msmbuilder.preprocessing.Imputer¶
-
class
msmbuilder.preprocessing.
Imputer
(missing_values='NaN', strategy='mean', axis=0, verbose=0, copy=True)¶ Imputation transformer for completing missing values.
Read more in the User Guide.
Parameters: - missing_values : integer or “NaN”, optional (default=”NaN”)
The placeholder for the missing values. All occurrences of missing_values will be imputed. For missing values encoded as np.nan, use the string value “NaN”.
- strategy : string, optional (default=”mean”)
The imputation strategy.
- If “mean”, then replace missing values using the mean along the axis.
- If “median”, then replace missing values using the median along the axis.
- If “most_frequent”, then replace missing using the most frequent value along the axis.
- axis : integer, optional (default=0)
The axis along which to impute.
- If axis=0, then impute along columns.
- If axis=1, then impute along rows.
- verbose : integer, optional (default=0)
Controls the verbosity of the imputer.
- copy : boolean, optional (default=True)
If True, a copy of X will be created. If False, imputation will be done in-place whenever possible. Note that, in the following cases, a new copy will always be made, even if copy=False:
- If X is not an array of floating values;
- If X is sparse and missing_values=0;
- If axis=0 and X is encoded as a CSR matrix;
- If axis=1 and X is encoded as a CSC matrix.
Notes
- When
axis=0
, columns which only contained missing values at fit are discarded upon transform. - When
axis=1
, an exception is raised if there are rows for which it is not possible to fill in the missing values (e.g., because they only contain missing values).
Attributes: - statistics_ : array of shape (n_features,)
The imputation fill value for each feature if axis == 0.
Methods
fit
(X[, y])Fit Preprocessing to X. fit_transform
(sequences[, y])Fit the model and apply preprocessing get_params
([deep])Get parameters for this estimator. 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__
(missing_values='NaN', strategy='mean', axis=0, verbose=0, copy=True)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([missing_values, strategy, axis, …])Initialize self. fit
(X[, y])Fit Preprocessing to X. fit_transform
(sequences[, y])Fit the model and apply preprocessing get_params
([deep])Get parameters for this estimator. 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
(X, y=None)¶ Fit Preprocessing to X.
Parameters: - sequence : array-like, [sequence_length, n_features]
A multivariate timeseries.
- y : None
Ignored
Returns: - self
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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)
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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.
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partial_fit
(sequence, y=None)¶ Fit Preprocessing to X. Parameters ———- sequence : array-like, [sequence_length, n_features]
A multivariate timeseries.- y : None
- Ignored
self
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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)
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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
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summarize
()¶ Return some diagnostic summary statistics about this Markov model
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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)