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import pandas as pd

# 2018.11.28 Created by Eamon.Zhang

class MeanEncoding():
    """
    replacing the label by the mean of the target for that label. 
    
    Parameters
    ----------
   
    """

    def __init__(self, mapping=None, cols=None):
        self.cols = cols
        self.mapping = mapping
        self._dim = None
        # self.threshold = threshold


    def fit(self, X, y=None, **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.
        """

        self._dim = X.shape[1]

        _, categories = self.mean_encoding(
            X,
            y,
            mapping=self.mapping,
            cols=self.cols
            # threshold=self.threshold
        )
        self.mapping = categories
        return self


    def transform(self, X):
        """Perform the transformation to new categorical data.
        Will use the mapping (if available) and the column list to encode the
        data.
        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]
        Returns
        -------
        X : Transformed values with encoding applied.
        """

        if self._dim is None:
            raise ValueError('Must train encoder before it can be used to transform data.')

        #  make sure that it is the right size
        if X.shape[1] != self._dim:
            raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,))

        X, _ = self.mean_encoding(
            X,
            mapping=self.mapping,
            cols=self.cols
            # threshold=self.threshold
        )

        return X 


    def mean_encoding(self, X_in, y=None, mapping=None, cols=None):
        """
        Grouping the observations that show rare labels into a unique category ('rare')
        """

        X = X_in.copy(deep=True)

#        if cols is None:
#            cols = X.columns.values

        if mapping is not None:  # transform
            mapping_out = mapping
            for i in mapping:
                column = i.get('col') # get the column name
                X[column] = X[column].map(i['mapping'])

#                try:
#                    X[column] = X[column].astype(int)
#                except ValueError as e:
#                    X[column] = X[column].astype(float)
        else: # fit
            mapping_out = []
            for col in cols:
#                if util.is_category(X[col].dtype):
#                    categories = X[col].cat.categories
#                else:
                mapping = X[y.name].groupby(X[col]).mean().to_dict()
                mapping = pd.Series(mapping)
                mapping_out.append({'col': col, 'mapping': mapping, 'data_type': X[col].dtype}, )

        return X, mapping_out

	import pandas as pd

	# 2018.11.28 Created by Eamon.Zhang

	class MeanEncoding():
	"""
	replacing the label by the mean of the target for that label.

	Parameters
	----------

	"""

	def __init__(self, mapping=None, cols=None):
	self.cols = cols
	self.mapping = mapping
	self._dim = None
	# self.threshold = threshold


	def fit(self, X, y=None, **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.
	"""

	self._dim = X.shape[1]

	_, categories = self.mean_encoding(
	X,
	y,
	mapping=self.mapping,
	cols=self.cols
	# threshold=self.threshold
	)
	self.mapping = categories
	return self


	def transform(self, X):
	"""Perform the transformation to new categorical data.
	Will use the mapping (if available) and the column list to encode the
	data.
	Parameters
	----------
	X : array-like, shape = [n_samples, n_features]
	Returns
	-------
	X : Transformed values with encoding applied.
	"""

	if self._dim is None:
	raise ValueError('Must train encoder before it can be used to transform data.')

	# make sure that it is the right size
	if X.shape[1] != self._dim:
	raise ValueError('Unexpected input dimension %d, expected %d' % (X.shape[1], self._dim,))

	X, _ = self.mean_encoding(
	X,
	mapping=self.mapping,
	cols=self.cols
	# threshold=self.threshold
	)

	return X


	def mean_encoding(self, X_in, y=None, mapping=None, cols=None):
	"""
	Grouping the observations that show rare labels into a unique category ('rare')

	"""

	X = X_in.copy(deep=True)

	# if cols is None:
	# cols = X.columns.values

	if mapping is not None: # transform
	mapping_out = mapping
	for i in mapping:
	column = i.get('col') # get the column name
	X[column] = X[column].map(i['mapping'])

	# try:
	# X[column] = X[column].astype(int)
	# except ValueError as e:
	# X[column] = X[column].astype(float)
	else: # fit
	mapping_out = []
	for col in cols:
	# if util.is_category(X[col].dtype):
	# categories = X[col].cat.categories
	# else:
	mapping = X[y.name].groupby(X[col]).mean().to_dict()
	mapping = pd.Series(mapping)
	mapping_out.append({'col': col, 'mapping': mapping, 'data_type': X[col].dtype}, )

	return X, mapping_out