def test_libact_first_try_results_are_the_same(self):
"""
test that the first libact example work the same way as the original example taken from github\
very long test !
"""
# self.skipTest(reason="too long")
cn.Inner_PredictionModel = SvmModel
cn.Feature_Extractor = AvgGloveExtractor
cn.load_codementor_sentiment_analysis_parameters()
kb_helper.load_WordNet_model()
quota = 5 # ask labeler to label 5 samples (tops)
base_training_df, validation_data_df = prepare_balanced_dataset()
pos_sents = pandas_util.get_all_positive_sentences(
base_training_df, cn.col_names.text, cn.col_names.tag, cn.pos_tags)
# prepare all data
generated_pool_df = sg.generate_sents_using_random_synthesis(
pos_sents, base_training_df, cn.col_names)
labeled_pool_df = label_df_with_expert(generated_pool_df, cn.col_names)
enriched_train_df = pd.concat([base_training_df, generated_pool_df],
ignore_index=True)
ideal_df = pd.concat([base_training_df, labeled_pool_df],
ignore_index=True)
extractor = cn.Feature_Extractor(
enriched_train_df, cn.col_names) # build the feature extractor
lbr = IdealTextLabeler(TextDataset(ideal_df, cn.col_names, extractor))
manager = multiprocessing.Manager()
return_dict = manager.dict()
jobs = []
# first job
p = multiprocessing.Process(target=self.libact_first_try_first_run,
args=(enriched_train_df, extractor, lbr,
quota, validation_data_df,
return_dict))
jobs.append(p)
p.start()
# second job
p = multiprocessing.Process(target=self.libact_first_try_second_run,
args=(enriched_train_df, extractor,
ideal_df, lbr, quota,
validation_data_df, return_dict))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
self.assertTrue(np.array_equal(return_dict[1], return_dict[2]))
def test_heuristic_test_data_gain_works(self):
cn.load_codementor_sentiment_analysis_parameters()
kb_helper.load_WordNet_model()
base_train_df, pool_df, validation_data_df = prepare_pool_based_dataset(
)
all_sents = list(base_train_df[cn.col_names.text])
tns = 2
pool_name, prep_pools = (
"orig pool", lambda *_:
(pool_df.iloc[:tns + 5], cn.labeled_pool_df.iloc[:tns + 5]))
train_with_pool_df = pd.concat([base_train_df, pool_df],
ignore_index=True)
generated_pool_df, labeled_pool_df = prep_pools(
all_sents, train_with_pool_df, cn.col_names, tns)
cn.experiment_purpose += pool_name + " "
trn_ds, lbr, extractor = prepare_trn_ds(base_train_df,
generated_pool_df,
labeled_pool_df)
final_scoring_fun = partial(
lambda en_df: run_classifier(en_df, validation_data_df).acc)
table_headers = ['#added examples']
data = [range(0, tns + 1)]
compared_heuristics = [("test-data-gain",
lambda: SynStateTestDataGain),
("random", lambda: "random")]
for (heuristic_name, prepare_usage) in compared_heuristics:
ss_type = prepare_usage()
table_headers.append(heuristic_name)
print heuristic_name
_, heur_scores = insert_in_AL_fashion(trn_ds,
final_scoring_fun,
lbr,
ss_type,
labeled_pool_df,
quota=tns)
data.append(heur_scores)
print data[1]
print data[2]
self.assertEqual(data[1][0], data[2][0], "starts are same")
self.assertGreater(data[1][-1], data[2][-1],
"test-data-gain should be better than random")
def setUpClass(cls):
cn.load_codementor_sentiment_analysis_parameters(d_measure=10)
cn.data_df = pretokenize_df(cn.data_df, cn.col_names)
def setUpClass(cls):
cn.load_codementor_sentiment_analysis_parameters()
ce.load_machine_expert(cn.col_names, cn.relevant_tags, cn.data_df)