class AllStateDataLoaderTest(unittest.TestCase): def setUp(self): self.data_loader = AllStateDataLoader() def testColumnsData2Train(self): self.data_2_train = self.data_loader.get_data_2_train() self.assertTrue("real_A" in self.data_2_train.columns) self.assertFalse("value_A_pt_2" in self.data_2_train.columns) self.assertFalse("value_A_pt_2_0" in self.data_2_train.columns) self.assertFalse("value_A_pt_3_0" in self.data_2_train.columns)
return np.array(np.where(tmp["real_%s" % letter] == value, 1, 0)) def get_y(letter, data): tmp = data.copy() return np.array(tmp["real_%s" % letter]) from sklearn import svm from sklearn.externals import joblib from sklearn import grid_search l = AllStateDataLoader() print("Extraction data_2...") data_2 = l.get_data_2_train() print("Extraction data_3...") data_3 = l.get_data_3_train() print("Extraction data_4...") data_4 = l.get_data_4_train() print("Extraction data_all...") data_all = l.get_data_all_train() def fit_and_save_log(parameters, dataset, letter, filename,verbose=2): log = svm.LinearSVC(class_weight="auto") X = get_X_without_scaler(dataset) y = get_y(letter, dataset) model = grid_search.GridSearchCV(log, parameters, verbose=verbose) model.fit(X,y)
def get_y(letter, data): tmp = data.copy() return np.array(tmp["real_%s" % letter]) from sklearn import linear_model from sklearn.externals import joblib from sklearn import grid_search l = AllStateDataLoader() print("Extraction data_2...") data_2 = l.get_data_2_train() print("Extraction data_3...") data_3 = l.get_data_3_train() print("Extraction data_all...") data_all = l.get_data_all_train() def fit_and_save_log(parameters, dataset, letter, filename, verbose=2): log = linear_model.LogisticRegression() X = get_X_without_scaler(dataset) y = get_y(letter, dataset) model = grid_search.GridSearchCV(log, parameters, verbose=verbose) model.fit(X, y)
return np.array(np.where(tmp["real_%s" % letter] == value, 1, 0)) def get_y(letter, data): tmp = data.copy() return np.array(tmp["real_%s" % letter]) from sklearn import svm from sklearn.externals import joblib from sklearn import grid_search l = AllStateDataLoader() print("Extraction data_2...") data_2 = l.get_data_2_train(with_location_view=True) print("Extraction data_3...") data_3 = l.get_data_3_train(with_location_view=True) print("Extraction data_4...") data_4 = l.get_data_4_train(with_location_view=True) print("Extraction data_all...") data_all = l.get_data_all_train(with_location_view=True) def fit_and_save_log(parameters, dataset, letter, filename,verbose=2): log = svm.LinearSVC(class_weight="auto") X = get_X_without_scaler(dataset) y = get_y(letter, dataset) model = grid_search.GridSearchCV(log, parameters, verbose=verbose) model.fit(X,y)