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September 26, 2020 20:22
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#Example from LightGBM documentation | |
#Import needed libraries | |
import lightgbm as lgb | |
import pandas as pd | |
from sklearn.metrics import mean_squared_error | |
print('Loading data...') | |
# load or create your dataset | |
df_train = pd.read_csv('../regression/regression.train', header=None, sep='\t') | |
df_test = pd.read_csv('../regression/regression.test', header=None, sep='\t') | |
y_train = df_train[0] | |
y_test = df_test[0] | |
X_train = df_train.drop(0, axis=1) | |
X_test = df_test.drop(0, axis=1) | |
# create dataset for lightgbm | |
lgb_train = lgb.Dataset(X_train, y_train) | |
lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train) | |
# specify your configurations as a dict | |
params = { | |
'boosting_type': 'gbdt', | |
'objective': 'regression', | |
'metric': {'l2', 'l1'}, | |
'num_leaves': 31, | |
'learning_rate': 0.05, | |
'feature_fraction': 0.9, | |
'bagging_fraction': 0.8, | |
'bagging_freq': 5, | |
'verbose': 0 | |
} | |
print('Starting training...') | |
# train | |
gbm = lgb.train(params, | |
lgb_train, | |
num_boost_round=20, | |
valid_sets=lgb_eval, | |
early_stopping_rounds=5) | |
print('Saving model...') | |
# save model to file | |
gbm.save_model('model.txt') | |
print('Starting predicting...') | |
# predict | |
y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration) | |
# eval | |
print('The rmse of prediction is:', mean_squared_error(y_test, y_pred) ** 0.5) |
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