from abc import ABC, abstractmethod
from evalml.exceptions import MethodPropertyNotFoundError
from evalml.utils import get_logger, get_random_state, log_subtitle
logger = get_logger(__file__)
[docs]class ComponentBase(ABC):
"Base class for all components"
[docs] def __init__(self, parameters, component_obj, random_state):
self.random_state = get_random_state(random_state)
self._component_obj = component_obj
self.parameters = parameters
@property
@classmethod
@abstractmethod
def name(cls):
"""Returns string name of this component"""
@property
@classmethod
@abstractmethod
def model_family(cls):
"""Returns ModelFamily of this component"""
[docs] def fit(self, X, y=None):
"""Fits component to data
Arguments:
X (pd.DataFrame or np.array): the input training data of shape [n_samples, n_features]
y (pd.Series, optional): the target training labels of length [n_samples]
Returns:
self
"""
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