wehub-resource-sync
53 lines
1.4 KiB
Python
53 lines
1.4 KiB
Python
# coding: utf-8
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from pathlib import Path
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import pandas as pd
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from sklearn.metrics import mean_squared_error
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import lightgbm as lgb
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print("Loading data...")
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# load or create your dataset
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regression_example_dir = Path(__file__).absolute().parents[1] / "regression"
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df_train = pd.read_csv(str(regression_example_dir / "regression.train"), header=None, sep="\t")
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df_test = pd.read_csv(str(regression_example_dir / "regression.test"), header=None, sep="\t")
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y_train = df_train[0]
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y_test = df_test[0]
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X_train = df_train.drop(0, axis=1)
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X_test = df_test.drop(0, axis=1)
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# create dataset for lightgbm
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lgb_train = lgb.Dataset(X_train, y_train)
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lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
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# specify your configurations as a dict
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params = {
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"boosting_type": "gbdt",
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"objective": "regression",
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"metric": {"l2", "l1"},
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"num_leaves": 31,
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"learning_rate": 0.05,
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"feature_fraction": 0.9,
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"bagging_fraction": 0.8,
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"bagging_freq": 5,
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"verbose": 0,
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}
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print("Starting training...")
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# train
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gbm = lgb.train(
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params, lgb_train, num_boost_round=20, valid_sets=lgb_eval, callbacks=[lgb.early_stopping(stopping_rounds=5)]
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)
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print("Saving model...")
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# save model to file
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gbm.save_model("model.txt")
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print("Starting predicting...")
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# predict
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y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
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# eval
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rmse_test = mean_squared_error(y_test, y_pred) ** 0.5
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print(f"The RMSE of prediction is: {rmse_test}")
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