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problem_unittests.py
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problem_unittests.py
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from unittest.mock import MagicMock, patch
import sklearn.naive_bayes
import numpy as np
import pandas as pd
import re
# test csv file
TEST_CSV = 'data/test_info.csv'
class AssertTest(object):
'''Defines general test behavior.'''
def __init__(self, params):
self.assert_param_message = '\n'.join([str(k) + ': ' + str(v) + '' for k, v in params.items()])
def test(self, assert_condition, assert_message):
assert assert_condition, assert_message + '\n\nUnit Test Function Parameters\n' + self.assert_param_message
def _print_success_message():
print('Tests Passed!')
# test clean_dataframe
def test_numerical_df(numerical_dataframe):
# test result
transformed_df = numerical_dataframe(TEST_CSV)
# Check type is a DataFrame
assert isinstance(transformed_df, pd.DataFrame), 'Returned type is {}.'.format(type(transformed_df))
# check columns
column_names = list(transformed_df)
assert 'File' in column_names, 'No File column, found.'
assert 'Task' in column_names, 'No Task column, found.'
assert 'Category' in column_names, 'No Category column, found.'
assert 'Class' in column_names, 'No Class column, found.'
# check conversion values
assert transformed_df.loc[0, 'Category'] == 1, '`heavy` plagiarism mapping test, failed.'
assert transformed_df.loc[2, 'Category'] == 0, '`non` plagiarism mapping test, failed.'
assert transformed_df.loc[30, 'Category'] == 3, '`cut` plagiarism mapping test, failed.'
assert transformed_df.loc[5, 'Category'] == 2, '`light` plagiarism mapping test, failed.'
assert transformed_df.loc[37, 'Category'] == -1, 'original file mapping test, failed; should have a Category = -1.'
assert transformed_df.loc[41, 'Category'] == -1, 'original file mapping test, failed; should have a Category = -1.'
_print_success_message()
def test_containment(complete_df, containment_fn):
# check basic format and value
# for n = 1 and just the fifth file
test_val = containment_fn(complete_df, 1, 'g0pA_taske.txt')
assert isinstance(test_val, float), 'Returned type is {}.'.format(type(test_val))
assert test_val<=1.0, 'It appears that the value is not normalized; expected a value <=1, got: '+str(test_val)
# known vals for first few files
filenames = ['g0pA_taska.txt', 'g0pA_taskb.txt', 'g0pA_taskc.txt', 'g0pA_taskd.txt']
ngram_1 = [0.39814814814814814, 1.0, 0.86936936936936937, 0.5935828877005348]
ngram_3 = [0.0093457943925233638, 0.96410256410256412, 0.61363636363636365, 0.15675675675675677]
# results for comparison
results_1gram = []
results_3gram = []
for i in range(4):
val_1 = containment_fn(complete_df, 1, filenames[i])
val_3 = containment_fn(complete_df, 3, filenames[i])
results_1gram.append(val_1)
results_3gram.append(val_3)
# check correct results
assert all(np.isclose(results_1gram, ngram_1, rtol=1e-04)), \
'n=1 calculations are incorrect. Double check the intersection calculation.'
# check correct results
assert all(np.isclose(results_3gram, ngram_3, rtol=1e-04)), \
'n=3 calculations are incorrect.'
_print_success_message()
def test_lcs(df, lcs_word):
test_index = 10 # file 10
# get answer file text
answer_text = df.loc[test_index, 'Text']
# get text for orig file
# find the associated task type (one character, a-e)
task = df.loc[test_index, 'Task']
# we know that source texts have Class = -1
orig_rows = df[(df['Class'] == -1)]
orig_row = orig_rows[(orig_rows['Task'] == task)]
source_text = orig_row['Text'].values[0]
# calculate LCS
test_val = lcs_word(answer_text, source_text)
# check type
assert isinstance(test_val, float), 'Returned type is {}.'.format(type(test_val))
assert test_val<=1.0, 'It appears that the value is not normalized; expected a value <=1, got: '+str(test_val)
# known vals for first few files
lcs_vals = [0.1917808219178082, 0.8207547169811321, 0.8464912280701754, 0.3160621761658031, 0.24257425742574257]
# results for comparison
results = []
for i in range(5):
# get answer and source text
answer_text = df.loc[i, 'Text']
task = df.loc[i, 'Task']
# we know that source texts have Class = -1
orig_rows = df[(df['Class'] == -1)]
orig_row = orig_rows[(orig_rows['Task'] == task)]
source_text = orig_row['Text'].values[0]
# calc lcs
val = lcs_word(answer_text, source_text)
results.append(val)
# check correct results
assert all(np.isclose(results, lcs_vals, rtol=1e-05)), 'LCS calculations are incorrect.'
_print_success_message()
def test_data_split(train_x, train_y, test_x, test_y):
# check types
assert isinstance(train_x, np.ndarray),\
'train_x is not an array, instead got type: {}'.format(type(train_x))
assert isinstance(train_y, np.ndarray),\
'train_y is not an array, instead got type: {}'.format(type(train_y))
assert isinstance(test_x, np.ndarray),\
'test_x is not an array, instead got type: {}'.format(type(test_x))
assert isinstance(test_y, np.ndarray),\
'test_y is not an array, instead got type: {}'.format(type(test_y))
# should hold all 95 submission files
assert len(train_x) + len(test_x) == 95, \
'Unexpected amount of train + test data. Expecting 95 answer text files, got ' +str(len(train_x) + len(test_x))
assert len(test_x) > 1, \
'Unexpected amount of test data. There should be multiple test files.'
# check shape
assert train_x.shape[1]==2, \
'train_x should have as many columns as selected features, got: {}'.format(train_x.shape[1])
assert len(train_y.shape)==1, \
'train_y should be a 1D array, got shape: {}'.format(train_y.shape)
_print_success_message()