def test_categories_to_integers_grid_search(self):
data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data",
"adult_set.txt")
df = pandas.read_csv(data, sep="\t")
X = df.drop('income', axis=1)
y = df['income']
pipe = make_pipeline(CategoriesToIntegers(), LogisticRegression())
self.assertRaise(lambda: test_sklearn_grid_search_cv(lambda: pipe, df),
ValueError)
self.assertRaise(
lambda: test_sklearn_grid_search_cv(
lambda: pipe, X, y, categoriestointegers__single=[True, False]
), ValueError, "Unable to find category value")
pipe = make_pipeline(CategoriesToIntegers(),
Imputer(strategy='most_frequent'),
LogisticRegression())
res = test_sklearn_grid_search_cv(
lambda: pipe,
X,
y,
categoriestointegers__single=[True, False],
categoriestointegers__skip_errors=[True])
self.assertIn('model', res)
self.assertIn('score', res)
self.assertGreater(res['score'], 0)
self.assertLesser(res['score'], 1)
def test_categories_to_integers(self):
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)
self.assertIsInstance(str(trans), str)
newdf = trans.transform(df)
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_grid_search(self):
data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data",
"adult_set.txt")
df = pandas.read_csv(data, sep="\t")
X = df.drop('income', axis=1)
y = df['income'] # pylint: disable=E1136
pipe = make_pipeline(CategoriesToIntegers(), LogisticRegression())
self.assertRaise(lambda: test_sklearn_grid_search_cv(lambda: pipe, df),
ValueError)
if (compare_module_version(sklver, "0.24") >= 0 and # pylint: disable=R1716
compare_module_version(pandas.__version__, "1.3") < 0):
self.assertRaise(
lambda: test_sklearn_grid_search_cv(
lambda: pipe,
X,
y,
categoriestointegers__single=[True, False]), ValueError,
"Unable to find category value")
pipe = make_pipeline(CategoriesToIntegers(),
Imputer(strategy='most_frequent'),
LogisticRegression(n_jobs=1))
try:
res = test_sklearn_grid_search_cv(
lambda: pipe,
X,
y,
categoriestointegers__single=[True, False],
categoriestointegers__skip_errors=[True])
except AttributeError as e:
if compare_module_version(sklver, "0.24") < 0:
return
raise e
self.assertIn('model', res)
self.assertIn('score', res)
self.assertGreater(res['score'], 0)
self.assertLesser(res['score'], 1)
def test_categories_to_integers_big(self):
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))
newdf2 = trans.fit_transform(df)
self.assertEqual(newdf, newdf2)
rep = repr(trans)
self.assertEqual("CategoriesToIntegers(columns=None,remove=None,single=True,skip_errors=False)",
rep.replace(" ", "").replace("\n", ""))
def test_categories_to_integers_big(self):
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)) # pylint: disable=E1101
newdf2 = trans.fit_transform(df)
self.assertEqual(newdf, newdf2)
rep = repr(trans)
self.assertStartsWith("CategoriesToIntegers(",
rep.replace(" ", "").replace("\n", ""))
self.assertIn("single=True", rep.replace(" ", "").replace("\n", ""))
def test_categories_to_integers_clone(self):
self.maxDiff = None
test_sklearn_clone(lambda: CategoriesToIntegers())
def test_categories_to_integers_pickle(self):
data = os.path.join(os.path.abspath(os.path.dirname(__file__)), "data",
"adult_set.txt")
df = pandas.read_csv(data, sep="\t")
test_sklearn_pickle(lambda: CategoriesToIntegers(skip_errors=True), df)
