def predict_dataset(self, df, device=None, verbose=None):
        """
        Args:
            x_tr (pd.DataFrame): CEPC format dataframe containing the pairs
            y_tr (pd.DataFrame or np.ndarray): labels associated to the pairs
            epochs (int): number of train epochs
            learning rate (float): learning rate of Adam
            verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)
            device (str): cuda or cpu device (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            pandas.DataFrame: dataframe containing the predicted causation coefficients
        """
        verbose, device = SETTINGS.get_default(('verbose', verbose),
                                               ('device', device))
        dataset = []
        for i, (idx, row) in enumerate(df.iterrows()):
            a = row['A'].reshape((len(row['A']), 1))
            b = row['B'].reshape((len(row['B']), 1))
            m = np.hstack((a, b))
            m = m.astype('float32')
            m = th.from_numpy(m).t().unsqueeze(0)
            dataset.append(m)

        dataset = [m.to(device) for m in dataset]
        return pd.DataFrame((th.cat([self.model(m) for m, t in zip(dataset, trange(len(dataset)),
                                                      disable=not verbose)]\
                       , 0).data.cpu().numpy() -.5) * 2)
def predict_dataset(self, df, device=None, verbose=None):
        """
        Args:
            x_tr (pd.DataFrame): CEPC format dataframe containing the pairs
            y_tr (pd.DataFrame or np.ndarray): labels associated to the pairs
            epochs (int): number of train epochs
            learning rate (float): learning rate of Adam
            verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)
            device (str): cuda or cpu device (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            pandas.DataFrame: dataframe containing the predicted causation coefficients
        """
        verbose, device = SETTINGS.get_default(('verbose', verbose),
                                               ('device', device))
        dataset = []
        for i, (idx, row) in enumerate(df.iterrows()):
            a = row['A'].reshape((len(row['A']), 1))
            b = row['B'].reshape((len(row['B']), 1))
            m = np.hstack((a, b))
            m = m.astype('float32')
            m = th.from_numpy(m).t().unsqueeze(0)
            dataset.append(m)

        dataset = [m.to(device) for m in dataset]
        return pd.DataFrame((th.cat([self.model(m) for m, t in zip(dataset, trange(len(dataset)),
                                                      disable=not verbose)]\
                       , 0).data.cpu().numpy() -.5) * 2)
def predict_dataset ( self , df , device = None , verbose = None ) : verbose , device = SETTINGS . get_default ( ( 'verbose' , verbose ) , ( 'device' , device ) ) dataset = [ ] for i , ( idx , row ) in enumerate ( df . iterrows ( ) ) : a = row [ 'A' ] . reshape ( ( len ( row [ 'A' ] ) , 1 ) ) b = row [ 'B' ] . reshape ( ( len ( row [ 'B' ] ) , 1 ) ) m = np . hstack ( ( a , b ) ) m = m . astype ( 'float32' ) m = th . from_numpy ( m ) . t ( ) . unsqueeze ( 0 ) dataset . append ( m ) dataset = [ m . to ( device ) for m in dataset ] return pd . DataFrame ( ( th . cat ( [ self . model ( m ) for m , t in zip ( dataset , trange ( len ( dataset ) ) , disable = not verbose ) ] , 0 ) . data . cpu ( ) . numpy ( ) - .5 ) * 2 )
Args:
            x_tr (pd.DataFrame): CEPC format dataframe containing the pairs
            y_tr (pd.DataFrame or np.ndarray): labels associated to the pairs
            epochs (int): number of train epochs
            learning rate (float): learning rate of Adam
            verbose (bool): verbosity (defaults to ``cdt.SETTINGS.verbose``)
            device (str): cuda or cpu device (defaults to ``cdt.SETTINGS.default_device``)

        Returns:
            pandas.DataFrame: dataframe containing the predicted causation coefficients
Args : x_tr ( pd . DataFrame ) : CEPC format dataframe containing the pairs y_tr ( pd . DataFrame or np . ndarray ) : labels associated to the pairs epochs ( int ) : number of train epochs learning rate ( float ) : learning rate of Adam verbose ( bool ) : verbosity ( defaults to cdt . SETTINGS . verbose ) device ( str ) : cuda or cpu device ( defaults to cdt . SETTINGS . default_device )