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https://github.com/clearml/clearml
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75 lines
1.7 KiB
Python
75 lines
1.7 KiB
Python
# ClearML - Example of LightGBM integration
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#
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import lightgbm as lgb
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import pandas as pd
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from sklearn.metrics import mean_squared_error
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from clearml import Task
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# Connecting ClearML with the current process,
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# from here on everything is logged automatically
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task = Task.init(project_name="examples", task_name="LightGBM")
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print('Loading data...')
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# Load or create your dataset
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df_train = pd.read_csv(
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'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.train',
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header=None, sep='\t'
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)
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df_test = pd.read_csv(
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'https://raw.githubusercontent.com/microsoft/LightGBM/master/examples/regression/regression.test',
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header=None, sep='\t'
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)
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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,
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lgb_train,
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num_boost_round=20,
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valid_sets=lgb_eval,
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early_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('Loading model to predict...')
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# Load model to predict
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bst = lgb.Booster(model_file='model.txt')
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# Can only predict with the best iteration (or the saving iteration)
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y_pred = bst.predict(X_test)
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# Eval with loaded model
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print("The rmse of loaded model's prediction is:", mean_squared_error(y_test, y_pred) ** 0.5)
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