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* Update gitignore * Bump version to 2.4.0 * Update readme * Pre-download california housing data * Use pre-downloaded california housing data * Pin lightning<=2.5.6 * Fix typo in find and replace * Fix estimators has no attribute __sklearn_tags__ * Pin torch to 2.2.2 in tests * Fix conflict * Update pytorch-forecasting * Update pytorch-forecasting * Update pytorch-forecasting * Use numpy<2 for testing * Update scikit-learn * Run Build and UT every other day * Pin pip<24.1 * Pin pip<24.1 in pipeline * Loosen pip, install pytorch_forecasting only in py311 * Add support to new versions of nlp dependecies * Fix formats * Remove redefinition * Update mlflow versions * Fix mlflow version syntax * Update gitignore * Clean up cache to free space * Remove clean up action cache * Fix blendsearch * Update test workflow * Update setup.py * Fix catboost version * Update workflow * Prepare for python 3.14 * Support no catboost * Fix tests * Fix python_requires * Update test workflow * Fix vw tests * Remove python 3.9 * Fix nlp tests * Fix prophet * Print pip freeze for better debugging * Fix Optuna search does not support parameters of type Float with samplers of type Quantized * Save dependencies for later inspection * Fix coverage.xml not exists * Fix github action permission * Handle python 3.13 * Address openml is not installed * Check dependencies before run tests * Update dependencies * Fix syntax error * Use bash * Update dependencies * Fix git error * Loose mlflow constraints * Add rerun, use mlflow-skinny * Fix git error * Remove ray tests * Update xgboost versions * Fix automl pickle error * Don't test python 3.10 on macos as it's stuck * Rebase before push * Reduce number of branches
29 lines
795 B
Python
29 lines
795 B
Python
from sklearn.datasets import fetch_california_housing
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from flaml import AutoML
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# Initialize an AutoML instance
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automl = AutoML()
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# Specify automl goal and constraint
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automl_settings = {
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"time_budget": 1, # in seconds
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"metric": "r2",
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"task": "regression",
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"log_file_name": "test/california.log",
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}
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X_train, y_train = fetch_california_housing(return_X_y=True, data_home="test")
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# Train with labeled input data
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automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
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print(automl.model)
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print(automl.model.estimator)
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print(automl.best_estimator)
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print(automl.best_config)
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print(automl.best_config_per_estimator)
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print(automl.best_config_train_time)
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print(automl.best_iteration)
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print(automl.best_loss)
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print(automl.time_to_find_best_model)
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print(automl.config_history)
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