def testInputArray(self):
X = np.random.normal(0, 1, size=(200, 3))
y = np.random.randint(0, 2, size=200)
lastochka = LastochkaTransformer()
lastochka.fit(X, y)
X_w = lastochka.transform(X)
self.assertIsInstance(X_w, np.ndarray)
def testSpecial(self):
_boston = load_boston()
X = pd.DataFrame(_boston["data"], columns=_boston["feature_names"])
y = (_boston["target"] >= np.median(_boston["target"])).astype(int)
specs = {"PTRATIO": [20.2, 14.7]}
lastochka = LastochkaTransformer(verbose=True, n_final=3, n_initial=10, specials=specs)
lastochka.fit(X, y)
lastochka.transform(X)
self.assertTrue(list(lastochka.get_transformer("PTRATIO").specials_stats.keys()) == specs["PTRATIO"])
def testVerbose(self):
N_SAMPLES = 100
X, y = make_classification(n_samples=N_SAMPLES, n_features=5, n_informative=2, random_state=42)
column_names = ['X%i' % i for i in range(5)]
X_df = pd.DataFrame(X, columns=column_names)
X_df["CAT"] = np.random.choice(list("ABCDF"), N_SAMPLES)
lastochka = LastochkaTransformer(verbose=False, n_final=3, n_initial=10)
lastochka.fit(X_df, y)
def testMissing(self):
_boston = load_boston()
X = pd.DataFrame(_boston["data"], columns=_boston["feature_names"])
np.random.seed(2)
indexes = np.random.choice(X.index.tolist(), 200)
X.loc[indexes, "ZN"] = np.nan
y = (_boston["target"] >= np.median(_boston["target"])).astype(int)
lastochka = LastochkaTransformer(verbose=True, n_final=3, n_initial=10)
lastochka.fit(X, y)
lastochka.transform(X)
self.assertTrue(lastochka.get_transformer("ZN").missing_woe_value is not None)
def testCategory(self):
_boston = load_boston()
X = pd.DataFrame(_boston["data"], columns=_boston["feature_names"])
X["RAD_CAT"] = X["RAD"].astype(str)
X = X.drop("RAD", axis=1)
y = (_boston["target"] >= np.median(_boston["target"])).astype(int)
lastochka = LastochkaTransformer(verbose=True, n_final=3, n_initial=10)
lastochka.fit(X, y)
lastochka.transform(X)
optimizer_instance = lastochka.get_transformer("RAD_CAT").optimizer_instance
self.assertIsInstance(optimizer_instance, CategoryOptimizer)