Auto Sklearn

Data Type

• np.ndarray
• pd.DataFrame
• scipy.sparse.csr_matrix
• python lists

Pros

• Support ensembling

Cons

• Cannot use 'object' data type
• Cannot handle missed categorical data

import autosklearn.classification
import autosklearn.regression
import warnings
warnings.filterwarnings('ignore')

Classsification

import sklearn.model_selection
import sklearn.datasets
from sklearn.metrics import classification_report

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

model = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=120, # time to search a model
    per_run_time_limit=30, # time to fit a model
    resampling_strategy = 'cv',
    resampling_strategy_arguments = {'folds': 3},
    ensemble_size=1 # one model in the ensemble
)
model.fit(X_train, y_train)

AutoSklearnClassifier(ensemble_size=1, per_run_time_limit=30,
                      resampling_strategy='cv',
                      resampling_strategy_arguments={'folds': 3},
                      time_left_for_this_task=120)

# if use cross-validation, need to refit since only partial data is used in fit()
# autosklearn does not automatically re-train as what cross-validation does in sklearn
model.refit(X_train, y_train)

AutoSklearnClassifier(ensemble_size=1, per_run_time_limit=30,
                      resampling_strategy='cv',
                      resampling_strategy_arguments={'folds': 3},
                      time_left_for_this_task=120)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.96      0.95      0.95        55
           1       0.97      0.98      0.97        88

    accuracy                           0.97       143
   macro avg       0.96      0.96      0.96       143
weighted avg       0.97      0.97      0.96       143

Regression

X, y = sklearn.datasets.load_boston(return_X_y=True)

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

model = autosklearn.regression.AutoSklearnRegressor(
    time_left_for_this_task=120, # time to search a model
    per_run_time_limit=30, # time to fit a model
    resampling_strategy = 'cv',
    resampling_strategy_arguments = {'folds': 3},
    ensemble_size=1 # at least 1
)
model.fit(X_train, y_train)

AutoSklearnRegressor(ensemble_size=1, per_run_time_limit=30,
                     resampling_strategy='cv',
                     resampling_strategy_arguments={'folds': 3},
                     time_left_for_this_task=120)

from sklearn.metrics import r2_score
y_pred = model.predict(X_test)
print(r2_score(y_test, y_pred))

0.8987230576926952

Train with a Single Type of Models

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

model = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=120, # time to search a model
    per_run_time_limit=30, # time to fit a model
    resampling_strategy = 'cv',
    resampling_strategy_arguments = {'folds': 3},
    include_estimators=['random_forest'],
)

model.fit(X_train, y_train)

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AutoSklearnClassifier(include_estimators=['random_forest'],
                      per_run_time_limit=30, resampling_strategy='cv',
                      resampling_strategy_arguments={'folds': 3},
                      time_left_for_this_task=120)

y_pred = model.predict(X_test)
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.94      0.91      0.93        55
           1       0.94      0.97      0.96        88

    accuracy                           0.94       143
   macro avg       0.94      0.94      0.94       143
weighted avg       0.94      0.94      0.94       143

Parallel

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

model = autosklearn.classification.AutoSklearnClassifier(
    time_left_for_this_task=120, # time to search a model
    per_run_time_limit=30, # time to fit a model
    resampling_strategy = 'cv',
    resampling_strategy_arguments = {'folds': 3},
    n_jobs=4,
    ensemble_size=1
)
model.fit(X_train, y_train)

AutoSklearnClassifier(ensemble_size=1, n_jobs=4, per_run_time_limit=30,
                      resampling_strategy='cv',
                      resampling_strategy_arguments={'folds': 3},
                      time_left_for_this_task=120)

Tools

model.show_models() # linear weighting of all models

model.sprint_statistics() # Report statistics

Reference

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