cascade-reconstruction-by-tree-samples/test_tree_stat.py at master · xiaohan2012/cascade-reconstruction-by-tree-samples · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import pytest
import numpy as np
from numpy.testing import assert_array_equal as assert_eq_np, assert_almost_equal
from graph_tool import Graph
from scipy.stats import entropy

from tree_stat import TreeBasedStatistics


@pytest.fixture
def trees():
    """
    0 0 1 0 0 1
    1 0 0 1 0 0
    1 0 0 1 1 0
    0 0 0 1 1 1
    1 0 1 1 0 0

sum 3 0 2 4 2 2
    """
    return [
        {2, 5},
        {0, 3},
        {0, 3, 4},
        {3, 4, 5},
        {0, 2, 3}
    ]


@pytest.fixture
def new_trees():
    return [
        {0, 1},
        {0, 4}
    ]


@pytest.fixture
def g():
    g = Graph(directed=False)
    g.add_vertex(6)
    return g


@pytest.fixture
def stat(g, trees):
    return TreeBasedStatistics(g, trees)


@pytest.mark.parametrize("targets", [None, set(range(6)), list(range(6))])
def test_unconditional_count_and_proba(stat, trees, targets):
    arr_c = np.array([3, 0, 2, 4, 2, 2])
    arr_p = arr_c / len(trees)
    assert_eq_np(stat.unconditional_count(targets),
                 arr_c)

    assert_eq_np(stat.unconditional_proba(targets),
                 arr_p)


@pytest.mark.parametrize("targets", [None, set(range(6)), list(range(6))])
def test_filter_out_extreme_targets(stat, trees, targets):
    # [3, 0, 2, 4, 2, 2] --> [2, 0, 2, 1, 2, 2] --> [2/5, 0, 2/5, 1/5, 2/5, 2/5]
    filtered_targets = stat.filter_out_extreme_targets(targets,
                                                       min_value=2/len(trees))  # 2/5
    assert set(filtered_targets) == set()

    filtered_targets = stat.filter_out_extreme_targets(targets,
                                                       min_value=1/len(trees))  # 1/5
    assert set(filtered_targets) == {0, 2, 4, 5}

    filtered_targets = stat.filter_out_extreme_targets(targets,
                                                       min_value=0/len(trees))  # 0
    assert set(filtered_targets) == {0, 2, 3, 4, 5}


def test_count_and_proba(stat, trees):
    targets = list(range(1, 6))
    query = 0

    arr_c0 = np.array([0, 1, 1, 1, 2])
    arr_c1 = np.array([0, 1, 3, 1, 0])

    assert_eq_np(stat.count(query, condition=0, targets=targets),
                 arr_c0)
    assert_eq_np(stat.count(query, condition=1, targets=targets),
                 arr_c1)

    assert_eq_np(stat.proba(query, condition=0, targets=targets),
                 arr_c0 / 2)
    assert_eq_np(stat.proba(query, condition=1, targets=targets),
                 arr_c1 / 3)


def test_update_trees(stat, new_trees):
    """
    1 1 0 0 0 0 (new*)
    1 0 0 1 0 0
    1 0 0 1 1 0
    1 0 0 0 1 0 (new*)
    1 0 1 1 0 0

sum 5 1 1 3 2 0
    """
    stat.update_trees(new_trees, {0: 1})

    query = 1
    targets = [2, 3, 4, 5]
    arr_c0 = [1, 3, 2, 0]
    arr_c1 = [0, 0, 0, 0]

    assert_eq_np(stat.count(query, condition=0, targets=targets),
                 arr_c0)
    assert_eq_np(stat.count(query, condition=1, targets=targets),
                 arr_c1)


def test_update_trees_multiple_nodes_update(stat, new_trees):
    """
    1 1 0 0 0 0 (new*)
    1 0 0 1 0 0
    1 0 0 0 0 0 (new*)
    1 0 0 0 1 0 (new*)
    1 0 1 1 0 0

sum 5 1 1 3 2 0
    """

    new_trees.append({1})
    stat.update_trees(new_trees, {0: 1, 4: 0})

    query = 1
    targets = [2, 3, 5]
    arr_c0 = [1, 2, 0]
    arr_c1 = [0, 0, 0]

    assert_eq_np(stat.count(query, condition=0, targets=targets),
                 arr_c0)
    assert_eq_np(stat.count(query, condition=1, targets=targets),
                 arr_c1)


def test_update_trees_insufficient_trees(stat, new_trees):
    with pytest.raises(AssertionError):
        # insufficient length
        stat.update_trees(new_trees[:1], {0: 1})


@pytest.fixture
def trees1():
    return [
        {0, 1, 2},
        {1, 2},
        {1, 2, 3},
        {1, 2, 3, 4},
    ]


@pytest.fixture
def stat1(g, trees1):
    return TreeBasedStatistics(g, trees1)


def test_prediction_error(stat1):
    actual = stat1.prediction_error(0, 0, [3, 4])
    expected = entropy([1/3, 2/3]) * 2
    assert_almost_equal(actual, expected)


def test_query_score(stat1):
    actual = stat1.query_score(0, [3, 4])
    expected = entropy([1/3, 2/3]) * 2 * 3/4  # + error = 0 for state=1
    assert_almost_equal(actual, expected)