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
from pprint import pprint

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

import autosklearn.classification

Data Loading

# Using reuters multilabel dataset --
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:
y[y == "TRUE"] = 1
y[y == "FALSE"] = 0
y = y.astype(int)

# Using type of target is a good way to make sure your data
# is properly formatted

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

Building the classifier

automl = autosklearn.classification.AutoSklearnClassifier(
    # Bellow two flags are provided to speed up calculations
    # Not recommended for a real implementation
    smac_scenario_args={"runcount_limit": 1},
), y_train, dataset_name="reuters")
AutoSklearnClassifier(ensemble_class=<class 'autosklearn.ensembles.ensemble_selection.EnsembleSelection'>,
                      smac_scenario_args={'runcount_limit': 1},

View the models found by auto-sklearn

          rank  ensemble_weight           type      cost  duration
2            1              1.0  random_forest  0.447294  4.031171

Get the Score of the final ensemble

predictions = automl.predict(X_test)
print("Accuracy score", sklearn.metrics.accuracy_score(y_test, predictions))
Accuracy score 0.604

Total running time of the script: ( 0 minutes 24.585 seconds)

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