def test_write_clobber(self):
# Build and write instance to folder
test_name = 'test'
test_batch_grammar_classifier = BatchGrammarClassifier(name=test_name)
test_new_model_name = 'model1'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
test_new_model_name = 'model2'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
test_batch_grammar_classifier.write_to_folder()
self.assertTrue(os.path.isdir('test'))
self.assertTrue(os.path.isfile('test/model1.pkl'))
self.assertTrue(os.path.isfile('test/model2.pkl'))
test_batch_grammar_classifier.write_to_folder()
self.assertTrue(os.path.isdir('test'))
self.assertTrue(os.path.isfile('test/model1.pkl'))
self.assertTrue(os.path.isfile('test/model2.pkl'))
# Delete created folder
shutil.rmtree('test')
def test_train_new(self):
'''
Verify that a new model can be trained when valid data is supplied.
'''
test_batch_grammar_classifier = BatchGrammarClassifier(name='test')
test_new_model_name = 'new_model'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
self.assertIn('new_model', test_batch_grammar_classifier.model_dict.keys())
def test_predictions_consistent_after_load(self):
'''
Verify that model output is unchanged after instance is read and
written to file.
'''
test_name = 'test'
test_batch_grammar_classifier = BatchGrammarClassifier(name=test_name)
test_new_model_name = 'model1'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
test_new_model_name = 'model2'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
test_examples = ['Here are two new examples', 'How grammatically correct are they?']
# Predictions before folder write
test_pre_write_predictions = test_batch_grammar_classifier.predict(test_examples)
# Write then retrive instance
test_batch_grammar_classifier.write_to_folder()
test_read_instance = BatchGrammarClassifier.load_from_folder(test_name)
self.assertEqual(test_read_instance.name, test_name)
# Predictions after folder write
test_post_write_predictions = test_read_instance.predict(test_examples)
pd.testing.assert_frame_equal(test_pre_write_predictions, test_post_write_predictions)
# Delete created folder
shutil.rmtree(test_name)
def test_format_examples(self):
'''
Verify that example formatting is performed correctly
'''
test_negative_examples = pd.DataFrame({
'example': ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
})
test_positive_examples = pd.DataFrame({
'example': ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
})
expected_formatted_examples = pd.DataFrame({
'example': ['Horrible here grammar.', 'Nasty one', 'Great grammar here!', 'Another great one'],
'label': [0, 0, 1, 1]
})
actual_formatted_examples = BatchGrammarClassifier.format_examples(test_negative_examples, test_positive_examples)
def test_train_new_unoriginal_model_name(self):
'''
Verify behavior when attempting to train a new model whose name
already exists.
'''
test_batch_grammar_classifier = BatchGrammarClassifier(name='test')
test_new_model_name = 'new_model'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
self.assertIn('new_model', test_batch_grammar_classifier.model_dict.keys())
with self.assertRaises(ValueError):
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
def test_label_apply(self):
def test_func_1(arg):
return 1 * arg
def test_func_2(arg):
return 2 * arg
def test_func_3(arg):
return 3 * arg
test_functions = [test_func_1, test_func_2, test_func_3]
test_data = [1, 2, 3, 4]
expected_output = pd.DataFrame({
'data': test_data,
'test_func_1': [1, 2, 3, 4],
'test_func_2': [2, 4, 6, 8],
'test_func_3': [3, 6, 9, 12],
})
actual_output = BatchGrammarClassifier.label_apply(test_data, test_functions)
pd.testing.assert_frame_equal(expected_output, actual_output)
def test_train_new_models_independent(self):
'''
Verify that when training a new model, the model is different than
any existing ones.
'''
test_batch_grammar_classifier = BatchGrammarClassifier(name='test')
test_new_model_name = 'model1'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
test_new_model_name = 'model2'
test_negative_examples = ['Horrible here grammar.', 'Nasty one', 'Does grammar work not', 'still work no']
test_positive_examples = ['Great grammar here!', 'Another great one', 'Spectacular grammar here', 'Exellent']
test_batch_grammar_classifier.train_new(test_new_model_name, test_negative_examples, test_positive_examples)
model1 = test_batch_grammar_classifier.model_dict['model1']
model2 = test_batch_grammar_classifier.model_dict['model2']
self.assertNotEqual(model1, model2)
from label_tools import *
from tweet_tool import *
import time
from random import shuffle
import pandas as pd
from parse_tools import ParseTools
from tweet_generation import *
from textgenrnn import textgenrnn
from batch_grammar_classifier import BatchGrammarClassifier
# Generate tweets
model = textgenrnn('textgenrnn_FET_model')
generated_tweets = generate(model, gen_count=1000, temperature=.6)
# Apply simple fixes to tweets (@ correction, apostrophe insertion, etc.)
cleaned_tweets = ParseTools.clean_tweets(generated_tweets)
# Sort tweets by calculated grammar score
bgc = BatchGrammarClassifier.load_from_folder('grammar_models')
ranked_tweets = bgc.rank(cleaned_tweets)
# Obtain hand labels
labelled_tweets = label_data(ranked_tweets, shuffle=False)
good_tweets = list(labelled_tweets.loc[labelled_tweets['response'] == '1']['data'])
# Schedule tweets
shuffle(good_tweets)
t = TweetTool()
times = t.get_times(len(good_tweets), 100, 100)
t.schedule_tweets(good_tweets, times)
def test_load_from_folder_non_existant_folder(self):
'''
Verify that a non-existant folder can be caught.
'''
with self.assertRaises(ValueError):
BatchGrammarClassifier.load_from_folder('not_a_folder')
def test_constructor(self):
test_batch_grammar_classifier = BatchGrammarClassifier('test')
def test_power_set(self):
test_set = [1, 2, 3]
expected_power_set = [None, [1], [2], [3], [1, 2], [1, 3], [2, 3], [1, 2, 3]]
actual_power_set = BatchGrammarClassifier.power_set(test_set)
self.assertEqual(expected_power_set, actual_power_set)