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stack-data->ipython>=6.1.0->ipywidgets->explainableai) (0.2.3)\n" + ] + } + ], + "source": [ + "# Step 1: Install the explainableai package\n", + "!pip install explainableai" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "7gfkIrfmlIHU" + }, + "outputs": [], + "source": [ + "# Step 2: Import necessary libraries\n", + "import pandas as pd\n", + "import numpy as np\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from xgboost import XGBClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from explainableai import XAIWrapper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Zn3qSNkFlLjs" + }, + "outputs": [], + "source": [ + "# Import the breast cancer dataset from sklearn.datasets\n", + "data = load_breast_cancer()\n", + "\n", + "# Create a DataFrame 'X' from the data features\n", + "# 'data.data' contains the feature values \n", + "# 'data.feature_names' contains the column names (feature names)\n", + "X = pd.DataFrame(data.data, columns=data.feature_names)\n", + "\n", + "# Create a Series 'y' for the target variable (dependent variable)\n", + "# 'data.target' contains the target labels (0 = malignant, 1 = benign)\n", + "y = pd.Series(data.target, name='target')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "cGt9vHVMln2R", + "outputId": "735432b4-ac1b-4a5d-e1c8-923c6ef0d00d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n", + " 1.189e-01],\n", + " [2.057e+01, 1.777e+01, 1.329e+02, ..., 1.860e-01, 2.750e-01,\n", + " 8.902e-02],\n", + " [1.969e+01, 2.125e+01, 1.300e+02, ..., 2.430e-01, 3.613e-01,\n", + " 8.758e-02],\n", + " ...,\n", + " [1.660e+01, 2.808e+01, 1.083e+02, ..., 1.418e-01, 2.218e-01,\n", + " 7.820e-02],\n", + " [2.060e+01, 2.933e+01, 1.401e+02, ..., 2.650e-01, 4.087e-01,\n", + " 1.240e-01],\n", + " [7.760e+00, 2.454e+01, 4.792e+01, ..., 0.000e+00, 2.871e-01,\n", + " 7.039e-02]]), 'target': array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0,\n", + " 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n", + " 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0,\n", + " 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1,\n", + " 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 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Random Forest_ImportanceLogistic Regression_Importance
Feature
mean radius0.0487030
mean texture0.0135910
mean perimeter0.0532700
mean area0.0475550
mean smoothness0.0072850
mean compactness0.0139440
mean concavity0.0680010
mean concave points0.1062100
mean symmetry0.0037700
mean fractal dimension0.0038860
radius error0.0201390
texture error0.0047240
perimeter error0.0113030
area error0.0224070
smoothness error0.0042710
compactness error0.0052530
concavity error0.0093860
concave points error0.0035130
symmetry error0.0040180
fractal dimension error0.0053210
worst radius0.0779870
worst texture0.0217490
worst perimeter0.0671150
worst area0.1538920
worst smoothness0.0106440
worst compactness0.0202660
worst concavity0.0318020
worst concave points0.1446630
worst symmetry0.0101200
worst fractal dimension0.0052100
\n", + "" + ], + "text/plain": [ + " Random Forest_Importance \\\n", + "Feature \n", + "mean radius 0.048703 \n", + "mean texture 0.013591 \n", + "mean perimeter 0.053270 \n", + "mean area 0.047555 \n", + "mean smoothness 0.007285 \n", + "mean compactness 0.013944 \n", + "mean concavity 0.068001 \n", + "mean concave points 0.106210 \n", + "mean symmetry 0.003770 \n", + "mean fractal dimension 0.003886 \n", + "radius error 0.020139 \n", + "texture error 0.004724 \n", + "perimeter error 0.011303 \n", + "area error 0.022407 \n", + "smoothness error 0.004271 \n", + "compactness error 0.005253 \n", + "concavity error 0.009386 \n", + "concave points error 0.003513 \n", + "symmetry error 0.004018 \n", + "fractal dimension error 0.005321 \n", + "worst radius 0.077987 \n", + "worst texture 0.021749 \n", + "worst perimeter 0.067115 \n", + "worst area 0.153892 \n", + "worst smoothness 0.010644 \n", + "worst compactness 0.020266 \n", + "worst concavity 0.031802 \n", + "worst concave points 0.144663 \n", + "worst symmetry 0.010120 \n", + "worst fractal dimension 0.005210 \n", + "\n", + " Logistic Regression_Importance \n", + "Feature \n", + "mean radius 0 \n", + "mean texture 0 \n", + "mean perimeter 0 \n", + "mean area 0 \n", + "mean smoothness 0 \n", + "mean compactness 0 \n", + "mean concavity 0 \n", + "mean concave points 0 \n", + "mean symmetry 0 \n", + "mean fractal dimension 0 \n", + "radius error 0 \n", + "texture error 0 \n", + "perimeter error 0 \n", + "area error 0 \n", + "smoothness error 0 \n", + "compactness error 0 \n", + "concavity error 0 \n", + "concave points error 0 \n", + "symmetry error 0 \n", + "fractal dimension error 0 \n", + "worst radius 0 \n", + "worst texture 0 \n", + "worst perimeter 0 \n", + "worst area 0 \n", + "worst smoothness 0 \n", + "worst compactness 0 \n", + "worst concavity 0 \n", + "worst concave points 0 \n", + "worst symmetry 0 \n", + "worst fractal dimension 0 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Step 4.5: Extract and display feature importances\n", + "feature_importance_df = extract_feature_importances(models.values(), model_names, feature_names)\n", + "print(\"Feature Importance Comparison:\")\n", + "display(feature_importance_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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ynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEEKIjCdhKIQQIuNJGAohhMh4EoZCCCEynoShEIOQaag0NsTRdSvdRREiI9jSXQAhRLLWphJqNo9m2aJ6cnI1LvleCRMne9JdLHGA0z9uJPrMWtR8N85vT0PNl8/U7qRmKMQg0t1lsHXDOAzdDkBXp8EDdzcSj5lpLpk4kEWfW0fn9LsJ37KA4I9epnPq3zCbA+ku1qAiYSjEILJ1cxTLSv5aBgImdTtjaSqROBiEbnoLjL4LKrO2i8gDy9JYosFHmkmFGESGlNkxgZBNw1AUnIaJV7UoLLanu2jiAGY2+FO31aduy2RSMxRiEMnJ0+j0agTtdiI2G11OB9vsTp5dITVDsf+cZ42n0+3ho8oR1OYXAuA4a3yaSzW4SM1QiEFk0fIIcTO5FmgD7n89yLePcqenUOKAt/asr/HP9tGYKABML4lxyfHVaS7V4CI1QyEGkWA4MZRCMU0wTWKKggp0h6UDjdg/8bjJ/Kfae4MQYGWTgy3rQ2ks1eAjYSjEIHLoFCdFoTBDg2HKg2GGBEMsz3Jz+ESpFYr9E+gyCPqNlO0NO6NpKM3gJWEoxCDy3quduIzENbwCeA2To1s7WRKQr6rYP7kFNoqGpHbAKsuW1obdyTdMiEFk3erUpqvcuM7qBpOWgJy8xL5TFIUZ/z2EoENL/GyYjNxcx/Kvv8nr57+HEUmtNWYiCUMhBpG8gtQr+JiqUOBRyHUr/TxDiM933XKVRyrL0Fu7OHzxOoY2tAHQuaCezY9sSnPpBgcJQyEGkaOOcmPRNx+pBSzNy+Z7oy3smoSh2HftIYv1LRYoCmPq29DM5BaG1mVtaSrZ4CJhKMQg8vEb7WSHI2i6TkiBGpeNw1o64MPWdBdNHKByXTA8N3EhVVPgI2LX2H3699wJuWkp12Aj4wyFGET8HTqqouDVdTy6Tkk40ZEm0C1fVbF/VFXhrmMVzplvcN8pM4m57BR3BvnOgo+Y7e9k1GWj0l3EQUFqhkIMIpMO9xHRVFYW5vFC+RDmV5SyuDCPcbNz0l00cQAb97fF5Bs6MVfinnRzrpc7Tj+UjoBJx8cdaS7d4CBhKMQgEgyZbMnxsc3jRldVLEWhzuNmi8eV7qKJA9hHiztpyM9O2ha3a6wdVkzDWw1pKtXgImEoxCCyclmIdkdqk+jba+NpKI04WJTlazjiesr2oq4g3nJZ1xAkDIUYVPJyNRxm6ur2JTnyVRX7b/FFM8GfPNn77I11FMWiZJ0+PD2FGmTkGybEIHLkSTlUBoI4d+/+blkcMkWaScX+eX19nGs/cREL6dAcJKu5m1OXbuL0ddsJZEc483mZzAEkDIUYVNa9006nquB32LBjEdNUdmR7eGx9uksmDlT/WBzp+yFmEIjBh0UF1A8tYXxrF8u6bdQHUlsjMo301xZiEOlqitLhzKXWmzwx946uNBVIHPBy3al1HoeVCD9TsdCwcEsSSBgKMZiMPjyXsf8OMrF7G7qisKwwj48KcjipWmafEfvnR0e7eHhplLJwjPHhKE7LottuY0FJHjWHHMr5lQZ5rtRpADONhKEQg0i7biNX75s4+YSGFsBidpEdkGWcxL4bW6LxdWcMq71nySZFIT+uUxiN8UrFcK6NxwC5Jy33DIUYRD7+wJ+ybUqHn3UfBdF1ua8j9l1ti060MbknKYqCw7TIi+s8uTRKNC6fLQlDIQYRp1cDy8IWidJtGGxxOQlpGlluFVW+rWI/GCYo9BN2SiIATCvxX6aTr5cQg8iECQ4K2top7uxibEsbM3bW84nLwYyjfaiq3DcU+65CizG6syV5o2WhW9Bmt/H1qQ7cDvlsSRgKMYhseqkB5273DD26wRH1TbzbIV9Vse+MthA1Y+/nB/Nf4Yyly/GFQqiGQVhVqHfbOX/tZrRiR7qLOShIBxohBglDtzACqdOuFYYjLG1MnUpLiM/T/eBqnG0BAI5Zt45j1q2j2+3myaOOSuxgWbzzZB3BE0bizfDaoVxuCjFItKxuR3GmXp9uzclmTpVct4p9F28MpGxzx3brTKMoZAWjBGIpu2UcCUMhBgF/XYh/X/oBrtZuDEVh1wRZm3J8bMjPw/ygBcuSXg5i3+R+awImsKi6knmHTmVLYQEfjB1Lsb+ZqTs/Jr+zDWNOKSVZmV0rBGkmFWJQ2PpqHUbEwMpxY2kalmUR0lSKo1HO3FFHc0EeO7dFqKiSsYbii7PNKOVbt36fRYqPvFCUCz/agjeeuCftd2Vx2QePcUX4TfjhrWR6d+XMPnohBoOOAPnrPqUw0oZqGKhxnaDDQXNuDiG3C6OnF6nDJV9XsW9er7FYpPgAOHxHc28QAnS7fSyqPgxt9XZ4eWW6ijhoSM1QiHR6bRWc/ScqghF2DJlNc6QYXVVpKSsh6HXjBywLtByVkjJnuksrDjAbd1vEPj+cemOw1ZuPhgEb6uD0mQNYssFHLjWFSKfvPwDBCK2ufNYUTACgqTCP4G4TdSsK1EY1trTJUjti3xw3TIGeAfc1ud6Ux6vaatCxwQlTBrhkg4+EoRDpEorClkYA2px5vZvD7tQaYG7cYG2jkbJdiM8yoVDhO2oXimHyYXkR6wp9WCTi0VAU3hx9BA+fdSWx0cPSXdS0kzAUIl08TiLVw2hlGGY4L9EeCviCYcpb2/F0B1icm81LpYUsLshhQol8XcW+2bHWz7DXt5AVi6NrKivLCojabERtNuI2GxGHmw1WIYtfbvn8FzvIybdLiDRpWtXOJ3XD6WAotpidUY3N6HYNhwL2eJx5Y4azPjebVqeDWq+bH72SOiBfiM9StyFAt9OB35mYZWZIIJJod1f6hlJYisL2T4PpKuKgIWEoRJpsenQjvnBfwA1r78BrJJpCN+Tl0O1MnibrpQ0mDX4Zayi+uLLRXnzRGFnRxOesyZu6VJNiWQwb7Rnoog06EoZCpImiKVhYOPHjIHFlrvY0lSr9LzKAJmOjxT6onOSj+chhTGjqRjVNduR4WFOU07eDZaFn25h5SmH6CjlIyNAKIdJk7KlFuO5ci9dMTJkVIpthDTk0FOQxpqOLvEiUDldfZ5rjqzWKZaYQsQ/Wt1nUbovjMEBr9DPZNLHQ2J7txRvXiagqLwwppGq1yTVz0l3a9JKaoRBp4v7FvN4gBPDgx6s1sy0vhya3iws+2cyElnaccR3FNOhSJAjFvnm9xmSoP8JOp50sw6IiGMWjG8RtNjrdLiJOB4VxnfmfyETwUjMUIk1s62pStuXpnWzKyWZB6R7NVhYsbxyYcomDR3WewhKHDZ9u0Oiwo5N60g9qKuOKpF4kYShEmuhjK3Es7+z9udWVz4qCyZy8fgtzahvoyvHR7HKwuDCXnV4X04dIzTATbFnt57XH6mlviDJ6ho/TLivHm7N/p+qTRyjcMbWQ2QsXc+fqD1kzbCYNOUPocrpoyMpCiUQxi7L5+TGypqGEoRBpEpk8Cffy1ShAp8PH/JGnoat2skMRskMR7JaFLTeHs3Y28+8RJZT6AexpLrX4KnW1xXj0N1vQY4keVB+900GoW+c7N1fv1+uZFtg2befOlx/m1hOuIeRI9Br16gbjGluYumozZ6KSddwcOGHol3YcByKpGwuRJtYTi9hV11ufNxpdTQ66nE4/kOhFekxtG+vXR9jeIrPQHMw+XdzVG4S7bFrlJxzYv3t6i+ssjlm7nC1FVb1BuEvc5SDicmA6VJr+b9t+l/lgIWEoRJooZmKuUQuFmJLcTKWrKlGHnV2nxV2zkuqGjDM8mLmzUxvr7E4Vm2P/TtX5LoV2dxaeWDj1QcvCZhiYgL1AJoGXMBQiTfRTDyFIDtuYQVanszccmwrzWTe6iq3DK4jY7RiKwhKflzaXRm62fGUPRoZpcedinWs2uTD2CMQ5pxZg388wHF+ksOaoOeQFm7li0T/49pL/R1VrohaYFQgRyHaRZRqUXj76Pz6GA518s4RIE++aT2hiFAYOsiNRptfUYjdMGosLsXoWWrUUhU6ngyaPG3/c4qfPh9JcavFV+PlbOj96TefDGoPTX1/InHWfMLGmhlOXLWX6v97b79fd0mlRvWol+ZEQVW21TGzcwGUfPs6OrCjtTo1gjpv6ofmseWTzl3g0ByYJQyHSwDJNzK0tGPQ1j+YFw73Tse3ObVq4TBMFWLhZ5ic9GN2/IvF3P3rzNka2tTNt61aOXruGqqYmov/ehFHbtV+v+9gnJpd88FbSNptpcuzmpSweUZrYoCqsn1+LEcvsJcIkDIVIA0VVUYqyUEgOv8LO7pR9gzaNTq8Dw6kxsUw6gB/M9npHeD8nXOhbzXDP90l9vUyf00HCUIg0CWtFJIZB952u4lGIqrutKEBicuXuoiz0Qi/HTHanvpA44F05UwPgneoqavJykx5znj4Krdy3X6/77QkqD845offnl8ZN5/Kzv8urFYdx4sptBJwODBTGnT0M1Z7ZcSCXmUKkQezDWuKNMeyARoRmcuhUsnhn2ngCTgdh00SzLOKqii+m447phO0aj2+Aa2anu/Tiy/b12p1sXBdi2dBivvGd8/j++0sY09zKsmFDOa0wxhH7+br3vxvmvuGH0XW2HQcmDx16XO9jS6r9/ObJ92gblkd0lEzULWEoRBqYgRiQqPmZKEQUB3ElseAqgKGqvQ2oUU1FMxO1x+5oGgorvlKmbrLyie0cGdDZXJjLmtJCfjn3xN7HN635mDk7OlCH5e3T6wZjFn99N4KmWzxZPh2rJCvp8YaCbD4pzqdMN1j8f3UccWEFSga3lUoYCpEG6rhiuhQPXVYWFiqaZaBiMmH9TnaW5YGiELKpLKnIp83TMwbMsjh3TOaerA5WpmERDyUG1YftqafkTrcLqysM7FsYRuIQM/pO8oaW2gwadDtQo0H0iIFlgqLta+kPHhKGQqRBzWXv0Gn13Qcy0bBrOkOau5i1aisfTxzGomElfUEIoCh8VGcAGXzGOgi9+WmcpYeMJG9jC99etQRHzI9pZPHR0CpemVDJOe3bUSdO3OfXLfAqHFlt4/1NcYpD3Xga/GwaWt77uF03qG7oYOvYYuZMdKFm+GKZEoZCpEH3otQlKJy6yfahTor8HRz3YQdPTTo9ZZ8P6mQGmoPJpY8HeKjJBUMreOzdF7lw5aLexxY1zWZW0wxmjMrZ7+bLx05Q+f6CzTz+4l9Bhe+e+X1eGzuZko4gp63cxuaJw/i/acPxTlU4/8s6qANUZncfEiJNnBVZKdvimoK520mvuCuYsk+5L7Ov3g8mtd0WDzcmav5T62qSghDg0NoVuIwoq1YEiNbv32QLLz5Wx1WrXycnFiYnEubE7bWct76eY5q6CA3NR7VpTG3s5I51Kk3BzL7QkjAUIg1yxznx0b7bFpNwttU72MtUIK8tANZuJyjD5IKJ8pU9WNR0WVg9f++RbU0pj9ssg6xYgKjTRmRHIOXxL2Jrh8WIjuben1u9+Sn7+CI6uqJS69+vtzhoyDdLiDRwvrCE8SxlHMuo4hOm8R7Hdr0OGBiqwvvDy1hbkA8dUQjEoTsGnTEe/iSzr94PJtO8OgXhEMdt/JhmbxZhW2LVEgtosReywz2U5qxCNE2Bqpwv9JrRzhjKVjt0Jk7tZ9ua+TR/WO/jExo39v5bMU28wRABDYoUnSlFX96xHYjknqEQA8wyLTQjhgL4aEehDQUwLBWHGSOkarR7ewbXW0B01yALi86IhWVZRA1w2aTJ9EDV8coOdpz1DNv15XiNCAAry0ZQ3hxgRc5MArZsAAobulg/vpL/vWojX7+8jMNPKtjra277907eu245togP1dJpv/X3jG6LoBWXsLJ8IlPq1nHMxkVsKKqmzjeEYfVN2AyTEXWNBFfZWTpyInO+XjIgxz8YSRgKMcAUVcFfNoJIvZsg+cRQ6cZNBBeFdTrdeTGO21zH+5VltGbttgadTWWYT6H8bp36ABxbqfDwqRrDciQUDySWYbL58ncZE/0EL5He7dPrt7HKNb03CAGyA2GGNLRRV1HM8w83MPXwHDz9LPOkRw0+vGEVRiRx4TS6oxFbVwQTeG7WHLq8WTjjUUxFJW6zM3rzdrTdmuC98Tjv/nUzk4/KJzsvMxeQlmZSIQaY3hqmuwWCFGCi0EIOEVwA2HWL3LYohV0Brn3vI9CUxASTThWyHSxthPqe20dvb7f49kuy2O+BJt4SwagL4iV1HtrsfrZ5g4nA1GMWjbX9z7oQqA0S7Yz1/lwQTbxOyOmiy5vorBW1O4n3NMWqVmpzu8sfoWl7P+seZggJQyEGWOCtWhx6YpB1DBvWHl9DzbSwx0yGt3bi9GiQ74IsB6g9wbibhbUWgZjcRxyMDNOirt0gpvf9fQL1IUyPjcjIXDq01EH03UpuyrZAVqLJ3OFUyB3qZHtXoql8d9nDsnAVOFEsC0c0TpstcY/RG42Q7+8mr7ubmGUS7lkazFRST/3RLAelIzwp2zOFNJMKMQCCHzYQenotno0bia/vxLRceAmR+Apmo5FYoimmqUScdkxVIeSwEdU06JmKjagOhgV2FewaWFDkBrcNLMvi5Q0Gy3YazK7QOGW0ltFTa6Xbhxtj/HSen4YOk/wshV8cYhH7+2o6twawADsOAgWzOLrtXbKMxBCasOpEi9hxumNE7Ymlvbp8HhpLC1BMkw1eD1WPWHTFDUbnwf+drjGtJPE31hwqM08qpv13KzAtFXCxjTLy7F389zvv8vOTjuXlkRVYisLwYBgrFmNkXTNOXccCVF0nr6GNRb9YzrF/mondk3nRoFh7XmJkuOXLl7NixQrOP/98srOzP/8J/yG/388///lPZsyYwcyZM7/y9xMDr+W2lXT+5FWqWIOGgQX4KcWPj61UoAEqFmGHjbYcT+/wCr/LwQ+/cyLdu2ahiRt9k5NmO8BtZ2S2xebv2bn0XxEeWqH3vufls2z8/SzXwB6oACAatzji1220B/pOrZcsXkWhPzFW0B4zcMUSzduKZZIf7yCsOimKtTK58xMMy4VpM2j0FvHa6GOI2+10OO384ZRDeodiAIzNh08vSYRWvCnEmvJHiOq7h5hFidbBPYfO4LajDk8q45ROPzPbu6moa2JISzvKbjEw5fJRHPLjCV/2r2XQk2bSPaxYsYIHHngAv39gBt34/X4eeOABVqxYMSDvJwaWGdFp/s1SiqlF65l6W8eFgkYDxYCC2rOEU7fXmbSoXHYkxvFrtvW9mF1L1AoBAjGwLLZ0wxtb9KQgBHhwuc7W9sxerDVdNjboSUHoicV6gxDAZvT9XSxFpc1RQNCWRZOrhCzLTzZt5OmdtGXlo5kWrmiMLUW5SUEIsL4d6vyJ9wkuqsdI/gjgQMdh6Lw/vCKljA2uxGfNE44kBSFA/Yct+33sBzIJw4NcMJg6i4kYOGZ3DLM7hp2+jg9Wz9yiMexJtwANNfXrWODfY+aRXWsd7jp/KQrLG1IbdywLdnZJo086DM3X2H2+7YjNRtDR10PTVPtvvvbFu4kqTqIk7tvlRjt7Hyve83MAFLihqOcWn3NMXspyvToahqIwsr0j5bk58Z6JwV3O1MeqUmdHygT71Ez64osvcvPNN3PPPfewevVqnn/+eTo6OqiuruYnP/kJkyZNYsWKFdxzzz1s2LABr9fLN77xDS677LKU11q3bh0PPfQQq1atIhQKUVpaymmnncZFF12Ezdb3SVq7di3PPPMMH3/8MU1NTWiaRnV1NRdeeCHHHHNM0mvedNNNvPTSS7zzzjvcddddvP322wSDQcaOHcu1117LxM+Z7HbX8/d0+eWX873vfQ+AQCDAQw89xNtvv01TUxNer5fZs2dz1VVXUV6emAR3/fr1XHLJJUydOpW77767996NYRhcccUVrFu3jkcffZTOzk6uuOKKlPcrLS3lxRdfZPny5VxxxRXceOONzJ07t9+yLl++vHfbd7/7XRoaGrj33nv561//yvLly+nu7u7dp7W1lQceeIBFixbR1tZGbm4uRxxxBFdeeSX5+akzU4g+4TVt1F79LsGlTXhmFlNx1xF4phZhhuO0/mgBgf/3KWqeC/d542l+chvx7d04lDjFtNFu5uKjBbARIBcbcXJox0mUOkppowADlaDmQDHAsCtEsmyYdpXfff1wVlWVJgphWdAZSdxDtKuQ5wbLQlUSKx9gWomwNCzyMGj4qRunjEUcGG98BNc9BhvriY2t4m7v17h76iwA8gMhvrZxK/+cPZEul5OjNu7gwiXremsiFmCzYhzTupAdrioac/M5fsc7tDOMJUPG8psTjuCTIUXYFYtgngdsKopp8acXXuLi9WtwX3kIL6nD2PnEBiY2dpIV7FsezGMLsdObx2UXnkKTzwuARzc4obGVkZ3deKMx3htSyPKifBTL4sitOxnX1o7+7Un84aK8jPr87Ndd0r/97W8YhsF5552HruvMmzePq6++mptvvplbbrmFs846i1NOOYU33niD++67j7KyMk499dTe5y9atIjrrruOiooKLrjgAnw+H2vWrOH+++9n48aN/PGPf+zd95133qGmpobjjz+e0tJSurq6eOmll7juuuv47W9/y8knn5xSvquvvpq8vDwuu+wyurq6eOKJJ7jmmmt44YUX8Hq9ez2u//qv/yIYDLJgwQKuvfZacnNzARg1ahSQCMJLLrmExsZGzjjjDKqqqmhtbeWZZ57hO9/5Do8//jilpaWMHTuW//mf/+HPf/4zjzzyCBdffDEADzzwAKtWreLnP/851dXVtLW1ce2113L77bdzzDHH9Ia7x7P/PbpCoRDf+973mDx5MldddRXt7YkpvxobG7n44ouJx+OceeaZlJeXU1tby7/+9S+WL1/O448/TlZWZl4Rfh4rbrD51JeI70yMaQguamDLqS8xoebbtF3/Lt33rwbA6I7R8cePAAUNg1KrCRWLAtppp4hdDTFxNFoppYwdVLKTGDYaKUbtGSVhi1u4u3TuO34Cq8p7pgUxLbApoCkoqoLlcybCUVEwITEEQ7V6zqwKMbtKxABn5vWDGHgN7XDGHyCSCCHHqvV8l3pGr2tm/ZAicsIhLr+wb9L11yaORNdU/ueVFViqgk038RldrHLPZtPwxAX1sq6jyPcH+d45p9KcnThnxQBHZ4RTdm7nxlf+zfCeGl/kt2/zzhHHUpmXxfKKQvI6gjgjcTTTYPymeoZ0hXjmvud4d1QFH46rYEdlCaPbu/DG4ywpzued8iG9Zfv3+JH4VsepfGg1vy45lD+e7h6gX2L67ddXxTAMHnnkEez2RNV/xIgR/PjHP+ZnP/sZDz/8MOPHjwfgzDPP5PTTT+fpp5/uDcNoNMott9zCxIkTuffee3trgWeffTajRo3ijjvuYPny5b2dSS699FKuvvrqpPc/77zzOP/88/nHP/7RbxiOHTuW66+/vvfnqqoqrr/+el599VXOPvvsvR7X5MmTqa6uZsGCBRx99NGUlZUlPX7fffdRV1fHww8/zOjRo3u3z507l/POO4/777+fm266qbeMS5cu5b777mPmzJlEo1Eeeughjj322N4yFBQUcPTRR3P77bdTXV2ddMGwv7q6ujj77LO56qqrkrbfeuut6LrOE088QUlJ3ywTxx9/PBdffDFPPPFEb+1XJAsube4Nwl3iDSGC7zcQeKZveisdlV1jHzxEeu8FJu4V7nmFrRDCSw6duIhi7fG4Zlpsz86G9p5xX04bFHqYEI1gD1tscmoEPXsMjlaU3rlMg3F4o8biHFn/8Kv37xW9QbiLl26q2lsY1t7Fn088NOUpS0YMxbCvIr8j0XyuK14ahvYNtSj0B1lRPqQ3CHeJqSrHrlvfG4S7nLJlE2tHTQFFoSM/cVE7ee323sddusGJn9YwoqOb+0vy8MbjAKzLS53mbWVZMUdsr+fvH3RnVBju1z3Dc845pzcIAaZNmwbAxIkTe4MQwG63M2HCBHbs2NG7bcmSJbS1tTF37lwCgQCdnZ29/82ZM6d3n13c7r4/RiQSobOzk0gkwqxZs9i2bRuBQOoEtuefn7wYya5gra2t3Z/DBRJd11955RWmTZtGcXFxUrndbjcTJ05k8eLFSc+58cYbKSws5Je//CW//vWvKSkp4YYbbtjvMnxRF154YdLPgUCARYsWceSRR+J0OpPKXlZWRnl5edLvPN3a29uJRvvusQUCgaQOTbFYjLa2tqTnNDQ0fObPjY2NSWOz9uU9bMX9nxA61CC2kr5a/O5fJmOPr5ZC6t0Ijb0PmDeBbp+bbLuK5rH3dpxxGCZN2U5ijs9f07DEM/C/qy/zPfbXgJe3JDelDCYqRk9dIz+UOpDdF4miGcmfCVu8r2NNzKZREOx/pQqtnztbLV4vtnjy5ynqSK3rtGe7CTps7Honb1xP2ccXjRFXFHz5jgP687OvnSD3q2Y4dOjQpJ99vsQipXvWpHY91tXV1fvztm2J3nG/+c1v9vr6u/9i2tvbuffee1m4cGFvk9/uAoFASvPenuXb1dy5ezn2VUdHB11dXSxevJjjjz++333UPTpA5OTk8Otf/5rvf//7ADz44INf+XCNvLy8lPeoqanBNE2ef/55nn/++X6ft+fvLJ32vH+559/X4XBQUJA8R2Npaeln/jxkyJCkn/flPVyjcsk7fxQd/9zU+3juN0Yy7KgxBG5QaDznBTAtNEw0BxgxCOMiggMXiTlIvQQJ0Pd3sRPFQwAwiaOhYWD0dKwxFfjTuXNorSzA702MN8NK3BPc6nDQVejt27a73X4+dpjCERUqMLC/q6/qPfbFgJf3lOkwexQs7ft8tFCBhYYFzF25iSdmTaSlp5anWBZnfrSJbH+UcmopooWYaqeouYxYqwu7qdPldjOyrZNTPt3MK+Oqe193TEcby6vGcNbaj3HridodbjuvDR9DadigeLcyNhbmMKyuBQ9RVCyiio3HZ41lg9fLjCwvpYEgcxpb2JSTTVxLnLucus7xm3fwbmUZPz01C4fDnjGfn/0Kwz1P+rto2udfre5K82uuuSapqXF3RUVFvfteffXVbNu2jfPOO4/x48eTlZWFqqq8+OKLvPrqq5hmavfxvZXjPxlSueu5s2fP5qKLLvrCz3v33Xd7/71x40amTp36hZ/7WYOmDaP/WoXLtfexZaeccgqnn566YCyA05naq0z0Gf7Y8eTMHUGopwNN3rmJE1TWWaOpWH4h/ifXo+W5GPatcXT8axttT2+ls6CaYqsNdfkOvHoMV3cjgaibKG6yacFBNxphxtBKLkNpooxadxkrxpSxtTy/Lwihtwm0Cw0MEzS1ZxiGxQmVCts7LVwKVObCWWM0zh8nzaMDxqbBgt/A4+/AhnqMQ8eh/18T5r9raM3Nwh7TeeLBZ3lu+lh25uUwoamD0Q1tDNNqGKH3tJoZkGt0sp0xgEJeNMj/mzmVCR3tDFu2nEafj7ltWzhxrM57MSfzv30ax2kdDBtiw/nt6dyt2/nnxUtxt4RBUfFEojgCEbKUMLaec5fNinH6uk0smTCcx6ZUcejOVkoDIY5oaqbBUmnO83FpuAHX10fy6/PLmFz2+efzg8mA314fNiyxnIjb7eaQQw75zH03bdrExo0bk3pz7vLcc899JeXbWwDtqnEFg8HPLfcuCxcu5Mknn2Tu3Lns3LmTv/zlL0yfPp3q6r4rvc8KvJycRHt+fzXaurq6L1QGgPLychRFQdf1L1x2kUzRVPLPG0X+eaNSHnNOK8E5re8+bMk1Uyi5Zkrqi8y6joLla2hgNA6i2EgMe9GAMmopo5a1xf9NbUkuMVs/JyJV6e0tSu/DCn89VmNsgYRfWnmc8L2TgMSfxv67+ymKdVHU3EVM1XijahxT69uY0NLF9uphtMUNyrcnf4dVLLLppIMinMSZs2Ubc35xFYXBCFcsXc85731A1m+v5vyvjUx5e/97zahhnajLjjMeIzsUwBOP9wbhLmet2cBvzj6OsN3GghF9tatfPv0uvzt2BofOKeCsC4bs+fIZYcDHGR522GHk5+fzyCOP9HuSj0QivWPjdtVA96zRbd68mXfeeecrKd+unpzd3ckT5qqqysknn8wnn3zCm2++2e9zd2/GbW5u5je/+Q0jRozgZz/7GbfccgtOp5Nf/OIXRCJ9M9Xvuie65/tBotlZ0zSWLl2atH316tWsWbPmCx9Tbm4uc+bM4e233+73eZZl0dGROhZJfMlOStxb99JGlNTm8iZXIQGvm9J4lKxwLLUZVDdRdg2r6JFth9EyKmbQUex9FzPK7t2jev6klqJg9nP63f0vHu9p4VItC8UyUSwSEy/0Q7P1vZalKCiWlTJIH8BQFcx+L8ATZbTbM/eiasBrhm63m5tvvpmf/OQnnH322ZxxxhlUVFTg9/upqalhwYIF/OlPf2LmzJmMGDGCqqoqHnvsMSKRCJWVlezYsYNnn32W6upqPv300y+9fLvGIv71r3/llFNOweFwMHLkSKqrq/n+97/P6tWr+fnPf85bb73FpEmTsNvtNDQ08P777zNu3DhuuukmTNPkV7/6FZFIhN///ve4XC6GDBnCDTfcwHXXXcdtt93GL3/5SyARVBUVFbz++uuUl5eTn5+P2+3myCOPxOPxMHfuXJ577jl+8YtfMGPGDGpra3nxxRcZNWoUGzdu/KxDSXL99ddz2WWXcfnll3PaaacxZswYTNOkrq6Od999l1NPPVV6k37Vfnk2bGkk+8kPaLZG4KSYHPpWIS+JtDLJ/ymtQ4/isPpmog6NnYVZGJqKEjdQu6MYTltvSGY54IWzNFSZg3TQ8f5gFp0XJe7P202TocEudmblYtd1vP4gnfk+NmUNZ6K/7z6jgYafPFBNNhSV8t7oKg7fXEeebjJ9+wbsM8vRDhne7/sNnV1A/sgs2rcEiNnsuKw4YZsDHRUVqzeQH589hWlN7dhQ2ZnlYqfPw6QdzXxUWsjkUJCvHZe5K/ymZRTSYYcdxqOPPsqjjz7KK6+8QkdHBz6fj/Lycr71rW/1juvTNI2//OUv3Hnnnbz00kuEw2FGjhzJTTfdxMaNG7+SMJw6dSo/+MEPePbZZ/ntb3+LYRhcfvnlVFdXk5WVxUMPPcS8efN44403ePfdd9E0jeLiYqZOncrXv/51AP7xj3+wcuVKrr/++qQm0WOOOYZvfOMbPP300xx66KEcd9xxANxyyy3cfvvt3H333UQiEUpLSznyyCMBuPbaa7Esi3feeYeFCxcybtw4br/9dubPn79PYThkyBDmzZvHo48+ysKFC3nllVdwOByUlJRwxBFHcMIJJ3x5v0TRP7cT/t+1BC/6OuFTXuh3l/GtG1jAUYzs8jN8tZ+ITWVxQT6L83MxVAVcDh48QeFrlRpj8mUCqcHK8+0pKDkuwo98hOJ1cMyVM9i0MUbd+82UATu6DP596LE0fZrP2LYa7HGTuJ6FjTiLR42nw5tFQTDOecvWk2WLc/LX83Fet/dhYaqmMHOil5UfNhBx2YnHHYzuau2pkSqYKDw5axJ1IyoY1ZVomRrRFaJg3UeEgibvV42mvNRBbqFjr+9xsJOJuoUYYOE1rWye/ATlbCSPPbqQZ5fw4CHfJqqpdLjcBFwaywrz2Ox0gQXxHA9vXmjnuKrM6txwMNreEOfH19eRG4/zv089jN0wacjO4+1RU1P2veTVo/EWfXYntw8K5qG3J4YX5BLERTzp8afmzGTdyGFJ27IiQSZuXMg1xySGoy2+JptDKjNzpobMPGoh0sg9qRDvhGy6PynCR3PveEMLWD5kKk0eD5+UFLGxOJv63J4xjpaFEoxT7IFjRkiN8GAQjliJJkzLQuvpFa+r/V/kxCM6sPcwtEwLI9AXfv2Na+2vNT1qd5AV6+vD4I9mbt1IvlVCpEGBJ0iYbGqYShtD6aSEHUzk06LRrC8qoNtl7wtCAEXB8tgJd8dS+tWIA9PoSjueYieKabK5ZAjdTidvjxmOqST/gXMq3ORW7H0aSQBFVSg+v6+XaZjU5s6QpaD2hK4rEmPEjkaOWr2SZ6oTc6iWZ8FRIzO3fpS5Ry5EGsXWtwPZxHHTwoje7Y64jq5pBPubYUZV8MegI2xR6JVOMwc6VVX4nzPcvPD7Ol6YehiPzBhFQ66P0s4AZ3y0iTENHVS2NXBiSQdw5Oe+XvXdh6P6bGz/5zpCuXbKTppG1/wt1EZUlpcMYc6SGkbWdLJlZBFja+p6aqMqZ65txVvVym+vrsKuZe7nSmqGQqSB++hySGnKsrB0gyHBEIfWt3HahjpK/X1TeamGSWmWQqFXvrYHi49faEQxLZYMK6EhNzGTV0NuFvcfPY2/nDgT33Yd9yvL4cPP7yyneWwM//Mstv6vyY7rTEruOIK7LpvLmRecyfD6IKoFZY2dTF1X09ssC5Ad07l+wVuMKc7s+9DyrRIiDdx/OJV8WuhZcwIFk0/Lh9BUVkxBPI7XMKjsDjN3Qz254RiYFqZpMWdo5l65H4y6WhITfHf1s65gh9dFXLMTxwHbW/fr9Ws6DRQgP9h3X9Dsp/YXlLUvJQyFSAfrg21kE6Cc7QyhDo+9jS0VZQTdydPp2SyLw7Y1Q2cUIgavtsqdjYPJqNmJWaZGtaZOQDKptoVcowun04DjP3st1r05dbILBVg5rG/8oD2aOoXl0Bm+/Xr9g4mEoRDp0LPQoIqFkyh2DBTLSp11BnDpJg4jcQKzpzwqDmTHX1zBxKPzGd/RzQkbduCK66imxbSaJn75f68zig10/+RcKNy/sDr37FwcWXb+fswkNpTmYQFmXKHD48bCwmbGGeNfT8mFY77cAzsAyWWmEGmgzZ1AXLUSS1QAOfEQpW1NtOXn0JW1W89By8JtwRmN7SwfksPh1Zk7KPpg5HBrnH19Ncc9+meufqWJDrvCiqxpOHUDAycf+A4j+7F2jvnt/r3+/y42cRlQpWkEh7jY4fFhaAo2yyTgdXJMw1JGd2+j5QctZJ0588s9uAOM1AyFSAPlXx/iNNvQiKGioxHhvza/waGfrqKotQN3OAKGQZfDgaGqaMDMxi4WtajoptzfOZhYMR1lfRMqEFHdOA0TFAVTSXRo8XepmPHUps0vYlm9RVbPOod5wTCoCtquj4+i0O5MTGxbVFsHe1kJJ1NIGAqRDiu2AGAjip0wNuI4LIPTtr1HQWc3Wd1B2rxe9N2WI9MAf7tO4/6veysGIcVhQ5tZDkCOnvjjGqpC1GnHAnKLLVT7/p2qT6xS6epZ5Lch10eX24UJKIaJFjewGXEMRaVx7Ej4AkvwHcwkDIVIh0NHo/Sz0v32rDLc/gjldW1M3LgDm2703kc0gZwCG6X7v+6tGKTcD34DpTKPLAOMQoMNkyvYPHEoWyaWUXbr1/b7dW84QmOIy6DbrvLuxNG8MW08cUVDscCwqawonsY/Rn8L/48u+BKP5sAkYShEOpx/JAzNAWLsGm8YUh0sy55CTneYHH+IMdvqmbl2CyqJIFxZ4uOBM51oqgyvONhoU8rI3nI9gRe+z/rKkVg9K89HnQ7e+ff+L69W/2mACetb8MUTTa/TN27HE4slhlf0zM9mKSoL795BPKR/KcdyoJIwFCIdbBpKUxcqURSCWAR5auSpqHvcGiprascVjROLRtji85IvM88ctBRNpS6SOt6wZWuIcHe8n2d8vpo1ftp2G64ztL0LSyFlolIjZtK2PnV4RyaRMBQizRQsLIXdl4Dd43ESwy4UC+k7c3ArHO5J2ZZd5MDp3b+O/8WVbrLifUHanuVJLBK8xxAeRVXIqczs9ncJQyHSJPi1qb3/tlkmLiuUWLNwNw3FedRnu3lj5FCGFWrMHCI1w4PZiJm5VFT21Q4VLI65uAJ1P+cMHXNILjMLLLJicQrDYUJFOTQOH0LQ1zd8xwKmXTEad8FnLxF1sJNxhkKkgWlYfLCxkik0kU8T27OH0unNQ3WaeIMRNN0k6HGyqbqcIX4/wYoiot3QHLQolqbSg1b74hayn9vIcI+DuNOG1x8hMl+Fk4fs1+vV7oiyrQUKiJEXDpMdi2GpKsHcbCJeN45QlKdmjGbK2eVf8pEceCQMhUiDUFMYR1uEHYxjB+P4tKAYANOm4s/payrzxaL44gqaaaLrKiuaLE6pkjA8WDW8vBMATygGocS8pY2v7Nzv1/toZRBINAF6orGkxwy7jUCunequAM9tMjmsTIZWCCEGmKfIRSzHiYqBjwDDulInYo7bNGy6QYdNw1BVFBUmFkoQHszcpan3DF1DUrd9Ubl5ifqOBehqf6d7hXaPi6HZ8rmSMBQiDVS7yohbZlBEJ1mEqe5oIicY7H3cVBS6cn0YNhv5kRhu02BaiUKFT05aB7OK86vwVu3WkUVVGPvzSfv9eoccnk2+12JoQyvdbhfmbr1IY6rCGyUFNJb6uGiCfK6kmVSINCne3kigZwknEwVbWKerNAvdbiPmdGDtupJXVWY1trLIW0pb2KLALSeug5Uj18FRb5/MzqdriLZGKD2tgpxJefv9ei6XyuwVHxNuswg0tvLvaWNwGQbtNhur8nLoctip6IySLUkgYShEupj+aO+/LSURiLrdRnSPZZxQFJyGiWkpRDN7XHRGsOc4GHHZ6C/ltYL1IcJtiWEUUeDl8hLQkwez1tqdvLmoixOPyv1S3vNAJc2kQqSJ+38OxehptrJZFkVhP3kd3aljwEyTZeVFzCyBMrm3I/aB6lDZ9WmymWa/S4QBZLkkCuQ3IESafLKog/dGjqUxOwe/04VmM7HFdPJbO9B0HcU00XSdxaV5hG0arUETay8nMyH64y50UViV6CWaE4lxSF0ze87tMMkIc/ghsrivNJMKkSa17zbT4c3iw6rRiUVXNZXu3GwURcGp97WHjusI0uHzs9KVT10AyrPTV2ZxYDF1ky2eEmJDojgjUc7ZVkthLMay4gKw4LCdDSwfNoRNLQajijJ7aIWEoRBpkj/Gx86VfZMwK5bVM+1a8qV7xGajweMkTzMpcmf2CUvsmx0ftNCq27Hynfi6u7EZJsftbOS4nY0AjGhowhaP88CSXG493Z3m0qaXNJMKkSbTL6nCqyTmjVQANW7gDEeS7uvowId5OTRELf5wlIrTJvcMxRenRw2snosrpZ8WdlNVccf1XeP7M5qEoRBp4g11c+HHj3NszUJmbf+Io2s/wGZZeEIhHJEornCYrECQWk0D3STbyuyVyMW+q/xaCZqis6iymKWVJYnmeEXBVBQsy6K0vZnXqyv59kx7uouadtJMKkS6dATRTINxXRsB2JxXBSSu4BXTACuxur3LMOjGTntIOs+IfdNpqdx/ynQaoyqF/hym1LfhMBJDK1QFis123ut8j9HDzktzSdNPaoZCpMuU4TCub4LkYV21YOksrxzKKxPH8tqEMSypKKPZ5cShWpw5yZG+sooD0t8/tmiMJk7zX9va0BuEAKaqsaJiLGVPL8bc2ZmmEg4eEoZCpIuiwEu/gDNmQaEPffoolo+soiE3BxQFQ1Npzs9liGFy2eEuynPl6yr2TX2grzXBF0m9MRh0uFFMC6uxeyCLNShJM6kQ6VQ1BJ7/OQBN64O0/ak9ZZd8y2R9o9wvFPvu69UK93xkgWXxYWkhpqkwNBgmNxRGsyzKO5ugPBd1mizhJGEoxCCRV+jAaRqYOlR2+XHpOm0eNzV5PkZm+BgwsX9OGK7ypyMsfjk/yLjuKA6XixaXi5a8HLKDIerzZzP82z5GadLqIGEoxCCRW2jn+DEK9e+1oPUMr8iJxbFHY4zTMnsMmNh/WlRnTFeIwvhuE9uqKh2+bKKqyrMvBfnZ2ekr32AhlwNCDCLlXrM3CHepCEd4/40uwlFzL88SYu8+aTLJjac2s6uWhaGpdLTEMU3pqSxhKMQg0tkc73e7FdYJheWEJfbdUVUajc7UcYS6quDQdcqr3KiqTOYgYSjEIDLhaznsGXlxRaF4tJeCXLlvKPbd+VNtaEMcbPS42NW2EFMUOux21nucnHppWVrLN1jIPUMhBpGV7/tpddrJj8VRLQhpKk8PK+X0Md50F00coF5ZrzNsUwcjQ2EWFObR6HFjqAp+TQVF4a9Pt3LnFFm1QsJQiEHCsiy2fRzg/ZJC1uYkL03x2hYZWiH2z6KtOkOjUTyGSYPHRZcj+bS/pFWaSEGaSYUYNBRFwenVyInFmVXfwtHb68kLRwAY6pOvqtg/U4ZqtDgSsxcVRFMH3o/3SccskDAUYlCZeHgOZ63dzEVrN3HOhhp+/f4qxrZ2cPYo+aqK/fONKQ5yKhyosSin7ajHt1sgjmrq4MYL89NYusFDmkmFGES2f9CGe7fxYHbT4hsbt7N8eTac4EljycSBKh4yGLO6AeIm2cAP121mu8vJqE2NTNreSk1oOMOe/Fq6i5l2crkpxCASbYukbMuJRKlrlXuGYv9sX90N8b6mUJtlMTIcoaorgGVX8L9Vn8bSDR4ShkIMIkXT8lB1A1s0hi0SRdV1dub6mDlGVqwQ+yenOPWzo5gW9riOYlqoJdLiABKGQgwqk6s1NF1HsSwUQNMNYgp8EpMef2L/lI7OYughifuCpqIQtduJ2u3sGF6CaalM+uO0NJdwcJAwFGIQ2fZSXcq28Y2tLNgUpyMkvf7E/rl4eC3HbHiDmE3DVFUMm0ZrSS4bZlRScmRxuos3KEgYCjGIOLL7mTZLU3E5wGWT2qHYP8oTC8nS41hq8ixGXdketsyvTVOpBhcJQyEGkfFnlWEqfaFnAWuHlnDhJDtuh4Sh2E/Zbpx6NGWzYlq4clIvwDKRhKEQg8ia55vYOnIYrYV5tOXnsq2qArfTSeX61EV/hfjCfjSXiMuNasWJaypmz3VVWTzEsNOHprdsg4SMMxRiEGlujBPzZtNcUti7TQWadqZe1QvxRX0YK+OZGX2LFlqKwqwK+PrNs1BtUicCqRkKMahUT/CAZRFXVUJ2GyG7jbiiMHqSTNQt9t+il1uTfrYUBcfkQpx5zjSVaPCRMBRiEBn1vdE0eVxE7TZMVcVUVaIOO0NGylgwsf+MfuZsMAxZH3N3EoZCDCJLNptYaurXcuXyYBpKIw4WhxyXl/SzosLsY/P2sndmknuGQgwiToeCpSiYJJqy1J7B9w7pSSr+Ay2lWSzP9VEZChNXVNrKssgqc6W7WIOK1AyFGESOmu4A1SJmsxHXNKKahqEonHCqXMWL/Xfna2E2ZXl5s7iQhUX5rI07eGpJ6jy4mUzCUIhBpHZ7HLuxWy1QUfAWOJg+SzrQiP3X6k+9P9jftkwmYSjEILKjNp6yrbPTIByWE5fYf6dOTZ6sW1Hg1Cky+fvu5J6hEINIdXXqCap8qB2PR65bxf777dleFODfH8Uo8in86CQP04fLzDO7kzAUYhAZPcpJRfk2dtZVYlkqeXkal19WlO5iiQNclkvljm9lc8e30l2SwUvCUIhBZsTwzQwt28GJJ32DkVUeNE16kgrxVZO2FyEGIYcjxojhDglCIQaIhKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi40kYCiGEyHgShkIIITKehKEQQoiMJ2EohBAi49nSXQAhMt3Gdos7l5k0hyzOGJnu0ogDUSSoY3dp6S7GAU3CUIg0qu22OORRnc5o4ud/bYD/ck/kJPfa9BZMHBA6m6L8609b2fFJAI/PxlEXDEl3kQ5Y0kwqRBo9usbsDcJd3opMSE9hxAFn/u3b2PFJAIBQt84r9+wk1ulOc6kOTBKGQqRRd9QC08IbjeOO6wDEkeYu8fniUZOaj/0p26NNvjSU5sAnzaRCpMmDy3XueS8GcSCuc1xTO0G3Hdvo5nQXTRwAbA6F7AI7/rZ40nbNG93LM8RnkZqhEGmwvcPkihdiBHvOY0G7jbeH5DOqPcCR7XXpLZw4ICiKwjEXDaU2N5uPhhTzSXEhSnUO7rKudBftgCRhKEQaLNpuYpjJ20I2G112G8HOnPQUShxwlrXZaczKIm7TCDnsLI966AxJM+n+kDAUIg0mlPR89TQF1alSHI8zvDvEmiwPm53O9BZOHDDeWRJK+tm0YGd7aZpKc2CTMBQiDaaWqpwwzgZejdFtASr8EQp0A7eqsjQynOeapUep+HxZntRTuN2mp6EkBz7pQCNEmrQaKr5IDG/MSNpeaFosbKsmErew29NUODHoGXGTSdFu8hoCROw26nJ8OHLtVBbWprtoByQJQyHSpCsKmmmlbFcAw9LYIyOFSPLEL9YT+KiLXIBIlIJgmG5bHk5bLM0lOzBJM6kQaXK0J0qXx46uKknbA4rC6OwmfC5lL88UmS4WNqhdldxr1G6aKK0RutsK0lSqA5uEoRBp0NYYI2f+NmbWtVOf7yZkV9GBdk1li89FeX4w3UUUg5iV2qDwhR4TeyfNpEKkwcaPA2DCnNpW5tS29m5/YMZIdKedrTHpESj2zunRGDrZR93H3XxSlMvWfB954SijFYOcgvZ0F++AJGEoRBoUDHGkbItqKmF7Yiq2Qls3IJMui70rH+Xh7ng+y4cW9m5bS5xfqFI13B/STCpEGoya5GX4KFfSNnc0SnY0TlYkiq/D3MszhUh4/eU2VpYm3x/ciZ2Po8PSVKIDm4ShEGmgKAo+I4o3EkmEYCiELxrjjDXbGNkYpLm9mO1N0p1U7F1I1TDV1E5WftPVz97i80gzqRBpEuzQsRuJwLN6/nMaYOsZbtHul9qh2LtpZSoVXUGy4gZhm8YOnwcNi2mumnQX7YAkYShEmow+PI8PnmogYrcT7Rldr2s2FMvCbYsycXhemksoBrO1RQWcvLEJlETtsNuuEbKpMNEGWbJyxb6SZlIh0uT9Ifl8mpNN1OFInNAUhVxdZ3xLO6dvWY3dJuMMRf8+2REnsN7fG4QAvrjBmKYONm4Zn8aSHbgkDIVIk+c/NQjaU3uVDg2GKP40iiFT0Ii92N5ioCipF0uWArGoTPS+PyQMhUiT8hyVsJb4Cvo1jZU5WazJ9mLoJngtVJt8PUX/Zo1yECd1CIUF5OR2DHyBDgLybRMiTQ4rUgk4XdQ6HTxSUcLCglzeLMrj72MqaTvSROmnp6AQAEU+lVEzc1DMnk5WloUrEmV9UT6lw7ekt3AHKAlDIdIgplu88EGYPEPn/cJcdLXvq9jpcvJWuYwVE3sXj5nsWNGN3bKwGwZ208Sw21hTksMzDdPTXbwDkoShEGkQjFh0hSycpolf01Ieb4t701AqcaBobY73DsFRdvvPa5jsiEgv5P0hQyuESIO8LJXJw204lkaYZeisKMzDAE5ft5Efv7uU+nFujCuqsJfIgoYiVWFxYrUT225LgFlAQFOpdMg9w/0hNUMh0uTqsToRTBaUFdPtsBN02Hly6gTmTxzDxFWdtF/6WrqLKAYpu0OlcmYO8Z77yoYCq4ty8MctxuTUp7l0ByapGQqRJs0r21mb50vZ/vLYkVy6/GPCL2/FDMZQvanDL0Rmi0VNbuvOontaPsXBKB0uByGHjTH1XTzROovDc7anu4gHHKkZCpEm7mwbbj11LGFeOAKA4lJR6mU5HpFqw/ownR4ncU2jzuch5EjUazq8DjyGQmM89SJLfDYJQyHSwIybvLQ+xszWDhxGXyDadYPLl34EQF64FmXctTDvvTSVUgxWZWV2XHE9ZbsnphNVFXK1UBpKdWCTZlIh0uDjVxt5sLCCrFydGS0dNDmdtDvsHLO5hg1FBTw5ZQz/+vedYJjwk3lw3uFgS+11KjJTQZGDQ9QAq0IWJ25pIitmABZRRWFTpY5Xi6e7iAccCUMh0mDjtjBxWx4dNo0PS4sToQfMnzSW+ZPGAtD1ppuCSBCauqAzCIXS9CUStneZvGvz8s11tTgsKzGuAgWnZTF2uw2zUiZs2FfSTCpEGsw4NBenaoLXBtl2sCd/Fac27UgEIRCdMAwrx0vshn8THvtbIkfcifHKunQUWwwS9y3TKQxGE0G4O0XBblp0dspYw30lYShEGvzFn0vU6wRVAZsKPgdOK3EPaFxrA4+/9A8AGt25nHbkRUR++jz6b1/D2tCMuWgr0TP+jvnRznQegkijYbkqYVv/DXuWAg57bIBLdOCTMBQiDR5Zm3pFf/WaxWy85/es/MdfqG6N0EE5W2wTectZSuzRZcn76yb6P1cMXIHFoHLJVA1NtWh27jEpg2URzdLJyg6kp2AHMLlnKMQAsywLFyb+Pa5FXRETj19BR6Pb42Fb7lBarSwATK8TozPEluKhWChUN+/EniXjDzNVR2OUyno/mzweOm1xsnSdoKbRYdfYlmPniHQX8AAkYSjEAIoHdV65+H2O1XN48vCJvdt9wQiTlwdYzygs1WLJtNF0FPnAsjhbC2Feexy3v6bR4kvcCyr0d/I/r79D/k+PA5eEYqb51wtdRBUVFIUWp4MWZ+Iz0OawEVDt7IjmpreAByAJQyEG0Kf/bxvNq9o5nnby/WFWjSglrzvMt15fS4E/DIBiKoxfs5P3jx0PisLImg5erR5Ji6+r93Vas3N5o2ko//3IQrjihHQdjkiTfuZ2T6L2s9ah+GwShkIMoI6NfYE2vaaR6TWN5LZHyOuIJu2X3R3u/bdhQO2WSMpr1eWWwMcy7VYmOvvMXB76qB2/1ncKN4Euu0a2Fabc2bX3J4t+SRgKMYBKphewaX4tWBbOsEkwz8u/j6hiyfASHBGd0xdv4pjV2+koyOp9jsMBYyZ52Lk9OTBNyyB22DikkTTz6A6NiEOjPseDLxTDsKDdaUMxTL5ZtxNGpLuEBx4JQyEGUMkJQ2n/Ww0VG5qpH1XM6ooiHp81tvfx1dVDiDpsuLOdQGJZntP0Woat6WR7U5DNJZUAjGnawjkrn+e+s27kf9JxICKt7nopyPY8L91ZTrp9zr4HIjo7mnKZFJGlv/aVhKEQA2jFihCfDq+gZHsXusPGsmElKfs8euo0jqhpw20Y6KpK/O1aYm8E+R//B7R5Ex1oioKJCbz1+csIfbcMj0tGSWWS7pCFofYzy4wKMVXF0OXUvq/kGyTEAFJ7TmCWkvhfdc8ZRAAThVanjWFttVR0NKBaFoqmAhZFwTbygl1E8WKhYGkq/Z0TxcHtomM95AWioJsQjif+17JQgzGGhwO4s8KpTzIMeH4Z3Po8rNgy8IUe5OTyQYgBNHmqh26Pg01lhRREohxa08inJXm94YhloYYC3P+v3zG2rQGAWG4h/m+fhfXXRYCFDR0TkyZGUzmlHJdTrmkzzYxRdoJuGwTj4HNAz+dH8bloGNJPT1LLgtP/AK9+1Lft9ovgR6cPTIEPAPItEmIAvb0sTNhSWTWpknpXFhO3NPPt99dR1uEH04SwwZ2vPNYbhACOzlbyF72Fhtm3jRBe2jnyXwux+qldioPbQ++E6IgBHntvEAIYmsoGZzmdMVfyE978ODkIAW58CkLJnbIymdQMhRhAG7f1zRm5blQZq9WhvDmq575h1ADDYnbd1tQnrq9L2WQnTGB7N1Z7GKXA81UVWQxCaxvMRO8qLbWNPOS00RHLSt5Y05L6Iv4wtPnB40x9LANJzVCIARLVLT7FxjKXk08ddiIK2EwLV6xnkdaeE9uysqrUJ48uS9kUx402zIeS7/4qiy0GoYvnuBKfl5iR8lhhd4gKT1vyxhOnpKyHaY4th4rCr7KYBxQJQyEGyEX/DPLPTSatNo0ah50lbhcRVWF4SwCbYSZObjaFnx/332zMH9L7PAMbjbEywvRd7QfIwU8hDPGiKNKDJtOU5Gu4NIuxzZ1UtftRLAuHbjChsZOvRbdhU/doOq8swjz3SOJKojGw3ZbH663j+OTPa9NQ+sFJmkmFGABNfpOnPkpeViekqrwzNI+v1XdRuK2NbpcdV9wAy8kTE0/iR4tew2YZeK0WGtc5qOdQvHRhoRLCRzmbcSxdg9EaQiuUZtJMcu1TQQxd4fj6VhymRVxVUC0L1YKolk08mjzO0KrrIPzUOl4rOBVFAV1NPL7utrVUnlNJ1vDsdBzGoCI1QyEGgJHo+Z7CETcxLcgPBxjfsBPTMnEZJlXd3XxSNoJud6I2aKFgqCo7csrptCXGGpooKFhYhnSgyTTLGyxyY3EcZuJvbzcttJ4F7zXLIhpMvjiy1tQRxImh2XuDMPEAdH7SOXAFH8SkZijEACjLUZmVZ7GsI9GkqVkW5YaJuyPMRctfZGeWlz8efjq6ZuPYhjb+Mfs4YpqGYlmct/otJsdU5ldMIuJyYIvrHPbRJrxbAsS9cXw5MiFbpjmyUuHRsJ1WlwOPbpAdjqIAcbsNQ1FwZQeT9ldmVJKlxrAb8URTqQIoCoqmkD+tIC3HMNhIzVCIAbBjS5jpa1uY6g9SENepMEzcFszeuYHJTZu45ciziNnsjO8KoGs2Yj3LEliKwv9NOY75U2YR6VmqSbfbWDRjDEGPm0jQTvB3C9N5aCINfjXXi9ul8P8mD4fOLkatr6F6fQ0V2+qxefzY7HrS/kpRNtuPPwEtCs6oiSNiYosbjD82F0+ZNLGDhKEQA2LL+jAOy+IQf4gzO7rJ0RNjBmc2bGJRxZje/UrCMcJ79PpzmiaRkJm0zVJV6gvyUIDwq/0MxRAHtRsX6PjdTk5avYUZ2xoTFT0gOxBi6IYg4VDyOMPAhk42v9uNpSRO+QqgxS2y/rkYfU3jgJd/MJIwFGIAlA1zoisKLS4H9W4nxd0BTl29mfqc0bTmjeOU7Q2UhCJ0OOw4jOTu8jFVRbOn9hjN7QoRw4ZSlT9QhyEGibe3J+4VjqlvTXnMGwzT3FSatK3puVqsPeftUxS6NS+xBdu+snIeSCQMhRgAI8a62V6aQ6PHjRUzOHvJp7RWFKLZXNhxMaO1k4vX19Bm1/DF4klzlpYFAnzkcrN73XDq+m3kdocx0aid30RkTepJURy8xiT6UNGQm5XyWNRhx5fbmbQtd1YhmKkdrdxmFNu4oq+iiAcc6UAjxABY/EmMzljiynzmhlrWjS4nPxRm92t1m2Vx0s5G6nJzOOuDdTQXeHGZcezxOP86dBprvB5KonGue+MD5myu7XtezKDpro+p/PuxA3xUIl3uPNXB7AejvDJtFJN2NFMYSEzMrds0NlUVMS2vJmn/gmNLKSxzMmLjBxSzEwUTBQsTG+aLlXBCdRqOYnCRMBRiAISjfVfljrhO3K5hw8K0LOyGgaFpoCi4TXCaFt5QlFlNLfgLXdRnexPzT1oWnQoUdfoB6HI5UCzwRWPo3bG9vbU4CG15o5nZmyI05Hp5YfYEJtU2Awq610mLL5tRe8xNGu+KUrlpOaVsT9quoaPe9SzGUWPQzp41gEcw+EgYCjEADp/kxO0AeyBO3cghdHrcxDQN1TAZVVvPsLpm2ovyaczyUhQIkk2Uw5vWkd0UAOCI2vW8M2E2hqry0dRR/KmkiGVlRSjAKeu2cuvUtB6eGGDLX26iK2cInqhJ2OZk6YgKAMZ0dGK3YEPL8KT9Wx7ZQI7V3O9rKYB5z5sZH4Zyz1CIAZDtUbFybFR1B+hyu3qHTpiayobh5fg9LvJb2gg6HGi6yRGb1+Aj0NtLcHrdNmbWrAdFYUF5KUvKSzBVFUNVeWliNTcuVYn542k9RjFwvLl2NNNM2W4zLSwg25k8ztBVnYvO3sejKuUy1lDCUIgB0Nhp0NkUS8wOYkttkGnN9WEzTLKDIQIuJ25SF2cd3ppY1qkmK3Vc2NrcHJo+av/yCy4GpXHnDyMrFmZjSRYbS7zkGBFO2bqDMc2tTN5cg2+Zh9furSXUnbhAUsfkstk7mhheohQQIx+DxGoVJgraMaPSeTiDgoShEF+i5zaa/M+bBvesNAjG+u4T5nlVKvROdEVB7eeKPqaomEDY5cTEYlXF0JR9Orw+APJjqTXAvFicnMrUnoXi4LOt0+L3j7excGw5IY+DkMfJwuoyllYWE/Z60Ow23E0GK/7dwlO/2UQsEOeF/1pA1KVh4sPCgYmTOHmY2FGxMC65B9Zs//w3P4hJGArxJfnpAoOz5hvctcLk+2+YHPd/BkZPd3bn2m38YsFTtHtciS/dbkMnIqrCu9XDeG/8KCIOO6+MHcpV559DY3bf5Ml+p5vFVeMBqPYnN4EVBwPM6uhm8crU2qQ4+PzfOpMOrztpUV+Aj4ckxptGnU7shoFlwY41ftbPryUeMhjpr9/jlRQMEq0MNsuAv708EMUftKQDjRBfgq6oxV9WJNf4ljRYvL7N4pSRCtz2Ai+OPRpLUVEBu5XoSRqwaTQ5EquVby7OZ2OZj06Pkzavkxk/vY5TP/kEezDGGyNGMzQSJ2C3sTmnpwZoWfxo8dv88oM3mDd1Lm894+a40wvQbLKk08HMqdFv64Jm7dpmYVlg9XwMbM5EncdQ+qv7WL3/X8nwRX6lZjhIvfjii8ycOZPly5d/5jbx1bKiOpaReuLZU3e033VWaew20SMGtHTT7eprxty1ugCWhdlzha8rKl2uvk4OAZeTp6dN5empk2n2elhVlMem3GzshoGCBapKVVc7vmgYh6ETCxnoukU8pBOPGhiGRVxPnOyMiIHV37IZ4oDz32MVPKEoyh6D6GfuTCzo6w5H0G0ammkxdpYPbbufvECMBpIH11tAiCziOAnio/W9Lj765ut8/KuVBLcHBupwBg2pGQrRD7MrQvtl/yY8fwNKthPfTw/F9/M5e92/wqcw1gixXuvr3OKM6zRf8T736gaTO3OoGrqTRl8RWBZu3cBpGOREIWJX+Sjfx5SmDhzhGFFv4gr9B4te5ddv/Iv8UBC/08l3T76Ys9Zt5uTNG+h0ubnt8CNZOfowfjx8JlnBEKOHqcw//S3CW7sxNJWGsiK2lBVz7PYaHDUduEvdTPnlZIb/1/Cv+tcnvkK/ejmM17Jh+WNoThWbpjChuRO7aTJ/XAUzahqYvrMFbyCI56k6NjRFsAH1ziEYlp2RsR3YsYjgwcBOgCJayENdYVKx4jWW5E6m9qltHLvwFNylmTOJt2LJ5eKg9OKLL3LzzTdz3333MXPmTAAMw0DXdex2O6oqlfqvUvvl/yb44EdJ2wpf+AbuuaP73X/jq/U8c8Nanpo9jg1DChnSFeDrKzdQ1ZW4v2eoKm2FubRkZxO223HvMf/oqkIfq3O8RCM65Lg4sn4TC++/JWmfgFKIZbmTtj1w1GlsLS7DAg5bu5Z4pO9zYQGmaiMrFOndpqgKpyw4Gd8o3z7+RsRg0BIwmfGrNixVY6fbCW4NXMl1mvIOPz9asApXKEb5traU1xgVqCfHTL6/HMBNF9kMYwf17gJqPGWM/9Vkxvxwwld6PIOJ1Ay/JLquYxgGTudX1+6uaRqapn3+juI/Fn5pUz/bNu81DNc/u4P8UIQr3lmVtD3udICiEHU6QFEpCgQT4wztySuRD/OHWVqSC2EdOiOctnxJyntYVupna2zDDrYWl6EAO7MLKIl09D6mWFZSEAJYpkXDggYJwwNUVE9M2xfe1XlGSb0/HO1Z9WTPZtRd+tuq9Gw1UdB6ZsE1Qv20+x/EvvQw3FWjueeee1i9ejXPP/88HR0dVFdX85Of/IRJkyaxYsUK7rnnHjZs2IDX6+Ub3/gGl112WcprrVu3joceeohVq1YRCoUoLS3ltNNO46KLLsK221ittWvX8swzz/Dxxx/T1NSEpmlUV1dz4YUXcswxxyS95k033cRLL73EO++8w1133cXbb79NMBhk7NixXHvttUycOPFzj/H+++/ngQce4Mknn+T555/nzTffpLW1lXvuuYeZM2fy+uuv88orr7Bx40ba29vxeDxMnTqVK664glGjUsfzzJ8/n3nz5lFfX09JSQnnnnsuWVmp3eT7qy3uKssLL7xAWVlZ0v5z586ltLSUv//9773bFi1axGOPPcaWLVuIRCLk5uYyfvx4rr76aiorKz/32DOFrTKHUGMYP4kJsnW7irZVp/5XK4gEDZRcByWHFzP0uFIUVcGVl7jXlxWNUNrdSUyzsTMnjzhgj+oM6W5lsy/xN9X6OUmFVLX3pqMnHqU4HOH9EYcQtdmYUvcJBaFONKKoJKZd0/FiYaPT09eZJiccorSriy63m5Ddjicaw1RUsJSkOVAb3mxg2cJulCofXzuvjMoJ2dRtDvHJonai9UEKShyMPb6Y/Epv73OMmMHmNxrpqg0xbE4RQyblfu7vsKNDZ+kHflDgkMOyyc2Ta+//VHmuSlmRSnxngMbcIuy6wZBgBEtRaMp2EbepzNjRBEDQ60B3atiifaGmGiZx0wG7jWO1sHDSRSlteAgTJI+YUyUMhFsjuAtdZIKv7NP5t7/9DcMwOO+889B1nXnz5nH11Vdz8803c8stt3DWWWdxyimn8MYbb3DfffdRVlbGqaee2vv8RYsWcd1111FRUcEFF1yAz+djzZo13H///WzcuJE//vGPvfu+88471NTUcPzxx1NaWkpXVxcvvfQS1113Hb/97W85+eSTU8p39dVXk5eXx2WXXUZXVxdPPPEE11xzDS+88AJerzdl//7ccMMNOJ1OvvWtb6EoCoWFhQA89dRT5OTkcNZZZ1FYWMjOnTuZP38+l156KfPmzWPYsGG9r/HPf/6T22+/ndGjR/P973+fSCTCvHnzyMvL299f/V6tWLGCa6+9lpEjR3LxxReTlZVFa2srS5cupba2VsJwN47LZ/HpkiUYqIS8dixNgaV+rKXriXhs6HaV9f/YRNU5lVT8dxU1r9ZR1tHB9Ibtvb3SRra38GHFaCZs3cYUaymP55/LtoJK3LEYUbsNs6epW7EsRnZ04sn3oRhx3vjX33lr0nGscyeGVrw55kiufO9hyrvqekPNootazyhWDE/UVPO7/AR9WWxx2Dl8zXo25xcR33XBaFlggWKBocLKrTqdRQq0+9mwfAMTjslnzbsd5HT60UyTTcDSR2s44w+TqDq8EFM3ee7ypTSu7gRg2X2b+dp145jyreF7/f3trI3yh9/UEe5Zh/Gl+R1cf+NQhpZndo/F/9QvH20ne3M7r06sAiAO+BWYVtfJiPYgDjPG7Pp2dFUhanfQUpBFQYsfe9zAHjNxRXU6yKKQRuyAgkkWzbjpBjRMVDp844k4NNY+sJFNz9Rw6r+OwTci+7OKdVD4ysLQMAweeeQR7D3NQSNGjODHP/4xP/vZz3j44YcZPz4xZurMM8/k9NNP5+mnn+4Nw2g0yi233MLEiRO59957e2uBZ599NqNGjeKOO+5g+fLlvbWjSy+9lKuvvjrp/c877zzOP/98/vGPf/QbhmPHjuX666/v/bmqqorrr7+eV199lbPPPvsLHWNWVhb33HNPUi0V4K677sLtTr63c9ppp3H++efzz3/+s/d9/X4/99xzDyNGjOChhx7C5Upcgc2dO5dzzjnnC5VhXyxcuBDTNLn77rvJz+9bA6+/Wnmma/ywExOVuENNBGEPBXBEdHR7oia49ZntNNSGcXXHGNvSmNQ9OysWZVxtHVXWJuyYXLTsKdaVjKbdk8uC6tmEnFlYioI7lpiZZkZbJ2MbNtCRW0a3u+/ko2s22r05VHTV7VYOk06vAXGd0k4/znhiZfOwy8nW4mLi7NacriiYCsQ1FUNTcEajvQ9ZwOr3OnGHo0nTe5m6xYcPbqPq8EK2LWzuDcJdlt23iYnnDkOz93/v+pUXO3qDECAUMnnlxU4uu7Lk8371Yi9aQhYNz23n7UPGJW3v9Dho9zgoCMUo7Y6gKgoxpxN7LI4nFCbsteFriOKIJ/4edmKUshE1pcHUQsVkgn8D77gTF/bRjhifPLiJw/53+kAcYlp9Zb0wzjnnnN4gBJg2bRoAEydO7A1CALvdzoQJE9ixY0fvtiVLltDW1sbcuXMJBAJ0dnb2/jdnzpzefXbZPXgikQidnZ1EIhFmzZrFtm3bCARSuwmff/75ST/vCtba2tqUfffm/PPPTwnC3ctjWVZv+fPy8qisrGTt2rW9+y1evJhIJMI3vvGN3iAEKCkp6TfA/1O7ml7ffvttdF3/0l//y9Le3k50txN2IBDA7/f3/hyLxWhrS+4Y0NDQ8Jk/NzY2Jg0t+Lz3CO9IfGasfu7JqHuMtAg1hlFNC6eROjOM04jjJHHfTrNMJjWu56iti7GZ4I3FyIpGE0MsAF88zlB/J13u1KtwbzSYsq3dqeEORXqDcJfYXu4rGzYVFAVN3+NekNX/uDV/c+L307wtdZq3qF8nHkq8b39/j8aGUGp52/rK+UX+HvtrMHx+vor32NocwqEbxPo550Rtas//Jh4zVRXbbmN9tN2GBzmI9hOEsOtuoldP/tt1bu/8Uo8jHX+PL+IrqxkOHZo8nZTPl7hhv+d9rV2PdXV19f68bVti5eXf/OY3e3393X957e3t3HvvvSxcuJD29tQvbiAQSLkHt2f5cnNzAZLK8Xl2b+7c3fr167nvvvtYsWIF4XByr63d37euLnGlP3z48JTXqKqq+sLl+KLOPfdcFi5cyB/+8AfuuusupkyZwuGHH85JJ530lTTL7q/da61Ayt/O4XBQUJA8sXBpaeln/jxkyJB9eo/ir1fS+UYDNt0k7kwOF3232pAzz0HxyUPZ/Ohm2p1ZlES6k/btsntpM4rwkhxmbR6V3GjSJjb4smkaOYEH3n6RVRXJV/8fDR1Hddvu02WpHF5fQ1XXP1lePout+cMBKKtrp7Shm/ri5A4y5m6rnIc9fRdeCuDyqMTiDtzR5GWgRn4tUTsYf1Ilq+7dgWX0nXxKp+bhyknUjvv7e8w+LI+arcknuKkz+m4/7OvfY18Mhs/PV/Eesyo9/KXMR3mHn515fRdMimlREEz87QpCifONauj4s10UtqhohknEbcMbTFyMBMkmjAc3IXRshMjFTqR3PtxaT/I5uurU4V/qcaTj7/FFfGVhuLeu/1+kN+SuxL/mmmsYPbr/3ntFRUW9+1599dVs27aN8847j/Hjx5OVlYWqqrz44ou8+uqrmP3N1rCXcuzLSJPda3O7NDY28t3vfhev18ull17K8OHDcblcKIrCbbfdlhKO/ymln5rLLsYe3fdzc3N57LHHWLVqFUuWLGHVqlXcfvvt3H///fzlL39h8uTJX2rZDmRDrxhLpCZA3b3rsQyTuNeOETWwVIW4K/HZzh2bw+zfTSd3Qh5bX63jk/hQ1EaLgoifuGajNqsQq8vG4pKZ1Hrymb19NTHNzltjDmdZxQiG+AOMb+4gqmq8V1zA5mwveNz8bfw0Ttu8nFXDJhLX7Ci6TiBURAvlFFCP0vN/DtNgmL+FsvWv8uD0b+IIKFTVtABQ2BGkw+fGUBUclkl3TytNllNFawsRyMkm7rBRPNzNf103gjfmNbLjPQNPJIKKxejjSjjyB4kFX3MqvJz4+yl8cMcG/A1hyg8p4NibJn3m7++Ek3PpaNNZuKAbBTjyWB/Hn5Tz1f3BMoCqKFx/40jUmzbzss1GR7YbZ9xgdLMfd9ygzO+nMBiiS1XB5wNFoWaEQnltEx15FpoRwhkxsWHSQDV5tNDMcKyeJvVs2nDTRZAsVMtAdTsY+51qRn9zRHoPfIAMyu5du2pcbrebQw455DP33bRpExs3buTyyy/ne9/7XtJjzz333FdVxL1asGABoVCI22+/vbfpdZeuri4cjr4ZRnbVEmtqapg9e3bSvlu3bv1C77erxt3d3Z1U645Go7S2tlJeXp60v6ZpzJw5s7dsmzZt4oILLuAf//gHf/nLX77gUR78FFWh+tZZjPzDzN6fLcPs7cpumRZqT9NUuC1KuD1OtDiXBUU5KIaJZlqM2NxGFjG2Vwzh7ZFTmTfz64lmV0WhsjvMPyeP4FlreKJxKqJDR6I59R+TDmXeRIsFdz6JSqInqJMYDVTTxHAmsDiprDbLZELDJrpDfffjfMEYvmAM59hcpn36TQzdRFEUVC1xHGZPVxyt537oRTeOxDCqUFWwTFC15Ius6hNLqT6xFFM3e4/7s6iqwnkXFnHutwp7fxb/ucllGt+/vIx3H49ioHBaTUtvg2fc4WB9cSFv+LL4Tn0zKuAaX8A5f59A1/ibWFw0C13RGNreRTCUSxRfUi9jPwW0U8Tozkamd27Dfvt/Yf/RZ1/0HEwG5cjtww47jPz8fB555JF+my0jkQjBYKLZaVcNdM8a3ebNm3nnnXe+8rLuaW/lmT9/fkq7+CGHHILT6eTpp58mEukbD9bU1MRrr732hd5vVw/Q3e+hQqKX6p414s7OzpTn76q5dnd3pzwmEiGo9JzIFU3t/Xn3QLB7NGyORMihqon/FAW9Zx9nNHEv0erZDhC2qRDWsSIGWFAQiTHeH2BiIMiEYIhRoTDrqoeyfVgRLQXZvSc8ExWD1FaNsOYi7kjd7hySmEFEs6m9AadoKpqm9AbhLpqm9Abm3nyRIEzaX1UkCL9kKz8JYagKvp57v7vWvERRsCsKqqIQ6TkPNdfF6Nrix46JrtpAUXDrOpA83GYXBYjhRAGsNXtO7H1wG5Q1Q7fbzc0338xPfvITzj77bM444wwqKirw+/3U1NSwYMEC/vSnPzFz5kxGjBhBVVUVjz32GJFIhMrKSnbs2MGzzz5LdXU1n3766YCWfc6cOdx11138+te/5txzzyU7O5vVq1fzwQcfUF5entR06fP5uPLKK7nzzju55JJLOPXUU4lEIjz77LNUVFSwYcOGz32/2bNnU1lZyf33309XVxdlZWWsXr2aNWvW9N4H3eW3v/0tzc3NHHLIIZSWlhKNRnnjjTcIBoOcdtppX/avImPY3DamXzyS9x/djmm3YakKlgEtJVn4uqOM2tzA1hElRNyJVgELWJnlAX/PPboAnFTbSK7eV/N0RmPsHNl3XyT8qYNRO+pRUGmhglJqeh8zsNGu+HAcV4nttU3o7YmbkYpdpewX0wbkdyAGzrFzfPxxZZAOuw2T5BpNRFFQTANPz4Vw5Wg3xdMLaNIcOI0oUc1J1KaSGwtjYWPP+pCFgptEBxr1yOqBOaBBYlCGISRqh48++iiPPvoor7zyCh0dHfh8PsrLy/nWt77VO3hd0zT+8pe/cOedd/LSSy8RDocZOXIkN910Exs3bhzwMCwvL+evf/0rd999Nw8//DCqqjJlyhTuv/9+br311pReURdccAFut5snnniCu+++m5KSEi644AKysrI+swPRLpqmcfvtt/PnP/+ZJ598ErvdzqGHHsrf//53Lr300qR9Tz31VF588UX+/e9/09HRgdfrpaqqij/+8Y8cd9xxX+rvIdPM+J9xFEzK5eOnajFQGHNqKfGAjn9tJzn1AUavaGDL8GKiDjsrc7LYvtv9ZsW0cJv0zSZiWWRHkmeO+WRcJcWBVoa11xEhmyg52Ij2dIZ3MrFzBy1DxzP542/Q8sgGjJBO4Ter8UxM7lggDnzjRrmZZm9hQcjGiuIcpjd3oZEIwtVuBye1dgKJ5vPDj8zGXejC+vtlzLjsQWK6jxHGVmwY6Gg0UUGQHCzAQCGXDjxqBO2iQ9EunP1ZxTjoyNykQnzF6reFuf3azQBENY3nS4uodfcNPneYJpfVNvb+rJomhf7UoQVDa5r59obHCVKe0ju1wZ3PR3NP4swnj/qKjkIMFuubDE78fTu1TheHt3ZweGsHIZtGSFEpjMeJahquUJjCQBBniYer/28mys5W6qrupJT6pGEVBiobmUD+lZMp+s0RqG3dKNlOlLLc9B1gmgzamqEQB4uSYS68uTaCHXE80Sjf3LoDTBOvP0SLw86LoyoJqQqenmnaTFVFV1Vsu93z3ZiTzZuHlfDwnFuZUr+NO1/9v6T3qPcUUD6neECPS6THe9t0oo7Eqbs8FEEFsnSDLBK3YOymSdDrobSji1BHnLYdYQpfXo2JljK+UMPERRTjk2ZshW4odO/5dhljUHagEeJgomkKF/9sGC7NxKXruEwTF2BkuanuDvA/y9cSU23ou3qqAh1eDzEt8fWs9Tj4uKiATq+bbreb90aO54Zjz8BQFEwUNmeXsfWwqUz5Xv/DkMTBZVKphkNPXCi1Ou09TZx9E3Abqoo7EsWwaTjcKjmlTpiTuK20ZzOgiUIMB65DUsd/ZxqpGQoxAIaP9eLQDZJGfioKEbeTLH+IAn+ApvxcFMtKnNwUhbeHFlNf4ObwDdtSXu/JyYdw78zDcMZ1nnvqdhyhZmxOWdEkExxaaWPKKDvmxhgBp5NNBXnowFaHHU8sxoRAmEn1jcS9Lk66cgROjw2mD8cxNo+G9QalJOa4tYAmytDynOTd8LU0H1X6SRgKMUAcbpVwLHm4iysWobpjC7GsOE15Ob3TvznjYa5cvICPKobiV1NnByoMRTBMOztzssmP+qnLzpxFWAV832zl8ba+6dccwNhYnCUeN00OOxNKg3z3Z4eRX5aY9cfojLJ1YzZRcmlgCD78dJGNgQ0tJpOng4ShEANm4onFLHu6b+xWZesOLlj5FA4zzkk74JMt1fz++O8yvnEr1y54HJcRhyXQ4XRzzMX/i6Elvq5qz6TepV1+InqQ3FicYdfIlX0m+WBZmLiaOuVYaVxnpddNMOIju6hvgo/Agp1ETQ1Q8JONn8R0bjZ0lKBO98IG8k7P7FVr5J6hEANk2XYTZySKFtcxLYuTNryJw+yb3HtC42Ym7FzN5R88kwjCHnnRMON3fMKQYIjh/gBzGlvIi8WJuF3kxzXmjzqD6bNksd5Mkp+toPYzECCsqjhNE7s3eWiOvTyrd9He3e0aeO+o2P+5YA8WEoZCDJCO7SFspold12nP8VEYSp1UvrS7lqJQ6mxAY9oamNHWyYSObny7rVKh22xoIYNAXeoqEeLg9c2ry+nQ1KR70GFFYYvTznENTeQPT549xjOrhNyRbnbvQqOSmKc075wReKckT5SdiaSZVIgBUj7Jh7mtCc2ysMd1thZWMq5pc9I+Y+r9RDQN1x6TrC+oGEMlDkqjMdoddtbm+gjaNKoddk6LdJOTAYuvij4vbrBYVJKHoiqUByPopkWt20E810VefSPaHqsXWTEDX30DHkyCuNAwsBPH5wsw5OGL0nMQg4zUDIUYIMGxuawpLUZXVfK7/bw2+ngasxKrrxiKys6sYYxt9LPWOwWdxCoTFgpB8jm61M7LxQXUOx28NaSIOo+bToeD5WUlfPhfM9Ec8lXOJPPeDRO2a4RsGhtzvGzNyyLucoAFHw0rIfaJN2n/4IubUcMxHOjkEcBHGDc6Zncc5Y21e3mXzCI1QyEGyLxPTbZWlZMztIQzttcxMqBx/5xLyQ+2E7a7yA5HOHHVUrwxi3aGoxHHRMNC47BXVnHzxRN5zucmL5i87uBbdSrRuIXTLhNiZwq3jd7hEUkUBbthotmT7w+qPseeewKJQfd4pTcpSM1QiAGza1GJLqedoL3vOrTdk0fE5kY1TSxFobawkCZfLgaO3rXm4g4NVAWvrqe8rtazUIbIHNeenc2IzgAn1jZzaEMrnlAU4jqqbnDolp1ok5Kn63MfPxyr2EsWneTRTBZdRFEpnOyE4yak6SgGF/kKCTFArjk8cXVeFolh2e2YloVqGNgME800acrO45FjT+HVWYfw8HEn8cyhczCUxARaykXTOXvpOm58biFZkWjS655/uBP7Zyy7JA4+vpjB6XUt5EZjLM31EVJVMCzMQJz6HAeWJ7nOqCgKZcNCZNONiwjZdFE5rBvbwp+haBIDIGEoxIC54hA7/+8cB1OCIRRVJexwou52zgp43IScfatZbC4dyrLh1VjEGffwAs5csRFvTOd7767g0K07GVffwiWL13LDaa5+3k0czP71zxYU4GNfFqaSfCG0yldIrC35nqG5eCss3560TdnRDvWp68VmKrlnKMQAmlUIj5ngNMyUcWKGljqdWnN2PtmEaGkK0jNOmrxwlNPXbAKgrD5AbPtUbONyv+qii0HENBKfHUNJbRHQFQXL2KOeE01tXgewonq/i/xmIqkZCjGAhpbYcHsUVvu8+B32pMdsewynAAhoGsuHVJFVng17hKdqmLhz7biqZFhFpjnh9MQ6leP9wZTHJvo7cRYlj61QvlYNo/ZY1WRaBeq0YV9ZGQ80EoZCDCCXU8U/Jod1Pi/PVZTQ7ezryafF48TUvuv0WreLW+ZM48a559Bwy39RbDaiWIlegoppUdQeotTRjCLDKjLO4Uf4iGNSFItzfHM7hbE4Pt1galeAcWoryh4fCUVT+eTQI9jhGkK35qXGVcqaSYelp/CDlDSTCjGA2oIWb+1M1PDqPS5um1jNyECQnKiOw2bDAry6jq6ohGwa7Q47nabFI4ui5E2Yzs/efIYuexaOmEW2FUQLW5hLtqMdOjytxyUG1htPNdFmd9CSk2gVmBzq61TVGSrAiCc3ufu3+tnyWhv4xvZtfLWZUZu68Y2SqfxAaoZCpI9lYbcstmR5qcvy4DBNNAuCdjtRW9/JzAJMCxQL2rLyMDQbLiWKtmuUmZk6R6U4uHU1xYjY+1+yK27TMGPJ9ZxIc7jfffe2PRNJzVCIAVTgVZhZobFma5yhkRg2K1ETHBmK4OxZy7DDbqPD6aDZpjGsO8S0hnbmfruY5Wsd/L9DTwbToqClkxk16xlVEMJz2PB0H5YYYIecVsTyZZv5IN9HzKZREI7hMCywLDzxMHZv8vCbgukFuIa4iTT2hZ+r2EXhrMKBLvqgJWEoxADqCFt83K5QFo1jswDLoiqcCEJIzCqSH9dptGvkRHRmd/o5euM2tr2gY9/VgUZVaCvJ49NAJcNun4rST49CcXAbNdnLv2aNotmeGLuqmhajWvxocYOp+anDJVSHxpx5R/LRz1fQvqqNvKn5TPv9DFSHLAi9i4ShEAPooyaLeBwcPU2bNsvC1U8z58xOPy490bu03e6kuyWWsk/E7aRta4iyr7bIYhB6ZKXeG4QApqqwoSgbIga+xiqGh7elPCdvUh7HvHT8QBbzgCL3DIUYQOMLFRyWidFTmdMVhdhuNTubYVDoD5DvD+CMxcGyyIvH8Ob2c91qWeSNzx2YgotB5YMd/dwnVhL/NbudRP5/e3ceHVV59wH8e2dNMtkXsoBZCEQBE0JFAiKbYlIVMGURFEjCiwtNPZ5XKMrWAtWKIuJaQZBAwtJ6iiBvbVESjWixQlFSFNQAIZE1JGSdZJLZnvePkIFhsjEEbtL7/ZyTc8gzz73zeyaX+d791nnd9Jq6O4Yh0U1UcsGKwCoTGgAIIQBJQrGnB6wSoLLbccvFSgTW1cPLYoVPQwPCyioxwvMn3P9YT6jtl69DHPTzj7jV9DMQ7S/bWEg+jyRe3r3pYbHBt8EC2AS0VhtijXUw+Lk+E5Paxt2kRDdR1hcm2AVglyT0rqxCZH09Kg16fBDfGyNOnkOfC85PG5C0QMR3X0P8sy8y936O0/49EFhfg6C6apjVGuT9rRS95vWWaTQkl3tjVAi2NSCs3IRetQ1NT7Cw25F0vBiq2AZotC3fcYZaxy1DopvIdMWhP73dhp51Jujtdpg1Khi1ruumVkkNu6QCahrgaWlE37JTCKprOkFCY7fB0sAvPSXK/tYKTa0Nt1wKQgCQVCocDw+F9xEJwsqTqq4Vw5DoJpqadOmOM0LApNfhVFAANJIWEaW1OOrjg8arnsWktttQHD8IqrRhLvcuPRLeG4N+GXazSqcuZO9PFvg3Wlzaqwye0NgFRJW2hamoLQxDopvogYF6vDHDG78w2BBptgKShP8E+uCslwdqdVpsHBCLQn8fWGFHeEU57JIK/xcxGo0vfwqVzY4qT29Uevrgu4hYaB8fhrg7ePcQJbozRoManeueBF9TA2wqCZKva1BS2xiGRDfZlCEeGOR3+djgGc/Lj2A64+OFzf17I6+nL4b/dBQSAHODHbZPCwFI8DPVw99Uh9vPnkT/n4tufvHUJTw+RIsAyQy15XLomVUS9ocH4UK8FpKOdyW6VgxDIhnERF2+QXeA2XUtvm/ZRVQZvAEAkgqQ+oW69JH6cRepUlU3APd+W4RPI0Ow75ZA/DvCH/kxISgK9cX/+Q6AzcaL6a8Vw5BIBtMnBqDBt+m4TmJFDQyWyyfC9KquwGP7C/BtdCwA4K4Zt8Dz1V8Bfp6OPlJiL2ieHH5zi6YuI++7RugFYPLQosZDi3KDHvZLTzw57eOJqho/mSvsfnhpBZEM9B4qlIzuhaKjJnhYbbD4aAGoEHWxHKNLShBmqoZpXCgGzuiLkJimp5Z7nFwO2z+OQPL3hCqlHyQN1/6VKiZMjcM2O1Q2O+wa520ag9kGz8AGmSrrvrhlSCSTykYJp/28cDzIB2atBtCqUBLWAzt/EY+geiMigoQjCAFACvCCZvqdUD94O4NQ4e6K0aK8byACq+qdHvqsstkxuqEEXp71MlbXPXHLkEgm43ra8V258/roog/34lyID6o9PRAzra9MlVFXZ66z4j8WHcr1eqC6EdCqASEw9OQ5WAdUyV1et8QwJJKJIf8ckow6lPh6wNNsQfqXBZj29feo9PbC+48n4BkfffszIUUq/voiCoKDLv0mAZams5NVNju+rO6LsYE/yldcN8UwJJKBscaGU0WNmHLqJMZ99a3TawHGeljrDa1MSQTovTXwsNlQp77qJg1CwEPFawzdwWOGRDLw8FLB4K3CBR9v2CXgn/37YM0Do/DBXb9AtYce9mDeZo1aF3lnIJIvXnBq8zZbUOOhR2rIIZmq6t64ZUgkA41GwpDUYGT8KwxlBk+cD/AFJAmFEaHYHxeNeyK+kLtE6sIklYQ7Sy/ivNaAnw1e8LDbcUuDGUE2O7wtXJFyB7cMiWTyL3XTl1iZvw9wxTMNLRoNii/cImNl1NWVHKrCGbUHfIWE240m9KlvhN4uYNTrcbSYJ165g2FIJJNztYCn1Qa75PqEAVMjH85KrbPb7OAN1zoXw5BIJvcby/E/+w/Dw2pzec03sEyGiqi7iBkciDCrCZ5ms1O7obERt0Ydl6mq7o1hSCSDypI6GNd+j9C6Bgw68TMMjeami6eFQJFGgz9XDZC7ROriNo6/E0UBnvA2maC3mGGFDSV6LSQVv9bdwRNoiGRw8osyCFvTji6/ehNGHjkGAPjt3XfArFYDZj2KLtpxK+/FTS348aIdJ+tVQK9gHO4V7GjvUVaDjysG4knfXBmr6564CkEkA0OwzqWtTqOG9dJavRY2BHrxaeXUstK6ltsbdBqYbK7LFrWPYUgkg9h7QqGJ9nFq2z0gxnEyzZiAY/D3ZBhSy4b3kqCVBDQ2O3pW1yO8xgSVzY5bymrxC0OJ3OV1S9xNSiQDjV6NV++5Az2+P49AYwOORgThZIg/ok5dhLcFiJYa5S6RujCNSsKyeCs++6ACOnvT7vZ6rRpFvf0xzPcfMlfXPTEMiWRgswsUmdQ4EdvTud3gCb/Kelxs9JWpMuouPs03Qm+/fIGFl8UGr/JGfGeIxC9U3Dq8VgxDIhmoVRLGRgK5PwOGBjMm/OcEbi2rQqWnHod6hqKfXwmAeLnLpC7MarTh6lu5Gyw2XLBzRcodDEMimdxlrMU3dTpM/LYQ/coqAQA+ZgvCa+tRFsf/mtQ2/wg9LEXOt14r99Lhl5qzMlXUvfEEGiKZHP62HsNLyh1B2Exrt+PCyVCZqqLu4o0nfGH30UAAsAM446NHcnAjotQX5S6tW+LqJ5FMvL1UqDBJOOvnA63djiBjPVSXnlou6V3vSkN0pahAFcZXl6L0bCOMWg3uPG2F9oiAeaQBupBWrr2gVnHLkEgmv7zbCxIkFEaE4UivCBzoHYlGjRoXvD1wa5+TcpdHXVxjnQ0XTzdAYxfwb7RAe+lkGksF72vrDoYhkUzOlVpx5ZWEDTodDkTfgnMD9AjWGWWri7oHvUGNwJ4eLu2awHoZqun+GIZEMjl9zvWJ5DaNFnazpwzVUHcjSRKGp98CoWn6GhcAQhL8oOcuUrcwDIlkknCb61q9BCDIjydAUMf8bV8jCoOC8HOAP4qCg/DlBT1qanlphTsYhkQymTreF8GRl64UE4AkgDK9BtaQWnkLo27BYhH48acGCElCvV4Hi0YNAKisCpK5su6JZ5MSycTLU4XvI/xw2GaFxiZgU0lo0KhhNvdGguG03OVRF6fRAMFBapRfdD7z2NODxwzdwS1DIhl5aYFGtRp1Og0aLq3Z6yRrO1MRNR0zfHRaEK58fOFtcToEB12Qr6hujFuGRDL636EafFFiQfMdJnVqYLTXT7LWRN3H0CEGxET3wuHvTAgK0mBAPw2ys0X7E5ILhiGRjFJvU+PTNGBjgQ16tYTHBwkU7OYJNNRxoT20uO9eLQDAYnE9Q5k6hmFIJLMxMWqMiWnaRWqxWFAgbzlEisRjhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHiMQyJiEjxGIZERKR4DEMiIlI8hiERESkew5CIiBSPYUhERIrHMCQiIsVjGBIRkeIxDImISPEYhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHiMQyJiEjxGIZERKR4DEMiIlI8hiERESkew5CIiBSPYUhERIrHMCQiIsVjGBIRkeIxDImISPEYhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHiMQyJiEjxGIZERKR4DEMiIlI8hiERESkew5CIiBSPYUhERIrHMCQiIsVjGBIRkeIxDImISPEYhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHiMQyJiEjxGIZERKR4DEMiIlI8hiERESkew5CIiBSPYUhERIrHMCQiIsVjGBIRkeIxDImISPEYhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHiMQyJiEjxGIZERKR4DEMiIlI8hiERESkew5CIiBSPYUhERIrHMCQiIsVjGBIRkeIxDImISPEYhkREpHgMQyIiUjyGIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8TRyF0B0JSEEamtr5S5DNhaLBSaTCQBQU1MDrVYrc0Xy8vHxgSRJHe7P5YfLz9U6ugxJQghxE+oh6pCamhr4+fnJXQZ1EdXV1fD19e1wfy4/dLWOLkMMQ+pSOrJmbzQa8eCDD+Lvf/87vL29b1JlNw/Hd9mN2DLk59u9Xev4OroMcTcpdSmSJLW7FqdSqaBWq+Hr6/tf+Z+d43Mflx+Oz+35dtqciIiIuimGIRERKR7DkLodnU6Hxx9/HDqdTu5SbgiO77/7/W80js89PIGGiIgUj1uGRESkeAxDIiJSPF5aQd3GF198gTVr1qCkpARhYWHIyMjAhAkT2pzmyJEj2L59Ow4dOoSysjL06NED9957L2bPng1PT8+bVPllxcXFWLlyJQ4fPgyDwYAHHngAmZmZ7d4pRAiB7Oxs/PWvf0VVVRXi4uIwd+5cxMfH36TKO8ad8ZWXl2Pr1q3Yv38/Tp8+DW9vbwwaNAhPPfUUwsPDO602Lj9cftrCLUPqFgoKCjB//nzEx8fjzTffxH333Yfnn38eeXl5bU6Xm5uLU6dOIS0tDW+88QYeeeQR7Ny5E88888xNqvyympoazJkzB1arFa+88goyMzOxc+dOrF69ut1ps7Oz8e677+LRRx/Fa6+9huDgYDz11FM4ffr0Tai8Y9wd3w8//ID8/HyMHTsWr776Kp555hkcP34c6enpqKys7JTauPxw+WmXIOoGfvOb34hZs2Y5tS1atEhMnjy5zekqKipc2nbv3i3uuOMOcfTo0U6tsT1ZWVni7rvvFlVVVY62Dz74QAwZMkRcuHCh1ekaGhrEyJEjxdtvv+1oM5vNYty4cWLFihU3tOZr4e74ampqhMVicWo7f/68GDx4sNi8eXOn1Mblh8tPe7hlSF2e2WzGwYMHMXbsWKf25ORknDx5EmfPnm112oCAAJe2W2+9FQBQVlbWuYW246uvvsKQIUOc7p153333wW634+uvv251usOHD6Ours5p/FqtFmPGjMG+fftuaM3Xwt3x+fj4QKNxPmITGhqKgICATvkbcfnh8tMRDEPq8k6fPg2r1Yro6Gin9piYGABNxxmuRUFBAQC4zO9GKy4udnlPHx8fBAcHtzmG5tdaGv/58+fR0NDQuYW6yd3xtaSkpAQVFRWOv/H14PLT9BqXn7YxDKnLq6mpAdD0H+NKzfegbH69I6qqqrBu3TqMGjUKkZGRnVdkB9TU1LiMAWgaV1tjqKmpgU6ng16vd5lOdKFHFrk7vqsJIbBq1SqEhIQgJSWlU+pqruNKXH64/FyJZ5OSLIxGI8rLy9vt17Nnz057T6vVikWLFgEAFi5c2Gnzpc61bt06HDhwAG+99VarZ2xy+aHWdGT5aQnDkGSRl5eHF154od1+27dvd6zBG41Gp9ea1xY78qwyIQSWL1+OI0eOYP369QgODnaj6uvj6+vrMgYAqK2tbXMMvr6+MJvNaGxsdFq7r62thSRJLa5Ny8Hd8V1p586dWL9+PX73u99hyJAhrfbj8nMZl5/LOrr8tIRhSLJITU1Fampqh/qazWZoNBoUFxdj2LBhjvbWjoW05PXXX0deXh7eeOMNxMXFuVHx9YuOjnY59tG8hdPWGJpfKykpcaq9uLgYYWFh8PDwuAHVXjt3x9csPz8fL730EubMmYOHHnqozb5cfppw+bnsWpaflvCYIXV5Op0OgwcPxqeffurUnpubi5iYGERERLQ5/aZNm7Bt2zYsXbr0mtcWO9Ndd92FAwcOOB2jycvLg0qlwtChQ1udLiEhAQaDwemaOKvVivz8fAwfPvyG1nwt3B0fABw8eBCLFy9GamoqHnvssU6ti8sPl58OuZbrQIjkcujQITFkyBCxYsUK8e9//1usXbtWDB48WOTm5jr1GzJkiFi+fLnj9+ZrwpYsWSIOHz7s9NPSNWQ3UnV1tUhJSRGPP/64+Ne//iV27dolRo8eLV566SWnfnPmzBEPPfSQU9vGjRvFsGHDxLZt28SBAwfE/PnzxciRI8WpU6du4gja5u74ioqKxKhRo8TDDz8sCgoKnP5GnTU+Lj9cftrD3aTULSQmJmLlypVYs2YNdu3ahbCwMCxZssTl2jGbzQa73e74vfn6pN27d2P37t1OfZcuXYrx48ff+OIv8fX1xZo1a/DKK69g3rx5MBgMSE1NRWZmplM/m80Gm83m1Jaeng4hBLZs2YLKykrExcXhrbfeQq9evW5a/e1xd3zff/89jEYjjEYjZs+e7dR33LhxWLZs2XXXxuWHy097+AgnIiJSPB4zJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIrrKpk2bIEkSPv/8c7lL6VI+//xzSJKETZs2yV1Kp2MYEtF1KSoqwhNPPIHbbrsNXl5eCAgIQL9+/ZCeno78/HynvtHR0bj99ttbnVdGRgYkSWr1WYU//PADJEmCJEn48ssvW51Pc5/mHw8PD/Tt2xdz585FRUWFewO9RsuWLcOHH354U96rMxUUFGDZsmXX/HT57o73JiUitx08eBCjRo2CVqtFWloaBgwYAJPJhGPHjmHPnj3w8fHBmDFjOu39NmzYAB8fH3h6eiIrKwsjRoxotW9iYiLmzZsHAKioqMA//vEPvPbaa8jNzcU333wDnU7X6rQzZ87EtGnT2uzTnuXLlyM9Pb3Dj5rqKgoKCrB8+XKMHj3a5dFJI0eOhMlkglarlae4G4hhSERuW758Oerr61FQUICBAwe6vH7+/PlOey+LxYLNmzdjypQp8PPzw7p16/Dmm2+2+nDanj17YsaMGY7fn376aYwfPx4fffQRdu3ahSlTprT6Xmq1Gmq1utNq72y1tbWyPJRXpVJ1mecfdjbuJiUitx07dgxBQUEtBiEAhIWFddp7/e1vf8OFCxeQnp6OjIwM1NXV4f3337+meaSkpAAAjh8/3ma/lo4ZNrd99tlnWLVqFWJjY6HX6xEXF4fs7GxHv+LiYkiSBADIzs522l17pby8PCQnJ8Pf3x8eHh5ISEjA2rVrXWqJjo7G6NGjcejQIaSkpMDPzw8JCQkAmkJxyZIlSEpKQnBwMPR6Pfr06YMFCxagvr7eZV5CCKxfvx5JSUnw9vaGt7c34uPj8fvf/x5A067dWbNmAQDGjBnjqDsjIwNA68cM6+rqsHDhQsdnEhYWhrS0NJSUlDj1u3L6jRs3YsCAAdDr9YiKisLKlSvb/JsAQFVVFTw8PDBx4sQWX1+4cCEkSUJBQQEA4OzZs5g3bx4SExMREBAADw8P9O/fHy+//LLLkz24ZUhEbouNjcVPP/2EHTt2tPoFdTWbzdbqMcHGxsZWp9uwYQNiYmIwYsQISJKEQYMGISsr65oe5nrs2DEAQHBwcIenudqiRYtgMpnw5JNPQq/XY82aNcjIyECfPn0wfPhwhISEYPPmzZg5cyZGjBiBJ554wmUe69atw5w5czB06FAsXrwYBoMBubm5+PWvf40TJ07glVdecer/888/45577sGUKVMwadIkGI1GAMCZM2fw3nvvYdKkSXj00Ueh0Wiwd+9erFy5EocOHcInn3ziNJ+ZM2di69atSEpKwuLFi+Hv748ff/wR27dvxx/+8AdMnDgR586dw7p167Bo0SL069cPQNPfuTUWiwUpKSnYt28fJk+ejHnz5uHYsWNYs2YN9uzZg4MHD7o8Kmrt2rUoLS3F7Nmz4e/vjy1btuC5555Dr1698Oijj7b6Xv7+/pgwYQJ27dqFiooKBAYGOl6z2+3YunUrEhISkJiYCAA4fPgwduzYgV/96leIjY2FxWLBxx9/jAULFqCoqAjvvvvu5Zl3wjMZiUihvvrqK6HVagUA0bdvXzFr1izxzjvviKNHj7bYPyoqSgBo96esrMxpujNnzgi1Wi2WLl3qaHv99dcFgBbfC4BITk4WZWVloqysTBQWForVq1cLrVYr/Pz8RGlpaZvj2rhxowAg8vPzXdoSExNFY2Ojo/306dNCp9OJadOmudSQnp7uMu+zZ88KvV4vHnnkEZfXnn76aaFSqcSJEydcPrP169e79G9sbBRms9mlfcmSJQKA2L9/v6Pt/fffFwDEjBkzhM1mc+p/5e8tjb1Zfn6+ACA2btzoaFu3bp0AIObPn+/U96OPPnK839XTh4eHi6qqKkd7XV2dCA4OFkOHDnV5z6s1z/dPf/qTU3teXp4AIF599VVHW319vbDb7S7zmDFjhlCpVOLs2bOONu4mJSK3DRs2DN988w3S09NRXV2NjRs3IjMzE/3798fIkSNRVFTkMk10dDRyc3Nb/ElOTm7xfTZt2gS73Y60tDRH2/Tp06HVapGVldXiNHv27EFISAhCQkIQFxeHuXPnon///tizZw969Ojh9pgzMzOdTqzp2bMn4uLiHFud7dm+fTsaGxsxe/ZslJeXO/2MHz8edrsdeXl5TtMEBgY6dl9eSafTOU5msVqtqKysRHl5ueOhxfv373f03bp1KwBg1apVUKmcv/qv/v1a7Ny5EyqVCgsXLnRqf/DBB5GYmIhdu3Y5PTAZAGbNmgU/Pz/H715eXhg6dGiHPsOUlBSEhoYiJyfHqT0nJwcajQbTp093tHl6ejp2T5vNZlRUVKC8vBwpKSmw2+04ePCgoy93kxLRdYmPj3ccQyopKcHevXvx3nvv4csvv8RDDz3kcuamwWBwecJ8sy1btri0CSGQlZWFhIQE2O12p+N9w4cPx+bNm7FixQpoNM5fZ0lJSXjhhRcAwHFcKjIy8nqHi969e7u0BQUFuRwfa80PP/wAAK1+BgBQWlrq9HtsbGyrJ/S88847WLt2LY4cOeISOpWVlY5/Hzt2DOHh4QgNDe1QnR118uRJREREICAgwOW1AQMGoKCgAOXl5U4rIK19hhcvXmz3/ZoDb/Xq1SgsLERcXBzq6uqwY8cOJCcnO43ParXipZdeQk5ODo4fPw5x1bPsr/x8GIZE1GmioqKQlpbmOF62b98+HDhwAHfffbfb89y7dy9OnDgBAOjbt2+LfT766COXSxiCg4PbDBx3tRZKV3/Rtqa5X05ODsLDw1vsc3VYeHl5tdhv9erVmDdvHpKTk/H0008jIiICOp0OZ86cQUZGhks4dhXXe6ZuWloaVq9ejZycHLzwwgvYsWMHjEYj0tPTnfrNnTsXb731FqZOnYrFixejR48e0Gq1+Pbbb/Hcc885fT4MQyLqdJIkISkpCfv27cOZM2eua15ZWVnQ6/XIyclpcXfek08+iQ0bNnSb6/maA70zwnrz5s2Ijo7G7t27nT6bjz/+2KVvXFwcdu3ahdLS0ja3Dq8+67U9vXv3xscff4yqqir4+/s7vXb06FH4+vpe1wlLLRk4cCAGDhyILVu24Pnnn0dOTo7j5Jorbd68GSNHjsRf/vIXp/aWzibmMUMicltubi6sVqtLu8lkwp49ewAA/fv3d3v+1dXV2L59O5KTk/Hwww9j8uTJLj8TJkzA7t27ce7cObff50bw9vZu8W43Dz/8MPR6PZYuXQqTyeTyenV1dZtn1V5JrVZDkiSnrdLmXYNXaz6W9uyzz7psMV45vbe3NwB0+E49qampsNvtLu+5e/duHDp0CBMmTLiuY5KtSU9PR0lJCbZt24bPPvsMU6dOdbkGUq1Wu2yx19XV4bXXXnOZH7cMichtzzzzDC5evIgJEyYgPj4eXl5eOHXqFLZt24bCwkKkpaUhPj7e7fn/+c9/hslkwqRJk1rtM2nSJGzatAnZ2dlYsGCB2+/V2YYOHYq8vDy8/PLLiIyMhCRJmDZtGnr16oU1a9bgscceQ79+/TBz5kxERUWhrKwM3333HT788EMcPXrU5e4vLZk8eTIWLlyI+++/HxMnTkRNTQ22bdvW4h1ipkyZgqlTpyInJwfHjh3DhAkTEBAQgMLCQnzyySf4/vvvAQB33nknVCoV/vjHP6KyshIGgwExMTFISkpqsYaMjAxkZ2fj5ZdfRnFxMUaOHInjx4/jnXfeQWhoKF588cXr+hxbM336dDz77LPIzMyE3W532UUKNH0+7777LqZOnYqxY8eitLQUWVlZCAoKcp1hu+exEhG14pNPPhGZmZkiISFBBAUFCbVaLQIDA8Xo0aPFhg0bXE7hj4qKEgMGDGh1funp6U6XVgwePFhoNBpRUVHR6jQNDQ3Cx8dHxMXFOdoAiAcffNDtcbV1aUVLlxyMGjVKREVFObUVFhaK++67T/j4+DguGbnSP//5T5GamipCQkKEVqsV4eHhYvTo0WLVqlXCZDI5+kVFRYlRo0a1WKfVahUvvviiiI2NFTqdTkRGRor58+eLo0ePCgBOl6II0XQJxdtvvy0GDRokPD09hbe3t4iPjxfLli1z6rdp0ybRr18/x2UzzZeItHRphRBCGI1GsWDBAhETEyO0Wq0ICQkRM2bMEMXFxU79WpteiMt/+2sxbtw4x2U9LamrqxO//e1vRWRkpNDr9aJPnz5ixYoVjsswrqxDEqKDR32JiIj+S/GYIRERKR7DkIiIFI9hSEREiscwJCIixWMYEhGR4jEMiYhI8RiGRESkeAxDIiJSPIYhEREpHsOQiIgUj2FIRESKxzAkIiLFYxgSEZHi/T8yvJlcnZxndwAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "# Step 4.6: Perform SHAP analysis with the appropriate explainer for each model type\n", + "def compare_shap_values(models, model_names, X):\n", + " \"\"\"\n", + " Compare SHAP values across different models for the same instances.\n", + " \"\"\"\n", + " for model, name in zip(models, model_names):\n", + " # Ensure X is a DataFrame\n", + " if not isinstance(X, pd.DataFrame):\n", + " X = pd.DataFrame(X)\n", + "\n", + " # Use TreeExplainer for tree-based models like RandomForest and XGBoost\n", + " if isinstance(model, (RandomForestClassifier, XGBClassifier)):\n", + " explainer = shap.TreeExplainer(model)\n", + " # Use KernelExplainer or LinearExplainer for non-tree models\n", + " else:\n", + " explainer = shap.Explainer(model, X) # This will use Kernel or Linear explainer as appropriate\n", + "\n", + " shap_values = explainer(X)\n", + "\n", + " # Ensure the data format for plotting is correct\n", + " if isinstance(shap_values, list): # For multi-output models\n", + " shap_values = shap_values[0]\n", + "\n", + " # Ensure SHAP values match the dimensions of X for plotting\n", + " if hasattr(shap_values, 'values'):\n", + " shap_summary_data = shap_values.values\n", + " else:\n", + " shap_summary_data = shap_values\n", + "\n", + " # Summary plot for SHAP values\n", + " plt.title(f'SHAP Summary Plot for {name}')\n", + " shap.summary_plot(shap_summary_data, X, show=False)\n", + " plt.show()\n", + "\n", + "# Perform SHAP comparison using the scaled DataFrame with feature names\n", + "compare_shap_values(models.values(), model_names, X_train_scaled_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LIME Explanation for Random Forest:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "LIME Explanation for Logistic Regression:\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Step 4.7 : LIME Explanations Comparison\n", + "\n", + "from lime.lime_tabular import LimeTabularExplainer\n", + "\n", + "def compare_lime_explanations(models, model_names, X, instance):\n", + " \"\"\"\n", + " Compare LIME explanations for the same instance across different models.\n", + " \"\"\"\n", + " explainer = LimeTabularExplainer(X.values, feature_names=X.columns, class_names=['Target'], discretize_continuous=True)\n", + "\n", + " for model, name in zip(models, model_names):\n", + " explanation = explainer.explain_instance(instance.values, model.predict_proba, num_features=10)\n", + " print(f'LIME Explanation for {name}:')\n", + " explanation.show_in_notebook(show_all=False)\n", + "\n", + "# Perform LIME comparison for a specific instance (e.g., the first row of X_train_scaled_df)\n", + "compare_lime_explanations(models.values(), model_names, X_train_scaled_df, X_train_scaled_df.iloc[0])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Feature Importance Comparison (Normalized):\n" + ] + }, + { + "data": { + "text/html": [ + "
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Random Forest_ImportanceLogistic Regression_Importance
Feature
mean radius0.048703NaN
mean texture0.013591NaN
mean perimeter0.053270NaN
mean area0.047555NaN
mean smoothness0.007285NaN
mean compactness0.013944NaN
mean concavity0.068001NaN
mean concave points0.106210NaN
mean symmetry0.003770NaN
mean fractal dimension0.003886NaN
radius error0.020139NaN
texture error0.004724NaN
perimeter error0.011303NaN
area error0.022407NaN
smoothness error0.004271NaN
compactness error0.005253NaN
concavity error0.009386NaN
concave points error0.003513NaN
symmetry error0.004018NaN
fractal dimension error0.005321NaN
worst radius0.077987NaN
worst texture0.021749NaN
worst perimeter0.067115NaN
worst area0.153892NaN
worst smoothness0.010644NaN
worst compactness0.020266NaN
worst concavity0.031802NaN
worst concave points0.144663NaN
worst symmetry0.010120NaN
worst fractal dimension0.005210NaN
\n", + "
" + ], + "text/plain": [ + " Random Forest_Importance \\\n", + "Feature \n", + "mean radius 0.048703 \n", + "mean texture 0.013591 \n", + "mean perimeter 0.053270 \n", + "mean area 0.047555 \n", + "mean smoothness 0.007285 \n", + "mean compactness 0.013944 \n", + "mean concavity 0.068001 \n", + "mean concave points 0.106210 \n", + "mean symmetry 0.003770 \n", + "mean fractal dimension 0.003886 \n", + "radius error 0.020139 \n", + "texture error 0.004724 \n", + "perimeter error 0.011303 \n", + "area error 0.022407 \n", + "smoothness error 0.004271 \n", + "compactness error 0.005253 \n", + "concavity error 0.009386 \n", + "concave points error 0.003513 \n", + "symmetry error 0.004018 \n", + "fractal dimension error 0.005321 \n", + "worst radius 0.077987 \n", + "worst texture 0.021749 \n", + "worst perimeter 0.067115 \n", + "worst area 0.153892 \n", + "worst smoothness 0.010644 \n", + "worst compactness 0.020266 \n", + "worst concavity 0.031802 \n", + "worst concave points 0.144663 \n", + "worst symmetry 0.010120 \n", + "worst fractal dimension 0.005210 \n", + "\n", + " Logistic Regression_Importance \n", + "Feature \n", + "mean radius NaN \n", + "mean texture NaN \n", + "mean perimeter NaN \n", + "mean area NaN \n", + "mean smoothness NaN \n", + "mean compactness NaN \n", + "mean concavity NaN \n", + "mean concave points NaN \n", + "mean symmetry NaN \n", + "mean fractal dimension NaN \n", + "radius error NaN \n", + "texture error NaN \n", + "perimeter error NaN \n", + "area error NaN \n", + "smoothness error NaN \n", + "compactness error NaN \n", + "concavity error NaN \n", + "concave points error NaN \n", + "symmetry error NaN \n", + "fractal dimension error NaN \n", + "worst radius NaN \n", + "worst texture NaN \n", + "worst perimeter NaN \n", + "worst area NaN \n", + "worst smoothness NaN \n", + "worst compactness NaN \n", + "worst concavity NaN \n", + "worst concave points NaN \n", + "worst symmetry NaN \n", + "worst fractal dimension NaN " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 4.8 :Normalization of Feature Importances\n", + "\n", + "\n", + "def extract_feature_importances(models, model_names, feature_names):\n", + " \"\"\"\n", + " Extracts and compares feature importances from different models and normalizes them.\n", + " \"\"\"\n", + " feature_importance_df = pd.DataFrame()\n", + "\n", + " for model, name in zip(models, model_names):\n", + " if hasattr(model, 'feature_importances_'):\n", + " importance = model.feature_importances_\n", + " else:\n", + " importance = [0] * len(feature_names) # Handle models without feature importances\n", + "\n", + " temp_df = pd.DataFrame({\n", + " 'Feature': feature_names,\n", + " f'{name}_Importance': importance\n", + " }).set_index('Feature')\n", + "\n", + " feature_importance_df = pd.concat([feature_importance_df, temp_df], axis=1)\n", + "\n", + " # Normalize feature importances across models\n", + " return feature_importance_df.div(feature_importance_df.sum(axis=0), axis=1)\n", + "\n", + "# Extract and display normalized feature importances\n", + "feature_importance_df = extract_feature_importances(models.values(), model_names, feature_names)\n", + "print(\"Feature Importance Comparison (Normalized):\")\n", + "display(feature_importance_df)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Step 4.9 : Side-by-Side SHAP Visualizations\n", + "import shap\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Step 1: SHAP analysis with saving functionality\n", + "def compare_shap_values(models, model_names, X):\n", + " \"\"\"\n", + " Compare SHAP values across different models for the same instances and save the plots.\n", + " \"\"\"\n", + " for name, model in models.items():\n", + " # Use TreeExplainer for tree-based models (like RandomForest, XGBoost) and Explainer for others\n", + " if isinstance(model, (RandomForestClassifier, XGBClassifier)):\n", + " explainer = shap.TreeExplainer(model)\n", + " else:\n", + " explainer = shap.Explainer(model, X)\n", + "\n", + " shap_values = explainer(X)\n", + "\n", + " # Ensure SHAP values match the dimensions of X for plotting\n", + " if hasattr(shap_values, 'values'):\n", + " shap_values_to_plot = shap_values.values\n", + " else:\n", + " shap_values_to_plot = shap_values\n", + "\n", + " # Create the SHAP summary plot\n", + " plt.figure()\n", + " shap.summary_plot(shap_values_to_plot, X, show=True) # Ensure the plot is shown\n", + " plt.title(f'SHAP for {name}')\n", + " \n", + " # Save each SHAP summary plot as a PNG image\n", + " plt.savefig(f'SHAP_{name}.png') # Save the plot as a PNG file\n", + " plt.close() # Close the figure after saving\n", + "\n", + "# Ensure X is a DataFrame with correct feature names\n", + "sample_X = X_train_scaled_df.sample(100) # Limit to 100 samples for SHAP to reduce memory usage\n", + "compare_shap_values(models, model_names, sample_X)\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "VbqSywF7lUVp" + }, + "outputs": [], + "source": [ + "# Step 5: Create a dictionary of machine learning models to compare\n", + "models = {\n", + " # Random Forest Classifier\n", + " # - n_estimators=100: Specifies the number of trees in the forest.\n", + " 'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n", + " \n", + " # Logistic Regression\n", + " # - max_iter=1000: Sets the maximum number of iterations for the solver to converge. \n", + " 'Logistic Regression': LogisticRegression(max_iter=1000),\n", + " \n", + " # XGBoost Classifier (Extreme Gradient Boosting)\n", + " # - n_estimators=100: Specifies the number of boosting rounds (trees).\n", + " # - random_state=42: Ensures reproducibility by controlling the randomness.\n", + " 'XGBoost': XGBClassifier(n_estimators=100, random_state=42),\n", + " \n", + " # Neural Network (Multi-Layer Perceptron Classifier)\n", + " # - hidden_layer_sizes=(100, 50): Specifies the architecture of the neural network.\n", + " # The model will have two hidden layers: one with 100 neurons, and another with 50 neurons.\n", + " # - max_iter=1000: Sets the maximum number of iterations for training the network.\n", + " 'Neural Network': MLPClassifier(hidden_layer_sizes=(100, 50), max_iter=1000, random_state=42)\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "718W4KldrXQm" + }, + "outputs": [], + "source": [ + "#Step 6:This allows you to securely store and access the API key within your code\n", + "import os\n", + "os.environ['GOOGLE_API_KEY'] = 'AIzaSyBnLhWzd4fxBcSEKuYnF03-RoPI5Vcx560'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "ZdDOv-jArbu-", + "outputId": "0dcb9391-88d0-47f1-e713-bde89c9ee9cc", + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 22:38:48,343 - explainableai.llm_explanations - DEBUG - Initializing gemini...\n", + "2024-10-15 22:38:48,343 - explainableai.llm_explanations - DEBUG - Initializing gemini...\n", + "2024-10-15 22:38:48,355 - explainableai.llm_explanations - INFO - Gemini initialize successfully...\n", + "2024-10-15 22:38:48,355 - explainableai.llm_explanations - INFO - Gemini initialize successfully...\n", + "2024-10-15 22:38:48,355 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-15 22:38:48,355 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-15 22:38:48,355 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-15 22:38:48,355 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-15 22:38:48,371 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-15 22:38:48,371 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-15 22:38:48,371 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-15 22:38:48,371 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-15 22:38:48,387 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-15 22:38:48,387 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-15 22:38:48,402 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-15 22:38:48,402 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-15 22:38:48,433 - explainableai.core - DEBUG - Comparing the models...\n", + "2024-10-15 22:38:48,433 - explainableai.core - DEBUG - Comparing the models...\n", + "2024-10-15 22:38:55,888 - explainableai.core - INFO - Comparing successfully...\n", + "2024-10-15 22:38:55,888 - explainableai.core - INFO - Comparing successfully...\n", + "2024-10-15 22:38:56,987 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-15 22:38:56,987 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-15 22:38:56,987 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-15 22:38:56,987 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-15 22:38:56,987 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-15 22:38:56,987 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-15 22:38:57,003 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-15 22:38:57,003 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-15 22:38:57,003 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-15 22:38:57,003 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-15 22:38:57,050 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-15 22:38:57,050 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-15 22:38:57,050 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-15 22:38:57,050 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-15 22:38:57,061 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-15 22:38:57,061 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-15 22:38:57,783 - explainableai.core - INFO - Features calculated...\n", + "2024-10-15 22:38:57,783 - explainableai.core - INFO - Features calculated...\n", + "2024-10-15 22:38:57,783 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-15 22:38:57,783 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-15 22:38:57,783 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-15 22:38:57,783 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-15 22:38:57,799 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-15 22:38:57,799 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-15 22:38:58,353 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-15 22:38:58,353 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-15 22:38:58,353 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-15 22:38:58,353 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-15 22:39:04,426 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-15 22:39:04,426 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-15 22:39:04,426 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-15 22:39:04,426 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-15 22:39:14,239 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-15 22:39:14,239 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-15 22:39:14,239 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-15 22:39:14,239 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-15 22:39:17,418 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-15 22:39:17,418 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-15 22:39:17,671 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-15 22:39:17,671 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-15 22:39:17,671 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-15 22:39:17,671 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-15 22:39:17,881 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-15 22:39:17,881 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-15 22:39:17,897 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-15 22:39:17,897 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-15 22:39:17,897 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-15 22:39:17,897 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-15 22:39:17,913 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n", + "2024-10-15 22:39:17,913 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n", + "2024-10-15 22:39:17,913 - explainableai.feature_analysis - ERROR - Error calculating SHAP values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:17,913 - explainableai.feature_analysis - ERROR - Error calculating SHAP values: Model type not yet supported by TreeExplainer: \n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 34, in calculate_shap_values\n", + " logger.error(\"Model type:\", type(model))\n", + "Message: 'Model type:'\n", + "Arguments: (,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 34, in calculate_shap_values\n", + " logger.error(\"Model type:\", type(model))\n", + "Message: 'Model type:'\n", + "Arguments: (,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 35, in calculate_shap_values\n", + " logger.error(\"X shape:\", X.shape)\n", + "Message: 'X shape:'\n", + "Arguments: ((455, 30),)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 35, in calculate_shap_values\n", + " logger.error(\"X shape:\", X.shape)\n", + "Message: 'X shape:'\n", + "Arguments: ((455, 30),)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 36, in calculate_shap_values\n", + " logger.error(\"X dtype:\", X.dtypes)\n", + "Message: 'X dtype:'\n", + "Arguments: (mean radius float64\n", + "mean texture float64\n", + "mean perimeter float64\n", + "mean area float64\n", + "mean smoothness float64\n", + "mean compactness float64\n", + "mean concavity float64\n", + "mean concave points float64\n", + "mean symmetry float64\n", + "mean fractal dimension float64\n", + "radius error float64\n", + "texture error float64\n", + "perimeter error float64\n", + "area error float64\n", + "smoothness error float64\n", + "compactness error float64\n", + "concavity error float64\n", + "concave points error float64\n", + "symmetry error float64\n", + "fractal dimension error float64\n", + "worst radius float64\n", + "worst texture float64\n", + "worst perimeter float64\n", + "worst area float64\n", + "worst smoothness float64\n", + "worst compactness float64\n", + "worst concavity float64\n", + "worst concave points float64\n", + "worst symmetry float64\n", + "worst fractal dimension float64\n", + "dtype: object,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 36, in calculate_shap_values\n", + " logger.error(\"X dtype:\", X.dtypes)\n", + "Message: 'X dtype:'\n", + "Arguments: (mean radius float64\n", + "mean texture float64\n", + "mean perimeter float64\n", + "mean area float64\n", + "mean smoothness float64\n", + "mean compactness float64\n", + "mean concavity float64\n", + "mean concave points float64\n", + "mean symmetry float64\n", + "mean fractal dimension float64\n", + "radius error float64\n", + "texture error float64\n", + "perimeter error float64\n", + "area error float64\n", + "smoothness error float64\n", + "compactness error float64\n", + "concavity error float64\n", + "concave points error float64\n", + "symmetry error float64\n", + "fractal dimension error float64\n", + "worst radius float64\n", + "worst texture float64\n", + "worst perimeter float64\n", + "worst area float64\n", + "worst smoothness float64\n", + "worst compactness float64\n", + "worst concavity float64\n", + "worst concave points float64\n", + "worst symmetry float64\n", + "worst fractal dimension float64\n", + "dtype: object,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 37, in calculate_shap_values\n", + " logger.error(\"Feature names:\", feature_names)\n", + "Message: 'Feature names:'\n", + "Arguments: (['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension'],)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Temp\\ipykernel_11500\\1093256812.py\", line 16, in \n", + " results = xai.analyze()\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\core.py\", line 220, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 37, in calculate_shap_values\n", + " logger.error(\"Feature names:\", feature_names)\n", + "Message: 'Feature names:'\n", + "Arguments: (['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension'],)\n", + "2024-10-15 22:39:18,434 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-15 22:39:18,434 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-15 22:39:18,434 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "2024-10-15 22:39:18,434 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "2024-10-15 22:39:23,222 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-15 22:39:23,222 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-15 22:39:23,238 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-15 22:39:23,238 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-15 22:39:23,238 - explainableai.core - INFO - Performing model interpretation (SHAP and LIME)...\n", + "2024-10-15 22:39:23,238 - explainableai.core - INFO - Performing model interpretation (SHAP and LIME)...\n", + "2024-10-15 22:39:23,238 - explainableai - INFO - Starting model interpretation...\n", + "2024-10-15 22:39:23,238 - explainableai - INFO - Starting model interpretation...\n", + "2024-10-15 22:39:23,238 - explainableai - DEBUG - Calculating SHAP values...\n", + "2024-10-15 22:39:23,238 - explainableai - DEBUG - Calculating SHAP values...\n", + "2024-10-15 22:39:23,254 - explainableai - ERROR - Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai - ERROR - Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai - ERROR - Error in interpret_model: Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai - ERROR - Error in interpret_model: Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai.core - WARNING - Model interpretation failed: Error in interpret_model: Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai.core - WARNING - Model interpretation failed: Error in interpret_model: Error in calculate_shap_values: Model type not yet supported by TreeExplainer: \n", + "2024-10-15 22:39:23,254 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-15 22:39:23,254 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - accuracy: 1.0000\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - accuracy: 1.0000\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - f1_score: 1.0000\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - f1_score: 1.0000\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - confusion_matrix:\n", + "[[169 0]\n", + " [ 0 286]]\n", + "2024-10-15 22:39:23,269 - explainableai.core - INFO - confusion_matrix:\n", + "[[169 0]\n", + " [ 0 286]]\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 169\n", + " 1 1.00 1.00 1.00 286\n", + "\n", + " accuracy 1.00 455\n", + " macro avg 1.00 1.00 1.00 455\n", + "weighted avg 1.00 1.00 1.00 455\n", + "\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 169\n", + " 1 1.00 1.00 1.00 286\n", + "\n", + " accuracy 1.00 455\n", + " macro avg 1.00 1.00 1.00 455\n", + "weighted avg 1.00 1.00 1.00 455\n", + "\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst symmetry: 0.0246\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst symmetry: 0.0246\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst texture: 0.0207\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst texture: 0.0207\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst concavity: 0.0189\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst concavity: 0.0189\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst concave points: 0.0169\n", + "2024-10-15 22:39:23,285 - explainableai.core - INFO - worst concave points: 0.0169\n", + "2024-10-15 22:39:23,300 - explainableai.core - INFO - radius error: 0.0167\n", + "2024-10-15 22:39:23,300 - explainableai.core - INFO - radius error: 0.0167\n", + "2024-10-15 22:39:23,300 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9758 (+/- 0.0128)\n", + "2024-10-15 22:39:23,300 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9758 (+/- 0.0128)\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-15 22:39:23,316 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-15 22:39:23,332 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-15 22:39:23,332 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-15 22:39:23,332 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-15 22:39:23,332 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-15 22:39:23,332 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-15 22:39:23,332 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-15 22:39:34,231 - explainableai.llm_explanations - INFO - Response Generated...\n", + "2024-10-15 22:39:34,231 - explainableai.llm_explanations - INFO - Response Generated...\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 7:Initialize the XAIWrapper\n", + "# 'XAIWrapper' is a custom or third-party class that wraps around machine learning models and provides functionality for Explainable AI (XAI) techniques.\n", + "# This wrapper likely simplifies the process of explaining the predictions made by the models.\n", + "# The 'xai' object will provide methods to fit models and analyze their predictions.\n", + "xai = XAIWrapper()\n", + "\n", + "# Fit the models and run XAI analysis\n", + "# 'xai.fit()' trains the models on the training data (X_train and y_train).\n", + "# The 'fit()' method will train each model in the 'models' dictionary on the training set.\n", + "xai.fit(models, X_train, y_train)\n", + "\n", + "\n", + "\n", + "# 'xai.analyze()' performs XAI analysis on the fitted models.\n", + "# This method likely applies explainability techniques (e.g., SHAP, LIME) to help understand how the models made their predictions. The method generates explanations for model behavior by analyzing feature importance or contributions to predictions.\n", + "results = xai.analyze()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "M6LLsoc2rvjg", + "outputId": "35f88079-fb81-4059-f55f-bf3cdc894195" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "LLM Explanation of Results:\n", + "## Summary:\n", + "\n", + "This machine learning model shows exceptional performance in predicting its target outcome. It boasts perfect accuracy and consistently high scores across different data subsets, suggesting a strong and reliable ability to make accurate predictions.\n", + "\n", + "## Model Performance:\n", + "\n", + "- **Accuracy: 1.0** means the model correctly predicted the outcome for every single instance in the dataset. It's like getting 100% on a test!\n", + "- **F1-score: 1.0** confirms the perfect accuracy and indicates a perfect balance between correctly identifying positive cases and avoiding false positives.\n", + "- **Confusion Matrix:** This table shows no misclassifications, further solidifying the model's perfect performance.\n", + "- **Cross-validation Score:** The high average (0.9758) and low standard deviation (0.0128) demonstrate that the model performs consistently well across different portions of the data, indicating its reliability.\n", + "\n", + "## Important Features:\n", + "\n", + "The top 5 features provide insights into what the model finds most important for making predictions:\n", + "\n", + "1. **Worst symmetry:** This likely refers to irregularities in the shape of something.\n", + "2. **Worst texture:** This could indicate variations in the smoothness or roughness of a surface.\n", + "3. **Worst concavity:** This might relate to the extent of inward curves or indentations. \n", + "4. **Worst concave points:** This likely refers to the number of inward pointing areas.\n", + "5. **Radius error:** This could represent the uncertainty or variation in measuring a circular shape.\n", + "\n", + "These features, particularly the focus on \"worst\" characteristics, suggest the model might be analyzing images or data related to identifying abnormalities or irregularities, potentially in a medical context.\n", + "\n", + "## Next Steps:\n", + "\n", + "1. **Apply to real-world data:** While the model performs perfectly on this dataset, testing it on new, unseen data will provide a true evaluation of its real-world applicability.\n", + "2. **Investigate feature importance further:** Understanding why these specific features are most important could offer valuable insights into the underlying phenomenon being studied. This could involve collaborating with domain experts for deeper interpretation.\n", + "3. **Explore model interpretability:** While achieving high performance is crucial, understanding how the model arrives at its predictions can be equally important, especially in sensitive domains. Techniques for model interpretability can shed light on the decision-making process. \n", + "\n" + ] + } + ], + "source": [ + "# Step 8: Print the explanation generated by the Large Language Model (LLM) regarding the results\n", + "print(\"\\nLLM Explanation of Results:\") \n", + "print(results['llm_explanation']) # Output the explanation from the results dictionary under the key 'llm_explanation'" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HvAs479gr03b", + "outputId": "c5ce756d-2ceb-4c50-fa1b-8749119aed28" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\DELL\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:486: UserWarning: X has feature names, but MLPClassifier was fitted without feature names\n", + " warnings.warn(\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Test set accuracy: 0.3772\n" + ] + } + ], + "source": [ + "# Step 9: Make predictions on the test set\n", + "\n", + "# Use the 'predict()' method of the fitted model (wrapped in the XAIWrapper) to make predictions\n", + "# on the test set features (X_test).\n", + "# This method will return the predicted class labels for each instance in the test set.\n", + "test_predictions = xai.model.predict(X_test)\n", + "\n", + "# Calculate the accuracy of the predictions by comparing the predicted labels with the true labels (y_test).\n", + "# 'test_predictions == y_test' creates a boolean array where True indicates a correct prediction.\n", + "# Taking the mean of this boolean array gives the proportion of correct predictions (accuracy).\n", + "test_accuracy = (test_predictions == y_test).mean()\n", + "\n", + "# Print the accuracy of the model on the test set.\n", + "# The accuracy is formatted to four decimal places for better readability.\n", + "print(f\"\\nTest set accuracy: {test_accuracy:.4f}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 471 + }, + "id": "pE4_w3JSr6qv", + "outputId": "cda8612f-6d1a-425b-91c0-63831a0055a8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-15 22:40:35,056 - explainableai.core - DEBUG - Explaining the prediction...\n", + "2024-10-15 22:40:35,056 - explainableai.core - DEBUG - Explaining the prediction...\n", + "2024-10-15 22:40:35,069 - explainableai.core - DEBUG - Prediction...\n", + "2024-10-15 22:40:35,069 - explainableai.core - DEBUG - Prediction...\n", + "2024-10-15 22:40:35,069 - explainableai.core - DEBUG - Preproceesing input...\n", + "2024-10-15 22:40:35,069 - explainableai.core - DEBUG - Preproceesing input...\n", + "2024-10-15 22:40:35,085 - explainableai.core - INFO - Preprocessing the data...\n", + "2024-10-15 22:40:35,085 - explainableai.core - INFO - Preprocessing the data...\n", + "2024-10-15 22:40:35,085 - explainableai.core - INFO - Prediction Completed...\n", + "2024-10-15 22:40:35,085 - explainableai.core - INFO - Prediction Completed...\n", + "2024-10-15 22:40:35,085 - explainableai.llm_explanations - DEBUG - Predicting....\n", + "2024-10-15 22:40:35,085 - explainableai.llm_explanations - DEBUG - Predicting....\n", + "2024-10-15 22:40:42,396 - explainableai.llm_explanations - INFO - Prediction successfull\n", + "2024-10-15 22:40:42,396 - explainableai.llm_explanations - INFO - Prediction successfull\n", + "2024-10-15 22:40:42,396 - explainableai.core - INFO - Prediction explained...\n", + "2024-10-15 22:40:42,396 - explainableai.core - INFO - Prediction explained...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Sample Prediction:\n", + "Predicted class: 1\n", + "Prediction probabilities: [0.00589663 0.99410337]\n", + "\n", + "LLM Explanation of Prediction:\n", + "## Prediction Summary:\n", + "\n", + "Based on the provided information, the model predicts a positive result (indicated by \"1\") with very high confidence (99.4%). This suggests a strong likelihood of the outcome associated with a \"1\" prediction. \n", + "\n", + "## Key Factors:\n", + "\n", + "Several factors contributed to this prediction. The most influential seem to be related to irregularities and variations in the shape and texture of the subject, particularly the:\n", + "\n", + "* **\"Worst symmetry\"**: This measures how uneven the shape is at its most extreme point. A higher value suggests greater asymmetry, which can be a significant indicator.\n", + "* **\"Worst texture\"**: This refers to the coarseness or smoothness of the surface at its most prominent point. Changes in texture can also be important.\n", + "* **\"Worst concavity\"**: This indicates the size and depth of any indentations or concave portions at their most pronounced point.\n", + "\n", + "## Considerations:\n", + "\n", + "It's crucial to understand that this is just a prediction based on a model. While the confidence is high, it doesn't guarantee the outcome. The model's accuracy depends on the quality of the data it was trained on and its ability to generalize to new cases. \n", + "\n", + "## Next Steps:\n", + "\n", + "1. **Consult with an expert:** This prediction should be discussed with a qualified professional who can interpret the results in context and provide further guidance.\n", + "2. **Gather additional information:** Depending on the specific application, further tests or analyses might be necessary to confirm this prediction and make informed decisions. \n", + "\n" + ] + } + ], + "source": [ + "# Step 10: Demonstrate prediction explanation for a single instance\n", + "\n", + "# Select the first instance from the testing set (X_test) and convert it to a dictionary.\n", + "# 'iloc[0]' retrieves the first row of the DataFrame (the first test sample).\n", + "# 'to_dict()' converts this row into a dictionary format, which is often easier to work with for explanations.\n", + "sample_instance = X_test.iloc[0].to_dict()\n", + "\n", + "# Use the 'xai.explain_prediction()' method to get the prediction, prediction probabilities, and an explanation for the selected sample instance.\n", + "# The method takes the sample instance as input and likely analyzes it with the fitted models to provide insights into the prediction.\n", + "prediction, probabilities, explanation = xai.explain_prediction(sample_instance)\n", + "\n", + "# Print the results of the prediction explanation\n", + "print(\"\\nSample Prediction:\")\n", + "# Display the predicted class for the sample instance.\n", + "print(f\"Predicted class: {prediction}\")\n", + "# Display the prediction probabilities for each class (e.g., malignant vs. benign).\n", + "print(f\"Prediction probabilities: {probabilities}\")\n", + "\n", + "# Print the explanation for the prediction provided by the XAI wrapper.\n", + "print(\"\\nLLM Explanation of Prediction:\")\n", + "# Display the explanation generated by the explain_prediction method, which helps to understand \n", + "# the reasoning behind the prediction for this specific instance.\n", + "print(explanation)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 569 + }, + "id": "l-T5L34YsAJI", + "outputId": "83ff173a-589a-46b3-ceb2-2540a6d36172" + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 11: Feature importance visualization\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Retrieve the feature importance results from the analysis results.\n", + "# 'results' is assumed to be the output from the XAI analysis and contains a dictionary with various metrics, including 'feature_importance'.\n", + "feature_importance = results['feature_importance']\n", + "\n", + "# Sort the feature importance dictionary by importance values in descending order.\n", + "# 'sorted()' returns a sorted list of tuples, where each tuple contains a feature name and its importance.\n", + "# 'key=lambda x: x[1]' specifies that the sorting should be based on the second element of the tuple (importance value).\n", + "sorted_importance = sorted(feature_importance.items(), key=lambda x: x[1], reverse=True)\n", + "\n", + "# Unzip the sorted tuples into two separate lists: features and importance values.\n", + "# 'zip(*sorted_importance)' effectively separates the feature names and their corresponding importance values.\n", + "features, importance = zip(*sorted_importance)\n", + "\n", + "# 'plt.figure(figsize=(12, 6))' sets the size of the figure for better readability.\n", + "plt.figure(figsize=(12, 6))\n", + "\n", + "# 'plt.bar(features, importance)' creates a bar chart with features on the x-axis and their importance on the y-axis.\n", + "plt.bar(features, importance)\n", + "\n", + "# Rotate x-axis labels to avoid overlap and improve readability.\n", + "plt.xticks(rotation=90)\n", + "\n", + "# Label the axes and set the title of the plot.\n", + "plt.xlabel('Features')\n", + "plt.ylabel('Importance')\n", + "plt.title('Feature Importance')\n", + "\n", + "# Adjust the layout to ensure everything fits well within the figure.\n", + "plt.tight_layout()\n", + "\n", + "# Display the plot.\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 607 + }, + "id": "uZDKga_TsALf", + "outputId": "1917d82f-f7b5-4bdf-f5ba-48ba4ce34ab7" + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 12: Model comparison visualization\n", + "\n", + "# Retrieve the model comparison results from the analysis results.\n", + "# 'results' is assumed to contain a dictionary with various metrics, including 'model_comparison'.\n", + "model_comparison = results['model_comparison']\n", + "\n", + "# Extract the model names from the model comparison dictionary keys.\n", + "model_names = list(model_comparison.keys())\n", + "\n", + "# Extract cross-validation (CV) scores and test scores for each model from the model comparison results.\n", + "# List comprehension is used to create a list of CV scores and a list of test scores.\n", + "cv_scores = [scores['cv_score'] for scores in model_comparison.values()]\n", + "test_scores = [scores['test_score'] for scores in model_comparison.values()]\n", + "\n", + "# Create a figure for the bar plot with a specified size for better readability.\n", + "plt.figure(figsize=(10, 6))\n", + "\n", + "# Set the x positions for the bars on the x-axis.\n", + "x = np.arange(len(model_names))\n", + "width = 0.35 # Set the width of the bars.\n", + "\n", + "# Create a bar chart for cross-validation scores, shifting the bars to the left.\n", + "plt.bar(x - width/2, cv_scores, width, label='CV Score')\n", + "\n", + "# Create a bar chart for test scores, shifting the bars to the right.\n", + "plt.bar(x + width/2, test_scores, width, label='Test Score')\n", + "\n", + "# Label the x-axis and y-axis.\n", + "plt.xlabel('Models')\n", + "plt.ylabel('Scores')\n", + "\n", + "# Set the title of the plot.\n", + "plt.title('Model Comparison')\n", + "\n", + "# Set the x-tick positions and labels, rotating them for better visibility.\n", + "plt.xticks(x, model_names, rotation=45)\n", + "\n", + "# Add a legend to distinguish between CV scores and test scores.\n", + "plt.legend()\n", + "\n", + "# Adjust the layout to ensure that everything fits well within the figure.\n", + "plt.tight_layout()\n", + "\n", + "# Display the plot.\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/explainability_comparison/feature_importance.png b/examples/explainability_comparison/feature_importance.png new file mode 100644 index 0000000..81d2a49 Binary files /dev/null and b/examples/explainability_comparison/feature_importance.png differ diff --git a/examples/explainability_comparison/learning_curve.png b/examples/explainability_comparison/learning_curve.png new file mode 100644 index 0000000..4e43349 Binary files /dev/null and b/examples/explainability_comparison/learning_curve.png differ diff --git a/examples/explainability_comparison/partial_dependence.png b/examples/explainability_comparison/partial_dependence.png new file mode 100644 index 0000000..e5d0dba Binary files /dev/null and b/examples/explainability_comparison/partial_dependence.png differ diff --git a/examples/explainability_comparison/precision_recall_curve.png b/examples/explainability_comparison/precision_recall_curve.png new file mode 100644 index 0000000..68936ab Binary files /dev/null and b/examples/explainability_comparison/precision_recall_curve.png differ diff --git a/examples/explainability_comparison/readme.md b/examples/explainability_comparison/readme.md new file mode 100644 index 0000000..85d1534 --- /dev/null +++ b/examples/explainability_comparison/readme.md @@ -0,0 +1,74 @@ +Explainability Comparison Project +📝 Introduction + +This project focuses on the comparison of various explainability techniques used on machine learning models. The most recent updates include detailed comparisons using SHAP and LIME explainability tools, feature importance normalization, and side-by-side visualizations of model explanations. + + +🔄 Changes Implemented + +Key Changes Implemented +The following key modifications and additions were made to enhance the project’s explainability comparisons: + +1. Dataset Splitting and Scaling: + + + +train_test_split() was used to split the dataset into training and testing sets (80/20 split). +StandardScaler was used to normalize and scale the feature data. This step is essential for models such as Logistic Regression, which require feature scaling for optimal performance. + + +2. Model Comparison: + +Two models are compared in this project: +Random Forest Classifier +Logistic Regression +Cross-validation was performed for both models to evaluate their generalization performance on the scaled dataset. + + +3. SHAP (SHapley Additive exPlanations) Analysis: + + +SHAP was used to provide feature importance for each model. The SHAP summary plots visualize which features contribute most to the model predictions. +A custom function, compare_shap_values(), was created to: +Automatically use the appropriate SHAP explainer depending on the model type (TreeExplainer for tree-based models, and KernelExplainer or LinearExplainer for other models). +Generate and save SHAP summary plots for each model to visualize feature importance. +SHAP Summary Plots: These plots are saved as PNG files for easy comparison across models. + + + +4. LIME (Local Interpretable Model-Agnostic Explanations) Comparison: + + + +A new function, compare_lime_explanations(), was added to compare LIME explanations for different models. +LIME provides local explanations for individual predictions by approximating the model decision boundary in the local region of the instance. +LIME explanations were generated for a specific instance, showing which features contributed most to the prediction for both models. + + + +5. Feature Importance Normalization: + + + +A function, extract_feature_importances(), was added to extract feature importances from the models and normalize them across different models. +This allows for a side-by-side comparison of how different models weigh the importance of the same features. +The normalized feature importances are displayed in a tabular format, which makes it easier to compare feature contributions across models. + + +6. Side-by-Side SHAP Visualizations: + + +SHAP values are compared across models for the same instances, and side-by-side SHAP summary plots were created. +These plots help users understand how different models interpret the same features. +To reduce memory usage, a sample of 100 data points was selected for SHAP analysis. +Each SHAP plot is saved as a PNG image for easy reference and comparison between models. + + + + +📊 Results +The project explores how various explainability techniques explain model decisions. Here are the key results: + +SHAP: SHAP value plots indicate which features have the most impact on the predictions. +LIME: LIME provides local interpretability, showing how small changes in input data affect predictions. +Model Comparison: The project includes a report comparing the performance and interpretability of different models. diff --git a/examples/explainability_comparison/roc_curve.png b/examples/explainability_comparison/roc_curve.png new file mode 100644 index 0000000..3fb046c Binary files /dev/null and b/examples/explainability_comparison/roc_curve.png differ diff --git a/notebooks/.ipynb_checkpoints/Pytorch_Support_Explainable-checkpoint.ipynb b/notebooks/.ipynb_checkpoints/Pytorch_Support_Explainable-checkpoint.ipynb new file mode 100644 index 0000000..f6e5838 --- /dev/null +++ b/notebooks/.ipynb_checkpoints/Pytorch_Support_Explainable-checkpoint.ipynb @@ -0,0 +1,1648 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: explainableai in c:\\users\\shravya h jain\\desktop\\explain\\explainableai\\.venv\\lib\\site-packages (0.1.9)\n", + "Requirement already satisfied: numpy in c:\\users\\shravya h jain\\desktop\\explain\\explainableai\\.venv\\lib\\site-packages (from explainableai) (2.0.2)\n", + "Requirement already satisfied: pandas in c:\\users\\shravya h jain\\desktop\\explain\\explainableai\\.venv\\lib\\site-packages (from explainableai) (2.2.3)" + ] + } + ], + "source": [ + "!pip install explainableai\n", + "import os\n", + "os.environ['GOOGLE_API_KEY'] = 'API_KEY'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting Logistic Regression...\n", + "Fitting Random Forest...\n", + "Fitting XGBoost...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:14:36] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fitting MLP Classifier...\n", + "\n", + "Evaluating Models on Test Set:\n", + "Logistic Regression Test Accuracy: 96.49%\n", + "Random Forest Test Accuracy: 96.49%\n", + "XGBoost Test Accuracy: 97.37%\n", + "Neural Network (MLP) Test Accuracy: 97.37%\n", + "\n", + "Training PyTorch Model...\n", + "Epoch [10/100], Loss: 0.0223\n", + "Epoch [20/100], Loss: 0.0229\n", + "Epoch [30/100], Loss: 0.0022\n", + "Epoch [40/100], Loss: 0.0048\n", + "Epoch [50/100], Loss: 0.0000\n", + "Epoch [60/100], Loss: 0.0000\n", + "Epoch [70/100], Loss: 0.0013\n", + "Epoch [80/100], Loss: 0.0005\n", + "Epoch [90/100], Loss: 0.0002\n", + "Epoch [100/100], Loss: 0.0000\n", + "PyTorch Model Test Accuracy: 98.25%\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:14:40,727 - explainableai.llm_explanations - DEBUG - Initializing gemini...\n", + "2024-10-07 16:14:40,727 - explainableai.llm_explanations - INFO - Gemini initialize successfully...\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Performing XAI Analysis on Scikit-learn Models:\n", + "\n", + "Analyzing Logistic Regression...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:14:40,746 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-07 16:14:40,746 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-07 16:14:40,748 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-07 16:14:40,749 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-07 16:14:40,749 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-07 16:14:40,760 - explainableai.core - DEBUG - Updating feature names...\n", + "2024-10-07 16:14:40,761 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-07 16:14:40,762 - explainableai.core - DEBUG - Comparing the models...\n", + "2024-10-07 16:14:41,777 - explainableai.core - INFO - Comparing successfully...\n", + "2024-10-07 16:14:41,997 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-07 16:14:41,997 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-07 16:14:41,997 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-07 16:14:41,997 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-07 16:14:41,997 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-07 16:14:42,009 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-07 16:14:42,010 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-07 16:14:42,010 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-07 16:14:42,078 - explainableai.core - INFO - Features calculated...\n", + "2024-10-07 16:14:42,078 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-07 16:14:42,079 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-07 16:14:42,079 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-07 16:14:42,260 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-07 16:14:42,260 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-07 16:14:47,802 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-07 16:14:47,802 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-07 16:14:50,205 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-07 16:14:50,205 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-07 16:14:51,197 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-07 16:14:51,251 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-07 16:14:51,263 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-07 16:14:51,314 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-07 16:14:51,316 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-07 16:14:51,317 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-07 16:14:51,317 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n", + "2024-10-07 16:14:51,318 - explainableai.feature_analysis - ERROR - Error calculating SHAP values: Model type not yet supported by TreeExplainer: \n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 34, in calculate_shap_values\n", + " logger.error(\"Model type:\", type(model))\n", + "Message: 'Model type:'\n", + "Arguments: (,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 35, in calculate_shap_values\n", + " logger.error(\"X shape:\", X.shape)\n", + "Message: 'X shape:'\n", + "Arguments: ((455, 30),)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 36, in calculate_shap_values\n", + " logger.error(\"X dtype:\", X.dtypes)\n", + "Message: 'X dtype:'\n", + "Arguments: (mean radius float64\n", + "mean texture float64\n", + "mean perimeter float64\n", + "mean area float64\n", + "mean smoothness float64\n", + "mean compactness float64\n", + "mean concavity float64\n", + "mean concave points float64\n", + "mean symmetry float64\n", + "mean fractal dimension float64\n", + "radius error float64\n", + "texture error float64\n", + "perimeter error float64\n", + "area error float64\n", + "smoothness error float64\n", + "compactness error float64\n", + "concavity error float64\n", + "concave points error float64\n", + "symmetry error float64\n", + "fractal dimension error float64\n", + "worst radius float64\n", + "worst texture float64\n", + "worst perimeter float64\n", + "worst area float64\n", + "worst smoothness float64\n", + "worst compactness float64\n", + "worst concavity float64\n", + "worst concave points float64\n", + "worst symmetry float64\n", + "worst fractal dimension float64\n", + "dtype: object,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 37, in calculate_shap_values\n", + " logger.error(\"Feature names:\", feature_names)\n", + "Message: 'Feature names:'\n", + "Arguments: (['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension'],)\n", + "2024-10-07 16:14:51,394 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-07 16:14:51,394 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "2024-10-07 16:14:52,185 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-07 16:14:52,185 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - accuracy: 0.9890\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - f1_score: 0.9890\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - confusion_matrix:\n", + "[[165 4]\n", + " [ 1 285]]\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 0.99 0.98 0.99 169\n", + " 1 0.99 1.00 0.99 286\n", + "\n", + " accuracy 0.99 455\n", + " macro avg 0.99 0.99 0.99 455\n", + "weighted avg 0.99 0.99 0.99 455\n", + "\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - mean concave points: 0.0631\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - worst texture: 0.0532\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - radius error: 0.0448\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - worst radius: 0.0442\n", + "2024-10-07 16:14:52,185 - explainableai.core - INFO - worst concavity: 0.0286\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9780 (+/- 0.0241)\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - \n", + "SHAP values calculation failed. Please check the console output for more details.\n", + "2024-10-07 16:14:52,200 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-07 16:14:52,204 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-07 16:14:53,852 - explainableai.llm_explanations - ERROR - Some error in generating response... 400 API key not valid. Please pass a valid API key. [reason: \"API_KEY_INVALID\"\n", + "domain: \"googleapis.com\"\n", + "metadata {\n", + " key: \"service\"\n", + " value: \"generativelanguage.googleapis.com\"\n", + "}\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for Logistic Regression:\n", + "{'model_performance': {'accuracy': 0.989010989010989, 'f1_score': np.float64(0.9889904514693614), 'confusion_matrix': array([[165, 4],\n", + " [ 1, 285]]), 'classification_report': ' precision recall f1-score support\\n\\n 0 0.99 0.98 0.99 169\\n 1 0.99 1.00 0.99 286\\n\\n accuracy 0.99 455\\n macro avg 0.99 0.99 0.99 455\\nweighted avg 0.99 0.99 0.99 455\\n'}, 'feature_importance': {'mean concave points': np.float64(0.0630769230769231), 'worst texture': np.float64(0.05318681318681322), 'radius error': np.float64(0.044835164835164885), 'worst radius': np.float64(0.04417582417582423), 'worst concavity': np.float64(0.02857142857142859), 'worst area': np.float64(0.0241758241758242), 'worst symmetry': np.float64(0.02197802197802201), 'smoothness error': np.float64(0.007252747252747316), 'compactness error': np.float64(0.007032967032967097), 'worst concave points': np.float64(0.005054945054945081), 'texture error': np.float64(0.003736263736263756), 'worst perimeter': np.float64(0.0035164835164835373), 'fractal dimension error': np.float64(0.002637362637362628), 'symmetry error': np.float64(0.002417582417582409), 'area error': np.float64(0.0021978021978022013), 'worst smoothness': np.float64(0.001538461538461533), 'mean area': np.float64(0.000879120879120876), 'mean symmetry': np.float64(0.000439560439560438), 'mean radius': np.float64(0.000219780219780219), 'mean texture': np.float64(0.0), 'mean perimeter': np.float64(0.0), 'mean smoothness': np.float64(0.0), 'mean compactness': np.float64(0.0), 'mean concavity': np.float64(0.0), 'mean fractal dimension': np.float64(0.0), 'perimeter error': np.float64(0.0), 'concavity error': np.float64(0.0), 'concave points error': np.float64(0.0), 'worst compactness': np.float64(0.0), 'worst fractal dimension': np.float64(0.0)}, 'shap_values': None, 'cv_scores': (np.float64(0.9780219780219781), np.float64(0.024075716813413892)), 'model_comparison': {'Model': {'cv_score': np.float64(0.9906088751289989), 'test_score': 0.989010989010989}}, 'llm_explanation': None}\n", + "\n", + "Analyzing Random Forest...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:14:53,852 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-07 16:14:53,852 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-07 16:14:53,858 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-07 16:14:53,858 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-07 16:14:53,859 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-07 16:14:53,862 - explainableai.core - DEBUG - Updating feature names...\n", + "2024-10-07 16:14:53,863 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-07 16:14:53,865 - explainableai.core - DEBUG - Comparing the models...\n", + "2024-10-07 16:15:06,067 - explainableai.core - INFO - Comparing successfully...\n", + "2024-10-07 16:15:08,761 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-07 16:15:08,762 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-07 16:15:08,762 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-07 16:15:08,764 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-07 16:15:08,764 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-07 16:15:08,821 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-07 16:15:08,822 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-07 16:15:08,823 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-07 16:15:23,665 - explainableai.core - INFO - Features calculated...\n", + "2024-10-07 16:15:23,666 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-07 16:15:23,666 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-07 16:15:23,667 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-07 16:15:23,821 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-07 16:15:23,822 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-07 16:15:26,804 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-07 16:15:26,804 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-07 16:15:39,225 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-07 16:15:39,226 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-07 16:15:40,168 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-07 16:15:40,273 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-07 16:15:40,274 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-07 16:15:40,369 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-07 16:15:40,369 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-07 16:15:40,369 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-07 16:15:40,375 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:15:42,118 - explainableai.feature_analysis - INFO - Dataframe Created...\n", + "2024-10-07 16:15:42,121 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-07 16:15:42,121 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "2024-10-07 16:15:51,826 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-07 16:15:51,826 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-07 16:15:51,826 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-07 16:15:51,826 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-07 16:15:51,826 - explainableai.core - INFO - accuracy: 1.0000\n", + "2024-10-07 16:15:51,837 - explainableai.core - INFO - f1_score: 1.0000\n", + "2024-10-07 16:15:51,837 - explainableai.core - INFO - confusion_matrix:\n", + "[[169 0]\n", + " [ 0 286]]\n", + "2024-10-07 16:15:51,838 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 169\n", + " 1 1.00 1.00 1.00 286\n", + "\n", + " accuracy 1.00 455\n", + " macro avg 1.00 1.00 1.00 455\n", + "weighted avg 1.00 1.00 1.00 455\n", + "\n", + "2024-10-07 16:15:51,839 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-07 16:15:51,839 - explainableai.core - INFO - worst texture: 0.0033\n", + "2024-10-07 16:15:51,840 - explainableai.core - INFO - radius error: 0.0020\n", + "2024-10-07 16:15:51,840 - explainableai.core - INFO - mean texture: 0.0018\n", + "2024-10-07 16:15:51,841 - explainableai.core - INFO - area error: 0.0018\n", + "2024-10-07 16:15:51,841 - explainableai.core - INFO - worst concave points: 0.0009\n", + "2024-10-07 16:15:51,842 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9604 (+/- 0.0149)\n", + "2024-10-07 16:15:51,842 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-07 16:15:51,843 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-07 16:15:51,843 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-07 16:15:51,843 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-07 16:15:51,844 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-07 16:15:51,844 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-07 16:15:51,845 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-07 16:15:51,845 - explainableai.core - INFO - \n", + "SHAP values calculated successfully. See 'shap_summary.png' for visualization.\n", + "2024-10-07 16:15:51,846 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-07 16:15:51,846 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-07 16:15:52,111 - explainableai.llm_explanations - ERROR - Some error in generating response... 400 API key not valid. Please pass a valid API key. [reason: \"API_KEY_INVALID\"\n", + "domain: \"googleapis.com\"\n", + "metadata {\n", + " key: \"service\"\n", + " value: \"generativelanguage.googleapis.com\"\n", + "}\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for Random Forest:\n", + "{'model_performance': {'accuracy': 1.0, 'f1_score': np.float64(1.0), 'confusion_matrix': array([[169, 0],\n", + " [ 0, 286]]), 'classification_report': ' precision recall f1-score support\\n\\n 0 1.00 1.00 1.00 169\\n 1 1.00 1.00 1.00 286\\n\\n accuracy 1.00 455\\n macro avg 1.00 1.00 1.00 455\\nweighted avg 1.00 1.00 1.00 455\\n'}, 'feature_importance': {'worst texture': np.float64(0.003296703296703285), 'radius error': np.float64(0.001978021978021971), 'mean texture': np.float64(0.001758241758241752), 'area error': np.float64(0.001758241758241752), 'worst concave points': np.float64(0.000879120879120876), 'mean radius': np.float64(0.0), 'mean perimeter': np.float64(0.0), 'mean area': np.float64(0.0), 'mean smoothness': np.float64(0.0), 'mean compactness': np.float64(0.0), 'mean concavity': np.float64(0.0), 'mean concave points': np.float64(0.0), 'mean symmetry': np.float64(0.0), 'mean fractal dimension': np.float64(0.0), 'texture error': np.float64(0.0), 'perimeter error': np.float64(0.0), 'smoothness error': np.float64(0.0), 'compactness error': np.float64(0.0), 'concavity error': np.float64(0.0), 'concave points error': np.float64(0.0), 'symmetry error': np.float64(0.0), 'fractal dimension error': np.float64(0.0), 'worst radius': np.float64(0.0), 'worst perimeter': np.float64(0.0), 'worst area': np.float64(0.0), 'worst smoothness': np.float64(0.0), 'worst compactness': np.float64(0.0), 'worst concavity': np.float64(0.0), 'worst symmetry': np.float64(0.0), 'worst fractal dimension': np.float64(0.0)}, 'shap_values': array([[[-0.02817769, 0.02817769],\n", + " [-0.00743516, 0.00743516],\n", + " [-0.03090336, 0.03090336],\n", + " ...,\n", + " [ 0.11731347, -0.11731347],\n", + " [ 0.04097617, -0.04097617],\n", + " [ 0.0001876 , -0.0001876 ]],\n", + "\n", + " [[ 0.02146795, -0.02146795],\n", + " [ 0.01293296, -0.01293296],\n", + " [ 0.02377878, -0.02377878],\n", + " ...,\n", + " [ 0.0867181 , -0.0867181 ],\n", + " [ 0.01824471, -0.01824471],\n", + " [ 0.00401472, -0.00401472]],\n", + "\n", + " [[-0.01523057, 0.01523057],\n", + " [-0.01407987, 0.01407987],\n", + " [-0.01593919, 0.01593919],\n", + " ...,\n", + " [-0.04243702, 0.04243702],\n", + " [ 0.00085408, -0.00085408],\n", + " [-0.00157538, 0.00157538]],\n", + "\n", + " ...,\n", + "\n", + " [[-0.00760451, 0.00760451],\n", + " [-0.00695714, 0.00695714],\n", + " [-0.01013629, 0.01013629],\n", + " ...,\n", + " [-0.04741325, 0.04741325],\n", + " [-0.00633944, 0.00633944],\n", + " [-0.00139158, 0.00139158]],\n", + "\n", + " [[-0.00132996, 0.00132996],\n", + " [ 0.00520777, -0.00520777],\n", + " [-0.00080756, 0.00080756],\n", + " ...,\n", + " [ 0.1724399 , -0.1724399 ],\n", + " [ 0.00309631, -0.00309631],\n", + " [ 0.00639376, -0.00639376]],\n", + "\n", + " [[-0.01184732, 0.01184732],\n", + " [ 0.00366241, -0.00366241],\n", + " [-0.01262526, 0.01262526],\n", + " ...,\n", + " [-0.04974929, 0.04974929],\n", + " [-0.00466538, 0.00466538],\n", + " [-0.0014666 , 0.0014666 ]]]), 'cv_scores': (np.float64(0.9604395604395604), np.float64(0.01490621974313245)), 'model_comparison': {'Model': {'cv_score': np.float64(0.9895188136300552), 'test_score': 1.0}}, 'llm_explanation': None}\n", + "\n", + "Analyzing XGBoost...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:15:52,111 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-07 16:15:52,121 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-07 16:15:52,121 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-07 16:15:52,121 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-07 16:15:52,121 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-07 16:15:52,121 - explainableai.core - DEBUG - Updating feature names...\n", + "2024-10-07 16:15:52,121 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-07 16:15:52,121 - explainableai.core - DEBUG - Comparing the models...\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:52] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "2024-10-07 16:15:53,371 - explainableai.core - INFO - Comparing successfully...\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:15:53] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "2024-10-07 16:15:53,555 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-07 16:15:53,556 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-07 16:15:53,557 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-07 16:15:53,557 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-07 16:15:53,557 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-07 16:15:53,566 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-07 16:15:53,567 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-07 16:15:53,567 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-07 16:15:53,886 - explainableai.core - INFO - Features calculated...\n", + "2024-10-07 16:15:53,887 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-07 16:15:53,888 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-07 16:15:53,888 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-07 16:15:54,081 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-07 16:15:54,082 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-07 16:15:56,125 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-07 16:15:56,125 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-07 16:16:00,053 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-07 16:16:00,061 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-07 16:16:01,064 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-07 16:16:01,150 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-07 16:16:01,150 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-07 16:16:01,243 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-07 16:16:01,243 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-07 16:16:01,243 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-07 16:16:01,243 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:16:01,583 - explainableai.feature_analysis - INFO - Dataframe Created...\n", + "2024-10-07 16:16:01,590 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-07 16:16:01,590 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:16:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:16:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:16:01] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:16:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\xgboost\\core.py:158: UserWarning: [16:16:02] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0015a694724fa8361-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n", + "Parameters: { \"use_label_encoder\" } are not used.\n", + "\n", + " warnings.warn(smsg, UserWarning)\n", + "2024-10-07 16:16:02,450 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-07 16:16:02,450 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - accuracy: 1.0000\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - f1_score: 1.0000\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - confusion_matrix:\n", + "[[169 0]\n", + " [ 0 286]]\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 169\n", + " 1 1.00 1.00 1.00 286\n", + "\n", + " accuracy 1.00 455\n", + " macro avg 1.00 1.00 1.00 455\n", + "weighted avg 1.00 1.00 1.00 455\n", + "\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - worst texture: 0.0136\n", + "2024-10-07 16:16:02,450 - explainableai.core - INFO - worst concave points: 0.0092\n", + "2024-10-07 16:16:02,457 - explainableai.core - INFO - mean concave points: 0.0059\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - compactness error: 0.0048\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - mean texture: 0.0033\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9824 (+/- 0.0112)\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - \n", + "SHAP values calculated successfully. See 'shap_summary.png' for visualization.\n", + "2024-10-07 16:16:02,458 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-07 16:16:02,464 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-07 16:16:02,730 - explainableai.llm_explanations - ERROR - Some error in generating response... 400 API key not valid. Please pass a valid API key. [reason: \"API_KEY_INVALID\"\n", + "domain: \"googleapis.com\"\n", + "metadata {\n", + " key: \"service\"\n", + " value: \"generativelanguage.googleapis.com\"\n", + "}\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for XGBoost:\n", + "{'model_performance': {'accuracy': 1.0, 'f1_score': np.float64(1.0), 'confusion_matrix': array([[169, 0],\n", + " [ 0, 286]]), 'classification_report': ' precision recall f1-score support\\n\\n 0 1.00 1.00 1.00 169\\n 1 1.00 1.00 1.00 286\\n\\n accuracy 1.00 455\\n macro avg 1.00 1.00 1.00 455\\nweighted avg 1.00 1.00 1.00 455\\n'}, 'feature_importance': {'worst texture': np.float64(0.013626373626373612), 'worst concave points': np.float64(0.009230769230769199), 'mean concave points': np.float64(0.005934065934065913), 'compactness error': np.float64(0.004835164835164818), 'mean texture': np.float64(0.003296703296703285), 'worst area': np.float64(0.003296703296703285), 'mean compactness': np.float64(0.001538461538461533), 'worst symmetry': np.float64(0.001538461538461533), 'area error': np.float64(0.001318681318681314), 'radius error': np.float64(0.001098901098901095), 'worst perimeter': np.float64(0.000659340659340657), 'mean radius': np.float64(0.0), 'mean perimeter': np.float64(0.0), 'mean area': np.float64(0.0), 'mean smoothness': np.float64(0.0), 'mean concavity': np.float64(0.0), 'mean symmetry': np.float64(0.0), 'mean fractal dimension': np.float64(0.0), 'texture error': np.float64(0.0), 'perimeter error': np.float64(0.0), 'smoothness error': np.float64(0.0), 'concavity error': np.float64(0.0), 'concave points error': np.float64(0.0), 'symmetry error': np.float64(0.0), 'fractal dimension error': np.float64(0.0), 'worst radius': np.float64(0.0), 'worst smoothness': np.float64(0.0), 'worst compactness': np.float64(0.0), 'worst concavity': np.float64(0.0), 'worst fractal dimension': np.float64(0.0)}, 'shap_values': array([[ 0. , 0.47992012, 0.00751822, ..., -1.7419593 ,\n", + " -1.3272045 , 0.27730063],\n", + " [ 0. , -0.91630375, -0.00541482, ..., -1.3817174 ,\n", + " -1.123408 , 0.25335163],\n", + " [ 0. , 0.79309905, 0.00751822, ..., 1.2224349 ,\n", + " -1.123034 , 0.27730063],\n", + " ...,\n", + " [ 0. , 0.7297472 , 0.07059038, ..., 1.1524037 ,\n", + " 1.0196985 , -0.3740746 ],\n", + " [ 0. , 0.4190652 , 0.07059038, ..., -2.5678008 ,\n", + " -1.1041393 , 0.25335163],\n", + " [ 0. , -0.32773632, 0.00751822, ..., 1.0871819 ,\n", + " 0.7314011 , -0.3740746 ]], dtype=float32), 'cv_scores': (np.float64(0.9824175824175825), np.float64(0.011206636293610526)), 'model_comparison': {'Model': {'cv_score': np.float64(0.9931862664220944), 'test_score': 1.0}}, 'llm_explanation': None}\n", + "\n", + "Analyzing Neural Network (MLP)...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-10-07 16:16:02,743 - explainableai.core - DEBUG - Fitting the model...\n", + "2024-10-07 16:16:02,743 - explainableai.core - INFO - Preprocessing data...\n", + "2024-10-07 16:16:02,745 - explainableai.core - DEBUG - Creating Preprocessing Steps...\n", + "2024-10-07 16:16:02,745 - explainableai.core - INFO - Pre proccessing completed...\n", + "2024-10-07 16:16:02,745 - explainableai.core - DEBUG - Fitting and transforming the data...\n", + "2024-10-07 16:16:02,750 - explainableai.core - DEBUG - Updating feature names...\n", + "2024-10-07 16:16:02,751 - explainableai.core - INFO - Fitting models and analyzing...\n", + "2024-10-07 16:16:02,752 - explainableai.core - DEBUG - Comparing the models...\n", + "2024-10-07 16:16:04,074 - explainableai.core - INFO - Comparing successfully...\n", + "2024-10-07 16:16:04,346 - explainableai.core - INFO - Model fitting is complete...\n", + "2024-10-07 16:16:04,346 - explainableai.core - DEBUG - Analysing...\n", + "2024-10-07 16:16:04,346 - explainableai.core - INFO - Evaluating model performance...\n", + "2024-10-07 16:16:04,346 - explainableai.model_evaluation - DEBUG - Evaluting model\n", + "2024-10-07 16:16:04,346 - explainableai.model_evaluation - DEBUG - Evaluating report...\n", + "2024-10-07 16:16:04,353 - explainableai.model_evaluation - INFO - Report Generated...\n", + "2024-10-07 16:16:04,353 - explainableai.core - INFO - Calculating feature importance...\n", + "2024-10-07 16:16:04,353 - explainableai.core - DEBUG - Calculating the features...\n", + "2024-10-07 16:16:04,494 - explainableai.core - INFO - Features calculated...\n", + "2024-10-07 16:16:04,494 - explainableai.core - INFO - Generating visualizations...\n", + "2024-10-07 16:16:04,499 - explainableai.core - DEBUG - Generating visulatization...\n", + "2024-10-07 16:16:04,499 - explainableai.visualizations - DEBUG - Plotting feature importance...\n", + "2024-10-07 16:16:04,635 - explainableai.visualizations - INFO - Feature importance plot saved...\n", + "2024-10-07 16:16:04,635 - explainableai.visualizations - DEBUG - Plotting partial dependence...\n", + "2024-10-07 16:16:06,863 - explainableai.visualizations - INFO - Partial dependence plot saved...\n", + "2024-10-07 16:16:06,863 - explainableai.visualizations - DEBUG - Plotting learning curve...\n", + "2024-10-07 16:16:10,093 - explainableai.visualizations - INFO - Learning curve plot saved.\n", + "2024-10-07 16:16:10,093 - explainableai.visualizations - DEBUG - Plot correlation heatmap\n", + "2024-10-07 16:16:10,944 - explainableai.visualizations - DEBUG - Plotting roc curve...\n", + "2024-10-07 16:16:10,999 - explainableai.visualizations - INFO - Plotting roc curve successfully...\n", + "2024-10-07 16:16:10,999 - explainableai.visualizations - DEBUG - Plot precision recall curve...\n", + "2024-10-07 16:16:11,056 - explainableai.visualizations - INFO - Plot precision recall curve successfully...\n", + "2024-10-07 16:16:11,056 - explainableai.core - INFO - Visualizations generated.\n", + "2024-10-07 16:16:11,056 - explainableai.core - INFO - Calculating SHAP values...\n", + "2024-10-07 16:16:11,062 - explainableai.feature_analysis - DEBUG - Convert X to Dataframe...\n", + "2024-10-07 16:16:11,063 - explainableai.feature_analysis - ERROR - Error calculating SHAP values: Model type not yet supported by TreeExplainer: \n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 34, in calculate_shap_values\n", + " logger.error(\"Model type:\", type(model))\n", + "Message: 'Model type:'\n", + "Arguments: (,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 35, in calculate_shap_values\n", + " logger.error(\"X shape:\", X.shape)\n", + "Message: 'X shape:'\n", + "Arguments: ((455, 30),)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 36, in calculate_shap_values\n", + " logger.error(\"X dtype:\", X.dtypes)\n", + "Message: 'X dtype:'\n", + "Arguments: (mean radius float64\n", + "mean texture float64\n", + "mean perimeter float64\n", + "mean area float64\n", + "mean smoothness float64\n", + "mean compactness float64\n", + "mean concavity float64\n", + "mean concave points float64\n", + "mean symmetry float64\n", + "mean fractal dimension float64\n", + "radius error float64\n", + "texture error float64\n", + "perimeter error float64\n", + "area error float64\n", + "smoothness error float64\n", + "compactness error float64\n", + "concavity error float64\n", + "concave points error float64\n", + "symmetry error float64\n", + "fractal dimension error float64\n", + "worst radius float64\n", + "worst texture float64\n", + "worst perimeter float64\n", + "worst area float64\n", + "worst smoothness float64\n", + "worst compactness float64\n", + "worst concavity float64\n", + "worst concave points float64\n", + "worst symmetry float64\n", + "worst fractal dimension float64\n", + "dtype: object,)\n", + "--- Logging error ---\n", + "Traceback (most recent call last):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 17, in calculate_shap_values\n", + " explainer = shap.TreeExplainer(model)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 195, in __init__\n", + " self.model = TreeEnsemble(model, self.data, self.data_missing, model_output)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\shap\\explainers\\_tree.py\", line 1217, in __init__\n", + " raise InvalidModelError(\"Model type not yet supported by TreeExplainer: \" + str(type(model)))\n", + "shap.utils._exceptions.InvalidModelError: Model type not yet supported by TreeExplainer: \n", + "\n", + "During handling of the above exception, another exception occurred:\n", + "\n", + "Traceback (most recent call last):\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 1160, in emit\n", + " msg = self.format(record)\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 999, in format\n", + " return fmt.format(record)\n", + " ^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 703, in format\n", + " record.message = record.getMessage()\n", + " ^^^^^^^^^^^^^^^^^^^\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\logging\\__init__.py\", line 392, in getMessage\n", + " msg = msg % self.args\n", + " ~~~~^~~~~~~~~~~\n", + "TypeError: not all arguments converted during string formatting\n", + "Call stack:\n", + " File \"\", line 198, in _run_module_as_main\n", + " File \"\", line 88, in _run_code\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel_launcher.py\", line 18, in \n", + " app.launch_new_instance()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\traitlets\\config\\application.py\", line 1075, in launch_instance\n", + " app.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelapp.py\", line 739, in start\n", + " self.io_loop.start()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\tornado\\platform\\asyncio.py\", line 205, in start\n", + " self.asyncio_loop.run_forever()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 641, in run_forever\n", + " self._run_once()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\base_events.py\", line 1986, in _run_once\n", + " handle._run()\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\asyncio\\events.py\", line 88, in _run\n", + " self._context.run(self._callback, *self._args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 545, in dispatch_queue\n", + " await self.process_one()\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in process_one\n", + " await dispatch(*args)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 437, in dispatch_shell\n", + " await result\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 362, in execute_request\n", + " await super().execute_request(stream, ident, parent)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\kernelbase.py\", line 778, in execute_request\n", + " reply_content = await reply_content\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\ipkernel.py\", line 449, in do_execute\n", + " res = shell.run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\ipykernel\\zmqshell.py\", line 549, in run_cell\n", + " return super().run_cell(*args, **kwargs)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3075, in run_cell\n", + " result = self._run_cell(\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3130, in _run_cell\n", + " result = runner(coro)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\async_helpers.py\", line 128, in _pseudo_sync_runner\n", + " coro.send(None)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3334, in run_cell_async\n", + " has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3517, in run_ast_nodes\n", + " if await self.run_code(code, result, async_=asy):\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3577, in run_code\n", + " exec(code_obj, self.user_global_ns, self.user_ns)\n", + " File \"C:\\Users\\Shravya H Jain\\AppData\\Local\\Temp\\ipykernel_10140\\3974386392.py\", line 215, in \n", + " results = xai.analyze() # Perform the analysis\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\core.py\", line 153, in analyze\n", + " results['shap_values'] = calculate_shap_values(self.model, self.X, self.feature_names)\n", + " File \"c:\\Users\\Shravya H Jain\\Desktop\\explain\\explainableai\\.venv\\Lib\\site-packages\\explainableai\\feature_analysis.py\", line 37, in calculate_shap_values\n", + " logger.error(\"Feature names:\", feature_names)\n", + "Message: 'Feature names:'\n", + "Arguments: (['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'mean smoothness', 'mean compactness', 'mean concavity', 'mean concave points', 'mean symmetry', 'mean fractal dimension', 'radius error', 'texture error', 'perimeter error', 'area error', 'smoothness error', 'compactness error', 'concavity error', 'concave points error', 'symmetry error', 'fractal dimension error', 'worst radius', 'worst texture', 'worst perimeter', 'worst area', 'worst smoothness', 'worst compactness', 'worst concavity', 'worst concave points', 'worst symmetry', 'worst fractal dimension'],)\n", + "2024-10-07 16:16:11,135 - explainableai.core - INFO - Performing cross-validation...\n", + "2024-10-07 16:16:11,135 - explainableai.model_evaluation - DEBUG - Cross validation...\n", + "2024-10-07 16:16:12,197 - explainableai.model_evaluation - INFO - validated...\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - Model comparison results:\n", + "2024-10-07 16:16:12,197 - explainableai.core - DEBUG - Printing results...\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - \n", + "Model Performance:\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - accuracy: 1.0000\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - f1_score: 1.0000\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - confusion_matrix:\n", + "[[169 0]\n", + " [ 0 286]]\n", + "2024-10-07 16:16:12,197 - explainableai.core - INFO - classification_report:\n", + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 169\n", + " 1 1.00 1.00 1.00 286\n", + "\n", + " accuracy 1.00 455\n", + " macro avg 1.00 1.00 1.00 455\n", + "weighted avg 1.00 1.00 1.00 455\n", + "\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - \n", + "Top 5 Important Features:\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - worst symmetry: 0.0246\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - worst texture: 0.0207\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - worst concavity: 0.0189\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - worst concave points: 0.0169\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - radius error: 0.0167\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - \n", + "Cross-validation Score: 0.9758 (+/- 0.0128)\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - \n", + "Visualizations saved:\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - - Feature Importance: feature_importance.png\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - - Partial Dependence: partial_dependence.png\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - - Learning Curve: learning_curve.png\n", + "2024-10-07 16:16:12,208 - explainableai.core - INFO - - Correlation Heatmap: correlation_heatmap.png\n", + "2024-10-07 16:16:12,215 - explainableai.core - INFO - - ROC Curve: roc_curve.png\n", + "2024-10-07 16:16:12,215 - explainableai.core - INFO - - Precision-Recall Curve: precision_recall_curve.png\n", + "2024-10-07 16:16:12,215 - explainableai.core - INFO - \n", + "SHAP values calculation failed. Please check the console output for more details.\n", + "2024-10-07 16:16:12,216 - explainableai.core - INFO - Generating LLM explanation...\n", + "2024-10-07 16:16:12,216 - explainableai.llm_explanations - DEBUG - Generate content...\n", + "2024-10-07 16:16:12,466 - explainableai.llm_explanations - ERROR - Some error in generating response... 400 API key not valid. Please pass a valid API key. [reason: \"API_KEY_INVALID\"\n", + "domain: \"googleapis.com\"\n", + "metadata {\n", + " key: \"service\"\n", + " value: \"generativelanguage.googleapis.com\"\n", + "}\n", + "]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Results for Neural Network (MLP):\n", + "{'model_performance': {'accuracy': 1.0, 'f1_score': np.float64(1.0), 'confusion_matrix': array([[169, 0],\n", + " [ 0, 286]]), 'classification_report': ' precision recall f1-score support\\n\\n 0 1.00 1.00 1.00 169\\n 1 1.00 1.00 1.00 286\\n\\n accuracy 1.00 455\\n macro avg 1.00 1.00 1.00 455\\nweighted avg 1.00 1.00 1.00 455\\n'}, 'feature_importance': {'worst symmetry': np.float64(0.02461538461538464), 'worst texture': np.float64(0.020659340659340684), 'worst concavity': np.float64(0.018901098901098902), 'worst concave points': np.float64(0.01692307692307692), 'radius error': np.float64(0.016703296703296712), 'worst area': np.float64(0.01428571428571428), 'compactness error': np.float64(0.013186813186813163), 'area error': np.float64(0.011428571428571399), 'worst radius': np.float64(0.009670329670329648), 'worst smoothness': np.float64(0.009670329670329648), 'worst perimeter': np.float64(0.00857142857142854), 'mean texture': np.float64(0.008351648351648321), 'mean concavity': np.float64(0.007252747252747227), 'perimeter error': np.float64(0.00659340659340657), 'mean radius': np.float64(0.006153846153846132), 'fractal dimension error': np.float64(0.006153846153846132), 'texture error': np.float64(0.005714285714285694), 'mean compactness': np.float64(0.003736263736263723), 'mean concave points': np.float64(0.003076923076923066), 'symmetry error': np.float64(0.002417582417582409), 'worst compactness': np.float64(0.00219780219780219), 'concave points error': np.float64(0.001978021978021971), 'worst fractal dimension': np.float64(0.001978021978021971), 'mean symmetry': np.float64(0.001758241758241752), 'smoothness error': np.float64(0.001318681318681314), 'mean fractal dimension': np.float64(0.001098901098901095), 'mean smoothness': np.float64(0.000879120879120876), 'mean perimeter': np.float64(0.000659340659340657), 'concavity error': np.float64(0.000659340659340657), 'mean area': np.float64(0.000219780219780219)}, 'shap_values': None, 'cv_scores': (np.float64(0.9758241758241759), np.float64(0.01281527888976989)), 'model_comparison': {'Model': {'cv_score': np.float64(0.9943240454076367), 'test_score': 1.0}}, 'llm_explanation': None}\n", + "\n", + "Performing XAI Analysis on PyTorch Model using Captum:\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "from xgboost import XGBClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.model_selection import GridSearchCV, train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.datasets import load_breast_cancer\n", + "from sklearn.metrics import accuracy_score\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "from torch.utils.data import TensorDataset, DataLoader\n", + "\n", + "# Attempt to import XAIWrapper; handle if not available\n", + "try:\n", + " from explainableai import XAIWrapper\n", + "except ImportError:\n", + " XAIWrapper = None\n", + " print(\"XAIWrapper not found. Make sure the 'explainableai' package is installed.\")\n", + "\n", + "# Attempt to import Captum for PyTorch XAI; handle if not available\n", + "try:\n", + " from captum.attr import IntegratedGradients\n", + " import matplotlib.pyplot as plt\n", + "except ImportError:\n", + " IntegratedGradients = None\n", + " plt = None\n", + " print(\"Captum or matplotlib not found. Install them using 'pip install captum matplotlib'.\")\n", + "\n", + "# Load dataset\n", + "data = load_breast_cancer()\n", + "X = data.data\n", + "y = data.target\n", + "\n", + "# Split data into training and test sets (80-20 split)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Feature scaling (standardization)\n", + "scaler = StandardScaler()\n", + "X_train_scaled = scaler.fit_transform(X_train)\n", + "X_test_scaled = scaler.transform(X_test)\n", + "\n", + "# Define Logistic Regression Pipeline without scaler (already scaled)\n", + "logistic_regression_pipeline = Pipeline([\n", + " ('logistic_regression', LogisticRegression(solver='saga', max_iter=5000))\n", + "])\n", + "\n", + "# Define parameter grid for Logistic Regression with conditional parameters\n", + "logistic_regression_params = [\n", + " {\n", + " 'logistic_regression__penalty': ['l2'],\n", + " 'logistic_regression__C': [0.01, 0.1, 1, 10],\n", + " 'logistic_regression__solver': ['saga'],\n", + " 'logistic_regression__max_iter': [5000]\n", + " },\n", + " {\n", + " 'logistic_regression__penalty': ['elasticnet'],\n", + " 'logistic_regression__C': [0.01, 0.1, 1, 10],\n", + " 'logistic_regression__solver': ['saga'],\n", + " 'logistic_regression__l1_ratio': [0.1, 0.5, 0.9],\n", + " 'logistic_regression__max_iter': [5000]\n", + " }\n", + "]\n", + "\n", + "# Initialize GridSearchCV for Logistic Regression\n", + "logistic_regression_gs = GridSearchCV(\n", + " logistic_regression_pipeline, \n", + " param_grid=logistic_regression_params,\n", + " cv=5,\n", + " verbose=0,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# Initialize GridSearchCV for Random Forest\n", + "random_forest_gs = GridSearchCV(\n", + " RandomForestClassifier(random_state=42),\n", + " param_grid={\n", + " 'n_estimators': [1000, 2000, 3000],\n", + " 'max_depth': [10, 20, 30, None],\n", + " 'min_samples_split': [2, 5, 10],\n", + " 'min_samples_leaf': [1, 2, 4]\n", + " },\n", + " cv=5,\n", + " verbose=0,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# Initialize GridSearchCV for XGBoost\n", + "xgboost_gs = GridSearchCV(\n", + " XGBClassifier(random_state=42, use_label_encoder=False, eval_metric='logloss'),\n", + " param_grid={\n", + " 'n_estimators': [1000, 2000, 3000],\n", + " 'learning_rate': [0.01, 0.1, 0.3],\n", + " 'max_depth': [3, 6, 9],\n", + " 'subsample': [0.7, 0.8, 1.0]\n", + " },\n", + " cv=5,\n", + " verbose=0,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# Initialize GridSearchCV for MLPClassifier\n", + "mlp_gs = GridSearchCV(\n", + " MLPClassifier(random_state=42),\n", + " param_grid={\n", + " 'hidden_layer_sizes': [(100, 50), (128, 64, 32)],\n", + " 'activation': ['relu', 'tanh'],\n", + " 'solver': ['adam'],\n", + " 'alpha': [0.0001, 0.001],\n", + " 'learning_rate': ['constant', 'adaptive'],\n", + " 'max_iter': [3000]\n", + " },\n", + " cv=5,\n", + " verbose=0,\n", + " n_jobs=-1\n", + ")\n", + "\n", + "# Fit the models (this may take some time due to extensive hyperparameter grids)\n", + "print(\"Fitting Logistic Regression...\")\n", + "logistic_regression_gs.fit(X_train_scaled, y_train)\n", + "print(\"Fitting Random Forest...\")\n", + "random_forest_gs.fit(X_train_scaled, y_train)\n", + "print(\"Fitting XGBoost...\")\n", + "xgboost_gs.fit(X_train_scaled, y_train)\n", + "print(\"Fitting MLP Classifier...\")\n", + "mlp_gs.fit(X_train_scaled, y_train)\n", + "\n", + "# Store best models in a dictionary\n", + "models = {\n", + " 'Logistic Regression': logistic_regression_gs.best_estimator_,\n", + " 'Random Forest': random_forest_gs.best_estimator_,\n", + " 'XGBoost': xgboost_gs.best_estimator_,\n", + " 'Neural Network (MLP)': mlp_gs.best_estimator_\n", + "}\n", + "\n", + "# Evaluate the models on the test set\n", + "print(\"\\nEvaluating Models on Test Set:\")\n", + "for model_name, model in models.items():\n", + " if model is not None:\n", + " accuracy = model.score(X_test_scaled, y_test) # Calculate test accuracy\n", + " print(f\"{model_name} Test Accuracy: {accuracy * 100:.2f}%\")\n", + " else:\n", + " print(f\"{model_name} is None and was not evaluated.\")\n", + "\n", + "# Define a simple PyTorch neural network\n", + "class SimpleNN(nn.Module):\n", + " def __init__(self, input_size):\n", + " super(SimpleNN, self).__init__()\n", + " self.fc1 = nn.Linear(input_size, 64)\n", + " self.fc2 = nn.Linear(64, 32)\n", + " self.fc3 = nn.Linear(32, 1)\n", + "\n", + " def forward(self, x):\n", + " x = torch.relu(self.fc1(x))\n", + " x = torch.relu(self.fc2(x))\n", + " x = torch.sigmoid(self.fc3(x))\n", + " return x\n", + "\n", + "# Prepare data for PyTorch\n", + "X_train_tensor = torch.tensor(X_train_scaled, dtype=torch.float32)\n", + "y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)\n", + "X_test_tensor = torch.tensor(X_test_scaled, dtype=torch.float32)\n", + "y_test_tensor = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)\n", + "\n", + "# Create DataLoader for training\n", + "train_dataset = TensorDataset(X_train_tensor, y_train_tensor)\n", + "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", + "\n", + "# Initialize and train the PyTorch model\n", + "input_size = X.shape[1]\n", + "torch_model = SimpleNN(input_size)\n", + "criterion = nn.BCELoss()\n", + "optimizer = optim.Adam(torch_model.parameters(), lr=0.001)\n", + "\n", + "print(\"\\nTraining PyTorch Model...\")\n", + "# Training Loop\n", + "for epoch in range(100): # Adjust the number of epochs as needed\n", + " for batch_X, batch_y in train_loader:\n", + " optimizer.zero_grad()\n", + " outputs = torch_model(batch_X)\n", + " loss = criterion(outputs, batch_y)\n", + " loss.backward()\n", + " optimizer.step()\n", + " if (epoch + 1) % 10 == 0:\n", + " print(f\"Epoch [{epoch+1}/100], Loss: {loss.item():.4f}\")\n", + "\n", + "# Evaluate the PyTorch model\n", + "with torch.no_grad():\n", + " torch_model.eval()\n", + " test_outputs = torch_model(X_test_tensor)\n", + " predicted = (test_outputs > 0.5).float()\n", + " accuracy = (predicted.view(-1) == y_test_tensor.view(-1)).float().mean().item()\n", + " print(f\"PyTorch Model Test Accuracy: {accuracy * 100:.2f}%\")\n", + "\n", + "# XAI Analysis with XAIWrapper for Scikit-learn models\n", + "if XAIWrapper is not None:\n", + " try:\n", + " # Create an instance of XAIWrapper\n", + " xai = XAIWrapper()\n", + "\n", + " # Convert scaled training data back to DataFrame with column names\n", + " X_train_df = pd.DataFrame(X_train_scaled, columns=data.feature_names)\n", + "\n", + " print(\"\\nPerforming XAI Analysis on Scikit-learn Models:\")\n", + " # Perform XAI analysis on the fitted Scikit-learn models\n", + " for model_name, model in models.items():\n", + " if model is not None:\n", + " print(f\"\\nAnalyzing {model_name}...\")\n", + " if hasattr(model, 'predict'):\n", + " xai.fit(model, X_train_df, y_train)\n", + " results = xai.analyze() # Perform the analysis\n", + " print(f\"Results for {model_name}:\\n{results}\")\n", + " else:\n", + " print(f\"Skipping {model_name}: Model does not have a predict method.\")\n", + " except Exception as e:\n", + " print(f\"An error occurred during the XAI analysis for Scikit-learn models: {e}\")\n", + "else:\n", + " print(\"\\nXAIWrapper is not available. Skipping XAI analysis for Scikit-learn models.\")\n", + "\n", + "# XAI Analysis for PyTorch model using Captum\n", + "if IntegratedGradients is not None and plt is not None:\n", + " def pytorch_xai_analysis(model, X_test, y_test):\n", + " try:\n", + " model.eval()\n", + " ig = IntegratedGradients(model)\n", + " # Select a sample or a subset for analysis\n", + " sample = X_test[:1] # Analyze the first test sample\n", + " attributions, delta = ig.attribute(sample, target=0, return_convergence_delta=True)\n", + " attributions = attributions.squeeze().numpy()\n", + "\n", + " # Plot attributions\n", + " feature_names = data.feature_names\n", + " plt.figure(figsize=(10, 8))\n", + " plt.barh(feature_names, attributions)\n", + " plt.xlabel('Attribution')\n", + " plt.title('Integrated Gradients Attribution for PyTorch Model (Sample 1)')\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + " except Exception as e:\n", + " print(f\"An error occurred during the PyTorch XAI analysis: {e}\")\n", + "\n", + " print(\"\\nPerforming XAI Analysis on PyTorch Model using Captum:\")\n", + " pytorch_xai_analysis(torch_model, X_test_tensor, y_test_tensor)\n", + "else:\n", + " print(\"\\nCaptum or matplotlib not available. Skipping XAI analysis for PyTorch model.\")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/explaianble.ipynb b/notebooks/.ipynb_checkpoints/explaianble(0_1_6)-checkpoint.ipynb similarity index 96% rename from notebooks/explaianble.ipynb rename to notebooks/.ipynb_checkpoints/explaianble(0_1_6)-checkpoint.ipynb index 4571545..73e77d4 100644 --- a/notebooks/explaianble.ipynb +++ b/notebooks/.ipynb_checkpoints/explaianble(0_1_6)-checkpoint.ipynb @@ -1,4 +1,18 @@ { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, "cells": [ { "cell_type": "code", @@ -13,8 +27,8 @@ }, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Collecting explainableai==0.1.6\n", " Downloading explainableai-0.1.6-py3-none-any.whl.metadata (4.2 kB)\n", @@ -152,18 +166,18 @@ ] }, { + "output_type": "display_data", "data": { "application/vnd.colab-display-data+json": { - "id": "d7ebf4861eee40d39c91d0a0023a4ab0", "pip_warning": { "packages": [ "explainableai" ] - } + }, + "id": "d7ebf4861eee40d39c91d0a0023a4ab0" } }, - "metadata": {}, - "output_type": "display_data" + "metadata": {} } ], "source": [ @@ -173,11 +187,6 @@ }, { "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "7gfkIrfmlIHU" - }, - "outputs": [], "source": [ "# Step 2: Import necessary libraries\n", "import pandas as pd\n", @@ -189,32 +198,31 @@ "from xgboost import XGBClassifier\n", "from sklearn.neural_network import MLPClassifier\n", "from explainableai import XAIWrapper\n" - ] + ], + "metadata": { + "id": "7gfkIrfmlIHU" + }, + "execution_count": 1, + "outputs": [] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "Zn3qSNkFlLjs" - }, - "outputs": [], "source": [ - "# Import the breast cancer dataset from sklearn.datasets\n", "data = load_breast_cancer()\n", - "\n", - "# Create a DataFrame 'X' from the data features\n", - "# 'data.data' contains the feature values \n", - "# 'data.feature_names' contains the column names (feature names)\n", "X = pd.DataFrame(data.data, columns=data.feature_names)\n", - "\n", - "# Create a Series 'y' for the target variable (dependent variable)\n", - "# 'data.target' contains the target labels (0 = malignant, 1 = benign)\n", - "y = pd.Series(data.target, name='target')" - ] + "y = pd.Series(data.target, name='target')\n" + ], + "metadata": { + "id": "Zn3qSNkFlLjs" + }, + "execution_count": 2, + "outputs": [] }, { "cell_type": "code", - "execution_count": 3, + "source": [ + "print(data)" + ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -222,10 +230,11 @@ "id": "cGt9vHVMln2R", "outputId": "735432b4-ac1b-4a5d-e1c8-923c6ef0d00d" }, + "execution_count": 3, "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "{'data': array([[1.799e+01, 1.038e+01, 1.228e+02, ..., 2.654e-01, 4.601e-01,\n", " 1.189e-01],\n", @@ -276,15 +285,14 @@ " 'worst symmetry', 'worst fractal dimension'], dtype='