transformers#
Components that transform data.
Subpackages#
- dimensionality_reduction
- encoders
- feature_selection
- imputers
- preprocessing
- datetime_featurizer
- decomposer
- drop_nan_rows_transformer
- drop_null_columns
- drop_rows_transformer
- featuretools
- log_transformer
- lsa
- natural_language_featurizer
- polynomial_decomposer
- replace_nullable_types
- stl_decomposer
- text_transformer
- time_series_featurizer
- time_series_regularizer
- transform_primitive_components
- samplers
- scalers
Submodules#
Package Contents#
Classes Summary#
Transformer that can automatically extract features from datetime columns. |
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Featuretools DFS component that generates features for the input features. |
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Drops specified columns in input data. |
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Transformer to drop rows with NaN values. |
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Transformer to drop features whose percentage of NaN values exceeds a specified threshold. |
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Transformer to drop rows specified by row indices. |
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Transformer that can automatically extract features from emails. |
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Selects top features based on importance weights. |
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Imputes missing data according to a specified imputation strategy. |
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A transformer that encodes target labels using values between 0 and num_classes - 1. |
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Reduces the number of features by using Linear Discriminant Analysis. |
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Applies a log transformation to the target data. |
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Transformer to calculate the Latent Semantic Analysis Values of text input. |
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Transformer that can automatically featurize text columns using featuretools' nlp_primitives. |
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A transformer that encodes categorical features in a one-hot numeric array. |
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A transformer that encodes ordinal features as an array of ordinal integers representing the relative order of categories. |
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SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component. |
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Reduces the number of features by using Principal Component Analysis (PCA). |
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Imputes missing data according to a specified imputation strategy per column. |
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Removes trends and seasonality from time series by fitting a polynomial and moving average to the data. |
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Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML. |
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Selects top features based on importance weights using a Random Forest classifier. |
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Selects top features based on importance weights using a Random Forest regressor. |
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Selects columns by specified Woodwork logical type or semantic tag in input data. |
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Selects specified columns in input data. |
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Imputes missing data according to a specified imputation strategy. Natural language columns are ignored. |
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A transformer that standardizes input features by removing the mean and scaling to unit variance. |
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Removes trends and seasonality from time series using the STL algorithm. |
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A transformer that encodes categorical features into target encodings. |
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Imputes missing target data according to a specified imputation strategy. |
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Transformer that delays input features and target variable for time series problems. |
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Imputes missing data according to a specified timeseries-specific imputation strategy. |
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Transformer that regularizes an inconsistently spaced datetime column. |
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A component that may or may not need fitting that transforms data. These components are used before an estimator. |
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Initializes an undersampling transformer to downsample the majority classes in the dataset. |
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Transformer that can automatically extract features from URL. |
Contents#
- class evalml.pipelines.components.transformers.DateTimeFeaturizer(features_to_extract=None, encode_as_categories=False, time_index=None, random_seed=0, **kwargs)[source]#
Transformer that can automatically extract features from datetime columns.
- Parameters
features_to_extract (list) – List of features to extract. Valid options include “year”, “month”, “day_of_week”, “hour”. Defaults to None.
encode_as_categories (bool) – Whether day-of-week and month features should be encoded as pandas “category” dtype. This allows OneHotEncoders to encode these features. Defaults to False.
time_index (str) – Name of the column containing the datetime information used to order the data. Ignored.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
DateTime Featurizer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fit the datetime featurizer component.
Fits on X and transforms X.
Gets the categories of each datetime feature.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by creating new features using existing DateTime columns, and then dropping those DateTime columns.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fit the datetime featurizer component.
- Parameters
X (pd.DataFrame) – Input features.
y (pd.Series, optional) – Target data. Ignored.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- get_feature_names(self)[source]#
Gets the categories of each datetime feature.
- Returns
- Dictionary, where each key-value pair is a column name and a dictionary
mapping the unique feature values to their integer encoding.
- Return type
dict
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.DFSTransformer(index='index', features=None, random_seed=0, **kwargs)[source]#
Featuretools DFS component that generates features for the input features.
- Parameters
index (string) – The name of the column that contains the indices. If no column with this name exists, then featuretools.EntitySet() creates a column with this name to serve as the index column. Defaults to ‘index’.
random_seed (int) – Seed for the random number generator. Defaults to 0.
features (list) – List of features to run DFS on. Defaults to None. Features will only be computed if the columns used by the feature exist in the input and if the feature itself is not in input.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
DFS Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the DFSTransformer Transformer component.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Computes the feature matrix for the input X using featuretools' dfs algorithm.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the DFSTransformer Transformer component.
- Parameters
X (pd.DataFrame, np.array) – The input data to transform, of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Computes the feature matrix for the input X using featuretools’ dfs algorithm.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data to transform. Has shape [n_samples, n_features]
y (pd.Series, optional) – Ignored.
- Returns
Feature matrix
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.DropColumns(columns=None, random_seed=0, **kwargs)[source]#
Drops specified columns in input data.
- Parameters
columns (list(string)) – List of column names, used to determine which columns to drop.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Drop Columns Transformer
needs_fitting
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the transformer by checking if column names are present in the dataset.
Fits on X and transforms X.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by dropping columns.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, ignored) – Targets.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.DropNaNRowsTransformer(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]#
Transformer to drop rows with NaN values.
- Parameters
random_seed (int) – Seed for the random number generator. Is not used by this component. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Drop NaN Rows Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data using fitted component.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.DropNullColumns(pct_null_threshold=1.0, random_seed=0, **kwargs)[source]#
Transformer to drop features whose percentage of NaN values exceeds a specified threshold.
- Parameters
pct_null_threshold (float) – The percentage of NaN values in an input feature to drop. Must be a value between [0, 1] inclusive. If equal to 0.0, will drop columns with any null values. If equal to 1.0, will drop columns with all null values. Defaults to 0.95.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Drop Null Columns Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by dropping columns that exceed the threshold of null values.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.DropRowsTransformer(indices_to_drop=None, random_seed=0)[source]#
Transformer to drop rows specified by row indices.
- Parameters
indices_to_drop (list) – List of indices to drop in the input data. Defaults to None.
random_seed (int) – Seed for the random number generator. Is not used by this component. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Drop Rows Transformer
training_only
True
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data using fitted component.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If indices to drop do not exist in input features or target.
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.EmailFeaturizer(random_seed=0, **kwargs)[source]#
Transformer that can automatically extract features from emails.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Email Featurizer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transforms data X.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- class evalml.pipelines.components.transformers.FeatureSelector(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]#
Selects top features based on importance weights.
- Parameters
parameters (dict) – Dictionary of parameters for the component. Defaults to None.
component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
modifies_features
True
modifies_target
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fit and transform data using the feature selector.
Get names of selected features.
Loads component at file path.
Returns string name of this component.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)[source]#
Fit and transform data using the feature selector.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- get_names(self)[source]#
Get names of selected features.
- Returns
List of the names of features selected.
- Return type
list[str]
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property name(cls)#
Returns string name of this component.
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data. Ignored.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If feature selector does not have a transform method or a component_obj that implements transform
- class evalml.pipelines.components.transformers.Imputer(categorical_impute_strategy='most_frequent', categorical_fill_value=None, numeric_impute_strategy='mean', numeric_fill_value=None, boolean_impute_strategy='most_frequent', boolean_fill_value=None, random_seed=0, **kwargs)[source]#
Imputes missing data according to a specified imputation strategy.
- Parameters
categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “most_frequent” and “constant”.
numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “mean”, “median”, “most_frequent”, and “constant”.
boolean_impute_strategy (string) – Impute strategy to use for boolean columns. Valid values include “most_frequent” and “constant”.
categorical_fill_value (string) – When categorical_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with the string “missing_value”.
numeric_fill_value (int, float) – When numeric_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with 0.
boolean_fill_value (bool) – When boolean_impute_strategy == “constant”, fill_value is used to replace missing data. The default value of None will fill with True.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “categorical_impute_strategy”: [“most_frequent”], “numeric_impute_strategy”: [“mean”, “median”, “most_frequent”, “knn”], “boolean_impute_strategy”: [“most_frequent”, “knn”]}
modifies_features
True
modifies_target
False
name
Imputer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputer to data. 'None' values are converted to np.nan before imputation and are treated as the same.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by imputing missing values.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits imputer to data. ‘None’ values are converted to np.nan before imputation and are treated as the same.
- Parameters
X (pd.DataFrame, np.ndarray) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.LabelEncoder(positive_label=None, random_seed=0, **kwargs)[source]#
A transformer that encodes target labels using values between 0 and num_classes - 1.
- Parameters
positive_label (int, str) – The label for the class that should be treated as positive (1) for binary classification problems. Ignored for multiclass problems. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0. Ignored.
Attributes
hyperparameter_ranges
{}
modifies_features
False
modifies_target
True
name
Label Encoder
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the label encoder.
Fit and transform data using the label encoder.
Decodes the target data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform the target using the fitted label encoder.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)[source]#
Fits the label encoder.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If input y is None.
- fit_transform(self, X, y)[source]#
Fit and transform data using the label encoder.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
The original features and an encoded version of the target.
- Return type
pd.DataFrame, pd.Series
- inverse_transform(self, y)[source]#
Decodes the target data.
- Parameters
y (pd.Series) – Target data.
- Returns
The decoded version of the target.
- Return type
pd.Series
- Raises
ValueError – If input y is None.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transform the target using the fitted label encoder.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]. Ignored.
y (pd.Series) – The target training data of length [n_samples].
- Returns
The original features and an encoded version of the target.
- Return type
pd.DataFrame, pd.Series
- Raises
ValueError – If input y is None.
- class evalml.pipelines.components.transformers.LinearDiscriminantAnalysis(n_components=None, random_seed=0, **kwargs)[source]#
Reduces the number of features by using Linear Discriminant Analysis.
- Parameters
n_components (int) – The number of features to maintain after computation. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Linear Discriminant Analysis Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the LDA component.
Fit and transform data using the LDA component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform data using the fitted LDA component.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)[source]#
Fits the LDA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If input data is not all numeric.
- fit_transform(self, X, y=None)[source]#
Fit and transform data using the LDA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- Raises
ValueError – If input data is not all numeric.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transform data using the fitted LDA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- Raises
ValueError – If input data is not all numeric.
- class evalml.pipelines.components.transformers.LogTransformer(random_seed=0)[source]#
Applies a log transformation to the target data.
Attributes
hyperparameter_ranges
{}
modifies_features
False
modifies_target
True
name
Log Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the LogTransformer.
Log transforms the target variable.
Apply exponential to target data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Log transforms the target variable.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the LogTransformer.
- Parameters
X (pd.DataFrame or np.ndarray) – Ignored.
y (pd.Series, optional) – Ignored.
- Returns
self
- fit_transform(self, X, y=None)[source]#
Log transforms the target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to log transform.
- Returns
- The input features are returned without modification. The target
variable y is log transformed.
- Return type
tuple of pd.DataFrame, pd.Series
- inverse_transform(self, y)[source]#
Apply exponential to target data.
- Parameters
y (pd.Series) – Target variable.
- Returns
Target with exponential applied.
- Return type
pd.Series
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Log transforms the target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target data to log transform.
- Returns
- The input features are returned without modification. The target
variable y is log transformed.
- Return type
tuple of pd.DataFrame, pd.Series
- class evalml.pipelines.components.transformers.LSA(random_seed=0, **kwargs)[source]#
Transformer to calculate the Latent Semantic Analysis Values of text input.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
LSA Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the input data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by applying the LSA pipeline.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the input data.
- Parameters
X (pd.DataFrame) – The data to transform.
y (pd.Series, optional) – Ignored.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transforms data X by applying the LSA pipeline.
- Parameters
X (pd.DataFrame) – The data to transform.
y (pd.Series, optional) – Ignored.
- Returns
- Transformed X. The original column is removed and replaced with two columns of the
format LSA(original_column_name)[feature_number], where feature_number is 0 or 1.
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.NaturalLanguageFeaturizer(random_seed=0, **kwargs)[source]#
Transformer that can automatically featurize text columns using featuretools’ nlp_primitives.
Since models cannot handle non-numeric data, any text must be broken down into features that provide useful information about that text. This component splits each text column into several informative features: Diversity Score, Mean Characters per Word, Polarity Score, LSA (Latent Semantic Analysis), Number of Characters, and Number of Words. Calling transform on this component will replace any text columns in the given dataset with these numeric columns.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Natural Language Featurizer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by creating new features using existing text columns.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits component to data.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (pd.Series) – The target training data of length [n_samples]
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.OneHotEncoder(top_n=10, features_to_encode=None, categories=None, drop='if_binary', handle_unknown='ignore', handle_missing='error', random_seed=0, **kwargs)[source]#
A transformer that encodes categorical features in a one-hot numeric array.
- Parameters
top_n (int) – Number of categories per column to encode. If None, all categories will be encoded. Otherwise, the n most frequent will be encoded and all others will be dropped. Defaults to 10.
features_to_encode (list[str]) – List of columns to encode. All other columns will remain untouched. If None, all appropriate columns will be encoded. Defaults to None.
categories (list) – A two dimensional list of categories, where categories[i] is a list of the categories for the column at index i. This can also be None, or “auto” if top_n is not None. Defaults to None.
drop (string, list) – Method (“first” or “if_binary”) to use to drop one category per feature. Can also be a list specifying which categories to drop for each feature. Defaults to ‘if_binary’.
handle_unknown (string) – Whether to ignore or error for unknown categories for a feature encountered during fit or transform. If either top_n or categories is used to limit the number of categories per column, this must be “ignore”. Defaults to “ignore”.
handle_missing (string) – Options for how to handle missing (NaN) values encountered during fit or transform. If this is set to “as_category” and NaN values are within the n most frequent, “nan” values will be encoded as their own column. If this is set to “error”, any missing values encountered will raise an error. Defaults to “error”.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
One Hot Encoder
training_only
False
Methods
Returns a list of the unique categories to be encoded for the particular feature, in order.
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the one-hot encoder component.
Fits on X and transforms X.
Return feature names for the categorical features after fitting.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
One-hot encode the input data.
- categories(self, feature_name)[source]#
Returns a list of the unique categories to be encoded for the particular feature, in order.
- Parameters
feature_name (str) – The name of any feature provided to one-hot encoder during fit.
- Returns
The unique categories, in the same dtype as they were provided during fit.
- Return type
np.ndarray
- Raises
ValueError – If feature was not provided to one-hot encoder as a training feature.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the one-hot encoder component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If encoding a column failed.
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- get_feature_names(self)[source]#
Return feature names for the categorical features after fitting.
Feature names are formatted as {column name}_{category name}. In the event of a duplicate name, an integer will be added at the end of the feature name to distinguish it.
For example, consider a dataframe with a column called “A” and category “x_y” and another column called “A_x” with “y”. In this example, the feature names would be “A_x_y” and “A_x_y_1”.
- Returns
The feature names after encoding, provided in the same order as input_features.
- Return type
np.ndarray
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.OrdinalEncoder(features_to_encode=None, categories=None, handle_unknown='error', unknown_value=None, encoded_missing_value=None, random_seed=0, **kwargs)[source]#
A transformer that encodes ordinal features as an array of ordinal integers representing the relative order of categories.
- Parameters
features_to_encode (list[str]) – List of columns to encode. All other columns will remain untouched. If None, all appropriate columns will be encoded. Defaults to None. The order of columns does not matter.
categories (dict[str, list[str]]) – A dictionary mapping column names to their categories in the dataframes passed in at fit and transform. The order of categories specified for a column does not matter. Any category found in the data that is not present in categories will be handled as an unknown value. To not have unknown values raise an error, set handle_unknown to “use_encoded_value”. Defaults to None.
handle_unknown ("error" or "use_encoded_value") – Whether to ignore or error for unknown categories for a feature encountered during fit or transform. When set to “error”, an error will be raised when an unknown category is found. When set to “use_encoded_value”, unknown categories will be encoded as the value given for the parameter unknown_value. Defaults to “error.”
unknown_value (int or np.nan) – The value to use for unknown categories seen during fit or transform. Required when the parameter handle_unknown is set to “use_encoded_value.” The value has to be distinct from the values used to encode any of the categories in fit. Defaults to None.
encoded_missing_value (int or np.nan) – The value to use for missing (null) values seen during fit or transform. Defaults to np.nan.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Ordinal Encoder
training_only
False
Methods
Returns a list of the unique categories to be encoded for the particular feature, in order.
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the ordinal encoder component.
Fits on X and transforms X.
Return feature names for the ordinal features after fitting.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Ordinally encode the input data.
- categories(self, feature_name)[source]#
Returns a list of the unique categories to be encoded for the particular feature, in order.
- Parameters
feature_name (str) – The name of any feature provided to ordinal encoder during fit.
- Returns
The unique categories, in the same dtype as they were provided during fit.
- Return type
np.ndarray
- Raises
ValueError – If feature was not provided to ordinal encoder as a training feature.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the ordinal encoder component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If encoding a column failed.
TypeError – If non-Ordinal columns are specified in features_to_encode.
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- get_feature_names(self)[source]#
Return feature names for the ordinal features after fitting.
Feature names are formatted as {column name}_ordinal_encoding.
- Returns
The feature names after encoding, provided in the same order as input_features.
- Return type
np.ndarray
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Ordinally encode the input data.
- Parameters
X (pd.DataFrame) – Features to encode.
y (pd.Series) – Ignored.
- Returns
Transformed data, where each ordinal feature has been encoded into a numerical column where ordinal integers represent the relative order of categories.
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.Oversampler(sampling_ratio=0.25, sampling_ratio_dict=None, k_neighbors_default=5, n_jobs=- 1, random_seed=0, **kwargs)[source]#
SMOTE Oversampler component. Will automatically select whether to use SMOTE, SMOTEN, or SMOTENC based on inputs to the component.
- Parameters
sampling_ratio (float) – This is the goal ratio of the minority to majority class, with range (0, 1]. A value of 0.25 means we want a 1:4 ratio of the minority to majority class after oversampling. We will create the a sampling dictionary using this ratio, with the keys corresponding to the class and the values responding to the number of samples. Defaults to 0.25.
sampling_ratio_dict (dict) – A dictionary specifying the desired balanced ratio for each target value. For instance, in a binary case where class 1 is the minority, we could specify: sampling_ratio_dict={0: 0.5, 1: 1}, which means we would undersample class 0 to have twice the number of samples as class 1 (minority:majority ratio = 0.5), and don’t sample class 1. Overrides sampling_ratio if provided. Defaults to None.
k_neighbors_default (int) – The number of nearest neighbors used to construct synthetic samples. This is the default value used, but the actual k_neighbors value might be smaller if there are less samples. Defaults to 5.
n_jobs (int) – The number of CPU cores to use. Defaults to -1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Oversampler
training_only
True
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits oversampler to data.
Fit and transform data using the sampler component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms the input data by sampling the data.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)[source]#
Fits oversampler to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y)#
Fit and transform data using the sampler component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
(pd.DataFrame, pd.Series)
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transforms the input data by sampling the data.
- Parameters
X (pd.DataFrame) – Training features.
y (pd.Series) – Target.
- Returns
Transformed features and target.
- Return type
pd.DataFrame, pd.Series
- class evalml.pipelines.components.transformers.PCA(variance=0.95, n_components=None, random_seed=0, **kwargs)[source]#
Reduces the number of features by using Principal Component Analysis (PCA).
- Parameters
variance (float) – The percentage of the original data variance that should be preserved when reducing the number of features. Defaults to 0.95.
n_components (int) – The number of features to maintain after computing SVD. Defaults to None, but will override variance variable if set.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
Real(0.25, 1)}:type: {“variance”
modifies_features
True
modifies_target
False
name
PCA Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the PCA component.
Fit and transform data using the PCA component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform data using fitted PCA component.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the PCA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If input data is not all numeric.
- fit_transform(self, X, y=None)[source]#
Fit and transform data using the PCA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- Raises
ValueError – If input data is not all numeric.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transform data using fitted PCA component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- Raises
ValueError – If input data is not all numeric.
- class evalml.pipelines.components.transformers.PerColumnImputer(impute_strategies=None, random_seed=0, **kwargs)[source]#
Imputes missing data according to a specified imputation strategy per column.
- Parameters
impute_strategies (dict) – Column and {“impute_strategy”: strategy, “fill_value”:value} pairings. Valid values for impute strategy include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to None, which uses “most_frequent” for all columns. When impute_strategy == “constant”, fill_value is used to replace missing data. When None, uses 0 when imputing numerical data and “missing_value” for strings or object data types.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Per Column Imputer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputers on input data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input data by imputing missing values.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits imputers on input data.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to fit.
y (pd.Series, optional) – The target training data of length [n_samples]. Ignored.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transforms input data by imputing missing values.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features] to transform.
y (pd.Series, optional) – The target training data of length [n_samples]. Ignored.
- Returns
Transformed X
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.PolynomialDecomposer(time_index: str = None, degree: int = 1, seasonal_period: int = - 1, random_seed: int = 0, **kwargs)[source]#
Removes trends and seasonality from time series by fitting a polynomial and moving average to the data.
- Scikit-learn’s PolynomialForecaster is used to generate the additive trend portion of the target data. A polynomial
will be fit to the data during fit. That additive polynomial trend will be removed during fit so that statsmodel’s seasonal_decompose can determine the addititve seasonality of the data by using rolling averages over the series’ inferred periodicity.
For example, daily time series data will generate rolling averages over the first week of data, normalize out the mean and return those 7 averages repeated over the entire length of the given series. Those seven averages, repeated as many times as necessary to match the length of the given target data, will be used as the seasonal signal of the data.
- Parameters
time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.
degree (int) – Degree for the polynomial. If 1, linear model is fit to the data. If 2, quadratic model is fit, etc. Defaults to 1.
seasonal_period (int) – The number of entries in the time series data that corresponds to one period of a cyclic signal. For instance, if data is known to possess a weekly seasonal signal, and if the data is daily data, seasonal_period should be 7. For daily data with a yearly seasonal signal, seasonal_period should be 365. Defaults to -1, which uses the statsmodels libarary’s freq_to_period function. statsmodels/statsmodels
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “degree”: Integer(1, 3)}
modifies_features
False
modifies_target
True
name
Polynomial Decomposer
needs_fitting
True
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
Fits the PolynomialDecomposer and determine the seasonal signal.
Removes fitted trend and seasonality from target variable.
Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.
Adds back fitted trend and seasonality to target variable.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Plots the decomposition of the target signal.
Saves component at file path.
Function to set the component's seasonal period based on the target's seasonality.
Transforms the target data by removing the polynomial trend and rolling average seasonality.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- determine_periodicity(self, X: pandas.DataFrame, y: pandas.Series, method: str = 'autocorrelation')#
Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
- Parameters
X (pandas.DataFrame) – The feature data of the time series problem.
y (pandas.Series) – The target data of a time series problem.
method (str) – Either “autocorrelation” or “partial-autocorrelation”. The method by which to determine the first period of the seasonal part of the target signal. “partial-autocorrelation” should currently not be used. Defaults to “autocorrelation”.
- Returns
- The integer numbers of entries in time series data over which the seasonal part of the target data
repeats. If the time series data is in days, then this is the number of days that it takes the target’s seasonal signal to repeat. Note: the target data can contain multiple seasonal signals. This function will only return the first, and thus, shortest period. E.g. if the target has both weekly and yearly seasonality, the function will only return “7” and not return “365”. If no period is detected, returns [None].
- Return type
(list[int])
- fit(self, X: pandas.DataFrame, y: pandas.Series = None) PolynomialDecomposer[source]#
Fits the PolynomialDecomposer and determine the seasonal signal.
Currently only fits the polynomial detrender. The seasonality is determined by removing the trend from the signal and using statsmodels’ seasonal_decompose(). Both the trend and seasonality are currently assumed to be additive.
- Parameters
X (pd.DataFrame, optional) – Conditionally used to build datetime index.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
self
- Raises
NotImplementedError – If the input data has a frequency of “month-begin”. This isn’t supported by statsmodels decompose as the freqstr “MS” is misinterpreted as milliseconds.
ValueError – If y is None.
ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data
- fit_transform(self, X: pandas.DataFrame, y: pandas.Series = None) tuple[pandas.DataFrame, pandas.Series]#
Removes fitted trend and seasonality from target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the fitted trend removed.
- Return type
tuple of pd.DataFrame, pd.Series
- get_trend_dataframe(self, X: pandas.DataFrame, y: pandas.Series) list[pandas.DataFrame][source]#
Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.
Scikit-learn’s PolynomialForecaster is used to generate the trend portion of the target data. statsmodel’s seasonal_decompose is used to generate the seasonality of the data.
- Parameters
X (pd.DataFrame) – Input data with time series data in index.
y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.
- Returns
- Each DataFrame contains the columns “signal”, “trend”, “seasonality” and “residual,”
with the latter 3 column values being the decomposed elements of the target data. The “signal” column is simply the input target signal but reindexed with a datetime index to match the input features.
- Return type
list of pd.DataFrame
- Raises
TypeError – If X does not have time-series data in the index.
ValueError – If time series index of X does not have an inferred frequency.
ValueError – If the forecaster associated with the detrender has not been fit yet.
TypeError – If y is not provided as a pandas Series or DataFrame.
- inverse_transform(self, y_t: pandas.Series) tuple[pandas.DataFrame, pandas.Series][source]#
Adds back fitted trend and seasonality to target variable.
The polynomial trend is added back into the signal, calling the detrender’s inverse_transform(). Then, the seasonality is projected forward to and added back into the signal.
- Parameters
y_t (pd.Series) – Target variable.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the trend and seasonality added back in.
- Return type
tuple of pd.DataFrame, pd.Series
- Raises
ValueError – If y is None.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- plot_decomposition(self, X: pandas.DataFrame, y: pandas.Series, show: bool = False) tuple[matplotlib.pyplot.Figure, list]#
Plots the decomposition of the target signal.
- Parameters
X (pd.DataFrame) – Input data with time series data in index.
y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.
show (bool) – Whether to display the plot or not. Defaults to False.
- Returns
- The figure and axes that have the decompositions
plotted on them
- Return type
matplotlib.pyplot.Figure, list[matplotlib.pyplot.Axes]
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- set_seasonal_period(self, X: pandas.DataFrame, y: pandas.Series)#
Function to set the component’s seasonal period based on the target’s seasonality.
- Parameters
X (pandas.DataFrame) – The feature data of the time series problem.
y (pandas.Series) – The target data of a time series problem.
- transform(self, X: pandas.DataFrame, y: pandas.Series = None) tuple[pandas.DataFrame, pandas.Series][source]#
Transforms the target data by removing the polynomial trend and rolling average seasonality.
Applies the fit polynomial detrender to the target data, removing the additive polynomial trend. Then, utilizes the first period’s worth of seasonal data determined in the .fit() function to extrapolate the seasonal signal of the data to be transformed. This seasonal signal is also assumed to be additive and is removed.
- Parameters
X (pd.DataFrame, optional) – Conditionally used to build datetime index.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
- The input features are returned without modification. The target
variable y is detrended and deseasonalized.
- Return type
tuple of pd.DataFrame, pd.Series
- Raises
ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data
- class evalml.pipelines.components.transformers.ReplaceNullableTypes(random_seed=0, **kwargs)[source]#
Transformer to replace features with the new nullable dtypes with a dtype that is compatible in EvalML.
Attributes
hyperparameter_ranges
None
modifies_features
True
modifies_target
{}
name
Replace Nullable Types Transformer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Substitutes non-nullable types for the new pandas nullable types in the data and target data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data by replacing columns that contain nullable types with the appropriate replacement type.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y=None)[source]#
Substitutes non-nullable types for the new pandas nullable types in the data and target data.
- Parameters
X (pd.DataFrame, optional) – Input features.
y (pd.Series) – Target data.
- Returns
The input features and target data with the non-nullable types set.
- Return type
tuple of pd.DataFrame, pd.Series
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transforms data by replacing columns that contain nullable types with the appropriate replacement type.
“float64” for nullable integers and “category” for nullable booleans.
- Parameters
X (pd.DataFrame) – Data to transform
y (pd.Series, optional) – Target data to transform
- Returns
Transformed X pd.Series: Transformed y
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.RFClassifierSelectFromModel(number_features=None, n_estimators=10, max_depth=None, percent_features=0.5, threshold='median', n_jobs=- 1, random_seed=0, **kwargs)[source]#
Selects top features based on importance weights using a Random Forest classifier.
- Parameters
number_features (int) – The maximum number of features to select. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5. Defaults to None.
n_estimators (float) – The number of trees in the forest. Defaults to 100.
max_depth (int) – Maximum tree depth for base learners. Defaults to 6.
percent_features (float) – Percentage of features to use. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5.
threshold (string or float) – The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median”, then the threshold value is the median of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. Defaults to -np.inf.
n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “percent_features”: Real(0.01, 1), “threshold”: [“mean”, “median”],}
modifies_features
True
modifies_target
False
name
RF Classifier Select From Model
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fit and transform data using the feature selector.
Get names of selected features.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)#
Fit and transform data using the feature selector.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- get_names(self)#
Get names of selected features.
- Returns
List of the names of features selected.
- Return type
list[str]
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data. Ignored.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If feature selector does not have a transform method or a component_obj that implements transform
- class evalml.pipelines.components.transformers.RFRegressorSelectFromModel(number_features=None, n_estimators=10, max_depth=None, percent_features=0.5, threshold='median', n_jobs=- 1, random_seed=0, **kwargs)[source]#
Selects top features based on importance weights using a Random Forest regressor.
- Parameters
number_features (int) – The maximum number of features to select. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5. Defaults to None.
n_estimators (float) – The number of trees in the forest. Defaults to 100.
max_depth (int) – Maximum tree depth for base learners. Defaults to 6.
percent_features (float) – Percentage of features to use. If both percent_features and number_features are specified, take the greater number of features. Defaults to 0.5.
threshold (string or float) – The threshold value to use for feature selection. Features whose importance is greater or equal are kept while the others are discarded. If “median”, then the threshold value is the median of the feature importances. A scaling factor (e.g., “1.25*mean”) may also be used. Defaults to -np.inf.
n_jobs (int or None) – Number of jobs to run in parallel. -1 uses all processes. Defaults to -1.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “percent_features”: Real(0.01, 1), “threshold”: [“mean”, “median”],}
modifies_features
True
modifies_target
False
name
RF Regressor Select From Model
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fit and transform data using the feature selector.
Get names of selected features.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)#
Fit and transform data using the feature selector.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- get_names(self)#
Get names of selected features.
- Returns
List of the names of features selected.
- Return type
list[str]
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transforms input data by selecting features. If the component_obj does not have a transform method, will raise an MethodPropertyNotFoundError exception.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data. Ignored.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If feature selector does not have a transform method or a component_obj that implements transform
- class evalml.pipelines.components.transformers.SelectByType(column_types=None, exclude=False, random_seed=0, **kwargs)[source]#
Selects columns by specified Woodwork logical type or semantic tag in input data.
- Parameters
column_types (string, ww.LogicalType, list(string), list(ww.LogicalType)) – List of Woodwork types or tags, used to determine which columns to select or exclude.
exclude (bool) – If true, exclude the column_types instead of including them. Defaults to False.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Select Columns By Type Transformer
needs_fitting
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the transformer by checking if column names are present in the dataset.
Fits on X and transforms X.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by selecting columns.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, ignored) – Targets.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.SelectColumns(columns=None, random_seed=0, **kwargs)[source]#
Selects specified columns in input data.
- Parameters
columns (list(string)) – List of column names, used to determine which columns to select. If columns are not present, they will not be selected.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Select Columns Transformer
needs_fitting
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the transformer by checking if column names are present in the dataset.
Fits on X and transforms X.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform data using fitted column selector component.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the transformer by checking if column names are present in the dataset.
- Parameters
X (pd.DataFrame) – Data to check.
y (pd.Series, optional) – Targets.
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transform data using fitted column selector component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.SimpleImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)[source]#
Imputes missing data according to a specified imputation strategy. Natural language columns are ignored.
- Parameters
impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types.
fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to 0 when imputing numerical data and “missing_value” for strings or object data types.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
modifies_features
True
modifies_target
False
name
Simple Imputer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputer to data. 'None' values are converted to np.nan before imputation and are treated as the same.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input by imputing missing values. 'None' and np.nan values are treated as the same.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits imputer to data. ‘None’ values are converted to np.nan before imputation and are treated as the same.
- Parameters
X (pd.DataFrame or np.ndarray) – the input training data of shape [n_samples, n_features]
y (pd.Series, optional) – the target training data of length [n_samples]
- Returns
self
- Raises
ValueError – if the SimpleImputer receives a dataframe with both Boolean and Categorical data.
- fit_transform(self, X, y=None)[source]#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform
y (pd.Series, optional) – Target data.
- Returns
Transformed X
- Return type
pd.DataFrame
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.StandardScaler(random_seed=0, **kwargs)[source]#
A transformer that standardizes input features by removing the mean and scaling to unit variance.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Standard Scaler
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the standard scalar on the given data.
Fit and transform data using the standard scaler component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform data using the fitted standard scaler.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the standard scalar on the given data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y=None)[source]#
Fit and transform data using the standard scaler component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.STLDecomposer(time_index: str = None, degree: int = 1, seasonal_period: int = 7, random_seed: int = 0, **kwargs)[source]#
Removes trends and seasonality from time series using the STL algorithm.
https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html
- Parameters
time_index (str) – Specifies the name of the column in X that provides the datetime objects. Defaults to None.
degree (int) – Not currently used. STL 3x “degree-like” values. None are able to be set at this time. Defaults to 1.
seasonal_period (int) – The number of entries in the time series data that corresponds to one period of a cyclic signal. For instance, if data is known to possess a weekly seasonal signal, and if the data is daily data, seasonal_period should be 7. For daily data with a yearly seasonal signal, seasonal_period should be 365. For compatibility with the underlying STL algorithm, must be odd. If an even number is provided, the next, highest odd number will be used. Defaults to 7.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
None
modifies_features
False
modifies_target
True
name
STL Decomposer
needs_fitting
True
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
Fits the STLDecomposer and determine the seasonal signal.
Removes fitted trend and seasonality from target variable.
Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.
Adds back fitted trend and seasonality to target variable.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Plots the decomposition of the target signal.
Saves component at file path.
Function to set the component's seasonal period based on the target's seasonality.
Transforms the target data by removing the STL trend and seasonality.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- determine_periodicity(self, X: pandas.DataFrame, y: pandas.Series, method: str = 'autocorrelation')#
Function that uses autocorrelative methods to determine the first, signficant period of the seasonal signal.
- Parameters
X (pandas.DataFrame) – The feature data of the time series problem.
y (pandas.Series) – The target data of a time series problem.
method (str) – Either “autocorrelation” or “partial-autocorrelation”. The method by which to determine the first period of the seasonal part of the target signal. “partial-autocorrelation” should currently not be used. Defaults to “autocorrelation”.
- Returns
- The integer numbers of entries in time series data over which the seasonal part of the target data
repeats. If the time series data is in days, then this is the number of days that it takes the target’s seasonal signal to repeat. Note: the target data can contain multiple seasonal signals. This function will only return the first, and thus, shortest period. E.g. if the target has both weekly and yearly seasonality, the function will only return “7” and not return “365”. If no period is detected, returns [None].
- Return type
(list[int])
- fit(self, X: pandas.DataFrame, y: pandas.Series = None) STLDecomposer[source]#
Fits the STLDecomposer and determine the seasonal signal.
Instantiates a statsmodels STL decompose object with the component’s stored parameters and fits it. Since the statsmodels object does not fit the sklearn api, it is not saved during __init__() in _component_obj and will be re-instantiated each time fit is called.
To emulate the sklearn API, when the STL decomposer is fit, the full seasonal component, a single period sample of the seasonal component, the full trend-cycle component and the residual are saved.
y(t) = S(t) + T(t) + R(t)
- Parameters
X (pd.DataFrame, optional) – Conditionally used to build datetime index.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
self
- Raises
ValueError – If y is None.
ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data
- fit_transform(self, X: pandas.DataFrame, y: pandas.Series = None) tuple[pandas.DataFrame, pandas.Series]#
Removes fitted trend and seasonality from target variable.
- Parameters
X (pd.DataFrame, optional) – Ignored.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the fitted trend removed.
- Return type
tuple of pd.DataFrame, pd.Series
- get_trend_dataframe(self, X, y)[source]#
Return a list of dataframes with 4 columns: signal, trend, seasonality, residual.
- Parameters
X (pd.DataFrame) – Input data with time series data in index.
y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.
- Returns
- Each DataFrame contains the columns “signal”, “trend”, “seasonality” and “residual,”
with the latter 3 column values being the decomposed elements of the target data. The “signal” column is simply the input target signal but reindexed with a datetime index to match the input features.
- Return type
list of pd.DataFrame
- Raises
TypeError – If X does not have time-series data in the index.
ValueError – If time series index of X does not have an inferred frequency.
ValueError – If the forecaster associated with the detrender has not been fit yet.
TypeError – If y is not provided as a pandas Series or DataFrame.
- inverse_transform(self, y_t: pandas.Series) tuple[pandas.DataFrame, pandas.Series][source]#
Adds back fitted trend and seasonality to target variable.
The STL trend is projected to cover the entire requested target range, then added back into the signal. Then, the seasonality is projected forward to and added back into the signal.
- Parameters
y_t (pd.Series) – Target variable.
- Returns
- The first element are the input features returned without modification.
The second element is the target variable y with the trend and seasonality added back in.
- Return type
tuple of pd.DataFrame, pd.Series
- Raises
ValueError – If y is None.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- plot_decomposition(self, X: pandas.DataFrame, y: pandas.Series, show: bool = False) tuple[matplotlib.pyplot.Figure, list]#
Plots the decomposition of the target signal.
- Parameters
X (pd.DataFrame) – Input data with time series data in index.
y (pd.Series or pd.DataFrame) – Target variable data provided as a Series for univariate problems or a DataFrame for multivariate problems.
show (bool) – Whether to display the plot or not. Defaults to False.
- Returns
- The figure and axes that have the decompositions
plotted on them
- Return type
matplotlib.pyplot.Figure, list[matplotlib.pyplot.Axes]
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- set_seasonal_period(self, X: pandas.DataFrame, y: pandas.Series)#
Function to set the component’s seasonal period based on the target’s seasonality.
- Parameters
X (pandas.DataFrame) – The feature data of the time series problem.
y (pandas.Series) – The target data of a time series problem.
- transform(self, X: pandas.DataFrame, y: pandas.Series = None) tuple[pandas.DataFrame, pandas.Series][source]#
Transforms the target data by removing the STL trend and seasonality.
Uses an ARIMA model to project forward the addititve trend and removes it. Then, utilizes the first period’s worth of seasonal data determined in the .fit() function to extrapolate the seasonal signal of the data to be transformed. This seasonal signal is also assumed to be additive and is removed.
- Parameters
X (pd.DataFrame, optional) – Conditionally used to build datetime index.
y (pd.Series) – Target variable to detrend and deseasonalize.
- Returns
- The input features are returned without modification. The target
variable y is detrended and deseasonalized.
- Return type
tuple of pd.DataFrame, pd.Series
- Raises
ValueError – If target data doesn’t have DatetimeIndex AND no Datetime features in features data
- class evalml.pipelines.components.transformers.TargetEncoder(cols=None, smoothing=1, handle_unknown='value', handle_missing='value', random_seed=0, **kwargs)[source]#
A transformer that encodes categorical features into target encodings.
- Parameters
cols (list) – Columns to encode. If None, all string columns will be encoded, otherwise only the columns provided will be encoded. Defaults to None
smoothing (float) – The smoothing factor to apply. The larger this value is, the more influence the expected target value has on the resulting target encodings. Must be strictly larger than 0. Defaults to 1.0
handle_unknown (string) – Determines how to handle unknown categories for a feature encountered. Options are ‘value’, ‘error’, nd ‘return_nan’. Defaults to ‘value’, which replaces with the target mean
handle_missing (string) – Determines how to handle missing values encountered during fit or transform. Options are ‘value’, ‘error’, and ‘return_nan’. Defaults to ‘value’, which replaces with the target mean
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
Target Encoder
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the target encoder.
Fit and transform data using the target encoder.
Return feature names for the input features after fitting.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transform data using the fitted target encoder.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)[source]#
Fits the target encoder.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- fit_transform(self, X, y)[source]#
Fit and transform data using the target encoder.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
pd.DataFrame
- get_feature_names(self)[source]#
Return feature names for the input features after fitting.
- Returns
The feature names after encoding.
- Return type
np.array
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.TargetImputer(impute_strategy='most_frequent', fill_value=None, random_seed=0, **kwargs)[source]#
Imputes missing target data according to a specified imputation strategy.
- Parameters
impute_strategy (string) – Impute strategy to use. Valid values include “mean”, “median”, “most_frequent”, “constant” for numerical data, and “most_frequent”, “constant” for object data types. Defaults to “most_frequent”.
fill_value (string) – When impute_strategy == “constant”, fill_value is used to replace missing data. Defaults to None which uses 0 when imputing numerical data and “missing_value” for strings or object data types.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “impute_strategy”: [“mean”, “median”, “most_frequent”]}
modifies_features
False
modifies_target
True
name
Target Imputer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputer to target data. 'None' values are converted to np.nan before imputation and are treated as the same.
Fits on and transforms the input target data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms input target data by imputing missing values. 'None' and np.nan values are treated as the same.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)[source]#
Fits imputer to target data. ‘None’ values are converted to np.nan before imputation and are treated as the same.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]. Ignored.
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
TypeError – If target is filled with all null values.
- fit_transform(self, X, y)[source]#
Fits on and transforms the input target data.
- Parameters
X (pd.DataFrame) – Features. Ignored.
y (pd.Series) – Target data to impute.
- Returns
The original X, transformed y
- Return type
(pd.DataFrame, pd.Series)
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y)[source]#
Transforms input target data by imputing missing values. ‘None’ and np.nan values are treated as the same.
- Parameters
X (pd.DataFrame) – Features. Ignored.
y (pd.Series) – Target data to impute.
- Returns
The original X, transformed y
- Return type
(pd.DataFrame, pd.Series)
- class evalml.pipelines.components.transformers.TimeSeriesFeaturizer(time_index=None, max_delay=2, gap=0, forecast_horizon=1, conf_level=0.05, rolling_window_size=0.25, delay_features=True, delay_target=True, random_seed=0, **kwargs)[source]#
Transformer that delays input features and target variable for time series problems.
This component uses an algorithm based on the autocorrelation values of the target variable to determine which lags to select from the set of all possible lags.
The algorithm is based on the idea that the local maxima of the autocorrelation function indicate the lags that have the most impact on the present time.
The algorithm computes the autocorrelation values and finds the local maxima, called “peaks”, that are significant at the given conf_level. Since lags in the range [0, 10] tend to be predictive but not local maxima, the union of the peaks is taken with the significant lags in the range [0, 10]. At the end, only selected lags in the range [0, max_delay] are used.
Parametrizing the algorithm by conf_level lets the AutoMLAlgorithm tune the set of lags chosen so that the chances of finding a good set of lags is higher.
Using conf_level value of 1 selects all possible lags.
- Parameters
time_index (str) – Name of the column containing the datetime information used to order the data. Ignored.
max_delay (int) – Maximum number of time units to delay each feature. Defaults to 2.
forecast_horizon (int) – The number of time periods the pipeline is expected to forecast.
conf_level (float) – Float in range (0, 1] that determines the confidence interval size used to select which lags to compute from the set of [1, max_delay]. A delay of 1 will always be computed. If 1, selects all possible lags in the set of [1, max_delay], inclusive.
rolling_window_size (float) – Float in range (0, 1] that determines the size of the window used for rolling features. Size is computed as rolling_window_size * max_delay.
delay_features (bool) – Whether to delay the input features. Defaults to True.
delay_target (bool) – Whether to delay the target. Defaults to True.
gap (int) – The number of time units between when the features are collected and when the target is collected. For example, if you are predicting the next time step’s target, gap=1. This is only needed because when gap=0, we need to be sure to start the lagging of the target variable at 1. Defaults to 1.
random_seed (int) – Seed for the random number generator. This transformer performs the same regardless of the random seed provided.
Attributes
hyperparameter_ranges
Real(0.001, 1.0), “rolling_window_size”: Real(0.001, 1.0)}:type: {“conf_level”
modifies_features
True
modifies_target
False
name
Time Series Featurizer
needs_fitting
True
target_colname_prefix
target_delay_{}
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the DelayFeatureTransformer.
Fit the component and transform the input data.
Loads component at file path.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Computes the delayed values and rolling means for X and y.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the DelayFeatureTransformer.
- Parameters
X (pd.DataFrame or np.ndarray) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
ValueError – if self.time_index is None
- fit_transform(self, X, y=None)[source]#
Fit the component and transform the input data.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, or None) – Target.
- Returns
Transformed X.
- Return type
pd.DataFrame
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Computes the delayed values and rolling means for X and y.
The chosen delays are determined by the autocorrelation function of the target variable. See the class docstring for more information on how they are chosen. If y is None, all possible lags are chosen.
If y is not None, it will also compute the delayed values for the target variable.
The rolling means for all numeric features in X and y, if y is numeric, are also returned.
- Parameters
X (pd.DataFrame or None) – Data to transform. None is expected when only the target variable is being used.
y (pd.Series, or None) – Target.
- Returns
Transformed X. No original features are returned.
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.TimeSeriesImputer(categorical_impute_strategy='forwards_fill', numeric_impute_strategy='interpolate', target_impute_strategy='forwards_fill', random_seed=0, **kwargs)[source]#
Imputes missing data according to a specified timeseries-specific imputation strategy.
This Transformer should be used after the TimeSeriesRegularizer in order to impute the missing values that were added to X and y (if passed).
- Parameters
categorical_impute_strategy (string) – Impute strategy to use for string, object, boolean, categorical dtypes. Valid values include “backwards_fill” and “forwards_fill”. Defaults to “forwards_fill”.
numeric_impute_strategy (string) – Impute strategy to use for numeric columns. Valid values include “backwards_fill”, “forwards_fill”, and “interpolate”. Defaults to “interpolate”.
target_impute_strategy (string) – Impute strategy to use for the target column. Valid values include “backwards_fill”, “forwards_fill”, and “interpolate”. Defaults to “forwards_fill”.
random_seed (int) – Seed for the random number generator. Defaults to 0.
- Raises
ValueError – If categorical_impute_strategy, numeric_impute_strategy, or target_impute_strategy is not one of the valid values.
Attributes
hyperparameter_ranges
{ “categorical_impute_strategy”: [“backwards_fill”, “forwards_fill”], “numeric_impute_strategy”: [“backwards_fill”, “forwards_fill”, “interpolate”], “target_impute_strategy”: [“backwards_fill”, “forwards_fill”, “interpolate”],}
modifies_features
True
modifies_target
True
name
Time Series Imputer
training_only
True
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits imputer to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X by imputing missing values using specified timeseries-specific strategies. 'None' values are converted to np.nan before imputation and are treated as the same.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits imputer to data.
‘None’ values are converted to np.nan before imputation and are treated as the same. If a value is missing at the beginning or end of a column, that value will be imputed using backwards fill or forwards fill as necessary, respectively.
- Parameters
X (pd.DataFrame, np.ndarray) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Transforms data X by imputing missing values using specified timeseries-specific strategies. ‘None’ values are converted to np.nan before imputation and are treated as the same.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Optionally, target data to transform.
- Returns
Transformed X and y
- Return type
pd.DataFrame
- class evalml.pipelines.components.transformers.TimeSeriesRegularizer(time_index=None, frequency_payload=None, window_length=4, threshold=0.4, random_seed=0, **kwargs)[source]#
Transformer that regularizes an inconsistently spaced datetime column.
If X is passed in to fit/transform, the column time_index will be checked for an inferrable offset frequency. If the time_index column is perfectly inferrable then this Transformer will do nothing and return the original X and y.
If X does not have a perfectly inferrable frequency but one can be estimated, then X and y will be reformatted based on the estimated frequency for time_index. In the original X and y passed: - Missing datetime values will be added and will have their corresponding columns in X and y set to None. - Duplicate datetime values will be dropped. - Extra datetime values will be dropped. - If it can be determined that a duplicate or extra value is misaligned, then it will be repositioned to take the place of a missing value.
This Transformer should be used before the TimeSeriesImputer in order to impute the missing values that were added to X and y (if passed).
- Parameters
time_index (string) – Name of the column containing the datetime information used to order the data, required. Defaults to None.
frequency_payload (tuple) – Payload returned from Woodwork’s infer_frequency function where debug is True. Defaults to None.
window_length (int) – The size of the rolling window over which inference is conducted to determine the prevalence of uninferrable frequencies.
5. (Lower values make this component more sensitive to recognizing numerous faulty datetime values. Defaults to) –
threshold (float) – The minimum percentage of windows that need to have been able to infer a frequency. Lower values make this component more
0.8. (sensitive to recognizing numerous faulty datetime values. Defaults to) –
random_seed (int) – Seed for the random number generator. This transformer performs the same regardless of the random seed provided.
0. (Defaults to) –
- Raises
ValueError – if the frequency_payload parameter has not been passed a tuple
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Time Series Regularizer
training_only
True
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the TimeSeriesRegularizer.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Regularizes a dataframe and target data to an inferrable offset frequency.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)[source]#
Fits the TimeSeriesRegularizer.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – if self.time_index is None, if X and y have different lengths, if time_index in X does not have an offset frequency that can be estimated
TypeError – if the time_index column is not of type Datetime
KeyError – if the time_index column doesn’t exist
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)[source]#
Regularizes a dataframe and target data to an inferrable offset frequency.
A ‘clean’ X and y (if y was passed in) are created based on an inferrable offset frequency and matching datetime values with the original X and y are imputed into the clean X and y. Datetime values identified as misaligned are shifted into their appropriate position.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Data with an inferrable time_index offset frequency.
- Return type
(pd.DataFrame, pd.Series)
- class evalml.pipelines.components.transformers.Transformer(parameters=None, component_obj=None, random_seed=0, **kwargs)[source]#
A component that may or may not need fitting that transforms data. These components are used before an estimator.
To implement a new Transformer, define your own class which is a subclass of Transformer, including a name and a list of acceptable ranges for any parameters to be tuned during the automl search (hyperparameters). Define an __init__ method which sets up any necessary state and objects. Make sure your __init__ only uses standard keyword arguments and calls super().__init__() with a parameters dict. You may also override the fit, transform, fit_transform and other methods in this class if appropriate.
To see some examples, check out the definitions of any Transformer component.
- Parameters
parameters (dict) – Dictionary of parameters for the component. Defaults to None.
component_obj (obj) – Third-party objects useful in component implementation. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
modifies_features
True
modifies_target
False
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns string name of this component.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)[source]#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- property name(cls)#
Returns string name of this component.
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- abstract transform(self, X, y=None)[source]#
Transforms data X.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- class evalml.pipelines.components.transformers.Undersampler(sampling_ratio=0.25, sampling_ratio_dict=None, min_samples=100, min_percentage=0.1, random_seed=0, **kwargs)[source]#
Initializes an undersampling transformer to downsample the majority classes in the dataset.
This component is only run during training and not during predict.
- Parameters
sampling_ratio (float) – The smallest minority:majority ratio that is accepted as ‘balanced’. For instance, a 1:4 ratio would be represented as 0.25, while a 1:1 ratio is 1.0. Must be between 0 and 1, inclusive. Defaults to 0.25.
sampling_ratio_dict (dict) – A dictionary specifying the desired balanced ratio for each target value. For instance, in a binary case where class 1 is the minority, we could specify: sampling_ratio_dict={0: 0.5, 1: 1}, which means we would undersample class 0 to have twice the number of samples as class 1 (minority:majority ratio = 0.5), and don’t sample class 1. Overrides sampling_ratio if provided. Defaults to None.
min_samples (int) – The minimum number of samples that we must have for any class, pre or post sampling. If a class must be downsampled, it will not be downsampled past this value. To determine severe imbalance, the minority class must occur less often than this and must have a class ratio below min_percentage. Must be greater than 0. Defaults to 100.
min_percentage (float) – The minimum percentage of the minimum class to total dataset that we tolerate, as long as it is above min_samples. If min_percentage and min_samples are not met, treat this as severely imbalanced, and we will not resample the data. Must be between 0 and 0.5, inclusive. Defaults to 0.1.
random_seed (int) – The seed to use for random sampling. Defaults to 0.
- Raises
ValueError – If sampling_ratio is not in the range (0, 1].
ValueError – If min_sample is not greater than 0.
ValueError – If min_percentage is not between 0 and 0.5, inclusive.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
True
name
Undersampler
training_only
True
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits the sampler to the data.
Resampling technique for this sampler.
Fit and transform data using the sampler component.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms the input data by sampling the data.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y)#
Fits the sampler to the data.
- Parameters
X (pd.DataFrame) – Input features.
y (pd.Series) – Target.
- Returns
self
- Raises
ValueError – If y is None.
- fit_resample(self, X, y)[source]#
Resampling technique for this sampler.
- Parameters
X (pd.DataFrame) – Training data to fit and resample.
y (pd.Series) – Training data targets to fit and resample.
- Returns
Indices to keep for training data.
- Return type
list
- fit_transform(self, X, y)#
Fit and transform data using the sampler component.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
Transformed data.
- Return type
(pd.DataFrame, pd.Series)
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- class evalml.pipelines.components.transformers.URLFeaturizer(random_seed=0, **kwargs)[source]#
Transformer that can automatically extract features from URL.
- Parameters
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
modifies_features
True
modifies_target
False
name
URL Featurizer
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Fits component to data.
Fits on X and transforms X.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Saves component at file path.
Transforms data X.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- fit(self, X, y=None)#
Fits component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features]
y (pd.Series, optional) – The target training data of length [n_samples]
- Returns
self
- Raises
MethodPropertyNotFoundError – If component does not have a fit method or a component_obj that implements fit.
- fit_transform(self, X, y=None)#
Fits on X and transforms X.
- Parameters
X (pd.DataFrame) – Data to fit and transform.
y (pd.Series) – Target data.
- Returns
Transformed X.
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.
- transform(self, X, y=None)#
Transforms data X.
- Parameters
X (pd.DataFrame) – Data to transform.
y (pd.Series, optional) – Target data.
- Returns
Transformed X
- Return type
pd.DataFrame
- Raises
MethodPropertyNotFoundError – If transformer does not have a transform method or a component_obj that implements transform.