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