category_encoders/target_encoder.py
def fit(self, X, y, **kwargs):
        """Fit encoder according to X and y.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.
        y : array-like, shape = [n_samples]
            Target values.

        Returns
        -------
        self : encoder
            Returns self.

        """

        # unite the input into pandas types
        X = util.convert_input(X)
        y = util.convert_input_vector(y, X.index)

        if X.shape[0] != y.shape[0]:
            raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")

        self._dim = X.shape[1]

        # if columns aren't passed, just use every string column
        if self.cols is None:
            self.cols = util.get_obj_cols(X)
        else:
            self.cols = util.convert_cols_to_list(self.cols)

        if self.handle_missing == 'error':
            if X[self.cols].isnull().any().bool():
                raise ValueError('Columns to be encoded can not contain null')

        self.ordinal_encoder = OrdinalEncoder(
            verbose=self.verbose,
            cols=self.cols,
            handle_unknown='value',
            handle_missing='value'
        )
        self.ordinal_encoder = self.ordinal_encoder.fit(X)
        X_ordinal = self.ordinal_encoder.transform(X)
        self.mapping = self.fit_target_encoding(X_ordinal, y)
        
        X_temp = self.transform(X, override_return_df=True)
        self.feature_names = list(X_temp.columns)

        if self.drop_invariant:
            self.drop_cols = []
            X_temp = self.transform(X)
            generated_cols = util.get_generated_cols(X, X_temp, self.cols)
            self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
            try:
                [self.feature_names.remove(x) for x in self.drop_cols]
            except KeyError as e:
                if self.verbose > 0:
                    print("Could not remove column from feature names."
                    "Not found in generated cols.\n{}".format(e))

        return self
def fit(self, X, y, **kwargs):
        """Fit encoder according to X and y.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.
        y : array-like, shape = [n_samples]
            Target values.

        Returns
        -------
        self : encoder
            Returns self.

        """

        # unite the input into pandas types
        X = util.convert_input(X)
        y = util.convert_input_vector(y, X.index)

        if X.shape[0] != y.shape[0]:
            raise ValueError("The length of X is " + str(X.shape[0]) + " but length of y is " + str(y.shape[0]) + ".")

        self._dim = X.shape[1]

        # if columns aren't passed, just use every string column
        if self.cols is None:
            self.cols = util.get_obj_cols(X)
        else:
            self.cols = util.convert_cols_to_list(self.cols)

        if self.handle_missing == 'error':
            if X[self.cols].isnull().any().bool():
                raise ValueError('Columns to be encoded can not contain null')

        self.ordinal_encoder = OrdinalEncoder(
            verbose=self.verbose,
            cols=self.cols,
            handle_unknown='value',
            handle_missing='value'
        )
        self.ordinal_encoder = self.ordinal_encoder.fit(X)
        X_ordinal = self.ordinal_encoder.transform(X)
        self.mapping = self.fit_target_encoding(X_ordinal, y)
        
        X_temp = self.transform(X, override_return_df=True)
        self.feature_names = list(X_temp.columns)

        if self.drop_invariant:
            self.drop_cols = []
            X_temp = self.transform(X)
            generated_cols = util.get_generated_cols(X, X_temp, self.cols)
            self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
            try:
                [self.feature_names.remove(x) for x in self.drop_cols]
            except KeyError as e:
                if self.verbose > 0:
                    print("Could not remove column from feature names."
                    "Not found in generated cols.\n{}".format(e))

        return self
def fit ( self , X , y , * * kwargs ) : # unite the input into pandas types X = util . convert_input ( X ) y = util . convert_input_vector ( y , X . index ) if X . shape [ 0 ] != y . shape [ 0 ] : raise ValueError ( "The length of X is " + str ( X . shape [ 0 ] ) + " but length of y is " + str ( y . shape [ 0 ] ) + "." ) self . _dim = X . shape [ 1 ] # if columns aren't passed, just use every string column if self . cols is None : self . cols = util . get_obj_cols ( X ) else : self . cols = util . convert_cols_to_list ( self . cols ) if self . handle_missing == 'error' : if X [ self . cols ] . isnull ( ) . any ( ) . bool ( ) : raise ValueError ( 'Columns to be encoded can not contain null' ) self . ordinal_encoder = OrdinalEncoder ( verbose = self . verbose , cols = self . cols , handle_unknown = 'value' , handle_missing = 'value' ) self . ordinal_encoder = self . ordinal_encoder . fit ( X ) X_ordinal = self . ordinal_encoder . transform ( X ) self . mapping = self . fit_target_encoding ( X_ordinal , y ) X_temp = self . transform ( X , override_return_df = True ) self . feature_names = list ( X_temp . columns ) if self . drop_invariant : self . drop_cols = [ ] X_temp = self . transform ( X ) generated_cols = util . get_generated_cols ( X , X_temp , self . cols ) self . drop_cols = [ x for x in generated_cols if X_temp [ x ] . var ( ) <= 10e-5 ] try : [ self . feature_names . remove ( x ) for x in self . drop_cols ] except KeyError as e : if self . verbose > 0 : print ( "Could not remove column from feature names." "Not found in generated cols.\n{}" . format ( e ) ) return self
Fit encoder according to X and y.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
            Training vectors, where n_samples is the number of samples
            and n_features is the number of features.
        y : array-like, shape = [n_samples]
            Target values.

        Returns
        -------
        self : encoder
            Returns self.
Fit encoder according to X and y .