コード例 #1
0
ファイル: lstm_anomaly.py プロジェクト: omri374/moda
    def fit_one_category(self, dataset, category=None, verbose=False):

        train = self.scale(dataset)

        as_windows_train = get_windowed_ts(train, window_size=self.window_size)
        train_X, train_y = split_history_and_current(as_windows_train)

        train_X = train_X.reshape(train_X.shape[0], train_X.shape[1], 1)

        # Get model definition
        model = self.lstm_forecast_model(window_size=self.window_size,
                                         verbose=verbose)

        # Fit model
        start = time.time()
        history = model.fit(
            train_X,
            train_y,
            batch_size=max(self.batch_size, len(train_X)),
            epochs=self.epochs,
            validation_split=0.1,
            callbacks=[EarlyStopping(patience=4)],
        )
        print("> Compilation Time : ", time.time() - start)

        self.model[category] = model
コード例 #2
0
ファイル: lstm_anomaly.py プロジェクト: omri374/moda
    def predict_one_category(self, X, category):
        test = self.scale(X)

        as_windows_test = get_windowed_ts(test, window_size=self.window_size)
        test_X, test_y = split_history_and_current(as_windows_test)

        test_X = test_X.reshape(test_X.shape[0], test_X.shape[1], 1)

        preds = self.model[category].predict(test_X)

        from sklearn.metrics import mean_squared_error

        # clear session
        clear_session()
        del self.model

        actual = test_y
        prediction = preds

        mse = mean_squared_error(actual, prediction)
        print("MSE=" + str(mse))

        results = pd.DataFrame({
            "date": X.index[self.window_size:],
            "prediction": preds.squeeze(),
            "actual": test_y,
            "diff": test_y - preds.squeeze(),
        })
        results["diff_rolling_std"] = (
            results["diff"].rolling(self.lookback,
                                    min_periods=self.min_periods).std()
        )  # naive version where we look at the standard deviation of the
        # difference between predicted and actual in a previous window.
        results["prediction"] = np.where(
            results["diff"] > self.num_of_std * results["diff_rolling_std"], 1,
            0)
        return results