floats
y_ordered = np.zeros(y.shape, dtype=float) y_ordered = np.zeros(y.shape, dtype=float)
else: else:
@@ -236,10 +242,12 @@ class Stree(BaseEstimator, ClassifierMixin):
# set a class for every sample in dataset # set a class for every sample in dataset
prediction = np.full((xp.shape[0], 1), node._class) prediction = np.full((xp.shape[0], 1), node._class)
return prediction, indices return prediction, indices
u, i_u, d, i_d, _, _ = self._split_data(node, xp, indices) down = self._split_criteria(self._distances(node, xp))
k, l = predict_class(d, i_d, node.get_down()) X_U, X_D = self._split_array(xp, down)
m, n = predict_class(u, i_u, node.get_up()) i_u, i_d = self._split_array(indices, down)
return np.append(k, m), np.append(l, n) prx_u, prin_u = predict_class(X_U, i_u, node.get_up())
prx_d, prin_d = predict_class(X_D, i_d, node.get_down())
return np.append(prx_u, prx_d), np.append(prin_u, prin_d)
# sklearn check # sklearn check
check_is_fitted(self, ['tree_']) check_is_fitted(self, ['tree_'])
@@ -276,10 +284,15 @@ class Stree(BaseEstimator, ClassifierMixin):
prediction = np.full((xp.shape[0], 1), node._class) prediction = np.full((xp.shape[0], 1), node._class)
prediction_proba = dist prediction_proba = dist
return np.append(prediction, prediction_proba, axis=1), indices return np.append(prediction, prediction_proba, axis=1), indices
u, i_u, d, i_d, r_u, r_d = self._split_data(node, xp, indices) distances = self._distances(node, xp)
k, l = predict_class(d, i_d, r_d, node.get_down()) down = self._split_criteria(distances)
m, n = predict_class(u, i_u, r_u, node.get_up())
return np.append(k, m), np.append(l, n) X_U, X_D = self._split_array(xp, down)
i_u, i_d = self._split_array(indices, down)
di_u, di_d = self._split_array(distances, down)
prx_u, prin_u = predict_class(X_U, i_u, di_u, node.get_up())
prx_d, prin_d = predict_class(X_D, i_d, di_d, node.get_down())
return np.append(prx_u, prx_d), np.append(prin_u, prin_d)
# sklearn check # sklearn check
check_is_fitted(self, ['tree_']) check_is_fitted(self, ['tree_'])
@@ -295,7 +308,7 @@ class Stree(BaseEstimator, ClassifierMixin):
# Probability of being 1 # Probability of being 1
result[:, 1] = 1 / (1 + np.exp(-result[:, 1])) result[:, 1] = 1 / (1 + np.exp(-result[:, 1]))
result[:, 0] = 1 - result[:, 1] # Probability of being 0 result[:, 0] = 1 - result[:, 1] # Probability of being 0
return self._reorder_results(result, indices, proba=True) return self._reorder_results(result, indices)
def score(self, X: np.array, y: np.array) -> float: def score(self, X: np.array, y: np.array) -> float:
"""Return accuracy """Return accuracy
@@ -319,35 +332,3 @@ class Stree(BaseEstimator, ClassifierMixin):
output += str(i) + '\n' output += str(i) + '\n'
return output return output
def get_folder(self) -> str:
return self.__folder
def _save_datasets(self, tree: Snode, catalog: typing.TextIO, number: int):
"""Save the dataset of the node in a csv file
:param tree: node with data to save
:type tree: Snode
:param catalog: catalog file handler
:type catalog: typing.TextIO
:param number: sequential number for the generated file name
:type number: int
"""
data = np.append(tree._X, tree._y.reshape(-1, 1), axis=1)
name = f"{self.__folder}dataset{number}.csv"
np.savetxt(name, data, delimiter=",")
catalog.write(f"{name}, - {str(tree)}")
if tree.is_leaf():
return
self._save_datasets(tree.get_down(), catalog, number + 1)
self._save_datasets(tree.get_up(), catalog, number + 2)
def get_catalog_name(self):
return self.__folder + "catalog.txt"
def save_sub_datasets(self):
"""Save the every dataset stored in the tree to check with manual classifier
"""
if not os.path.isdir(self.__folder):
os.mkdir(self.__folder)
with open(self.get_catalog_name(), 'w', encoding='utf-8') as catalog:
self._save_datasets(self.tree_, catalog, 1)

23
stree/tests/Strees_test.py
View File

@@ -107,24 +107,6 @@ class Stree_test(unittest.TestCase):
res.append(y_original[row]) res.append(y_original[row])
return res return res
def test_subdatasets(self):
"""Check if the subdatasets files have the same labels as the original dataset
"""
self._clf.save_sub_datasets()
with open(self._clf.get_catalog_name()) as cat_file:
catalog = csv.reader(cat_file, delimiter=',')
for row in catalog:
X, y = self._get_Xy()
x_file, y_file = self._get_file_data(row[0])
y_original = np.array(self._find_out(x_file, X, y), dtype=int)
self.assertTrue(np.array_equal(y_file, y_original))
if os.path.isdir(self._clf.get_folder()):
try:
os.remove(f"{self._clf.get_folder()}*")
os.rmdir(self._clf.get_folder())
except:
pass
def test_single_prediction(self): def test_single_prediction(self):
X, y = self._get_Xy() X, y = self._get_Xy()
yp = self._clf.predict((X[0, :].reshape(-1, X.shape[1]))) yp = self._clf.predict((X[0, :].reshape(-1, X.shape[1])))
@@ -141,10 +123,9 @@ class Stree_test(unittest.TestCase):
X, y = self._get_Xy() X, y = self._get_Xy()
accuracy_score = self._clf.score(X, y) accuracy_score = self._clf.score(X, y)
yp = self._clf.predict(X) yp = self._clf.predict(X)
right = (yp == y).astype(int) accuracy_computed = np.mean(yp == y)
accuracy_computed = sum(right) / len(y)
self.assertEqual(accuracy_score, accuracy_computed) self.assertEqual(accuracy_score, accuracy_computed)
self.assertGreater(accuracy_score, 0.8) self.assertGreater(accuracy_score, 0.9)
def test_single_predict_proba(self): def test_single_predict_proba(self):
"""Check that element 28 has a prediction different that the current label """Check that element 28 has a prediction different that the current label