import numpy as np from sklearn.ensemble import RandomForestRegressor from preprocessing.to_onehot import to_labels from gini_normalized import normalized_gini # joined = pd.read_csv('../data/joined.csv') # # train = joined[joined['Hazard'] != -1] # test = joined[joined['Hazard'] == -1] train = pd.read_csv('../data/train_new.csv') hold = pd.read_csv('../data/hold_new.csv') test = pd.read_csv('../data/test.csv') # hold = pd.read_csv('../data/hold_new.csv') train, hold = to_labels((train, hold)) y = train['Hazard'] X = train.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_hold = hold.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_test = hold.drop(['Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) random_state = 42 ind = 1 if ind == 1: rs = ShuffleSplit(len(y), n_iter=10, test_size=0.5, random_state=random_state)
import numpy as np from sklearn.ensemble import RandomForestRegressor from preprocessing.to_onehot import to_labels from gini_normalized import normalized_gini # joined = pd.read_csv('../data/joined.csv') # # train = joined[joined['Hazard'] != -1] # test = joined[joined['Hazard'] == -1] train = pd.read_csv('../data/train_new.csv') hold = pd.read_csv('../data/hold_new.csv') test = pd.read_csv('../data/test.csv') # hold = pd.read_csv('../data/hold_new.csv') train, hold = to_labels((train, hold)) y = train['Hazard'] X = train.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_hold = hold.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_test = hold.drop(['Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) random_state = 42 ind = 1 if ind == 1: rs = ShuffleSplit(len(y), n_iter=10, test_size=0.5,
joined = pd.read_csv('../data/joined.csv') train = joined[joined['Hazard'] != -1] test = joined[joined['Hazard'] == -1] y_train = train['Hazard'] X_train = train.drop(['Hazard', 'Id'], 1) X_test = test.drop(['Hazard', 'Id'], 1) train = pd.read_csv('../data/train_new.csv') hold = pd.read_csv('../data/hold_new.csv') test = pd.read_csv('../data/test.csv') # hold = pd.read_csv('../data/hold_new.csv') train, hold, test = to_labels((train, hold, test)) y = train['Hazard'] X = train.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_hold = hold.drop(['Hazard', 'Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) X_test = hold.drop(['Id', 'T2_V10', 'T2_V7', 'T1_V13', 'T1_V10'], 1) net1 = NeuralNet( layers=[ # three layers: one hidden layer ('input', layers.InputLayer), # ('dropout1', DropoutLayer), ('hidden0', layers.DenseLayer), # ('dropout2', DropoutLayer), # ('hidden1', layers.DenseLayer), ('output', layers.DenseLayer),