{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import json\n", "\n", "from sklearn.feature_selection import SelectKBest\n", "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n", "from sklearn.metrics import f1_score, classification_report\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.model_selection import train_test_split\n", "\n", "\n", "from sklearn.linear_model import LogisticRegression, SGDClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Dataframes" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv', index_col=[0,1])\n", "df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv', index_col=[0,1])\n", "df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv', index_col=[0,1])\n", "\n", "# df_1km = pd.read_csv('Datasets/Machine Learning/Dataframes/1km_All_Birds_DF.csv')\n", "# df_5km = pd.read_csv('Datasets/Machine Learning/Dataframes/5km_All_Birds_DF.csv')\n", "# df_10km = pd.read_csv('Datasets/Machine Learning/Dataframes/10km_All_Birds_DF.csv')\n", "\n", "df_dicts = [\n", " {'name' : '1km', 'dataframe' : df_1km},\n", " {'name' : '5km', 'dataframe' : df_5km},\n", " {'name' : '10km', 'dataframe' : df_10km}\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Cleaning" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1km data before drop: \n", " Occurrence\n", "0 876916\n", "1 33084\n", "dtype: int64 \n", "\n", "1km data after drop: \n", " Occurrence\n", "0 33084\n", "1 33084\n", "dtype: int64 \n", "\n", "5km data before drop: \n", " Occurrence\n", "0 28435\n", "1 7965\n", "dtype: int64 \n", "\n", "5km data after drop: \n", " Occurrence\n", "0 7965\n", "1 7965\n", "dtype: int64 \n", "\n", "10km data before drop: \n", " Occurrence\n", "0 6428\n", "1 2672\n", "dtype: int64 \n", "\n", "10km data after drop: \n", " Occurrence\n", "0 2672\n", "1 2672\n", "dtype: int64 \n", "\n" ] } ], "source": [ "# Drop random rows with Occurence=0 to even out the datasets\n", "for dict in df_dicts:\n", " cur_df = dict[\"dataframe\"]\n", " cur_df_name = dict[\"name\"]\n", "\n", " print(f'{cur_df_name} data before drop: \\n {cur_df.value_counts(\"Occurrence\")} \\n')\n", " \n", " no_occurences = cur_df[cur_df['Occurrence']==0].index # Retrieve the indices of the all negative cellsa\n", " sample_size = sum(cur_df['Occurrence']==0) - sum(cur_df['Occurrence']==1) # Number of samples to be dropped from the dataset\n", " random_indices = np.random.choice(no_occurences, sample_size, replace=False) # Randomly choosing the negative samples to be dropped\n", " dict[\"dataframe\"] = cur_df.drop(random_indices) # Dropping the randomly chosen negaative samples from the dataset\n", " \n", " print(f'{cur_df_name} data after drop: \\n {dict[\"dataframe\"].value_counts(\"Occurrence\")} \\n')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Standardisation" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def standardise(X):\n", " scaler = StandardScaler()\n", " X_scaled = scaler.fit_transform(X)\n", "\n", " # Add headers back\n", " X_scaled_df = pd.DataFrame(X_scaled, columns=X.columns)\n", "\n", " # Revert 'Surface type' back to non-standardised column as it is a categorical feature\n", " X_scaled_df['Surface type'] = X['Surface type'].values\n", " return X_scaled_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Feature Selection" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Check if any columns have NaN in them\n", "# nan_columns = []\n", "# for column in X_scaled_df:\n", "# if X_scaled_df[column].isnull().values.any():\n", "# nan_columns.append(column)\n", "# print(nan_columns if len(nan_columns)!= 0 else 'None')\n", "\n", "\n", "# Using ANOVA F-Score as a feature selection method\n", "def feature_select(X, y):\n", " k_nums = [10, 15, 20, 25, 30, 35]\n", " kbest_dict = {}\n", " for num in k_nums:\n", " # Needs to be 1d array, y.values.ravel() converts y into a 1d array\n", " best_X = SelectKBest(f_classif, k=num).fit(X, y.values.ravel())\n", " kbest_dict[str(num)] = best_X.get_feature_names_out().tolist()\n", " kbest_dict['40'] = list(X.columns)\n", "\n", " best_X = SelectKBest(f_classif, k='all').fit(X, y.values.ravel())\n", "\n", " # Append all relevant data to dataframe\n", " feat_scores = pd.DataFrame()\n", " feat_scores[\"F Score\"] = best_X.scores_\n", " feat_scores[\"P Value\"] = best_X.pvalues_\n", " feat_scores[\"Attribute\"] = X.columns\n", " kbest_dict['Dataframe'] = feat_scores.sort_values([\"F Score\", \"P Value\"], ascending=[False, False])\n", "\n", " print(f'K-Best Features Dataframe: \\n{kbest_dict[\"Dataframe\"]} \\n')\n", " # print(json.dumps(kbest_dict, indent=4))\n", " return kbest_dict" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pipeline for all three cell sizes\n", "This section is the main pipeline that will be used for evaluation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training with 1km cells... \n", "\n", "K-Best Features Dataframe: \n", " F Score P Value Attribute\n", "23 76729.029046 0.000000e+00 Surface type\n", "30 74271.628192 0.000000e+00 Glyphosate\n", "34 74271.628192 0.000000e+00 Pendimethalin\n", "29 74257.759387 0.000000e+00 Chlorothalonil\n", "31 74243.894287 0.000000e+00 Mancozeb\n", "32 74230.032889 0.000000e+00 Mecoprop-P\n", "25 68226.190120 0.000000e+00 Inflowing drainage direction\n", "21 60291.246246 0.000000e+00 Elevation\n", "24 47941.537465 0.000000e+00 Outflowing drainage direction\n", "26 36673.961922 0.000000e+00 Fertiliser K\n", "27 36673.961922 0.000000e+00 Fertiliser N\n", "28 36673.961922 0.000000e+00 Fertiliser P\n", "37 29859.194589 0.000000e+00 Sulphur\n", "36 29729.783810 0.000000e+00 Prosulfocarb\n", "38 29543.187566 0.000000e+00 Tri-allate\n", "22 20009.446503 0.000000e+00 Cumulative catchment area\n", "35 19364.922744 0.000000e+00 PropamocarbHydrochloride\n", "33 18690.083436 0.000000e+00 Metamitron\n", "3 15369.688547 0.000000e+00 Improve grassland\n", "2 10864.199283 0.000000e+00 Arable\n", "20 9533.108615 0.000000e+00 Suburban\n", "0 7140.266361 0.000000e+00 Deciduous woodland\n", "19 3491.123620 0.000000e+00 Urban\n", "13 1879.385814 0.000000e+00 Freshwater\n", "4 1061.356044 5.629280e-231 Neutral grassland\n", "17 644.494414 1.681829e-141 Littoral sediment\n", "18 638.597401 3.133112e-140 Saltmarsh\n", "7 322.455755 6.267762e-72 Fen\n", "15 306.541250 1.767767e-68 Supralittoral sediment\n", "12 181.200360 3.004066e-41 Saltwater\n", "5 176.730917 2.824674e-40 Calcareous grassland\n", "16 136.742986 1.474325e-31 Littoral rock\n", "11 34.577575 4.115579e-09 Inland rock\n", "1 22.972701 1.646743e-06 Coniferous woodland\n", "14 19.848747 8.395487e-06 Supralittoral rock\n", "6 15.454821 8.458709e-05 Acid grassland\n", "10 10.513464 1.185665e-03 Bog\n", "9 6.220743 1.262863e-02 Heather grassland\n", "8 0.583017 4.451351e-01 Heather \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Classification report: \n", " precision recall f1-score support\n", "\n", " 0 0.93 0.82 0.87 8196\n", " 1 0.84 0.94 0.89 8346\n", "\n", " accuracy 0.88 16542\n", " macro avg 0.89 0.88 0.88 16542\n", "weighted avg 0.89 0.88 0.88 16542\n", " \n", "\n", "Training with 5km cells... \n", "\n", "K-Best Features Dataframe: \n", " F Score P Value Attribute\n", "21 28578.391332 0.000000e+00 Elevation\n", "23 28476.697719 0.000000e+00 Surface type\n", "25 25200.435147 0.000000e+00 Inflowing drainage direction\n", "26 9398.275845 0.000000e+00 Fertiliser K\n", "27 9398.275845 0.000000e+00 Fertiliser N\n", "28 9398.275845 0.000000e+00 Fertiliser P\n", "29 6054.369903 0.000000e+00 Chlorothalonil_5km\n", "30 6054.369903 0.000000e+00 Glyphosate_5km\n", "31 6054.369903 0.000000e+00 Mancozeb_5km\n", "32 6054.369903 0.000000e+00 Mecoprop-P_5km\n", "34 6054.369903 0.000000e+00 Pendimethalin_5km\n", "3 5946.261693 0.000000e+00 Improve grassland\n", "38 4354.491845 0.000000e+00 Tri-allate_5km\n", "36 4320.388658 0.000000e+00 Prosulfocarb_5km\n", "37 4318.544553 0.000000e+00 Sulphur_5km\n", "2 4210.119135 0.000000e+00 Arable\n", "24 4158.237438 0.000000e+00 Outflowing drainage direction\n", "35 3358.253214 0.000000e+00 PropamocarbHydrochloride_5km\n", "33 3309.009971 0.000000e+00 Metamitron_5km\n", "0 2552.537614 0.000000e+00 Deciduous woodland\n", "20 1595.127665 0.000000e+00 Suburban\n", "22 1538.605309 2.465388e-321 Cumulative catchment area\n", "19 416.543429 2.031969e-91 Urban\n", "1 230.246573 1.209489e-51 Coniferous woodland\n", "6 163.553568 2.888030e-37 Acid grassland\n", "4 160.465001 1.344532e-36 Neutral grassland\n", "5 127.854957 1.564816e-29 Calcareous grassland\n", "17 123.622232 1.298764e-28 Littoral sediment\n", "13 105.081884 1.397197e-24 Freshwater\n", "7 88.391534 6.084608e-21 Fen\n", "18 76.703071 2.183725e-18 Saltmarsh\n", "9 58.851987 1.797918e-14 Heather grassland\n", "16 57.834198 3.010424e-14 Littoral rock\n", "12 52.309060 4.957490e-13 Saltwater\n", "15 48.444215 3.530746e-12 Supralittoral sediment\n", "8 47.980640 4.469198e-12 Heather\n", "10 45.655915 1.458453e-11 Bog\n", "14 41.383564 1.286725e-10 Supralittoral rock\n", "11 0.672196 4.122992e-01 Inland rock \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Classification report: \n", " precision recall f1-score support\n", "\n", " 0 0.93 0.89 0.91 2001\n", " 1 0.90 0.93 0.91 1982\n", "\n", " accuracy 0.91 3983\n", " macro avg 0.91 0.91 0.91 3983\n", "weighted avg 0.91 0.91 0.91 3983\n", " \n", "\n", "Training with 10km cells... \n", "\n", "K-Best Features Dataframe: \n", " F Score P Value Attribute\n", "21 13578.608189 0.000000e+00 Elevation\n", "25 12353.048339 0.000000e+00 Inflowing drainage direction\n", "23 10499.510176 0.000000e+00 Surface type\n", "26 2226.785286 0.000000e+00 Fertiliser K\n", "27 2226.785286 0.000000e+00 Fertiliser N\n", "28 2226.785286 0.000000e+00 Fertiliser P\n", "29 2145.579506 0.000000e+00 Chlorothalonil_10km\n", "30 2145.579506 0.000000e+00 Glyphosate_10km\n", "34 2145.579506 0.000000e+00 Pendimethalin_10km\n", "31 2142.070589 0.000000e+00 Mancozeb_10km\n", "32 2142.070589 0.000000e+00 Mecoprop-P_10km\n", "3 1711.252209 0.000000e+00 Improve grassland\n", "24 1501.778069 9.745438e-290 Outflowing drainage direction\n", "38 1403.857820 5.216418e-273 Tri-allate_10km\n", "36 1359.234150 2.641195e-265 Prosulfocarb_10km\n", "37 1359.234150 2.641195e-265 Sulphur_10km\n", "2 1154.191034 3.150172e-229 Arable\n", "35 1088.966134 1.639018e-217 PropamocarbHydrochloride_10km\n", "33 1060.840425 2.012198e-212 Metamitron_10km\n", "0 808.026807 1.215740e-165 Deciduous woodland\n", "20 399.323014 9.815263e-86 Suburban\n", "6 197.553557 4.322818e-44 Acid grassland\n", "1 172.637652 7.816177e-39 Coniferous woodland\n", "19 109.280288 2.470054e-25 Urban\n", "22 99.274923 3.497966e-23 Cumulative catchment area\n", "8 75.541379 4.694683e-18 Heather\n", "9 62.541702 3.146329e-15 Heather grassland\n", "13 52.109548 5.984009e-13 Freshwater\n", "4 46.582027 9.761540e-12 Neutral grassland\n", "5 43.612337 4.391280e-11 Calcareous grassland\n", "10 33.220299 8.689575e-09 Bog\n", "17 32.007357 1.615781e-08 Littoral sediment\n", "18 27.347755 1.764384e-07 Saltmarsh\n", "16 25.241928 5.221762e-07 Littoral rock\n", "12 19.481877 1.035592e-05 Saltwater\n", "7 18.837341 1.449810e-05 Fen\n", "15 18.322188 1.897873e-05 Supralittoral sediment\n", "14 16.522327 4.877122e-05 Supralittoral rock\n", "11 3.100609 7.832022e-02 Inland rock \n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:98: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n", "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\preprocessing\\_label.py:133: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n", " y = column_or_1d(y, warn=True)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Classification report: \n", " precision recall f1-score support\n", "\n", " 0 0.93 0.95 0.94 701\n", " 1 0.94 0.92 0.93 635\n", "\n", " accuracy 0.93 1336\n", " macro avg 0.93 0.93 0.93 1336\n", "weighted avg 0.93 0.93 0.93 1336\n", " \n", "\n" ] } ], "source": [ "seed = 42\n", "verbose = False\n", "\n", "# List of estimators\n", "estimators = [\n", " ('lr', LogisticRegression(max_iter=10000, solver='saga', random_state=seed, penalty='l2', verbose=verbose)),\n", " ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n", " ('sgd', SGDClassifier( max_iter=10000, loss='modified_huber', random_state=seed, penalty='l2', verbose=verbose)),\n", " ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=seed, verbose=verbose))\n", "]\n", "\n", "\n", "for dict in df_dicts:\n", " print(f'Training with {dict[\"name\"]} cells... \\n')\n", " # coords, X, y = data['dataframe'].iloc[:, :2], data['dataframe'].iloc[:, 2:-1], data['dataframe'].iloc[:, [-1]]\n", " # data['coords'] = coords\n", " \n", " # Declaring features (X) and declaring labels (y)\n", " X, y = dict['dataframe'].iloc[:, 0:-1], dict['dataframe'].iloc[:, [-1]], \n", "\n", " # Adding values to dictionary\n", " dict['X'] = standardise(X)\n", " dict['y'] = y\n", " dict['kbest'] = feature_select(X, y)\n", "\n", " # Train-test split\n", " X_train, X_test, y_train, y_test = train_test_split(dict['X'], dict['y'], random_state=seed)\n", " dict['X_train'], dict['X_test'], dict['y_train'], dict['y_test'] = X_train, X_test, y_train, y_test\n", "\n", " # Stacking classifier initialisation\n", " stack_clf = StackingClassifier(\n", " estimators=estimators, \n", " final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=seed)\n", " )\n", "\n", " # Fit training data\n", " stack_clf.fit(X_train, y_train)\n", " y_pred = stack_clf.predict(X_test)\n", " \n", " dict['predictions'] = y_pred\n", " dict['report'] = classification_report(y_test, y_pred)\n", " \n", " # Print results\n", " print(f'Classification report: \\n {dict[\"report\"]} \\n')\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Occurrence\n", "y x \n", "571500.0 434500.0 1\n", "685500.0 316500.0 1\n", "596500.0 681500.0 0\n", "52500.0 639500.0 0\n", "867500.0 399500.0 1\n", "... ...\n", "891500.0 64500.0 0\n", "245500.0 330500.0 1\n", "1044500.0 443500.0 0\n", "377500.0 680500.0 0\n", "586500.0 81500.0 1\n", "\n", "[16542 rows x 1 columns]\n", "(16542,)\n", "[1 1 0 ... 0 0 0]\n", " Occurrence\n", "y x \n", "652500.0 467500.0 0\n", "262500.0 632500.0 1\n", "1097500.0 517500.0 0\n", "462500.0 682500.0 0\n", "392500.0 612500.0 0\n", "... ...\n", "472500.0 477500.0 1\n", "512500.0 627500.0 0\n", "1152500.0 362500.0 0\n", "102500.0 452500.0 1\n", "402500.0 142500.0 0\n", "\n", "[3983 rows x 1 columns]\n", "(3983,)\n", "[0 1 0 ... 0 1 0]\n", " Occurrence\n", "y x \n", "325000.0 625000.0 1\n", "455000.0 405000.0 1\n", "475000.0 275000.0 0\n", "105000.0 35000.0 0\n", "1245000.0 415000.0 0\n", "... ...\n", "155000.0 665000.0 0\n", "95000.0 165000.0 0\n", "805000.0 305000.0 0\n", "495000.0 135000.0 1\n", "105000.0 485000.0 1\n", "\n", "[1336 rows x 1 columns]\n", "(1336,)\n", "[1 1 0 ... 1 0 1]\n" ] } ], "source": [ "for dict in df_dicts:\n", " print(dict[\"y_test\"])\n", " print(dict[\"predictions\"].shape)\n", "\n", " # dict[\"predictions\"] = dict[\"y\"].insert(len(dict[\"y\"].columns), 'Predictions', dict[\"predictions\"])\n", " print(f'{dict[\"predictions\"]}')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# Show features\n", "# for dict in df_dicts:\n", "# print(f\"{dict['name']} dataset feature dictionary: {json.dumps(dict['kbest']['10'], indent=4)}\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Individual model testing\n", "This section is for testing the effectiveness of individual models with different machine learning algorithms and methods " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train-Test Split" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "# Train test split (change this since I made the coordinates the index)\n", "coords, X, y = df_5km.iloc[:, :2], df_5km.iloc[:, 2:-1], df_5km.iloc[:, [-1]]\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y.values.ravel(), random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Individual models" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score (training): 0.892\n", "Accuracy score (testing): 0.889\n", " precision recall f1-score support\n", "\n", " 0 0.89 0.98 0.93 7084\n", " 1 0.89 0.57 0.70 2016\n", "\n", " accuracy 0.89 9100\n", " macro avg 0.89 0.78 0.81 9100\n", "weighted avg 0.89 0.89 0.88 9100\n", "\n" ] } ], "source": [ "# Logistic regression\n", "logreg_clf= LogisticRegression(max_iter=1000, solver='saga', random_state=42, penalty='l1', n_jobs=2).fit(X_train, y_train)\n", "\n", "print(\"Accuracy score (training): {0:.3f}\".format(logreg_clf.score(X_train, y_train)))\n", "print(\"Accuracy score (testing): {0:.3f}\".format(logreg_clf.score(X_test, y_test)))\n", "# f1_score(y_test, logreg_clf.predict(X_test))\n", "\n", "y_pred = logreg_clf.predict(X_test)\n", "# print(len(y_pred)==len(y_test))\n", "print(classification_report(y_test, y_pred))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score (training): 0.894\n", "Accuracy score (testing): 0.887\n", " precision recall f1-score support\n", "\n", " 0 0.91 0.95 0.93 7084\n", " 1 0.79 0.67 0.72 2016\n", "\n", " accuracy 0.89 9100\n", " macro avg 0.85 0.81 0.83 9100\n", "weighted avg 0.88 0.89 0.88 9100\n", "\n" ] } ], "source": [ "# K Nearest Neigbours\n", "knn_clf = KNeighborsClassifier(n_neighbors=5, weights='distance').fit(X_train, y_train)\n", "print(\"Accuracy score (training): {0:.3f}\".format(knn_clf.score(X_train, y_train)))\n", "print(\"Accuracy score (testing): {0:.3f}\".format(knn_clf.score(X_test, y_test)))\n", "# f1_score(y_test, knn_clf.predict(X_test))\n", "\n", "print(classification_report(y_test, knn_clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score (training): 0.874\n", "Accuracy score (testing): 0.873\n", " precision recall f1-score support\n", "\n", " 0 0.90 0.94 0.92 7084\n", " 1 0.75 0.64 0.69 2016\n", "\n", " accuracy 0.87 9100\n", " macro avg 0.83 0.79 0.81 9100\n", "weighted avg 0.87 0.87 0.87 9100\n", "\n" ] } ], "source": [ "sgd_clf = SGDClassifier(loss='modified_huber', max_iter=1000, random_state=42, penalty='l2')\n", "sgd_clf.fit(X_train, y_train)\n", "\n", "print(\"Accuracy score (training): {0:.3f}\".format(sgd_clf.score(X_train, y_train)))\n", "print(\"Accuracy score (testing): {0:.3f}\".format(sgd_clf.score(X_test, y_test)))\n", "# f1_score(y_test, clf1.predict(X_test))\n", "print(classification_report(y_test, sgd_clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accuracy score (training): 0.982\n", "Accuracy score (testing): 0.928\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.95 7084\n", " 1 0.85 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.90 0.89 0.89 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", "\n" ] } ], "source": [ "rf_clf = RandomForestClassifier(n_estimators=20,max_features=None, random_state=42)\n", "rf_clf.fit(X_train, y_train)\n", "\n", "print(\"Accuracy score (training): {0:.3f}\".format(rf_clf.score(X_train, y_train)))\n", "print(\"Accuracy score (testing): {0:.3f}\".format(rf_clf.score(X_test, y_test)))\n", "# print(\"F1-score: {0:.3f}\".format(f1_score(y_test, rf_clf.predict(X_test))))\n", "print(classification_report(y_test, rf_clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Learning rate: 0.05\n", "Accuracy score (training): 0.926\n", "Accuracy score (testing): 0.927\n", " precision recall f1-score support\n", "\n", " 0 0.94 0.97 0.95 7084\n", " 1 0.87 0.79 0.83 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.88 0.89 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 0.075\n", "Accuracy score (training): 0.928\n", "Accuracy score (testing): 0.930\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.97 0.96 7084\n", " 1 0.87 0.81 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 0.1\n", "Accuracy score (training): 0.930\n", "Accuracy score (testing): 0.933\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.96 7084\n", " 1 0.87 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 0.25\n", "Accuracy score (training): 0.933\n", "Accuracy score (testing): 0.933\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.96 7084\n", " 1 0.86 0.83 0.85 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.90 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 0.5\n", "Accuracy score (training): 0.936\n", "Accuracy score (testing): 0.932\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.96 7084\n", " 1 0.87 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 0.75\n", "Accuracy score (training): 0.937\n", "Accuracy score (testing): 0.931\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.96 7084\n", " 1 0.86 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.91 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n", "Learning rate: 1\n", "Accuracy score (training): 0.937\n", "Accuracy score (testing): 0.930\n", " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.96 7084\n", " 1 0.86 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.90 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n" ] } ], "source": [ "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n", "\n", "lr_list1 = [1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 9, 10]\n", "\n", "lr_list2 = [15, 20, 25, 30, 35, 40] # Not Worth\n", "\n", "lr_list3 = np.arange(2.0, 2.15, 0.01)\n", "\n", "for learning_rate in lr_list:\n", " gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=None, max_depth=3, random_state=42)\n", " gb_clf.fit(X_train, y_train)\n", "\n", " print(\"Learning rate: \", learning_rate)\n", " print(\"Accuracy score (training): {0:.3f}\".format(gb_clf.score(X_train, y_train)))\n", " print(\"Accuracy score (testing): {0:.3f}\".format(gb_clf.score(X_test, y_test)))\n", " print(classification_report(y_test, gb_clf.predict(X_test)), '\\n')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ensemble Models" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "c:\\Users\\Timmo\\Documents\\Workspaces\\Master's Dissertation\\env\\lib\\site-packages\\sklearn\\linear_model\\_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.95 0.96 0.95 7084\n", " 1 0.85 0.82 0.84 2016\n", "\n", " accuracy 0.93 9100\n", " macro avg 0.90 0.89 0.90 9100\n", "weighted avg 0.93 0.93 0.93 9100\n", " \n", "\n" ] } ], "source": [ "# Ensemble learning\n", "estimators = [\n", " ('lr', LogisticRegression(max_iter=1000, solver='saga', random_state=42, penalty='l2')),\n", " ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n", " ('sgd', SGDClassifier(loss='modified_huber', max_iter=1000, random_state=42, penalty='l2')),\n", " ('rf', RandomForestClassifier(n_estimators=20,max_features=None, random_state=42))\n", "]\n", "\n", "stack_clf = StackingClassifier(\n", " estimators=estimators, \n", " final_estimator=GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=42)\n", ")\n", "\n", "stack_clf.fit(X_train, y_train)\n", "print(classification_report(y_test, stack_clf.predict(X_test)), '\\n')\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.91 0.97 0.94 7084\n", " 1 0.87 0.68 0.76 2016\n", "\n", " accuracy 0.91 9100\n", " macro avg 0.89 0.82 0.85 9100\n", "weighted avg 0.90 0.91 0.90 9100\n", " \n", "\n" ] } ], "source": [ "estimators = [\n", " ('lr', LogisticRegression(max_iter=1000, solver='saga', random_state=42, penalty='l2')),\n", " ('knn', KNeighborsClassifier(n_neighbors=5, weights='distance')),\n", " ('sgd', SGDClassifier(loss='modified_huber', max_iter=1000, random_state=42, penalty='l2')),\n", " ('rf', RandomForestClassifier(n_estimators=20, max_features=None, random_state=42)),\n", " ('gb', GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=None, max_depth=2, random_state=42))\n", "]\n", "\n", "vote_clf = VotingClassifier(estimators)\n", "\n", "vote_clf.fit(X_train, y_train)\n", "print(classification_report(y_test, vote_clf.predict(X_test)), '\\n')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot Graphs" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " , , ]], dtype=object)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot distribution of colummns\n", "%matplotlib inline\n", "X.hist(figsize=(20, 50), layout=(10, 4))\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " ,\n", " ,\n", " ],\n", " [,\n", " , , ]], dtype=object)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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