{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import os\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import roc_auc_score","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train = pd.read_csv('../input/ranzcr-clip-catheter-line-classification/train.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_true = train.iloc[:, 1:-1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_pred = pd.concat([y_true.mean()] * y_true.shape[0], axis=1).T","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Roc AUC when flattening the vectors:"},{"metadata":{"trusted":true},"cell_type":"code","source":"y_true_flat = y_true.values.reshape(-1)\ny_pred_flat = y_pred.values.reshape(-1)\n\nroc_auc_score(y_true_flat, y_pred_flat)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"ROC AUC when calculating each column separately:"},{"metadata":{"trusted":true},"cell_type":"code","source":"for col in y_true.columns:\n    print(col, roc_auc_score(y_true[col], y_pred[col]))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"ROC AUC as mean over columns:"},{"metadata":{"trusted":true},"cell_type":"code","source":"roc_auc_score(y_true, y_pred)","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}