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* precommit: end-of-file-fixer * exclude .gitignore * apply --------- Co-authored-by: Shaokun <shaokunzhang529@gmail.com>
63 lines
3.6 KiB
Markdown
63 lines
3.6 KiB
Markdown
# AutoML - Classification
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### A basic classification example
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```python
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from flaml import AutoML
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from sklearn.datasets import load_iris
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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": 'accuracy',
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"task": 'classification',
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"log_file_name": "iris.log",
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}
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X_train, y_train = load_iris(return_X_y=True)
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# Train with labeled input data
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automl.fit(X_train=X_train, y_train=y_train,
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**automl_settings)
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# Predict
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print(automl.predict_proba(X_train))
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# Print the best model
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print(automl.model.estimator)
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```
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#### Sample of output
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```
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[flaml.automl: 11-12 18:21:44] {1485} INFO - Data split method: stratified
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[flaml.automl: 11-12 18:21:44] {1489} INFO - Evaluation method: cv
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[flaml.automl: 11-12 18:21:44] {1540} INFO - Minimizing error metric: 1-accuracy
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[flaml.automl: 11-12 18:21:44] {1577} INFO - List of ML learners in AutoML Run: ['lgbm', 'rf', 'catboost', 'xgboost', 'extra_tree', 'lrl1']
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 0, current learner lgbm
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[flaml.automl: 11-12 18:21:44] {1944} INFO - Estimated sufficient time budget=1285s. Estimated necessary time budget=23s.
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[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.2s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 1, current learner lgbm
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[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.3s, estimator lgbm's best error=0.0733, best estimator lgbm's best error=0.0733
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 2, current learner lgbm
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[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.4s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 3, current learner lgbm
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[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 4, current learner lgbm
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[flaml.automl: 11-12 18:21:44] {2029} INFO - at 0.6s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:44] {1826} INFO - iteration 5, current learner xgboost
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[flaml.automl: 11-12 18:21:45] {2029} INFO - at 0.9s, estimator xgboost's best error=0.0600, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 6, current learner lgbm
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[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.0s, estimator lgbm's best error=0.0533, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:45] {1826} INFO - iteration 7, current learner extra_tree
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[flaml.automl: 11-12 18:21:45] {2029} INFO - at 1.1s, estimator extra_tree's best error=0.0667, best estimator lgbm's best error=0.0533
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[flaml.automl: 11-12 18:21:45] {2242} INFO - retrain lgbm for 0.0s
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[flaml.automl: 11-12 18:21:45] {2247} INFO - retrained model: LGBMClassifier(learning_rate=0.2677050123105203, max_bin=127,
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min_child_samples=12, n_estimators=4, num_leaves=4,
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reg_alpha=0.001348364934537134, reg_lambda=1.4442580148221913,
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verbose=-1)
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[flaml.automl: 11-12 18:21:45] {1608} INFO - fit succeeded
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[flaml.automl: 11-12 18:21:45] {1610} INFO - Time taken to find the best model: 0.3756711483001709
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```
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### A more advanced example including custom learner and metric
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[Link to notebook](https://github.com/microsoft/FLAML/blob/main/notebook/automl_classification.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/automl_classification.ipynb)
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