import copy
from abc import ABC, abstractmethod
import cloudpickle
from evalml.exceptions import MethodPropertyNotFoundError
from evalml.pipelines.components.component_base_meta import ComponentBaseMeta
from evalml.utils import (
classproperty,
get_logger,
infer_feature_types,
log_subtitle,
safe_repr
)
logger = get_logger(__file__)
[docs]class ComponentBase(ABC, metaclass=ComponentBaseMeta):
"""Base class for all components."""
_default_parameters = None
[docs] def __init__(self, parameters=None, component_obj=None, random_seed=0, **kwargs):
self.random_seed = random_seed
self._component_obj = component_obj
self._parameters = parameters or {}
self._is_fitted = False
@property
@classmethod
@abstractmethod
def name(cls):
"""Returns string name of this component"""
@property
@classmethod
@abstractmethod
def model_family(cls):
"""Returns ModelFamily of this component"""
@classproperty
def 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."""
return True
@property
def parameters(self):
"""Returns the parameters which were used to initialize the component"""
return copy.copy(self._parameters)
@classproperty
def default_parameters(cls):
"""Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
Returns:
dict: default parameters for this component.
"""
if cls._default_parameters is None:
cls._default_parameters = cls().parameters
return cls._default_parameters
[docs] def 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.
"""
return self.__class__(**self.parameters, random_seed=self.random_seed)
[docs] def fit(self, X, y=None):
"""Fits component to data
Arguments:
X (list, pd.DataFrame or np.ndarray): The input training data of shape [n_samples, n_features]
y (list, pd.Series, np.ndarray, optional): The target training data of length [n_samples]
Returns:
self
"""
X = infer_feature_types(X)
if y is not None:
y = infer_feature_types(y)
try:
self._component_obj.fit(X, y)
return self
except AttributeError:
raise MethodPropertyNotFoundError("Component requires a fit method or a component_obj that implements fit")
[docs] def describe(self, print_name=False, return_dict=False):
"""Describe a component and its parameters
Arguments:
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:
None or dict: prints and returns dictionary
"""
if print_name:
title = self.name
log_subtitle(logger, title)
for parameter in self.parameters:
parameter_str = ("\t * {} : {}").format(parameter, self.parameters[parameter])
logger.info(parameter_str)
if return_dict:
component_dict = {"name": self.name}
component_dict.update({"parameters": self.parameters})
return component_dict
[docs] def save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL):
"""Saves component at file path
Arguments:
file_path (str): Location to save file
pickle_protocol (int): The pickle data stream format.
Returns:
None
"""
with open(file_path, 'wb') as f:
cloudpickle.dump(self, f, protocol=pickle_protocol)
[docs] @staticmethod
def load(file_path):
"""Loads component at file path
Arguments:
file_path (str): Location to load file
Returns:
ComponentBase object
"""
with open(file_path, 'rb') as f:
return cloudpickle.load(f)
def __eq__(self, other):
if not isinstance(other, self.__class__):
return False
random_seed_eq = self.random_seed == other.random_seed
if not random_seed_eq:
return False
attributes_to_check = ['_parameters', '_is_fitted']
for attribute in attributes_to_check:
if getattr(self, attribute) != getattr(other, attribute):
return False
return True
def __str__(self):
return self.name
def __repr__(self):
parameters_repr = ', '.join([f'{key}={safe_repr(value)}' for key, value in self.parameters.items()])
return f'{(type(self).__name__)}({parameters_repr})'