{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"_kg_hide-output":true},"cell_type":"code","source":"import sys\n!cp ../input/rapids/rapids.0.13.0 /opt/conda/envs/rapids.tar.gz\n!cd /opt/conda/envs/ && tar -xzvf rapids.tar.gz > /dev/null\nsys.path = [\"/opt/conda/envs/rapids/lib/python3.6/site-packages\"] + sys.path\nsys.path = [\"/opt/conda/envs/rapids/lib/python3.6\"] + sys.path\nsys.path = [\"/opt/conda/envs/rapids/lib\"] + sys.path\n!cp /opt/conda/envs/rapids/lib/libxgboost.so /opt/conda/lib/","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import numpy as np\n# import pandas as pd\nimport cudf\nimport cupy as cp\nfrom cuml.neighbors import KNeighborsRegressor\nfrom cuml import SVR\nfrom cuml.linear_model import Ridge, Lasso\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import roc_auc_score\nfrom cuml.metrics import mean_absolute_error, mean_squared_error","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = cudf.read_csv(\"../input/siim-isic-melanoma-classification/train.csv\")\ntest = cudf.read_csv(\"../input/siim-isic-melanoma-classification/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train_32 = cp.load('../input/siimisic-melanoma-resized-images/x_train_32.npy')\nx_test_32 = cp.load('../input/siimisic-melanoma-resized-images/x_test_32.npy')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train_32 = x_train_32.reshape((x_train_32.shape[0], 32*32*3))\nx_train_32.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test_32 = x_test_32.reshape((x_test_32.shape[0], 32*32*3))\nx_test_32.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"NUM_FOLDS = 5\nkf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_oof = cp.zeros(train.shape[0])\ny_test = cp.zeros(test.shape[0])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = train['target'].values.reshape(-1,1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for f, (train_ind, val_ind) in enumerate(kf.split(train, train)):\n    print(f)\n    train_, val_ = x_train_32[train_ind].astype('float32'), x_train_32[val_ind].astype('float32')\n    y_tr, y_vl = y[train_ind].astype('float32'), y[val_ind].astype('float32')\n    \n        \n    model = SVR(C=0.2, cache_size=5000.0)\n    model.fit(train_, y_tr)\n    \n    val_pred = model.predict(val_)\n    y_oof[val_ind] = val_pred\n    \n    y_test += model.predict(x_test_32.astype('float32')).values/NUM_FOLDS\n    \n    print(\"Fold AUC:\", roc_auc_score(cp.asnumpy(y_vl.flatten()), cp.asnumpy(val_pred.values)))\n    \n    \n\n\nprint(\"Total AUC:\", roc_auc_score(cp.asnumpy(y.flatten()), cp.asnumpy(y_oof)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission = cudf.read_csv('../input/siim-isic-melanoma-classification/sample_submission.csv')\nsample_submission.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample_submission['target'] = y_test\nsample_submission.to_csv('submission_32x32_svr.csv', index=False)","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":4}