def generic_multiplesplits(self, cls, repeat):
self.maxDiff = None
all_datasets = [
generate_dataset(intercept=i,
coeff=i,
size=50,
min_x=(i - 1) * 10,
max_x=i * 10,
cls=cls,
repeat=repeat) for i in range(1, 9)
]
dataset = sum(all_datasets, [])
reg = compute_regression(dataset)
flat_reg = reg.flatify()
self.assertEqual(list(flat_reg), list(sorted(dataset)))
# TODO should be 7, but is 8 in reality because of the non-optimality of the algorithm
self.assertEqual(reg.nb_params, flat_reg.nb_params)
self.assertEqual(reg.breakpoints, flat_reg.breakpoints)
self.assertTrue(flat_reg.null_RSS)
self.assertTrue(flat_reg.rss_equal(flat_reg.RSS, 0))
self.assertIn(len(flat_reg.breakpoints), (7, 8))
self.assertAlmostIncluded(range(10, 80, 10),
flat_reg.breakpoints,
epsilon=2)
for x, y in dataset:
prediction = flat_reg.predict(x)
self.assertAlmostEqual(y, prediction)
other_flat = compute_regression(dataset,
breakpoints=flat_reg.breakpoints)
self.assertEqual(str(other_flat), str(flat_reg))
def test_singlesplit(self):
intercept_1 = random.uniform(0, 50)
coeff_1 = random.uniform(0, 50)
intercept_2 = random.uniform(50, 100)
coeff_2 = random.uniform(50, 100)
split = random.uniform(30, 60)
dataset1 = generate_dataset(intercept=intercept_1,
coeff=coeff_1,
size=50,
min_x=0,
max_x=split)
dataset2 = generate_dataset(intercept=intercept_2,
coeff=coeff_2,
size=50,
min_x=split,
max_x=100)
dataset = dataset1 + dataset2
random.shuffle(dataset)
reg = compute_regression(dataset)
self.assertIsInstance(reg, Node)
self.assertAlmostEqual(reg.RSS, 0, delta=1e-3)
self.assertIsInstance(reg.left, Leaf)
self.assertAlmostEqual(reg.left.intercept, intercept_1)
self.assertAlmostEqual(reg.left.coeff, coeff_1)
self.assertIsInstance(reg.right, Leaf)
self.assertAlmostEqual(reg.right.intercept, intercept_2)
self.assertAlmostEqual(reg.right.coeff, coeff_2)
self.assertEqual(reg.split, max(dataset1)[0])
self.assertEqual(reg.breakpoints, [reg.split])
self.assertEqual(list(reg), list(sorted(dataset)))
def test_plot_error(self, mock_show):
all_datasets = [
generate_dataset(intercept=i,
coeff=i,
size=50,
min_x=(i - 1) * 10,
max_x=i * 10) for i in range(1, 9)
]
dataset = sum(all_datasets, [])
reg = compute_regression(dataset)
reg.plot_error()
reg.plot_error(log=True)
reg.plot_error(log_x=True)
reg.plot_error(log_y=True)
def generic_multiplesplits_simplify(self, cls, repeat):
self.maxDiff = None
all_datasets = [
generate_dataset(intercept=i,
coeff=i,
size=50,
min_x=(i - 1) * 10,
max_x=i * 10,
cls=cls,
repeat=repeat) for i in range(1, 9)
]
dataset = sum(all_datasets, [])
reg = compute_regression(dataset)
merged = reg.merge()
simple_df = reg.simplify()
self.assertEqual(len(simple_df), len(reg.breakpoints) + 1)
self.assertEqual(list(simple_df.nb_breakpoints),
list(range(len(reg.breakpoints), -1, -1)))
self.assertTrue(reg.rss_equal(reg.RSS, simple_df.RSS[0]))
self.assertTrue(reg.rss_equal(list(simple_df.RSS)[-1], merged.RSS))
self.assertTrue(reg.error_equal(reg.BIC, simple_df.BIC[0]))
self.assertTrue(reg.error_equal(list(simple_df.BIC)[-1], merged.BIC))
for old_rss, new_rss in zip(simple_df.RSS, simple_df.RSS[1:]):
if not reg.rss_equal(old_rss, new_rss):
self.assertLess(old_rss, new_rss)
for nb_breakpoints, new_reg in zip(simple_df.nb_breakpoints,
simple_df.regression):
self.assertEqual(list(reg), list(new_reg))
self.assertEqual(nb_breakpoints, len(new_reg.breakpoints))
self.assertTrue(set(new_reg.breakpoints) <= set(reg.breakpoints))
simple_reg = reg.auto_simplify()
expected_reg = simple_df.regression[1]
self.assertEqual(simple_reg.breakpoints, expected_reg.breakpoints)
self.assertEqual(simple_reg.RSS, expected_reg.RSS)
self.assertEqual(simple_reg.BIC, expected_reg.BIC)
# Checking that the auto_simplify() is a fix-point
new_reg = simple_reg.auto_simplify()
self.assertEqual(simple_reg.breakpoints, new_reg.breakpoints)
# Checking to_pandas method
df = new_reg.to_pandas()
self.assertEqual(len(df), len(new_reg.segments))
for (_, row), ((min_x, max_x), leaf) in zip(df.iterrows(),
new_reg.segments):
self.assertEqual(row['min_x'], min_x)
self.assertEqual(row['max_x'], max_x)
self.assertEqual(row['intercept'], leaf.intercept)
self.assertEqual(row['coefficient'], leaf.coeff)
self.assertEqual(row['RSS'], leaf.RSS)
self.assertEqual(row['MSE'], leaf.MSE)
def test_nosplit(self):
intercept = random.uniform(0, 100)
coeff = random.uniform(0, 100)
dataset = generate_dataset(intercept=intercept,
coeff=coeff,
size=50,
min_x=0,
max_x=100)
reg = compute_regression(dataset)
self.assertIsInstance(reg, Leaf)
self.assertAlmostEqual(reg.intercept, intercept)
self.assertAlmostEqual(reg.coeff, coeff)
self.assertAlmostEqual(reg.RSS, 0, delta=1e-3)
self.assertEqual(reg.breakpoints, [])
self.assertEqual(list(reg), list(sorted(dataset)))
def test_plot_dataset(self, mock_show):
all_datasets = [
generate_dataset(intercept=i,
coeff=i,
size=50,
min_x=(i - 1) * 10,
max_x=i * 10) for i in range(1, 9)
]
dataset = sum(all_datasets, [])
reg = compute_regression(dataset)
reg.plot_dataset()
reg.plot_dataset(log=True)
reg.plot_dataset(log_x=True)
reg.plot_dataset(log_y=True)
reg.plot_dataset(plot_merged_reg=True)
reg.plot_dataset(color=False)
reg.plot_dataset(color='green')
reg.plot_dataset(color=['green', 'blue', 'red'])
def generic_multiplesplits(self, cls, repeat):
all_datasets = [
generate_dataset(intercept=i,
coeff=i,
size=50,
min_x=(i - 1) * 10,
max_x=i * 10,
cls=cls,
repeat=repeat) for i in range(1, 9)
]
dataset = sum(all_datasets, [])
reg = compute_regression(dataset)
self.assertEqual(list(reg), list(sorted(dataset)))
# TODO should be 7, but is 8 in reality because of the non-optimality of the algorithm
self.assertIn(len(reg.breakpoints), (7, 8))
self.assertAlmostIncluded(range(10, 80, 10),
reg.breakpoints,
epsilon=2)
for x, y in dataset:
prediction = reg.predict(x)
self.assertAlmostEqual(y, prediction)
