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Refactor election expenses classifier #89

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Nov 4, 2017
Merged

Refactor election expenses classifier #89

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e47610b
refactor X to a more meaningful name in election expenses classifier
Oct 19, 2017
993bab4
Remove a Rails accent of use subject in favor of Zen of Python: expli…
Oct 19, 2017
ee324e9
changes from dataset to dataframe metions in election expenses classi…
Oct 19, 2017
e572e9b
Gives a more meaningful constant name for a election legal entity use…
Oct 19, 2017
afce56e
Makes the assertion more clear about what is been asserted
Oct 19, 2017
8fa947e
fixes a typo on transform for election expense classifier test
Oct 19, 2017
4ea4974
Gives a more meaningful name for transform test on election expenses …
lipemorais Oct 27, 2017
d906fe7
Gives a more meaningful name for fit test on election expenses classi…
lipemorais Oct 27, 2017
250022a
refactor test to just read from fixture file when necessary to improv…
lipemorais Oct 27, 2017
6025ef5
Merge branch 'master' into refactor-election-expenses-classifier
lipemorais Oct 31, 2017
d63ba5d
Create dataframe focused on each test scenario
lipemorais Nov 2, 2017
e9c3ac8
DRY dataframe creation
lipemorais Nov 2, 2017
1fc676f
Merge branch 'master' into refactor-election-expenses-classifier
lipemorais Nov 3, 2017
a3bd233
Moves ELECTION_LEGAL_ENTITY constant to a module level
Nov 3, 2017
8b14e51
Make test data easier to read on test_election_expenses_classifier
Nov 3, 2017
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14 changes: 8 additions & 6 deletions rosie/chamber_of_deputies/classifiers/election_expenses_classifier.py
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Expand Up @@ -14,11 +14,13 @@ class ElectionExpensesClassifier(TransformerMixin):
Brazilian Federal Revenue category of companies, preceded by its code.
"""

def fit(self, X):
return self
def fit(self, dataframe):
pass

def transform(self, X=None):
return self
def transform(self, dataframe=None):
pass

def predict(self, X):
return X['legal_entity'] == '409-0 - CANDIDATO A CARGO POLITICO ELETIVO'
def predict(self, dataframe):
ELECTION_LEGAL_ENTITY = '409-0 - CANDIDATO A CARGO POLITICO ELETIVO'
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PEP8 says that constants usually should be defined in the module level (not method level), would you mind changing this minor detail?


return dataframe['legal_entity'] == ELECTION_LEGAL_ENTITY
31 changes: 20 additions & 11 deletions rosie/chamber_of_deputies/tests/test_election_expenses_classifier.py
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Expand Up @@ -9,18 +9,27 @@
class TestElectionExpensesClassifier(TestCase):

def setUp(self):
self.dataset = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/election_expenses_classifier.csv',
dtype={'name': np.str, 'legal_entity': np.str})
self.subject = ElectionExpensesClassifier()
self.election_expenser_classifier = ElectionExpensesClassifier()
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Just wondering: as each test file refers to only one classifier I wouldn't bother shortening it to self.classifier…

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I aiming to achieve people that have no context of a classifier, to understand what is this referencing to.

How do you self.classifier helping with it? Or is this tip addressing something else that I didn't get here?

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I aiming to achieve people that have no context of a classifier

Hopefully readers know which file they are reading… or maybe I'm just too optimistic.

But nothing against self.election_expenser_classifier per se except that I was trying something a bit shorter (minor enhancement in code readability) and as meaningful as before (guessing the file name would be a context to inform the reader about which classifier we're talking about). But that was just a minor suggestion… feel free to throw it away ; )

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In that way, I'm with @cuducos, I prefer something meaningful but simple at the same way :)


def test_is_election_company(self):
self.assertEqual(self.subject.predict(self.dataset)[0], True)
def test_legal_entity_is_a_election_company(self):
self.dataframe = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/election_expenses_classifier.csv',
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Hey @anaschwendler @cuducos

How could I create a data frame here from a string or a data structure? Just to avoid have tests accessing file system and be able to be more specific with just what is necessary for this test.
Something like: pd.read('just the information necessary for this test)

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A dataframe is a structure of rows and columns. So something like pd.DataFrame(data=[['my text']]) might work (the first list is the row, the inside list is the single column I guess).

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Nice with your tip and some search I was able to do what I was planing. :)

dtype={'name': np.str, 'legal_entity': np.str})

def test_is_not_election_company(self):
self.assertEqual(self.subject.predict(self.dataset)[1], False)
prediction_result = self.election_expenser_classifier.predict(self.dataframe)

def test_fit(self):
self.assertEqual(self.subject.fit(self.dataset), self.subject)
self.assertEqual(prediction_result[0], True)

def test_tranform(self):
self.assertEqual(self.subject.transform(), self.subject)
def test_legal_entity_is_not_election_company(self):
self.dataframe = pd.read_csv('rosie/chamber_of_deputies/tests/fixtures/election_expenses_classifier.csv',
dtype={'name': np.str, 'legal_entity': np.str})

prediction_result = self.election_expenser_classifier.predict(self.dataframe)

self.assertEqual(prediction_result[1], False)

def test_fit_just_for_formality_because_its_never_used(self):
empty_dataframe = pd.DataFrame()
self.assertTrue(self.election_expenser_classifier.fit(empty_dataframe) is None)

def test_transform_just_for_formality_because_its_never_used(self):
self.assertTrue(self.election_expenser_classifier.transform() is None)