# Authors: Alexandre Gramfort # Mathieu Blondel # Olivier Grisel # Andreas Mueller # Eric Martin # Giorgio Patrini # Eric Chang # License: BSD 3 clause import warnings from numbers import Integral, Real import numpy as np from scipy import optimize, sparse, stats from scipy.special import boxcox from ..base import ( BaseEstimator, ClassNamePrefixFeaturesOutMixin, OneToOneFeatureMixin, TransformerMixin, _fit_context, ) from ..utils import _array_api, check_array from ..utils._array_api import get_namespace from ..utils._param_validation import Interval, Options, StrOptions, validate_params from ..utils.extmath import _incremental_mean_and_var, row_norms from ..utils.sparsefuncs import ( incr_mean_variance_axis, inplace_column_scale, mean_variance_axis, min_max_axis, ) from ..utils.sparsefuncs_fast import ( inplace_csr_row_normalize_l1, inplace_csr_row_normalize_l2, ) from ..utils.validation import ( FLOAT_DTYPES, _check_sample_weight, check_is_fitted, check_random_state, ) from ._encoders import OneHotEncoder BOUNDS_THRESHOLD = 1e-7 __all__ = [ "Binarizer", "KernelCenterer", "MinMaxScaler", "MaxAbsScaler", "Normalizer", "OneHotEncoder", "RobustScaler", "StandardScaler", "QuantileTransformer", "PowerTransformer", "add_dummy_feature", "binarize", "normalize", "scale", "robust_scale", "maxabs_scale", "minmax_scale", "quantile_transform", "power_transform", ] def _is_constant_feature(var, mean, n_samples): """Detect if a feature is indistinguishable from a constant feature. The detection is based on its computed variance and on the theoretical error bounds of the '2 pass algorithm' for variance computation. See "Algorithms for computing the sample variance: analysis and recommendations", by Chan, Golub, and LeVeque. """ # In scikit-learn, variance is always computed using float64 accumulators. eps = np.finfo(np.float64).eps upper_bound = n_samples * eps * var + (n_samples * mean * eps) ** 2 return var <= upper_bound def _handle_zeros_in_scale(scale, copy=True, constant_mask=None): """Set scales of near constant features to 1. The goal is to avoid division by very small or zero values. Near constant features are detected automatically by identifying scales close to machine precision unless they are precomputed by the caller and passed with the `constant_mask` kwarg. Typically for standard scaling, the scales are the standard deviation while near constant features are better detected on the computed variances which are closer to machine precision by construction. """ # if we are fitting on 1D arrays, scale might be a scalar if np.isscalar(scale): if scale == 0.0: scale = 1.0 return scale # scale is an array else: xp, _ = get_namespace(scale) if constant_mask is None: # Detect near constant values to avoid dividing by a very small # value that could lead to surprising results and numerical # stability issues. constant_mask = scale < 10 * xp.finfo(scale.dtype).eps if copy: # New array to avoid side-effects scale = xp.asarray(scale, copy=True) scale[constant_mask] = 1.0 return scale @validate_params( { "X": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})], "with_mean": ["boolean"], "with_std": ["boolean"], "copy": ["boolean"], }, prefer_skip_nested_validation=True, ) def scale(X, *, axis=0, with_mean=True, with_std=True, copy=True): """Standardize a dataset along any axis. Center to the mean and component wise scale to unit variance. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to center and scale. axis : {0, 1}, default=0 Axis used to compute the means and standard deviations along. If 0, independently standardize each feature, otherwise (if 1) standardize each sample. with_mean : bool, default=True If True, center the data before scaling. with_std : bool, default=True If True, scale the data to unit variance (or equivalently, unit standard deviation). copy : bool, default=True If False, try to avoid a copy and scale in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) The transformed data. See Also -------- StandardScaler : Performs scaling to unit variance using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Notes ----- This implementation will refuse to center scipy.sparse matrices since it would make them non-sparse and would potentially crash the program with memory exhaustion problems. Instead the caller is expected to either set explicitly `with_mean=False` (in that case, only variance scaling will be performed on the features of the CSC matrix) or to call `X.toarray()` if he/she expects the materialized dense array to fit in memory. To avoid memory copy the caller should pass a CSC matrix. NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation. We use a biased estimator for the standard deviation, equivalent to `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to affect model performance. For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. .. warning:: Risk of data leak Do not use :func:`~sklearn.preprocessing.scale` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.StandardScaler` within a :ref:`Pipeline ` in order to prevent most risks of data leaking: `pipe = make_pipeline(StandardScaler(), LogisticRegression())`. Examples -------- >>> from sklearn.preprocessing import scale >>> X = [[-2, 1, 2], [-1, 0, 1]] >>> scale(X, axis=0) # scaling each column independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>> scale(X, axis=1) # scaling each row independently array([[-1.37..., 0.39..., 0.98...], [-1.22..., 0. , 1.22...]]) """ X = check_array( X, accept_sparse="csc", copy=copy, ensure_2d=False, estimator="the scale function", dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) if sparse.issparse(X): if with_mean: raise ValueError( "Cannot center sparse matrices: pass `with_mean=False` instead" " See docstring for motivation and alternatives." ) if axis != 0: raise ValueError( "Can only scale sparse matrix on axis=0, got axis=%d" % axis ) if with_std: _, var = mean_variance_axis(X, axis=0) var = _handle_zeros_in_scale(var, copy=False) inplace_column_scale(X, 1 / np.sqrt(var)) else: X = np.asarray(X) if with_mean: mean_ = np.nanmean(X, axis) if with_std: scale_ = np.nanstd(X, axis) # Xr is a view on the original array that enables easy use of # broadcasting on the axis in which we are interested in Xr = np.rollaxis(X, axis) if with_mean: Xr -= mean_ mean_1 = np.nanmean(Xr, axis=0) # Verify that mean_1 is 'close to zero'. If X contains very # large values, mean_1 can also be very large, due to a lack of # precision of mean_. In this case, a pre-scaling of the # concerned feature is efficient, for instance by its mean or # maximum. if not np.allclose(mean_1, 0): warnings.warn( "Numerical issues were encountered " "when centering the data " "and might not be solved. Dataset may " "contain too large values. You may need " "to prescale your features." ) Xr -= mean_1 if with_std: scale_ = _handle_zeros_in_scale(scale_, copy=False) Xr /= scale_ if with_mean: mean_2 = np.nanmean(Xr, axis=0) # If mean_2 is not 'close to zero', it comes from the fact that # scale_ is very small so that mean_2 = mean_1/scale_ > 0, even # if mean_1 was close to zero. The problem is thus essentially # due to the lack of precision of mean_. A solution is then to # subtract the mean again: if not np.allclose(mean_2, 0): warnings.warn( "Numerical issues were encountered " "when scaling the data " "and might not be solved. The standard " "deviation of the data is probably " "very close to 0. " ) Xr -= mean_2 return X class MinMaxScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one. The transformation is given by:: X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min where min, max = feature_range. This transformation is often used as an alternative to zero mean, unit variance scaling. `MinMaxScaler` doesn't reduce the effect of outliers, but it linearly scales them down into a fixed range, where the largest occurring data point corresponds to the maximum value and the smallest one corresponds to the minimum value. For an example visualization, refer to :ref:`Compare MinMaxScaler with other scalers `. Read more in the :ref:`User Guide `. Parameters ---------- feature_range : tuple (min, max), default=(0, 1) Desired range of transformed data. copy : bool, default=True Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array). clip : bool, default=False Set to True to clip transformed values of held-out data to provided `feature range`. .. versionadded:: 0.24 Attributes ---------- min_ : ndarray of shape (n_features,) Per feature adjustment for minimum. Equivalent to ``min - X.min(axis=0) * self.scale_`` scale_ : ndarray of shape (n_features,) Per feature relative scaling of the data. Equivalent to ``(max - min) / (X.max(axis=0) - X.min(axis=0))`` .. versionadded:: 0.17 *scale_* attribute. data_min_ : ndarray of shape (n_features,) Per feature minimum seen in the data .. versionadded:: 0.17 *data_min_* data_max_ : ndarray of shape (n_features,) Per feature maximum seen in the data .. versionadded:: 0.17 *data_max_* data_range_ : ndarray of shape (n_features,) Per feature range ``(data_max_ - data_min_)`` seen in the data .. versionadded:: 0.17 *data_range_* n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 n_samples_seen_ : int The number of samples processed by the estimator. It will be reset on new calls to fit, but increments across ``partial_fit`` calls. feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- minmax_scale : Equivalent function without the estimator API. Notes ----- NaNs are treated as missing values: disregarded in fit, and maintained in transform. Examples -------- >>> from sklearn.preprocessing import MinMaxScaler >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler() >>> 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. ]] """ _parameter_constraints: dict = { "feature_range": [tuple], "copy": ["boolean"], "clip": ["boolean"], } def __init__(self, feature_range=(0, 1), *, copy=True, clip=False): self.feature_range = feature_range self.copy = copy self.clip = clip def _reset(self): """Reset internal data-dependent state of the scaler, if necessary. __init__ parameters are not touched. """ # Checking one attribute is enough, because they are all set together # in partial_fit if 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 of shape (n_samples, n_features) The data used to compute the per-feature minimum and maximum used for later scaling along the features axis. y : None Ignored. Returns ------- self : object Fitted scaler. """ # Reset internal state before fitting self._reset() return self.partial_fit(X, y) @_fit_context(prefer_skip_nested_validation=True) 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 cases when :meth:`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 of shape (n_samples, n_features) The data used to compute the mean and standard deviation used for later scaling along the features axis. y : None Ignored. Returns ------- self : object Fitted scaler. """ feature_range = self.feature_range if 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 not support sparse input. " "Consider using MaxAbsScaler instead." ) xp, _ = get_namespace(X) first_pass = not hasattr(self, "n_samples_seen_") X = self._validate_data( X, reset=first_pass, dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", ) data_min = _array_api._nanmin(X, axis=0) data_max = _array_api._nanmax(X, axis=0) if first_pass: self.n_samples_seen_ = X.shape[0] else: data_min = xp.minimum(self.data_min_, data_min) data_max = xp.maximum(self.data_max_, data_max) self.n_samples_seen_ += X.shape[0] data_range = data_max - data_min self.scale_ = (feature_range[1] - feature_range[0]) / _handle_zeros_in_scale( data_range, copy=True ) self.min_ = feature_range[0] - data_min * self.scale_ self.data_min_ = data_min self.data_max_ = data_max self.data_range_ = data_range return self def transform(self, X): """Scale features of X according to feature_range. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data that will be transformed. Returns ------- Xt : ndarray of shape (n_samples, n_features) Transformed data. """ check_is_fitted(self) xp, _ = get_namespace(X) X = self._validate_data( X, copy=self.copy, dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", reset=False, ) X *= self.scale_ X += self.min_ if self.clip: xp.clip(X, self.feature_range[0], self.feature_range[1], out=X) return X def inverse_transform(self, X): """Undo the scaling of X according to feature_range. Parameters ---------- X : array-like of shape (n_samples, n_features) Input data that will be transformed. It cannot be sparse. Returns ------- Xt : ndarray of shape (n_samples, n_features) Transformed data. """ check_is_fitted(self) xp, _ = get_namespace(X) X = check_array( X, copy=self.copy, dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", ) X -= self.min_ X /= self.scale_ return X def _more_tags(self): return {"allow_nan": True} @validate_params( { "X": ["array-like"], "axis": [Options(Integral, {0, 1})], }, prefer_skip_nested_validation=False, ) def minmax_scale(X, feature_range=(0, 1), *, axis=0, copy=True): """Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, i.e. between zero and one. The transformation is given by (when ``axis=0``):: X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min where min, max = feature_range. The transformation is calculated as (when ``axis=0``):: X_scaled = scale * X + min - X.min(axis=0) * scale where scale = (max - min) / (X.max(axis=0) - X.min(axis=0)) This transformation is often used as an alternative to zero mean, unit variance scaling. Read more in the :ref:`User Guide `. .. versionadded:: 0.17 *minmax_scale* function interface to :class:`~sklearn.preprocessing.MinMaxScaler`. Parameters ---------- X : array-like of shape (n_samples, n_features) The data. feature_range : tuple (min, max), default=(0, 1) Desired range of transformed data. axis : {0, 1}, default=0 Axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample. copy : bool, default=True If False, try to avoid a copy and scale in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. Returns ------- X_tr : ndarray of shape (n_samples, n_features) The transformed data. .. warning:: Risk of data leak Do not use :func:`~sklearn.preprocessing.minmax_scale` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.MinMaxScaler` within a :ref:`Pipeline ` in order to prevent most risks of data leaking: `pipe = make_pipeline(MinMaxScaler(), LogisticRegression())`. See Also -------- MinMaxScaler : Performs scaling to a given range using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Notes ----- For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. Examples -------- >>> from sklearn.preprocessing import minmax_scale >>> X = [[-2, 1, 2], [-1, 0, 1]] >>> minmax_scale(X, axis=0) # scale each column independently array([[0., 1., 1.], [1., 0., 0.]]) >>> minmax_scale(X, axis=1) # scale each row independently array([[0. , 0.75, 1. ], [0. , 0.5 , 1. ]]) """ # Unlike the scaler object, this function allows 1d input. # If copy is required, it will be done inside the scaler object. X = check_array( X, copy=False, ensure_2d=False, dtype=FLOAT_DTYPES, force_all_finite="allow-nan" ) original_ndim = X.ndim if original_ndim == 1: X = X.reshape(X.shape[0], 1) s = MinMaxScaler(feature_range=feature_range, copy=copy) if axis == 0: X = s.fit_transform(X) else: X = s.fit_transform(X.T).T if original_ndim == 1: X = X.ravel() return X class StandardScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Standardize features by removing the mean and scaling to unit variance. The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if `with_mean=False`, and `s` is the standard deviation of the training samples or one if `with_std=False`. Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Mean and standard deviation are then stored to be used on later data using :meth:`transform`. Standardization of a dataset is a common requirement for many machine learning estimators: they might behave badly if the individual features do not more or less look like standard normally distributed data (e.g. Gaussian with 0 mean and unit variance). For instance many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the L1 and L2 regularizers of linear models) assume that all features are centered around 0 and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected. `StandardScaler` is sensitive to outliers, and the features may scale differently from each other in the presence of outliers. For an example visualization, refer to :ref:`Compare StandardScaler with other scalers `. This scaler can also be applied to sparse CSR or CSC matrices by passing `with_mean=False` to avoid breaking the sparsity structure of the data. Read more in the :ref:`User Guide `. Parameters ---------- copy : bool, default=True If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. with_mean : bool, default=True If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory. with_std : bool, default=True If True, scale the data to unit variance (or equivalently, unit standard deviation). Attributes ---------- scale_ : ndarray of shape (n_features,) or None Per feature relative scaling of the data to achieve zero mean and unit variance. Generally this is calculated using `np.sqrt(var_)`. If a variance is zero, we can't achieve unit variance, and the data is left as-is, giving a scaling factor of 1. `scale_` is equal to `None` when `with_std=False`. .. versionadded:: 0.17 *scale_* mean_ : ndarray of shape (n_features,) or None The mean value for each feature in the training set. Equal to ``None`` when ``with_mean=False`` and ``with_std=False``. var_ : ndarray of shape (n_features,) or None The variance for each feature in the training set. Used to compute `scale_`. Equal to ``None`` when ``with_mean=False`` and ``with_std=False``. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_samples_seen_ : int or ndarray of shape (n_features,) The number of samples processed by the estimator for each feature. If there are no missing samples, the ``n_samples_seen`` will be an integer, otherwise it will be an array of dtype int. If `sample_weights` are used it will be a float (if no missing data) or an array of dtype float that sums the weights seen so far. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. See Also -------- scale : Equivalent function without the estimator API. :class:`~sklearn.decomposition.PCA` : Further removes the linear correlation across features with 'whiten=True'. Notes ----- NaNs are treated as missing values: disregarded in fit, and maintained in transform. We use a biased estimator for the standard deviation, equivalent to `numpy.std(x, ddof=0)`. Note that the choice of `ddof` is unlikely to affect model performance. Examples -------- >>> from sklearn.preprocessing import StandardScaler >>> data = [[0, 0], [0, 0], [1, 1], [1, 1]] >>> scaler = StandardScaler() >>> print(scaler.fit(data)) StandardScaler() >>> print(scaler.mean_) [0.5 0.5] >>> print(scaler.transform(data)) [[-1. -1.] [-1. -1.] [ 1. 1.] [ 1. 1.]] >>> print(scaler.transform([[2, 2]])) [[3. 3.]] """ _parameter_constraints: dict = { "copy": ["boolean"], "with_mean": ["boolean"], "with_std": ["boolean"], } def __init__(self, *, copy=True, with_mean=True, with_std=True): self.with_mean = with_mean self.with_std = with_std self.copy = copy def _reset(self): """Reset internal data-dependent state of the scaler, if necessary. __init__ parameters are not touched. """ # Checking one attribute is enough, because they are all set together # in partial_fit if hasattr(self, "scale_"): del self.scale_ del self.n_samples_seen_ del self.mean_ del self.var_ def fit(self, X, y=None, sample_weight=None): """Compute the mean and std to be used for later scaling. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the mean and standard deviation used for later scaling along the features axis. y : None Ignored. sample_weight : array-like of shape (n_samples,), default=None Individual weights for each sample. .. versionadded:: 0.24 parameter *sample_weight* support to StandardScaler. Returns ------- self : object Fitted scaler. """ # Reset internal state before fitting self._reset() return self.partial_fit(X, y, sample_weight) @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None, sample_weight=None): """Online computation of mean and std on X for later scaling. All of X is processed as a single batch. This is intended for cases when :meth:`fit` is not feasible due to very large number of `n_samples` or because X is read from a continuous stream. The algorithm for incremental mean and std is given in Equation 1.5a,b in Chan, Tony F., Gene H. Golub, and Randall J. LeVeque. "Algorithms for computing the sample variance: Analysis and recommendations." The American Statistician 37.3 (1983): 242-247: Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the mean and standard deviation used for later scaling along the features axis. y : None Ignored. sample_weight : array-like of shape (n_samples,), default=None Individual weights for each sample. .. versionadded:: 0.24 parameter *sample_weight* support to StandardScaler. Returns ------- self : object Fitted scaler. """ first_call = not hasattr(self, "n_samples_seen_") X = self._validate_data( X, accept_sparse=("csr", "csc"), dtype=FLOAT_DTYPES, force_all_finite="allow-nan", reset=first_call, ) n_features = X.shape[1] if sample_weight is not None: sample_weight = _check_sample_weight(sample_weight, X, dtype=X.dtype) # Even in the case of `with_mean=False`, we update the mean anyway # This is needed for the incremental computation of the var # See incr_mean_variance_axis and _incremental_mean_variance_axis # if n_samples_seen_ is an integer (i.e. no missing values), we need to # transform it to a NumPy array of shape (n_features,) required by # incr_mean_variance_axis and _incremental_variance_axis dtype = np.int64 if sample_weight is None else X.dtype if not hasattr(self, "n_samples_seen_"): self.n_samples_seen_ = np.zeros(n_features, dtype=dtype) elif np.size(self.n_samples_seen_) == 1: self.n_samples_seen_ = np.repeat(self.n_samples_seen_, X.shape[1]) self.n_samples_seen_ = self.n_samples_seen_.astype(dtype, copy=False) if sparse.issparse(X): if self.with_mean: raise ValueError( "Cannot center sparse matrices: pass `with_mean=False` " "instead. See docstring for motivation and alternatives." ) sparse_constructor = ( sparse.csr_matrix if X.format == "csr" else sparse.csc_matrix ) if self.with_std: # First pass if not hasattr(self, "scale_"): self.mean_, self.var_, self.n_samples_seen_ = mean_variance_axis( X, axis=0, weights=sample_weight, return_sum_weights=True ) # Next passes else: ( self.mean_, self.var_, self.n_samples_seen_, ) = incr_mean_variance_axis( X, axis=0, last_mean=self.mean_, last_var=self.var_, last_n=self.n_samples_seen_, weights=sample_weight, ) # We force the mean and variance to float64 for large arrays # See https://github.com/scikit-learn/scikit-learn/pull/12338 self.mean_ = self.mean_.astype(np.float64, copy=False) self.var_ = self.var_.astype(np.float64, copy=False) else: self.mean_ = None # as with_mean must be False for sparse self.var_ = None weights = _check_sample_weight(sample_weight, X) sum_weights_nan = weights @ sparse_constructor( (np.isnan(X.data), X.indices, X.indptr), shape=X.shape ) self.n_samples_seen_ += (np.sum(weights) - sum_weights_nan).astype( dtype ) else: # First pass if not hasattr(self, "scale_"): self.mean_ = 0.0 if self.with_std: self.var_ = 0.0 else: self.var_ = None if not self.with_mean and not self.with_std: self.mean_ = None self.var_ = None self.n_samples_seen_ += X.shape[0] - np.isnan(X).sum(axis=0) else: self.mean_, self.var_, self.n_samples_seen_ = _incremental_mean_and_var( X, self.mean_, self.var_, self.n_samples_seen_, sample_weight=sample_weight, ) # for backward-compatibility, reduce n_samples_seen_ to an integer # if the number of samples is the same for each feature (i.e. no # missing values) if np.ptp(self.n_samples_seen_) == 0: self.n_samples_seen_ = self.n_samples_seen_[0] if self.with_std: # Extract the list of near constant features on the raw variances, # before taking the square root. constant_mask = _is_constant_feature( self.var_, self.mean_, self.n_samples_seen_ ) self.scale_ = _handle_zeros_in_scale( np.sqrt(self.var_), copy=False, constant_mask=constant_mask ) else: self.scale_ = None return self def transform(self, X, copy=None): """Perform standardization by centering and scaling. Parameters ---------- X : {array-like, sparse matrix of shape (n_samples, n_features) The data used to scale along the features axis. copy : bool, default=None Copy the input X or not. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) copy = copy if copy is not None else self.copy X = self._validate_data( X, reset=False, accept_sparse="csr", copy=copy, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) if sparse.issparse(X): if self.with_mean: raise ValueError( "Cannot center sparse matrices: pass `with_mean=False` " "instead. See docstring for motivation and alternatives." ) if self.scale_ is not None: inplace_column_scale(X, 1 / self.scale_) else: if self.with_mean: X -= self.mean_ if self.with_std: X /= self.scale_ return X def inverse_transform(self, X, copy=None): """Scale back the data to the original representation. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to scale along the features axis. copy : bool, default=None Copy the input X or not. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) copy = copy if copy is not None else self.copy X = check_array( X, accept_sparse="csr", copy=copy, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) if sparse.issparse(X): if self.with_mean: raise ValueError( "Cannot uncenter sparse matrices: pass `with_mean=False` " "instead See docstring for motivation and alternatives." ) if self.scale_ is not None: inplace_column_scale(X, self.scale_) else: if self.with_std: X *= self.scale_ if self.with_mean: X += self.mean_ return X def _more_tags(self): return {"allow_nan": True, "preserves_dtype": [np.float64, np.float32]} class MaxAbsScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity. This scaler can also be applied to sparse CSR or CSC matrices. `MaxAbsScaler` doesn't reduce the effect of outliers; it only linearly scales them down. For an example visualization, refer to :ref:`Compare MaxAbsScaler with other scalers `. .. versionadded:: 0.17 Parameters ---------- copy : bool, default=True Set to False to perform inplace scaling and avoid a copy (if the input is already a numpy array). Attributes ---------- scale_ : ndarray of shape (n_features,) Per feature relative scaling of the data. .. versionadded:: 0.17 *scale_* attribute. max_abs_ : ndarray of shape (n_features,) Per feature maximum absolute value. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 n_samples_seen_ : int The number of samples processed by the estimator. Will be reset on new calls to fit, but increments across ``partial_fit`` calls. See Also -------- maxabs_scale : Equivalent function without the estimator API. Notes ----- NaNs are treated as missing values: disregarded in fit, and maintained in transform. Examples -------- >>> from sklearn.preprocessing import MaxAbsScaler >>> X = [[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]] >>> transformer = MaxAbsScaler().fit(X) >>> transformer MaxAbsScaler() >>> transformer.transform(X) array([[ 0.5, -1. , 1. ], [ 1. , 0. , 0. ], [ 0. , 1. , -0.5]]) """ _parameter_constraints: dict = {"copy": ["boolean"]} def __init__(self, *, copy=True): self.copy = copy def _reset(self): """Reset internal data-dependent state of the scaler, if necessary. __init__ parameters are not touched. """ # Checking one attribute is enough, because they are all set together # in partial_fit if hasattr(self, "scale_"): del self.scale_ del self.n_samples_seen_ del self.max_abs_ def fit(self, X, y=None): """Compute the maximum absolute value to be used for later scaling. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the per-feature minimum and maximum used for later scaling along the features axis. y : None Ignored. Returns ------- self : object Fitted scaler. """ # Reset internal state before fitting self._reset() return self.partial_fit(X, y) @_fit_context(prefer_skip_nested_validation=True) def partial_fit(self, X, y=None): """Online computation of max absolute value of X for later scaling. All of X is processed as a single batch. This is intended for cases when :meth:`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, sparse matrix} of shape (n_samples, n_features) The data used to compute the mean and standard deviation used for later scaling along the features axis. y : None Ignored. Returns ------- self : object Fitted scaler. """ xp, _ = get_namespace(X) first_pass = not hasattr(self, "n_samples_seen_") X = self._validate_data( X, reset=first_pass, accept_sparse=("csr", "csc"), dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", ) if sparse.issparse(X): mins, maxs = min_max_axis(X, axis=0, ignore_nan=True) max_abs = np.maximum(np.abs(mins), np.abs(maxs)) else: max_abs = _array_api._nanmax(xp.abs(X), axis=0) if first_pass: self.n_samples_seen_ = X.shape[0] else: max_abs = xp.maximum(self.max_abs_, max_abs) self.n_samples_seen_ += X.shape[0] self.max_abs_ = max_abs self.scale_ = _handle_zeros_in_scale(max_abs, copy=True) return self def transform(self, X): """Scale the data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data that should be scaled. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) xp, _ = get_namespace(X) X = self._validate_data( X, accept_sparse=("csr", "csc"), copy=self.copy, reset=False, dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", ) if sparse.issparse(X): inplace_column_scale(X, 1.0 / self.scale_) else: X /= self.scale_ return X def inverse_transform(self, X): """Scale back the data to the original representation. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data that should be transformed back. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) xp, _ = get_namespace(X) X = check_array( X, accept_sparse=("csr", "csc"), copy=self.copy, dtype=_array_api.supported_float_dtypes(xp), force_all_finite="allow-nan", ) if sparse.issparse(X): inplace_column_scale(X, self.scale_) else: X *= self.scale_ return X def _more_tags(self): return {"allow_nan": True} @validate_params( { "X": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})], }, prefer_skip_nested_validation=False, ) def maxabs_scale(X, *, axis=0, copy=True): """Scale each feature to the [-1, 1] range without breaking the sparsity. This estimator scales each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. This scaler can also be applied to sparse CSR or CSC matrices. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data. axis : {0, 1}, default=0 Axis used to scale along. If 0, independently scale each feature, otherwise (if 1) scale each sample. copy : bool, default=True If False, try to avoid a copy and scale in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) The transformed data. .. warning:: Risk of data leak Do not use :func:`~sklearn.preprocessing.maxabs_scale` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.MaxAbsScaler` within a :ref:`Pipeline ` in order to prevent most risks of data leaking: `pipe = make_pipeline(MaxAbsScaler(), LogisticRegression())`. See Also -------- MaxAbsScaler : Performs scaling to the [-1, 1] range using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Notes ----- NaNs are treated as missing values: disregarded to compute the statistics, and maintained during the data transformation. For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. Examples -------- >>> from sklearn.preprocessing import maxabs_scale >>> X = [[-2, 1, 2], [-1, 0, 1]] >>> maxabs_scale(X, axis=0) # scale each column independently array([[-1. , 1. , 1. ], [-0.5, 0. , 0.5]]) >>> maxabs_scale(X, axis=1) # scale each row independently array([[-1. , 0.5, 1. ], [-1. , 0. , 1. ]]) """ # Unlike the scaler object, this function allows 1d input. # If copy is required, it will be done inside the scaler object. X = check_array( X, accept_sparse=("csr", "csc"), copy=False, ensure_2d=False, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) original_ndim = X.ndim if original_ndim == 1: X = X.reshape(X.shape[0], 1) s = MaxAbsScaler(copy=copy) if axis == 0: X = s.fit_transform(X) else: X = s.fit_transform(X.T).T if original_ndim == 1: X = X.ravel() return X class RobustScaler(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the range between the 1st quartile (25th quantile) and the 3rd quartile (75th quantile). Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training set. Median and interquartile range are then stored to be used on later data using the :meth:`transform` method. Standardization of a dataset is a common preprocessing for many machine learning estimators. Typically this is done by removing the mean and scaling to unit variance. However, outliers can often influence the sample mean / variance in a negative way. In such cases, using the median and the interquartile range often give better results. For an example visualization and comparison to other scalers, refer to :ref:`Compare RobustScaler with other scalers `. .. versionadded:: 0.17 Read more in the :ref:`User Guide `. Parameters ---------- with_centering : bool, default=True If `True`, center the data before scaling. This will cause :meth:`transform` to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory. with_scaling : bool, default=True If `True`, scale the data to interquartile range. quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, \ default=(25.0, 75.0) Quantile range used to calculate `scale_`. By default this is equal to the IQR, i.e., `q_min` is the first quantile and `q_max` is the third quantile. .. versionadded:: 0.18 copy : bool, default=True If `False`, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. unit_variance : bool, default=False If `True`, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of `q_max` and `q_min` for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up. .. versionadded:: 0.24 Attributes ---------- center_ : array of floats The median value for each feature in the training set. scale_ : array of floats The (scaled) interquartile range for each feature in the training set. .. versionadded:: 0.17 *scale_* attribute. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- robust_scale : Equivalent function without the estimator API. sklearn.decomposition.PCA : Further removes the linear correlation across features with 'whiten=True'. Notes ----- https://en.wikipedia.org/wiki/Median https://en.wikipedia.org/wiki/Interquartile_range Examples -------- >>> from sklearn.preprocessing import RobustScaler >>> X = [[ 1., -2., 2.], ... [ -2., 1., 3.], ... [ 4., 1., -2.]] >>> transformer = RobustScaler().fit(X) >>> transformer RobustScaler() >>> transformer.transform(X) array([[ 0. , -2. , 0. ], [-1. , 0. , 0.4], [ 1. , 0. , -1.6]]) """ _parameter_constraints: dict = { "with_centering": ["boolean"], "with_scaling": ["boolean"], "quantile_range": [tuple], "copy": ["boolean"], "unit_variance": ["boolean"], } def __init__( self, *, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False, ): self.with_centering = with_centering self.with_scaling = with_scaling self.quantile_range = quantile_range self.unit_variance = unit_variance self.copy = copy @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Compute the median and quantiles to be used for scaling. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to compute the median and quantiles used for later scaling along the features axis. y : Ignored Not used, present here for API consistency by convention. Returns ------- self : object Fitted scaler. """ # at fit, convert sparse matrices to csc for optimized computation of # the quantiles X = self._validate_data( X, accept_sparse="csc", dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) q_min, q_max = self.quantile_range if not 0 <= q_min <= q_max <= 100: raise ValueError("Invalid quantile range: %s" % str(self.quantile_range)) if self.with_centering: if sparse.issparse(X): raise ValueError( "Cannot center sparse matrices: use `with_centering=False`" " instead. See docstring for motivation and alternatives." ) self.center_ = np.nanmedian(X, axis=0) else: self.center_ = None if self.with_scaling: quantiles = [] for feature_idx in range(X.shape[1]): if sparse.issparse(X): column_nnz_data = X.data[ X.indptr[feature_idx] : X.indptr[feature_idx + 1] ] column_data = np.zeros(shape=X.shape[0], dtype=X.dtype) column_data[: len(column_nnz_data)] = column_nnz_data else: column_data = X[:, feature_idx] quantiles.append(np.nanpercentile(column_data, self.quantile_range)) quantiles = np.transpose(quantiles) self.scale_ = quantiles[1] - quantiles[0] self.scale_ = _handle_zeros_in_scale(self.scale_, copy=False) if self.unit_variance: adjust = stats.norm.ppf(q_max / 100.0) - stats.norm.ppf(q_min / 100.0) self.scale_ = self.scale_ / adjust else: self.scale_ = None return self def transform(self, X): """Center and scale the data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to scale along the specified axis. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) X = self._validate_data( X, accept_sparse=("csr", "csc"), copy=self.copy, dtype=FLOAT_DTYPES, reset=False, force_all_finite="allow-nan", ) if sparse.issparse(X): if self.with_scaling: inplace_column_scale(X, 1.0 / self.scale_) else: if self.with_centering: X -= self.center_ if self.with_scaling: X /= self.scale_ return X def inverse_transform(self, X): """Scale back the data to the original representation. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The rescaled data to be transformed back. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ check_is_fitted(self) X = check_array( X, accept_sparse=("csr", "csc"), copy=self.copy, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) if sparse.issparse(X): if self.with_scaling: inplace_column_scale(X, self.scale_) else: if self.with_scaling: X *= self.scale_ if self.with_centering: X += self.center_ return X def _more_tags(self): return {"allow_nan": True} @validate_params( {"X": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})]}, prefer_skip_nested_validation=False, ) def robust_scale( X, *, axis=0, with_centering=True, with_scaling=True, quantile_range=(25.0, 75.0), copy=True, unit_variance=False, ): """Standardize a dataset along any axis. Center to the median and component wise scale according to the interquartile range. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_sample, n_features) The data to center and scale. axis : int, default=0 Axis used to compute the medians and IQR along. If 0, independently scale each feature, otherwise (if 1) scale each sample. with_centering : bool, default=True If `True`, center the data before scaling. with_scaling : bool, default=True If `True`, scale the data to unit variance (or equivalently, unit standard deviation). quantile_range : tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0,\ default=(25.0, 75.0) Quantile range used to calculate `scale_`. By default this is equal to the IQR, i.e., `q_min` is the first quantile and `q_max` is the third quantile. .. versionadded:: 0.18 copy : bool, default=True If False, try to avoid a copy and scale in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. unit_variance : bool, default=False If `True`, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of `q_max` and `q_min` for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up. .. versionadded:: 0.24 Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) The transformed data. See Also -------- RobustScaler : Performs centering and scaling using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Notes ----- This implementation will refuse to center scipy.sparse matrices since it would make them non-sparse and would potentially crash the program with memory exhaustion problems. Instead the caller is expected to either set explicitly `with_centering=False` (in that case, only variance scaling will be performed on the features of the CSR matrix) or to call `X.toarray()` if he/she expects the materialized dense array to fit in memory. To avoid memory copy the caller should pass a CSR matrix. For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. .. warning:: Risk of data leak Do not use :func:`~sklearn.preprocessing.robust_scale` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.RobustScaler` within a :ref:`Pipeline ` in order to prevent most risks of data leaking: `pipe = make_pipeline(RobustScaler(), LogisticRegression())`. Examples -------- >>> from sklearn.preprocessing import robust_scale >>> X = [[-2, 1, 2], [-1, 0, 1]] >>> robust_scale(X, axis=0) # scale each column independently array([[-1., 1., 1.], [ 1., -1., -1.]]) >>> robust_scale(X, axis=1) # scale each row independently array([[-1.5, 0. , 0.5], [-1. , 0. , 1. ]]) """ X = check_array( X, accept_sparse=("csr", "csc"), copy=False, ensure_2d=False, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) original_ndim = X.ndim if original_ndim == 1: X = X.reshape(X.shape[0], 1) s = RobustScaler( with_centering=with_centering, with_scaling=with_scaling, quantile_range=quantile_range, unit_variance=unit_variance, copy=copy, ) if axis == 0: X = s.fit_transform(X) else: X = s.fit_transform(X.T).T if original_ndim == 1: X = X.ravel() return X @validate_params( { "X": ["array-like", "sparse matrix"], "norm": [StrOptions({"l1", "l2", "max"})], "axis": [Options(Integral, {0, 1})], "copy": ["boolean"], "return_norm": ["boolean"], }, prefer_skip_nested_validation=True, ) def normalize(X, norm="l2", *, axis=1, copy=True, return_norm=False): """Scale input vectors individually to unit norm (vector length). Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to normalize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy. norm : {'l1', 'l2', 'max'}, default='l2' The norm to use to normalize each non zero sample (or each non-zero feature if axis is 0). axis : {0, 1}, default=1 Define axis used to normalize the data along. If 1, independently normalize each sample, otherwise (if 0) normalize each feature. copy : bool, default=True If False, try to avoid a copy and normalize in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. return_norm : bool, default=False Whether to return the computed norms. Returns ------- X : {ndarray, sparse matrix} of shape (n_samples, n_features) Normalized input X. norms : ndarray of shape (n_samples, ) if axis=1 else (n_features, ) An array of norms along given axis for X. When X is sparse, a NotImplementedError will be raised for norm 'l1' or 'l2'. See Also -------- Normalizer : Performs normalization using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Notes ----- For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. Examples -------- >>> from sklearn.preprocessing import normalize >>> X = [[-2, 1, 2], [-1, 0, 1]] >>> normalize(X, norm="l1") # L1 normalization each row independently array([[-0.4, 0.2, 0.4], [-0.5, 0. , 0.5]]) >>> normalize(X, norm="l2") # L2 normalization each row independently array([[-0.66..., 0.33..., 0.66...], [-0.70..., 0. , 0.70...]]) """ if axis == 0: sparse_format = "csc" else: # axis == 1: sparse_format = "csr" xp, _ = get_namespace(X) X = check_array( X, accept_sparse=sparse_format, copy=copy, estimator="the normalize function", dtype=_array_api.supported_float_dtypes(xp), ) if axis == 0: X = X.T if sparse.issparse(X): if return_norm and norm in ("l1", "l2"): raise NotImplementedError( "return_norm=True is not implemented " "for sparse matrices with norm 'l1' " "or norm 'l2'" ) if norm == "l1": inplace_csr_row_normalize_l1(X) elif norm == "l2": inplace_csr_row_normalize_l2(X) elif norm == "max": mins, maxes = min_max_axis(X, 1) norms = np.maximum(abs(mins), maxes) norms_elementwise = norms.repeat(np.diff(X.indptr)) mask = norms_elementwise != 0 X.data[mask] /= norms_elementwise[mask] else: if norm == "l1": norms = xp.sum(xp.abs(X), axis=1) elif norm == "l2": norms = row_norms(X) elif norm == "max": norms = xp.max(xp.abs(X), axis=1) norms = _handle_zeros_in_scale(norms, copy=False) X /= norms[:, None] if axis == 0: X = X.T if return_norm: return X, norms else: return X class Normalizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Normalize samples individually to unit norm. Each sample (i.e. each row of the data matrix) with at least one non zero component is rescaled independently of other samples so that its norm (l1, l2 or inf) equals one. This transformer is able to work both with dense numpy arrays and scipy.sparse matrix (use CSR format if you want to avoid the burden of a copy / conversion). Scaling inputs to unit norms is a common operation for text classification or clustering for instance. For instance the dot product of two l2-normalized TF-IDF vectors is the cosine similarity of the vectors and is the base similarity metric for the Vector Space Model commonly used by the Information Retrieval community. For an example visualization, refer to :ref:`Compare Normalizer with other scalers `. Read more in the :ref:`User Guide `. Parameters ---------- norm : {'l1', 'l2', 'max'}, default='l2' The norm to use to normalize each non zero sample. If norm='max' is used, values will be rescaled by the maximum of the absolute values. copy : bool, default=True Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix). Attributes ---------- n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- normalize : Equivalent function without the estimator API. Notes ----- This estimator is :term:`stateless` and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.preprocessing import Normalizer >>> X = [[4, 1, 2, 2], ... [1, 3, 9, 3], ... [5, 7, 5, 1]] >>> transformer = Normalizer().fit(X) # fit does nothing. >>> transformer Normalizer() >>> transformer.transform(X) array([[0.8, 0.2, 0.4, 0.4], [0.1, 0.3, 0.9, 0.3], [0.5, 0.7, 0.5, 0.1]]) """ _parameter_constraints: dict = { "norm": [StrOptions({"l1", "l2", "max"})], "copy": ["boolean"], } def __init__(self, norm="l2", *, copy=True): self.norm = norm self.copy = copy @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to estimate the normalization parameters. y : Ignored Not used, present here for API consistency by convention. Returns ------- self : object Fitted transformer. """ self._validate_data(X, accept_sparse="csr") return self def transform(self, X, copy=None): """Scale each non zero row of X to unit norm. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to normalize, row by row. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy. copy : bool, default=None Copy the input X or not. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ copy = copy if copy is not None else self.copy X = self._validate_data(X, accept_sparse="csr", reset=False) return normalize(X, norm=self.norm, axis=1, copy=copy) def _more_tags(self): return {"stateless": True, "array_api_support": True} @validate_params( { "X": ["array-like", "sparse matrix"], "threshold": [Interval(Real, None, None, closed="neither")], "copy": ["boolean"], }, prefer_skip_nested_validation=True, ) def binarize(X, *, threshold=0.0, copy=True): """Boolean thresholding of array-like or scipy.sparse matrix. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to binarize, element by element. scipy.sparse matrices should be in CSR or CSC format to avoid an un-necessary copy. threshold : float, default=0.0 Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. copy : bool, default=True If False, try to avoid a copy and binarize in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an object dtype, a copy will be returned even with copy=False. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) The transformed data. See Also -------- Binarizer : Performs binarization using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). Examples -------- >>> from sklearn.preprocessing import binarize >>> X = [[0.4, 0.6, 0.5], [0.6, 0.1, 0.2]] >>> binarize(X, threshold=0.5) array([[0., 1., 0.], [1., 0., 0.]]) """ X = check_array(X, accept_sparse=["csr", "csc"], copy=copy) if sparse.issparse(X): if threshold < 0: raise ValueError("Cannot binarize a sparse matrix with threshold < 0") cond = X.data > threshold not_cond = np.logical_not(cond) X.data[cond] = 1 X.data[not_cond] = 0 X.eliminate_zeros() else: cond = X > threshold not_cond = np.logical_not(cond) X[cond] = 1 X[not_cond] = 0 return X class Binarizer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Binarize data (set feature values to 0 or 1) according to a threshold. Values greater than the threshold map to 1, while values less than or equal to the threshold map to 0. With the default threshold of 0, only positive values map to 1. Binarization is a common operation on text count data where the analyst can decide to only consider the presence or absence of a feature rather than a quantified number of occurrences for instance. It can also be used as a pre-processing step for estimators that consider boolean random variables (e.g. modelled using the Bernoulli distribution in a Bayesian setting). Read more in the :ref:`User Guide `. Parameters ---------- threshold : float, default=0.0 Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. copy : bool, default=True Set to False to perform inplace binarization and avoid a copy (if the input is already a numpy array or a scipy.sparse CSR matrix). Attributes ---------- n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- binarize : Equivalent function without the estimator API. KBinsDiscretizer : Bin continuous data into intervals. OneHotEncoder : Encode categorical features as a one-hot numeric array. Notes ----- If the input is a sparse matrix, only the non-zero values are subject to update by the :class:`Binarizer` class. This estimator is :term:`stateless` and does not need to be fitted. However, we recommend to call :meth:`fit_transform` instead of :meth:`transform`, as parameter validation is only performed in :meth:`fit`. Examples -------- >>> from sklearn.preprocessing import Binarizer >>> X = [[ 1., -1., 2.], ... [ 2., 0., 0.], ... [ 0., 1., -1.]] >>> transformer = Binarizer().fit(X) # fit does nothing. >>> transformer Binarizer() >>> transformer.transform(X) array([[1., 0., 1.], [1., 0., 0.], [0., 1., 0.]]) """ _parameter_constraints: dict = { "threshold": [Real], "copy": ["boolean"], } def __init__(self, *, threshold=0.0, copy=True): self.threshold = threshold self.copy = copy @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Only validates estimator's parameters. This method allows to: (i) validate the estimator's parameters and (ii) be consistent with the scikit-learn transformer API. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data. y : None Ignored. Returns ------- self : object Fitted transformer. """ self._validate_data(X, accept_sparse="csr") return self def transform(self, X, copy=None): """Binarize each element of X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy. copy : bool Copy the input X or not. Returns ------- X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features) Transformed array. """ copy = copy if copy is not None else self.copy # TODO: This should be refactored because binarize also calls # check_array X = self._validate_data(X, accept_sparse=["csr", "csc"], copy=copy, reset=False) return binarize(X, threshold=self.threshold, copy=False) def _more_tags(self): return {"stateless": True} class KernelCenterer(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): r"""Center an arbitrary kernel matrix :math:`K`. Let define a kernel :math:`K` such that: .. math:: K(X, Y) = \phi(X) . \phi(Y)^{T} :math:`\phi(X)` is a function mapping of rows of :math:`X` to a Hilbert space and :math:`K` is of shape `(n_samples, n_samples)`. This class allows to compute :math:`\tilde{K}(X, Y)` such that: .. math:: \tilde{K(X, Y)} = \tilde{\phi}(X) . \tilde{\phi}(Y)^{T} :math:`\tilde{\phi}(X)` is the centered mapped data in the Hilbert space. `KernelCenterer` centers the features without explicitly computing the mapping :math:`\phi(\cdot)`. Working with centered kernels is sometime expected when dealing with algebra computation such as eigendecomposition for :class:`~sklearn.decomposition.KernelPCA` for instance. Read more in the :ref:`User Guide `. Attributes ---------- K_fit_rows_ : ndarray of shape (n_samples,) Average of each column of kernel matrix. K_fit_all_ : float Average of kernel matrix. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- sklearn.kernel_approximation.Nystroem : Approximate a kernel map using a subset of the training data. References ---------- .. [1] `Schölkopf, Bernhard, Alexander Smola, and Klaus-Robert Müller. "Nonlinear component analysis as a kernel eigenvalue problem." Neural computation 10.5 (1998): 1299-1319. `_ Examples -------- >>> from sklearn.preprocessing import KernelCenterer >>> from sklearn.metrics.pairwise import pairwise_kernels >>> X = [[ 1., -2., 2.], ... [ -2., 1., 3.], ... [ 4., 1., -2.]] >>> K = pairwise_kernels(X, metric='linear') >>> K array([[ 9., 2., -2.], [ 2., 14., -13.], [ -2., -13., 21.]]) >>> transformer = KernelCenterer().fit(K) >>> transformer KernelCenterer() >>> transformer.transform(K) array([[ 5., 0., -5.], [ 0., 14., -14.], [ -5., -14., 19.]]) """ def __init__(self): # Needed for backported inspect.signature compatibility with PyPy pass def fit(self, K, y=None): """Fit KernelCenterer. Parameters ---------- K : ndarray of shape (n_samples, n_samples) Kernel matrix. y : None Ignored. Returns ------- self : object Returns the instance itself. """ xp, _ = get_namespace(K) K = self._validate_data(K, dtype=_array_api.supported_float_dtypes(xp)) if K.shape[0] != K.shape[1]: raise ValueError( "Kernel matrix must be a square matrix." " Input is a {}x{} matrix.".format(K.shape[0], K.shape[1]) ) n_samples = K.shape[0] self.K_fit_rows_ = xp.sum(K, axis=0) / n_samples self.K_fit_all_ = xp.sum(self.K_fit_rows_) / n_samples return self def transform(self, K, copy=True): """Center kernel matrix. Parameters ---------- K : ndarray of shape (n_samples1, n_samples2) Kernel matrix. copy : bool, default=True Set to False to perform inplace computation. Returns ------- K_new : ndarray of shape (n_samples1, n_samples2) Returns the instance itself. """ check_is_fitted(self) xp, _ = get_namespace(K) K = self._validate_data( K, copy=copy, dtype=_array_api.supported_float_dtypes(xp), reset=False ) K_pred_cols = (xp.sum(K, axis=1) / self.K_fit_rows_.shape[0])[:, None] K -= self.K_fit_rows_ K -= K_pred_cols K += self.K_fit_all_ return K @property def _n_features_out(self): """Number of transformed output features.""" # Used by ClassNamePrefixFeaturesOutMixin. This model preserves the # number of input features but this is not a one-to-one mapping in the # usual sense. Hence the choice not to use OneToOneFeatureMixin to # implement get_feature_names_out for this class. return self.n_features_in_ def _more_tags(self): return {"pairwise": True, "array_api_support": True} @validate_params( { "X": ["array-like", "sparse matrix"], "value": [Interval(Real, None, None, closed="neither")], }, prefer_skip_nested_validation=True, ) def add_dummy_feature(X, value=1.0): """Augment dataset with an additional dummy feature. This is useful for fitting an intercept term with implementations which cannot otherwise fit it directly. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Data. value : float Value to use for the dummy feature. Returns ------- X : {ndarray, sparse matrix} of shape (n_samples, n_features + 1) Same data with dummy feature added as first column. Examples -------- >>> from sklearn.preprocessing import add_dummy_feature >>> add_dummy_feature([[0, 1], [1, 0]]) array([[1., 0., 1.], [1., 1., 0.]]) """ X = check_array(X, accept_sparse=["csc", "csr", "coo"], dtype=FLOAT_DTYPES) n_samples, n_features = X.shape shape = (n_samples, n_features + 1) if sparse.issparse(X): if X.format == "coo": # Shift columns to the right. col = X.col + 1 # Column indices of dummy feature are 0 everywhere. col = np.concatenate((np.zeros(n_samples), col)) # Row indices of dummy feature are 0, ..., n_samples-1. row = np.concatenate((np.arange(n_samples), X.row)) # Prepend the dummy feature n_samples times. data = np.concatenate((np.full(n_samples, value), X.data)) return sparse.coo_matrix((data, (row, col)), shape) elif X.format == "csc": # Shift index pointers since we need to add n_samples elements. indptr = X.indptr + n_samples # indptr[0] must be 0. indptr = np.concatenate((np.array([0]), indptr)) # Row indices of dummy feature are 0, ..., n_samples-1. indices = np.concatenate((np.arange(n_samples), X.indices)) # Prepend the dummy feature n_samples times. data = np.concatenate((np.full(n_samples, value), X.data)) return sparse.csc_matrix((data, indices, indptr), shape) else: klass = X.__class__ return klass(add_dummy_feature(X.tocoo(), value)) else: return np.hstack((np.full((n_samples, 1), value), X)) class QuantileTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Transform features using quantiles information. This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme. The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. For example visualizations, refer to :ref:`Compare QuantileTransformer with other scalers `. Read more in the :ref:`User Guide `. .. versionadded:: 0.19 Parameters ---------- n_quantiles : int, default=1000 or n_samples Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator. output_distribution : {'uniform', 'normal'}, default='uniform' Marginal distribution for the transformed data. The choices are 'uniform' (default) or 'normal'. ignore_implicit_zeros : bool, default=False Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros. subsample : int, default=10_000 Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices. random_state : int, RandomState instance or None, default=None Determines random number generation for subsampling and smoothing noise. Please see ``subsample`` for more details. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. copy : bool, default=True Set to False to perform inplace transformation and avoid a copy (if the input is already a numpy array). Attributes ---------- n_quantiles_ : int The actual number of quantiles used to discretize the cumulative distribution function. quantiles_ : ndarray of shape (n_quantiles, n_features) The values corresponding the quantiles of reference. references_ : ndarray of shape (n_quantiles, ) Quantiles of references. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- quantile_transform : Equivalent function without the estimator API. PowerTransformer : Perform mapping to a normal distribution using a power transform. StandardScaler : Perform standardization that is faster, but less robust to outliers. RobustScaler : Perform robust standardization that removes the influence of outliers but does not put outliers and inliers on the same scale. Notes ----- NaNs are treated as missing values: disregarded in fit, and maintained in transform. Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import QuantileTransformer >>> rng = np.random.RandomState(0) >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) >>> qt = QuantileTransformer(n_quantiles=10, random_state=0) >>> qt.fit_transform(X) array([...]) """ _parameter_constraints: dict = { "n_quantiles": [Interval(Integral, 1, None, closed="left")], "output_distribution": [StrOptions({"uniform", "normal"})], "ignore_implicit_zeros": ["boolean"], "subsample": [Interval(Integral, 1, None, closed="left")], "random_state": ["random_state"], "copy": ["boolean"], } def __init__( self, *, n_quantiles=1000, output_distribution="uniform", ignore_implicit_zeros=False, subsample=10_000, random_state=None, copy=True, ): self.n_quantiles = n_quantiles self.output_distribution = output_distribution self.ignore_implicit_zeros = ignore_implicit_zeros self.subsample = subsample self.random_state = random_state self.copy = copy def _dense_fit(self, X, random_state): """Compute percentiles for dense matrices. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data used to scale along the features axis. """ if self.ignore_implicit_zeros: warnings.warn( "'ignore_implicit_zeros' takes effect only with" " sparse matrix. This parameter has no effect." ) n_samples, n_features = X.shape references = self.references_ * 100 self.quantiles_ = [] for col in X.T: if self.subsample < n_samples: subsample_idx = random_state.choice( n_samples, size=self.subsample, replace=False ) col = col.take(subsample_idx, mode="clip") self.quantiles_.append(np.nanpercentile(col, references)) self.quantiles_ = np.transpose(self.quantiles_) # Due to floating-point precision error in `np.nanpercentile`, # make sure that quantiles are monotonically increasing. # Upstream issue in numpy: # https://github.com/numpy/numpy/issues/14685 self.quantiles_ = np.maximum.accumulate(self.quantiles_) def _sparse_fit(self, X, random_state): """Compute percentiles for sparse matrices. Parameters ---------- X : sparse matrix of shape (n_samples, n_features) The data used to scale along the features axis. The sparse matrix needs to be nonnegative. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``. """ n_samples, n_features = X.shape references = self.references_ * 100 self.quantiles_ = [] for feature_idx in range(n_features): column_nnz_data = X.data[X.indptr[feature_idx] : X.indptr[feature_idx + 1]] if len(column_nnz_data) > self.subsample: column_subsample = self.subsample * len(column_nnz_data) // n_samples if self.ignore_implicit_zeros: column_data = np.zeros(shape=column_subsample, dtype=X.dtype) else: column_data = np.zeros(shape=self.subsample, dtype=X.dtype) column_data[:column_subsample] = random_state.choice( column_nnz_data, size=column_subsample, replace=False ) else: if self.ignore_implicit_zeros: column_data = np.zeros(shape=len(column_nnz_data), dtype=X.dtype) else: column_data = np.zeros(shape=n_samples, dtype=X.dtype) column_data[: len(column_nnz_data)] = column_nnz_data if not column_data.size: # if no nnz, an error will be raised for computing the # quantiles. Force the quantiles to be zeros. self.quantiles_.append([0] * len(references)) else: self.quantiles_.append(np.nanpercentile(column_data, references)) self.quantiles_ = np.transpose(self.quantiles_) # due to floating-point precision error in `np.nanpercentile`, # make sure the quantiles are monotonically increasing # Upstream issue in numpy: # https://github.com/numpy/numpy/issues/14685 self.quantiles_ = np.maximum.accumulate(self.quantiles_) @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Compute the quantiles used for transforming. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False. y : None Ignored. Returns ------- self : object Fitted transformer. """ if self.n_quantiles > self.subsample: raise ValueError( "The number of quantiles cannot be greater than" " the number of samples used. Got {} quantiles" " and {} samples.".format(self.n_quantiles, self.subsample) ) X = self._check_inputs(X, in_fit=True, copy=False) n_samples = X.shape[0] if self.n_quantiles > n_samples: warnings.warn( "n_quantiles (%s) is greater than the total number " "of samples (%s). n_quantiles is set to " "n_samples." % (self.n_quantiles, n_samples) ) self.n_quantiles_ = max(1, min(self.n_quantiles, n_samples)) rng = check_random_state(self.random_state) # Create the quantiles of reference self.references_ = np.linspace(0, 1, self.n_quantiles_, endpoint=True) if sparse.issparse(X): self._sparse_fit(X, rng) else: self._dense_fit(X, rng) return self def _transform_col(self, X_col, quantiles, inverse): """Private function to transform a single feature.""" output_distribution = self.output_distribution if not inverse: lower_bound_x = quantiles[0] upper_bound_x = quantiles[-1] lower_bound_y = 0 upper_bound_y = 1 else: lower_bound_x = 0 upper_bound_x = 1 lower_bound_y = quantiles[0] upper_bound_y = quantiles[-1] # for inverse transform, match a uniform distribution with np.errstate(invalid="ignore"): # hide NaN comparison warnings if output_distribution == "normal": X_col = stats.norm.cdf(X_col) # else output distribution is already a uniform distribution # find index for lower and higher bounds with np.errstate(invalid="ignore"): # hide NaN comparison warnings if output_distribution == "normal": lower_bounds_idx = X_col - BOUNDS_THRESHOLD < lower_bound_x upper_bounds_idx = X_col + BOUNDS_THRESHOLD > upper_bound_x if output_distribution == "uniform": lower_bounds_idx = X_col == lower_bound_x upper_bounds_idx = X_col == upper_bound_x isfinite_mask = ~np.isnan(X_col) X_col_finite = X_col[isfinite_mask] if not inverse: # Interpolate in one direction and in the other and take the # mean. This is in case of repeated values in the features # and hence repeated quantiles # # If we don't do this, only one extreme of the duplicated is # used (the upper when we do ascending, and the # lower for descending). We take the mean of these two X_col[isfinite_mask] = 0.5 * ( np.interp(X_col_finite, quantiles, self.references_) - np.interp(-X_col_finite, -quantiles[::-1], -self.references_[::-1]) ) else: X_col[isfinite_mask] = np.interp(X_col_finite, self.references_, quantiles) X_col[upper_bounds_idx] = upper_bound_y X_col[lower_bounds_idx] = lower_bound_y # for forward transform, match the output distribution if not inverse: with np.errstate(invalid="ignore"): # hide NaN comparison warnings if output_distribution == "normal": X_col = stats.norm.ppf(X_col) # find the value to clip the data to avoid mapping to # infinity. Clip such that the inverse transform will be # consistent clip_min = stats.norm.ppf(BOUNDS_THRESHOLD - np.spacing(1)) clip_max = stats.norm.ppf(1 - (BOUNDS_THRESHOLD - np.spacing(1))) X_col = np.clip(X_col, clip_min, clip_max) # else output distribution is uniform and the ppf is the # identity function so we let X_col unchanged return X_col def _check_inputs(self, X, in_fit, accept_sparse_negative=False, copy=False): """Check inputs before fit and transform.""" X = self._validate_data( X, reset=in_fit, accept_sparse="csc", copy=copy, dtype=FLOAT_DTYPES, force_all_finite="allow-nan", ) # we only accept positive sparse matrix when ignore_implicit_zeros is # false and that we call fit or transform. with np.errstate(invalid="ignore"): # hide NaN comparison warnings if ( not accept_sparse_negative and not self.ignore_implicit_zeros and (sparse.issparse(X) and np.any(X.data < 0)) ): raise ValueError( "QuantileTransformer only accepts non-negative sparse matrices." ) return X def _transform(self, X, inverse=False): """Forward and inverse transform. Parameters ---------- X : ndarray of shape (n_samples, n_features) The data used to scale along the features axis. inverse : bool, default=False If False, apply forward transform. If True, apply inverse transform. Returns ------- X : ndarray of shape (n_samples, n_features) Projected data. """ if sparse.issparse(X): for feature_idx in range(X.shape[1]): column_slice = slice(X.indptr[feature_idx], X.indptr[feature_idx + 1]) X.data[column_slice] = self._transform_col( X.data[column_slice], self.quantiles_[:, feature_idx], inverse ) else: for feature_idx in range(X.shape[1]): X[:, feature_idx] = self._transform_col( X[:, feature_idx], self.quantiles_[:, feature_idx], inverse ) return X def transform(self, X): """Feature-wise transformation of the data. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False. Returns ------- Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) The projected data. """ check_is_fitted(self) X = self._check_inputs(X, in_fit=False, copy=self.copy) return self._transform(X, inverse=False) def inverse_transform(self, X): """Back-projection to the original space. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data used to scale along the features axis. If a sparse matrix is provided, it will be converted into a sparse ``csc_matrix``. Additionally, the sparse matrix needs to be nonnegative if `ignore_implicit_zeros` is False. Returns ------- Xt : {ndarray, sparse matrix} of (n_samples, n_features) The projected data. """ check_is_fitted(self) X = self._check_inputs( X, in_fit=False, accept_sparse_negative=True, copy=self.copy ) return self._transform(X, inverse=True) def _more_tags(self): return {"allow_nan": True} @validate_params( {"X": ["array-like", "sparse matrix"], "axis": [Options(Integral, {0, 1})]}, prefer_skip_nested_validation=False, ) def quantile_transform( X, *, axis=0, n_quantiles=1000, output_distribution="uniform", ignore_implicit_zeros=False, subsample=int(1e5), random_state=None, copy=True, ): """Transform features using quantiles information. This method transforms the features to follow a uniform or a normal distribution. Therefore, for a given feature, this transformation tends to spread out the most frequent values. It also reduces the impact of (marginal) outliers: this is therefore a robust preprocessing scheme. The transformation is applied on each feature independently. First an estimate of the cumulative distribution function of a feature is used to map the original values to a uniform distribution. The obtained values are then mapped to the desired output distribution using the associated quantile function. Features values of new/unseen data that fall below or above the fitted range will be mapped to the bounds of the output distribution. Note that this transform is non-linear. It may distort linear correlations between variables measured at the same scale but renders variables measured at different scales more directly comparable. Read more in the :ref:`User Guide `. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to transform. axis : int, default=0 Axis used to compute the means and standard deviations along. If 0, transform each feature, otherwise (if 1) transform each sample. n_quantiles : int, default=1000 or n_samples Number of quantiles to be computed. It corresponds to the number of landmarks used to discretize the cumulative distribution function. If n_quantiles is larger than the number of samples, n_quantiles is set to the number of samples as a larger number of quantiles does not give a better approximation of the cumulative distribution function estimator. output_distribution : {'uniform', 'normal'}, default='uniform' Marginal distribution for the transformed data. The choices are 'uniform' (default) or 'normal'. ignore_implicit_zeros : bool, default=False Only applies to sparse matrices. If True, the sparse entries of the matrix are discarded to compute the quantile statistics. If False, these entries are treated as zeros. subsample : int, default=1e5 Maximum number of samples used to estimate the quantiles for computational efficiency. Note that the subsampling procedure may differ for value-identical sparse and dense matrices. random_state : int, RandomState instance or None, default=None Determines random number generation for subsampling and smoothing noise. Please see ``subsample`` for more details. Pass an int for reproducible results across multiple function calls. See :term:`Glossary `. copy : bool, default=True If False, try to avoid a copy and transform in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. .. versionchanged:: 0.23 The default value of `copy` changed from False to True in 0.23. Returns ------- Xt : {ndarray, sparse matrix} of shape (n_samples, n_features) The transformed data. See Also -------- QuantileTransformer : Performs quantile-based scaling using the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). power_transform : Maps data to a normal distribution using a power transformation. scale : Performs standardization that is faster, but less robust to outliers. robust_scale : Performs robust standardization that removes the influence of outliers but does not put outliers and inliers on the same scale. Notes ----- NaNs are treated as missing values: disregarded in fit, and maintained in transform. .. warning:: Risk of data leak Do not use :func:`~sklearn.preprocessing.quantile_transform` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.QuantileTransformer` within a :ref:`Pipeline ` in order to prevent most risks of data leaking:`pipe = make_pipeline(QuantileTransformer(), LogisticRegression())`. For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import quantile_transform >>> rng = np.random.RandomState(0) >>> X = np.sort(rng.normal(loc=0.5, scale=0.25, size=(25, 1)), axis=0) >>> quantile_transform(X, n_quantiles=10, random_state=0, copy=True) array([...]) """ n = QuantileTransformer( n_quantiles=n_quantiles, output_distribution=output_distribution, subsample=subsample, ignore_implicit_zeros=ignore_implicit_zeros, random_state=random_state, copy=copy, ) if axis == 0: X = n.fit_transform(X) else: # axis == 1 X = n.fit_transform(X.T).T return X class PowerTransformer(OneToOneFeatureMixin, TransformerMixin, BaseEstimator): """Apply a power transform featurewise to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired. Currently, PowerTransformer supports the Box-Cox transform and the Yeo-Johnson transform. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood. Box-Cox requires input data to be strictly positive, while Yeo-Johnson supports both positive or negative data. By default, zero-mean, unit-variance normalization is applied to the transformed data. For an example visualization, refer to :ref:`Compare PowerTransformer with other scalers `. To see the effect of Box-Cox and Yeo-Johnson transformations on different distributions, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_map_data_to_normal.py`. Read more in the :ref:`User Guide `. .. versionadded:: 0.20 Parameters ---------- method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson' The power transform method. Available methods are: - 'yeo-johnson' [1]_, works with positive and negative values - 'box-cox' [2]_, only works with strictly positive values standardize : bool, default=True Set to True to apply zero-mean, unit-variance normalization to the transformed output. copy : bool, default=True Set to False to perform inplace computation during transformation. Attributes ---------- lambdas_ : ndarray of float of shape (n_features,) The parameters of the power transformation for the selected features. n_features_in_ : int Number of features seen during :term:`fit`. .. versionadded:: 0.24 feature_names_in_ : ndarray of shape (`n_features_in_`,) Names of features seen during :term:`fit`. Defined only when `X` has feature names that are all strings. .. versionadded:: 1.0 See Also -------- power_transform : Equivalent function without the estimator API. QuantileTransformer : Maps data to a standard normal distribution with the parameter `output_distribution='normal'`. Notes ----- NaNs are treated as missing values: disregarded in ``fit``, and maintained in ``transform``. References ---------- .. [1] :doi:`I.K. Yeo and R.A. Johnson, "A new family of power transformations to improve normality or symmetry." Biometrika, 87(4), pp.954-959, (2000). <10.1093/biomet/87.4.954>` .. [2] :doi:`G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal of the Royal Statistical Society B, 26, 211-252 (1964). <10.1111/j.2517-6161.1964.tb00553.x>` Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import PowerTransformer >>> pt = PowerTransformer() >>> data = [[1, 2], [3, 2], [4, 5]] >>> print(pt.fit(data)) PowerTransformer() >>> print(pt.lambdas_) [ 1.386... -3.100...] >>> print(pt.transform(data)) [[-1.316... -0.707...] [ 0.209... -0.707...] [ 1.106... 1.414...]] """ _parameter_constraints: dict = { "method": [StrOptions({"yeo-johnson", "box-cox"})], "standardize": ["boolean"], "copy": ["boolean"], } def __init__(self, method="yeo-johnson", *, standardize=True, copy=True): self.method = method self.standardize = standardize self.copy = copy @_fit_context(prefer_skip_nested_validation=True) def fit(self, X, y=None): """Estimate the optimal parameter lambda for each feature. The optimal lambda parameter for minimizing skewness is estimated on each feature independently using maximum likelihood. Parameters ---------- X : array-like of shape (n_samples, n_features) The data used to estimate the optimal transformation parameters. y : None Ignored. Returns ------- self : object Fitted transformer. """ self._fit(X, y=y, force_transform=False) return self @_fit_context(prefer_skip_nested_validation=True) def fit_transform(self, X, y=None): """Fit `PowerTransformer` to `X`, then transform `X`. Parameters ---------- X : array-like of shape (n_samples, n_features) The data used to estimate the optimal transformation parameters and to be transformed using a power transformation. y : Ignored Not used, present for API consistency by convention. Returns ------- X_new : ndarray of shape (n_samples, n_features) Transformed data. """ return self._fit(X, y, force_transform=True) def _fit(self, X, y=None, force_transform=False): X = self._check_input(X, in_fit=True, check_positive=True) if not self.copy and not force_transform: # if call from fit() X = X.copy() # force copy so that fit does not change X inplace n_samples = X.shape[0] mean = np.mean(X, axis=0, dtype=np.float64) var = np.var(X, axis=0, dtype=np.float64) optim_function = { "box-cox": self._box_cox_optimize, "yeo-johnson": self._yeo_johnson_optimize, }[self.method] transform_function = { "box-cox": boxcox, "yeo-johnson": self._yeo_johnson_transform, }[self.method] with np.errstate(invalid="ignore"): # hide NaN warnings self.lambdas_ = np.empty(X.shape[1], dtype=X.dtype) for i, col in enumerate(X.T): # For yeo-johnson, leave constant features unchanged # lambda=1 corresponds to the identity transformation is_constant_feature = _is_constant_feature(var[i], mean[i], n_samples) if self.method == "yeo-johnson" and is_constant_feature: self.lambdas_[i] = 1.0 continue self.lambdas_[i] = optim_function(col) if self.standardize or force_transform: X[:, i] = transform_function(X[:, i], self.lambdas_[i]) if self.standardize: self._scaler = StandardScaler(copy=False).set_output(transform="default") if force_transform: X = self._scaler.fit_transform(X) else: self._scaler.fit(X) return X def transform(self, X): """Apply the power transform to each feature using the fitted lambdas. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to be transformed using a power transformation. Returns ------- X_trans : ndarray of shape (n_samples, n_features) The transformed data. """ check_is_fitted(self) X = self._check_input(X, in_fit=False, check_positive=True, check_shape=True) transform_function = { "box-cox": boxcox, "yeo-johnson": self._yeo_johnson_transform, }[self.method] for i, lmbda in enumerate(self.lambdas_): with np.errstate(invalid="ignore"): # hide NaN warnings X[:, i] = transform_function(X[:, i], lmbda) if self.standardize: X = self._scaler.transform(X) return X def inverse_transform(self, X): """Apply the inverse power transformation using the fitted lambdas. The inverse of the Box-Cox transformation is given by:: if lambda_ == 0: X = exp(X_trans) else: X = (X_trans * lambda_ + 1) ** (1 / lambda_) The inverse of the Yeo-Johnson transformation is given by:: if X >= 0 and lambda_ == 0: X = exp(X_trans) - 1 elif X >= 0 and lambda_ != 0: X = (X_trans * lambda_ + 1) ** (1 / lambda_) - 1 elif X < 0 and lambda_ != 2: X = 1 - (-(2 - lambda_) * X_trans + 1) ** (1 / (2 - lambda_)) elif X < 0 and lambda_ == 2: X = 1 - exp(-X_trans) Parameters ---------- X : array-like of shape (n_samples, n_features) The transformed data. Returns ------- X : ndarray of shape (n_samples, n_features) The original data. """ check_is_fitted(self) X = self._check_input(X, in_fit=False, check_shape=True) if self.standardize: X = self._scaler.inverse_transform(X) inv_fun = { "box-cox": self._box_cox_inverse_tranform, "yeo-johnson": self._yeo_johnson_inverse_transform, }[self.method] for i, lmbda in enumerate(self.lambdas_): with np.errstate(invalid="ignore"): # hide NaN warnings X[:, i] = inv_fun(X[:, i], lmbda) return X def _box_cox_inverse_tranform(self, x, lmbda): """Return inverse-transformed input x following Box-Cox inverse transform with parameter lambda. """ if lmbda == 0: x_inv = np.exp(x) else: x_inv = (x * lmbda + 1) ** (1 / lmbda) return x_inv def _yeo_johnson_inverse_transform(self, x, lmbda): """Return inverse-transformed input x following Yeo-Johnson inverse transform with parameter lambda. """ x_inv = np.zeros_like(x) pos = x >= 0 # when x >= 0 if abs(lmbda) < np.spacing(1.0): x_inv[pos] = np.exp(x[pos]) - 1 else: # lmbda != 0 x_inv[pos] = np.power(x[pos] * lmbda + 1, 1 / lmbda) - 1 # when x < 0 if abs(lmbda - 2) > np.spacing(1.0): x_inv[~pos] = 1 - np.power(-(2 - lmbda) * x[~pos] + 1, 1 / (2 - lmbda)) else: # lmbda == 2 x_inv[~pos] = 1 - np.exp(-x[~pos]) return x_inv def _yeo_johnson_transform(self, x, lmbda): """Return transformed input x following Yeo-Johnson transform with parameter lambda. """ out = np.zeros_like(x) pos = x >= 0 # binary mask # when x >= 0 if abs(lmbda) < np.spacing(1.0): out[pos] = np.log1p(x[pos]) else: # lmbda != 0 out[pos] = (np.power(x[pos] + 1, lmbda) - 1) / lmbda # when x < 0 if abs(lmbda - 2) > np.spacing(1.0): out[~pos] = -(np.power(-x[~pos] + 1, 2 - lmbda) - 1) / (2 - lmbda) else: # lmbda == 2 out[~pos] = -np.log1p(-x[~pos]) return out def _box_cox_optimize(self, x): """Find and return optimal lambda parameter of the Box-Cox transform by MLE, for observed data x. We here use scipy builtins which uses the brent optimizer. """ mask = np.isnan(x) if np.all(mask): raise ValueError("Column must not be all nan.") # the computation of lambda is influenced by NaNs so we need to # get rid of them _, lmbda = stats.boxcox(x[~mask], lmbda=None) return lmbda def _yeo_johnson_optimize(self, x): """Find and return optimal lambda parameter of the Yeo-Johnson transform by MLE, for observed data x. Like for Box-Cox, MLE is done via the brent optimizer. """ x_tiny = np.finfo(np.float64).tiny def _neg_log_likelihood(lmbda): """Return the negative log likelihood of the observed data x as a function of lambda.""" x_trans = self._yeo_johnson_transform(x, lmbda) n_samples = x.shape[0] x_trans_var = x_trans.var() # Reject transformed data that would raise a RuntimeWarning in np.log if x_trans_var < x_tiny: return np.inf log_var = np.log(x_trans_var) loglike = -n_samples / 2 * log_var loglike += (lmbda - 1) * (np.sign(x) * np.log1p(np.abs(x))).sum() return -loglike # the computation of lambda is influenced by NaNs so we need to # get rid of them x = x[~np.isnan(x)] # choosing bracket -2, 2 like for boxcox return optimize.brent(_neg_log_likelihood, brack=(-2, 2)) def _check_input(self, X, in_fit, check_positive=False, check_shape=False): """Validate the input before fit and transform. Parameters ---------- X : array-like of shape (n_samples, n_features) in_fit : bool Whether or not `_check_input` is called from `fit` or other methods, e.g. `predict`, `transform`, etc. check_positive : bool, default=False If True, check that all data is positive and non-zero (only if ``self.method=='box-cox'``). check_shape : bool, default=False If True, check that n_features matches the length of self.lambdas_ """ X = self._validate_data( X, ensure_2d=True, dtype=FLOAT_DTYPES, copy=self.copy, force_all_finite="allow-nan", reset=in_fit, ) with warnings.catch_warnings(): warnings.filterwarnings("ignore", r"All-NaN (slice|axis) encountered") if check_positive and self.method == "box-cox" and np.nanmin(X) <= 0: raise ValueError( "The Box-Cox transformation can only be " "applied to strictly positive data" ) if check_shape and not X.shape[1] == len(self.lambdas_): raise ValueError( "Input data has a different number of features " "than fitting data. Should have {n}, data has {m}".format( n=len(self.lambdas_), m=X.shape[1] ) ) return X def _more_tags(self): return {"allow_nan": True} @validate_params( {"X": ["array-like"]}, prefer_skip_nested_validation=False, ) def power_transform(X, method="yeo-johnson", *, standardize=True, copy=True): """Parametric, monotonic transformation to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is useful for modeling issues related to heteroscedasticity (non-constant variance), or other situations where normality is desired. Currently, power_transform supports the Box-Cox transform and the Yeo-Johnson transform. The optimal parameter for stabilizing variance and minimizing skewness is estimated through maximum likelihood. Box-Cox requires input data to be strictly positive, while Yeo-Johnson supports both positive or negative data. By default, zero-mean, unit-variance normalization is applied to the transformed data. Read more in the :ref:`User Guide `. Parameters ---------- X : array-like of shape (n_samples, n_features) The data to be transformed using a power transformation. method : {'yeo-johnson', 'box-cox'}, default='yeo-johnson' The power transform method. Available methods are: - 'yeo-johnson' [1]_, works with positive and negative values - 'box-cox' [2]_, only works with strictly positive values .. versionchanged:: 0.23 The default value of the `method` parameter changed from 'box-cox' to 'yeo-johnson' in 0.23. standardize : bool, default=True Set to True to apply zero-mean, unit-variance normalization to the transformed output. copy : bool, default=True If False, try to avoid a copy and transform in place. This is not guaranteed to always work in place; e.g. if the data is a numpy array with an int dtype, a copy will be returned even with copy=False. Returns ------- X_trans : ndarray of shape (n_samples, n_features) The transformed data. See Also -------- PowerTransformer : Equivalent transformation with the Transformer API (e.g. as part of a preprocessing :class:`~sklearn.pipeline.Pipeline`). quantile_transform : Maps data to a standard normal distribution with the parameter `output_distribution='normal'`. Notes ----- NaNs are treated as missing values: disregarded in ``fit``, and maintained in ``transform``. For a comparison of the different scalers, transformers, and normalizers, see: :ref:`sphx_glr_auto_examples_preprocessing_plot_all_scaling.py`. References ---------- .. [1] I.K. Yeo and R.A. Johnson, "A new family of power transformations to improve normality or symmetry." Biometrika, 87(4), pp.954-959, (2000). .. [2] G.E.P. Box and D.R. Cox, "An Analysis of Transformations", Journal of the Royal Statistical Society B, 26, 211-252 (1964). Examples -------- >>> import numpy as np >>> from sklearn.preprocessing import power_transform >>> data = [[1, 2], [3, 2], [4, 5]] >>> print(power_transform(data, method='box-cox')) [[-1.332... -0.707...] [ 0.256... -0.707...] [ 1.076... 1.414...]] .. warning:: Risk of data leak. Do not use :func:`~sklearn.preprocessing.power_transform` unless you know what you are doing. A common mistake is to apply it to the entire data *before* splitting into training and test sets. This will bias the model evaluation because information would have leaked from the test set to the training set. In general, we recommend using :class:`~sklearn.preprocessing.PowerTransformer` within a :ref:`Pipeline ` in order to prevent most risks of data leaking, e.g.: `pipe = make_pipeline(PowerTransformer(), LogisticRegression())`. """ pt = PowerTransformer(method=method, standardize=standardize, copy=copy) return pt.fit_transform(X)