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58 lines
1.5 KiB
Python
58 lines
1.5 KiB
Python
#!/usr/bin/env python
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# # Class Imbalance Model Training Example
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#
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# This example trains a model utilizing a standard config, and then a config using oversampling
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import logging
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import shutil
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# Import required libraries
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from ludwig.api import LudwigModel
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from ludwig.datasets import imbalanced_insurance
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from ludwig.visualize import compare_performance
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# clean out old results
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shutil.rmtree("./results", ignore_errors=True)
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shutil.rmtree("./visualizations", ignore_errors=True)
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# list models to train
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list_of_model_ids = ["standard_model", "balanced_model"]
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list_of_eval_stats = []
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training_set, val_set, test_set = imbalanced_insurance.load()
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# Train models
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for model_id in list_of_model_ids:
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print(">>>> training: ", model_id)
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# Define Ludwig model object that drive model training
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model = LudwigModel(config=model_id + "_config.yaml", logging_level=logging.WARN)
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# initiate model training
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train_stats, _, _ = model.train(
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training_set=training_set,
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validation_set=val_set,
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test_set=test_set,
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experiment_name="balance_example",
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model_name=model_id,
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skip_save_model=True,
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)
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# evaluate model on test_set
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eval_stats, _, _ = model.evaluate(test_set)
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# save eval stats for later use
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list_of_eval_stats.append(eval_stats)
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print(">>>>>>> completed: ", model_id, "\n")
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compare_performance(
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list_of_eval_stats,
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"Response",
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model_names=list_of_model_ids,
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output_directory="./visualizations",
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file_format="png",
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)
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