Multi-label Classification

This examples shows how to format the targets for a multilabel classification problem. Details on multilabel classification can be found here.

import numpy as np

import sklearn.datasets
import sklearn.metrics
from sklearn.utils.multiclass import type_of_target

import autosklearn.classification

Data Loading

# Using reuters multilabel dataset -- https://www.openml.org/d/40594
X, y = sklearn.datasets.fetch_openml(data_id=40594, return_X_y=True, as_frame=False)

# fetch openml downloads a numpy array with TRUE/FALSE strings. Re-map it to
# integer dtype with ones and zeros
# This is to comply with Scikit-learn requirement:
# "Positive classes are indicated with 1 and negative classes with 0 or -1."
# More information on: https://scikit-learn.org/stable/modules/multiclass.html
y[y == 'TRUE'] = 1
y[y == 'FALSE'] = 0
y = y.astype(np.int)

# Using type of target is a good way to make sure your data
# is properly formatted
print(f"type_of_target={type_of_target(y)}")

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

Out:

/home/runner/work/auto-sklearn/auto-sklearn/examples/20_basic/example_multilabel_classification.py:33: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
  y = y.astype(np.int)
type_of_target=multilabel-indicator

Building the classifier

automl = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=60,
    per_run_time_limit=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': 1},
)
automl.fit(X_train, y_train, dataset_name='reuters')

Out:

AutoSklearnClassifier(initial_configurations_via_metalearning=0,
                      per_run_time_limit=30,
                      smac_scenario_args={'runcount_limit': 1},
                      time_left_for_this_task=60)

View the models found by auto-sklearn

print(automl.leaderboard())

Out:

          rank  ensemble_weight           type      cost  duration
model_id
2            1              1.0  random_forest  0.447294  3.636021

Get the Score of the final ensemble

predictions = automl.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))

Out:

Accuracy score 0.604

Total running time of the script: ( 0 minutes 15.973 seconds)

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