How to use the tigramite.models.Models function in tigramite

To help you get started, we’ve selected a few tigramite examples, based on popular ways it is used in public projects.

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github jakobrunge / tigramite / tigramite / models.py View on Github external
Returns
        -------
        amce : array of shape (N,)
            Average Mediated Causal Effect.
        """
        amce = np.zeros(self.N)
        for k in range(self.N):
            amce[k] = self.get_amce(k,
                                    lag_mode=lag_mode,
                                    exclude_k=exclude_k,
                                    exclude_self_effects=exclude_self_effects)

        return amce


class Prediction(Models, PCMCI):
    r"""Prediction class for time series models.

    Allows to fit and predict from any sklearn model. The optimal predictors can
    be estimated using PCMCI. Also takes care of missing values, masking and
    preprocessing.

    Parameters
    ----------
    dataframe : data object
        Tigramite dataframe object. It must have the attributes dataframe.values
        yielding a numpy array of shape (observations T, variables N) and
        optionally a mask of the same shape and a missing values flag.

    train_indices : array-like
        Either boolean array or time indices marking the training data.
github jakobrunge / tigramite / tigramite / models.py View on Github external
Returns
        -------
        coeffs : dictionary
            Dictionary of dictionaries for each variable with keys given by the
            parents and the regression coefficients as values.
        """
        coeffs = {}
        for j in self.selected_variables:
            coeffs[j] = {}
            for ipar, par in enumerate(self.all_parents[j]):
                coeffs[j][par] = self.fit_results[j]['model'].coef_[ipar]
        return coeffs


class LinearMediation(Models):
    r"""Linear mediation analysis for time series models.

    Fits linear model to parents and provides functions to return measures such
    as causal effect, mediated causal effect, average causal effect, etc. as
    described in [4]_.

    Notes
    -----
    This class implements the following causal mediation measures introduced in
    [4]_:

      * causal effect (CE)
      * mediated causal effect (MCE)
      * average causal effect (ACE)
      * average causal susceptibility (ACS)
      * average mediated causal effect (AMCE)