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January 21, 2021 09:48
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XGBoost vs LightGBM vs CatBoost.ipynb
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| { | |
| "cells": [ | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-16T07:50:23.788402Z", | |
| "start_time": "2020-12-16T07:50:10.804419Z" | |
| }, | |
| "scrolled": true, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "import pandas as pd\nimport numpy as np\nimport time\nfrom sklearn.model_selection import train_test_split\n\n# Read data\ndata = pd.read_csv(\"flights.csv\")\ndata = data.sample(frac = 0.1, random_state=10)\n\n# Data preprocessing\ndata = data[[\"MONTH\",\"DAY\",\"DAY_OF_WEEK\",\"AIRLINE\",\"FLIGHT_NUMBER\",\"DESTINATION_AIRPORT\",\n \"ORIGIN_AIRPORT\",\"AIR_TIME\", \"DEPARTURE_TIME\",\"DISTANCE\",\"ARRIVAL_DELAY\"]]\ndata.dropna(inplace=True)\n\ndata[\"ARRIVAL_DELAY\"] = (data[\"ARRIVAL_DELAY\"] > 10)*1\n\ncols = [\"AIRLINE\",\"FLIGHT_NUMBER\",\"DESTINATION_AIRPORT\",\"ORIGIN_AIRPORT\"]\nfor item in cols:\n data[item] = data[item].astype(\"category\").cat.codes +1\n\nprint(data.head())\n\n# train test split\ntrain, test, y_train, y_test = train_test_split(data.drop([\"ARRIVAL_DELAY\"], axis=1), data[\"ARRIVAL_DELAY\"],\n random_state=10, test_size=0.25)", | |
| "execution_count": 1, | |
| "outputs": [ | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": "C:\\Users\\desse\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3058: DtypeWarning: Columns (7,8) have mixed types.Specify dtype option on import or set low_memory=False.\n interactivity=interactivity, compiler=compiler, result=result)\n" | |
| }, | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": " MONTH DAY DAY_OF_WEEK AIRLINE FLIGHT_NUMBER DESTINATION_AIRPORT \\\n411984 1 28 3 14 102 717 \n3591965 8 11 2 3 152 748 \n526451 2 4 3 4 1184 597 \n1336011 3 27 5 14 170 770 \n3424502 8 1 6 14 4321 772 \n\n ORIGIN_AIRPORT AIR_TIME DEPARTURE_TIME DISTANCE ARRIVAL_DELAY \n411984 608 102.0 713.0 634 0 \n3591965 690 134.0 111.0 1028 1 \n526451 740 111.0 1734.0 931 0 \n1336011 609 173.0 1807.0 1436 0 \n3424502 544 63.0 2151.0 481 1 \n" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-16T00:59:34.610328Z", | |
| "start_time": "2020-12-15T22:30:15.126489Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# Example of XGBoost\nimport xgboost as xgb\nfrom sklearn import metrics\nfrom sklearn.model_selection import GridSearchCV\n\n\ndef auc(m, train, test):\n return (metrics.roc_auc_score(y_train, m.predict_proba(train)[:, 1]),\n metrics.roc_auc_score(y_test, m.predict_proba(test)[:, 1]))\n\n\n# Parameter Tuning\nmodel = xgb.XGBClassifier()\nparam_dist = {\"max_depth\": [10, 30, 50],\n \"min_child_weight\": [1, 3, 6],\n \"n_estimators\": [200],\n \"learning_rate\": [0.05, 0.1, 0.16], }\ngrid_search = GridSearchCV(model, param_grid=param_dist, cv=3,\n verbose=10, n_jobs=-1)\ngrid_search.fit(train, y_train)\n\ngrid_search.best_estimator_\n\nmodel = xgb.XGBClassifier(max_depth=50, min_child_weight=1, n_estimators=200,\n n_jobs=-1, verbose=1, learning_rate=0.16)\nmodel.fit(train, y_train)\n\nauc(model, train, test)", | |
| "execution_count": null, | |
| "outputs": [] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-16T08:17:01.050293Z", | |
| "start_time": "2020-12-16T08:12:36.708419Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# Example of LightGBM\nimport lightgbm as lgb\nfrom sklearn import metrics\nfrom sklearn.model_selection import GridSearchCV\n\n\ndef auc2(m, train, test): \n return (metrics.roc_auc_score(y_train,m.predict(train)),\n metrics.roc_auc_score(y_test,m.predict(test)))\n\nlg = lgb.LGBMClassifier(silent=False)\nparam_dist = {\"max_depth\": [25,50, 75],\n \"learning_rate\" : [0.01,0.05,0.1],\n \"num_leaves\": [300,900,1200],\n }\ngrid_search = GridSearchCV(lg, n_jobs=-1, param_grid=param_dist, cv = 3, scoring=\"roc_auc\", verbose=5)\ngrid_search.fit(train,y_train)\ngrid_search.best_estimator_\n\n# n_estimator of LightGBM is called num_boost_round\ndata_train = lgb.Dataset(train, label=y_train, free_raw_data=False)\nparams = {\"max_depth\": 50, \"learning_rate\" : 0.1, \"num_leaves\": 900, \"num_boost_round\": 300}\n\n# Without Categorical Features\nmodel_without_categorical = lgb.train(params, data_train)\nauc2(model_without_categorical, train, test)\n\n#With Catgeorical Features\ncate_features_name = [\"MONTH\",\"DAY\",\"DAY_OF_WEEK\",\"AIRLINE\",\"DESTINATION_AIRPORT\",\n \"ORIGIN_AIRPORT\"]\nmodel_with_categorical = lgb.train(params, data_train, categorical_feature = cate_features_name)\nauc2(model_with_categorical, train, test)", | |
| "execution_count": 4, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
| "text": "Fitting 3 folds for each of 27 candidates, totalling 81 fits\n" | |
| }, | |
| { | |
| "name": "stderr", | |
| "output_type": "stream", | |
| "text": "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 12 concurrent workers.\n[Parallel(n_jobs=-1)]: Done 48 tasks | elapsed: 2.6min\n[Parallel(n_jobs=-1)]: Done 75 out of 81 | elapsed: 3.5min remaining: 16.8s\n[Parallel(n_jobs=-1)]: Done 81 out of 81 | elapsed: 3.6min finished\nC:\\Users\\desse\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\nC:\\Users\\desse\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\lightgbm\\engine.py:148: UserWarning: Found `num_boost_round` in params. Will use it instead of argument\n warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\nC:\\Users\\desse\\AppData\\Local\\Programs\\Python\\Python36\\lib\\site-packages\\lightgbm\\basic.py:1295: UserWarning: categorical_feature in Dataset is overridden.\nNew categorical_feature is ['AIRLINE', 'DAY', 'DAY_OF_WEEK', 'DESTINATION_AIRPORT', 'MONTH', 'ORIGIN_AIRPORT']\n 'New categorical_feature is {}'.format(sorted(list(categorical_feature))))\n" | |
| }, | |
| { | |
| "data": { | |
| "text/plain": "(0.9981758315059435, 0.7733837850395386)" | |
| }, | |
| "execution_count": 4, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| } | |
| ] | |
| }, | |
| { | |
| "metadata": { | |
| "ExecuteTime": { | |
| "end_time": "2020-12-16T08:47:22.338572Z", | |
| "start_time": "2020-12-16T08:30:47.061842Z" | |
| }, | |
| "trusted": true | |
| }, | |
| "cell_type": "code", | |
| "source": "# Example of Catboost\nimport catboost as cb\n\ncat_features_index = [0,1,2,3,4,5,6]\n\ndef auc(m, train, test): \n return (metrics.roc_auc_score(y_train,m.predict_proba(train)[:,1]),\n metrics.roc_auc_score(y_test,m.predict_proba(test)[:,1]))\n\n# n_estimator of LightGBM is called iterations\nparams = {'depth': [4, 7, 10],\n 'learning_rate' : [0.03, 0.1, 0.15],\n 'l2_leaf_reg': [1,4,9],\n 'iterations': [300]}\ncb = cb.CatBoostClassifier()\ncb_model = GridSearchCV(cb, params, scoring=\"roc_auc\", cv = 3)\ncb_model.fit(train, y_train)\n\n# Without Categorical features\nclf_without_categorical = cb.CatBoostClassifier(eval_metric=\"AUC\", depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15)\nclf_without_categorical.fit(train,y_train)\nauc(clf_without_categorical, train, test)\n\n# With Categorical features\nclf_with_categorical = cb.CatBoostClassifier(eval_metric=\"AUC\",one_hot_max_size=31, \n depth=10, iterations= 500, l2_leaf_reg= 9, learning_rate= 0.15)\nclf_with_categorical.fit(train,y_train, cat_features= cat_features_index)\nauc(clf_with_categorical, train, test)", | |
| "execution_count": 5, | |
| "outputs": [ | |
| { | |
| "name": "stdout", | |
| "output_type": "stream", | |
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