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 ]