feature_name = ['QNH', 'TEMP', 'RH', 'absolute_temp', 'WS2A', 'CW2A']
train = data[200:, :]
test = data[:200, :]
train_target = target[200:]
test_target = target[:200]
from sklearn.ensemble import GradientBoostingRegressor
gb = GradientBoostingRegressor(n_estimators=500,
max_depth=10,
learning_rate=0.01)
relu_fit = RuleFit()
relu_fit.max_iter = 4000
relu_fit.tree_generator = gb
relu_fit.fit(train, train_target, feature_names=feature_name)
f = relu_fit.predict(test)
ff = relu_fit.predict(train)
rule = relu_fit.get_rules()
truth = 0
for i in range(test_target.shape[0]):
if abs(test_target[i] - f[i]) / test_target[i] < 0.1:
truth += 1
print("truth: ", truth / test_target.shape[0])
#print(rule)
ruleset = pd.DataFrame(data=rule)
writer = pd.ExcelWriter('./rules.xlsx')
ruleset.to_excel(writer)
writer.save()
