def test_pickle():
ftdf = pd.DataFrame(
data=[['woof woof', 0], ['meow meow', 1]],
columns=['txt', 'lbl']
)
ft_clf = FirstColFtClassifier()
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
fd, pic_fpath = tempfile.mkstemp()
with open(pic_fpath, 'wb+') as bfile:
pickle.dump(ft_clf, bfile)
with open(pic_fpath, 'rb') as bfile:
ft_clf2 = pickle.load(bfile)
assert ft_clf2 != ft_clf
assert ft_clf2.predict([['woof woof']])[0] == 0
assert ft_clf2.predict([['meow meow']])[0] == 1
assert ft_clf2.predict([['meow']])[0] == 1
assert ft_clf2.predict([['woof lol']])[0] == 0
assert ft_clf2.predict([['meow lolz']])[0] == 1
# Clean up
os.close(fd) # Prevent a file-handle leak
os.unlink(pic_fpath)
def test_pickle():
ftdf = pd.DataFrame(data=[['woof woof', 0], ['meow meow', 1]],
columns=['txt', 'lbl'])
ft_clf = FirstColFtClassifier()
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
fd, pic_fpath = tempfile.mkstemp()
with open(pic_fpath, 'wb+') as bfile:
pickle.dump(ft_clf, bfile)
with open(pic_fpath, 'rb') as bfile:
ft_clf2 = pickle.load(bfile)
assert ft_clf2 != ft_clf
assert ft_clf2.predict([['woof woof']])[0] == 0
assert ft_clf2.predict([['meow meow']])[0] == 1
assert ft_clf2.predict([['meow']])[0] == 1
assert ft_clf2.predict([['woof lol']])[0] == 0
assert ft_clf2.predict([['meow lolz']])[0] == 1
# Clean up
os.close(fd) # Prevent a file-handle leak
os.unlink(pic_fpath)
def test_pickle_unfitted():
ftdf = pd.DataFrame(data=[['woof woof', 0], ['meow meow', 1]],
columns=['txt', 'lbl'])
ft_clf = FirstColFtClassifier()
pic_fpath = os.path.expanduser('~/.temp/ttemp_ft_model.ft')
with open(pic_fpath, 'wb+') as bfile:
pickle.dump(ft_clf, bfile)
with open(pic_fpath, 'rb') as bfile:
ft_clf2 = pickle.load(bfile)
with pytest.raises(NotFittedError):
assert ft_clf.predict([['woof woof']])[0] == 0
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
assert ft_clf2 != ft_clf
with pytest.raises(NotFittedError):
assert ft_clf2.predict([['woof woof']])[0] == 0
ft_clf2.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf2.predict([['woof woof']])[0] == 0
assert ft_clf2.predict([['meow meow']])[0] == 1
assert ft_clf2.predict([['meow']])[0] == 1
assert ft_clf2.predict([['woof lol']])[0] == 0
assert ft_clf2.predict([['meow lolz']])[0] == 1
def test_predict():
ftdf = _ftdf()
ft_clf = FirstColFtClassifier()
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
def test_predict():
ftdf = _ftdf()
ft_clf = FirstColFtClassifier()
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
def test_pickle(quantize):
ftdf = pd.DataFrame(data=[['woof woof', 0], ['meow meow', 1]],
columns=['txt', 'lbl'])
ft_clf = FirstColFtClassifier()
ft_clf.fit(ftdf[['txt']], ftdf['lbl'])
if quantize:
with pytest.raises(ValueError):
ft_clf.quantize(cutoff=1)
assert not ft_clf.is_quantized()
return
assert ft_clf.predict([['woof woof']])[0] == 0
assert ft_clf.predict([['meow meow']])[0] == 1
assert ft_clf.predict([['meow']])[0] == 1
assert ft_clf.predict([['woof lol']])[0] == 0
assert ft_clf.predict([['meow lolz']])[0] == 1
fd, pic_fpath = tempfile.mkstemp()
with open(pic_fpath, 'wb+') as bfile:
pickle.dump(ft_clf, bfile)
with open(pic_fpath, 'rb') as bfile:
ft_clf2 = pickle.load(bfile)
assert ft_clf2 != ft_clf
assert ft_clf2.predict([['woof woof']])[0] == 0
assert ft_clf2.predict([['meow meow']])[0] == 1
assert ft_clf2.predict([['meow']])[0] == 1
assert ft_clf2.predict([['woof lol']])[0] == 0
assert ft_clf2.predict([['meow lolz']])[0] == 1
if quantize:
assert not ft_clf2.is_quantized()
# Clean up
os.close(fd) # Prevent a file-handle leak
os.unlink(pic_fpath)
