wehub-resource-sync
63 lines
1.8 KiB
Python
63 lines
1.8 KiB
Python
# coding: utf-8
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from pathlib import Path
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import matplotlib.pyplot as plt
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import pandas as pd
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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(
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X_train,
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y_train,
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feature_name=[f"f{i + 1}" for i in range(X_train.shape[-1])],
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categorical_feature=[21],
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)
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lgb_test = lgb.Dataset(X_test, y_test, reference=lgb_train)
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# specify your configurations as a dict
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params = {"num_leaves": 5, "metric": ("l1", "l2"), "verbose": 0}
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evals_result = {} # to record eval results for plotting
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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=100,
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valid_sets=[lgb_train, lgb_test],
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callbacks=[lgb.log_evaluation(10), lgb.record_evaluation(evals_result)],
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)
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print("Plotting metrics recorded during training...")
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ax = lgb.plot_metric(evals_result, metric="l1")
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plt.show()
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print("Plotting feature importances...")
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ax = lgb.plot_importance(gbm, max_num_features=10)
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plt.show()
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print("Plotting split value histogram...")
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ax = lgb.plot_split_value_histogram(gbm, feature="f26", bins="auto")
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plt.show()
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print("Plotting 54th tree...") # one tree use categorical feature to split
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ax = lgb.plot_tree(gbm, tree_index=53, figsize=(15, 15), show_info=["split_gain"])
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plt.show()
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print("Plotting 54th tree with graphviz...")
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graph = lgb.create_tree_digraph(gbm, tree_index=53, name="Tree54")
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graph.render(view=True)
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