niaaml.preprocessing
¶
-
class
niaaml.preprocessing.
PreprocessingAlgorithm
(**kwargs)¶ Bases:
niaaml.pipeline_component.PipelineComponent
Class for implementing preprocessing algorithms.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- See Also:
niaaml.pipeline_component.PipelineComponent
niaaml.preprocessing.feature_selection
¶
-
class
niaaml.preprocessing.feature_selection.
BatAlgorithm
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using BA algorithm.
- Date:
2020
- Author:
Luka Pečnik
- Reference:
The implementation is adapted according to the following article: D. Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
- Reference URL:
- License:
MIT
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Bat Algorithm'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
pandas.core.frame.DataFrame: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
DifferentialEvolution
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using DE algorithm.
- Date:
2020
- Author:
Luka Pečnik
- Reference:
The implementation is adapted according to the following article: D. Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
- Reference URL:
- License:
MIT
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Differential Evolution'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
FeatureSelectionAlgorithm
(**kwargs)¶ Bases:
niaaml.preprocessing.preprocessing_algorithm.PreprocessingAlgorithm
Class for implementing feature selection algorithms.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- See Also:
niaaml.preprocessing.preprocessing_algorithm.PreprocessingAlgorithm
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
class
niaaml.preprocessing.feature_selection.
FeatureSelectionAlgorithmFactory
(**kwargs)¶ Bases:
niaaml.utilities.Factory
Class with string mappings to feature selection algorithms.
- Attributes:
_entities (Dict[str, FeatureSelectionAlgorithm]): Mapping from strings to feature selection algorithms.
- See Also:
niaaml.utilities.Factory
-
class
niaaml.preprocessing.feature_selection.
GreyWolfOptimizer
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using GWO algorithm.
- Date:
2020
- Author:
Luka Pečnik
- Reference:
The implementation is adapted according to the following article: D. Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
- Reference URL:
- License:
MIT
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Grey Wolf Optimizer'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
ParticleSwarmOptimization
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using PSO algorithm.
- Date:
2020
- Author:
Luka Pečnik
- Reference:
The implementation is adapted according to the following article: D. Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
- Reference URL:
- License:
MIT
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Particle Swarm Optimization'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
SelectKBest
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using selection of k best features according to used score function.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Select K Best'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
SelectPercentile
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using percentile selection of best features according to used score function.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectPercentile.html
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Select Percentile'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
VarianceThreshold
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of feature selection using variance threshold.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.VarianceThreshold.html
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Variance Threshold'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
class
niaaml.preprocessing.feature_selection.
jDEFSTH
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
Implementation of self-adaptive differential evolution for feature selection using threshold mechanism.
- Date:
2020
- Author:
Iztok Fister Jr.
- Reference:
Fister, I. Fister, T. Jagrič, I. Fister Jr., J. Brest. A novel self-adaptive differential evolution for feature selection using threshold mechanism . In: Proceedings of the 2018 IEEE Symposium on Computational Intelligence (SSCI 2018), pp. 17-24, 2018.
- Reference URL:
- License:
MIT
- See Also:
niaaml.preprocessing.feature_selection.feature_selection_algorithm.FeatureSelectionAlgorithm
-
Name
= 'Self-Adaptive Differential Evolution'¶
-
select_features
(x, y, **kwargs)¶ Perform the feature selection process.
- Arguments:
x (pandas.core.frame.DataFrame): Array of original features. y (pandas.core.series.Series) Expected classifier results.
- Returns:
numpy.ndarray[bool]: Mask of selected features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
niaaml.preprocessing.feature_transform
¶
-
class
niaaml.preprocessing.feature_transform.
FeatureTransformAlgorithm
(**kwargs)¶ Bases:
niaaml.preprocessing.preprocessing_algorithm.PreprocessingAlgorithm
Class for implementing feature transform algorithms.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- See Also:
niaaml.preprocessing.preprocessing_algorithm.PreprocessingAlgorithm
-
fit
(x, **kwargs)¶ Fit implemented feature transform algorithm.
- Arguments:
x (pandas.core.frame.DataFrame): n samples to fit transformation algorithm.
-
transform
(x, **kwargs)¶ Transforms the given x data.
- Arguments:
x (pandas.core.frame.DataFrame): Data to transform.
- Returns:
pandas.core.frame.DataFrame: Transformed data.
-
class
niaaml.preprocessing.feature_transform.
FeatureTransformAlgorithmFactory
(**kwargs)¶ Bases:
niaaml.utilities.Factory
Class with string mappings to feature transform algorithms.
- Attributes:
_entities (Dict[str, FeatureTransformAlgorithm]): Mapping from strings to feature transform algorithms.
-
class
niaaml.preprocessing.feature_transform.
MaxAbsScaler
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_transform.feature_transform_algorithm.FeatureTransformAlgorithm
Implementation of feature scaling by its maximum absolute value.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
- See Also:
-
Name
= 'Maximum Absolute Scaler'¶
-
fit
(x, **kwargs)¶ Fit implemented transformation algorithm.
- Arguments:
x (pandas.core.frame.DataFrame): n samples to fit transformation algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(x, **kwargs)¶ Transforms the given x data.
- Arguments:
x (pandas.core.frame.DataFrame): Data to transform.
- Returns:
pandas.core.frame.DataFrame: Transformed data.
-
class
niaaml.preprocessing.feature_transform.
Normalizer
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_transform.feature_transform_algorithm.FeatureTransformAlgorithm
Implementation of feature normalization algorithm.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Normalizer
- See Also:
-
Name
= 'Normalizer'¶
-
fit
(x, **kwargs)¶ Fit implemented transformation algorithm.
- Arguments:
x (pandas.core.frame.DataFrame): n samples to fit transformation algorithm.
-
set_parameters
(**kwargs)¶ Set the parameters/arguments of the algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(x, **kwargs)¶ Transforms the given x data.
- Arguments:
x (pandas.core.frame.DataFrame): Data to transform.
- Returns:
pandas.core.frame.DataFrame: Transformed data.
-
class
niaaml.preprocessing.feature_transform.
RobustScaler
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_transform.feature_transform_algorithm.FeatureTransformAlgorithm
Implementation of the robust scaler.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
- See Also:
-
Name
= 'Robust Scaler'¶
-
fit
(x, **kwargs)¶ Fit implemented transformation algorithm.
- Arguments:
x (pandas.core.frame.DataFrame): n samples to fit transformation algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(x, **kwargs)¶ Transforms the given x data.
- Arguments:
x (pandas.core.frame.DataFrame): Data to transform.
- Returns:
pandas.core.frame.DataFrame: Transformed data.
-
class
niaaml.preprocessing.feature_transform.
StandardScaler
(**kwargs)¶ Bases:
niaaml.preprocessing.feature_transform.feature_transform_algorithm.FeatureTransformAlgorithm
Implementation of feature standard scaling algorithm.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
- See Also:
-
Name
= 'Standard Scaler'¶
-
fit
(x, **kwargs)¶ Fit implemented transformation algorithm.
- Arguments:
x (pandas.core.frame.DataFrame): n samples to fit transformation algorithm.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(x, **kwargs)¶ Transforms the given x data.
- Arguments:
x (pandas.core.frame.DataFrame): Data to transform.
- Returns:
pandas.core.frame.DataFrame: Transformed data.
niaaml.preprocessing.encoding
¶
-
class
niaaml.preprocessing.encoding.
EncoderFactory
(**kwargs)¶ Bases:
niaaml.utilities.Factory
Class with string mappings to encoders.
- Attributes:
_entities (Dict[str, FeatureEncoder]): Mapping from strings to encoders.
- See Also:
niaaml.utilities.Factory
-
class
niaaml.preprocessing.encoding.
FeatureEncoder
(**kwargs)¶ Bases:
object
Class for implementing feature encoders.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Attributes:
Name (str): Name of the feature encoder.
-
Name
= None¶
-
fit
(feature)¶ Fit feature encoder.
- Arguments:
feature (pandas.core.frame.DataFrame): A column (categorical) from DataFrame of features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(feature)¶ Transform feature’s values.
- Arguments:
feature (pandas.core.frame.DataFrame): A column (categorical) from DataFrame of features.
- Returns:
pandas.core.frame.DataFrame: A transformed column.
-
class
niaaml.preprocessing.encoding.
OneHotEncoder
(**kwargs)¶ Bases:
niaaml.preprocessing.encoding.feature_encoder.FeatureEncoder
Implementation of one-hot encoder.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Reference:
Seger, Cedric. “An investigation of categorical variable encoding techniques in machine learning: binary versus one-hot and feature hashing.” (2018).
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html
- See Also:
-
Name
= 'One-Hot Encoder'¶
-
fit
(feature)¶ Fit feature encoder.
- Arguments:
feature (pandas.core.frame.DataFrame): A column (categorical) from DataFrame of features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(feature)¶ Transform feature’s values.
- Arguments:
feature (pandas.core.frame.DataFrame): A column (categorical) from DataFrame of features.
- Returns:
pandas.core.frame.DataFrame: A transformed column.
-
niaaml.preprocessing.encoding.
encode_categorical_features
(features, encoder)¶ Encode categorical features.
- Arguments:
features (pandas.core.frame.DataFrame): DataFrame of features. encoder (str): Name of the encoder to use.
- Returns:
- Tuple[pandas.core.frame.DataFrame, Iterable[FeatureEncoder]]:
Converted dataframe.
Dictionary of encoders for all categorical features.
niaaml.preprocessing.imputation
¶
-
class
niaaml.preprocessing.imputation.
Imputer
(**kwargs)¶ Bases:
object
Class for implementing imputers.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Attributes:
Name (str): Name of the imputer.
-
Name
= None¶
-
fit
(feature)¶ Fit imputer.
- Arguments:
feature (pandas.core.frame.DataFrame): A column from DataFrame of features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(feature)¶ Transform feature’s values.
- Arguments:
feature (pandas.core.frame.DataFrame): A column from DataFrame of features.
- Returns:
pandas.core.frame.DataFrame: A transformed column.
-
class
niaaml.preprocessing.imputation.
ImputerFactory
(**kwargs)¶ Bases:
niaaml.utilities.Factory
Class with string mappings to imputers.
- Attributes:
_entities (Dict[str, Imputer]): Mapping from strings to imputers.
- See Also:
niaaml.utilities.Factory
-
class
niaaml.preprocessing.imputation.
SimpleImputer
(**kwargs)¶ Bases:
niaaml.preprocessing.imputation.imputer.Imputer
Implementation of simple imputer.
- Date:
2020
- Author:
Luka Pečnik
- License:
MIT
- Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.impute.SimpleImputer.html
- See Also:
-
Name
= 'Simple Imputer'¶
-
fit
(feature)¶ Fit imputer.
- Arguments:
feature (pandas.core.frame.DataFrame): A column from DataFrame of features.
-
to_string
()¶ User friendly representation of the object.
- Returns:
str: User friendly representation of the object.
-
transform
(feature)¶ Transform feature’s values.
- Arguments:
feature (pandas.core.frame.DataFrame): A column from DataFrame of features.
- Returns:
pandas.core.frame.DataFrame: A transformed column.
-
niaaml.preprocessing.imputation.
impute_features
(features, imputer)¶ Impute features with missing data.
- Arguments:
features (pandas.core.frame.DataFrame): DataFrame of features. imputer (str): Name of the imputer to use.
- Returns:
- Tuple[pandas.core.frame.DataFrame, Dict[Imputer]]:
Converted dataframe.
Dictionary of imputers for all features with missing data.