Logging and debugging

This example shows how to provide a custom logging configuration to auto-sklearn. We will be fitting 2 pipelines and showing any INFO-level msg on console. Even if you do not provide a logging_configuration, autosklearn creates a log file in the temporal working directory. This directory can be specified via the tmp_folder as exemplified below.

This example also highlights additional information about auto-sklearn internal directory structure.

import pathlib

import sklearn.datasets
import sklearn.metrics
import sklearn.model_selection

import autosklearn.classification

Data Loading

Load kr-vs-kp dataset from https://www.openml.org/d/3

X, y = data = sklearn.datasets.fetch_openml(data_id=3, return_X_y=True, as_frame=True)

X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
    X, y, random_state=1
)

Create a logging config

auto-sklearn uses a default logging config We will instead create a custom one as follows:

logging_config = {
    "version": 1,
    "disable_existing_loggers": True,
    "formatters": {
        "custom": {
            # More format options are available in the official
            # `documentation <https://docs.python.org/3/howto/logging-cookbook.html>`_
            "format": "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
        }
    },
    # Any INFO level msg will be printed to the console
    "handlers": {
        "console": {
            "level": "INFO",
            "formatter": "custom",
            "class": "logging.StreamHandler",
            "stream": "ext://sys.stdout",
        },
    },
    "loggers": {
        "": {  # root logger
            "level": "DEBUG",
        },
        "Client-EnsembleBuilder": {
            "level": "DEBUG",
            "handlers": ["console"],
        },
    },
}

Build and fit a classifier

cls = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=30,
    # Bellow two flags are provided to speed up calculations
    # Not recommended for a real implementation
    initial_configurations_via_metalearning=0,
    smac_scenario_args={"runcount_limit": 2},
    # Pass the config file we created
    logging_config=logging_config,
    # *auto-sklearn* generates temporal files under tmp_folder
    tmp_folder="./tmp_folder",
    # By default tmp_folder is deleted. We will preserve it
    # for debug purposes
    delete_tmp_folder_after_terminate=False,
)
cls.fit(X_train, y_train, X_test, y_test)

# *auto-sklearn* generates intermediate files which can be of interest
# Dask multiprocessing information. Useful on multi-core runs:
#   * tmp_folder/distributed.log
# The individual fitted estimators are written to disk on:
#   * tmp_folder/.auto-sklearn/runs
# SMAC output is stored in this directory.
# For more info, you can check the `SMAC documentation <https://github.com/automl/SMAC3>`_
#   * tmp_folder/smac3-output
# Auto-sklearn always outputs to this log file
# tmp_folder/AutoML*.log
for filename in pathlib.Path("./tmp_folder").glob("*"):
    print(filename)

Fitting to the training data:   0%|          | 0/30 [00:00<?, ?it/s, The total time budget for this task is 0:00:30]/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/data/target_validator.py:187: UserWarning: Fitting transformer with a pandas series which has the dtype category. Inverse transform may not be able preserve dtype when converting to np.ndarray
  warnings.warn(

Fitting to the training data:   3%|3         | 1/30 [00:01<00:29,  1.00s/it, The total time budget for this task is 0:00:30]
Fitting to the training data:   7%|6         | 2/30 [00:02<00:28,  1.00s/it, The total time budget for this task is 0:00:30]
Fitting to the training data:  10%|#         | 3/30 [00:03<00:27,  1.00s/it, The total time budget for this task is 0:00:30]
Fitting to the training data:  13%|#3        | 4/30 [00:04<00:26,  1.00s/it, The total time budget for this task is 0:00:30]2022-11-24 12:39:35,781 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-11-24 12:39:35.758860'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.11.24-12.39.35 for iteration 0.

Fitting to the training data:  17%|#6        | 5/30 [00:05<00:25,  1.01s/it, The total time budget for this task is 0:00:30]
Fitting to the training data:  20%|##        | 6/30 [00:06<00:24,  1.01s/it, The total time budget for this task is 0:00:30]
Fitting to the training data:  23%|##3       | 7/30 [00:07<00:23,  1.01s/it, The total time budget for this task is 0:00:30]2022-11-24 12:39:38,861 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-11-24 12:39:38.842278'), 'ensemble_optimization_score': 0.9406565656565656, 'ensemble_test_score': 0.9411764705882353}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.11.24-12.39.38 for iteration 1.

Fitting to the training data:  27%|##6       | 8/30 [00:08<00:22,  1.01s/it, The total time budget for this task is 0:00:30]
Fitting to the training data: 100%|##########| 30/30 [00:08<00:00,  3.73it/s, The total time budget for this task is 0:00:30]
tmp_folder/distributed.log
tmp_folder/AutoML(1):069654fc-6bf5-11ed-87b6-77edb579fc6c.log
tmp_folder/space.json
tmp_folder/smac3-output
tmp_folder/.auto-sklearn