def score(tunings, dataset): df1, df2 = data.readDataset(dataset) # Training Set X_Train_DF = df1.ix[:, 3:23] X_Train = X_Train_DF.values.astype(float) Y_Train = np.asarray(list(df1["bug"])) # print X_Train # print Y_Train # Testing Set X_Test_DF = df2.ix[:, 3:23] X_Test = X_Test_DF.values.astype(float) Y_Test = np.asarray(list(df2["bug"])) # print X_Test # print Y_Test p = cart(X_Train, Y_Train, X_Test, Y_Test, tunings) return p
def score(candidate, datasets): df1, df2 = data.readDataset(datasets) # Training Set X_Train_DF = df1.ix[:, 3:23] X_Train = X_Train_DF.values.astype(float) Y_Train = np.asarray(list(df1["bug"])) # print X_Train # print Y_Train # Testing Set X_Test_DF = df2.ix[:, 3:23] X_Test = X_Test_DF.values.astype(float) Y_Test = np.asarray(list(df2["bug"])) # print X_Test # print Y_Test p = random_forest(X_Train, Y_Train, X_Test, Y_Test, candidate) return p