# utils¶

Utility methods for EvalML components.

## Module Contents¶

### Classes Summary¶

 WrappedSKClassifier Scikit-learn classifier wrapper class. WrappedSKRegressor Scikit-learn regressor wrapper class.

### Functions¶

 all_components Get all available components. allowed_model_families List the model types allowed for a particular problem type. generate_component_code Creates and returns a string that contains the Python imports and code required for running the EvalML component. get_estimators Returns the estimators allowed for a particular problem type. handle_component_class Standardizes input from a string name to a ComponentBase subclass if necessary. make_balancing_dictionary Makes dictionary for oversampler components. Find ratio of each class to the majority. If the ratio is smaller than the sampling_ratio, we want to oversample, otherwise, we don’t want to sample at all, and we leave the data as is. scikit_learn_wrapped_estimator Wraps an EvalML object as a scikit-learn estimator.

### Contents¶

evalml.pipelines.components.utils.all_components()[source]

Get all available components.

evalml.pipelines.components.utils.allowed_model_families(problem_type)[source]

List the model types allowed for a particular problem type.

Parameters

problem_type (ProblemTypes or str) – ProblemTypes enum or string.

Returns

A list of model families.

Return type

list[ModelFamily]

evalml.pipelines.components.utils.generate_component_code(element)[source]

Creates and returns a string that contains the Python imports and code required for running the EvalML component.

Parameters

element (component instance) – The instance of the component to generate string Python code for.

Returns

String representation of Python code that can be run separately in order to recreate the component instance. Does not include code for custom component implementation.

Raises

ValueError – If the input element is not a component instance.

evalml.pipelines.components.utils.get_estimators(problem_type, model_families=None)[source]

Returns the estimators allowed for a particular problem type.

Can also optionally filter by a list of model types.

Parameters
• problem_type (ProblemTypes or str) – Problem type to filter for.

• model_families (list[ModelFamily] or list[str]) – Model families to filter for.

Returns

A list of estimator subclasses.

Return type

list[class]

Raises
• TypeError – If the model_families parameter is not a list.

• RuntimeError – If a model family is not valid for the problem type.

evalml.pipelines.components.utils.handle_component_class(component_class)[source]

Standardizes input from a string name to a ComponentBase subclass if necessary.

If a str is provided, will attempt to look up a ComponentBase class by that name and return a new instance. Otherwise if a ComponentBase subclass or Component instance is provided, will return that without modification.

Parameters

component_class (str, ComponentBase) – Input to be standardized.

Returns

ComponentBase

Raises
• ValueError – If input is not a valid component class.

• MissingComponentError – If the component cannot be found.

evalml.pipelines.components.utils.make_balancing_dictionary(y, sampling_ratio)[source]

Makes dictionary for oversampler components. Find ratio of each class to the majority. If the ratio is smaller than the sampling_ratio, we want to oversample, otherwise, we don’t want to sample at all, and we leave the data as is.

Parameters
• y (pd.Series) – Target data.

• sampling_ratio (float) – The balanced ratio we want the samples to meet.

Returns

Dictionary where keys are the classes, and the corresponding values are the counts of samples for each class that will satisfy sampling_ratio.

Return type

dict

Raises

ValueError – If sampling ratio is not in the range (0, 1] or the target is empty.

evalml.pipelines.components.utils.scikit_learn_wrapped_estimator(evalml_obj)[source]

Wraps an EvalML object as a scikit-learn estimator.

class evalml.pipelines.components.utils.WrappedSKClassifier(pipeline)[source]

Scikit-learn classifier wrapper class.

Methods

 fit Fits component to data. get_params Get parameters for this estimator. predict Make predictions using selected features. predict_proba Make probability estimates for labels. score Return the mean accuracy on the given test data and labels. set_params Set the parameters of this estimator.
fit(self, X, y)[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, optional) – The target training data of length [n_samples].

Returns

self

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(self, X)[source]

Make predictions using selected features.

Parameters

X (pd.DataFrame) – Features

Returns

Predicted values.

Return type

np.ndarray

predict_proba(self, X)[source]

Make probability estimates for labels.

Parameters

X (pd.DataFrame) – Features.

Returns

Probability estimates.

Return type

np.ndarray

score(self, X, y, sample_weight=None)

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters
• X (array-like of shape (n_samples, n_features)) – Test samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True labels for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score – Mean accuracy of self.predict(X) wrt. y.

Return type

float

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

class evalml.pipelines.components.utils.WrappedSKRegressor(pipeline)[source]

Scikit-learn regressor wrapper class.

Methods

 fit Fits component to data. get_params Get parameters for this estimator. predict Make predictions using selected features. score Return the coefficient of determination $$R^2$$ of the set_params Set the parameters of this estimator.
fit(self, X, y)[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, optional) – the target training data of length [n_samples]

Returns

self

get_params(self, deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

predict(self, X)[source]

Make predictions using selected features.

Parameters

X (pd.DataFrame) – Features.

Returns

Predicted values.

Return type

np.ndarray

score(self, X, y, sample_weight=None)

Return the coefficient of determination $$R^2$$ of the prediction.

The coefficient $$R^2$$ is defined as $$(1 - \frac{u}{v})$$, where $$u$$ is the residual sum of squares ((y_true - y_pred) ** 2).sum() and $$v$$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $$R^2$$ score of 0.0.

Parameters
• X (array-like of shape (n_samples, n_features)) – Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs)) – True values for X.

• sample_weight (array-like of shape (n_samples,), default=None) – Sample weights.

Returns

score$$R^2$$ of self.predict(X) wrt. y.

Return type

float

Notes

The $$R^2$$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_params(self, **params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance