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jblitzar
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github-python

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feature_subset=10))
]''',
one_shot_algos='''[
('LR-0.01', CrossValidationAgent, dict(learner=LogisticRegression,
learner_kwargs=dict(C=0.01),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('LR-100', CrossValidationAgent, dict(learner=LogisticRegression,
learner_kwargs=dict(C=100),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('KNN-1', CrossValidationAgent, dict(learner=KNeighborsClassifier,
learner_kwargs=dict(n_neighbors=1),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('KNN-5', CrossValidationAgent, dict(learner=KNeighborsClassifier,
learner_kwargs=dict(n_neighbors=3),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('KNN-25', CrossValidationAgent, dict(learner=KNeighborsClassifier,
learner_kwargs=dict(n_neighbors=9),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('GNB', CrossValidationAgent, dict(learner=GaussianNB,
learner_kwargs=dict(),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('DTC-1', CrossValidationAgent, dict(learner=DecisionTreeClassifier,
learner_kwargs=dict(min_samples_leaf=1),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('DTC-5', CrossValidationAgent, dict(learner=DecisionTreeClassifier,
learner_kwargs=dict(min_samples_leaf=9),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('DTC-25', CrossValidationAgent, dict(learner=DecisionTreeClassifier,
learner_kwargs=dict(min_samples_leaf=27),
agent=OneShotLearnerAgent,
agent_kwargs=dict())),
('LR-l1-1', CrossValidationAgent, dict(learner=LogisticRegression,
learner_kwargs=dict(C=1, penalty='l1'),
agent=OneShotLearnerAgent,
agent_kwargs=dict()))
]''',
anytime_algos='''[
('RF-1', CrossValidationAgent, dict(learner=WarmLearner,
learner_kwargs=dict(base_model=RandomForestClassifier,
base_model_kwargs=dict(min_samples_leaf=1,
n_estimators=1)),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('RF-54', CrossValidationAgent, dict(learner=WarmLearner,
learner_kwargs=dict(base_model=RandomForestClassifier,
base_model_kwargs=dict(min_samples_leaf=54,
n_estimators=1)),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-1', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=1, warm_start=True,
n_estimators=1),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-54', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=54, warm_start=True,
n_estimators=1),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-1-5', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=1, warm_start=True,
n_estimators=1, max_depth=5),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-54-5', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=54, warm_start=True,
n_estimators=1, max_depth=5),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('RF-3', CrossValidationAgent, dict(learner=WarmLearner,
learner_kwargs=dict(base_model=RandomForestClassifier,
base_model_kwargs=dict(min_samples_leaf=3,
n_estimators=1)),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('RF-27', CrossValidationAgent, dict(learner=WarmLearner,
learner_kwargs=dict(base_model=RandomForestClassifier,
base_model_kwargs=dict(min_samples_leaf=27,
n_estimators=1)),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-3', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=3, warm_start=True,
n_estimators=1),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-27', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=27, warm_start=True,
n_estimators=1),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-3-5', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=3, warm_start=True,
n_estimators=1, max_depth=5),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('GBM-27-5', CrossValidationAgent, dict(learner=GradientBoostingClassifier,
learner_kwargs=dict(min_samples_leaf=27, warm_start=True,
n_estimators=1, max_depth=5),
agent_kwargs=dict(time_quantum=self.compute_quantum))),
('RF-9', CrossValidationAgent, dict(learner=WarmLearner,
learner_kwargs=dict(base_model=RandomForestClassifier,
base_model_kwargs=dict(min_samples_leaf=9,