text_featurizer¶
Module Contents¶
Classes Summary¶
Transformer that can automatically featurize text columns using featuretools’ nlp_primitives. |
Contents¶
-
class
evalml.pipelines.components.transformers.preprocessing.text_featurizer.TextFeaturizer(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, and LSA (Latent Semantic Analysis). 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
{}
model_family
ModelFamily.NONE
modifies_features
True
modifies_target
False
name
Text Featurization Component
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
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
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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.
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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
prints and returns dictionary
- Return type
None or dict
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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, np.ndarray, optional) – The target training data of length [n_samples]
- Returns
self
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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
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static
load(file_path)¶ Loads component at file path
- Parameters
file_path (str) – Location to load file
- Returns
ComponentBase object
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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.
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property
parameters(self)¶ Returns the parameters which were used to initialize the component
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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.
- Returns
None