def test_allequal(self):
df = pd.DataFrame()
df["c1"] = [chr(0) for _ in range(100)]
df["c2"] = [1 for _ in range(100)]
pmi = self.mi(df, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertEqual(r, pmi)
r = mutual_info(1, 0, df)[0]
self.assertEqual(r, pmi)
def test_allnull(self):
df = pd.DataFrame()
df["c1"] = [None for i in range(100)]
df["c2"] = [np.NaN for i in range(100)]
pmi = self.mi(df, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertEqual(r, pmi)
r = mutual_info(1, 0, df)[0]
self.assertEqual(r, pmi)
def test_halfnull_halfequal(self):
df = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c1.extend([None for _ in range(50)])
c2.extend([np.NaN for _ in range(50)])
df["c1"] = c1
df["c2"] = c2
pmi = self.mi(df, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_empty(self):
df = pd.DataFrame()
df["c1"] = []
df["c2"] = []
r1 = mutual_info(0, 1, df)[0]
self.assertEqual(r1, 0.)
def test_halfhalf_shuffled(self):
for _ in range(2):
df = pd.DataFrame()
c1 = [chr(1) for _ in range(50)]
c2 = [2 for _ in range(50)]
c3 = [0.7 for _ in range(50)]
c1.extend(["zz" for _ in range(50)])
c2.extend([100 for _ in range(50)])
c3.extend([32. for _ in range(50)])
random.shuffle(c1)
random.shuffle(c2)
random.shuffle(c3)
df["c1"] = c1
df["c2"] = c2
df["c3"] = c3
pmi = self.mi(df, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(df, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(df, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
def test_mixed_shuffled_with_null(self):
for _ in range(2):
df = pd.DataFrame()
c1 = [chr(i) for i in range(50)]
c2 = [i for i in range(1, 51)]
c3 = [i / 0.7 for i in range(1, 51)]
c1.extend([None for _ in range(50)])
c2.extend([np.NaN for _ in range(50)])
c3.extend([None for _ in range(50)])
random.shuffle(c1)
random.shuffle(c2)
random.shuffle(c3)
df["c1"] = c1
df["c2"] = c2
df["c3"] = c3
pmi = self.mi(df, "c1", "c2")
r = mutual_info(0, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(1, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(df, "c1", "c3")
r = mutual_info(0, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 0, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
pmi = self.mi(df, "c2", "c3")
r = mutual_info(1, 2, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
r = mutual_info(2, 1, df)[0]
self.assertAlmostEqual(r, pmi, delta=0.000001)
