model=LogisticRegression(penalty='l1',
solver='liblinear'),
performance_metric='accuracy_score',
stopping_criteria='num_of_queries',
stopping_value=stop)
al_unc.split_AL(test_ratio=trn_tst_split,
initial_label_rate=init_labels,
split_count=splits,
all_class=True)
al_unc.set_query_strategy(strategy='QueryInstanceUncertainty')
al_unc.set_performance_metric(performance_metric='accuracy_score')
al_unc.start_query(multi_thread=False)
# print(al.get_experiment_result())
# al.plot_learning_curve()
analyser = ExperimentAnalyser(x_axis='num_of_queries')
analyser.add_method('uncertainty', al_unc.get_experiment_result())
al_qbc = AlExperiment(X,
y,
model=LogisticRegression(penalty='l1',
solver='liblinear'),
performance_metric='accuracy_score',
stopping_criteria='num_of_queries',
stopping_value=stop)
al_qbc.split_AL(test_ratio=trn_tst_split,
initial_label_rate=init_labels,
split_count=splits,
all_class=True)
al_qbc.set_query_strategy(strategy='QueryInstanceQBC')
al_qbc.set_performance_metric(performance_metric='accuracy_score')
al_qbc.start_query(multi_thread=False)
analyser.add_method('by committee', al_qbc.get_experiment_result())
label_idx=lab,
unlabel_idx=unlab)
# set the query strategy
# using the a pre-defined strategy
al.set_query_strategy(strategy=strategy)
# or using your own query strategy
# class my_qs_class:
# def __init__(self, X=None, y=None, **kwargs):
# pass
# def select(self, label_index, unlabel_index, batch_size=1, **kwargs):
# """Select instances to query."""
# pass
# al.set_query_strategy(strategy=my_qs_class(), **kwargs)
# set the metric for experiment.
al.set_performance_metric('accuracy_score')
# by default,run in multi-thread.
al.start_query(multi_thread=False)
# or execute sequentially
# al.start_query(multi_thread=False)
# get the experiemnt result
stateIO = al.get_experiment_result()
# get a brief description of the experiment
# al.plot_learning_curve(title='Alexperiment result')