from abc import abstractmethod
from evalml.exceptions import MethodPropertyNotFoundError
from evalml.model_family import ModelFamily
from evalml.pipelines.components import ComponentBase
from evalml.utils import infer_feature_types
[docs]class Estimator(ComponentBase):
"""A component that fits and predicts given data.
To implement a new Estimator, define your own class which is a subclass of Estimator, 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 Estimator component.
"""
# We can't use the inspect module to dynamically determine this because of issue 1582
predict_uses_y = False
model_family = ModelFamily.NONE
@property
@classmethod
@abstractmethod
def supported_problem_types(cls):
"""Problem types this estimator supports"""
[docs] def __init__(self, parameters=None, component_obj=None, random_seed=0, **kwargs):
self.input_feature_names = None
super().__init__(parameters=parameters, component_obj=component_obj, random_seed=random_seed, **kwargs)
def _manage_woodwork(self, X, y=None):
"""Function to convert the input and target data to Pandas data structures."""
if X is not None:
X = infer_feature_types(X)
if y is not None:
y = infer_feature_types(y)
return X, y
[docs] def fit(self, X, y=None):
X, y = self._manage_woodwork(X, y)
self.input_feature_names = list(X.columns)
self._component_obj.fit(X, y)
return self
[docs] def predict(self, X):
"""Make predictions using selected features.
Arguments:
X (pd.DataFrame, np.ndarray): Data of shape [n_samples, n_features]
Returns:
pd.Series: Predicted values
"""
try:
X = infer_feature_types(X)
predictions = self._component_obj.predict(X)
except AttributeError:
raise MethodPropertyNotFoundError("Estimator requires a predict method or a component_obj that implements predict")
return infer_feature_types(predictions)
[docs] def predict_proba(self, X):
"""Make probability estimates for labels.
Arguments:
X (pd.DataFrame, or np.ndarray): Features
Returns:
pd.Series: Probability estimates
"""
try:
X = infer_feature_types(X)
pred_proba = self._component_obj.predict_proba(X)
except AttributeError:
raise MethodPropertyNotFoundError("Estimator requires a predict_proba method or a component_obj that implements predict_proba")
return infer_feature_types(pred_proba)
@property
def feature_importance(self):
"""Returns importance associated with each feature.
Returns:
np.ndarray: Importance associated with each feature
"""
try:
return self._component_obj.feature_importances_
except AttributeError:
raise MethodPropertyNotFoundError("Estimator requires a feature_importance property or a component_obj that implements feature_importances_")
def __eq__(self, other):
return super().__eq__(other) and self.supported_problem_types == other.supported_problem_types