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Extending Auto-Sklearn with Preprocessor Component¶
The following example demonstrates how to create a wrapper around the linear discriminant analysis (LDA) algorithm from sklearn and use it as a preprocessor in auto-sklearn.
from pprint import pprint
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import UniformFloatHyperparameter, CategoricalHyperparameter
from ConfigSpace.conditions import InCondition
import sklearn.metrics
import autosklearn.classification
import autosklearn.pipeline.components.feature_preprocessing
from autosklearn.pipeline.components.base \
import AutoSklearnPreprocessingAlgorithm
from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, \
UNSIGNED_DATA
from autosklearn.util.common import check_none
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
Create LDA component for auto-sklearn¶
class LDA(AutoSklearnPreprocessingAlgorithm):
def __init__(self, solver, tol, shrinkage=None, random_state=None):
self.solver = solver
self.shrinkage = shrinkage
self.tol = tol
self.random_state = random_state
self.preprocessor = None
def fit(self, X, y=None):
if check_none(self.shrinkage):
self.shrinkage = None
else:
self.shrinkage = float(self.shrinkage)
self.tol = float(self.tol)
import sklearn.discriminant_analysis
self.preprocessor = sklearn.discriminant_analysis.LinearDiscriminantAnalysis(
shrinkage=self.shrinkage,
solver=self.solver,
tol=self.tol,
)
self.preprocessor.fit(X, y)
return self
def transform(self, X):
if self.preprocessor is None:
raise NotImplementedError()
return self.preprocessor.transform(X)
@staticmethod
def get_properties(dataset_properties=None):
return {
'shortname': 'LDA',
'name': 'Linear Discriminant Analysis',
'handles_regression': False,
'handles_classification': True,
'handles_multiclass': False,
'handles_multilabel': False,
'handles_multioutput': False,
'is_deterministic': True,
'input': (DENSE, UNSIGNED_DATA, SIGNED_DATA),
'output': (DENSE, UNSIGNED_DATA, SIGNED_DATA)
}
@staticmethod
def get_hyperparameter_search_space(dataset_properties=None):
cs = ConfigurationSpace()
solver = CategoricalHyperparameter(
name="solver", choices=['svd', 'lsqr', 'eigen'], default_value='svd'
)
shrinkage = UniformFloatHyperparameter(
name="shrinkage", lower=0.0, upper=1.0, default_value=0.5
)
tol = UniformFloatHyperparameter(
name="tol", lower=0.0001, upper=1, default_value=0.0001
)
cs.add_hyperparameters([solver, shrinkage, tol])
shrinkage_condition = InCondition(shrinkage, solver, ['lsqr', 'eigen'])
cs.add_condition(shrinkage_condition)
return cs
# Add LDA component to auto-sklearn.
autosklearn.pipeline.components.feature_preprocessing.add_preprocessor(LDA)
Create dataset¶
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
Configuration space¶
cs = LDA.get_hyperparameter_search_space()
print(cs)
Out:
Configuration space object:
Hyperparameters:
shrinkage, Type: UniformFloat, Range: [0.0, 1.0], Default: 0.5
solver, Type: Categorical, Choices: {svd, lsqr, eigen}, Default: svd
tol, Type: UniformFloat, Range: [0.0001, 1.0], Default: 0.0001
Conditions:
shrinkage | solver in {'lsqr', 'eigen'}
Fit the model using LDA as preprocessor¶
clf = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=30,
include={
'feature_preprocessor': ['LDA']
},
# 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': 5},
)
clf.fit(X_train, y_train)
Out:
AutoSklearnClassifier(include={'feature_preprocessor': ['LDA']},
initial_configurations_via_metalearning=0,
per_run_time_limit=3,
smac_scenario_args={'runcount_limit': 5},
time_left_for_this_task=30)
Print prediction score and statistics¶
y_pred = clf.predict(X_test)
print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
pprint(clf.show_models(), indent=4)
Out:
accuracy: 0.951048951048951
{ 2: { 'balancing': Balancing(random_state=1),
'classifier': <autosklearn.pipeline.components.classification.ClassifierChoice object at 0x7fb0e68e1880>,
'cost': 0.06382978723404253,
'data_preprocessor': <autosklearn.pipeline.components.data_preprocessing.DataPreprocessorChoice object at 0x7fb0e68e1160>,
'ensemble_weight': 1.0,
'feature_preprocessor': <autosklearn.pipeline.components.feature_preprocessing.FeaturePreprocessorChoice object at 0x7fb0ea200ca0>,
'model_id': 2,
'rank': 1,
'sklearn_classifier': RandomForestClassifier(max_features=1, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)}}
Total running time of the script: ( 0 minutes 26.457 seconds)