{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.experimental import enable_hist_gradient_boosting\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score, roc_auc_score\nfrom scipy.io import mmread\n\nfrom scipy.sparse.csr import csr_matrix\n\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\nimport gc\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"#tr_0 = mmread('/kaggle/input/let-s-overfit-some-345c17/tr_0.mm')\n#te_0 = mmread('/kaggle/input/let-s-overfit-some-345c17/te_0.mm')\ntrain = pd.read_csv(\"/kaggle/input/multi-cat-encodings/X_train_te.csv\")\ntest = pd.read_csv(\"/kaggle/input/multi-cat-encodings/X_test_te.csv\")\nsample_submission = pd.read_csv('../input/cat-in-the-dat-ii/sample_submission.csv')\ntarget = np.load('../input/multi-cat-encodings/target.npy')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(target)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#train = tr_0.tocsr()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_oof = np.zeros((600000,))\ntest_preds = 0\ntrain_oof.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#test = te_0.tocsr()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"features = test.columns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"%%time\nn_splits = 5\nkf = KFold(n_splits=n_splits, random_state=137)\nscores = []\n\nfor jj, (train_index, val_index) in enumerate(kf.split(train)):\n    print(\"Fitting fold\", jj+1)\n    train_features = train.loc[train['fold_column'] != jj][features]\n    train_target = train.loc[train['fold_column'] != jj]['target'].values.astype(float)\n    \n    val_features = train.loc[train['fold_column'] == jj][features]\n    val_target = train.loc[train['fold_column'] == jj]['target'].values.astype(float)\n    \n    model = RandomForestRegressor(n_estimators=100)\n    model.fit(train_features, train_target)\n    val_pred = model.predict(val_features)\n    train_oof[val_index] = val_pred\n    score = roc_auc_score(val_target, val_pred)\n    scores.append(score)\n    print(\"Fold AUC:\", score)\n    test_preds += model.predict(test)/n_splits\n    del train_features, train_target, val_features, val_target\n    gc.collect()\n    \nprint(\"Mean AUC:\", np.mean(scores))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nroc_auc_score(target, train_oof)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission['target'] = test_preds\nsample_submission.to_csv('submission.csv', index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.save('test_preds', test_preds)\nnp.save('train_oof', train_oof)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat":4,"nbformat_minor":1}