Hugging Face's logo

Datasets:
hc99
/
FEA-Bench

Dataset card Files
xet
 Community
FEA-Bench
File size: 4,555 Bytes
fc0f7bd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

import nbformat as nbf
import papermill as pm
import pytest
import scrapbook as sb


class ScrapSpec:
    def __init__(self, code, expected):
        self.code = code
        self.expected = expected

    @property
    def code(self):
        """The code to be inserted (string)."""  # noqa:D401
        return self._code

    @code.setter
    def code(self, value):
        self._code = value

    @property
    def expected(self):
        """The expected evaluation of the code (Python object)."""  # noqa:D401
        return self._expected

    @expected.setter
    def expected(self, value):
        self._expected = value


def append_scrapbook_commands(input_nb_path, output_nb_path, scrap_specs):
    notebook = nbf.read(input_nb_path, as_version=nbf.NO_CONVERT)

    scrapbook_cells = []
    # Always need to import nteract-scrapbook
    scrapbook_cells.append(nbf.v4.new_code_cell(source="import scrapbook as sb"))

    # Create a cell to store each key and value in the scrapbook
    for k, v in scrap_specs.items():
        source = "sb.glue(\"{0}\", {1})".format(k, v.code)
        scrapbook_cells.append(nbf.v4.new_code_cell(source=source))

    # Append the cells to the notebook
    [notebook['cells'].append(c) for c in scrapbook_cells]

    # Write out the new notebook
    nbf.write(notebook, output_nb_path)


def assay_one_notebook(notebook_name, test_values):
    """Test a single notebook.

    This uses nbformat to append `nteract-scrapbook` commands to the
    specified notebook. The content of the commands and their expected
    values are stored in the `test_values` dictionary. The keys of this
    dictionary are strings to be used as scrapbook keys. They corresponding
    value is a `ScrapSpec` tuple. The `code` member of this tuple is
    the code (as a string) to be run to generate the scrapbook value. The
    `expected` member is a Python object which is checked for equality with
    the scrapbook value

    Makes certain assumptions about directory layout.
    """
    input_notebook = "notebooks/" + notebook_name + ".ipynb"
    processed_notebook = "./test/notebooks/" + notebook_name + ".processed.ipynb"
    output_notebook = "./test/notebooks/" + notebook_name + ".output.ipynb"

    append_scrapbook_commands(input_notebook, processed_notebook, test_values)
    pm.execute_notebook(processed_notebook, output_notebook)
    nb = sb.read_notebook(output_notebook)

    for k, v in test_values.items():
        assert nb.scraps[k].data == v.expected


@pytest.mark.notebooks
def test_group_metrics_notebook():
    overall_recall_key = "overall_recall"
    by_groups_key = "recall_by_groups"

    test_values = {}
    test_values[overall_recall_key] = ScrapSpec("group_metrics.overall", 0.5)
    test_values[by_groups_key] = ScrapSpec(
        "results.by_group", {'a': 0.0, 'b': 0.5, 'c': 0.75, 'd': 0.0})

    assay_one_notebook("Group Metrics", test_values)


@pytest.mark.notebooks
def test_grid_search_for_binary_classification():
    nb_name = "Grid Search for Binary Classification"

    test_values = {}
    test_values["best_lambda_second_grid"] = ScrapSpec(
        "lambda_best_second", pytest.approx(0.8333333333))
    test_values["best_coeff_second0"] = ScrapSpec(
        "second_sweep.best_result.predictor.coef_[0][0]", pytest.approx(2.53725364))

    assay_one_notebook(nb_name, test_values)


@pytest.mark.notebooks
def test_binary_classification_on_compas_dataset():
    nb_name = "Binary Classification on COMPAS dataset"

    test_values = {}
    test_values["pp_eo_aa_pignore"] = ScrapSpec(
        "postprocessed_predictor_EO._post_processed_predictor_by_sensitive_feature['African-American']._p_ignore",  # noqa: E501
        pytest.approx(0.2320703126)
    )

    assay_one_notebook(nb_name, test_values)


@pytest.mark.notebooks
def test_grid_search_with_census_data():
    nb_name = "Grid Search with Census Data"
    test_values = {}
    test_values["len_nondominated"] = ScrapSpec("len(non_dominated)", 13)
    assay_one_notebook(nb_name, test_values)


@pytest.mark.notebooks
def test_mitigating_disparities_in_ranking_from_binary_data():
    nb_name = "Mitigating Disparities in Ranking from Binary Data"
    test_values = {}
    # Needs wider bound due to randomness in ExponentiatedGradient
    test_values["sel_eg_X_alt_disparity"] = ScrapSpec(
        "sel_expgrad_X_alt.loc[ 'disparity', :][0]",
        pytest.approx(0.35, abs=0.04))
    assay_one_notebook(nb_name, test_values)