from pandas import DataFrame
from numpy import isnan


class Normalizer:
	def __init__(self, ignore_columns=None):
		self._columns = None
		self._means = None
		self._standard_deviations = None

		if isinstance(ignore_columns, str):
			ignore_columns = [ignore_columns]
		self._ignore_columns = ignore_columns or []

	def __getstate__(self):
		return {'_columns': self._columns, '_means': self._means, '_standard_deviations': self._standard_deviations}

	def __setstate__(self, state):
		for key, value in state.items():
			setattr(self, key, value)

	def fit(self, X, y=None):
		"""
		:type X: DataFrame
		"""
		if self._means is not None or self._standard_deviations is not None:
			raise RuntimeError('normalizer is already fit!')

		self._means = {
			column: X[column].mean(skipna=True)
			for column in X.columns if column not in self._ignore_columns
		}

		self._standard_deviations = {
			column: X[column].std(skipna=True)
			for column in X.columns if column not in self._ignore_columns
		}

		nan_values = []
		for column, m in self._means.items():
			if isnan(m) or isnan(self._standard_deviations[column]):
				nan_values.append(str(column))
		if len(nan_values) > 0:
			raise ValueError(f'{", ".join(nan_values)} produce nan values!')

	def transform(self, X):
		"""
		:type X: DataFrame
		:rtype: DataFrame
		"""
		if self._means is None or self._standard_deviations is None:
			raise RuntimeError('normalizer is not fit!')

		result = X.copy()

		for column in self._means.keys():
			if self._standard_deviations[column] != 0:
				result[column] = (result[column] - self._means[column]) / self._standard_deviations[column]
			else:
				result[column] = (result[column] - self._means[column])

		return result

	def fit_transform(self, X, y=None):
		self.fit(X=X, y=y)
		return self.transform(X=X)

	def untransform(self, X):
		"""
		:type X: DataFrame
		:rtype: DataFrame
		"""
		if self._means is None or self._standard_deviations is None:
			raise RuntimeError('normalizer is not fit!')

		result = X.copy()

		for column in self._means.keys():
			if self._standard_deviations[column] != 0:
				result[column] = result[column] * self._standard_deviations[column] + self._means[column]
			else:
				result[column] = result[column] + self._means[column]

		return result

	@property
	def parameters(self):
		"""
		:rtype: DataFrame
		"""
		return DataFrame.from_records([
			{'column': column, 'mean': self._means[column], 'sd': self._standard_deviations[column]}
			for column in self._means.keys()
		])

	@classmethod
	def get_normalizer_and_normalized(cls, data, **kwargs):
		"""
		:rtype: tuple[Normalizer, DataFrame]
		"""
		normalizer = cls(**kwargs)
		normalized = normalizer.fit_transform(X=data)
		return normalizer, normalized


def get_normalizer_and_normalized(data, **kwargs):
	"""
	:rtype: tuple[Normalizer, DataFrame]
	"""
	return Normalizer.get_normalizer_and_normalized(data=data, **kwargs)
9fb040a288a1a5dd5b9c702a910a298e852bfcdb
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class Normalizer:
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>
    <mask token>


<mask token>

<mask token>


class Normalizer:

    def __init__(self, ignore_columns=None):
        self._columns = None
        self._means = None
        self._standard_deviations = None
        if isinstance(ignore_columns, str):
            ignore_columns = [ignore_columns]
        self._ignore_columns = ignore_columns or []

    def __getstate__(self):
        return {'_columns': self._columns, '_means': self._means,
            '_standard_deviations': self._standard_deviations}

    def __setstate__(self, state):
        for key, value in state.items():
            setattr(self, key, value)

    def fit(self, X, y=None):
        """
		:type X: DataFrame
		"""
        if self._means is not None or self._standard_deviations is not None:
            raise RuntimeError('normalizer is already fit!')
        self._means = {column: X[column].mean(skipna=True) for column in X.
            columns if column not in self._ignore_columns}
        self._standard_deviations = {column: X[column].std(skipna=True) for
            column in X.columns if column not in self._ignore_columns}
        nan_values = []
        for column, m in self._means.items():
            if isnan(m) or isnan(self._standard_deviations[column]):
                nan_values.append(str(column))
        if len(nan_values) > 0:
            raise ValueError(f"{', '.join(nan_values)} produce nan values!")
    <mask token>
    <mask token>

    def untransform(self, X):
        """
		:type X: DataFrame
		:rtype: DataFrame
		"""
        if self._means is None or self._standard_deviations is None:
            raise RuntimeError('normalizer is not fit!')
        result = X.copy()
        for column in self._means.keys():
            if self._standard_deviations[column] != 0:
                result[column] = result[column] * self._standard_deviations[
                    column] + self._means[column]
            else:
                result[column] = result[column] + self._means[column]
        return result

    @property
    def parameters(self):
        """
		:rtype: DataFrame
		"""
        return DataFrame.from_records([{'column': column, 'mean': self.
            _means[column], 'sd': self._standard_deviations[column]} for
            column in self._means.keys()])

    @classmethod
    def get_normalizer_and_normalized(cls, data, **kwargs):
        """
		:rtype: tuple[Normalizer, DataFrame]
		"""
        normalizer = cls(**kwargs)
        normalized = normalizer.fit_transform(X=data)
        return normalizer, normalized


<mask token>

<mask token>


class Normalizer:

    def __init__(self, ignore_columns=None):
        self._columns = None
        self._means = None
        self._standard_deviations = None
        if isinstance(ignore_columns, str):
            ignore_columns = [ignore_columns]
        self._ignore_columns = ignore_columns or []

    def __getstate__(self):
        return {'_columns': self._columns, '_means': self._means,
            '_standard_deviations': self._standard_deviations}

    def __setstate__(self, state):
        for key, value in state.items():
            setattr(self, key, value)

    def fit(self, X, y=None):
        """
		:type X: DataFrame
		"""
        if self._means is not None or self._standard_deviations is not None:
            raise RuntimeError('normalizer is already fit!')
        self._means = {column: X[column].mean(skipna=True) for column in X.
            columns if column not in self._ignore_columns}
        self._standard_deviations = {column: X[column].std(skipna=True) for
            column in X.columns if column not in self._ignore_columns}
        nan_values = []
        for column, m in self._means.items():
            if isnan(m) or isnan(self._standard_deviations[column]):
                nan_values.append(str(column))
        if len(nan_values) > 0:
            raise ValueError(f"{', '.join(nan_values)} produce nan values!")

    def transform(self, X):
        """
		:type X: DataFrame
		:rtype: DataFrame
		"""
        if self._means is None or self._standard_deviations is None:
            raise RuntimeError('normalizer is not fit!')
        result = X.copy()
        for column in self._means.keys():
            if self._standard_deviations[column] != 0:
                result[column] = (result[column] - self._means[column]
                    ) / self._standard_deviations[column]
            else:
                result[column] = result[column] - self._means[column]
        return result

    def fit_transform(self, X, y=None):
        self.fit(X=X, y=y)
        return self.transform(X=X)

    def untransform(self, X):
        """
		:type X: DataFrame
		:rtype: DataFrame
		"""
        if self._means is None or self._standard_deviations is None:
            raise RuntimeError('normalizer is not fit!')
        result = X.copy()
        for column in self._means.keys():
            if self._standard_deviations[column] != 0:
                result[column] = result[column] * self._standard_deviations[
                    column] + self._means[column]
            else:
                result[column] = result[column] + self._means[column]
        return result

    @property
    def parameters(self):
        """
		:rtype: DataFrame
		"""
        return DataFrame.from_records([{'column': column, 'mean': self.
            _means[column], 'sd': self._standard_deviations[column]} for
            column in self._means.keys()])

    @classmethod
    def get_normalizer_and_normalized(cls, data, **kwargs):
        """
		:rtype: tuple[Normalizer, DataFrame]
		"""
        normalizer = cls(**kwargs)
        normalized = normalizer.fit_transform(X=data)
        return normalizer, normalized


<mask token>

from pandas import DataFrame
from numpy import isnan


class Normalizer:

    def __init__(self, ignore_columns=None):
        self._columns = None
        self._means = None
        self._standard_deviations = None
        if isinstance(ignore_columns, str):
            ignore_columns = [ignore_columns]
        self._ignore_columns = ignore_columns or []

    def __getstate__(self):
        return {'_columns': self._columns, '_means': self._means,
            '_standard_deviations': self._standard_deviations}

    def __setstate__(self, state):
        for key, value in state.items():
            setattr(self, key, value)

    def fit(self, X, y=None):
        """
		:type X: DataFrame
		"""
        if self._means is not None or self._standard_deviations is not None:
            raise RuntimeError('normalizer is already fit!')
        self._means = {column: X[column].mean(skipna=True) for column in X.
            columns if column not in self._ignore_columns}
        self._standard_deviations = {column: X[column].std(skipna=True) for
            column in X.columns if column not in self._ignore_columns}
        nan_values = []
        for column, m in self._means.items():
            if isnan(m) or isnan(self._standard_deviations[column]):
                nan_values.append(str(column))
        if len(nan_values) > 0:
            raise ValueError(f"{', '.join(nan_values)} produce nan values!")

    def transform(self, X):
        """
		:type X: DataFrame
		:rtype: DataFrame
		"""
        if self._means is None or self._standard_deviations is None:
            raise RuntimeError('normalizer is not fit!')
        result = X.copy()
        for column in self._means.keys():
            if self._standard_deviations[column] != 0:
                result[column] = (result[column] - self._means[column]
                    ) / self._standard_deviations[column]
            else:
                result[column] = result[column] - self._means[column]
        return result

    def fit_transform(self, X, y=None):
        self.fit(X=X, y=y)
        return self.transform(X=X)

    def untransform(self, X):
        """
		:type X: DataFrame
		:rtype: DataFrame
		"""
        if self._means is None or self._standard_deviations is None:
            raise RuntimeError('normalizer is not fit!')
        result = X.copy()
        for column in self._means.keys():
            if self._standard_deviations[column] != 0:
                result[column] = result[column] * self._standard_deviations[
                    column] + self._means[column]
            else:
                result[column] = result[column] + self._means[column]
        return result

    @property
    def parameters(self):
        """
		:rtype: DataFrame
		"""
        return DataFrame.from_records([{'column': column, 'mean': self.
            _means[column], 'sd': self._standard_deviations[column]} for
            column in self._means.keys()])

    @classmethod
    def get_normalizer_and_normalized(cls, data, **kwargs):
        """
		:rtype: tuple[Normalizer, DataFrame]
		"""
        normalizer = cls(**kwargs)
        normalized = normalizer.fit_transform(X=data)
        return normalizer, normalized


def get_normalizer_and_normalized(data, **kwargs):
    """
	:rtype: tuple[Normalizer, DataFrame]
	"""
    return Normalizer.get_normalizer_and_normalized(data=data, **kwargs)

from pandas import DataFrame
from numpy import isnan


class Normalizer:
	def __init__(self, ignore_columns=None):
		self._columns = None
		self._means = None
		self._standard_deviations = None

		if isinstance(ignore_columns, str):
			ignore_columns = [ignore_columns]
		self._ignore_columns = ignore_columns or []

	def __getstate__(self):
		return {'_columns': self._columns, '_means': self._means, '_standard_deviations': self._standard_deviations}

	def __setstate__(self, state):
		for key, value in state.items():
			setattr(self, key, value)

	def fit(self, X, y=None):
		"""
		:type X: DataFrame
		"""
		if self._means is not None or self._standard_deviations is not None:
			raise RuntimeError('normalizer is already fit!')

		self._means = {
			column: X[column].mean(skipna=True)
			for column in X.columns if column not in self._ignore_columns
		}

		self._standard_deviations = {
			column: X[column].std(skipna=True)
			for column in X.columns if column not in self._ignore_columns
		}

		nan_values = []
		for column, m in self._means.items():
			if isnan(m) or isnan(self._standard_deviations[column]):
				nan_values.append(str(column))
		if len(nan_values) > 0:
			raise ValueError(f'{", ".join(nan_values)} produce nan values!')

	def transform(self, X):
		"""
		:type X: DataFrame
		:rtype: DataFrame
		"""
		if self._means is None or self._standard_deviations is None:
			raise RuntimeError('normalizer is not fit!')

		result = X.copy()

		for column in self._means.keys():
			if self._standard_deviations[column] != 0:
				result[column] = (result[column] - self._means[column]) / self._standard_deviations[column]
			else:
				result[column] = (result[column] - self._means[column])

		return result

	def fit_transform(self, X, y=None):
		self.fit(X=X, y=y)
		return self.transform(X=X)

	def untransform(self, X):
		"""
		:type X: DataFrame
		:rtype: DataFrame
		"""
		if self._means is None or self._standard_deviations is None:
			raise RuntimeError('normalizer is not fit!')

		result = X.copy()

		for column in self._means.keys():
			if self._standard_deviations[column] != 0:
				result[column] = result[column] * self._standard_deviations[column] + self._means[column]
			else:
				result[column] = result[column] + self._means[column]

		return result

	@property
	def parameters(self):
		"""
		:rtype: DataFrame
		"""
		return DataFrame.from_records([
			{'column': column, 'mean': self._means[column], 'sd': self._standard_deviations[column]}
			for column in self._means.keys()
		])

	@classmethod
	def get_normalizer_and_normalized(cls, data, **kwargs):
		"""
		:rtype: tuple[Normalizer, DataFrame]
		"""
		normalizer = cls(**kwargs)
		normalized = normalizer.fit_transform(X=data)
		return normalizer, normalized


def get_normalizer_and_normalized(data, **kwargs):
	"""
	:rtype: tuple[Normalizer, DataFrame]
	"""
	return Normalizer.get_normalizer_and_normalized(data=data, **kwargs)