from evalml.exceptions import MethodPropertyNotFoundError
from evalml.utils import Logger
class ComponentBase:
def __init__(self, parameters, component_obj, random_state):
self.random_state = random_state
self._component_obj = component_obj
self.parameters = parameters
self.logger = Logger()
attributes_to_check = ["name"]
for attribute in attributes_to_check:
if not hasattr(self, attribute):
raise AttributeError("Component missing attribute: `{}`".format(attribute))
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")
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
self.logger.log_subtitle(title)
for parameter in self.parameters:
parameter_str = ("\t * {} : {}").format(parameter, self.parameters[parameter])
self.logger.log(parameter_str)
if return_dict:
component_dict = {"name": self.name}
component_dict.update({"parameters": self.parameters})
return component_dict