def predict(self, X):
"""Execute the synthesized push program on a dataset.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The set of cases to predict.
verbose : bool, optional
Indicates if verbose printing should be used during searching.
Default is False.
Returns
-------
y_hat : pandas dataframe of shape = [n_samples, n_outputs]
"""
check_is_fitted(self, "_result")
return [
self.interpreter.run(
self._result.program,
inputs,
self._result.output_types,
verbosity_config=self.search.config.verbosity_config)
for inputs in X
]
def save(self, filepath: str):
"""Load the found solution to a JSON file.
Parameters
----------
filepath
Filepath to write the serialized search result to.
"""
check_is_fitted(self, "solution")
self.solution.save(filepath)
def save(self, filepath: str):
"""Load the found solution to a JSON file.
Parameters
----------
filepath
Filepath to write the serialized search result to.
"""
check_is_fitted(self, "_result")
self._result.to_json(filepath)
def score(self, X, y):
"""Run the search algorithm to synthesize a push program.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : list, array-like, or pandas dataframe.
The target values (class labels in classification, real numbers in
regression). Shape = [n_samples] or [n_samples, n_outputs]
"""
check_is_fitted(self, "solution")
X, y, arity, y_types = check_X_y(X, y)
self.evaluator = DatasetEvaluator(X, y, interpreter=self.interpreter)
return self.evaluator.evaluate(self.solution.program)
def predict(self, X):
"""Execute the synthesized push program on a dataset.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The set of cases to predict.
Returns
-------
y_hat : pandas dataframe of shape = [n_samples, n_outputs]
"""
check_is_fitted(self, "solution")
return [
self.interpreter.run(self.solution.program, inputs) for inputs in X
]
def score(self, X, y):
"""Run the search algorithm to synthesize a push program.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The training input samples.
y : list, array-like, or pandas dataframe.
The target values (class labels in classification, real numbers in
regression). Shape = [n_samples] or [n_samples, n_outputs]
"""
check_is_fitted(self, "_result")
X, y, arity, y_types = check_X_y(X, y)
self.evaluator = DatasetEvaluator(X, y, interpreter=self.interpreter)
return self.evaluator.evaluate(self._result.program)
def predict(self, X):
"""Execute the synthesized push program on a dataset.
Parameters
----------
X : pandas dataframe of shape = [n_samples, n_features]
The set of cases to predict.
verbose : bool, optional
Indicates if verbose printing should be used during searching.
Default is False.
Returns
-------
y_hat : pandas dataframe of shape = [n_samples, n_outputs]
"""
check_is_fitted(self, "_result")
return [
self.interpreter.run(
self._result.program,
inputs,
self._result.output_types
) for inputs in X
]