"""A component that fits and predicts given data."""
from abc import abstractmethod
from pandas.core.indexes import range
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 subclass.
Args:
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.
"""
# We can't use the inspect module to dynamically determine this because of issue 1582
predict_uses_y = False
model_family = ModelFamily.NONE
"""ModelFamily.NONE"""
modifies_features = True
modifies_target = False
training_only = False
@property
@classmethod
@abstractmethod
def model_family(cls):
"""Returns ModelFamily of this component."""
@property
@classmethod
@abstractmethod
def supported_problem_types(cls):
"""Problem types this estimator supports."""
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):
"""Fits estimator to data.
Args:
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
"""
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.
Args:
X (pd.DataFrame): Data of shape [n_samples, n_features].
Returns:
pd.Series: Predicted values.
Raises:
MethodPropertyNotFoundError: If estimator does not have a predict method or a component_obj that implements predict.
"""
try:
X = infer_feature_types(X)
if isinstance(X.columns, range.RangeIndex):
X.columns = [x for x in X.columns]
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.
Args:
X (pd.DataFrame): Features.
Returns:
pd.Series: Probability estimates.
Raises:
MethodPropertyNotFoundError: If estimator does not have a predict_proba method or a component_obj that implements predict_proba.
"""
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.
Raises:
MethodPropertyNotFoundError: If estimator does not have a feature_importance method or a component_obj that implements feature_importance.
"""
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):
"""Check for equality."""
return (
super().__eq__(other)
and self.supported_problem_types == other.supported_problem_types
)