def test_numpy_random():
"""
Tests whether the monkey patching of ('numpy.random', 'random') works
"""
test_code = cleandoc("""
import numpy as np
np.random.seed(42)
test = np.random.random(100)
assert len(test) == 100
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
extracted_node: DagNode = list(inspector_result.dag.nodes)[0]
expected_node = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('numpy.random', 'random')),
DagNodeDetails('random', ['array']),
OptionalCodeInfo(CodeReference(3, 7, 3, 28), "np.random.random(100)"))
compare(extracted_node, expected_node)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
extracted_node]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[0.5, {LineageId(0, 0)}], [0.5, {LineageId(0, 1)}]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
atol=1)
def test_frame_merge_sorted():
"""
Tests whether the monkey patching of ('pandas.core.frame', 'merge') works if the sort option is set to True
"""
test_code = cleandoc("""
import pandas as pd
df_a = pd.DataFrame({'A': [0, 2, 4, 8, 5], 'B': [7, 5, 4, 2, 1]})
df_b = pd.DataFrame({'B': [1, 4, 3, 2, 5], 'C': [1, 5, 4, 11, None]})
df_merged = df_a.merge(df_b, on='B', sort=True)
df_expected = pd.DataFrame({'A': [5, 8, 4, 2], 'B': [1, 2, 4, 5], 'C': [1, 11, 5, None]})
pd.testing.assert_frame_equal(df_merged, df_expected)
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(5)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[3])
expected_dag = networkx.DiGraph()
expected_a = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A', 'B']),
OptionalCodeInfo(
CodeReference(3, 7, 3, 65),
"pd.DataFrame({'A': [0, 2, 4, 8, 5], 'B': [7, 5, 4, 2, 1]})"))
expected_b = DagNode(
1, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['B', 'C']),
OptionalCodeInfo(
CodeReference(4, 7, 4, 69),
"pd.DataFrame({'B': [1, 4, 3, 2, 5], 'C': [1, 5, 4, 11, None]})"))
expected_join = DagNode(
2, BasicCodeLocation("<string-source>", 5),
OperatorContext(OperatorType.JOIN,
FunctionInfo('pandas.core.frame', 'merge')),
DagNodeDetails("on 'B'", ['A', 'B', 'C']),
OptionalCodeInfo(CodeReference(5, 12, 5, 47),
"df_a.merge(df_b, on='B', sort=True)"))
expected_dag.add_edge(expected_a, expected_join)
expected_dag.add_edge(expected_b, expected_join)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_join]
lineage_output = inspection_results_data_source[RowLineage(5)]
expected_lineage_df = DataFrame(
[[5, 1, 1., {LineageId(0, 4), LineageId(1, 0)}],
[8, 2, 11., {LineageId(0, 3), LineageId(1, 3)}],
[4, 4, 5., {LineageId(0, 2), LineageId(1, 1)}],
[2, 5, math.nan, {LineageId(0, 1), LineageId(1, 4)}]],
columns=['A', 'B', 'C', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_frame__init__():
"""
Tests whether the monkey patching of ('pandas.core.frame', 'DataFrame') works
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame([0, 1, 2], columns=['A'])
assert len(df) == 3
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
extracted_node: DagNode = list(inspector_result.dag.nodes)[0]
expected_node = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A']),
OptionalCodeInfo(CodeReference(3, 5, 3, 43),
"pd.DataFrame([0, 1, 2], columns=['A'])"))
compare(extracted_node, expected_node)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
extracted_node]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[0, {LineageId(0, 0)}], [1, {LineageId(0, 1)}]],
columns=['A', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_get_rdataset():
"""
Tests whether the monkey patching of ('statsmodels.datasets', 'get_rdataset') works
"""
test_code = cleandoc("""
import statsmodels.api as sm
dat = sm.datasets.get_rdataset("Guerry", "HistData").data
assert len(dat) == 86
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
extracted_node: DagNode = list(inspector_result.dag.nodes)[0]
expected_node = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('statsmodels.datasets', 'get_rdataset')),
DagNodeDetails(
'Data from A.-M. Guerry, "Essay on the Moral Statistics of France"',
[
'dept', 'Region', 'Department', 'Crime_pers', 'Crime_prop',
'Literacy', 'Donations', 'Infants', 'Suicides', 'MainCity',
'Wealth', 'Commerce', 'Clergy', 'Crime_parents', 'Infanticide',
'Donation_clergy', 'Lottery', 'Desertion', 'Instruction',
'Prostitutes', 'Distance', 'Area', 'Pop1831'
]),
OptionalCodeInfo(CodeReference(3, 6, 3, 52),
"""sm.datasets.get_rdataset("Guerry", "HistData")"""))
compare(extracted_node, expected_node)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
extracted_node]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[
1, 'E', 'Ain', 28870, 15890, 37, 5098, 33120, 35039, '2:Med', 73,
58, 11, 71, 60, 69, 41, 55, 46, 13, 218.372, 5762, 346.03,
{LineageId(0, 0)}
],
[
2, 'N', 'Aisne', 26226, 5521, 51, 8901, 14572, 12831, '2:Med', 22,
10, 82, 4, 82, 36, 38, 82, 24, 327, 65.945, 7369, 513.0,
{LineageId(0, 1)}
]],
columns=[
'dept', 'Region', 'Department', 'Crime_pers', 'Crime_prop',
'Literacy', 'Donations', 'Infants', 'Suicides', 'MainCity',
'Wealth', 'Commerce', 'Clergy', 'Crime_parents', 'Infanticide',
'Donation_clergy', 'Lottery', 'Desertion', 'Instruction',
'Prostitutes', 'Distance', 'Area', 'Pop1831', 'mlinspect_lineage'
])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_statsmodels_add_constant():
"""
Tests whether the monkey patching of ('statsmodel.api', 'add_constant') works
"""
test_code = cleandoc("""
import numpy as np
import statsmodels.api as sm
np.random.seed(42)
test = np.random.random(100)
test = sm.add_constant(test)
assert len(test) == 100
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
expected_dag = networkx.DiGraph()
expected_random = DagNode(
0, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('numpy.random', 'random')),
DagNodeDetails('random', ['array']),
OptionalCodeInfo(CodeReference(4, 7, 4, 28), "np.random.random(100)"))
expected_constant = DagNode(
1, BasicCodeLocation("<string-source>", 5),
OperatorContext(OperatorType.PROJECTION_MODIFY,
FunctionInfo('statsmodel.api', 'add_constant')),
DagNodeDetails('Adds const column', ['array']),
OptionalCodeInfo(CodeReference(5, 7, 5, 28), "sm.add_constant(test)"))
expected_dag.add_edge(expected_random, expected_constant)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_random]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[0.5, {LineageId(0, 0)}], [0.5, {LineageId(0, 1)}]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
atol=1)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_constant]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[numpy.array([0.5, 1.]), {LineageId(0, 0)}],
[numpy.array([0.5, 1.]), {LineageId(0, 1)}]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
atol=1)
def test_read_csv():
"""
Tests whether the monkey patching of ('pandas.io.parsers', 'read_csv') works
"""
test_code = cleandoc("""
import os
import pandas as pd
from mlinspect.utils import get_project_root
train_file = os.path.join(str(get_project_root()), "example_pipelines", "adult_complex", "adult_train.csv")
raw_data = pd.read_csv(train_file, na_values='?', index_col=0)
assert len(raw_data) == 22792
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
extracted_node: DagNode = list(inspector_result.dag.nodes)[0]
expected_node = DagNode(
0, BasicCodeLocation("<string-source>", 6),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.io.parsers', 'read_csv')),
DagNodeDetails(StringComparison(r".*\.csv"), [
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
'income-per-year'
]),
OptionalCodeInfo(
CodeReference(6, 11, 6, 62),
"pd.read_csv(train_file, na_values='?', index_col=0)"))
compare(extracted_node, expected_node)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
extracted_node]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[
46, 'Private', 128645, 'Some-college', 10, 'Divorced',
'Prof-specialty', 'Not-in-family', 'White', 'Female', 0, 0, 40,
'United-States', '<=50K', {LineageId(0, 0)}
],
[
29, 'Local-gov', 115585, 'Some-college', 10, 'Never-married',
'Handlers-cleaners', 'Not-in-family', 'White', 'Male', 0, 0, 50,
'United-States', '<=50K', {LineageId(0, 1)}
]],
columns=[
'age', 'workclass', 'fnlwgt', 'education', 'education-num',
'marital-status', 'occupation', 'relationship', 'race', 'sex',
'capital-gain', 'capital-loss', 'hours-per-week', 'native-country',
'income-per-year', 'mlinspect_lineage'
])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_frame__setitem__():
"""
Tests whether the monkey patching of ('pandas.core.frame', '__setitem__') works
"""
test_code = cleandoc("""
import pandas as pd
pandas_df = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
'baz': [1, 2, 3, 4, 5, 6],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
pandas_df['baz'] = pandas_df['baz'] + 1
df_expected = pd.DataFrame({'foo': ['one', 'one', 'one', 'two', 'two', 'two'],
'bar': ['A', 'B', 'C', 'A', 'B', 'C'],
'baz': [2, 3, 4, 5, 6, 7],
'zoo': ['x', 'y', 'z', 'q', 'w', 't']})
pd.testing.assert_frame_equal(pandas_df, df_expected)
""")
inspector_result = _pipeline_executor.singleton.run(python_code=test_code, track_code_references=True,
inspections=[RowLineage(2)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[3])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(0,
BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['foo', 'bar', 'baz', 'zoo']),
OptionalCodeInfo(CodeReference(3, 12, 6, 53),
"pd.DataFrame({'foo': ['one', 'one', 'one', 'two', "
"'two', 'two'],\n"
" 'bar': ['A', 'B', 'C', 'A', 'B', 'C'],\n"
" 'baz': [1, 2, 3, 4, 5, 6],\n"
" 'zoo': ['x', 'y', 'z', 'q', 'w', 't']})"))
expected_project = DagNode(1,
BasicCodeLocation("<string-source>", 7),
OperatorContext(OperatorType.PROJECTION,
FunctionInfo('pandas.core.frame', '__getitem__')),
DagNodeDetails("to ['baz']", ['baz']),
OptionalCodeInfo(CodeReference(7, 19, 7, 35), "pandas_df['baz']"))
expected_dag.add_edge(expected_data_source, expected_project)
expected_project_modify = DagNode(2,
BasicCodeLocation("<string-source>", 7),
OperatorContext(OperatorType.PROJECTION_MODIFY,
FunctionInfo('pandas.core.frame', '__setitem__')),
DagNodeDetails("modifies ['baz']", ['foo', 'bar', 'baz', 'zoo']),
OptionalCodeInfo(CodeReference(7, 0, 7, 39),
"pandas_df['baz'] = pandas_df['baz'] + 1"))
expected_dag.add_edge(expected_data_source, expected_project_modify)
compare(networkx.to_dict_of_dicts(inspector_result.dag), networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[expected_project_modify]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame([['one', 'A', 2, 'x', {LineageId(0, 0)}],
['one', 'B', 3, 'y', {LineageId(0, 1)}]],
columns=['foo', 'bar', 'baz', 'zoo', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(lineage_output.reset_index(drop=True), expected_lineage_df.reset_index(drop=True))
def test_my_word_to_vec_transformer():
"""
Tests whether the monkey patching of ('example_pipelines.healthcare.healthcare_utils', 'MyW2VTransformer') works
"""
test_code = cleandoc("""
import pandas as pd
from example_pipelines.healthcare.healthcare_utils import MyW2VTransformer
import numpy as np
df = pd.DataFrame({'A': ['cat_a', 'cat_b', 'cat_a', 'cat_c']})
word_to_vec = MyW2VTransformer(min_count=2, size=2, workers=1)
encoded_data = word_to_vec.fit_transform(df)
assert encoded_data.shape == (4, 2)
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(3)],
custom_monkey_patching=[custom_monkeypatching])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(
0, BasicCodeLocation("<string-source>", 5),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A']),
OptionalCodeInfo(
CodeReference(5, 5, 5, 62),
"pd.DataFrame({'A': ['cat_a', 'cat_b', 'cat_a', 'cat_c']})"))
expected_estimator = DagNode(
1, BasicCodeLocation("<string-source>", 6),
OperatorContext(
OperatorType.TRANSFORMER,
FunctionInfo('example_pipelines.healthcare.healthcare_utils',
'MyW2VTransformer')),
DagNodeDetails('Word2Vec', ['array']),
OptionalCodeInfo(CodeReference(6, 14, 6, 62),
'MyW2VTransformer(min_count=2, size=2, workers=1)'))
expected_dag.add_edge(expected_data_source, expected_estimator)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_estimator]
lineage_output = inspection_results_data_source[RowLineage(3)]
expected_lineage_df = DataFrame(
[[numpy.array([0.0, 0.0, 0.0]), {LineageId(0, 0)}],
[numpy.array([0.0, 0.0, 0.0]), {LineageId(0, 1)}],
[numpy.array([0.0, 0.0, 0.0]), {LineageId(0, 2)}]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_series_equal(
lineage_output["mlinspect_lineage"],
expected_lineage_df["mlinspect_lineage"])
assert expected_lineage_df.iloc[0, 0].shape == (3, )
def test_frame__getitem__selection():
"""
Tests whether the monkey patching of ('pandas.core.frame', '__getitem__') works for filtering
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame({'A': [0, 2, 4, 8, 5], 'B': [1, 5, 4, 11, None]})
df_selection = df[df['A'] > 3]
df_expected = pd.DataFrame({'A': [4, 8, 5], 'B': [4, 11, None]})
pd.testing.assert_frame_equal(df_selection.reset_index(drop=True), df_expected.reset_index(drop=True))
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[3])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A', 'B']),
OptionalCodeInfo(
CodeReference(3, 5, 3, 67),
"pd.DataFrame({'A': [0, 2, 4, 8, 5], 'B': [1, 5, 4, 11, None]})"))
expected_projection = DagNode(
1, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.PROJECTION,
FunctionInfo('pandas.core.frame', '__getitem__')),
DagNodeDetails("to ['A']", ['A']),
OptionalCodeInfo(CodeReference(4, 18, 4, 25), "df['A']"))
expected_dag.add_edge(expected_data_source, expected_projection)
expected_selection = DagNode(
2, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.SELECTION,
FunctionInfo('pandas.core.frame', '__getitem__')),
DagNodeDetails("Select by Series: df[df['A'] > 3]", ['A', 'B']),
OptionalCodeInfo(CodeReference(4, 15, 4, 30), "df[df['A'] > 3]"))
expected_dag.add_edge(expected_data_source, expected_selection)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_selection]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[4, 4., {LineageId(0, 2)}], [8, 11., {LineageId(0, 3)}]],
columns=['A', 'B', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def run_row_index_annotation_testing_analyzer(code):
"""
An utility function to test backends
"""
result = PipelineInspector \
.on_pipeline_from_string(code) \
.add_required_inspection(RowLineage(10)) \
.execute()
inspection_results = result.inspection_to_annotations
assert RowLineage(10) in inspection_results
result = inspection_results[RowLineage(10)]
return result
def test_groupby_agg():
"""
Tests whether the monkey patching of ('pandas.core.frame', 'groupby') and ('pandas.core.groupbygeneric', 'agg')
works.
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame({'group': ['A', 'B', 'A', 'C', 'B'], 'value': [1, 2, 1, 3, 4]})
df_groupby_agg = df.groupby('group').agg(mean_value=('value', 'mean'))
df_expected = pd.DataFrame({'group': ['A', 'B', 'C'], 'mean_value': [1, 3, 3]})
pd.testing.assert_frame_equal(df_groupby_agg.reset_index(drop=False), df_expected.reset_index(drop=True))
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[2])
expected_dag = networkx.DiGraph()
expected_data = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['group', 'value']),
OptionalCodeInfo(
CodeReference(3, 5, 3, 81),
"pd.DataFrame({'group': ['A', 'B', 'A', 'C', 'B'], "
"'value': [1, 2, 1, 3, 4]})"))
expected_groupby_agg = DagNode(
1, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.GROUP_BY_AGG,
FunctionInfo('pandas.core.groupby.generic', 'agg')),
DagNodeDetails(
"Groupby 'group', Aggregate: '{'mean_value': ('value', 'mean')}'",
['group', 'mean_value']),
OptionalCodeInfo(
CodeReference(4, 17, 4, 70),
"df.groupby('group').agg(mean_value=('value', 'mean'))"))
expected_dag.add_edge(expected_data, expected_groupby_agg)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_groupby_agg]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[["A", 1, {LineageId(1, 0)}], ['B', 3, {LineageId(1, 1)}]],
columns=['group', 'mean_value', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_frame__getitem__frame():
"""
Tests whether the monkey patching of ('pandas.core.frame', '__getitem__') works for multiple string arguments
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame([[0, None, 2], [1, 2, 3], [4, None, 2], [9, 2, 3], [6, 1, 2], [1, 2, 3]],
columns=['A', 'B', 'C'])
df_projection = df[['A', 'C']]
df_expected = pd.DataFrame([[0, 2], [1, 3], [4, 2], [9, 3], [6, 2], [1, 3]], columns=['A', 'C'])
pd.testing.assert_frame_equal(df_projection, df_expected)
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[2])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A', 'B', 'C']),
OptionalCodeInfo(
CodeReference(3, 5, 4, 28),
"pd.DataFrame([[0, None, 2], [1, 2, 3], [4, None, 2], "
"[9, 2, 3], [6, 1, 2], [1, 2, 3]], \n"
" columns=['A', 'B', 'C'])"))
expected_project = DagNode(
1, BasicCodeLocation("<string-source>", 5),
OperatorContext(OperatorType.PROJECTION,
FunctionInfo('pandas.core.frame', '__getitem__')),
DagNodeDetails("to ['A', 'C']", ['A', 'C']),
OptionalCodeInfo(CodeReference(5, 16, 5, 30), "df[['A', 'C']]"))
expected_dag.add_edge(expected_data_source, expected_project)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_project]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[0, 2, {LineageId(0, 0)}], [1, 3, {LineageId(0, 1)}]],
columns=['A', 'C', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def run_row_index_annotation_testing_analyzer(code):
"""
An utility function to test backends
"""
result = PipelineInspector \
.on_pipeline_from_string(code) \
.add_required_inspection(RowLineage(10)) \
.execute()
inspection_results = result.dag_node_to_inspection_results
dag_node_to_lineage_inspection = {}
for dag_node, inspection_result in inspection_results.items():
assert RowLineage(10) in inspection_result
dag_node_to_lineage_inspection[dag_node] = inspection_result[
RowLineage(10)]
return dag_node_to_lineage_inspection
def test_frame_replace():
"""
Tests whether the monkey patching of ('pandas.core.frame', 'replace') works
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame(['Low', 'Medium', 'Low', 'High', None], columns=['A'])
df_replace = df.replace('Medium', 'Low')
df_expected = pd.DataFrame(['Low', 'Low', 'Low', 'High', None], columns=['A'])
pd.testing.assert_frame_equal(df_replace.reset_index(drop=True), df_expected.reset_index(drop=True))
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[2])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A']),
OptionalCodeInfo(
CodeReference(3, 5, 3, 72),
"pd.DataFrame(['Low', 'Medium', 'Low', 'High', None], "
"columns=['A'])"))
expected_modify = DagNode(
1, BasicCodeLocation("<string-source>", 4),
OperatorContext(OperatorType.PROJECTION_MODIFY,
FunctionInfo('pandas.core.frame', 'replace')),
DagNodeDetails("Replace 'Medium' with 'Low'", ['A']),
OptionalCodeInfo(CodeReference(4, 13, 4, 40),
"df.replace('Medium', 'Low')"))
expected_dag.add_edge(expected_data_source, expected_modify)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_modify]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[['Low', {LineageId(0, 0)}], ['Low', {LineageId(0, 1)}]],
columns=['A', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def test_frame_dropna():
"""
Tests whether the monkey patching of ('pandas.core.frame', 'dropna') works
"""
test_code = cleandoc("""
import pandas as pd
df = pd.DataFrame([0, 2, 4, 5, None], columns=['A'])
assert len(df) == 5
df = df.dropna()
assert len(df) == 4
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(2)])
expected_dag = networkx.DiGraph()
expected_data_source = DagNode(
0, BasicCodeLocation("<string-source>", 3),
OperatorContext(OperatorType.DATA_SOURCE,
FunctionInfo('pandas.core.frame', 'DataFrame')),
DagNodeDetails(None, ['A']),
OptionalCodeInfo(CodeReference(3, 5, 3, 52),
"pd.DataFrame([0, 2, 4, 5, None], columns=['A'])"))
expected_select = DagNode(
1, BasicCodeLocation("<string-source>", 5),
OperatorContext(OperatorType.SELECTION,
FunctionInfo('pandas.core.frame', 'dropna')),
DagNodeDetails('dropna', ['A']),
OptionalCodeInfo(CodeReference(5, 5, 5, 16), 'df.dropna()'))
expected_dag.add_edge(expected_data_source, expected_select)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_select]
lineage_output = inspection_results_data_source[RowLineage(2)]
expected_lineage_df = DataFrame(
[[0., {LineageId(0, 0)}], [2., {LineageId(0, 1)}]],
columns=['A', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True))
def run_and_assert_all_op_outputs_inspected(py_file_path,
sensitive_columns,
dag_png_path,
custom_monkey_patching=None):
"""
Execute the pipeline with a few checks and inspections.
Assert that mlinspect properly lets inspections inspect all DAG nodes
"""
if custom_monkey_patching is None:
custom_monkey_patching = []
inspector_result = PipelineInspector \
.on_pipeline_from_py_file(py_file_path) \
.add_check(NoBiasIntroducedFor(sensitive_columns)) \
.add_check(NoIllegalFeatures()) \
.add_required_inspection(MissingEmbeddings(20)) \
.add_required_inspection(RowLineage(5)) \
.add_required_inspection(MaterializeFirstOutputRows(5)) \
.add_custom_monkey_patching_modules(custom_monkey_patching) \
.execute()
for dag_node, inspection_result in inspector_result.dag_node_to_inspection_results.items(
):
assert dag_node.operator_info.operator != OperatorType.MISSING_OP
assert MaterializeFirstOutputRows(5) in inspection_result
assert RowLineage(5) in inspection_result
assert MissingEmbeddings(20) in inspection_result
assert HistogramForColumns(sensitive_columns) in inspection_result
if dag_node.operator_info.operator != OperatorType.ESTIMATOR: # Estimator does not have output
assert inspection_result[MaterializeFirstOutputRows(5)] is not None
assert inspection_result[RowLineage(5)] is not None
assert inspection_result[HistogramForColumns(
sensitive_columns)] is not None
else:
assert inspection_result[MaterializeFirstOutputRows(5)] is None
assert inspection_result[RowLineage(5)] is not None
assert inspection_result[HistogramForColumns(
sensitive_columns)] is None
save_fig_to_path(inspector_result.dag, dag_png_path)
assert os.path.isfile(dag_png_path)
return inspector_result.dag
def run_multiple_test_analyzers(code):
"""
An utility function to test backends.
Also useful to debug annotation propagation.
"""
analyzers = [RandomAnnotationTestingInspection(2), MaterializeFirstOutputRows(5),
RowLineage(2)]
result = PipelineInspector \
.on_pipeline_from_string(code) \
.add_required_inspections(analyzers) \
.execute()
inspection_results = result.inspection_to_annotations
return inspection_results, analyzers
def run_and_assert_all_op_outputs_inspected(py_file_path, sensitive_columns, dag_png_path):
"""
Execute the pipeline with a few checks and inspections.
Assert that mlinspect properly lets inspections inspect all DAG nodes
"""
inspector_result = PipelineInspector \
.on_pipeline_from_py_file(py_file_path) \
.add_check(NoBiasIntroducedFor(sensitive_columns)) \
.add_check(NoIllegalFeatures()) \
.add_required_inspection(MissingEmbeddings(20)) \
.add_required_inspection(RowLineage(5)) \
.add_required_inspection(MaterializeFirstOutputRows(5)) \
.execute()
materialize_output = inspector_result.inspection_to_annotations[MaterializeFirstOutputRows(5)]
assert len(materialize_output) == (len(inspector_result.dag.nodes) - 1) # Estimator does not have output
save_fig_to_path(inspector_result.dag, dag_png_path)
assert os.path.isfile(dag_png_path)
def test_ols_fit():
"""
Tests whether the monkey patching of ('statsmodels.regression.linear_model.OLS', 'fit') works
"""
test_code = cleandoc("""
import numpy as np
import statsmodels.api as sm
np.random.seed(42)
nobs = 100
X = np.random.random((nobs, 2))
X = sm.add_constant(X)
beta = [1, .1, .5]
e = np.random.random(nobs)
y = np.dot(X, beta) + e
results = sm.OLS(y, X).fit()
assert results.summary() is not None
""")
inspector_result = _pipeline_executor.singleton.run(
python_code=test_code,
track_code_references=True,
inspections=[RowLineage(3)])
inspector_result.dag.remove_nodes_from(
list(inspector_result.dag.nodes)[0:4])
inspector_result.dag.remove_node(list(inspector_result.dag.nodes)[1])
expected_dag = networkx.DiGraph()
expected_train_data = DagNode(
3, BasicCodeLocation("<string-source>", 10),
OperatorContext(OperatorType.TRAIN_DATA,
FunctionInfo('statsmodel.api.OLS', 'fit')),
DagNodeDetails(None, ['array']),
OptionalCodeInfo(CodeReference(10, 10, 10, 22), 'sm.OLS(y, X)'))
expected_train_labels = DagNode(
4, BasicCodeLocation("<string-source>", 10),
OperatorContext(OperatorType.TRAIN_LABELS,
FunctionInfo('statsmodel.api.OLS', 'fit')),
DagNodeDetails(None, ['array']),
OptionalCodeInfo(CodeReference(10, 10, 10, 22), 'sm.OLS(y, X)'))
expected_ols = DagNode(
5, BasicCodeLocation("<string-source>", 10),
OperatorContext(OperatorType.ESTIMATOR,
FunctionInfo('statsmodel.api.OLS', 'fit')),
DagNodeDetails('Decision Tree', []),
OptionalCodeInfo(CodeReference(10, 10, 10, 22), 'sm.OLS(y, X)'))
expected_dag.add_edge(expected_train_data, expected_ols)
expected_dag.add_edge(expected_train_labels, expected_ols)
compare(networkx.to_dict_of_dicts(inspector_result.dag),
networkx.to_dict_of_dicts(expected_dag))
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_train_data]
lineage_output = inspection_results_data_source[RowLineage(3)]
expected_lineage_df = DataFrame(
[[
numpy.array([1.0, 0.3745401188473625, 0.9507143064099162]),
{LineageId(3, 0)}
],
[
numpy.array([1.0, 0.7319939418114051, 0.5986584841970366]),
{LineageId(3, 1)}
],
[
numpy.array([1.0, 0.15601864044243652, 0.15599452033620265]),
{LineageId(3, 2)}
]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
atol=0.1)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_train_labels]
lineage_output = inspection_results_data_source[RowLineage(3)]
expected_lineage_df = DataFrame([[2.154842811243982, {LineageId(5, 0)}],
[1.4566686012747074, {LineageId(5, 1)}],
[1.2552278383069588, {LineageId(5, 2)}]],
columns=['array', 'mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
atol=0.1)
inspection_results_data_source = inspector_result.dag_node_to_inspection_results[
expected_ols]
lineage_output = inspection_results_data_source[RowLineage(3)]
expected_lineage_df = DataFrame(
[[{LineageId(5, 0), LineageId(3, 0)}],
[{LineageId(5, 1), LineageId(3, 1)}],
[{LineageId(5, 2), LineageId(3, 2)}]],
columns=['mlinspect_lineage'])
pandas.testing.assert_frame_equal(
lineage_output.reset_index(drop=True),
expected_lineage_df.reset_index(drop=True),
check_column_type=False)
