{"cells":[{"metadata":{},"cell_type":"markdown","source":"# Categorical II Catboost Pool CV Bayes_Opt GPU\n_By Nick Brooks, 2020-01-06_\n\n[Replication of my code from first competition..](https://www.kaggle.com/nicapotato/categorical-catboost-pool-cv-bayes-opt)"},{"metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport catboost\nfrom sklearn import preprocessing\nfrom contextlib import contextmanager\nimport time\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport gc\n\nfrom bayes_opt import BayesianOptimization\nimport warnings\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n \nfrom catboost.utils import get_gpu_device_count\nprint('\\n%i GPU devices available' % get_gpu_device_count())\n\n# Any results you write to the current directory are saved as output.\nprint(catboost.__version__)\n\nnotebookstart = time.time()\nseed = 25\n\ntop_boosting_rounds = 9000\nearly_stopping_rounds = 100","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"@contextmanager\ndef timer(name):\n \"\"\"\n Time Each Process\n \"\"\"\n t0 = time.time()\n yield\n print('\\n[{}] done in {} Minutes'.format(name, round((time.time() - t0)/60,2)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with timer(\"Load\"):\n nrow = None\n\n PATH = \"/kaggle/input/cat-in-the-dat-ii/\"\n train = pd.read_csv(PATH + \"train.csv\", index_col = 'id', nrows = nrow)\n test = pd.read_csv(PATH + \"test.csv\", index_col = 'id')\n submission_df = pd.read_csv(PATH + \"sample_submission.csv\")\n [print(x.shape) for x in [train, test, submission_df]]\n\n traindex = train.index\n testdex = test.index\n\n y = train.target.copy()\n\n df = pd.concat([train.drop('target',axis = 1), test], axis = 0)\n del train, test, submission_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with timer(\"Categorical Processing\"):\n categorical = df.columns\n # Encoder:\n for col in categorical:\n diff = list(set(df.loc[testdex, col].unique()) - set(df.loc[traindex,col].unique()))\n if diff:\n print(\"Column {} has {} unseen categories in test set\".format(col, len(diff)))\n df.loc[df[col].isin(diff),col] = 999\n if df[col].dtypes == object:\n df[col] = df[col].astype(str)\n lbl = preprocessing.LabelEncoder()\n df[col] = pd.Series(lbl.fit_transform(df[col].values)).astype('category')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## CV Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Prepare Data Object\ncategorical_index = list(range(0, len(categorical)))\nfeatures_names = df.columns\n\ncatboost_pool = catboost.Pool(df.loc[traindex,:],\n label=y,\n cat_features=categorical_index)\n\ntest_pool = catboost.Pool(data=df.loc[testdex,:],\n cat_features = categorical_index)\n\ndel df\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def catboost_blackbox(max_depth, reg_lambda):\n # num_leaves removed\n param = {\n 'learning_rate': 0.2,\n 'bagging_temperature': 0.1, \n 'l2_leaf_reg': reg_lambda,\n 'depth': int(max_depth), \n# 'max_leaves': int(num_leaves),\n# 'max_bin':255,\n 'iterations' : top_boosting_rounds,\n 'task_type':'GPU',\n# 'grow_policy': 'Lossguide '\n 'loss_function' : \"Logloss\",\n 'objective':'Logloss',\n 'eval_metric' : \"AUC\",\n 'bootstrap_type' : 'Bayesian',\n 'random_seed': seed,\n 'early_stopping_rounds' : early_stopping_rounds,\n 'use_best_model': False,\n \"verbose\": False\n }\n \n modelstart= time.time()\n scores = catboost.cv(catboost_pool,\n param,\n fold_count = 2,\n stratified = True,\n shuffle = True,\n partition_random_seed = seed,\n plot = False\n )\n runtime = (time.time() - modelstart)/60\n \n optimise = scores.loc[scores['test-AUC-mean'].idxmax(),'test-AUC-mean'] - scores.loc[scores['test-AUC-mean'].idxmax(),'test-AUC-std']\n optimisation_info.append([scores['test-AUC-mean'].idxmax(), optimise, runtime, param, scores['test-AUC-mean'].idxmax()])\n \n \n return optimise","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"parameter_bounds = {\n# 'num_leaves': (31, 500), \n 'reg_lambda': (0.1, 10),\n 'max_depth':(3,16)\n}\n\ninit_points = 2\nn_iter = 4\n\noptimisation_info = []\nCATBOOST_BO = BayesianOptimization(catboost_blackbox,\n parameter_bounds,\n random_state=seed)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with timer(\"Bayesian Optimisation - {} Iterations\".format(init_points + n_iter)):\n with warnings.catch_warnings():\n warnings.filterwarnings('ignore')\n CATBOOST_BO.maximize(init_points = init_points,\n n_iter = n_iter,\n acq = 'ucb',\n xi = 0.0,\n alpha = 1e-6)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Best Score: {}\".format(CATBOOST_BO.max['target']))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"CATBOOST_BO.max['params']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Lower Learning Rate with Best Parameters"},{"metadata":{"trusted":true},"cell_type":"code","source":"optimisation_pd = pd.DataFrame(optimisation_info, columns = ['Best Round', 'Score', 'Runtime','Param', 'Iterations'])\noptimisation_pd.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"optimisation_pd.describe()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"best_param = optimisation_pd.loc[optimisation_pd['Score'].idxmax(),'Param']\nbest_param['iterations'] = top_boosting_rounds*3\nbest_param['learning_rate'] = 0.04\nbest_param['early_stopping_rounds'] = early_stopping_rounds\n\nbest_param","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with timer(\"Catboost CV\"):\n scores = catboost.cv(catboost_pool,\n best_param,\n fold_count = 3,\n stratified = True,\n partition_random_seed = seed,\n plot = True,\n shuffle = True,\n )\n\ndisplay(scores.tail())\n\nfig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 4))\naxes[0].plot(scores['iterations'],scores['test-Logloss-mean'], label='test-Logloss-mean')\naxes[0].plot(scores['iterations'],scores['train-Logloss-mean'], label='train-Logloss-mean')\naxes[0].legend()\n\naxes[1].plot(scores['iterations'],scores['test-AUC-mean'], label='validation_rocauc')\naxes[1].legend()\nplt.show()\n\nbest_iteration = scores['test-AUC-mean'].idxmax()\nprint(\"Best Iteration: {}\".format(best_iteration))\n\ndisplay(scores.loc[best_iteration,:])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Single Model"},{"metadata":{"trusted":true},"cell_type":"code","source":"with timer(\"Catboost Single Model\"):\n best_param['iterations'] = best_iteration\n model = catboost.CatBoostClassifier(**best_param)\n model.fit(catboost_pool)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"feat_imp = pd.DataFrame()\nfeat_imp['importance'] = model.get_feature_importance()\nfeat_imp['features'] = features_names\nfeat_imp.sort_values(by = 'importance', inplace = True, ascending = False)\n\nsns.barplot(y = feat_imp['features'], x = feat_imp['importance'])\nplt.title(\"Feature Importance\")\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"cm = catboost.utils.get_confusion_matrix(model, catboost_pool)\nprint(cm)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"roc_curve_values = catboost.utils.get_roc_curve(model, catboost_pool, plot=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(thresholds, fnr) = catboost.utils.get_fnr_curve(curve=roc_curve_values, plot=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(thresholds, fpr) = catboost.utils.get_fpr_curve(curve=roc_curve_values, plot=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"results = model.predict_proba(test_pool)[:, 1]\nsubmission = pd.DataFrame({'id': testdex, 'target': results})\nsubmission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!head submission.csv","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"Notebook Runtime: %0.2f Hours\"%((time.time() - notebookstart)/60/60))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 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