{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\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","collapsed":true,"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":false},"cell_type":"code","source":"\"\"\"\nAn example to check the AUC score on a validation set for each N epochs.\nI hope it will be helpful for optimizing number of epochs.\n\"\"\"\nimport logging\nfrom sklearn.metrics import roc_auc_score\nfrom keras.callbacks import Callback\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class AUCCallback(Callback):\n    \"\"\"\n    Calculates AUC for train and val sets and passes them as lists every n epochs\n    \n    Args: \n        interval: how often to validate. Default = 1\n    \"\"\"\n    def __init__(self, train_data: tuple =(), validation_data: tuple=(), interval: int=1):\n        super(Callback, self).__init__()\n\n        self.interval = interval\n        self.X_train, self.y_train = train_data\n        self.X_val, self.y_val = validation_data\n        self.auc = []\n        self.auc_train = []\n\n    def on_epoch_end(self, epoch, logs={}):\n        if epoch % self.interval == 0:\n            #auc for train data\n            y_pred_train = self.model.predict(self.X_train, verbose=0)\n            score_train = roc_auc_score(self.y_train, y_pred_train)\n            self.auc_train.append(score_train)\n            #auc for validation data\n            y_pred = self.model.predict(self.X_val, verbose=0)\n            score = roc_auc_score(self.y_val, y_pred)\n            self.auc.append(score)\n            print(\"epoch: {:d} - roc_auc_score train: {:.6f}\".format(epoch, score_train))\n            print(\"epoch: {:d} - roc_auc_score val: {:.6f}\".format(epoch, score))\n            logging.info(\"epoch: {:d} - score: {:.6f}\".format(epoch, score))\n","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}