def score(X: str, y: str) -> float: """ Return the coefficient of determination R^2 of the prediction. :param X: Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. :type X: array :param y: True values for X. :type y: array :param sample_weight: Sample weights. :type sample_weight: array :param return: R^2 of self.predict(X) wrt. y. :type return: float """ X_input = "~/.cloudmesh/upload-file/" + f"{X}" + ".csv" y_input = "~/.cloudmesh/upload-file/" + f"{y}" + ".csv" X = pd.read_csv(X_input) y = pd.read_csv(y_input) model = ResultCache().load("Linregnew") float = model.score(X, y) return float
def score(X: array, y: array, sample_weight: array) -> float: """ Return the mean accuracy on the given test data and labels. :param X: Test samples. :type X: array :param y: True labels for X. :type y: array :param sample_weight: Sample weights. :type sample_weight: array :param return: Mean accuracy of self.predict(X) wrt. y. :type return: float """ model = ResultCache().load("RidgeClassifier") float = model.score(X, y, sample_weight) return float
def score(X: array, y: array, sample_weight: array) -> float: """ Return the coefficient of determination R^2 of the prediction. :param X: Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator. :type X: array :param y: True values for X. :type y: array :param sample_weight: Sample weights. :type sample_weight: array :param return: R^2 of self.predict(X) wrt. y. :type return: float """ model = ResultCache().load("RidgeCV") float = model.score(X, y, sample_weight) return float