├── .DS_Store ├── .ipynb_checkpoints ├── Isolation_forest-checkpoint.ipynb ├── Lasso, Ridge and Elastic Net-checkpoint.ipynb ├── PCA-checkpoint.ipynb ├── Simple Neural Network from the Scratch-checkpoint.ipynb ├── Untitled-checkpoint.ipynb └── XGBoost-IRIS-checkpoint.ipynb ├── Auto ARIMA hyperparameter search.ipynb ├── Breath_cancer_with_shap_values.ipynb ├── DBScan clustering algorithm.ipynb ├── Isolation_forest.ipynb ├── K-means clustering.ipynb ├── LICENSE ├── Lasso, Ridge and Elastic Net.ipynb ├── Linear Programming ├── .ipynb_checkpoints │ └── Linear_programming_with_gurobipy_teachers_example-checkpoint.ipynb ├── Linear_programming_with_gurobipy_teachers_example.ipynb └── TEACHERS.lp ├── Online_Payments_Fraud_Detection.ipynb ├── PCA.ipynb ├── Polynomial Regression.ipynb ├── README.md ├── Random Forest.ipynb ├── Rule_based_approach for removing the outliers.ipynb ├── Simple Neural Network from the Scratch.ipynb ├── Timeseries comprehensive.ipynb ├── WordEmbeddingsLecture.ipynb ├── XGBoost-IRIS.ipynb ├── XGBoost.ipynb ├── __pycache__ └── planar_utils.cpython-38.pyc ├── cost_sensitive1.png ├── pictures └── lasso.png ├── planar_utils.py ├── tree.dot └── tree.png /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vitomir84/ML_algorithms/3082d7ca60dec864758c58bb27455f13da3a4c76/.DS_Store -------------------------------------------------------------------------------- /.ipynb_checkpoints/PCA-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /Isolation_forest.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "6c4de066", 6 | "metadata": {}, 7 | "source": [ 8 | "## Isolation Forest algorithm" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "bff40f41", 14 | "metadata": {}, 15 | "source": [ 16 | "Isolation Forest, like any tree ensemble method, is built on the basis of decision trees. \n", 17 | "In these trees, partitions are created by first randomly selecting a feature and then selecting a random split value \n", 18 | "between the minimum and maximum value of the selected feature.\n", 19 | "In principle, outliers are less frequent than regular observations \n", 20 | "and are different from them in terms of values (they lie further away from the regular observations in the feature space). \n", 21 | "That is why by using such random partitioning they should be identified closer to the root of the tree (shorter average path length, i.e., the number of edges an observation must pass in the tree going from the root to the terminal node), with fewer splits necessary.\n", 22 | "\n", 23 | "In the following example we will use the ISO forest algorithm on a famous boston data set to detect the cities with the highest crime rate." 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 1, 29 | "id": "d5a6704a", 30 | "metadata": {}, 31 | "outputs": [], 32 | "source": [ 33 | "from sklearn.datasets import load_boston\n", 34 | "import numpy as np\n", 35 | "import pandas as pd\n", 36 | "import matplotlib.pyplot as plt\n", 37 | "import warnings" 38 | ] 39 | }, 40 | { 41 | "cell_type": "code", 42 | "execution_count": 2, 43 | "id": "0c5f7e8c", 44 | "metadata": { 45 | "scrolled": true 46 | }, 47 | "outputs": [], 48 | "source": [ 49 | "boston =load_boston()\n", 50 | "names=list(boston['feature_names'])\n", 51 | "X=boston['data']\n", 52 | "df = pd.DataFrame(X, columns=names)" 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 3, 58 | "id": "7e82f2cd", 59 | "metadata": {}, 60 | "outputs": [ 61 | { 62 | "name": "stdout", 63 | "output_type": "stream", 64 | "text": [ 65 | ".. _boston_dataset:\n", 66 | "\n", 67 | "Boston house prices dataset\n", 68 | "---------------------------\n", 69 | "\n", 70 | "**Data Set Characteristics:** \n", 71 | "\n", 72 | " :Number of Instances: 506 \n", 73 | "\n", 74 | " :Number of Attributes: 13 numeric/categorical predictive. Median Value (attribute 14) is usually the target.\n", 75 | "\n", 76 | " :Attribute Information (in order):\n", 77 | " - CRIM per capita crime rate by town\n", 78 | " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", 79 | " - INDUS proportion of non-retail business acres per town\n", 80 | " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", 81 | " - NOX nitric oxides concentration (parts per 10 million)\n", 82 | " - RM average number of rooms per dwelling\n", 83 | " - AGE proportion of owner-occupied units built prior to 1940\n", 84 | " - DIS weighted distances to five Boston employment centres\n", 85 | " - RAD index of accessibility to radial highways\n", 86 | " - TAX full-value property-tax rate per $10,000\n", 87 | " - PTRATIO pupil-teacher ratio by town\n", 88 | " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", 89 | " - LSTAT % lower status of the population\n", 90 | " - MEDV Median value of owner-occupied homes in $1000's\n", 91 | "\n", 92 | " :Missing Attribute Values: None\n", 93 | "\n", 94 | " :Creator: Harrison, D. and Rubinfeld, D.L.\n", 95 | "\n", 96 | "This is a copy of UCI ML housing dataset.\n", 97 | "https://archive.ics.uci.edu/ml/machine-learning-databases/housing/\n", 98 | "\n", 99 | "\n", 100 | "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", 101 | "\n", 102 | "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", 103 | "prices and the demand for clean air', J. Environ. Economics & Management,\n", 104 | "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", 105 | "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", 106 | "pages 244-261 of the latter.\n", 107 | "\n", 108 | "The Boston house-price data has been used in many machine learning papers that address regression\n", 109 | "problems. \n", 110 | " \n", 111 | ".. topic:: References\n", 112 | "\n", 113 | " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", 114 | " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", 115 | "\n" 116 | ] 117 | } 118 | ], 119 | "source": [ 120 | "print(boston.DESCR)" 121 | ] 122 | }, 123 | { 124 | "cell_type": "code", 125 | "execution_count": 4, 126 | "id": "77654100", 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [ 130 | "from sklearn.ensemble import IsolationForest\n", 131 | "\n", 132 | "def iso_forest_detection(df, outliers_fraction, target_feature:str, plotting = True):\n", 133 | " x=df[target_feature].to_numpy().reshape(len(df[target_feature]),-1)\n", 134 | " model = IsolationForest(contamination=outliers_fraction)\n", 135 | " model.fit(x)\n", 136 | " predictions = model.predict(x) # anomalies are -1\n", 137 | " df[target_feature+'_anomalies'] = pd.Series(predictions, index = df.index)\n", 138 | " decision_function = model.decision_function(x)\n", 139 | " df['decision_function'] = pd.Series(decision_function, index = df.index)\n", 140 | " \n", 141 | " if plotting:\n", 142 | " a = df.loc[df[target_feature+'_anomalies'] == -1] #anomaly\n", 143 | " plt.figure(figsize=(6,6))\n", 144 | " plt.plot(df[target_feature], color='blue', label= f'original {target_feature}')\n", 145 | " plt.plot(a[target_feature], linestyle='none', marker='X', color='red', markersize=8, label='Anomaly')\n", 146 | " plt.plot(df['decision_function'], color='green', label='decision function', alpha=0.3) # The anomaly score of the input samples.\n", 147 | " # The lower, the more abnormal. Negative scores represent outliers,\n", 148 | " # positive scores represent inliers.\n", 149 | " plt.xlabel('Rows')\n", 150 | " plt.ylabel(f'{target_feature}value')\n", 151 | " plt.title('Anomalies')\n", 152 | " plt.legend(loc='best')\n", 153 | " plt.show()\n", 154 | " return df" 155 | ] 156 | }, 157 | { 158 | "cell_type": "code", 159 | "execution_count": 5, 160 | "id": "4fc946c6", 161 | "metadata": {}, 162 | "outputs": [], 163 | "source": [ 164 | "#Trying isolation forest\n", 165 | "\n", 166 | "# Import IsolationForest\n", 167 | "from sklearn.ensemble import IsolationForest\n", 168 | "# Assume that 13% of the entire data set are anomalies\n", 169 | " \n", 170 | "outliers_fraction = 0.01\n", 171 | "x=df['CRIM'].to_numpy().reshape(506,-1)\n", 172 | "model = IsolationForest(contamination=outliers_fraction)\n", 173 | "model.fit(x)\n", 174 | "predictions = model.predict(x) # anomalies are -1\n", 175 | "# predictions = predictions.reshape(2375,-1)\n", 176 | "df['CRIM_anomalies'] = pd.Series(predictions, index = df.index)\n", 177 | "decision_function = model.decision_function(x)\n", 178 | "df['decision_function'] = pd.Series(decision_function, index = df.index)\n" 179 | ] 180 | }, 181 | { 182 | "cell_type": "code", 183 | "execution_count": 6, 184 | "id": "8541aaf0", 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "data": { 189 | "image/png": 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" 192 | ] 193 | }, 194 | "metadata": { 195 | "needs_background": "light" 196 | }, 197 | "output_type": "display_data" 198 | } 199 | ], 200 | "source": [ 201 | "df['CRIM'].hist(bins=40);" 202 | ] 203 | }, 204 | { 205 | "cell_type": "code", 206 | "execution_count": 7, 207 | "id": "9e355b74", 208 | "metadata": { 209 | "scrolled": true 210 | }, 211 | "outputs": [ 212 | { 213 | "data": { 214 | "image/png": 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\n", 215 | "text/plain": [ 216 | "
" 217 | ] 218 | }, 219 | "metadata": { 220 | "needs_background": "light" 221 | }, 222 | "output_type": "display_data" 223 | } 224 | ], 225 | "source": [ 226 | "a = df.loc[df['CRIM_anomalies'] == -1] #anomaly\n", 227 | "plt.figure(figsize=(6,4))\n", 228 | "plt.plot(df['CRIM'], color='blue', label='original CRIM ')\n", 229 | "plt.plot(a['CRIM'], linestyle='none', marker='X', color='red', markersize=8, label='Anomaly - cities with the highest crime rate')\n", 230 | "plt.plot(df['decision_function'], color='green', label='decision function', alpha=0.3) # The anomaly score of the input samples.\n", 231 | "# The lower, the more abnormal. Negative scores represent outliers,\n", 232 | "# positive scores represent inliers.\n", 233 | "plt.xlabel('Rows')\n", 234 | "plt.ylabel('CRIM value')\n", 235 | "plt.title('Anomalies')\n", 236 | "plt.legend(loc='best')\n", 237 | "plt.show()" 238 | ] 239 | }, 240 | { 241 | "cell_type": "code", 242 | "execution_count": 8, 243 | "id": "4777da29", 244 | "metadata": {}, 245 | "outputs": [ 246 | { 247 | "data": { 248 | "text/plain": [ 249 | "380 88.9762\n", 250 | "404 41.5292\n", 251 | "405 67.9208\n", 252 | "410 51.1358\n", 253 | "414 45.7461\n", 254 | "418 73.5341\n", 255 | "Name: CRIM, dtype: float64" 256 | ] 257 | }, 258 | "execution_count": 8, 259 | "metadata": {}, 260 | "output_type": "execute_result" 261 | } 262 | ], 263 | "source": [ 264 | "# cities with the highest crime rate\n", 265 | "a['CRIM']" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 9, 271 | "id": "66c1ce66", 272 | "metadata": {}, 273 | "outputs": [ 274 | { 275 | "data": { 276 | "image/png": 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Jp2CRgTILEZFkChYplFmI1D7XXXcdt9xyS5XWPfbYY8v9fPjw4WzYsKFK2060du1aBgwYQJ8+fZgzZ061txdz//33s3LlytL3559/PosXL87Z9itKo6EyUGYhUn+89tpr5X4+Y8aMnHzPCy+8wBFHHMEDDzyQk+3F3H///fTs2ZP9998fgHvuuSen268oZRYplFmI1A6TJk3i8MMP5+STT2bp0qWl5R9++CHDhg2jX79+DBw4sPTGSKtXr2bMmDH07t2b3r17lwaJ2M2LVq1axaBBgyguLqZnz56lZ/9du3bl888/B+DWW2+lZ8+e9OzZs3Raj+XLl9O9e3cuuOACevTowSmnnMKXX36ZVNeFCxdyxRVXMGPGDIqLi/nyyy+Tbpr06KOPMiGa+mTChAn84Ac/4Nhjj+Xggw/m0UcfLV3u5ptvplevXvTu3ZurrrqKRx99lHnz5nHWWWeVbnfIkCHELjieOnUqvXr1omfPnlx55ZWl22nZsiU//elP6d27N0cffXTapIhVocwiA2UWInGL1ixi446NOd1mm2Zt6NGx7LmV5s+fz7Rp03j77bfZtWsXffv2pV+/fgBMnDiRP/zhDxx66KHMnTuXiy++mBdffJEf/OAHDB48mOnTp7N79262bNmStM2HH36YoUOH8tOf/pTdu3ezLWXyw/nz5zN58mTmzp2LuzNgwAAGDx5M27Zt+eCDD5g6dSp33303//mf/8ljjz3Gd7/73dJ1i4uL+cUvfsG8efP43e9+l3X/V61axSuvvML777/PyJEj+da3vsWzzz7L448/zty5c2nRogXr16+nXbt2/O53v+OWW26hpCT5ouqVK1dy5ZVXMn/+fNq2bcspp5zC448/zujRo9m6dStHH300kyZN4oorruDuu+/mZz/7WdZ6lUfBIoWGzooU3pw5cxgzZgwtonmmYlOMb9myhddee40zzjijdNnYTY5efPFFHnzwQSDMRpt6lfVRRx3Feeedx86dOxk9ejTFxcVJn7/yyiuMGTOGvffeG4DTTz+dOXPmMHLkSLp161a6fL9+/VhezSvUR48eTaNGjTjyyCNLz/pnzpzJueeeW7rP7dq1K3cbb731FkOGDKFDhw5AmN9q9uzZjB49mqZNmzJixIjS+j7//PPVqi8oWKRRM5RIsvIygHyyDP+Me/bsYZ999mHhwoWV3t6gQYOYPXs2zzzzDGeffTY/+clPOOecc0o/L2+evNRp0VOboTJJrH/qdOeJ24t9r7tn3OeylFffoqKi0m3FpnGvLvVZZKDMQqSwBg0axPTp0/nyyy/ZvHkzTz31FACtW7emW7du/OUvfwHCAfOdd94B4KSTTiq9O97u3bvZtGlT0jY/+eQTOnbsyAUXXMD3vve90jvvJX7n448/zrZt29i6dSvTp09n4MCBVd6HTp06sWTJEvbs2cP06dOzLn/KKadw3333lTaPrV+/HoBWrVqxefPmtOUHDBjAyy+/zOeff87u3buZOnVqpaZlrywFixTKLEQKr2/fvnz729+muLiYsWPHJh20p0yZwr333kvv3r3p0aNH6X2xb7/9dmbNmkWvXr3o168fixYtStrmSy+9RHFxMX369OGxxx7jhz/8Ydp3Tpgwgf79+zNgwADOP/98+vTpU+V9uOmmmxgxYgQnnngi++23X9blhw0bxsiRIykpKaG4uLh0qPCECRO48MILSzu4Y/bbbz9uvPFGTjjhBHr37k3fvn0ZNWpUleubjaYoTzFwIBQVwYsv5qFSInVEpaYolzqrMlOUK7NIocxCRCSdgkUG9TTZEhGpMgWLFBo6KyKSTsEihZqhRIL62p8pQWV/vwoWGeh/RBq65s2bs27dOgWMesrdWbduHc2bN6/wOrooL4WaoUSgS5curFixgrVr1xa6KpInzZs3p0uXLhVeXsEiAwULaeiKioro1q1boashtYiaoVKoz0JEJJ2CRQbKLEREkilYpFBmISKSTsEiA2UWIiLJFCxSaDSUiEi6vAULMzvAzGaZ2RIzW2RmP4zKrzOzf5nZwugxPGGdq81smZktNbOhCeX9zOzd6LM7rDKTvle63vnasohI3ZXPobO7gMvdfYGZtQLmm1nsdk2/cfdbEhc2syOBcUAPYH9gppkd5u67gbuAicAbwAxgGPBsviquzEJEJFneMgt3X+XuC6LXm4ElQOdyVhkFTHP3He7+MbAM6G9m+wGt3f11D5eTPgiMzle9lVmIiKSrkT4LM+sK9AHmRkX/z8z+YWb3mVnbqKwz8FnCaiuiss7R69TyvFFmISKSLO/BwsxaAo8Bl7r7JkKT0iFAMbAK+HVs0Qyreznlmb5ropnNM7N5VZ2mQJmFiEi6vAYLMysiBIop7v5XAHdf7e673X0PcDfQP1p8BXBAwupdgJVReZcM5Wnc/U/uXuLuJR06dKhyvZVZiIgky+doKAPuBZa4+60J5Yk3ox0DvBe9fhIYZ2bNzKwbcCjwpruvAjab2dHRNs8BnshfvRUsRERS5XM01HHA2cC7ZrYwKrsGONPMiglNScuB/wJw90Vm9giwmDCS6pJoJBTARcD9wF6EUVB5GwmlZigRkXR5Cxbu/gqZ+xtmlLPOJGBShvJ5QM/c1a58yixERJLpCu4UyixERNIpWGSgzEJEJJmCRQplFiIi6RQsMlBmISKSTMEihTILEZF0ChYZKLMQEUmmYJFCF+WJiKRTsEihZigRkXQKFhkosxARSaZgkUKZhYhIOgWLDJRZiIgkU7BIocxCRCSdgkUGyixERJIpWKTQ0FkRkXQKFinUDCUikk7BIgNlFiIiyRQsUiizEBFJp2CRgTILEZFkChYplFmIiKRTsMhAmYWISDIFixTKLERE0ilYZKDMQkQkmYJFCl2UJyKSTsEihZqhRETSKVhkoMxCRCSZgkUKZRYiIukULDJQZiEikkzBIoUyCxGRdAoWGSizEBFJpmCRQkNnRUTSKVikUDOUiEg6BYsMlFmIiCRTsEihzEJEJJ2CRQbKLEREkilYpFBmISKSTsEiA2UWIiLJFCxSKLMQEUmnYJGBMgsRkWQKFil0UZ6ISDoFixRqhhIRSadgkYEyCxGRZHkLFmZ2gJnNMrMlZrbIzH4Ylbczs+fN7IPouW3COleb2TIzW2pmQxPK+5nZu9Fnd5jl7/xfmYWISLp8Zha7gMvdvTtwNHCJmR0JXAW84O6HAi9E74k+Gwf0AIYBvzezxtG27gImAodGj2F5rLcyCxGRFHkLFu6+yt0XRK83A0uAzsAo4IFosQeA0dHrUcA0d9/h7h8Dy4D+ZrYf0NrdX3d3Bx5MWCfnlFmIiKSrkT4LM+sK9AHmAp3cfRWEgAJ0jBbrDHyWsNqKqKxz9Dq1PNP3TDSzeWY2b+3atVWurzILEZFkeQ8WZtYSeAy41N03lbdohjIvpzy90P1P7l7i7iUdOnSofGXR0FkRkUzyGizMrIgQKKa4+1+j4tVR0xLR85qofAVwQMLqXYCVUXmXDOV5qnO+tiwiUnflczSUAfcCS9z91oSPngTGR6/HA08klI8zs2Zm1o3Qkf1m1FS12cyOjrZ5TsI6eaHMQkQkWZM8bvs44GzgXTNbGJVdA9wEPGJm3wM+Bc4AcPdFZvYIsJgwkuoSd98drXcRcD+wF/Bs9MgLZRYiIunyFizc/RUy9zcAnFTGOpOASRnK5wE9c1e78imzEBFJpiu4UyizEBFJp2CRgTILEZFkChYpNHRWRCSdgkUKNUOJiKRTsMhAmYWISDIFixTKLERE0ilYZKDMQkQkmYJFCmUWIiLpFCwyUGYhIpJMwSKFMgsRkXQKFhkosxARSaZgkUIX5YmIpFOwSKFmKBGRdAoWGSizEBFJpmCRQpmFiEg6BYsMlFmIiCRTsEihzEJEJJ2CRQbKLEREkilYpNDQWRGRdAoWKdQMJSKSTsEiA2UWIiLJFCxSKLMQEUmXNViYWSczu9fMno3eH2lm38t/1QpHmYWISLKKZBb3A88B+0fv/wlcmqf6FJwyCxGRdBUJFvu6+yPAHgB33wXszmutCkyZhYhIsooEi61m1h5wADM7GtiY11oVkDILEZF0TSqwzGXAk8AhZvYq0AH4Vl5rVWDKLEREkmUNFu6+wMwGA4cDBix19515r1mB6KI8EZF0WYOFmZ2TUtTXzHD3B/NUp4JSM5SISLqKNEMdlfC6OXASsACol8EClFmIiKSqSDPU9xPfm1kb4KG81ajAlFmIiKSryhXc24BDc12R2kSZhYhIsor0WTxFNGyWEFyOBB7JZ6UKSZmFiEi6ivRZ3JLwehfwibuvyFN9agVlFiIiySrSZ/FyTVSkttDQWRGRdGUGCzPbTLz5KekjwN29dd5qVUBqhhIRSVdmsHD3VjVZkdpEmYWISLKK9FkAYGYdCddZAODun+alRgWmzEJEJF1F7mcx0sw+AD4GXgaWA8/muV4FpcxCRCRZRa6zuAE4Gvinu3cjXMH9al5rVUDKLERE0lUkWOx093VAIzNr5O6zgOJsK5nZfWa2xszeSyi7zsz+ZWYLo8fwhM+uNrNlZrbUzIYmlPczs3ejz+4wy//hXJmFiEiyigSLDWbWEpgNTDGz2wnXW2RzPzAsQ/lv3L04esyAcKtWYBzQI1rn92bWOFr+LmAi4arxQ8vYZs4osxARSVeRYDGKMMXHj4C/AR8Cp2Vbyd1nA+srWI9RwDR33+HuHwPLgP5mth/Q2t1fd3cnTF44uoLbrDJlFiIiySoSLCYC+7v7Lnd/wN3viJqlqur/mdk/omaqtlFZZ+CzhGVWRGWdo9ep5Xmji/JERNJVJFi0Bp4zszlmdomZdarG990FHELo81gF/Doqz9T44+WUZ2RmE81snpnNW7t2bZUqqGYoEZF0WYOFu1/v7j2AS4D9gZfNbGZVvszdV7v7bnffA9wN9I8+WgEckLBoF2BlVN4lQ3lZ2/+Tu5e4e0mHDh2qUkUREcmgMlOUrwH+DawDOlbly6I+iJgxQGyk1JPAODNrZmbdCB3Zb7r7KmCzmR0djYI6B3iiKt9d8Trmc+siInVTRaYovwj4NtABeBS4wN0XV2C9qcAQYF8zWwH8HBhiZsWEpqTlwH8BuPsiM3sEWEwYaXWJu++ONnURYWTVXoSLAWvkgkB3BQ4RkZiKTPdxEHCpuy+szIbd/cwMxfeWs/wkYFKG8nlAz8p8d3UoQIiIpCuzGcrMjgJw96tSA4WZnZ3nehWcRkSJiMSV12dxr5ndFd1zGwAz62lms4Gx+a9aYcQyCwULEZG48oJFX+BTYKGZnWdmvwEeA25299E1UblCUDOUiEi68u5nsQu40cx2AfcQhqz2d/cyh67WJ8osRETiyuuzOMTMngNOALoT7sU928zOranKFYIyCxGRdOU1Qz0H3O3uw919qbvfBgwChppZvZ2iPEaZhYhIXHlDZ4vdfUtiQdQENc7MTs5vtQpHmYWISLry+iy2pJaZ2SHAmYTpxGvs2odCUGYhIhJXkduq7mdml5rZm8AioDEhYNRLyixERNKV18F9gZm9SLjv9r7A+cCqaGLBd2uqgoWizEJEJK68Pos7gdeB70RTbmBm9f4QqovyRETSlRcsuhCu1L41uofFI0BRjdSqgNQMJSKSrrw+i7+5+13uPgg4CdgIrDGzJWb2y5qpXuEosxARiSsvWJSeY7v7Cne/xd37Ee6XvSPvNSsQZRYiIunKa4bqYGaXlfHZ5nxUpjZRZiEiEldesGgMtKSS98Gu65RZiIikKy9YrHL3X9RYTWoZZRYiInEV6rNoSDR0VkQkXXnB4qQaq0UtomYoEZF0ZQYLd19fkxWpbZRZiIjEZZ0bqqFRZiEikk7BogzKLERE4hQsUiizEBFJp2BRBmUWIiJxChYpNHRWRCSdgkUKNUOJiKRTsCiDMgsRkTgFixTKLERE0ilYlEGZhYhInIJFCmUWIiLpFCzKoMxCRCROwSKFMgsRkXQKFmVQZiEiEqdgkUIX5YmIpFOwSKFmKBGRdAoWZVBmISISp2CRQpmFiEg6BYsyKLMQEYlTsEihzEJEJJ2CRRmUWYiIxClYpNDQWRGRdHkLFmZ2n5mtMbP3EsramdnzZvZB9Nw24bOrzWyZmS01s6EJ5f3M7N3oszvM8ttQpGYoEZF0+cws7geGpZRdBbzg7ocCL0TvMbMjgXFAj2id35tZ42idu4CJwKHRI3WbeaHMQkQkLm/Bwt1nA+tTikcBD0SvHwBGJ5RPc/cd7v4xsAzob2b7Aa3d/XV3d+DBhHXyQpmFiEi6mu6z6OTuqwCi545ReWfgs4TlVkRlnaPXqeUZmdlEM5tnZvPWrl1brYoqsxARiastHdyZzue9nPKM3P1P7l7i7iUdOnSoWkWUWYiIpKnpYLE6aloiel4Tla8ADkhYrguwMirvkqE875RZiIjE1XSweBIYH70eDzyRUD7OzJqZWTdCR/abUVPVZjM7OhoFdU7COnmhzEJEJF2TfG3YzKYCQ4B9zWwF8HPgJuARM/se8ClwBoC7LzKzR4DFwC7gEnffHW3qIsLIqr2AZ6NH3imzEBGJy1uwcPczy/jopDKWnwRMylA+D+iZw6qVSxfliYikqy0d3LWGmqFERNIpWJRBmYWISJyCRQplFiIi6RQsyqDMQkQkTsEihTILEZF0ChZlUGYhIhKnYJFCQ2dFRNIpWKRQM5SISDoFizIosxARiVOwSKHMQkQknYJFGZRZiIjEKVikUGYhIpJOwaIMyixEROIULFIosxARSadgUQZlFiIicQoWKXRRnohIOgWLFGqGEhFJp2BRBmUWIiJxChYplFmIiKRTsCiDMgsRKbhZs6Bdu3AW26gRXHxx8vtbb62xqjSpsW+qI5RZiEitMGsWDB0KO3eG9+5w113xz93h8svD68suy3t1lFmUQZmFiBTUuefGA0V5rrgi/3VBwSKNhs6KSK0weTIUFWVf7pZb8l8XFCzSqBlKRGqFE06A//mf0DdRluHD4dJLa6Q6ChZlUGYhIgU1axb87GewZ0/Zy8yYAbfdViPVUbBIocxCRGqFivZZ/PjH+a8LGg1VJmUWIrmzaRNcey1s2VLomtQdhx85mUs/HUqRxwOGA2nnszffXCP1UbBIocxCJPdefx1uvx06dICmTQtdm7rhb5zAW+2f40/rxtLKv6CRGatPv4imj02lLV9gZqFzuwaGzYKCRZmUWYjkTqw1ZcYMKCkpbF3qlhOA9aXvVi6Afo/dyRNPwMiRNVsT9Vmk0NBZkdyLBYuKjASVssV+fhXpysg1BYsUaoYSyb3Ywa2J2jKqRcGiFlJmIZI7yixyIxZsFSxqAWUWIrm3a1d4VrCoHmUWtZAyC5HcUWaRGwoWtYgyC5HcU59FbihY1ELKLERyR81QuaFgUYsosxDJPTVD5YaCRS2kzEIkd9QMlRuxYBHL1GqSgkUKXZQnknvKLHJDmUUtomYokdyLnQkrs6geM2jcuAEFCzNbbmbvmtlCM5sXlbUzs+fN7IPouW3C8leb2TIzW2pmQ2uijsosRHJn584QKHQyVn1NmjSgYBE5wd2L3T02rdhVwAvufijwQvQeMzsSGAf0AIYBvzezxvmqlP6YRXIvFiyk+oqKGl6wSDUKeCB6/QAwOqF8mrvvcPePgWVA/3xXRpmFSO7s2qX+ilxpaMHCgb+b2XwzmxiVdXL3VQDRc8eovDPwWcK6K6KyvFBmIZJ7O3cqWORKoYJFoRLD49x9pZl1BJ43s/fLWTbT4TvjeX8UeCYCHHjggdWqoDILkdxRM1TuNKjMwt1XRs9rgOmEZqXVZrYfQPS8Jlp8BXBAwupdgJVlbPdP7l7i7iUdOnSoUt1qw9DZL78s3HeL5IMyi9wpKmog11mY2d5m1ir2GjgFeA94EhgfLTYeeCJ6/SQwzsyamVk34FDgzfzVL19brphnnoEWLeCttwpbD5FcUp9F7jSkZqhOwHQLR+UmwMPu/jczewt4xMy+B3wKnAHg7ovM7BFgMbALuMTdd+e7koXKLJ59Njy/8QYcdVRh6iCSa2qGyp0GEyzc/SOgd4bydcBJZawzCZiU56oBhc8sROojNUPlToMJFnVFoTKL2PfWhqD1xBPw7rvJZU2bwgUXQNvoksnFi+G442Dr1vK39bOfwbXX5qeekn9vvgnr14ffdatWlV9fwSJ3CnVRnoJFitpwkIbC18Mdzjwzc2f7vvvCeeeF1x98ABs2hPedOmXe1l/+Ak8/rWBRV336KQwYEF5fdRXceGP6Mu+8E/5Wjj468zbUZ5E7yixqmUJnFoX273+Hf/477oCLLgpla9fC/vvDtm3x5XbsCM+XXQY9emTe1pdfwh//CLt3h3ltpG75/PP467VrMy9TXByeE/9+v/oKpk2Dnj3VZ5FLCha1RG04o69sPT7/PN4MtNde0LFj+ctXxPLl4fmQQ+L/5C1ahOevvoovF3vdrFnZ2/r610PAOOus+DaqauxY+OY3y1/miy9g06bwumlT2G+/+GfTp8M//wmXX66DV0UlNjFu2VLx9V54AcaPDycYhx+uzCJXioriJ2k1Sf8uZagtZ/jZLFkSzugT6/vmm9UfSfXxx+G5a9d4WdOm4TlTsIh9lsmJJ8IRR8Brr1WvTmvWwIcflh8s1q8PwSGxjk88ASNHhtennx6ejz0WBg6sXn0aisRgsXlz+ctu3Aht2oTXq1aF5zVrwkmHgkVuFBWF/4M77oiXHXwwjBiR3+9VsEhRGy7Kq4yPPgp1vfba8Ef03/8Nn3xS8WAxcybcdFP6/n4WTbCSKVgkntVUJFgcdFAIatV12mmwYkX5y6xZE+r0X/8Vmj++//3w84DkC5k+/bT69WkoYsGibdvsmcWnn0KvXuF1rPmqTZvQbNK8ef7q2JAcfDA89xz88IfxskaNQvNweRl+ddWmiQRrhUI3Q8UknhmX54svwvN3vxsekP3sL9Gjj8Ls2eH7Eh+dOsHFFyc3GzVuHB6JdYsFjvKCRa60aRPOXMsTq8/QoXD++eF17AC3bl18uc8+QyooFiw6dcoeLKZOjb+O9W/ERu8os8iNO+8Mf8uxx003wZ49lfu/rwplFmUodAd3ZYNF27bh7ALi7fUVsXFjOPOfM6diyzdtmjmzyOcZTUzr1tmDxfbt8fo0axaCW+wAl9g521CCxaefhjPOww6L/31UVmxAQ6dOsDLjRDtxN94Yzng7dYpnFjt2KFjkkhm0axd/HxuFuHlzGKmYL8osUhQ6s4gFi4p2YMWCRZs28fHvlTnD2LQpHIQrqlmzyvdZ5EqbNqG+5QXy2M+tefPwu2zZMn5mvGZNfLnZs+tOU2NVvfdeOBHo3h1uv73q24n9/P7jPzJnFu4hEB17bHh/ySXw8svx4Lxjh4bO5lPs/74yJ4lVoWBRhkIdSHZHE5lkyiz27IF77w2dhd27h7b4L74IB8SiovBo1qxywSKxQ7IimjbN3AxVEyOL2rQJB53yJlqMZRax9vG9907PLLp2DQfShx/OW1VrhWXL4q9ffrnq28nWDLVjR/jbHDECTj0VHn889BWlZhYafZYfsZM9NUPVsEJnFrGDb6ZgcfnlcNttoYPr/fdD09EXX8Svpobwh1OZM4xNm8L2KqpZs/RmqKZNa+bnFvun2LSp7CG4ic1QEALpli1hZM5LL4WyKVPClcj/+ldeq8u772YeAda2LZxxRv5/ZqtXh+cTToDXXw+DGU48sfLNUVu3hp9nmzbhZ+meXPdYMGnZEmbMgAsvDNfVxOzZEy7eLOuCPameqrQoVIWCRRkKlVnEDsSZmqE++CA8v/FGGB66dGm4ejoxWLRqVbOZxVdf1Ux/BcTruXFjaBLJJLEZCuLB4pJLwjUWrVtD//7hYJfvf66zzw5XNmeyzz5wyin5/f5Ys9vYsTBrFnzjG+HahxNPrNx2tm4NGVqrVuH/Ytu28D4mlm3EyhKv8xkzJvzcIcwIILmnYFEgiUNnd+6EX/86+Uy9W7fKn521alXxjqfYwe7228NopMMOi3+2eTMMGgQdOoR6LF0aMot99kn+rspmFtXts6iJ/gqI17O8Tu5MzVBbt4az7OOOg0ceCc0hsSBSEatXJ1+1XpbGjeGAA8Lf0LZtIbO47DL48Y/jy+zaBX36hNFab70FJSVlb6+6Vq8OJxIXXhj6Lk47LZ5tVEYsWLRsGd5v3pwcLGKZRWqw6NABBg+OB4tTT63afkj5FCwKJDG9njsXrr46HAQaNQr/6FXJOBo3DllBt27Zl03MKMaOTZ7Ib/PmcDUshIvc/vrXUJ/TTosv07p1xf9o3EOwqGxmkVjHHTtqLljE6rl6dfwA1bx58hQimZqh1q4Nj8GD4z+/li0r9nNauDAc3Ctq1CgYMiQ0ce3ZE74z8QpygF/9KgzrnTu3+sHi2WfhmGOSTxhiVq8O/QyNG0PfvqFs06Zw4eKIEWUH3T594KGH4v8LiZkFwPXXw113xZdPbIaCeLBo2rTmss6GLLF5Np8ULMrgHh+XP3cu9OsXymbOzD58MNHatfCTn4T+hcoGi/Xrkz/bvDn+D/vTn8a3N25cfJlWrSp+9rh1azigVSazyNQMVVPBIjZcMHY1NoQL7xIDaqZmqI8/Dk0yiTdPbNWqYpnF7Nnh+c47k8+mM/nHP0Kf0hNPxL/7mGPSlxs/Pszcmzg6qyreeAOGDw+vhwyBk08OJzfPPBP2bfHi+IE7sQnvH/8IfRhDhqRnvKtWhT6ds86K92WtWRP2PdZ89frryeuU1QzVrFnN/W00ZIkZXz4pWKRIzCxiw1JjBymz0O5bGXv2wA03hAnVGjcO65c3d1PigXjlSjj00Pj7ZctCZyWEzsJMHYatWoV/5h/9KExtUdaUFhs2xKdjqExmkakZqqbOHnv0gHvuiQfR558PwXvPnnizYKZmqDVrwgiqxGARyyw++SR8FrvgMPZo0iQ8v/FGODu/6KKKdUhff338SvHmzTNftdykCbRvX/1g8dBD8dcvvRRGeB1/fHIwjV2Y2KJF2J+NG+MnQbfdBr1T7iyzbl3IvmJBKOYb3wjlEyaEn3mi1GaoWJaTmFloAsn8adIkzAmnYFEg7vGDUuIFMJXVqFE4g3vyydBkcP75cPfdZS+f2rHdqlUYJvu3v8Xfl+e448L3/Pa3MH9+ODOeMwdeeSW+zKuvhrPPmMQO8myaNk0eulqTzVBm8L3vxd83ahQCxubN8YAX+/klNkPFfo+pmcXzzydPZ1KWb36z4iOXYmd52XTsWPYMrhX14Yfx123ahKGq8+aF9y+/HIJcLDswi4+UiwWL9u3Tt9m+ffib+eij5PLYFOX77pt8JTzE9yOWpcR+pldfrWBRU1q1gj//OT767sUXc38Sp2CRIvGgsH59OCBV5WYviR55JFwxfPHFyQftTFKDxQ03hIPVySeHkSzZ6vL974fHxReH6wjcwzQgiXMhFRWFYbgHHBDOfCszAVmzZslt3TXZDJUqsWkl9nr79vA7i43pT2xiS80sYtM833lnWG737syPYcNyX/cOHeCxx0LHcyZt2oTfd2KdUyU2Uw4aBE89FdZp1ixkGKmDMGLTpZQXLCAEhlhwSNW+fThZ2LYtPnw51iwb65vZZ594395TT4VnXWORX9//fvK1NPkYlq1fYRnc49cwVHWahJhmzeBrXwtNSNdcA7/5TWgmyiQ1WMT+aWMpfkUD19e/HjohH3ggBIrf/ja0k0PYn6peTZtpuo/aECxitm8PP+/YP8v558P//m8IDKmZBYTlLrig5q8ujl2w9rWvwYEHJn+2fn3IRN98M5wo3HhjCGgQ6v3CC6FJKDFYDB4cDswzZ4ZtZvqbTcws9torPCorlj2sW5ccLNq1y9zkpsyiZvzsZ+GRT7qCO0XsIPPrX4d/xso00WQTmx77+uvTL7r73e/CiJXYfSQgnCnE/jkrGyz69QvP554bnk89NT5fUnUOjIW8ziJVrG08MVjs2JF80OrWLRzY/vjH5Jl4Yz/H/fcvzDQUsazmwQdh8uTkx333hc/++c/w/NBDYZ+OOSZcjPnqq6E8NbOIbfdrX8v8nYmZRVlZRTaxv8fEGyKtXBkfZZYq9rehzKLu068wRffu4R/z738Pwxqr01+R6vDDwxnjyJHhzD/xILtoURjZc+qpYRmIHwCg8jcNKikJnZ6bNoX28UMOqXb1gfQO7h07qt9MV1VlZRapZ7itWsHEicllsb6F1LP6mvLXv4aBCJ07p3/Wvn04SfnnP0Pn8fvvhynor7giNF0tWRKaxzZsCAGwSZMw3PW++0KH/Zgxmb+zdetwYK9OsIitp2DR8OhXmKJ9+3C16zHHhGaAoUNzu/1TTglNI4n/bBBGNt10UzhIZGpvjAWLilwcBmEbgwdXr66Z1JVmqGxifRkHHJD7elVEjx5l34YWwsWYU6aEZiX3kCm2aBH6OObPh7ffDuVnnRW/r0EsiyxLmzbhb3v58spdO5Iollmcfnr8975hA5xzTublFSzqD/0KM+jfH77znTA+P9dTFDRrVv5oKAid4alBITbEsUuX3Nansgp5BXeqTMEitRmqLGefHdb7znfyU7fq+slP4veGGDgwPmS6V6+Qecayz8pkvt/6VggUsSBTFYcfHppRE0dymZUdqBQs6g/9CjNo1Cic1RVKpoBw3nmhLTqxaaoQMs06W6g+i4o2Q2VyyCFhoEFtNXZseKT67W/DQf9HPwp9FpVpnhwzpuwmqopq1Cg0iVVU7ERCHdx1n4JFHZGvZqXKatYsDJ188MHw/osvCpdZNG8evvtXv4pna6tWhf6g+uqgg0KTz957h6BRVmd2bbFnT3hWZlH36VcoldK5cxhxM358vKyszs18M4Nf/jLM35Qo8Qrm+mrs2DB4oVCDCyoqNppw9OiCVkNywLye3i6spKTE58UuZ5WccQ/XbcRu0mQWznarey2K1F8rV8YnNJTaz8zmu3vaFJfKLKRSYsFBpKIKlXlKbul8UEREslKwEBGRrBQsREQkKwULERHJSsFCRESyUrAQEZGsFCxERCQrBQsREclKwUJERLJSsBARkawULEREJCsFCxERyUrBQkREsqozwcLMhpnZUjNbZmZXFbo+IiINSZ0IFmbWGLgTOBU4EjjTzI4sbK1ERBqOunI/i/7AMnf/CMDMpgGjgMW5/qIFqxaw9autOE59vTGUiNRvAw8aSCPLbS5QV4JFZ+CzhPcrgAGpC5nZRGAiwIEHHlilL2psjWnauClmhmFV2oaISH1TV4JFpqN22mm/u/8J+BOE26pW5Yt6/0fvqqwmIlKv1Yk+C0ImcUDC+y7AygLVRUSkwakrweIt4FAz62ZmTYFxwJMFrpOISINRJ5qh3H2Xmf0/4DmgMXCfuy8qcLVERBqMOhEsANx9BjCj0PUQEWmI6kozlIiIFJCChYiIZKVgISIiWSlYiIhIVgoWIiKSlYKFiIhkpWAhIiJZKViIiEhWChYiIpKV1dd7NpjZWuCTKq6+L/B5DqtT22l/67+Gts/a36o7yN07pBbW22BRHWY2z91LCl2PmqL9rf8a2j5rf3NPzVAiIpKVgoWIiGSlYJHZnwpdgRqm/a3/Gto+a39zTH0WIiKSlTILERHJSsEigZkNM7OlZrbMzK4qdH1yxczuM7M1ZvZeQlk7M3vezD6IntsmfHZ19DNYamZDC1PrqjOzA8xslpktMbNFZvbDqLxe7rOZNTezN83snWh/r4/K6+X+xphZYzN728yejt7X9/1dbmbvmtlCM5sXldXcPru7HqEprjHwIXAw0BR4Bziy0PXK0b4NAvoC7yWU3QxcFb2+CvhV9PrIaN+bAd2in0njQu9DJfd3P6Bv9LoV8M9ov+rlPgMGtIxeFwFzgaPr6/4m7PdlwMPA09H7+r6/y4F9U8pqbJ+VWcT1B5a5+0fu/hUwDRhV4DrlhLvPBtanFI8CHohePwCMTiif5u473P1jYBnhZ1NnuPsqd18Qvd4MLAE6U0/32YMt0dui6OHU0/0FMLMuwDeBexKK6+3+lqPG9lnBIq4z8FnC+xVRWX3Vyd1XQTi4Ah2j8nr1czCzrkAfwtl2vd3nqElmIbAGeN7d6/X+ArcBVwB7Esrq8/5COAH4u5nNN7OJUVmN7XOT6qxcz1iGsoY4VKze/BzMrCXwGHCpu28yy7RrYdEMZXVqn919N1BsZvsA082sZzmL1+n9NbMRwBp3n29mQyqySoayOrO/CY5z95Vm1hF43szeL2fZnO+zMou4FcABCe+7ACsLVJeasNrM9gOIntdE5fXi52BmRYRAMcXd/xoV1+t9BnD3DcBLwDDq7/4eB4w0s+WE5uITzezP1N/9BcDdV0bPa4DphGalGttnBYu4t4BDzaybmTUFxgFPFrhO+fQkMD56PR54IqF8nJk1M7NuwKHAmwWoX5VZSCHuBZa4+60JH9XLfTazDlFGgZntBZwMvE893V93v9rdu7h7V8L/6Yvu/l3q6f4CmNneZtYq9ho4BXiPmtznQvfw16YHMJwwcuZD4KeFrk8O92sqsArYSTjj+B7QHngB+CB6bpew/E+jn8FS4NRC178K+3s8IeX+B7Awegyvr/sMfB14O9rf94Bro/J6ub8p+z6E+Gioeru/hFGa70SPRbHjU03us67gFhGRrNQMJSIiWSlYiIhIVgoWIiKSlYKFiIhkpWAhIiJZ6QpukWoys93Au4T/p4+Bsz1cHCdSbyizEKm+L9292N17EiZsvKTQFRLJNQULkdx6nWjCNjMrNrM3zOwfZjbdzNqaWUczmx993tvM3MwOjN5/aGYtzOwMM3svuj/F7ALui0gpBQuRHDGzxsBJxKeJeRC40t2/Tmim+rmHeX2am1lrYCAwDxhoZgcRJsfbBlwLDHX33sDImt4PkUwULESqb69oevB1QDvCjKBtgH3c/eVomQcIN6ECeI0wGd4g4JfR80BgTvT5q8D9ZnYB4aZcIgWnYCFSfV+6ezFwEOEui9n6LOYQgsNBhInfehPms5oN4O4XAj8jzBq60Mza56faIhWnYCGSI+6+EfgB8GNgG/CFmQ2MPj4biGUZs4HvAh+4+x5Cp/hwQkaBmR3i7nPd/Vrgc5KnmhYpCA2dFckhd3/bzN4hTJ09HviDmbUAPgLOjZZZHt2IKdZ5/QrQxd2/iN7/r5kdSriBzQuEmUZFCkqzzoqISFZqhhIRkawULEREJCsFCxERyUrBQkREslKwEBGRrBQsREQkKwULERHJSsFCRESy+v9AW3F27EK+nwAAAABJRU5ErkJggg==\n", 277 | "text/plain": [ 278 | "
" 279 | ] 280 | }, 281 | "metadata": { 282 | "needs_background": "light" 283 | }, 284 | "output_type": "display_data" 285 | } 286 | ], 287 | "source": [ 288 | "#now we try ISO FOREST for different outlier fraction and different target feature\n", 289 | "# and we create fake outlier to the unrealistically high tax \n", 290 | "warnings.filterwarnings(\"ignore\")\n", 291 | "df['TAX'][0]=3000\n", 292 | "\n", 293 | "outliers_fraction=0.01\n", 294 | "df = iso_forest_detection(df, outliers_fraction, 'TAX', plotting=True)" 295 | ] 296 | } 297 | ], 298 | "metadata": { 299 | "kernelspec": { 300 | "display_name": "Python 3", 301 | "language": "python", 302 | "name": "python3" 303 | }, 304 | "language_info": { 305 | "codemirror_mode": { 306 | "name": "ipython", 307 | "version": 3 308 | }, 309 | "file_extension": ".py", 310 | "mimetype": "text/x-python", 311 | "name": "python", 312 | "nbconvert_exporter": "python", 313 | "pygments_lexer": "ipython3", 314 | "version": "3.8.5" 315 | } 316 | }, 317 | "nbformat": 4, 318 | "nbformat_minor": 5 319 | } 320 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. By contrast, the GNU General Public 13 | License is intended to guarantee your freedom to share and change free 14 | software--to make sure the software is free for all its users. This 15 | General Public License applies to most of the Free Software 16 | Foundation's software and to any other program whose authors commit to 17 | using it. (Some other Free Software Foundation software is covered by 18 | the GNU Lesser General Public License instead.) You can apply it to 19 | your programs, too. 20 | 21 | When we speak of free software, we are referring to freedom, not 22 | price. 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It is safest 289 | to attach them to the start of each source file to most effectively 290 | convey the exclusion of warranty; and each file should have at least 291 | the "copyright" line and a pointer to where the full notice is found. 292 | 293 | 294 | Copyright (C) 295 | 296 | This program is free software; you can redistribute it and/or modify 297 | it under the terms of the GNU General Public License as published by 298 | the Free Software Foundation; either version 2 of the License, or 299 | (at your option) any later version. 300 | 301 | This program is distributed in the hope that it will be useful, 302 | but WITHOUT ANY WARRANTY; without even the implied warranty of 303 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 304 | GNU General Public License for more details. 305 | 306 | You should have received a copy of the GNU General Public License along 307 | with this program; if not, write to the Free Software Foundation, Inc., 308 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. 309 | 310 | Also add information on how to contact you by electronic and paper mail. 311 | 312 | If the program is interactive, make it output a short notice like this 313 | when it starts in an interactive mode: 314 | 315 | Gnomovision version 69, Copyright (C) year name of author 316 | Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 317 | This is free software, and you are welcome to redistribute it 318 | under certain conditions; type `show c' for details. 319 | 320 | The hypothetical commands `show w' and `show c' should show the appropriate 321 | parts of the General Public License. Of course, the commands you use may 322 | be called something other than `show w' and `show c'; they could even be 323 | mouse-clicks or menu items--whatever suits your program. 324 | 325 | You should also get your employer (if you work as a programmer) or your 326 | school, if any, to sign a "copyright disclaimer" for the program, if 327 | necessary. Here is a sample; alter the names: 328 | 329 | Yoyodyne, Inc., hereby disclaims all copyright interest in the program 330 | `Gnomovision' (which makes passes at compilers) written by James Hacker. 331 | 332 | , 1 April 1989 333 | Ty Coon, President of Vice 334 | 335 | This General Public License does not permit incorporating your program into 336 | proprietary programs. If your program is a subroutine library, you may 337 | consider it more useful to permit linking proprietary applications with the 338 | library. If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /Linear Programming/.ipynb_checkpoints/Linear_programming_with_gurobipy_teachers_example-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "98a7c74a", 6 | "metadata": {}, 7 | "source": [ 8 | "### Linear programming \n", 9 | "Linear programming is an optimization technique for a system of linear constraints and a linear objective function. An objective function defines the quantity to be optimized, and the goal of linear programming is to find the values of the variables that maximize or minimize the objective function.\n", 10 | "Linear programming is useful for many problems that require an optimization of resources. It could be applied to manufacturing, to calculate how to assign labor and machinery to minimize cost of operations. It could be applied in high-level business operations, to decide which products to sell and in what quantity in order to maximize profit. It could also be applied in logistics, to decide how to apply resources to get a job done in the minimum amount of time.\n", 11 | "Good introduction you can use here: https://brilliant.org/wiki/linear-programming/\n", 12 | "\n", 13 | "Here we created one interesting task to illustrate LP problem." 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 1, 19 | "id": "5cee4b45", 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import pandas as pd\n", 24 | "import numpy as np\n", 25 | "from gurobipy import *" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "8c0fb67c", 31 | "metadata": {}, 32 | "source": [ 33 | "### Task description\n", 34 | "Imagine you are local community officer in new suburban municipality and you need to hire teachers for new schools. You are responsible for ten schools and you have asked the teachers where they live. You have read an article that people are more motivated for a job if they do not have to travell a long way, so you want to hire teachers in the school which is the nearest to their home. Also, teachers have some salary expectations and you have some budget constraints. Every school needs one new teacher. How would you solve this? By linear programming! But, let's create some fake data first." 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "id": "f36d9dba", 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# Let's create the list of the teachers first:\n", 45 | "teachers = []\n", 46 | "for i in range(1,101):\n", 47 | " teachers.append(f'teacher_{i}')" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 3, 53 | "id": "78619b91", 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "#then schools\n", 58 | "schools = []\n", 59 | "for i in range(1,11):\n", 60 | " schools.append(f'School_{i}')" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 4, 66 | "id": "c23b99da", 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/plain": [ 72 | "1000" 73 | ] 74 | }, 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "#now we will for each teacher and school create some fake distance that in maximum could be 20 km\n", 82 | "distances = []\n", 83 | "for teacher in teachers:\n", 84 | " for school in schools:\n", 85 | " distances.append(np.random.randint(2, 20, size=1))\n", 86 | "len(distances) " 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 5, 92 | "id": "24e83b14", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "values = np.asarray(distances).reshape(100,-10)\n", 97 | "df = pd.DataFrame(values, index=teachers, columns = schools)" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 6, 103 | "id": "d620e934", 104 | "metadata": { 105 | "scrolled": true 106 | }, 107 | "outputs": [ 108 | { 109 | "data": { 110 | "text/html": [ 111 | "
\n", 112 | "\n", 125 | "\n", 126 | " \n", 127 | " \n", 128 | " \n", 129 | " \n", 130 | " \n", 131 | " \n", 132 | " \n", 133 | " \n", 134 | " \n", 135 | " \n", 136 | " \n", 137 | " \n", 138 | " \n", 139 | " \n", 140 | " \n", 141 | " \n", 142 | " \n", 143 | " \n", 144 | " \n", 145 | " \n", 146 | " \n", 147 | " \n", 148 | " \n", 149 | " \n", 150 | " \n", 151 | " \n", 152 | " \n", 153 | " \n", 154 | " \n", 155 | " \n", 156 | " \n", 157 | " \n", 158 | " \n", 159 | " \n", 160 | " \n", 161 | " \n", 162 | " \n", 163 | " \n", 164 | " \n", 165 | " \n", 166 | " \n", 167 | " \n", 168 | " \n", 169 | " \n", 170 | " \n", 171 | " \n", 172 | " \n", 173 | " \n", 174 | " \n", 175 | " \n", 176 | " \n", 177 | " \n", 178 | " \n", 179 | " \n", 180 | " \n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | "
School_1School_2School_3School_4School_5School_6School_7School_8School_9School_10
teacher_141461516161181714
teacher_258617918177159
teacher_31813101771771952
teacher_444211196771117
teacher_531522315916152
.................................
teacher_969111011912111696
teacher_9781314174314697
teacher_98715151710115171712
teacher_9913151114123116315
teacher_1001871761038151616
\n", 287 | "

100 rows × 10 columns

\n", 288 | "
" 289 | ], 290 | "text/plain": [ 291 | " School_1 School_2 School_3 School_4 School_5 School_6 \\\n", 292 | "teacher_1 4 14 6 15 16 16 \n", 293 | "teacher_2 5 8 6 17 9 18 \n", 294 | "teacher_3 18 13 10 17 7 17 \n", 295 | "teacher_4 4 4 2 11 19 6 \n", 296 | "teacher_5 3 15 2 2 3 15 \n", 297 | "... ... ... ... ... ... ... \n", 298 | "teacher_96 9 11 10 11 9 12 \n", 299 | "teacher_97 8 13 14 17 4 3 \n", 300 | "teacher_98 7 15 15 17 10 11 \n", 301 | "teacher_99 13 15 11 14 12 3 \n", 302 | "teacher_100 18 7 17 6 10 3 \n", 303 | "\n", 304 | " School_7 School_8 School_9 School_10 \n", 305 | "teacher_1 11 8 17 14 \n", 306 | "teacher_2 17 7 15 9 \n", 307 | "teacher_3 7 19 5 2 \n", 308 | "teacher_4 7 7 11 17 \n", 309 | "teacher_5 9 16 15 2 \n", 310 | "... ... ... ... ... \n", 311 | "teacher_96 11 16 9 6 \n", 312 | "teacher_97 14 6 9 7 \n", 313 | "teacher_98 5 17 17 12 \n", 314 | "teacher_99 11 6 3 15 \n", 315 | "teacher_100 8 15 16 16 \n", 316 | "\n", 317 | "[100 rows x 10 columns]" 318 | ] 319 | }, 320 | "execution_count": 6, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "df" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 7, 332 | "id": "90c61ca1", 333 | "metadata": {}, 334 | "outputs": [], 335 | "source": [ 336 | "#At the end, let's add some fake salary expectaitons for each teacher, maximal teacher salary could be 8000 dollars\n", 337 | "salary_expectation = []\n", 338 | "for i in range(1,101):\n", 339 | " salary_expectation.append(np.random.randint(8000, size=1))\n", 340 | "salary_expectation = np.asarray(salary_expectation).reshape(100,)" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 8, 346 | "id": "1bf97aef", 347 | "metadata": {}, 348 | "outputs": [], 349 | "source": [ 350 | "salary_pd = pd.Series(salary_expectation, index = df.index)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 9, 356 | "id": "b9cc896d", 357 | "metadata": {}, 358 | "outputs": [], 359 | "source": [ 360 | "df['salary_expectation'] = salary_pd" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": 10, 366 | "id": "842630c9", 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "#We must also calculate total distance for each teacher which we will use for objective function (we sum all school distances for each teacher)\n", 371 | "df['total_distance'] = df.iloc[:,:-2].sum(axis=1)" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 11, 377 | "id": "4225aaec", 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/html": [ 383 | "
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School_1School_2School_3School_4School_5School_6School_7School_8School_9School_10salary_expectationtotal_distance
teacher_1414615161611817145820107
teacher_2586179181771591251102
teacher_318131017717719521815113
teacher_444211196771117625371
teacher_531522315916152452180
\n", 493 | "
" 494 | ], 495 | "text/plain": [ 496 | " School_1 School_2 School_3 School_4 School_5 School_6 \\\n", 497 | "teacher_1 4 14 6 15 16 16 \n", 498 | "teacher_2 5 8 6 17 9 18 \n", 499 | "teacher_3 18 13 10 17 7 17 \n", 500 | "teacher_4 4 4 2 11 19 6 \n", 501 | "teacher_5 3 15 2 2 3 15 \n", 502 | "\n", 503 | " School_7 School_8 School_9 School_10 salary_expectation \\\n", 504 | "teacher_1 11 8 17 14 5820 \n", 505 | "teacher_2 17 7 15 9 1251 \n", 506 | "teacher_3 7 19 5 2 1815 \n", 507 | "teacher_4 7 7 11 17 6253 \n", 508 | "teacher_5 9 16 15 2 4521 \n", 509 | "\n", 510 | " total_distance \n", 511 | "teacher_1 107 \n", 512 | "teacher_2 102 \n", 513 | "teacher_3 113 \n", 514 | "teacher_4 71 \n", 515 | "teacher_5 80 " 516 | ] 517 | }, 518 | "execution_count": 11, 519 | "metadata": {}, 520 | "output_type": "execute_result" 521 | } 522 | ], 523 | "source": [ 524 | "df.head()" 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 12, 530 | "id": "d7a0780e", 531 | "metadata": {}, 532 | "outputs": [ 533 | { 534 | "name": "stdout", 535 | "output_type": "stream", 536 | "text": [ 537 | "Restricted license - for non-production use only - expires 2023-10-25\n" 538 | ] 539 | } 540 | ], 541 | "source": [ 542 | "#Declare and initialise the model\n", 543 | "#Resource Assignment problem - RAP\n", 544 | "#Four components of the model: data, desicion variables, constraints and objective function\n", 545 | "\n", 546 | "m = Model('RAP')" 547 | ] 548 | }, 549 | { 550 | "cell_type": "code", 551 | "execution_count": 13, 552 | "id": "a88a4862", 553 | "metadata": {}, 554 | "outputs": [ 555 | { 556 | "data": { 557 | "text/plain": [ 558 | "[('teacher_1', 'School_1'),\n", 559 | " ('teacher_1', 'School_2'),\n", 560 | " ('teacher_1', 'School_3'),\n", 561 | " ('teacher_1', 'School_4'),\n", 562 | " ('teacher_1', 'School_5'),\n", 563 | " ('teacher_1', 'School_6'),\n", 564 | " ('teacher_1', 'School_7'),\n", 565 | " ('teacher_1', 'School_8'),\n", 566 | " ('teacher_1', 'School_9'),\n", 567 | " ('teacher_1', 'School_10')]" 568 | ] 569 | }, 570 | "execution_count": 13, 571 | "metadata": {}, 572 | "output_type": "execute_result" 573 | } 574 | ], 575 | "source": [ 576 | "from itertools import product\n", 577 | "\n", 578 | "combinations = list(product(teachers, schools))\n", 579 | "combinations[:10]" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": 14, 585 | "id": "2848adfd", 586 | "metadata": {}, 587 | "outputs": [], 588 | "source": [ 589 | "# x is our decistion variable, in this case it could be only 1 (meaning this teacher goes to this school) or 0 (not assigned to this school)\n", 590 | "# in combinations we created all possible combinations of each teacher and each school\n", 591 | "x = m.addVars(combinations, name='assign')" 592 | ] 593 | }, 594 | { 595 | "cell_type": "code", 596 | "execution_count": 15, 597 | "id": "c5848965", 598 | "metadata": {}, 599 | "outputs": [], 600 | "source": [ 601 | "#now we create constraint that teacher can be assigned only to one school\n", 602 | "jobs = m.addConstrs((x.sum('*',school)==1 for school in schools), 'job')" 603 | ] 604 | }, 605 | { 606 | "cell_type": "code", 607 | "execution_count": 16, 608 | "id": "994b6046", 609 | "metadata": {}, 610 | "outputs": [], 611 | "source": [ 612 | "# for all teachers in the list of teachers with index j must be less 1, i.e. some teachers could not be chosen\n", 613 | "#teachers must be assigned maximum to 1 job, but it could happen teacher will not be chosen at all if he or she lives to far\n", 614 | "# or have unrealistic salary expectation\n", 615 | "resources = m.addConstrs((x.sum(teacher,'*')<=1 for teacher in teachers), 'teacher')" 616 | ] 617 | }, 618 | { 619 | "cell_type": "code", 620 | "execution_count": 17, 621 | "id": "72dbc863", 622 | "metadata": {}, 623 | "outputs": [], 624 | "source": [ 625 | "# just transform salary expectation to dict for gurobypi for budget constraint \n", 626 | "cost = {}\n", 627 | "for i, teacher in enumerate(teachers):\n", 628 | " for j, school in enumerate(schools):\n", 629 | " cost[(teacher,school)] = df['salary_expectation'][i]" 630 | ] 631 | }, 632 | { 633 | "cell_type": "code", 634 | "execution_count": 18, 635 | "id": "d0d2e27a", 636 | "metadata": {}, 637 | "outputs": [], 638 | "source": [ 639 | "#We also have some budget constraint. Out total budget is 400 000 dolars. So salary expectations of selected teachers\n", 640 | "#must not exceed 4000 000 dollars.\n", 641 | "budget = 400000\n", 642 | "budget = m.addConstr((x.prod(cost)<=budget), 'budget')" 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 19, 648 | "id": "857957ff", 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "# now we transpose our pandas data frame in multidict gurobipy could use \n", 653 | "obj_func = {}\n", 654 | "for i, teacher in enumerate(teachers):\n", 655 | " for j, school in enumerate(schools):\n", 656 | " obj_func[(teacher,school)] = df.iloc[i,j]" 657 | ] 658 | }, 659 | { 660 | "cell_type": "code", 661 | "execution_count": 20, 662 | "id": "2b3ef173", 663 | "metadata": {}, 664 | "outputs": [ 665 | { 666 | "data": { 667 | "text/plain": [ 668 | "1000" 669 | ] 670 | }, 671 | "execution_count": 20, 672 | "metadata": {}, 673 | "output_type": "execute_result" 674 | } 675 | ], 676 | "source": [ 677 | "len(obj_func)" 678 | ] 679 | }, 680 | { 681 | "cell_type": "code", 682 | "execution_count": 22, 683 | "id": "2f6c5aec", 684 | "metadata": {}, 685 | "outputs": [], 686 | "source": [ 687 | "#Now we should define our objective function. It is a distance for each teacher from each school. We want to minimize it.\n", 688 | "m.setObjective(x.prod(obj_func), GRB.MINIMIZE)" 689 | ] 690 | }, 691 | { 692 | "cell_type": "code", 693 | "execution_count": 28, 694 | "id": "7ac74dda", 695 | "metadata": {}, 696 | "outputs": [], 697 | "source": [ 698 | "#We can save our model\n", 699 | "# save model for inspection - you can see it in folder of this task.\n", 700 | "m.write('TEACHERS.lp')" 701 | ] 702 | }, 703 | { 704 | "cell_type": "code", 705 | "execution_count": 24, 706 | "id": "1a935ccc", 707 | "metadata": {}, 708 | "outputs": [ 709 | { 710 | "name": "stdout", 711 | "output_type": "stream", 712 | "text": [ 713 | "Gurobi Optimizer version 9.5.0 build v9.5.0rc5 (mac64[x86])\n", 714 | "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n", 715 | "Optimize a model with 111 rows, 1000 columns and 3000 nonzeros\n", 716 | "Model fingerprint: 0xf363be96\n", 717 | "Coefficient statistics:\n", 718 | " Matrix range [1e+00, 8e+03]\n", 719 | " Objective range [2e+00, 2e+01]\n", 720 | " Bounds range [0e+00, 0e+00]\n", 721 | " RHS range [1e+00, 4e+05]\n", 722 | "Presolve time: 0.01s\n", 723 | "Presolved: 111 rows, 1000 columns, 3000 nonzeros\n", 724 | "\n", 725 | "Iteration Objective Primal Inf. Dual Inf. Time\n", 726 | " 0 0.0000000e+00 4.000000e+01 0.000000e+00 0s\n", 727 | " 10 2.0000000e+01 0.000000e+00 0.000000e+00 0s\n", 728 | "\n", 729 | "Solved in 10 iterations and 0.01 seconds (0.00 work units)\n", 730 | "Optimal objective 2.000000000e+01\n" 731 | ] 732 | } 733 | ], 734 | "source": [ 735 | "# run optimisation engine\n", 736 | "m.optimize()" 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 25, 742 | "id": "7d7a13ac", 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "name": "stdout", 747 | "output_type": "stream", 748 | "text": [ 749 | "assign[teacher_6,School_2] 1.0\n", 750 | "assign[teacher_12,School_1] 1.0\n", 751 | "assign[teacher_16,School_6] 1.0\n", 752 | "assign[teacher_38,School_8] 1.0\n", 753 | "assign[teacher_54,School_7] 1.0\n", 754 | "assign[teacher_61,School_10] 1.0\n", 755 | "assign[teacher_66,School_3] 1.0\n", 756 | "assign[teacher_72,School_9] 1.0\n", 757 | "assign[teacher_85,School_5] 1.0\n", 758 | "assign[teacher_94,School_4] 1.0\n" 759 | ] 760 | } 761 | ], 762 | "source": [ 763 | "#display optimal values of desicion variables:\n", 764 | "#we print which teachers our solver selected for each school\n", 765 | "for var in m.getVars():\n", 766 | " if var.x==1:\n", 767 | " print(var.varName, var.x)" 768 | ] 769 | }, 770 | { 771 | "cell_type": "code", 772 | "execution_count": 26, 773 | "id": "b32b30a7", 774 | "metadata": {}, 775 | "outputs": [], 776 | "source": [ 777 | "#We basically solved the problem. Teacher 6 goes to school 2, teacher 12 to school 1, etc. \n", 778 | "#Maybe we could see values of our objective function:" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 27, 784 | "id": "d13c08eb", 785 | "metadata": {}, 786 | "outputs": [ 787 | { 788 | "name": "stdout", 789 | "output_type": "stream", 790 | "text": [ 791 | "total optimised distance 20.0\n", 792 | "average distance of all teachers 92.86\n" 793 | ] 794 | } 795 | ], 796 | "source": [ 797 | "#display optimal matching score\n", 798 | "print('total optimised distance', m.ObjVal)\n", 799 | "print('average distance of all teachers', df['total_distance'].mean())" 800 | ] 801 | }, 802 | { 803 | "cell_type": "markdown", 804 | "id": "4ab7a7e7", 805 | "metadata": {}, 806 | "source": [ 807 | "It was easy for our solver to solve this. It is easy problem. Imagine now that we have 100 schools and 1000 teachers. The procedure would be completely the same. " 808 | ] 809 | } 810 | ], 811 | "metadata": { 812 | "kernelspec": { 813 | "display_name": "Python 3 (ipykernel)", 814 | "language": "python", 815 | "name": "python3" 816 | }, 817 | "language_info": { 818 | "codemirror_mode": { 819 | "name": "ipython", 820 | "version": 3 821 | }, 822 | "file_extension": ".py", 823 | "mimetype": "text/x-python", 824 | "name": "python", 825 | "nbconvert_exporter": "python", 826 | "pygments_lexer": "ipython3", 827 | "version": "3.8.5" 828 | } 829 | }, 830 | "nbformat": 4, 831 | "nbformat_minor": 5 832 | } 833 | -------------------------------------------------------------------------------- /Linear Programming/Linear_programming_with_gurobipy_teachers_example.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "98a7c74a", 6 | "metadata": {}, 7 | "source": [ 8 | "### Linear programming \n", 9 | "Linear programming is an optimization technique for a system of linear constraints and a linear objective function. An objective function defines the quantity to be optimized, and the goal of linear programming is to find the values of the variables that maximize or minimize the objective function.\n", 10 | "Linear programming is useful for many problems that require an optimization of resources. It could be applied to manufacturing, to calculate how to assign labor and machinery to minimize cost of operations. It could be applied in high-level business operations, to decide which products to sell and in what quantity in order to maximize profit. It could also be applied in logistics, to decide how to apply resources to get a job done in the minimum amount of time.\n", 11 | "Good introduction you can use here: https://brilliant.org/wiki/linear-programming/\n", 12 | "\n", 13 | "Here we created one interesting task to illustrate LP problem." 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 1, 19 | "id": "5cee4b45", 20 | "metadata": {}, 21 | "outputs": [], 22 | "source": [ 23 | "import pandas as pd\n", 24 | "import numpy as np\n", 25 | "from gurobipy import *" 26 | ] 27 | }, 28 | { 29 | "cell_type": "markdown", 30 | "id": "8c0fb67c", 31 | "metadata": {}, 32 | "source": [ 33 | "### Task description\n", 34 | "Imagine you are local community officer in new suburban municipality and you need to hire teachers for new schools. You are responsible for ten schools and you have asked the teachers where they live. You have read an article that people are more motivated for a job if they do not have to travell a long way, so you want to hire teachers in the school which is the nearest to their home. Also, teachers have some salary expectations and you have some budget constraints. Every school needs one new teacher. How would you solve this? By linear programming! But, let's create some fake data first." 35 | ] 36 | }, 37 | { 38 | "cell_type": "code", 39 | "execution_count": 2, 40 | "id": "f36d9dba", 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "# Let's create the list of the teachers first:\n", 45 | "teachers = []\n", 46 | "for i in range(1,101):\n", 47 | " teachers.append(f'teacher_{i}')" 48 | ] 49 | }, 50 | { 51 | "cell_type": "code", 52 | "execution_count": 3, 53 | "id": "78619b91", 54 | "metadata": {}, 55 | "outputs": [], 56 | "source": [ 57 | "#then schools\n", 58 | "schools = []\n", 59 | "for i in range(1,11):\n", 60 | " schools.append(f'School_{i}')" 61 | ] 62 | }, 63 | { 64 | "cell_type": "code", 65 | "execution_count": 4, 66 | "id": "c23b99da", 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "text/plain": [ 72 | "1000" 73 | ] 74 | }, 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "output_type": "execute_result" 78 | } 79 | ], 80 | "source": [ 81 | "#now we will for each teacher and school create some fake distance that in maximum could be 20 km\n", 82 | "distances = []\n", 83 | "for teacher in teachers:\n", 84 | " for school in schools:\n", 85 | " distances.append(np.random.randint(2, 20, size=1))\n", 86 | "len(distances) " 87 | ] 88 | }, 89 | { 90 | "cell_type": "code", 91 | "execution_count": 5, 92 | "id": "24e83b14", 93 | "metadata": {}, 94 | "outputs": [], 95 | "source": [ 96 | "values = np.asarray(distances).reshape(100,-10)\n", 97 | "df = pd.DataFrame(values, index=teachers, columns = schools)" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 6, 103 | "id": "d620e934", 104 | "metadata": { 105 | "scrolled": true 106 | }, 107 | "outputs": [ 108 | { 109 | "data": { 110 | "text/html": [ 111 | "
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School_1School_2School_3School_4School_5School_6School_7School_8School_9School_10
teacher_141461516161181714
teacher_258617918177159
teacher_31813101771771952
teacher_444211196771117
teacher_531522315916152
.................................
teacher_969111011912111696
teacher_9781314174314697
teacher_98715151710115171712
teacher_9913151114123116315
teacher_1001871761038151616
\n", 287 | "

100 rows × 10 columns

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" 289 | ], 290 | "text/plain": [ 291 | " School_1 School_2 School_3 School_4 School_5 School_6 \\\n", 292 | "teacher_1 4 14 6 15 16 16 \n", 293 | "teacher_2 5 8 6 17 9 18 \n", 294 | "teacher_3 18 13 10 17 7 17 \n", 295 | "teacher_4 4 4 2 11 19 6 \n", 296 | "teacher_5 3 15 2 2 3 15 \n", 297 | "... ... ... ... ... ... ... \n", 298 | "teacher_96 9 11 10 11 9 12 \n", 299 | "teacher_97 8 13 14 17 4 3 \n", 300 | "teacher_98 7 15 15 17 10 11 \n", 301 | "teacher_99 13 15 11 14 12 3 \n", 302 | "teacher_100 18 7 17 6 10 3 \n", 303 | "\n", 304 | " School_7 School_8 School_9 School_10 \n", 305 | "teacher_1 11 8 17 14 \n", 306 | "teacher_2 17 7 15 9 \n", 307 | "teacher_3 7 19 5 2 \n", 308 | "teacher_4 7 7 11 17 \n", 309 | "teacher_5 9 16 15 2 \n", 310 | "... ... ... ... ... \n", 311 | "teacher_96 11 16 9 6 \n", 312 | "teacher_97 14 6 9 7 \n", 313 | "teacher_98 5 17 17 12 \n", 314 | "teacher_99 11 6 3 15 \n", 315 | "teacher_100 8 15 16 16 \n", 316 | "\n", 317 | "[100 rows x 10 columns]" 318 | ] 319 | }, 320 | "execution_count": 6, 321 | "metadata": {}, 322 | "output_type": "execute_result" 323 | } 324 | ], 325 | "source": [ 326 | "df" 327 | ] 328 | }, 329 | { 330 | "cell_type": "code", 331 | "execution_count": 7, 332 | "id": "90c61ca1", 333 | "metadata": {}, 334 | "outputs": [], 335 | "source": [ 336 | "#At the end, let's add some fake salary expectaitons for each teacher, maximal teacher salary could be 8000 dollars\n", 337 | "salary_expectation = []\n", 338 | "for i in range(1,101):\n", 339 | " salary_expectation.append(np.random.randint(8000, size=1))\n", 340 | "salary_expectation = np.asarray(salary_expectation).reshape(100,)" 341 | ] 342 | }, 343 | { 344 | "cell_type": "code", 345 | "execution_count": 8, 346 | "id": "1bf97aef", 347 | "metadata": {}, 348 | "outputs": [], 349 | "source": [ 350 | "salary_pd = pd.Series(salary_expectation, index = df.index)" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": 9, 356 | "id": "b9cc896d", 357 | "metadata": {}, 358 | "outputs": [], 359 | "source": [ 360 | "df['salary_expectation'] = salary_pd" 361 | ] 362 | }, 363 | { 364 | "cell_type": "code", 365 | "execution_count": 10, 366 | "id": "842630c9", 367 | "metadata": {}, 368 | "outputs": [], 369 | "source": [ 370 | "#We must also calculate total distance for each teacher which we will use for objective function (we sum all school distances for each teacher)\n", 371 | "df['total_distance'] = df.iloc[:,:-2].sum(axis=1)" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 11, 377 | "id": "4225aaec", 378 | "metadata": {}, 379 | "outputs": [ 380 | { 381 | "data": { 382 | "text/html": [ 383 | "
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School_1School_2School_3School_4School_5School_6School_7School_8School_9School_10salary_expectationtotal_distance
teacher_1414615161611817145820107
teacher_2586179181771591251102
teacher_318131017717719521815113
teacher_444211196771117625371
teacher_531522315916152452180
\n", 493 | "
" 494 | ], 495 | "text/plain": [ 496 | " School_1 School_2 School_3 School_4 School_5 School_6 \\\n", 497 | "teacher_1 4 14 6 15 16 16 \n", 498 | "teacher_2 5 8 6 17 9 18 \n", 499 | "teacher_3 18 13 10 17 7 17 \n", 500 | "teacher_4 4 4 2 11 19 6 \n", 501 | "teacher_5 3 15 2 2 3 15 \n", 502 | "\n", 503 | " School_7 School_8 School_9 School_10 salary_expectation \\\n", 504 | "teacher_1 11 8 17 14 5820 \n", 505 | "teacher_2 17 7 15 9 1251 \n", 506 | "teacher_3 7 19 5 2 1815 \n", 507 | "teacher_4 7 7 11 17 6253 \n", 508 | "teacher_5 9 16 15 2 4521 \n", 509 | "\n", 510 | " total_distance \n", 511 | "teacher_1 107 \n", 512 | "teacher_2 102 \n", 513 | "teacher_3 113 \n", 514 | "teacher_4 71 \n", 515 | "teacher_5 80 " 516 | ] 517 | }, 518 | "execution_count": 11, 519 | "metadata": {}, 520 | "output_type": "execute_result" 521 | } 522 | ], 523 | "source": [ 524 | "df.head()" 525 | ] 526 | }, 527 | { 528 | "cell_type": "code", 529 | "execution_count": 12, 530 | "id": "d7a0780e", 531 | "metadata": {}, 532 | "outputs": [ 533 | { 534 | "name": "stdout", 535 | "output_type": "stream", 536 | "text": [ 537 | "Restricted license - for non-production use only - expires 2023-10-25\n" 538 | ] 539 | } 540 | ], 541 | "source": [ 542 | "#Declare and initialise the model\n", 543 | "#Resource Assignment problem - RAP\n", 544 | "#Four components of the model: data, desicion variables, constraints and objective function\n", 545 | "\n", 546 | "m = Model('RAP')" 547 | ] 548 | }, 549 | { 550 | "cell_type": "code", 551 | "execution_count": 13, 552 | "id": "a88a4862", 553 | "metadata": {}, 554 | "outputs": [ 555 | { 556 | "data": { 557 | "text/plain": [ 558 | "[('teacher_1', 'School_1'),\n", 559 | " ('teacher_1', 'School_2'),\n", 560 | " ('teacher_1', 'School_3'),\n", 561 | " ('teacher_1', 'School_4'),\n", 562 | " ('teacher_1', 'School_5'),\n", 563 | " ('teacher_1', 'School_6'),\n", 564 | " ('teacher_1', 'School_7'),\n", 565 | " ('teacher_1', 'School_8'),\n", 566 | " ('teacher_1', 'School_9'),\n", 567 | " ('teacher_1', 'School_10')]" 568 | ] 569 | }, 570 | "execution_count": 13, 571 | "metadata": {}, 572 | "output_type": "execute_result" 573 | } 574 | ], 575 | "source": [ 576 | "from itertools import product\n", 577 | "\n", 578 | "combinations = list(product(teachers, schools))\n", 579 | "combinations[:10]" 580 | ] 581 | }, 582 | { 583 | "cell_type": "code", 584 | "execution_count": 14, 585 | "id": "2848adfd", 586 | "metadata": {}, 587 | "outputs": [], 588 | "source": [ 589 | "# x is our decistion variable, in this case it could be only 1 (meaning this teacher goes to this school) or 0 (not assigned to this school)\n", 590 | "# in combinations we created all possible combinations of each teacher and each school\n", 591 | "x = m.addVars(combinations, name='assign')" 592 | ] 593 | }, 594 | { 595 | "cell_type": "code", 596 | "execution_count": 15, 597 | "id": "c5848965", 598 | "metadata": {}, 599 | "outputs": [], 600 | "source": [ 601 | "#now we create constraint that teacher can be assigned only to one school\n", 602 | "jobs = m.addConstrs((x.sum('*',school)==1 for school in schools), 'job')" 603 | ] 604 | }, 605 | { 606 | "cell_type": "code", 607 | "execution_count": 16, 608 | "id": "994b6046", 609 | "metadata": {}, 610 | "outputs": [], 611 | "source": [ 612 | "# for all teachers in the list of teachers with index j must be less 1, i.e. some teachers could not be chosen\n", 613 | "#teachers must be assigned maximum to 1 job, but it could happen teacher will not be chosen at all if he or she lives to far\n", 614 | "# or have unrealistic salary expectation\n", 615 | "resources = m.addConstrs((x.sum(teacher,'*')<=1 for teacher in teachers), 'teacher')" 616 | ] 617 | }, 618 | { 619 | "cell_type": "code", 620 | "execution_count": 17, 621 | "id": "72dbc863", 622 | "metadata": {}, 623 | "outputs": [], 624 | "source": [ 625 | "# just transform salary expectation to dict for gurobypi for budget constraint \n", 626 | "cost = {}\n", 627 | "for i, teacher in enumerate(teachers):\n", 628 | " for j, school in enumerate(schools):\n", 629 | " cost[(teacher,school)] = df['salary_expectation'][i]" 630 | ] 631 | }, 632 | { 633 | "cell_type": "code", 634 | "execution_count": 18, 635 | "id": "d0d2e27a", 636 | "metadata": {}, 637 | "outputs": [], 638 | "source": [ 639 | "#We also have some budget constraint. Out total budget is 400 000 dolars. So salary expectations of selected teachers\n", 640 | "#must not exceed 4000 000 dollars.\n", 641 | "budget = 400000\n", 642 | "budget = m.addConstr((x.prod(cost)<=budget), 'budget')" 643 | ] 644 | }, 645 | { 646 | "cell_type": "code", 647 | "execution_count": 19, 648 | "id": "857957ff", 649 | "metadata": {}, 650 | "outputs": [], 651 | "source": [ 652 | "# now we transpose our pandas data frame in multidict gurobipy could use \n", 653 | "obj_func = {}\n", 654 | "for i, teacher in enumerate(teachers):\n", 655 | " for j, school in enumerate(schools):\n", 656 | " obj_func[(teacher,school)] = df.iloc[i,j]" 657 | ] 658 | }, 659 | { 660 | "cell_type": "code", 661 | "execution_count": 20, 662 | "id": "2b3ef173", 663 | "metadata": {}, 664 | "outputs": [ 665 | { 666 | "data": { 667 | "text/plain": [ 668 | "1000" 669 | ] 670 | }, 671 | "execution_count": 20, 672 | "metadata": {}, 673 | "output_type": "execute_result" 674 | } 675 | ], 676 | "source": [ 677 | "len(obj_func)" 678 | ] 679 | }, 680 | { 681 | "cell_type": "code", 682 | "execution_count": 22, 683 | "id": "2f6c5aec", 684 | "metadata": {}, 685 | "outputs": [], 686 | "source": [ 687 | "#Now we should define our objective function. It is a distance for each teacher from each school. We want to minimize it.\n", 688 | "m.setObjective(x.prod(obj_func), GRB.MINIMIZE)" 689 | ] 690 | }, 691 | { 692 | "cell_type": "code", 693 | "execution_count": 28, 694 | "id": "7ac74dda", 695 | "metadata": {}, 696 | "outputs": [], 697 | "source": [ 698 | "#We can save our model\n", 699 | "# save model for inspection - you can see it in folder of this task.\n", 700 | "m.write('TEACHERS.lp')" 701 | ] 702 | }, 703 | { 704 | "cell_type": "code", 705 | "execution_count": 24, 706 | "id": "1a935ccc", 707 | "metadata": {}, 708 | "outputs": [ 709 | { 710 | "name": "stdout", 711 | "output_type": "stream", 712 | "text": [ 713 | "Gurobi Optimizer version 9.5.0 build v9.5.0rc5 (mac64[x86])\n", 714 | "Thread count: 4 physical cores, 8 logical processors, using up to 8 threads\n", 715 | "Optimize a model with 111 rows, 1000 columns and 3000 nonzeros\n", 716 | "Model fingerprint: 0xf363be96\n", 717 | "Coefficient statistics:\n", 718 | " Matrix range [1e+00, 8e+03]\n", 719 | " Objective range [2e+00, 2e+01]\n", 720 | " Bounds range [0e+00, 0e+00]\n", 721 | " RHS range [1e+00, 4e+05]\n", 722 | "Presolve time: 0.01s\n", 723 | "Presolved: 111 rows, 1000 columns, 3000 nonzeros\n", 724 | "\n", 725 | "Iteration Objective Primal Inf. Dual Inf. Time\n", 726 | " 0 0.0000000e+00 4.000000e+01 0.000000e+00 0s\n", 727 | " 10 2.0000000e+01 0.000000e+00 0.000000e+00 0s\n", 728 | "\n", 729 | "Solved in 10 iterations and 0.01 seconds (0.00 work units)\n", 730 | "Optimal objective 2.000000000e+01\n" 731 | ] 732 | } 733 | ], 734 | "source": [ 735 | "# run optimisation engine\n", 736 | "m.optimize()" 737 | ] 738 | }, 739 | { 740 | "cell_type": "code", 741 | "execution_count": 25, 742 | "id": "7d7a13ac", 743 | "metadata": {}, 744 | "outputs": [ 745 | { 746 | "name": "stdout", 747 | "output_type": "stream", 748 | "text": [ 749 | "assign[teacher_6,School_2] 1.0\n", 750 | "assign[teacher_12,School_1] 1.0\n", 751 | "assign[teacher_16,School_6] 1.0\n", 752 | "assign[teacher_38,School_8] 1.0\n", 753 | "assign[teacher_54,School_7] 1.0\n", 754 | "assign[teacher_61,School_10] 1.0\n", 755 | "assign[teacher_66,School_3] 1.0\n", 756 | "assign[teacher_72,School_9] 1.0\n", 757 | "assign[teacher_85,School_5] 1.0\n", 758 | "assign[teacher_94,School_4] 1.0\n" 759 | ] 760 | } 761 | ], 762 | "source": [ 763 | "#display optimal values of desicion variables:\n", 764 | "#we print which teachers our solver selected for each school\n", 765 | "for var in m.getVars():\n", 766 | " if var.x==1:\n", 767 | " print(var.varName, var.x)" 768 | ] 769 | }, 770 | { 771 | "cell_type": "code", 772 | "execution_count": 26, 773 | "id": "b32b30a7", 774 | "metadata": {}, 775 | "outputs": [], 776 | "source": [ 777 | "#We basically solved the problem. Teacher 6 goes to school 2, teacher 12 to school 1, etc. \n", 778 | "#Maybe we could see values of our objective function:" 779 | ] 780 | }, 781 | { 782 | "cell_type": "code", 783 | "execution_count": 27, 784 | "id": "d13c08eb", 785 | "metadata": {}, 786 | "outputs": [ 787 | { 788 | "name": "stdout", 789 | "output_type": "stream", 790 | "text": [ 791 | "total optimised distance 20.0\n", 792 | "average distance of all teachers 92.86\n" 793 | ] 794 | } 795 | ], 796 | "source": [ 797 | "#display optimal matching score\n", 798 | "print('total optimised distance', m.ObjVal)\n", 799 | "print('average distance of all teachers', df['total_distance'].mean())" 800 | ] 801 | }, 802 | { 803 | "cell_type": "markdown", 804 | "id": "4ab7a7e7", 805 | "metadata": {}, 806 | "source": [ 807 | "It was easy for our solver to solve this. It is easy problem. Imagine now that we have 100 schools and 1000 teachers. The procedure would be completely the same. " 808 | ] 809 | } 810 | ], 811 | "metadata": { 812 | "kernelspec": { 813 | "display_name": "Python 3 (ipykernel)", 814 | "language": "python", 815 | "name": "python3" 816 | }, 817 | "language_info": { 818 | "codemirror_mode": { 819 | "name": "ipython", 820 | "version": 3 821 | }, 822 | "file_extension": ".py", 823 | "mimetype": "text/x-python", 824 | "name": "python", 825 | "nbconvert_exporter": "python", 826 | "pygments_lexer": "ipython3", 827 | "version": "3.8.5" 828 | } 829 | }, 830 | "nbformat": 4, 831 | "nbformat_minor": 5 832 | } 833 | -------------------------------------------------------------------------------- /PCA.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "984475ed", 6 | "metadata": {}, 7 | "source": [ 8 | "### PCA\n", 9 | "Principal component analysis is ML technique for feature reduction. It searches in the space of the features, latent vectors that explains the highest amount of variance among original features (original matrix). The latent features are eigenvectors in matrix decomposition with their own eigenvalues of matrix of covariance of original features. In that way, we can search the more fundamental structure of some matrix, and we can explain the high dimensional space of feature with only a few principal components which are dimension that reflect the \"inner structure\" of our original matrix (e.g. imagine that we applied 20 IQ tests to our subjects, and it will create a matrix with a lot of variance - some subject will underperform on some tests, due to the tiredness, attention, etc. but we should be able to extract one principal component that should reflect the global IQ of the subject). Principal components are usually not correlated (they are ortogonal in our feature space) but some rotation (oblique Promax) allows correlation between components (in our example it could be an verbal and non-verba IQ component)." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "id": "356fb66e", 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import pandas as pd\n", 20 | "from sklearn.preprocessing import LabelEncoder" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "id": "9eab8a3b", 27 | "metadata": {}, 28 | "outputs": [], 29 | "source": [ 30 | "df = pd.read_csv('iq.csv')" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 3, 36 | "id": "e1e93f79", 37 | "metadata": {}, 38 | "outputs": [ 39 | { 40 | "data": { 41 | "text/html": [ 42 | "
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VQ1sVQ1aVQ1eVQ2sVQ2aVQ2eVQ3sVQ3aVQ3eVQ4s...MQ5sMQ5aMQ5eMQ6sMQ6aMQ6edateloadintroelapsetestelapseendelapse
042,4,3,1838233,2,1746031,4,252133...417,21,18,208540326,23,2289742019-01-11 18:29:36967440
134,3,14202931,2,35658234,1,2234242...420,21,17,1910112426,71,23,25,24,22133872019-01-11 18:31:22370346
243,4,2,12484421,21601934,2,193873...417,20,21,1815522525,24,22,23,26101982019-01-11 18:36:292162757
344,2,3,11218821,21354232,4,193092...217,68,20,218469370,24,23,22,26122412019-01-11 19:06:543084895
441,4,2,39023111265132,4,160892...219,20,18,6912028072,22123962019-01-11 19:15:144756168
\n", 206 | "

5 rows × 61 columns

\n", 207 | "
" 208 | ], 209 | "text/plain": [ 210 | " VQ1s VQ1a VQ1e VQ2s VQ2a VQ2e VQ3s VQ3a VQ3e VQ4s ... \\\n", 211 | "0 4 2,4,3,1 8382 3 3,2,1 7460 3 1,4,2 5213 3 ... \n", 212 | "1 3 4,3,1 42029 3 1,2,3 56582 3 4,1,2 23424 2 ... \n", 213 | "2 4 3,4,2,1 24844 2 1,2 16019 3 4,2,1 9387 3 ... \n", 214 | "3 4 4,2,3,1 12188 2 1,2 13542 3 2,4,1 9309 2 ... \n", 215 | "4 4 1,4,2,3 9023 1 1 12651 3 2,4,1 6089 2 ... \n", 216 | "\n", 217 | " MQ5s MQ5a MQ5e MQ6s MQ6a MQ6e \\\n", 218 | "0 4 17,21,18,20 8540 3 26,23,22 8974 \n", 219 | "1 4 20,21,17,19 10112 4 26,71,23,25,24,22 13387 \n", 220 | "2 4 17,20,21,18 15522 5 25,24,22,23,26 10198 \n", 221 | "3 2 17,68,20,21 8469 3 70,24,23,22,26 12241 \n", 222 | "4 2 19,20,18,69 12028 0 72,22 12396 \n", 223 | "\n", 224 | " dateload introelapse testelapse endelapse \n", 225 | "0 2019-01-11 18:29:36 9 674 40 \n", 226 | "1 2019-01-11 18:31:22 3 703 46 \n", 227 | "2 2019-01-11 18:36:29 2 1627 57 \n", 228 | "3 2019-01-11 19:06:54 30 848 95 \n", 229 | "4 2019-01-11 19:15:14 4 756 168 \n", 230 | "\n", 231 | "[5 rows x 61 columns]" 232 | ] 233 | }, 234 | "execution_count": 3, 235 | "metadata": {}, 236 | "output_type": "execute_result" 237 | } 238 | ], 239 | "source": [ 240 | "df.head()" 241 | ] 242 | }, 243 | { 244 | "cell_type": "markdown", 245 | "id": "8fd6fd6c", 246 | "metadata": {}, 247 | "source": [ 248 | "This is data from an on-line objective test advertised as \"Full Scale IQ Test\".\n", 249 | "https://www.kaggle.com/mpwolke/alpha-version-iq-test/data\n", 250 | "\n", 251 | "The test had 3 sections. The first was a vocabulary test, the second had mental rotation items, and the third was a short term memory test. Each question had 8 possible answers, of which 3-5 were correct so each question was more like a composite of several questions. A demo of the test in included.\n", 252 | "\n", 253 | "For each question, multiple values are recorded. e.g. for the first vocabulary question:\n", 254 | "\n", 255 | "VQ1s\tThe individuals score on that question. +1 point for each correct answer, -1 point for each wrong answer\n", 256 | "VQ1a\tThe actual answers selected by the user for this question\n", 257 | "VQ1e\tThe elapsed time in milliseconds on this question\n", 258 | "\n", 259 | "\n", 260 | "The other values were also recorded:\n", 261 | "dateload\t\n", 262 | "introelapse\ttime spent on the landing page in seconds\n", 263 | "testelapse\ttime spent on the test page in seconds\n", 264 | "endelapse\ttime spent on the page where they agreed to donate their data\n", 265 | "\n", 266 | "This file only contains individuals who indicated their data was appropriate for research; records from subjects where there were indications that it was not their first time taking this were also removed." 267 | ] 268 | }, 269 | { 270 | "cell_type": "code", 271 | "execution_count": 4, 272 | "id": "89429029", 273 | "metadata": {}, 274 | "outputs": [], 275 | "source": [ 276 | "def missing_values_table(df):\n", 277 | " # Total missing values\n", 278 | " mis_val = df.isnull().sum()\n", 279 | " \n", 280 | " # Percentage of missing values\n", 281 | " mis_val_percent = 100 * df.isnull().sum() / len(df)\n", 282 | " \n", 283 | " # Make a table with the results\n", 284 | " mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n", 285 | " \n", 286 | " # Rename the columns\n", 287 | " mis_val_table_ren_columns = mis_val_table.rename(\n", 288 | " columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n", 289 | " \n", 290 | " # Sort the table by percentage of missing descending\n", 291 | " mis_val_table_ren_columns = mis_val_table_ren_columns[\n", 292 | " mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n", 293 | " '% of Total Values', ascending=False).round(1)\n", 294 | " \n", 295 | " # Print some summary information\n", 296 | " print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\" \n", 297 | " \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n", 298 | " \" columns that have missing values.\")\n", 299 | " \n", 300 | " # Return the dataframe with missing information\n", 301 | " return mis_val_table_ren_columns" 302 | ] 303 | }, 304 | { 305 | "cell_type": "code", 306 | "execution_count": 5, 307 | "id": "0638ddda", 308 | "metadata": {}, 309 | "outputs": [ 310 | { 311 | "data": { 312 | "text/plain": [ 313 | "count 3194.000000\n", 314 | "mean 3.251096\n", 315 | "std 1.084963\n", 316 | "min -1.000000\n", 317 | "25% 3.000000\n", 318 | "50% 4.000000\n", 319 | "75% 4.000000\n", 320 | "max 4.000000\n", 321 | "Name: VQ1s, dtype: float64" 322 | ] 323 | }, 324 | "execution_count": 5, 325 | "metadata": {}, 326 | "output_type": "execute_result" 327 | } 328 | ], 329 | "source": [ 330 | "df['VQ1s'].describe()" 331 | ] 332 | }, 333 | { 334 | "cell_type": "code", 335 | "execution_count": 6, 336 | "id": "38509138", 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "data": { 341 | "text/plain": [ 342 | "['VQ1s',\n", 343 | " 'VQ2s',\n", 344 | " 'VQ3s',\n", 345 | " 'VQ4s',\n", 346 | " 'VQ5s',\n", 347 | " 'VQ6s',\n", 348 | " 'VQ7s',\n", 349 | " 'RQ1s',\n", 350 | " 'RQ2s',\n", 351 | " 'RQ3s',\n", 352 | " 'RQ4s',\n", 353 | " 'RQ5s',\n", 354 | " 'RQ6s',\n", 355 | " 'MQ1s',\n", 356 | " 'MQ2s',\n", 357 | " 'MQ3s',\n", 358 | " 'MQ4s',\n", 359 | " 'MQ5s',\n", 360 | " 'MQ6s']" 361 | ] 362 | }, 363 | "execution_count": 6, 364 | "metadata": {}, 365 | "output_type": "execute_result" 366 | } 367 | ], 368 | "source": [ 369 | "assessments = [] # we extract only columns with assessed iq tasks \n", 370 | "for col in df.columns:\n", 371 | " if col.endswith('s'):\n", 372 | " assessments.append(col)\n", 373 | "assessments" 374 | ] 375 | }, 376 | { 377 | "cell_type": "code", 378 | "execution_count": 7, 379 | "id": "95825f14", 380 | "metadata": {}, 381 | "outputs": [], 382 | "source": [ 383 | "df = df[assessments]" 384 | ] 385 | }, 386 | { 387 | "cell_type": "code", 388 | "execution_count": 8, 389 | "id": "54732f19", 390 | "metadata": {}, 391 | "outputs": [ 392 | { 393 | "data": { 394 | "text/html": [ 395 | "
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VQ1sVQ2sVQ3sVQ4sVQ5sVQ6sVQ7sRQ1sRQ2sRQ3sRQ4sRQ5sRQ6sMQ1sMQ2sMQ3sMQ4sMQ5sMQ6s
04333333133445424443
13332443-154344423544
24233333144444334545
34232343344444434423
44132323021412423320
\n", 547 | "
" 548 | ], 549 | "text/plain": [ 550 | " VQ1s VQ2s VQ3s VQ4s VQ5s VQ6s VQ7s RQ1s RQ2s RQ3s RQ4s RQ5s \\\n", 551 | "0 4 3 3 3 3 3 3 1 3 3 4 4 \n", 552 | "1 3 3 3 2 4 4 3 -1 5 4 3 4 \n", 553 | "2 4 2 3 3 3 3 3 1 4 4 4 4 \n", 554 | "3 4 2 3 2 3 4 3 3 4 4 4 4 \n", 555 | "4 4 1 3 2 3 2 3 0 2 1 4 1 \n", 556 | "\n", 557 | " RQ6s MQ1s MQ2s MQ3s MQ4s MQ5s MQ6s \n", 558 | "0 5 4 2 4 4 4 3 \n", 559 | "1 4 4 2 3 5 4 4 \n", 560 | "2 4 3 3 4 5 4 5 \n", 561 | "3 4 4 3 4 4 2 3 \n", 562 | "4 2 4 2 3 3 2 0 " 563 | ] 564 | }, 565 | "execution_count": 8, 566 | "metadata": {}, 567 | "output_type": "execute_result" 568 | } 569 | ], 570 | "source": [ 571 | "df.head()" 572 | ] 573 | }, 574 | { 575 | "cell_type": "code", 576 | "execution_count": 9, 577 | "id": "fce66322", 578 | "metadata": { 579 | "scrolled": true 580 | }, 581 | "outputs": [ 582 | { 583 | "name": "stdout", 584 | "output_type": "stream", 585 | "text": [ 586 | "Your selected dataframe has 19 columns.\n", 587 | "There are 0 columns that have missing values.\n" 588 | ] 589 | }, 590 | { 591 | "data": { 592 | "text/html": [ 593 | "
\n", 594 | "\n", 607 | "\n", 608 | " \n", 609 | " \n", 610 | " \n", 611 | " \n", 612 | " \n", 613 | " \n", 614 | " \n", 615 | " \n", 616 | " \n", 617 | "
Missing Values% of Total Values
\n", 618 | "
" 619 | ], 620 | "text/plain": [ 621 | "Empty DataFrame\n", 622 | "Columns: [Missing Values, % of Total Values]\n", 623 | "Index: []" 624 | ] 625 | }, 626 | "execution_count": 9, 627 | "metadata": {}, 628 | "output_type": "execute_result" 629 | } 630 | ], 631 | "source": [ 632 | "df_missing= missing_values_table(df)\n", 633 | "df_missing" 634 | ] 635 | }, 636 | { 637 | "cell_type": "code", 638 | "execution_count": 10, 639 | "id": "3873cc56", 640 | "metadata": {}, 641 | "outputs": [], 642 | "source": [ 643 | "from sklearn.preprocessing import StandardScaler\n", 644 | "\n", 645 | "sc = StandardScaler()\n", 646 | "df_std = sc.fit_transform(df)" 647 | ] 648 | }, 649 | { 650 | "cell_type": "code", 651 | "execution_count": 11, 652 | "id": "76f46beb", 653 | "metadata": {}, 654 | "outputs": [ 655 | { 656 | "name": "stdout", 657 | "output_type": "stream", 658 | "text": [ 659 | "\n", 660 | "Eigenvalues \n", 661 | "[11.32384655 3.10317769 2.38987079 1.53470763 0.38844001 0.4047044\n", 662 | " 0.43405762 0.45174528 0.56935699 0.58265879 1.26507255 0.71717052\n", 663 | " 0.76601852 0.80238905 0.89512253 1.13175707 1.09794158 1.07614948\n", 664 | " 0.97820066]\n" 665 | ] 666 | } 667 | ], 668 | "source": [ 669 | "import numpy as np\n", 670 | "cov_mat = np.cov(df.T) # we create matrix of covariance\n", 671 | "eigen_vals, eigen_vecs = np.linalg.eig(cov_mat) # We find the eigen values and eigen vectors\n", 672 | "\n", 673 | "print('\\nEigenvalues \\n%s' % eigen_vals)" 674 | ] 675 | }, 676 | { 677 | "cell_type": "code", 678 | "execution_count": 12, 679 | "id": "50a6c191", 680 | "metadata": {}, 681 | "outputs": [], 682 | "source": [ 683 | "tot = sum(eigen_vals)\n", 684 | "var_exp = [(i / tot) for i in sorted(eigen_vals, reverse=True)]\n", 685 | "cum_var_exp = np.cumsum(var_exp) # we calculate the percentage of variance expleind by eigen vector (principal component)" 686 | ] 687 | }, 688 | { 689 | "cell_type": "code", 690 | "execution_count": 13, 691 | "id": "52826a50", 692 | "metadata": {}, 693 | "outputs": [ 694 | { 695 | "data": { 696 | "text/plain": [ 697 | "19" 698 | ] 699 | }, 700 | "execution_count": 13, 701 | "metadata": {}, 702 | "output_type": "execute_result" 703 | } 704 | ], 705 | "source": [ 706 | "len(var_exp)" 707 | ] 708 | }, 709 | { 710 | "cell_type": "code", 711 | "execution_count": 14, 712 | "id": "88e03bde", 713 | "metadata": {}, 714 | "outputs": [ 715 | { 716 | "data": { 717 | "text/plain": [ 718 | "[0.37856712307452156,\n", 719 | " 0.1037422261239888,\n", 720 | " 0.07989568777315426,\n", 721 | " 0.05130675762325875,\n", 722 | " 0.04229259665452779,\n", 723 | " 0.03783573143733723,\n", 724 | " 0.036705247090664965,\n", 725 | " 0.03597671595775853,\n", 726 | " 0.032702192394812174,\n", 727 | " 0.029924810346714657,\n", 728 | " 0.026824640651322597,\n", 729 | " 0.025608738751444194,\n", 730 | " 0.023975702892830887,\n", 731 | " 0.0194788458905116,\n", 732 | " 0.01903415390745789,\n", 733 | " 0.015102280997196191,\n", 734 | " 0.014510965178426144,\n", 735 | " 0.01352965872469495,\n", 736 | " 0.012985924529377134]" 737 | ] 738 | }, 739 | "execution_count": 14, 740 | "metadata": {}, 741 | "output_type": "execute_result" 742 | } 743 | ], 744 | "source": [ 745 | "var_exp" 746 | ] 747 | }, 748 | { 749 | "cell_type": "code", 750 | "execution_count": 15, 751 | "id": "ebd5c002", 752 | "metadata": {}, 753 | "outputs": [ 754 | { 755 | "data": { 756 | "image/png": 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\n", 757 | "text/plain": [ 758 | "
" 759 | ] 760 | }, 761 | "metadata": { 762 | "needs_background": "light" 763 | }, 764 | "output_type": "display_data" 765 | } 766 | ], 767 | "source": [ 768 | "import matplotlib.pyplot as plt\n", 769 | "\n", 770 | "\n", 771 | "plt.bar(range(1, 20), var_exp, alpha=0.5, align='center',\n", 772 | " label='individual explained variance')\n", 773 | "plt.step(range(1, 20), cum_var_exp, where='mid',\n", 774 | " label='cumulative explained variance')\n", 775 | "plt.ylabel('Explained variance ratio')\n", 776 | "plt.xlabel('Principal components')\n", 777 | "plt.legend(loc='best')\n", 778 | "plt.tight_layout()\n", 779 | "# plt.savefig('./figures/pca1.png', dpi=300)\n", 780 | "plt.show()" 781 | ] 782 | }, 783 | { 784 | "cell_type": "code", 785 | "execution_count": 16, 786 | "id": "48e99549", 787 | "metadata": {}, 788 | "outputs": [ 789 | { 790 | "data": { 791 | "text/plain": [ 792 | "array([0.28627341, 0.12096431])" 793 | ] 794 | }, 795 | "execution_count": 16, 796 | "metadata": {}, 797 | "output_type": "execute_result" 798 | } 799 | ], 800 | "source": [ 801 | "from sklearn.decomposition import PCA\n", 802 | "\n", 803 | "pca = PCA(n_components=2)\n", 804 | "df_pca2 = pca.fit_transform(df_std)\n", 805 | "pca.explained_variance_ratio_" 806 | ] 807 | }, 808 | { 809 | "cell_type": "code", 810 | "execution_count": 17, 811 | "id": "8bdd85aa", 812 | "metadata": {}, 813 | "outputs": [], 814 | "source": [ 815 | "pca = PCA(n_components=2) # we want to extract two principal components\n", 816 | "df_pca2 = pca.fit_transform(df_std) # we get scores on two main principal components" 817 | ] 818 | }, 819 | { 820 | "cell_type": "code", 821 | "execution_count": 18, 822 | "id": "10c8b133", 823 | "metadata": {}, 824 | "outputs": [], 825 | "source": [ 826 | "pca_data = pd.DataFrame(data=df_pca2, columns=[\"PC1\", \"PC2\"], index=df.index)" 827 | ] 828 | }, 829 | { 830 | "cell_type": "code", 831 | "execution_count": 19, 832 | "id": "cd624574", 833 | "metadata": {}, 834 | "outputs": [], 835 | "source": [ 836 | "df_std = pd.DataFrame(df_std, columns = df.columns)\n", 837 | "df_all = df_std.merge(pca_data, how='inner', on = df.index)" 838 | ] 839 | }, 840 | { 841 | "cell_type": "code", 842 | "execution_count": 20, 843 | "id": "cde82a5d", 844 | "metadata": {}, 845 | "outputs": [], 846 | "source": [ 847 | "df_all = df_all.drop('key_0', axis=1)" 848 | ] 849 | }, 850 | { 851 | "cell_type": "code", 852 | "execution_count": 21, 853 | "id": "36941b56", 854 | "metadata": {}, 855 | "outputs": [], 856 | "source": [ 857 | "correlations = df_all.corr() # we take correlations between features and PCs" 858 | ] 859 | }, 860 | { 861 | "cell_type": "code", 862 | "execution_count": 45, 863 | "id": "ff29901b", 864 | "metadata": {}, 865 | "outputs": [ 866 | { 867 | "data": { 868 | "image/png": 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" 871 | ] 872 | }, 873 | "metadata": { 874 | "needs_background": "light" 875 | }, 876 | "output_type": "display_data" 877 | } 878 | ], 879 | "source": [ 880 | "import seaborn as sns\n", 881 | "correlations = correlations[['PC1', 'PC2']].apply(lambda x: round(x,2))\n", 882 | "sns.heatmap(correlations, cmap = 'autumn_r', vmin = -1, vmax=1); # from this heatmap we clearly see\n", 883 | "#that first component (PC1) is more correlated with manipulative tests (RQ) while second PCA2 is more connected \n", 884 | "#with verbal tasks" 885 | ] 886 | } 887 | ], 888 | "metadata": { 889 | "kernelspec": { 890 | "display_name": "Python 3", 891 | "language": "python", 892 | "name": "python3" 893 | }, 894 | "language_info": { 895 | "codemirror_mode": { 896 | "name": "ipython", 897 | "version": 3 898 | }, 899 | "file_extension": ".py", 900 | "mimetype": "text/x-python", 901 | "name": "python", 902 | "nbconvert_exporter": "python", 903 | "pygments_lexer": "ipython3", 904 | "version": "3.8.5" 905 | } 906 | }, 907 | "nbformat": 4, 908 | "nbformat_minor": 5 909 | } 910 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ML_algorithms 2 | Basic and advanced ML algorithms with customised functions 3 | 4 | #### DBScan ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/DBScan%20clustering%20algorithm.ipynb)) 5 | DBscan is clustering algorithm but it, unlike K-means, does not have centroids, so it is more sensitive to the nonlinear patterns of connections between features we want to group and identify hidden paterns. DBScan thus uses radius and group values of the data if they belong in to the area of some hypotesised radius. 6 | In this script we loop for different noise hyperparameter on toy data set and find different precision of solution. 7 | 8 | #### Isolation forest ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Isolation_forest.ipynb)) 9 | 10 | Isolation Forest, like any tree ensemble method, is built on the basis of decision trees. In these trees, partitions are created by first randomly selecting a feature and then selecting a random split value between the minimum and maximum value of the selected feature. In principle, outliers are less frequent than regular observations and are different from them in terms of values (they lie further away from the regular observations in the feature space). That is why by using such random partitioning they should be identified closer to the root of the tree (shorter average path length, i.e., the number of edges an observation must pass in the tree going from the root to the terminal node), with fewer splits necessary. 11 | 12 | In the following example we will use the ISO forest algorithm on a famous boston data set to detect the cities with the highest crime rate. 13 | 14 | #### K-means clusters ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/K-means%20clustering.ipynb)) 15 | 16 | K-means clusters work by initialising the centroids of the data and then categorise the data by some measure of distance (e.g. Eucledian). The backside of this clustering algorithm is that we must assume number of clusters a priori, but then we can check the measure of goodness of various solutions (i.e. k-number of clusters), comapring variance between clasters and within clasters. The bigger the ratio in favor of huge variance between clusters, solution is better. This relatively simple analysis could give very important business value insights in terms of better understaning the charachteristics and typology of the some pheonomena (e.g. customers, products, etc.). 17 | 18 | In this script we make 3-D plots on Iris data set as ground truth and varoious solutions, as well as implement elbow rule. 19 | 20 | #### Lasso, Ridge and Elastic Net ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Lasso%2C%20Ridge%20and%20Elastic%20Net.ipynb)) 21 | 22 | In this script we apply three different types of regularisation on polinomial and regular regression model. Then we plot the parameter shrinkage. 23 | 24 | #### Principal component analysis (PCA) ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/PCA.ipynb)) 25 | 26 | Principal component analysis is ML technique for feature reduction. It searches in the space of the features, latent vectors that explains the highest amount of variance among original features (original matrix). The latent features are eigenvectors in matrix decomposition with their own eigenvalues of matrix of covariance of original features. In that way, we can search the more fundamental structure of some matrix, and we can explain the high dimensional space of feature with only a few principal components which are dimension that reflect the "inner structure" of our original matrix (e.g. imagine that we applied 20 IQ tests to our subjects, and it will create a matrix with a lot of variance - some subject will underperform on some tests, due to the tiredness, attention, etc. but we should be able to extract one principal component that should reflect the global IQ of the subject). Principal components are usually not correlated (they are ortogonal in our feature space) but some rotation (oblique Promax) allows correlation between components (in our example it could be an verbal and non-verba IQ component). 27 | 28 | We do real example with IQ data set. 29 | 30 | #### Polynomial regression ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Polynomial%20Regression.ipynb)) 31 | 32 | We did this small practise to illustrate the overfitting problem when we raise the polinomial parameter of regression model. 33 | Interesting plotting helps in understanding how polinomial regression could help in reducing bias but also how it can lead to overfitting. 34 | 35 | Polinomial regression describes polynomial functions in contrast to linear one, which is more complex and describes nonlinear relationships between predictor and target feature. We will do a little play with some fake data as illustration. PolynomialFeatures with degree three for two features a and b adds not only , , , but also , , . Some optimisation, like Akaike information criteria is needed to determine the smallest mean square error but in relation to the number of parameters, due to computational complexity. 36 | 37 | 38 | #### Random Forest ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Random%20Forest.ipynb)) 39 | 40 | Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of reducing the variance. This comes at the expense of a small increase in the bias and some loss of interpretability, but generally greatly boosts the performance in the final model. Trees that are grown very deep tend to learn highly irregular patterns: they overfit their training sets, i.e. have low bias, but very high variance. Random forests are a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of reducing the variance. 41 | 42 | The training algorithm for random forests applies the general technique of bootstrap aggregating, or bagging, to tree learners. Given a training set X with responses Y bagging repeatedly (B times) selects a random sample with replacement of the training set and fits trees to these samples: 43 | 44 | Sample, with replacement, n training examples from X, Y; call these Xb, Yb. Random Forest trains a classification or regression tree fb on Xb, Yb. After training, predictions for unseen samples x' can be made by averaging the predictions from all the individual regression trees on x' or by taking the majority vote in the case of classification trees. 45 | 46 | We make in this script full RF model on a wine data set with all relevant graphic illustrations. 47 | 48 | 49 | #### Simple Neural Network from the scratch ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Simple%20Neural%20Network%20from%20the%20Scratch.ipynb)) 50 | 51 | With the help of material of Andrew Ng, we make a simple NN from the scratch for a toy data set. Very good illustration gives the intuition of what NN are capable of. 52 | 53 | #### XGBoost ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/XGBoost-IRIS.ipynb)) 54 | 55 | XGBoost is an implementation of gradient boosted decision trees designed for speed and performance. Boosting is an ensemble technique where new models are added to correct the errors made by existing models. Models are added sequentially until no further improvements can be made. A popular example is the AdaBoost algorithm that weights data points that are hard to predict. 56 | 57 | Gradient boosting is an approach where new models are created that predict the residuals or errors of prior models and then added together to make the final prediction. It is called gradient boosting because it uses a gradient descent algorithm to minimize the loss when adding new models. 58 | 59 | We will do a quick XGboost model on an Iris data set with all relevant illustrations. 60 | 61 | #### Linear Programming ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Linear%20Programming/Linear_programming_with_gurobipy_teachers_example.ipynb)) 62 | 63 | Linear programming is an optimization technique for a system of linear constraints and a linear objective function. An objective function defines the quantity to be optimized, and the goal of linear programming is to find the values of the variables that maximize or minimize the objective function. 64 | Here we created one interesting task. Imagine we have 100 teachers and 10 schools. We have the data what is the distance from each school from each teacher. 65 | We want that our teachers be satisfied, that they does not travell a lot, in our intent, as municipality officer to assign one new teacher to the each school. 66 | We also have some budget constraing and salary expectation for each teacher. How would you do the optimal assignment of teachers to school? By LP! 67 | The formalisation of all equtations could be seen here: ([Teachers.lp](https://github.com/Vitomir84/ML_algorithms/blob/main/Linear%20Programming/TEACHERS.lp)) 68 | 69 | 70 | #### TimeSeries forecasting with SARIMAX ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Timeseries%20comprehensive.ipynb)) 71 | 72 | This scripts explains the basic concepts for understanding timeseries: trend, seasonality, white noise, stationarity and makes forecasting example with autoregresive, differencing and moving averages parameters. This is mandatory step for understanding any timeseries data. 73 | 74 | #### Automatised search for hyperparameters for SARIMA forecasting model ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Auto%20ARIMA%20hyperparameter%20search.ipynb)) 75 | 76 | Here you cand find a tool that will do auto hyperparameter search for autoregressive, differencing, moving average and seasonality parameters for SARIMA forecasting model. 77 | 78 | #### SMOTE and Cost Learning for imbalanced dataset in Fraud Detection ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Online_Payments_Fraud_Detection.ipynb)) 79 | 80 | This part is not finished and some parts of explanations are in Serbian, but the majority of the code is there, with interesting visualisation of SMOTE syntheting data oversampling, as well as grid search for weights for imbalanced cost sensitive learning that stresses the misclasified cases. 81 | 82 | #### XGBoost model on breast_cancer_dataset with precision-recall curve, ROC curve and shap values ([Link](https://github.com/Vitomir84/ML_algorithms/blob/main/Breath_cancer_with_shap_values.ipynb)) 83 | 84 | This part contains ML model of very important dataset with an ilustration of importance of particular features in cancer prediction. Shap package offers very nice visualisation of feature importance for interpretable ML models 85 | 86 | 87 | 88 | -------------------------------------------------------------------------------- /Rule_based_approach for removing the outliers.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "7d3a7d79", 6 | "metadata": {}, 7 | "source": [ 8 | "## Remove outliers\n", 9 | "Imagine we have some data from normal distribution and we want to see are there some outliers." 10 | ] 11 | }, 12 | { 13 | "cell_type": "code", 14 | "execution_count": 1, 15 | "id": "cbf08402", 16 | "metadata": {}, 17 | "outputs": [], 18 | "source": [ 19 | "import numpy as np\n", 20 | "import pandas as pd\n", 21 | "from sklearn.datasets import load_boston\n", 22 | "import matplotlib.pyplot as plt" 23 | ] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "execution_count": 2, 28 | "id": "c734bd58", 29 | "metadata": {}, 30 | "outputs": [], 31 | "source": [ 32 | "#we create a function that will remove selected percentage of top and bottom cases and made replace with median values\n", 33 | "def remove_outliers(df, tf: str, bottom:float, top:float):\n", 34 | " # remove outliers\n", 35 | " maxi = df[tf].quantile(top)\n", 36 | " mini = df[tf].quantile(bottom)\n", 37 | " median=df[tf].median()\n", 38 | " \n", 39 | " # fill outliers with median\n", 40 | " df['outlier'] = ~df[tf].between(mini, maxi)\n", 41 | " df[tf+'_without_outlier'] = np.where(df['outlier'] == True, median, df[tf])\n", 42 | " return df" 43 | ] 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": 3, 48 | "id": "8be481a3", 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "data": { 53 | "text/html": [ 54 | "
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.653063
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.141062
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.730000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.950000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.360000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.955000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.970000
\n", 218 | "
" 219 | ], 220 | "text/plain": [ 221 | " CRIM ZN INDUS CHAS NOX RM \\\n", 222 | "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", 223 | "mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \n", 224 | "std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \n", 225 | "min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \n", 226 | "25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \n", 227 | "50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \n", 228 | "75% 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 \n", 229 | "max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \n", 230 | "\n", 231 | " AGE DIS RAD TAX PTRATIO B \\\n", 232 | "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", 233 | "mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \n", 234 | "std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \n", 235 | "min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \n", 236 | "25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \n", 237 | "50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \n", 238 | "75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \n", 239 | "max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \n", 240 | "\n", 241 | " LSTAT \n", 242 | "count 506.000000 \n", 243 | "mean 12.653063 \n", 244 | "std 7.141062 \n", 245 | "min 1.730000 \n", 246 | "25% 6.950000 \n", 247 | "50% 11.360000 \n", 248 | "75% 16.955000 \n", 249 | "max 37.970000 " 250 | ] 251 | }, 252 | "execution_count": 3, 253 | "metadata": {}, 254 | "output_type": "execute_result" 255 | } 256 | ], 257 | "source": [ 258 | "boston =load_boston()\n", 259 | "names=list(boston['feature_names'])\n", 260 | "X=boston['data']\n", 261 | "df = pd.DataFrame(X, columns=names)\n", 262 | "df.describe()" 263 | ] 264 | }, 265 | { 266 | "cell_type": "code", 267 | "execution_count": 29, 268 | "id": "60cfd4e7", 269 | "metadata": {}, 270 | "outputs": [ 271 | { 272 | "data": { 273 | "image/png": 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OAbua7ZKW6O9PgXcBR5pc+fPWj+ul9JJUkzNwSSrKAJekogxwSSrKAJekogxwSSrKAJekogxwSSrq/wGQWegP5rI8TwAAAABJRU5ErkJggg==\n", 274 | "text/plain": [ 275 | "
" 276 | ] 277 | }, 278 | "metadata": { 279 | "needs_background": "light" 280 | }, 281 | "output_type": "display_data" 282 | } 283 | ], 284 | "source": [ 285 | "df['DIS'].hist(bins=50);" 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": 30, 291 | "id": "d6a3c030", 292 | "metadata": {}, 293 | "outputs": [], 294 | "source": [ 295 | "df = remove_outliers(df, 'DIS', 0.01, 0.98)" 296 | ] 297 | }, 298 | { 299 | "cell_type": "code", 300 | "execution_count": 32, 301 | "id": "d4b3f3a1", 302 | "metadata": {}, 303 | "outputs": [ 304 | { 305 | "data": { 306 | "image/png": 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" 309 | ] 310 | }, 311 | "metadata": { 312 | "needs_background": "light" 313 | }, 314 | "output_type": "display_data" 315 | } 316 | ], 317 | "source": [ 318 | "df['DIS_without_outlier'].hist(bins=50);" 319 | ] 320 | } 321 | ], 322 | "metadata": { 323 | "kernelspec": { 324 | "display_name": "Python 3", 325 | "language": "python", 326 | "name": "python3" 327 | }, 328 | "language_info": { 329 | "codemirror_mode": { 330 | "name": "ipython", 331 | "version": 3 332 | }, 333 | "file_extension": ".py", 334 | "mimetype": "text/x-python", 335 | "name": "python", 336 | "nbconvert_exporter": "python", 337 | "pygments_lexer": "ipython3", 338 | "version": "3.8.5" 339 | } 340 | }, 341 | "nbformat": 4, 342 | "nbformat_minor": 5 343 | } 344 | -------------------------------------------------------------------------------- /__pycache__/planar_utils.cpython-38.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vitomir84/ML_algorithms/3082d7ca60dec864758c58bb27455f13da3a4c76/__pycache__/planar_utils.cpython-38.pyc -------------------------------------------------------------------------------- /cost_sensitive1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vitomir84/ML_algorithms/3082d7ca60dec864758c58bb27455f13da3a4c76/cost_sensitive1.png -------------------------------------------------------------------------------- /pictures/lasso.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vitomir84/ML_algorithms/3082d7ca60dec864758c58bb27455f13da3a4c76/pictures/lasso.png -------------------------------------------------------------------------------- /planar_utils.py: -------------------------------------------------------------------------------- 1 | import matplotlib.pyplot as plt 2 | import numpy as np 3 | import sklearn 4 | import sklearn.datasets 5 | import sklearn.linear_model 6 | 7 | def plot_decision_boundary(model, X, y): 8 | # Set min and max values and give it some padding 9 | x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1 10 | y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1 11 | h = 0.01 12 | # Generate a grid of points with distance h between them 13 | xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h)) 14 | # Predict the function value for the whole grid 15 | Z = model(np.c_[xx.ravel(), yy.ravel()]) 16 | Z = Z.reshape(xx.shape) 17 | # Plot the contour and training examples 18 | plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral) 19 | plt.ylabel('x2') 20 | plt.xlabel('x1') 21 | plt.scatter(X[0, :], X[1, :], c=y, cmap=plt.cm.Spectral) 22 | 23 | 24 | def sigmoid(x): 25 | """ 26 | Compute the sigmoid of x 27 | 28 | Arguments: 29 | x -- A scalar or numpy array of any size. 30 | 31 | Return: 32 | s -- sigmoid(x) 33 | """ 34 | s = 1/(1+np.exp(-x)) 35 | return s 36 | 37 | def load_planar_dataset(): 38 | np.random.seed(1) 39 | m = 400 # number of examples 40 | N = int(m/2) # number of points per class 41 | D = 2 # dimensionality 42 | X = np.zeros((m,D)) # data matrix where each row is a single example 43 | Y = np.zeros((m,1), dtype='uint8') # labels vector (0 for red, 1 for blue) 44 | a = 4 # maximum ray of the flower 45 | 46 | for j in range(2): 47 | ix = range(N*j,N*(j+1)) 48 | t = np.linspace(j*3.12,(j+1)*3.12,N) + np.random.randn(N)*0.2 # theta 49 | r = a*np.sin(4*t) + np.random.randn(N)*0.2 # radius 50 | X[ix] = np.c_[r*np.sin(t), r*np.cos(t)] 51 | Y[ix] = j 52 | 53 | X = X.T 54 | Y = Y.T 55 | 56 | return X, Y 57 | 58 | def load_extra_datasets(): 59 | N = 200 60 | noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3) 61 | noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2) 62 | blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6) 63 | gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2, n_classes=2, shuffle=True, random_state=None) 64 | no_structure = np.random.rand(N, 2), np.random.rand(N, 2) 65 | 66 | return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure -------------------------------------------------------------------------------- /tree.dot: -------------------------------------------------------------------------------- 1 | digraph Tree { 2 | node [shape=box, style="filled", color="black"] ; 3 | 0 [label="alcohol <= 12.55\ngini = 0.643\nsamples = 74\nvalue = [33, 57, 34]\nclass = class 1", fillcolor="#ccf8df"] ; 4 | 1 [label="color_intensity <= 3.825\ngini = 0.194\nsamples = 27\nvalue = [0, 41, 5]\nclass = class 1", fillcolor="#51e890"] ; 5 | 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ; 6 | 2 [label="gini = 0.0\nsamples = 21\nvalue = [0, 39, 0]\nclass = class 1", fillcolor="#39e581"] ; 7 | 1 -> 2 ; 8 | 3 [label="gini = 0.408\nsamples = 6\nvalue = [0, 2, 5]\nclass = class 2", fillcolor="#b388ef"] ; 9 | 1 -> 3 ; 10 | 4 [label="od280/od315_of_diluted_wines <= 1.985\ngini = 0.641\nsamples = 47\nvalue = [33, 16, 29]\nclass = class 0", fillcolor="#fdf5ef"] ; 11 | 0 -> 4 [labeldistance=2.5, labelangle=-45, headlabel="False"] ; 12 | 5 [label="gini = 0.0\nsamples = 18\nvalue = [0, 0, 28]\nclass = class 2", fillcolor="#8139e5"] ; 13 | 4 -> 5 ; 14 | 6 [label="magnesium <= 94.5\ngini = 0.462\nsamples = 29\nvalue = [33, 16, 1]\nclass = class 0", fillcolor="#f2c09c"] ; 15 | 4 -> 6 ; 16 | 7 [label="alcalinity_of_ash <= 20.35\ngini = 0.29\nsamples = 10\nvalue = [2, 15, 1]\nclass = class 1", fillcolor="#5eea99"] ; 17 | 6 -> 7 ; 18 | 8 [label="gini = 0.531\nsamples = 6\nvalue = [2, 5, 1]\nclass = class 1", fillcolor="#9cf2c0"] ; 19 | 7 -> 8 ; 20 | 9 [label="gini = 0.0\nsamples = 4\nvalue = [0, 10, 0]\nclass = class 1", fillcolor="#39e581"] ; 21 | 7 -> 9 ; 22 | 10 [label="magnesium <= 117.0\ngini = 0.061\nsamples = 19\nvalue = [31, 1, 0]\nclass = class 0", fillcolor="#e6853f"] ; 23 | 6 -> 10 ; 24 | 11 [label="gini = 0.0\nsamples = 14\nvalue = [20, 0, 0]\nclass = class 0", fillcolor="#e58139"] ; 25 | 10 -> 11 ; 26 | 12 [label="gini = 0.153\nsamples = 5\nvalue = [11, 1, 0]\nclass = class 0", fillcolor="#e78c4b"] ; 27 | 10 -> 12 ; 28 | } -------------------------------------------------------------------------------- /tree.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Vitomir84/ML_algorithms/3082d7ca60dec864758c58bb27455f13da3a4c76/tree.png --------------------------------------------------------------------------------