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lowprofool_l_1-norm_bugs.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyOjnRWINzdPm9zXXGAv47MY",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/ZhipengHe/ff432a368f720c3504ec64398137bb39/lowprofool_l_1-norm_bugs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "facfLGIuHSx_",
"outputId": "8106b78e-9bfa-49e8-b908-66f74daad8d0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Requirement already satisfied: adversarial-robustness-toolbox in /usr/local/lib/python3.8/dist-packages (1.12.2)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (4.64.1)\n",
"Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (1.7.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (1.15.0)\n",
"Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (1.21.6)\n",
"Requirement already satisfied: scikit-learn<1.1.0,>=0.22.2 in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (1.0.2)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (from adversarial-robustness-toolbox) (57.4.0)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox) (3.1.0)\n",
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn<1.1.0,>=0.22.2->adversarial-robustness-toolbox) (1.2.0)\n"
]
}
],
"source": [
"!pip install adversarial-robustness-toolbox\n",
"\n",
"import logging\n",
"import pytest\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn import datasets\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.model_selection import StratifiedShuffleSplit\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import SVC\n",
"from scipy.stats import pearsonr\n",
"\n",
"from art.attacks.evasion import LowProFool\n",
"from art.estimators.classification.scikitlearn import ScikitlearnLogisticRegression\n",
"from art.estimators.classification import PyTorchClassifier\n",
"from art.estimators.classification.scikitlearn import ScikitlearnSVC\n",
"\n",
"from tests.utils import ARTTestException\n",
"\n",
"logger = logging.getLogger(__name__)"
]
},
{
"cell_type": "code",
"source": [
"def splitter():\n",
" return StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=0)\n",
"\n",
"@pytest.fixture\n",
"def breast_cancer_dataset(splitter):\n",
" cancer = datasets.load_breast_cancer()\n",
" design_matrix = pd.DataFrame(data=cancer[\"data\"], columns=cancer[\"feature_names\"])\n",
" labels = pd.Series(data=cancer[\"target\"])\n",
"\n",
" scaler = StandardScaler().fit(design_matrix)\n",
" design_matrix = pd.DataFrame(data=scaler.transform(design_matrix), columns=design_matrix.columns)\n",
" clip_values = (design_matrix.min(), design_matrix.max())\n",
"\n",
" [[train_idx, valid_idx]] = list(splitter.split(design_matrix, labels))\n",
" x_train = design_matrix.iloc[train_idx].copy()\n",
" x_valid = design_matrix.iloc[valid_idx].copy()\n",
" y_train = labels.iloc[train_idx].copy()\n",
" y_valid = labels.iloc[valid_idx].copy()\n",
"\n",
" return (x_train, y_train, x_valid, y_valid), scaler, clip_values\n"
],
"metadata": {
"id": "iFjGUPIbHZCl"
},
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"def test_general_cancer_lr(breast_cancer_dataset, norm):\n",
" \"\"\"\n",
" Check whether the produced adversaries are correct,\n",
" given Logistic Regression classifier and breast cancer wisconsin dataset.\n",
" \"\"\"\n",
" (x_train, y_train, x_valid, y_valid), _, clip_values = breast_cancer_dataset\n",
"\n",
" # Setup classifier\n",
" lr_clf = LogisticRegression(penalty=\"none\")\n",
" lr_clf.fit(x_train, y_train)\n",
" clf_slr = ScikitlearnLogisticRegression(model=lr_clf, clip_values=clip_values)\n",
"\n",
" lpf_slr = LowProFool(classifier=clf_slr, n_steps=30, eta=0.02, lambd=1.5, norm=norm) # change the norm \n",
" lpf_slr.fit_importances(x_train, y_train)\n",
"\n",
" sample = x_valid\n",
"\n",
" # Draw targets different from original labels and then save as one-hot encoded.\n",
" target = np.eye(2)[np.array(y_valid.apply(lambda x: np.random.choice([i for i in [0, 1] if i != x])))]\n",
"\n",
" adversaries = lpf_slr.generate(x=sample, y=target)\n",
" expected = np.argmax(target, axis=1)\n",
" predicted = np.argmax(lr_clf.predict_proba(adversaries), axis=1)\n",
" correct = expected == predicted\n",
"\n",
" success_rate = np.sum(correct) / correct.shape[0]\n",
"\n",
" return success_rate"
],
"metadata": {
"id": "tqevzRqBHf4z"
},
"execution_count": 14,
"outputs": []
},
{
"cell_type": "code",
"source": [
"success_rate=test_general_cancer_lr(breast_cancer_dataset(splitter()), norm=2)\n",
"print(success_rate)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7cwVUF5fII6T",
"outputId": "e08ae7e3-64f1-47dd-a006-4bc921355b96"
},
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"success_rate=test_general_cancer_lr(breast_cancer_dataset(splitter()), norm=1)\n",
"print(success_rate)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 467
},
"id": "XAA7v0G9IrfE",
"outputId": "cbf6573b-65c1-4d34-936d-3c637b7fe670"
},
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n",
"/usr/local/lib/python3.8/dist-packages/art/attacks/evasion/lowprofool.py:159: RuntimeWarning: divide by zero encountered in power\n",
" self.importance_vec * self.importance_vec * perturbations * np.power(np.abs(perturbations), norm - 2)\n",
"/usr/local/lib/python3.8/dist-packages/art/attacks/evasion/lowprofool.py:159: RuntimeWarning: invalid value encountered in multiply\n",
" self.importance_vec * self.importance_vec * perturbations * np.power(np.abs(perturbations), norm - 2)\n",
"/usr/local/lib/python3.8/dist-packages/sklearn/base.py:450: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
" warnings.warn(\n"
]
},
{
"output_type": "error",
"ename": "ValueError",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-17-92e804381fc0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msuccess_rate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtest_general_cancer_lr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbreast_cancer_dataset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplitter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuccess_rate\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/lib/python3.8/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Validation should be done on X, y or both.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 565\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mno_val_y\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_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 567\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 568\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mno_val_X\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mno_val_y\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/lib/python3.8/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[1;32m 798\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 799\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 800\u001b[0;31m \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_all_finite\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"allow-nan\"\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 801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype)\u001b[0m\n\u001b[1;32m 112\u001b[0m ):\n\u001b[1;32m 113\u001b[0m \u001b[0mtype_err\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"infinity\"\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m\"NaN, infinity\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 114\u001b[0;31m raise ValueError(\n\u001b[0m\u001b[1;32m 115\u001b[0m msg_err.format(\n\u001b[1;32m 116\u001b[0m \u001b[0mtype_err\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg_dtype\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmsg_dtype\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
]
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "MiOcYrdbJo7S"
},
"execution_count": null,
"outputs": []
}
]
}
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