def test_init_2():
"""
Test initialization of the class NLPFrame
"""
initial_df = nlp.NLPFrame({'text_col' : ['Today is a beautiful Monday and I would love getting a coffee. However, startbucks is closed.','It has been an amazing day today!']}, index = [0,1], column = 'non_existing')
assert initial_df.column == 'text_col'
def test_init_3():
"""
Test initialization of the class NLPFrame that doesn't have text column
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
assert initial_df.column == None
def test_positive_sentence_stopwords():
"""
Test function to ensure that the `sentence_stopwords` function is properly outputting a positive count.
"""
initial_df = nlp.NLPFrame({'text_col': ['The coffee is good', 'I would like to have some']}, index = [0,1])
res = initial_df.sentence_stopwords()
assert ((res.iloc[0,0] >= 0) & (res.iloc[0,1] >= 0)) , 'Count should be positive'
def test_polarity_count():
"""
Test function to ensure that the `polarity` function returns correct count values.
"""
initial_df = nlp.NLPFrame({'text_col': ['sam is living a great life','he treats them so well']}, index = [0,1])
res = initial_df.polarity()
assert ((res.iloc[0,0] >= 0) & (res.iloc[0,1] >= 0)) , 'Count should be positive'
def test_get_nlp_summary_2():
"""
Tests get_nlp_summary function for NLPFrame with wrong column specified
"""
initial_df = nlp.NLPFrame({'text_col' : ['Today is a beautiful Monday and I would love getting a coffee. However, startbucks is closed.','It has been an amazing day today!']}, index = [0,1], column = 'non_existing')
res = initial_df.get_nlp_summary(column = 'non_existing')
assert res.equals(pd.DataFrame())
def test_polarity_input():
"""
Test function to ensure that the `polarity` function properly classifies between "good" and "bad" text.
"""
initial_df = nlp.NLPFrame({'text_col': ['he is a good guy','his behaviour is bad']}, index = [0,1])
res = initial_df.polarity()
b = ['positive_words', 'negative_words']
a = list(res.columns)
assert a==b, 'Default columns should be positive, negative'
def test_df_sentence_stopwords():
"""
Test function to ensure that the input data is in string format.
"""
test_df = nlp.NLPFrame({'text_col' : ['Today is a great day \
and I wish I spent more time outside. I decided to go outside.\
English!']})
input_data = test_df['text_col'][0]
assert type(input_data) == str, 'The inputs are not strings'
def test_invalid_pos_input_1():
"""
Test function to ensure that the `get_part_of_speech` function handles the input of multilingual values in pd Series.
"""
initial_df = nlp.NLPFrame({'text_col': ['彼は新しい仕事に本当に満足している','It has been an amazing day today!']})
try:
res = initial_df.get_part_of_speech(show_only=5)
assert False, 'The function should not be executed when the show only input is not an iterable object!!'
except TypeError:
pass
def test_pos_output_columns():
"""
Test function to ensure that the `get_part_of_speech` function outputs the POS that are given as input
"""
initial_df = nlp.NLPFrame({'text_col': ['彼は新しい仕事に本当に満足している','It has been an amazing day today!']}, index = [0,1])
res = initial_df.get_part_of_speech(show_only=False)
a = len(res.columns)
assert a>0, 'The function should return an output even for mixture of English and non-English sentences!'
def test_pos_chinese_input():
"""
Test function to ensure that a Chinese string of text is understood by the `get_part_of_speech` function.
"""
initial_df = nlp.NLPFrame({'text_col': ['彼は新しい仕事に本当に満足している']}, index = [0])
res = initial_df.get_part_of_speech()
b = ['adjective', 'noun', 'verb']
a = list(res.columns)
assert a==b, 'The function should return an output even for non-English text'
def test_pos_mixture_input():
"""
Test function to ensure that the `get_part_of_speech` function can read different languages of strings.
"""
initial_df = nlp.NLPFrame({'text_col': ['彼は新しい仕事に本当に満足している','It has been an amazing day today!']}, index = [0,1])
res = initial_df.get_part_of_speech()
b = ['adjective', 'noun', 'verb']
a = list(res.columns)
assert a==b, 'The function should return an output even for mixture of English and non-English sentences!'
def test_pos_output_columns_3():
"""
Test function to ensure that the `get_part_of_speech` function error handling
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
try:
initial_df.get_part_of_speech()
assert False
except ValueError:
pass
def test_language_2():
"""
Tests detect_language function for NLPFrame with wrong column specified
"""
initial_df = nlp.NLPFrame({'text_col' : ['Today is a beautiful Monday and I would love getting a coffee. However, startbucks is closed.','It has been an amazing day today!']}, index = [0,1], column = 'non_existing')
try:
initial_df.detect_language(column = 'non_existing')
assert False
except ValueError:
pass
def test_get_nlp_summary_1():
"""
Tests get_nlp_summary function for NLPFrame without column with text
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
try:
initial_df.get_nlp_summary()
assert False
except ValueError:
pass
def test_language_1():
"""
Tests detect_language function for NLPFrame without column with text
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
try:
initial_df.detect_language()
assert False
except ValueError:
pass
def test_pos_output_columns_2():
"""
Test function to ensure that the `get_part_of_speech` function error handling
"""
initial_df = nlp.NLPFrame({'text_col': ['彼は新しい仕事に本当に満足している','It has been an amazing day today!']}, index = [0,1])
try:
res = initial_df.get_part_of_speech(show_only=5)
assert False, 'The function should throw an error when show_only argument is not valid'
except TypeError:
pass
def test_sentence_stopwords_1():
"""
Tests sentence_stopwords function for NLPFrame without column with text
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
try:
initial_df.sentence_stopwords()
assert False
except ValueError:
pass
def test_polarity_1():
"""
Tests polarity function for NLPFrame without column with text
"""
initial_df = nlp.NLPFrame({'text_col' : [5,6,8]})
try:
initial_df.polarity()
assert False
except ValueError:
pass
def test_pos_english_input():
"""
Test function to ensure that an English string is understood by the `get_part_of_speech` function.
"""
true_results = pd.DataFrame({'adjective': 0.0741, 'noun': 0.2222, 'verb': 0.2963},index = [0])
initial_df = nlp.NLPFrame({'text_col' : ['Today is a beautiful Monday and I would love getting a coffee. However, startbucks is closed.','It has been an amazing day today!']}, index = [0,1])
b = ['adjective', 'noun', 'verb']
res = initial_df.get_part_of_speech()
a = list(res.columns)
assert a==b , 'Default columns should be adjective, noun and verb'
def test_language_works():
"""
Test function to ensure that the `detect_language` function is properly returning correct languages.
"""
test_df = nlp.NLPFrame({'english_text' : ['I am a happy person'],
'mandarin_text': ['戰國策的版本存在非常多的錯誤'],
'spanish_text': ['Hola mi nombre es Bill y me gustan los gatos']
})
assert test_df.detect_language(column = 'english_text' )['language'][0] == 'English', 'Incorrect Language'
assert test_df.detect_language(column = 'mandarin_text')['language'][0] == 'Chinese', 'Incorrect Language'
assert test_df.detect_language(column = 'spanish_text' )['language'][0] == 'Spanish', 'Incorrect Language'