def test_categories_to_integers_big(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data",
"adult_set.txt")
df = pandas.read_csv(data, sep="\t")
trans = CategoriesToIntegers(single=True)
trans.fit(df)
newdf = trans.transform(df)
self.assertEqual(len(newdf.columns), len(df.columns))
self.assertEqual(list(newdf.columns), list(df.columns))
def test_categories_to_integers_big(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = os.path.join(os.path.abspath(
os.path.dirname(__file__)), "data", "adult_set.txt")
df = pandas.read_csv(data, sep="\t")
trans = CategoriesToIntegers(single=True)
trans.fit(df)
newdf = trans.transform(df)
self.assertEqual(len(newdf.columns), len(df.columns))
self.assertEqual(list(newdf.columns), list(df.columns))
def test_categories_to_integers(self):
fLOG(
__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = os.path.join(os.path.abspath(
os.path.dirname(__file__)), "data", "adult_set.txt")
df = pandas.read_csv(data, sep="\t")
trans = CategoriesToIntegers()
trans.fit(df)
newdf = trans.transform(df)
# fLOG(newdf.columns)
fLOG(list(newdf.columns))
exp = ['age', 'final_weight', 'education_num', 'capital_gain', 'capital_loss',
'hours_per_week', 'marital_status= Divorced',
'marital_status= Married-AF-spouse',
'marital_status= Married-civ-spouse',
'marital_status= Married-spouse-absent',
'marital_status= Never-married', 'marital_status= Separated',
'marital_status= Widowed', 'sex= Female', 'sex= Male',
'education= 10th', 'education= 11th', 'education= 12th',
'education= 1st-4th', 'education= 5th-6th', 'education= 7th-8th',
'education= 9th', 'education= Assoc-acdm', 'education= Assoc-voc',
'education= Bachelors', 'education= Doctorate', 'education= HS-grad',
'education= Masters', 'education= Preschool', 'education= Prof-school',
'education= Some-college', 'native_country= ?',
'native_country= Cambodia', 'native_country= Canada',
'native_country= China', 'native_country= Columbia',
'native_country= Cuba', 'native_country= Dominican-Republic',
'native_country= Ecuador', 'native_country= El-Salvador',
'native_country= England', 'native_country= France',
'native_country= Germany', 'native_country= Guatemala',
'native_country= Haiti', 'native_country= Honduras',
'native_country= India', 'native_country= Iran',
'native_country= Italy', 'native_country= Jamaica',
'native_country= Laos', 'native_country= Mexico',
'native_country= Philippines', 'native_country= Poland',
'native_country= Portugal', 'native_country= Puerto-Rico',
'native_country= South', 'native_country= Taiwan',
'native_country= Thailand', 'native_country= United-States',
'race= Amer-Indian-Eskimo', 'race= Asian-Pac-Islander', 'race= Black',
'race= Other', 'race= White', 'relationship= Husband',
'relationship= Not-in-family', 'relationship= Other-relative',
'relationship= Own-child', 'relationship= Unmarried',
'relationship= Wife', 'workclass= ?', 'workclass= Federal-gov',
'workclass= Local-gov', 'workclass= Private', 'workclass= Self-emp-inc',
'workclass= Self-emp-not-inc', 'workclass= State-gov', 'income= <=50K',
'income= >50K', 'occupation= ?', 'occupation= Adm-clerical',
'occupation= Armed-Forces', 'occupation= Craft-repair',
'occupation= Exec-managerial', 'occupation= Farming-fishing',
'occupation= Handlers-cleaners', 'occupation= Machine-op-inspct',
'occupation= Other-service', 'occupation= Priv-house-serv',
'occupation= Prof-specialty', 'occupation= Protective-serv',
'occupation= Sales', 'occupation= Tech-support',
'occupation= Transport-moving']
exp.sort()
ret = list(newdf.columns)
ret.sort()
self.assertEqual(len(ret), len(exp))
self.assertEqual(exp, ret)
def test_categories_to_integers(self):
fLOG(__file__,
self._testMethodName,
OutputPrint=__name__ == "__main__")
data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data",
"adult_set.txt")
df = pandas.read_csv(data, sep="\t")
trans = CategoriesToIntegers()
trans.fit(df)
newdf = trans.transform(df)
# fLOG(newdf.columns)
fLOG(list(newdf.columns))
exp = [
'age', 'final_weight', 'education_num', 'capital_gain',
'capital_loss', 'hours_per_week', 'marital_status= Divorced',
'marital_status= Married-AF-spouse',
'marital_status= Married-civ-spouse',
'marital_status= Married-spouse-absent',
'marital_status= Never-married', 'marital_status= Separated',
'marital_status= Widowed', 'sex= Female', 'sex= Male',
'education= 10th', 'education= 11th', 'education= 12th',
'education= 1st-4th', 'education= 5th-6th', 'education= 7th-8th',
'education= 9th', 'education= Assoc-acdm', 'education= Assoc-voc',
'education= Bachelors', 'education= Doctorate',
'education= HS-grad', 'education= Masters', 'education= Preschool',
'education= Prof-school', 'education= Some-college',
'native_country= ?', 'native_country= Cambodia',
'native_country= Canada', 'native_country= China',
'native_country= Columbia', 'native_country= Cuba',
'native_country= Dominican-Republic', 'native_country= Ecuador',
'native_country= El-Salvador', 'native_country= England',
'native_country= France', 'native_country= Germany',
'native_country= Guatemala', 'native_country= Haiti',
'native_country= Honduras', 'native_country= India',
'native_country= Iran', 'native_country= Italy',
'native_country= Jamaica', 'native_country= Laos',
'native_country= Mexico', 'native_country= Philippines',
'native_country= Poland', 'native_country= Portugal',
'native_country= Puerto-Rico', 'native_country= South',
'native_country= Taiwan', 'native_country= Thailand',
'native_country= United-States', 'race= Amer-Indian-Eskimo',
'race= Asian-Pac-Islander', 'race= Black', 'race= Other',
'race= White', 'relationship= Husband',
'relationship= Not-in-family', 'relationship= Other-relative',
'relationship= Own-child', 'relationship= Unmarried',
'relationship= Wife', 'workclass= ?', 'workclass= Federal-gov',
'workclass= Local-gov', 'workclass= Private',
'workclass= Self-emp-inc', 'workclass= Self-emp-not-inc',
'workclass= State-gov', 'income= <=50K', 'income= >50K',
'occupation= ?', 'occupation= Adm-clerical',
'occupation= Armed-Forces', 'occupation= Craft-repair',
'occupation= Exec-managerial', 'occupation= Farming-fishing',
'occupation= Handlers-cleaners', 'occupation= Machine-op-inspct',
'occupation= Other-service', 'occupation= Priv-house-serv',
'occupation= Prof-specialty', 'occupation= Protective-serv',
'occupation= Sales', 'occupation= Tech-support',
'occupation= Transport-moving'
]
exp.sort()
ret = list(newdf.columns)
ret.sort()
self.assertEqual(len(ret), len(exp))
self.assertEqual(exp, ret)