sklearn：sklearn.preprocessing的MinMaxScaler简介、使用方法之详细攻略

目录

MinMaxScaler简介

MinMaxScaler函数解释

MinMaxScaler底层代码

MinMaxScaler的使用方法

1、基础案例

MinMaxScaler简介

MinMaxScaler函数解释

MinMaxScaler底层代码

class MinMaxScaler Found at: sklearn.preprocessing.dataclass MinMaxScaler(BaseEstimator, TransformerMixin):def __init__(self, feature_range=(0, 1), copy=True):self.feature_range = feature_rangeself.copy = copydef _reset(self):"""Reset internal data-dependent state of the scaler, if necessary.__init__ parameters are not touched."""# Checking one attribute is enough, becase they are all set together# in partial_fitif hasattr(self, 'scale_'):del self.scale_del self.min_del self.n_samples_seen_del self.data_min_del self.data_max_del self.data_range_def fit(self, X, y=None):"""Compute the minimum and maximum to be used for later scaling.Parameters----------X : array-like, shape [n_samples, n_features]The data used to compute the per-feature minimum and maximumused for later scaling along the features axis."""# Reset internal state before fittingself._reset()return self.partial_fit(X, y)def partial_fit(self, X, y=None):"""Online computation of min and max on X for later scaling.All of X is processed as a single batch. This is intended for caseswhen `fit` is not feasible due to very large number of `n_samples`or because X is read from a continuous stream.Parameters----------X : array-like, shape [n_samples, n_features]The data used to compute the mean and standard deviationused for later scaling along the features axis.y : Passthrough for ``Pipeline`` compatibility."""feature_range = self.feature_rangeif feature_range[0] >= feature_range[1]:raise ValueError("Minimum of desired feature range must be smaller"" than maximum. Got %s." % str(feature_range))if sparse.issparse(X):raise TypeError("MinMaxScaler does no support sparse input. ""You may consider to use MaxAbsScaler instead.")X = check_array(X, copy=self.copy, warn_on_dtype=True, estimator=self, dtype=FLOAT_DTYPES)data_min = np.min(X, axis=0)data_max = np.max(X, axis=0)# First passif not hasattr(self, 'n_samples_seen_'):self.n_samples_seen_ = X.shape[0]else:data_min = np.minimum(self.data_min_, data_min)data_max = np.maximum(self.data_max_, data_max)self.n_samples_seen_ += X.shape[0] # Next stepsdata_range = data_max - data_minself.scale_ = (feature_range[1] - feature_range[0]) / _handle_zeros_in_scale(data_range)self.min_ = feature_range[0] - data_min * self.scale_self.data_min_ = data_minself.data_max_ = data_maxself.data_range_ = data_rangereturn selfdef transform(self, X):"""Scaling features of X according to feature_range.Parameters----------X : array-like, shape [n_samples, n_features]Input data that will be transformed."""check_is_fitted(self, 'scale_')X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)X *= self.scale_X += self.min_return Xdef inverse_transform(self, X):"""Undo the scaling of X according to feature_range.Parameters----------X : array-like, shape [n_samples, n_features]Input data that will be transformed. It cannot be sparse."""check_is_fitted(self, 'scale_')X = check_array(X, copy=self.copy, dtype=FLOAT_DTYPES)X -= self.min_X /= self.scale_return X

MinMaxScaler的使用方法

1、基础案例

    >>> from sklearn.preprocessing import MinMaxScaler>>>>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]>>> scaler = MinMaxScaler()>>> print(scaler.fit(data))MinMaxScaler(copy=True, feature_range=(0, 1))>>> print(scaler.data_max_)[  1.  18.]>>> print(scaler.transform(data))[[ 0.    0.  ][ 0.25  0.25][ 0.5   0.5 ][ 1.    1.  ]]>>> print(scaler.transform([[2, 2]]))[[ 1.5  0. ]]