{"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"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":22962,"databundleVersionId":3171193,"sourceType":"competition"}],"dockerImageVersionId":30626,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Define label encoder\nfit label encoder\ndefine threthouds","metadata":{}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nOther = [\"Other\"]\nencoder_other = LabelEncoder()\nOther = encoder_other.fit_transform(Other)\nprediction = []\nfor pred in tqdm(preds):\n    if pred.max() > 0.5:\n        pred_labels = encoder.inverse_transform(np.argmax([pred], 1)).item()\n        prediction.append(pred_labels)\n    else:\n        pred_labels = encoder_other.inverse_transform([0]).item()\n        prediction.append(pred_labels)\nprediction","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]}]}