# Source code for eqc_models.ml.forecast

    import sys
    import numpy as np
    import pandas as pd
    from sklearn.linear_model import LinearRegression, Ridge

    from .reservoir import QciReservoir
    from .forecastbase import BaseForecastModel

    class ReservoirForecastModel(BaseForecastModel, QciReservoir):
        """
        A reservoir based forecast model.

        Parameters
        ----------

        ip_addr: The IP address of the device.

        num_nodes: Number of reservoir network nodes.

        feature_scaling: The factor used to scale the reservoir output.

        num_pads: Size of the pad used in the reservoir input;
        default: 0.

        reg_coef: L2 regularization coefficient for linear regression;
        default: 0.

        device: The QCi reservoir device. Currently only 'EmuCore' is
        supported; default: EmuCore.

        Examples
        ---------

        >>> MAX_TRAIN_DAY = 800
        >>> IP_ADDR = "172.22.19.49"
        >>> FEATURE_SCALING = 0.1
        >>> NUM_NODES = 1000
        >>> NUM_PADS = 100
        >>> LAGS = 2
        >>> from contextlib import redirect_stdout
        >>> import io
        >>> f = io.StringIO()
        >>> from eqc_models.ml import ReservoirForecastModel
        >>> with redirect_stdout(f):
        ...    model = ReservoirForecastModel(
        ...        ip_addr=IP_ADDR,
        ...        num_nodes=NUM_NODES,
        ...        feature_scaling=FEATURE_SCALING,
        ...        num_pads=NUM_PADS,
        ...        device="EmuCore",
        ...    )
        ...    model.fit(
        ...        data=train_df,
        ...        feature_fields=["norm_cell_prod"],
        ...        target_fields=["norm_cell_prod"],
        ...        lags=LAGS,
        ...        horizon_size=1,
        ...    )
        ...    y_train_pred = model.predict(train_df, mode="in_sample")
        ...    y_test_pred = model.predict(test_df, mode="in_sample")
        >>> model.close()

        """

        def __init__(
            self,
            ip_addr,
            num_nodes,
            feature_scaling,
            num_pads: int = 0,
            reg_coef: float = 0.0,
            device: str = "EmuCore",
        ):
            super(ReservoirForecastModel).__init__()
            BaseForecastModel.__init__(self)
            QciReservoir.__init__(self, ip_addr, num_nodes)

            assert device == "EmuCore", "Unknown device!"

            self.ip_addr = ip_addr
            self.num_nodes = num_nodes
            self.feature_scaling = feature_scaling
            self.num_pads = num_pads
            self.reg_coef = reg_coef
            self.device = device

            self.lock_id = None
            self.lin_model = None
            self.feature_fields = None
            self.target_fields = None
            self.lags = None
            self.horizon_size = None
            self.zero_pad_data = None
            self.train_pad_data = None

            self.init_reservoir()

        def close(self):
            self.release_lock()

        def fit(
            self,
            data: pd.DataFrame,
            feature_fields: list,
            target_fields: list,
            lags: int = 0,
            horizon_size: int = 1,
        ):
            """A function to train a forecast model.

            Parameters
            ----------

            data: A pandas data frame that contain the time series.

            feature_fields: A list of fields in the data frame that are as
            inputs to the reservoir.

            target_fields: A list of fields in teh data frame that are to be
            forecasted.

            lags: Number of lags used; default = 0.

            horizon_size: Size of the horizon, e.g. number of forecast
            steps.

            """

            # Pad input
            num_pads = self.num_pads
            if num_pads is not None and num_pads > 0:
                self.zero_pad_data = pd.DataFrame()
                for item in data.columns:
                    self.zero_pad_data[item] = np.zeros(shape=(num_pads))

                data = pd.concat([self.zero_pad_data, data])

            # Prep data
            fea_data = np.array(data[feature_fields])
            targ_data = np.array(data[target_fields])

            X_train, y_train, steps = self.prep_fea_targs(
                fea_data=fea_data,
                targ_data=targ_data,
                window_size=lags + 1,
                horizon_size=horizon_size,
            )

            # Save some parameters
            self.feature_fields = feature_fields
            self.target_fields = target_fields
            self.lags = lags
            self.horizon_size = horizon_size

            # Push to reservoir
            X_train_resp = self.push_reservoir(X_train)

            if num_pads is not None and num_pads > 0:
                X_train_resp = X_train_resp[num_pads:]
                y_train = y_train[num_pads:]

            # Build linear model
            #self.lin_model = LinearRegression(fit_intercept=True)
            self.lin_model = Ridge(alpha=self.reg_coef, fit_intercept=True)
            self.lin_model.fit(X_train_resp, y_train)

            # Get predictions
            y_train_pred = self.lin_model.predict(X_train_resp)

            # Echo some stats
            train_stats = self.get_stats(y_train, y_train_pred)

            print("Training stats:", train_stats)

            if num_pads is not None and num_pads > 0:
                self.train_pad_data = data.tail(num_pads)

            return

        def predict(
            self,
            data: pd.DataFrame,
            pad_mode: str = "zero",
            mode: str = "in_sample",
        ):
            """A function to get predictions from forecast model.

            Parameters
            ----------

            data: A pandas data frame that contain the time series.

            pad_mode: Mode of the reservoir input padding, either
            'last_train' or 'zero'; default: 'zero.

            mode: A value of 'out_of_sample' predicts the horizon
            following the time series. A value of 'in_sample' predicts in
            sample (used for testing); default: in_sample.

            Returns
            -------

            The predictions: numpy.array((horizon_size, num_dims)).

            """

            assert self.lin_model is not None, "Model not train yet!"
            assert mode in ["in_sample", "out_of_sample"], (
                "Unknown mode <%s>!" % mode
            )

            num_pads = self.num_pads
            if num_pads is not None and num_pads > 0:
                if pad_mode == "last_train":
                    pad_data = self.train_pad_data
                else:
                    pad_data = self.zero_pad_data

                data = pd.concat([pad_data, data])

            num_records = data.shape[0]
            fea_data = np.array(data[self.feature_fields])
            targ_data = np.array(data[self.target_fields])

            if mode == "in_sample":
                X, y, _ = self.prep_fea_targs(
                    fea_data=fea_data,
                    targ_data=targ_data,
                    window_size=self.lags + 1,
                    horizon_size=self.horizon_size,
                )
            elif mode == "out_of_sample":
                X = self.prep_out_of_sample(
                    fea_data=fea_data,
                    window_size=self.lags + 1,
                    horizon_size=self.horizon_size,
                )
            else:
                assert False, "Unknown mode <%s>!" % mode

            X_resp = self.push_reservoir(X)

            if self.num_pads is not None and self.num_pads > 0:
                X_resp = X_resp[self.num_pads:]
                y = y[self.num_pads:]

            y_pred = self.lin_model.predict(X_resp)

            # Echo some stats
            if mode == "in_sample":
                stats = self.get_stats(y, y_pred)
                print("In-sample prediction stats:", stats)

            return y_pred