def setUp(self):
df, y = load_driftbif(100, 10, classification=True, seed=42)
df['my_id'] = df['id'].astype('str')
del df["id"]
self.df = df
def test_regression_labels(self):
Nsamples = 10
X, y = load_driftbif(Nsamples, 100, classification=False)
self.assertEqual(
y.size,
np.unique(y).size,
'For regression the target vector is expected to not contain any dublicated labels.'
)
def test_relevant_feature_extraction(self):
df, y = load_driftbif(100, 10, classification=False)
df['id'] = df['id'].astype('str')
y.index = y.index.astype('str')
X = extract_relevant_features(df,
y,
column_id="id",
column_sort="time",
column_kind="dimension",
column_value="value")
self.assertGreater(len(X.columns), 10)
def test_default_dimensionality(self):
Nsamples = 10
Nt = 100
X, y = load_driftbif(Nsamples, Nt)
self.assertEqual(X.shape, (2 * Nt * Nsamples, 4))
def test_classification_labels(self):
X, y = load_driftbif(10, 100)
self.assertEqual(set(y), set([0, 1]))
def test_configured_dimensionality(self):
Nsamples = 10
Nt = 100
X, y = load_driftbif(Nsamples, Nt, m=3)
self.assertEqual(X.shape, (3 * Nt * Nsamples, 4))
def test_configured_dimensionality(self):
Nsamples = 10
Nt = 100
X, y = load_driftbif(Nsamples, Nt, m=3)
self.assertEqual(X.shape, (3 * Nt * Nsamples, 4))
def test_default_dimensionality(self):
Nsamples = 10
Nt = 100
X, y = load_driftbif(Nsamples, Nt)
self.assertEqual(X.shape, (2 * Nt * Nsamples, 4))
def test_regression_labels(self):
Nsamples = 10
X, y = load_driftbif(Nsamples, 100, classification=False)
self.assertEqual(y.size, np.unique(y).size,
'For regression the target vector is expected to not contain any dublicated labels.')
def test_classification_labels(self):
X, y = load_driftbif(10, 100)
self.assertEqual(set(y), set([0,1]))
