msmbuilder.preprocessing.MinMaxScaler¶
-
class
msmbuilder.preprocessing.
MinMaxScaler
(feature_range=(0, 1), copy=True)¶ Transforms 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:
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.
Read more in the User Guide.
Parameters: - feature_range : tuple (min, max), default=(0, 1)
Desired range of transformed data.
- copy : boolean, optional, default True
Set to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array).
See also
minmax_scale
- Equivalent function without the estimator API.
Notes
For a comparison of the different scalers, transformers, and normalizers, see examples/preprocessing/plot_all_scaling.py.
Examples
>>> from sklearn.preprocessing import MinMaxScaler >>> >>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]] >>> scaler = MinMaxScaler() >>> print(scaler.fit(data)) MinMaxScaler(copy=True, feature_range=(0, 1)) >>> print(scaler.data_max_) [ 1. 18.] >>> print(scaler.transform(data)) [[ 0. 0. ] [ 0.25 0.25] [ 0.5 0.5 ] [ 1. 1. ]] >>> print(scaler.transform([[2, 2]])) [[ 1.5 0. ]]
Attributes: - min_ : ndarray, shape (n_features,)
Per feature adjustment for minimum.
- scale_ : ndarray, shape (n_features,)
Per feature relative scaling of the data.
New in version 0.17: scale_ attribute.
- data_min_ : ndarray, shape (n_features,)
Per feature minimum seen in the data
New in version 0.17: data_min_
- data_max_ : ndarray, shape (n_features,)
Per feature maximum seen in the data
New in version 0.17: data_max_
- data_range_ : ndarray, shape (n_features,)
Per feature range
(data_max_ - data_min_)
seen in the dataNew in version 0.17: data_range_
Methods
fit
(sequences[, y])Fit Preprocessing to X. fit_transform
(sequences[, y])Fit the model and apply preprocessing get_params
([deep])Get parameters for this estimator. inverse_transform
(X)Undo the scaling of X according to feature_range. partial_fit
(sequence[, y])Fit Preprocessing to X. partial_transform
(sequence)Apply preprocessing to single sequence set_params
(**params)Set the parameters of this estimator. summarize
()Return some diagnostic summary statistics about this Markov model transform
(sequences)Apply preprocessing to sequences -
__init__
(feature_range=(0, 1), copy=True)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([feature_range, copy])Initialize self. fit
(sequences[, y])Fit Preprocessing to X. fit_transform
(sequences[, y])Fit the model and apply preprocessing get_params
([deep])Get parameters for this estimator. inverse_transform
(X)Undo the scaling of X according to feature_range. partial_fit
(sequence[, y])Fit Preprocessing to X. partial_transform
(sequence)Apply preprocessing to single sequence set_params
(**params)Set the parameters of this estimator. summarize
()Return some diagnostic summary statistics about this Markov model transform
(sequences)Apply preprocessing to sequences -
fit
(sequences, y=None)¶ Fit Preprocessing to X.
Parameters: - sequences : list of array-like, each of shape [sequence_length, n_features]
A list of multivariate timeseries. Each sequence may have a different length, but they all must have the same number of features.
- y : None
Ignored
Returns: - self
-
fit_transform
(sequences, y=None)¶ Fit the model and apply preprocessing
Parameters: - sequences: list of array-like, each of shape (n_samples_i, n_features)
Training data, where n_samples_i in the number of samples in sequence i and n_features is the number of features.
- y : None
Ignored
Returns: - sequence_new : list of array-like, each of shape (n_samples_i, n_components)
-
get_params
(deep=True)¶ Get parameters for this estimator.
Parameters: - deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: - params : mapping of string to any
Parameter names mapped to their values.
-
inverse_transform
(X)¶ Undo the scaling of X according to feature_range.
Parameters: - X : array-like, shape [n_samples, n_features]
Input data that will be transformed. It cannot be sparse.
-
partial_fit
(sequence, y=None)¶ Fit Preprocessing to X. Parameters ———- sequence : array-like, [sequence_length, n_features]
A multivariate timeseries.- y : None
- Ignored
self
-
partial_transform
(sequence)¶ Apply preprocessing to single sequence
Parameters: - sequence: array like, shape (n_samples, n_features)
A single sequence to transform
Returns: - out : array like, shape (n_samples, n_features)
-
set_params
(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>
so that it’s possible to update each component of a nested object.Returns: - self
-
summarize
()¶ Return some diagnostic summary statistics about this Markov model
-
transform
(sequences)¶ Apply preprocessing to sequences
Parameters: - sequences: list of array-like, each of shape (n_samples_i, n_features)
Sequence data to transform, where n_samples_i in the number of samples in sequence i and n_features is the number of features.
Returns: - sequence_new : list of array-like, each of shape (n_samples_i, n_components)