build/lib/AutoMxL/__main__.py
def select_features(self, method='pca', verbose=False):
        """ fit and apply features selection (optional)

        Parameters
        ----------
        method : string (Default pca)
            method use to select features
        verbose : boolean (Default False)
            Get logging information

        """
        assert self.step in ['preprocess'], 'apply preprocess method'

        target = self.target

        if verbose:
            start_time = time()
            print_title1('Features Selection')

        df_local = self.copy()

        l_select_var = [col for col in df_local.columns.tolist() if col != self.target]

        # df_local = select_features(df=df_local, target=self.target, method=method, verbose=verbose)

        features_selector = FeatSelector(method=method)
        features_selector.fit(df_local, l_var=l_select_var, verbose=verbose)
        df_local = features_selector.transform(df_local, verbose=verbose)

        self.__dict__.update(df_local.__dict__)
        self.target = target
        self.features_selector = features_selector
        self.is_fitted_selector = True
        self.step = 'features_selection'

        if verbose :
            print('\n\t\t>>>', 'select_features execution time:', round(time() - start_time, 4), 'secs. <<<')
 fit and apply features selection (optional)

        Parameters
        ----------
        method : string (Default pca)
            method use to select features
        verbose : boolean (Default False)
            Get logging information

        
fit and apply features selection (optional)
Parameters

method : string (Default pca)
method use to select features
verbose : boolean (Default False)
Get logging information
fit and apply features selection ( optional ) Parameters method : string ( Default pca ) method use to select features verbose : boolean ( Default False ) Get logging information
def select_features(self, method='pca', verbose=False):
        assert self.step in ['preprocess'], 'apply preprocess method'
        target = self.target
        if verbose:
            start_time = time()
            print_title1('Features Selection')
        df_local = self.copy()
        l_select_var = [col for col in df_local.columns.tolist() if col != self.target]
        features_selector = FeatSelector(method=method)
        features_selector.fit(df_local, l_var=l_select_var, verbose=verbose)
        df_local = features_selector.transform(df_local, verbose=verbose)
        self.__dict__.update(df_local.__dict__)
        self.target = target
        self.features_selector = features_selector
        self.is_fitted_selector = True
        self.step = 'features_selection'
        if verbose :
            print('\n\t\t>>>', 'select_features execution time:', round(time() - start_time, 4), 'secs. <<<')
def select_features ( self , method = 'pca' , verbose = False ) : assert self . step in [ 'preprocess' ] , 'apply preprocess method' target = self . target if verbose : start_time = time ( ) print_title1 ( 'Features Selection' ) df_local = self . copy ( ) l_select_var = [ col for col in df_local . columns . tolist ( ) if col != self . target ] features_selector = FeatSelector ( method = method ) features_selector . fit ( df_local , l_var = l_select_var , verbose = verbose ) df_local = features_selector . transform ( df_local , verbose = verbose ) self . __dict__ . update ( df_local . __dict__ ) self . target = target self . features_selector = features_selector self . is_fitted_selector = True self . step = 'features_selection' if verbose : print ( '\n\t\t>>>' , 'select_features execution time:' , round ( time ( ) - start_time , 4 ) , 'secs. <<<' )
fit and apply features selection (optional)
Parameters
fit and apply features selection ( optional ) Parameters
""" fit and apply features selection (optional)

        Parameters
        ----------
        method : string (Default pca)
            method use to select features
        verbose : boolean (Default False)
            Get logging information

        """ # df_local = select_features(df=df_local, target=self.target, method=method, verbose=verbose)
self
method
verbose