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from Packages import *
def metrics(train, cv=None, test = None):
C = pd.DataFrame()
CV = pd.DataFrame()
T = pd.DataFrame()
if train is not None and cv is not None and test is not None:
C["r"] = [np.corrcoef(train[0], train[1])[0, 1]]
C["r2"] = [r2_score(train[0], train[1])]
C["rmse"] = [np.sqrt(mean_squared_error(train[0], train[1]))]
C["mae"] = [mean_absolute_error(train[0], train[1])]
C.index = ['perf']
METRICS = C
CV["r"] = [np.corrcoef(cv[0], cv[1])[0, 1]]
CV["r2"] = [r2_score(cv[0], cv[1])]
CV["rmse"] = [np.sqrt(mean_squared_error(cv[0], cv[1]))]
CV["mae"] = [mean_absolute_error(cv[0], cv[1])]
CV.index = ['perf']
T["r"] = [np.corrcoef(test[0], test[1])[0, 1]]
T["r2"] = [r2_score(test[0], test[1])]
T["rmse"] = [np.sqrt(mean_squared_error(test[0], test[1]))]
T["mae"] = [mean_absolute_error(test[0], test[1])]
T.index = ['perf']
METRICS = pd.concat([C, CV, T], axis=1)
elif train is not None and cv is not None and test is None:
C["r"] = [np.corrcoef(train[0], train[1])[0, 1]]
C["r2"] = [r2_score(train[0], train[1])]
C["rmse"] = [np.sqrt(mean_squared_error(train[0], train[1]))]
C["mae"] = [mean_absolute_error(train[0], train[1])]
C.index = ['perf']
CV["r"] = [np.corrcoef(cv[0], cv[1])[0, 1]]
CV["r2"] = [r2_score(cv[0], cv[1])]
CV["rmse"] = [np.sqrt(mean_squared_error(cv[0], cv[1]))]
CV["mae"] = [mean_absolute_error(cv[0], cv[1])]
CV.index = ['perf']
METRICS = pd.concat([C, CV], axis=1)
elif train is not None and cv is None and test is not None:
C["r"] = [np.corrcoef(train[0], train[1])[0, 1]]
C["r2"] = [r2_score(train[0], train[1])]
C["rmse"] = [np.sqrt(mean_squared_error(train[0], train[1]))]
C["mae"] = [mean_absolute_error(train[0], train[1])]
C.index = ['perf']
T["r"] = [np.corrcoef(test[0], test[1])[0, 1]]
T["r2"] = [r2_score(test[0], test[1])]
T["rmse"] = [np.sqrt(mean_squared_error(test[0], test[1]))]
T["mae"] = [mean_absolute_error(test[0], test[1])]
T.index = ['perf']
METRICS = pd.concat([C, T], axis=1)
elif train is None and cv is not None and test is not None:
CV["r"] = [np.corrcoef(cv[0], cv[1])[0, 1]]
CV["r2"] = [r2_score(cv[0], cv[1])]
CV["rmse"] = [np.sqrt(mean_squared_error(cv[0], cv[1]))]
CV["mae"] = [mean_absolute_error(cv[0], cv[1])]
CV.index = ['perf']
T["r"] = [np.corrcoef(test[0], test[1])[0, 1]]
T["r2"] = [r2_score(test[0], test[1])]
T["rmse"] = [np.sqrt(mean_squared_error(test[0], test[1]))]
T["mae"] = [mean_absolute_error(test[0], test[1])]
T.index = ['perf']
METRICS = pd.concat([CV, T], axis=1)
elif train is not None and cv is None and test is None:
C["r"] = [np.corrcoef(train[0], train[1])[0, 1]]
C["r2"] = [r2_score(train[0], train[1])]
C["rmse"] = [np.sqrt(mean_squared_error(train[0], train[1]))]
C["mae"] = [mean_absolute_error(train[0], train[1])]
C.index = ['perf']
METRICS = C
if train is None and cv is not None and test is None:
CV["r"] = [np.corrcoef(cv[0], cv[1])[0, 1]]
CV["r2"] = [r2_score(cv[0], cv[1])]
CV["rmse"] = [np.sqrt(mean_squared_error(cv[0], cv[1]))]
CV["mae"] = [mean_absolute_error(cv[0], cv[1])]
CV.index = ['perf']
METRICS = CV
if train is None and cv is None and test is not None:
T["r"] = [np.corrcoef(test[0], test[1])[0, 1]]
T["r2"] = [r2_score(test[0], test[1])]
T["rmse"] = [np.sqrt(mean_squared_error(test[0], test[1]))]
T["mae"] = [mean_absolute_error(test[0], test[1])]
T.index = ['perf']
METRICS = T
return METRICS