repo_id stringclasses 409
values | prefix large_stringlengths 34 36.3k | target large_stringlengths 1 498 | assertion_type stringclasses 31
values | difficulty stringclasses 8
values | test_file stringlengths 10 121 | test_function stringlengths 1 104 | test_class stringlengths 0 51 | lineno int32 2 11.3k | commit_idx int32 |
|---|---|---|---|---|---|---|---|---|---|
ing-bank/popmon | from copy import deepcopy
from pathlib import Path
import pandas as pd
import pytest
from popmon.hist.filling import make_histograms
from popmon.pipeline.metrics import df_stability_metrics
def spark_context():
if not spark_found:
return None
current_path = Path(__file__).parent
scala = "2.12" ... | (v1, v2) | assert | collection | tests/popmon/spark/test_spark.py | test_spark_make_histograms | 124 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_compute_traffic_light_bounds():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
"monitoring_rules": {
"the_feature:mae": [8, 4, 2, 2],
"dummy_feature:*": [0, 0, 0, 0],
... | [8, 4, 2, 2] | assert | collection | tests/popmon/alerting/test_compute_tl_bounds.py | test_compute_traffic_light_bounds | 39 | null | |
ing-bank/popmon | import numpy as np
import pytest
from popmon.hist.filling import get_bin_specs, make_histograms
from popmon.stitching import stitch_histograms
def test_histogram_stitching():
features1 = sorted(["date:isActive", "date:eyeColor", "date:latitude"])
features2 = sorted(["isActive", "eyeColor", "latitude", "age"])... | 800 | assert | numeric_literal | tests/popmon/stitching/test_histogram_stitching.py | test_histogram_stitching | 21 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
from pathlib import Path
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def spark_context():
if not spark_found:
return None
current_path = Path(__file__).parent
scala = "2.12" if int(pyspark_version[0]) == 3... | ValueError) | pytest.raises | variable | tests/popmon/spark/test_split_dataset_spark.py | test_split_dataset_spark_int_underflow | 74 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | 2 | assert | numeric_literal | tests/popmon/analysis/test_hist_numpy.py | test_histogram | 85 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon.alerting import AlertsSummary, ComputeTLBounds, traffic_light_summary
from popmon.analysis.apply_func import ApplyFunc
from popmon.base import Pipeline
def test_integration_alerting():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
"monitorin... | 4 | assert | numeric_literal | tests/popmon/alerting/test_integration.py | test_integration_alerting | 49 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | entries0) | assert_* | variable | tests/popmon/analysis/test_hist_numpy.py | test_get_consistent_numpy_entries | 337 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.comparison.hist_comparer import (
ExpandingHistComparer,
ReferenceHistComparer,
RollingHistComparer,
hist_compare,
)
from popmon.analysis.functions imp... | 16 | assert | numeric_literal | tests/popmon/analysis/comparison/test_hist_comparer.py | test_reference_hist_comparer | 117 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.comparison.hist_comparer import ReferenceHistComparer
from popmon.base import Pipeline
from popmon.config import Settings
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
from popmon.visualization imp... | len(features) | assert | func_call | tests/popmon/visualization/test_report_generator.py | test_report_generator | 52 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.comparison import (
ExpandingNormHistComparer,
ReferenceNormHistComparer,
RollingNormHistComparer,
)
from popmon.analysis.functions import (
expand,
... | 3.217532467532462) | assert_* | numeric_literal | tests/popmon/analysis/test_functions.py | test_chi_squared2 | 613 | null | |
ing-bank/popmon | import numpy as np
from popmon.analysis.profiling.profiles import profile_fraction_of_true
def test_fraction_of_true():
res = profile_fraction_of_true([], [])
assert np.isnan(res)
res = profile_fraction_of_true(["a"], [10])
assert np.isnan(res)
res = profile_fraction_of_true(["a", "b", "c"], [10, ... | 1.0 | assert | numeric_literal | tests/popmon/analysis/profiling/test_profiles.py | test_fraction_of_true | 22 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
from pathlib import Path
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def spark_context():
if not spark_found:
return None
current_path = Path(__file__).parent
scala = "2.12" if int(pyspark_version[0]) == 3... | 51 | assert | numeric_literal | tests/popmon/spark/test_split_dataset_spark.py | test_split_dataset_spark_condition | 172 | null | |
ing-bank/popmon | import numpy as np
import pytest
from popmon.base import Module
def test_popmon_module(test_module):
datastore = {"x": np.arange(10)}
datastore = test_module.transform(datastore)
assert "x" in datastore # check if key 'x' is still in the datastore
np.testing.assert_almost_equal(np.mean(datastore["sc... | 0.3) | assert_* | numeric_literal | tests/popmon/base/test_module.py | test_popmon_module | 37 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_condition(test_dataframe_pandas):
reference, df = split_dataset(
test_dataframe_pandas,
split=test_dataframe_pandas.date
< da... | 949 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_condition | 99 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | test_data | assert | variable | tests/popmon/alerting/test_apply_tl_bounds.py | test_apply_monitoring_business_rules | 64 | null | |
ing-bank/popmon | import copy
import json
import pandas as pd
import pytest
from popmon.io import FileWriter
DATA = {"name": ["Name"], "surname": ["Surname"]}
def get_ready_ds():
return copy.deepcopy({"my_data": DATA})
def to_json(data, **kwargs):
return json.dumps(data, **kwargs)
def to_pandas(data):
return pd.DataFra... | TypeError) | pytest.raises | variable | tests/popmon/io/test_file_writer.py | test_file_writer_not_a_func | 38 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | -0.5) | assert_* | numeric_literal | tests/popmon/alerting/test_apply_tl_bounds.py | test_apply_dynamic_traffic_light_bounds | 131 | null | |
ing-bank/popmon | import itertools
import numpy as np
from scipy import linalg, stats
from popmon.stats.numpy import (
mean,
probability_distribution_mean_covariance,
quantile,
std,
)
def get_data():
rng = np.random.default_rng(5)
a = rng.integers(0, 10, size=(3, 4, 5, 6))
w = rng.integers(0, 10, size=(3, ... | quantile(a, 0.1, w, axis=(1, 2), keepdims=False)) | assert_* | func_call | tests/popmon/stats/test_numpy.py | test_statistics_1 | 184 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | ValueError) | pytest.raises | variable | tests/popmon/analysis/test_hist_numpy.py | test_get_consistent_numpy_2dgrids | 213 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_int(test_dataframe_pandas):
reference, df = split_dataset(test_dataframe_pandas, split=3, time_axis="date")
assert reference.shape[0] == 3
... | 997 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_int | 26 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.comparison.hist_comparer import ReferenceHistComparer
from popmon.base import Pipeline
from popmon.config import Settings
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
from popmon.visualization imp... | datastore["comparison"] | assert | complex_expr | tests/popmon/visualization/test_report_generator.py | test_report_generator | 54 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists
from popmon.base import Pipeline
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
def test_hist_splitter():
hist_list = [
"date... | datastore["output_hist"] | assert | complex_expr | tests/popmon/hist/test_hist_splitter.py | test_hist_splitter | 43 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | centers) | assert_* | variable | tests/popmon/analysis/test_hist_numpy.py | test_get_consistent_numpy_entries | 343 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
from conftest import make_mixed_dataframe
from popmon.hist.hist_utils import (
is_numeric,
is_timestamp,
project_on_x,
project_split2dhist_on_axis,
sparse_bin_centers_x,
split_hist_along_first_dimension,
sum_entries,
sum_o... | splitC2 | assert | variable | tests/popmon/hist/test_histogram.py | test_project_split2dhist_on_axis | 325 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_float_round(test_dataframe_pandas):
reference, df = split_dataset(test_dataframe_pandas, split=0.8888, time_axis="date")
assert reference.shape[... | 112 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_float_round | 61 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
from popmon.analysis.profiling.hist_profiler import HistProfiler
from popmon.hist.hist_utils import get_bin_centers
def test_profile_hist1d():
num_bins = 1000
num_entries = 10000
hist_name = "histogram"
split_len = 10
split = []
... | profiles[0] | assert | complex_expr | tests/popmon/analysis/profiling/test_hist_profiler.py | test_profile_hist1d | 31 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_collect_traffic_light_bounds():
test_dict = {"a": 2, "b:c": 5, "b:d": 6, "x:y:z": 17}
pkeys, nkeys = collect_traffic_light_bounds(test_dict)
assert nkeys == | ["a"] | assert | collection | tests/popmon/alerting/test_compute_tl_bounds.py | test_collect_traffic_light_bounds | 11 | null | |
ing-bank/popmon | import pandas as pd
from popmon.analysis.merge_statistics import MergeStatistics
def test_merge_statistics():
df1 = pd.DataFrame(
{
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"],
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"],
... | df2) | assert_* | variable | tests/popmon/analysis/test_merge_statistics.py | test_merge_statistics | 47 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.base import Pipeline
from popmon.config import Settings
from popmon.io import JsonReader
from popmon.pipeline.metrics import df_stability_metrics, stability_metrics
def test_hists_stability_metrics():
settings = Settings(reference_type="ro... | list(ds.keys()) | assert | func_call | tests/popmon/pipeline/test_metrics.py | test_hists_stability_metrics | 38 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_compute_traffic_light_bounds():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
"monitoring_rules": {
"the_feature:mae": [8, 4, 2, 2],
"dummy_feature:*": [0, 0, 0, 0],
... | output.keys() | assert | func_call | tests/popmon/alerting/test_compute_tl_bounds.py | test_compute_traffic_light_bounds | 38 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_compute_traffic_light_funcs():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
"monitoring_rules": {
"the_feature:mae": [8, 4, 2, 2],
"dummy_feature:*": [0, 0, 0, 0],
... | ["mse"] | assert | collection | tests/popmon/alerting/test_compute_tl_bounds.py | test_compute_traffic_light_funcs | 74 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_int_underflow(test_dataframe_pandas):
with pytest.raises( | ValueError) | pytest.raises | variable | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_int_underflow | 32 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | -2.0) | assert_* | numeric_literal | tests/popmon/alerting/test_apply_tl_bounds.py | test_apply_dynamic_traffic_light_bounds | 132 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists
from popmon.base import Pipeline
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
def test_hist_splitter():
hist_list = [
"date... | len(features) | assert | func_call | tests/popmon/hist/test_hist_splitter.py | test_hist_splitter | 41 | null | |
ing-bank/popmon | import copy
import json
import pandas as pd
import pytest
from popmon.io import FileWriter
DATA = {"name": ["Name"], "surname": ["Surname"]}
def get_ready_ds():
return copy.deepcopy({"my_data": DATA})
def to_json(data, **kwargs):
return json.dumps(data, **kwargs)
def to_pandas(data):
return pd.DataFra... | DATA | assert | variable | tests/popmon/io/test_file_writer.py | test_file_writer_df | 47 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists
from popmon.base import Pipeline
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
@pytest.mark.filterwarnings("ignore:Input histograms have... | False | assert | bool_literal | tests/popmon/hist/test_hist_splitter.py | test_hist_splitter_filter | 94 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.analysis.apply_func import ApplyFunc, apply_func, apply_func_array
from popmon.analysis.functions import pull
from popmon.analysis.profiling.pull_calculator import (
ExpandingPullCalculator,
ReferencePullCalculator,
RefMedianMadPullCalculator... | -0.6745) | assert_* | numeric_literal | tests/popmon/analysis/profiling/test_apply_func.py | test_median_mad_pull_comparer | 138 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | datastore | assert | variable | tests/popmon/alerting/test_apply_tl_bounds.py | test_apply_monitoring_business_rules | 61 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_condition(test_dataframe_pandas):
reference, df = split_dataset(
test_dataframe_pandas,
split=test_dataframe_pandas.date
< da... | 51 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_condition | 98 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.analysis.apply_func import ApplyFunc, apply_func, apply_func_array
from popmon.analysis.functions import pull
from popmon.analysis.profiling.pull_calculator import (
ExpandingPullCalculator,
ReferencePullCalculator,
RefMedianMadPullCalculator... | 6) | assert_* | numeric_literal | tests/popmon/analysis/profiling/test_apply_func.py | test_variance_comparer | 99 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_float_round(test_dataframe_pandas):
reference, df = split_dataset(test_dataframe_pandas, split=0.8888, time_axis="date")
assert reference.shape... | 888 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_float_round | 60 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def test_split_dataset_pandas_int(test_dataframe_pandas):
reference, df = split_dataset(test_dataframe_pandas, split=3, time_axis="date")
assert reference.shape[0] == | 3 | assert | numeric_literal | tests/popmon/pipeline/test_split_dataset.py | test_split_dataset_pandas_int | 25 | null | |
ing-bank/popmon | from datetime import datetime, timedelta
from pathlib import Path
import pandas as pd
import pytest
from popmon.pipeline.dataset_splitter import split_dataset
def spark_context():
if not spark_found:
return None
current_path = Path(__file__).parent
scala = "2.12" if int(pyspark_version[0]) == 3... | 888 | assert | numeric_literal | tests/popmon/spark/test_split_dataset_spark.py | test_split_dataset_spark_float_round | 117 | null | |
ing-bank/popmon | import numpy as np
import pytest
from popmon.hist.filling import get_bin_specs, make_histograms
from popmon.stitching import stitch_histograms
def test_histogram_stitching():
features1 = sorted(["date:isActive", "date:eyeColor", "date:latitude"])
features2 = sorted(["isActive", "eyeColor", "latitude", "age"])... | 400 | assert | numeric_literal | tests/popmon/stitching/test_histogram_stitching.py | test_histogram_stitching | 22 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_collect_traffic_light_bounds():
test_dict = {"a": 2, "b:c": 5, "b:d": 6, "x:y:z": 17}
pkeys, nkeys = collect_traffic_light_bounds(test_dict)
assert nkeys == ["a"]
assert len(pkeys) == 2
assert pkeys[... | {"c", "d"} | assert | collection | tests/popmon/alerting/test_compute_tl_bounds.py | test_collect_traffic_light_bounds | 14 | null | |
ing-bank/popmon | import pytest
from popmon.alerting import ComputeTLBounds, collect_traffic_light_bounds
def test_compute_traffic_light_funcs():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
"monitoring_rules": {
"the_feature:mae": [8, 4, 2, 2],
"dummy_feature:*": [0, 0, 0, 0],
... | ["mae"] | assert | collection | tests/popmon/alerting/test_compute_tl_bounds.py | test_compute_traffic_light_funcs | 66 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | 5 | assert | numeric_literal | tests/popmon/analysis/test_hist_numpy.py | test_histogram | 80 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | 1 | assert | numeric_literal | tests/popmon/alerting/test_apply_tl_bounds.py | test_traffic_light | 35 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.comparison.hist_comparer import ReferenceHistComparer
from popmon.base import Pipeline
from popmon.config import Settings
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
from popmon.visualization imp... | 0 | assert | numeric_literal | tests/popmon/visualization/test_report_generator.py | test_report_generator | 59 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
from conftest import make_mixed_dataframe
from popmon.hist.hist_utils import (
is_numeric,
is_timestamp,
project_on_x,
project_split2dhist_on_axis,
sparse_bin_centers_x,
split_hist_along_first_dimension,
sum_entries,
sum_o... | bin_edges3) | assert_* | variable | tests/popmon/hist/test_histogram.py | test_sum_over_x | 210 | null | |
ing-bank/popmon | from popmon import resources
from popmon.io import JsonReader
def test_json_reader():
jr = JsonReader(file_path=resources.data("example.json"), store_key="example")
datastore = jr.transform(datastore={})
assert datastore["example"]["boolean"]
assert len(datastore["example"]["array"]) == 3
assert ... | 0 | assert | numeric_literal | tests/popmon/io/test_json_reader.py | test_json_reader | 11 | null | |
ing-bank/popmon | import pytest
from pydantic.error_wrappers import ValidationError
from popmon import Settings
def test_settings_docs_example():
settings = Settings()
settings.time_axis = "date"
assert settings.time_axis == | "date" | assert | string_literal | tests/popmon/test_config.py | test_settings_docs_example | 10 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
from conftest import make_mixed_dataframe
from popmon.hist.hist_utils import (
is_numeric,
is_timestamp,
project_on_x,
project_split2dhist_on_axis,
sparse_bin_centers_x,
split_hist_along_first_dimension,
sum_entries,
sum_o... | splitA1 | assert | variable | tests/popmon/hist/test_histogram.py | test_project_split2dhist_on_axis | 305 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
from conftest import make_mixed_dataframe
from popmon.hist.hist_utils import (
is_numeric,
is_timestamp,
project_on_x,
project_split2dhist_on_axis,
sparse_bin_centers_x,
split_hist_along_first_dimension,
sum_entries,
sum_o... | check3a) | assert_* | variable | tests/popmon/hist/test_histogram.py | test_split_hist_along_first_dimension | 116 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | [np.nan] * 9 + [1.0] * 91) | assert_* | collection | tests/popmon/alerting/test_apply_tl_bounds.py | test_rolling_window_funcs | 218 | null | |
ing-bank/popmon | import pandas as pd
from popmon.analysis.merge_statistics import MergeStatistics
def test_merge_statistics():
df1 = pd.DataFrame(
{
"A": ["A0", "A1", "A2", "A3"],
"B": ["B0", "B1", "B2", "B3"],
"C": ["C0", "C1", "C2", "C3"],
"D": ["D0", "D1", "D2", "D3"],
... | df1) | assert_* | variable | tests/popmon/analysis/test_merge_statistics.py | test_merge_statistics | 48 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon.alerting import AlertsSummary, ComputeTLBounds, traffic_light_summary
from popmon.analysis.apply_func import ApplyFunc
from popmon.base import Pipeline
def test_traffic_light_summary_combination():
datastore = {"test_data": pytest.test_comparer_df}
conf = {
... | alerts | assert | variable | tests/popmon/alerting/test_integration.py | test_traffic_light_summary_combination | 136 | null | |
ing-bank/popmon | from copy import deepcopy
from pathlib import Path
import pandas as pd
import pytest
from popmon.hist.filling import make_histograms
from popmon.pipeline.metrics import df_stability_metrics
def spark_context():
if not spark_found:
return None
current_path = Path(__file__).parent
scala = "2.12" ... | list(ds.keys()) | assert | func_call | tests/popmon/spark/test_spark.py | test_spark_stability_metrics | 67 | null | |
ing-bank/popmon | import histogrammar as hg
import numpy as np
import pandas as pd
import pytest
from conftest import make_mixed_dataframe
from popmon.analysis.hist_numpy import (
assert_similar_hists,
check_similar_hists,
get_2dgrid,
get_consistent_numpy_1dhists,
get_consistent_numpy_2dgrids,
get_consistent_num... | "Bin" | assert | string_literal | tests/popmon/analysis/test_hist_numpy.py | test_get_contentType | 98 | null | |
ing-bank/popmon | import pytest
from popmon.base.registry import Registry
def test_registry_duplicate():
DuplicatedRegistry = Registry()
@DuplicatedRegistry.register(key="test", description="me")
def func1():
pass
with pytest.raises(ValueError) as e:
@DuplicatedRegistry.register(key="another", descri... | "Key 'test' has already been registered." | assert | string_literal | tests/popmon/base/test_registry.py | test_registry_duplicate | 72 | null | |
ing-bank/popmon | import numpy as np
import pytest
from popmon.base import Module
def test_popmon_module(test_module):
datastore = {"x": np.arange(10)}
datastore = test_module.transform(datastore)
assert "x" in | datastore | assert | variable | tests/popmon/base/test_module.py | test_popmon_module | 35 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.comparison import (
ExpandingNormHistComparer,
ReferenceNormHistComparer,
RollingNormHistComparer,
)
from popmon.analysis.functions import (
expand,
... | 4.25) | assert_* | numeric_literal | tests/popmon/analysis/test_functions.py | test_chi_squared1 | 550 | null | |
ing-bank/popmon | import itertools
import numpy as np
from scipy import linalg, stats
from popmon.stats.numpy import (
mean,
probability_distribution_mean_covariance,
quantile,
std,
)
def get_data():
rng = np.random.default_rng(5)
a = rng.integers(0, 10, size=(3, 4, 5, 6))
w = rng.integers(0, 10, size=(3, ... | mean(a, w, axis=(1, 2), keepdims=True)) | assert_* | func_call | tests/popmon/stats/test_numpy.py | test_statistics_1 | 149 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.hist_numpy import assert_similar_hists, check_similar_hists
from popmon.base import Pipeline
from popmon.hist.hist_splitter import HistSplitter
from popmon.io import JsonReader
def test_hist_splitter():
hist_list = [
"date... | hlist) | assert_* | variable | tests/popmon/hist/test_hist_splitter.py | test_hist_splitter | 51 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon.alerting import (
ComputeTLBounds,
DynamicBounds,
StaticBounds,
TrafficLightAlerts,
pull_bounds,
traffic_light,
)
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.functions import (
expanding_mean,
exp... | 2 | assert | numeric_literal | tests/popmon/alerting/test_apply_tl_bounds.py | test_traffic_light | 34 | null | |
ing-bank/popmon | import copy
import json
import pandas as pd
import pytest
from popmon.io import FileWriter
DATA = {"name": ["Name"], "surname": ["Surname"]}
def get_ready_ds():
return copy.deepcopy({"my_data": DATA})
def to_json(data, **kwargs):
return json.dumps(data, **kwargs)
def to_pandas(data):
return pd.DataFra... | to_json(DATA, indent=4) | assert | func_call | tests/popmon/io/test_file_writer.py | test_file_writer_json_with_kwargument | 33 | null | |
ing-bank/popmon | import pandas as pd
import pytest
from popmon import resources
from popmon.base import Pipeline
from popmon.config import Settings
from popmon.hist.filling import get_bin_specs
from popmon.io import JsonReader
from popmon.pipeline.report import df_stability_report, stability_report
def test_df_stability_report_self()... | bin_specs["date:latitude"][0]["binWidth"] | assert | complex_expr | tests/popmon/pipeline/test_report.py | test_df_stability_report_self | 97 | null | |
ing-bank/popmon | import numpy as np
import pytest
from popmon.analysis.comparison.comparisons import (
jensen_shannon_divergence,
ks_prob,
ks_test,
kullback_leibler_divergence,
population_stability_index,
uu_chi2,
)
@pytest.mark.filterwarnings("ignore:invalid value encountered in true_divide")
def test_uu_chi2... | 4) | assert_* | numeric_literal | tests/popmon/analysis/comparison/test_comparisons.py | test_uu_chi2 | 22 | null | |
ing-bank/popmon | import numpy as np
import pandas as pd
import pytest
from popmon import resources
from popmon.analysis.apply_func import ApplyFunc
from popmon.analysis.comparison import (
ExpandingNormHistComparer,
ReferenceNormHistComparer,
RollingNormHistComparer,
)
from popmon.analysis.functions import (
expand,
... | datastore | assert | variable | tests/popmon/analysis/test_functions.py | test_normalized_hist_mean_cov | 342 | null | |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | len(exec_plan.get_output("input_partitions").load_partitioned_datasets(npartitions, data_partition_column, hive_partitioning))) | self.assertEqual | func_call | tests/test_partition.py | run_test_plan | TestPartition | 292 | null |
deepseek-ai/smallpond | from typing import List
import pandas as pd
import pyarrow as pa
import pytest
from smallpond.dataframe import Session
def test_arrow(sp: Session):
arrow_table = pa.table({"a": [1, 2, 3], "b": [4, 5, 6]})
df = sp.from_arrow(arrow_table)
assert df.to_arrow() == | arrow_table | assert | variable | tests/test_dataframe.py | test_arrow | 19 | null | |
deepseek-ai/smallpond | import os.path
import queue
import sys
import unittest
from concurrent.futures import ThreadPoolExecutor
from datetime import datetime
from multiprocessing import Manager, Process
from typing import List, Optional
import fsspec
import numpy as np
import psutil
import pyarrow as arrow
import pyarrow.compute as pc
impor... | actual.shape) | self.assertEqual | complex_expr | tests/test_fabric.py | _compare_arrow_tables | TestFabric | 265 | null |
deepseek-ai/smallpond | import glob
import os.path
import tempfile
import unittest
import pyarrow.parquet as parquet
from loguru import logger
from smallpond.io.arrow import (
RowRange,
build_batch_reader_from_files,
cast_columns_to_large_string,
dump_to_parquet_files,
load_from_parquet_files,
)
from smallpond.utility im... | table.num_rows) | self.assertEqual | complex_expr | tests/test_arrow.py | test_table_to_batches | TestArrow | 112 | null |
deepseek-ai/smallpond | import itertools
import unittest
import numpy as np
from hypothesis import given
from hypothesis import strategies as st
from smallpond.common import get_nth_partition, split_into_cols, split_into_rows
from tests.test_fabric import TestFabric
class TestCommon(TestFabric, unittest.TestCase):
@given(st.data())
... | large_size) | self.assertEqual | variable | tests/test_common.py | test_split_into_cols | TestCommon | 54 | null |
deepseek-ai/smallpond | import functools
import os.path
import socket
import tempfile
import time
import unittest
from datetime import datetime
from typing import Iterable, List, Tuple
import pandas
import pyarrow as arrow
from loguru import logger
from pandas.core.api import DataFrame as DataFrame
from smallpond.common import GB, MB, split... | os.path.exists(os.path.join(output_path, "url_counts"))) | self.assertTrue | func_call | tests/test_execution.py | test_override_output_path | TestExecution | 678 | null |
deepseek-ai/smallpond | import itertools
import unittest
import numpy as np
from hypothesis import given
from hypothesis import strategies as st
from smallpond.common import get_nth_partition, split_into_cols, split_into_rows
from tests.test_fabric import TestFabric
class TestCommon(TestFabric, unittest.TestCase):
@given(st.data())
... | [x for row in itertools.zip_longest(*chunks) for x in row if x is not None]) | self.assertListEqual | collection | tests/test_common.py | test_split_into_cols | TestCommon | 47 | null |
deepseek-ai/smallpond | import functools
import os.path
import socket
import tempfile
import time
import unittest
from datetime import datetime
from typing import Iterable, List, Tuple
import pandas
import pyarrow as arrow
from loguru import logger
from pandas.core.api import DataFrame as DataFrame
from smallpond.common import GB, MB, split... | len(os.listdir(copy_path))) | self.assertEqual | func_call | tests/test_execution.py | test_data_sink_avoid_filename_conflicts | TestExecution | 699 | null |
deepseek-ai/smallpond | import itertools
import unittest
import numpy as np
from hypothesis import given
from hypothesis import strategies as st
from smallpond.common import get_nth_partition, split_into_cols, split_into_rows
from tests.test_fabric import TestFabric
class TestCommon(TestFabric, unittest.TestCase):
@given(st.data())
... | computed) | self.assertEqual | variable | tests/test_common.py | test_split_into_rows | TestCommon | 38 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | len( exec_plan.get_output("nested_hash_partitions").load_partitioned_datasets(npartitions_nested, "nested_hash_partitions") )) | self.assertEqual | func_call | tests/test_partition.py | test_empty_hash_partition | TestPartition | 228 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | exec_plan.final_output.num_rows) | self.assertEqual | complex_expr | tests/test_partition.py | test_empty_hash_partition | TestPartition | 215 | null |
deepseek-ai/smallpond | import itertools
import unittest
import numpy as np
from hypothesis import given
from hypothesis import strategies as st
from smallpond.common import get_nth_partition, split_into_cols, split_into_rows
from tests.test_fabric import TestFabric
class TestCommon(TestFabric, unittest.TestCase):
def test_get_nth_part... | get_nth_partition(items, 1, 2)) | self.assertListEqual | func_call | tests/test_common.py | test_get_nth_partition | TestCommon | 19 | null |
deepseek-ai/smallpond | import glob
import os.path
import tempfile
import unittest
import pyarrow.parquet as parquet
from loguru import logger
from smallpond.io.arrow import (
RowRange,
build_batch_reader_from_files,
cast_columns_to_large_string,
dump_to_parquet_files,
load_from_parquet_files,
)
from smallpond.utility im... | loaded_table.num_rows) | self.assertEqual | complex_expr | tests/test_arrow.py | test_load_mixed_string_types | TestArrow | 138 | null |
deepseek-ai/smallpond | from typing import List
import pandas as pd
import pyarrow as pa
import pytest
from smallpond.dataframe import Session
def test_csv(sp: Session):
df = sp.read_csv(
"tests/data/mock_urls/*.tsv",
schema={"urlstr": "varchar", "valstr": "varchar"},
delim=r"\t",
)
assert df.count() ==... | 1000 | assert | numeric_literal | tests/test_dataframe.py | test_csv | 35 | null | |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | pc.sum(exec_plan.final_output.to_arrow_table().column("row_count")).as_py()) | self.assertEqual | func_call | tests/test_partition.py | test_hash_partition | TestPartition | 140 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | dataset.num_rows) | self.assertEqual | complex_expr | tests/test_partition.py | test_many_row_partitions | TestPartition | 71 | null |
deepseek-ai/smallpond | import os.path
import random
import time
import unittest
from typing import List, Tuple
from loguru import logger
from smallpond.execution.scheduler import ExecutorState
from smallpond.execution.task import PythonScriptTask, RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.l... | 0) | self.assertGreater | numeric_literal | tests/test_scheduler.py | test_failed_executors | TestScheduler | 120 | null |
deepseek-ai/smallpond | import functools
import os.path
import socket
import tempfile
import time
import unittest
from datetime import datetime
from typing import Iterable, List, Tuple
import pandas
import pyarrow as arrow
from loguru import logger
from pandas.core.api import DataFrame as DataFrame
from smallpond.common import GB, MB, split... | len(os.listdir(link_path))) | self.assertEqual | func_call | tests/test_execution.py | test_data_sink_avoid_filename_conflicts | TestExecution | 698 | null |
deepseek-ai/smallpond | import os
from smallpond.dataframe import Session
def test_shutdown_cleanup(sp: Session):
assert os.path.exists(sp._runtime_ctx.queue_root), "queue directory should exist"
assert os.path.exists(sp._runtime_ctx.staging_root), "staging directory should exist"
assert os.path.exists(sp._runtime_ctx.temp_root)... | fin.read() | assert | func_call | tests/test_session.py | test_shutdown_cleanup | 21 | null | |
deepseek-ai/smallpond | from typing import List
import pandas as pd
import pyarrow as pa
import pytest
from smallpond.dataframe import Session
def test_limit(sp: Session):
df = sp.from_items(list(range(1000))).repartition(10, by_rows=True)
assert df.limit(2).count() == | 2 | assert | numeric_literal | tests/test_dataframe.py | test_limit | 136 | null | |
deepseek-ai/smallpond | import functools
import os.path
import socket
import tempfile
import time
import unittest
from datetime import datetime
from typing import Iterable, List, Tuple
import pandas
import pyarrow as arrow
from loguru import logger
from pandas.core.api import DataFrame as DataFrame
from smallpond.common import GB, MB, split... | exec_plan.get_output("random_urls_k10").to_arrow_table().num_rows) | self.assertEqual | func_call | tests/test_execution.py | test_partial_process_func | TestExecution | 755 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import threading
import unittest
from smallpond.io.filesystem import dump, load
from tests.test_fabric import TestFabric
class TestFilesystem(TestFabric, unittest.TestCase):
def test_pickle_runtime_ctx(self):
with tempfile.TemporaryDirectory(dir=self.output_root_abspath) as ... | runtime_ctx.job_id) | self.assertEqual | complex_expr | tests/test_filesystem.py | test_pickle_runtime_ctx | TestFilesystem | 16 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | len(exec_plan.final_output.load_partitioned_datasets(npartitions_nested, "nested_hash_partitions"))) | self.assertEqual | func_call | tests/test_partition.py | test_empty_hash_partition | TestPartition | 220 | null |
deepseek-ai/smallpond | import os.path
import random
import time
import unittest
from typing import List, Tuple
from loguru import logger
from smallpond.execution.scheduler import ExecutorState
from smallpond.execution.task import PythonScriptTask, RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.l... | latest_sched_state.success) | self.assertTrue | complex_expr | tests/test_scheduler.py | test_failed_executors | TestScheduler | 119 | null |
deepseek-ai/smallpond | import functools
import os.path
import socket
import tempfile
import time
import unittest
from datetime import datetime
from typing import Iterable, List, Tuple
import pandas
import pyarrow as arrow
from loguru import logger
from pandas.core.api import DataFrame as DataFrame
from smallpond.common import GB, MB, split... | set(filename for filename in os.listdir(copy_input_path) if filename.endswith(".parquet"))) | self.assertEqual | func_call | tests/test_execution.py | test_data_sink_avoid_filename_conflicts | TestExecution | 701 | null |
deepseek-ai/smallpond | import glob
import os.path
import unittest
from pathlib import PurePath
import duckdb
import pandas
import pyarrow as arrow
import pytest
from loguru import logger
from smallpond.common import DEFAULT_ROW_GROUP_SIZE, MB
from smallpond.logical.dataset import ParquetDataSet
from smallpond.utility import ConcurrentIter
... | len(dataset_with_wildcards.resolved_paths)) | self.assertEqual | func_call | tests/test_dataset.py | test_paths_with_char_ranges | TestDataSet | 107 | null |
deepseek-ai/smallpond | import os.path
import tempfile
import unittest
from typing import List
import pyarrow.compute as pc
from smallpond.common import DATA_PARTITION_COLUMN_NAME, GB
from smallpond.execution.task import RuntimeContext
from smallpond.logical.dataset import DataSet, ParquetDataSet
from smallpond.logical.node import (
Arr... | len(exec_plan.get_output("hash_partitions").load_partitioned_datasets(npartitions, "hash_partitions"))) | self.assertEqual | func_call | tests/test_partition.py | test_empty_hash_partition | TestPartition | 224 | null |
deepseek-ai/smallpond | import itertools
import unittest
import numpy as np
from hypothesis import given
from hypothesis import strategies as st
from smallpond.common import get_nth_partition, split_into_cols, split_into_rows
from tests.test_fabric import TestFabric
class TestCommon(TestFabric, unittest.TestCase):
def test_get_nth_part... | get_nth_partition(items, 1, 3)) | self.assertListEqual | func_call | tests/test_common.py | test_get_nth_partition | TestCommon | 22 | null |
deepseek-ai/smallpond | import os.path
import unittest
import uuid
from loguru import logger
from benchmarks.gray_sort_benchmark import gray_sort_benchmark
from examples.sort_mock_urls import sort_mock_urls
from smallpond.common import GB, MB
from smallpond.execution.driver import Driver
from tests.test_fabric import TestFabric
class TestD... | 0) | self.assertGreater | numeric_literal | tests/test_driver.py | test_standalone_mode | TestDriver | 31 | null |
deepseek-ai/smallpond | from typing import List
import pandas as pd
import pyarrow as pa
import pytest
from smallpond.dataframe import Session
def test_filter(sp: Session):
df = sp.from_arrow(pa.table({"a": [1, 2, 3], "b": [4, 5, 6]}))
df1 = df.filter("a > 1")
assert df1.to_arrow() == | pa.table({"a": [2, 3], "b": [5, 6]}) | assert | func_call | tests/test_dataframe.py | test_filter | 88 | null | |
deepseek-ai/smallpond | import glob
import os.path
import tempfile
import unittest
import pyarrow.parquet as parquet
from loguru import logger
from smallpond.io.arrow import (
RowRange,
build_batch_reader_from_files,
cast_columns_to_large_string,
dump_to_parquet_files,
load_from_parquet_files,
)
from smallpond.utility im... | sum(batch.num_rows for batch in batch_reader)) | self.assertEqual | func_call | tests/test_arrow.py | test_load_mixed_string_types | TestArrow | 140 | null |
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