Merge pull request #14 from Ivan-Pokhabov/dev/graph-tests

feat & test: make first version of graphs criterion and tests for them

Author: Ivan-Pokhabov

stattest/test/exponent.py

@@ -7,6 +7,10 @@
 
 from stattest.core.distribution import expon
 from stattest.test.goodness_of_fit import AbstractGoodnessOfFitTestStatistic
+from stattest.test.graph_goodness_of_fit import (
+    GraphEdgesNumberTestStatistic,
+    GraphMaxDegreeTestStatistic,
+)
 
 
 class AbstractExponentialityTestStatistic(AbstractGoodnessOfFitTestStatistic, ABC):
@@ -813,4 +817,22 @@ def execute_statistic(self, rvs, **kwargs):
         return hg
 
 
+class GraphEdgesNumberExpTest(AbstractExponentialityTestStatistic, GraphEdgesNumberTestStatistic):
+    @staticmethod
+    @override
+    def code():
+        super_class = AbstractExponentialityTestStatistic
+        parent_code = super(super_class, super_class).code()
+        return f"EdgesNumber_{parent_code}"
+
+
+class GraphMaxDegreeExpTest(AbstractExponentialityTestStatistic, GraphMaxDegreeTestStatistic):
+    @staticmethod
+    @override
+    def code():
+        super_class = AbstractExponentialityTestStatistic
+        parent_code = super(super_class, super_class).code()
+        return f"MaxDegree_{parent_code}"
+
+
 # TODO: check all mistype warnings

stattest/test/graph_goodness_of_fit.py

@@ -0,0 +1,52 @@
+from abc import ABC
+from typing import Union
+
+from numpy import float64
+from typing_extensions import override
+
+from stattest.test.goodness_of_fit import AbstractGoodnessOfFitTestStatistic
+
+
+class AbstractGraphTestStatistic(AbstractGoodnessOfFitTestStatistic, ABC):
+    @override
+    def execute_statistic(self, rvs, **kwargs) -> Union[float, float64]:
+        dist = self._compute_dist(rvs)
+
+        adjacency_list = self._make_adjacency_list(rvs, dist)
+        statistic = self.get_graph_stat(adjacency_list)
+        return statistic
+
+    @staticmethod
+    def _make_adjacency_list(rvs, dist: float) -> list[list[int]]:
+        adjacency_list: list[list[int]] = []
+
+        for i in range(len(rvs)):
+            adjacency_list.append([])
+            for j in range(i):
+                if abs(rvs[i] - rvs[j]) < dist:
+                    adjacency_list[i].append(j)
+                    adjacency_list[j].append(i)
+
+        return adjacency_list
+
+    @staticmethod
+    def _compute_dist(rvs):  # TODO (normalize for different distributions)
+        return (max(rvs) - min(rvs)) / 10
+
+    @staticmethod
+    def get_graph_stat(graph: list[list[int]]):
+        raise NotImplementedError("Method is not implemented")
+
+
+class GraphEdgesNumberTestStatistic(AbstractGraphTestStatistic, ABC):
+    @staticmethod
+    @override
+    def get_graph_stat(graph):
+        return sum(map(len, graph)) // 2
+
+
+class GraphMaxDegreeTestStatistic(AbstractGraphTestStatistic, ABC):
+    @staticmethod
+    @override
+    def get_graph_stat(graph):
+        return max(map(len, graph))

stattest/test/normal.py

@@ -8,6 +8,10 @@
 
 from stattest.test.common import ADTestStatistic, KSTestStatistic, LillieforsTest
 from stattest.test.goodness_of_fit import AbstractGoodnessOfFitTestStatistic
+from stattest.test.graph_goodness_of_fit import (
+    GraphEdgesNumberTestStatistic,
+    GraphMaxDegreeTestStatistic,
+)
 
 
 class AbstractNormalityTestStatistic(AbstractGoodnessOfFitTestStatistic, ABC):
@@ -275,8 +279,7 @@ def skew_test(a):
         n = len(a)
         if n < 8:
             raise ValueError(
-                "skew test is not valid with less than 8 samples; %i samples"
-                " were given." % int(n)
+                "skew test is not valid with less than 8 samples; %i samples were given." % int(n)
             )
         b2 = scipy_stats.skew(a, axis=0)
         y = b2 * math.sqrt(((n + 1) * (n + 3)) / (6.0 * (n - 2)))
@@ -2175,5 +2178,23 @@ def stat35(x):
             return rn  # Here is the test statistic value
 
 
+class GraphEdgesNumberNormTest(AbstractNormalityTestStatistic, GraphEdgesNumberTestStatistic):
+    @staticmethod
+    @override
+    def code():
+        super_class = AbstractNormalityTestStatistic
+        parent_code = super(super_class, super_class).code()
+        return f"EdgesNumber_{parent_code}"
+
+
+class GraphMaxDegreeNormTest(AbstractNormalityTestStatistic, GraphMaxDegreeTestStatistic):
+    @staticmethod
+    @override
+    def code():
+        super_class = AbstractNormalityTestStatistic
+        parent_code = super(super_class, super_class).code()
+        return f"MaxDegree_{parent_code}"
+
+
 # TODO: fix all weak warnings
 # TODO: check tests

tests/exponentiality/gredg_exp_test.py

@@ -0,0 +1,17 @@
+import pytest as pytest
+
+from stattest.test.exponent import GraphEdgesNumberExpTest
+from tests.exponentiality.abstract_exponentiality_test_case import AbstractExponentialityTestCase
+
+
+@pytest.mark.parametrize(
+    ("data", "result"),
+    [
+        ([0.713, 0.644, 2.625, 0.740, 0.501, 0.185, 0.982, 1.028, 1.152, 0.267], 12),
+        ([0.039, 3.036, 0.626, 1.107, 0.139, 1.629, 0.050, 0.118, 0.978, 2.699], 7),
+    ],
+)
+class TestCaseGraphEdgesNumberExponentialityTest(AbstractExponentialityTestCase):
+    @pytest.fixture
+    def statistic_test(self):
+        return GraphEdgesNumberExpTest()

tests/exponentiality/grmd_exp_test.py

@@ -0,0 +1,17 @@
+import pytest as pytest
+
+from stattest.test.exponent import GraphMaxDegreeExpTest
+from tests.exponentiality.abstract_exponentiality_test_case import AbstractExponentialityTestCase
+
+
+@pytest.mark.parametrize(
+    ("data", "result"),
+    [
+        ([0.713, 0.644, 2.625, 0.740, 0.501, 0.185, 0.982, 1.028, 1.152, 0.267], 4),
+        ([0.039, 3.036, 0.626, 1.107, 0.139, 1.629, 0.050, 0.118, 0.978, 2.699], 3),
+    ],
+)
+class TestCaseGraphMaxDegreeExponentialityTest(AbstractExponentialityTestCase):
+    @pytest.fixture
+    def statistic_test(self):
+        return GraphMaxDegreeExpTest()

tests/normality/graph_edgesnum_test.py

@@ -0,0 +1,17 @@
+import pytest as pytest
+
+from stattest.test.normal import GraphEdgesNumberNormTest
+from tests.normality.abstract_normality_test_case import AbstractNormalityTestCase
+
+
+@pytest.mark.parametrize(
+    ("data", "result"),
+    [
+        ([-0.264, 0.031, 0.919, 1.751, -0.038, 0.133, 0.643, -0.480, 0.094, -0.527], 9),
+        ([1.311, 3.761, 0.415, 0.764, 0.100, -0.028, -1.516, -0.108, 2.248, 0.229], 11),
+    ],
+)
+class TestCaseGraphEdgesNumbernormalityTest(AbstractNormalityTestCase):
+    @pytest.fixture
+    def statistic_test(self):
+        return GraphEdgesNumberNormTest()

tests/normality/graph_maxdegree_test.py

@@ -0,0 +1,17 @@
+import pytest as pytest
+
+from stattest.test.normal import GraphMaxDegreeNormTest
+from tests.normality.abstract_normality_test_case import AbstractNormalityTestCase
+
+
+@pytest.mark.parametrize(
+    ("data", "result"),
+    [
+        ([-0.264, 0.031, 0.919, 1.751, -0.038, 0.133, 0.643, -0.480, 0.094, -0.527], 4),
+        ([1.311, 3.761, 0.415, 0.764, 0.100, -0.028, -1.516, -0.108, 2.248, 0.229], 5),
+    ],
+)
+class TestCaseGraphEdgesNumbernormalityTest(AbstractNormalityTestCase):
+    @pytest.fixture
+    def statistic_test(self):
+        return GraphMaxDegreeNormTest()