Quickly compare neural network against xgb, lgb and random forest

compare_models.py

from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
import lightgbm as lgb

def rmspe_calc(y_true, y_pred):
  # Compute Root Mean Square Percentage Error between two arrays.
  return np.sqrt(np.mean(np.square(((y_true - y_pred) / y_true)), axis=0))


models = [
 xgb.XGBRegressor(),
 lgb.LGBMRegressor(),
 RandomForestRegressor()
]

results = pd.DataFrame(columns=["Regressor", "RMSPE"])

for model in models:
  
  name = model.__class__.__name__

  model.fit(X_train, y_train)
  rmspe = rmspe_calc(y_valid, model.predict(X_valid))

  df2 = pd.DataFrame(
  {"Regressor": name, \
  "RMSPE": rmspe*100}, index = [0]
  )
  results = results.append(df2, ignore_index = True)