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NDArraysRegressionFixture: regression on arrays with arbitrary shape. #72

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merged 4 commits into from Sep 15, 2021
Merged

NDArraysRegressionFixture: regression on arrays with arbitrary shape. #72

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62e86ed
NDArraysRegressionFixture: regression on arrays with arbitrary shape.
tovrstra Sep 3, 2021
fade52f
Improve NDArraysRegression code
tovrstra Sep 7, 2021
af561d7
Improve error messages and add tests for corner cases
tovrstra Sep 8, 2021
f03cdce
Fix broken tests
tovrstra Sep 8, 2021
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15 changes: 7 additions & 8 deletions src/pytest_regressions/dataframe_regression.py
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Expand Up @@ -42,7 +42,7 @@ def _check_data_types(self, key, obtained_column, expected_column):
try:
import numpy as np
except ModuleNotFoundError:
raise ModuleNotFoundError(import_error_message("Numpy"))
raise ModuleNotFoundError(import_error_message("NumPy"))

__tracebackhide__ = True
obtained_data_type = obtained_column.values.dtype
Expand Down Expand Up @@ -89,7 +89,7 @@ def _check_fn(self, obtained_filename, expected_filename):
try:
import numpy as np
except ModuleNotFoundError:
raise ModuleNotFoundError(import_error_message("Numpy"))
raise ModuleNotFoundError(import_error_message("NumPy"))
try:
import pandas as pd
except ModuleNotFoundError:
Expand Down Expand Up @@ -123,8 +123,7 @@ def _check_fn(self, obtained_filename, expected_filename):
self._check_data_types(k, obtained_column, expected_column)
self._check_data_shapes(obtained_column, expected_column)

data_type = obtained_column.values.dtype
if data_type in [float, np.float16, np.float32, np.float64]:
if np.issubdtype(obtained_column.values.dtype, np.inexact):
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Please keep in mind that this will change behavior a bit, because complex numbers are also np.inexact:

>>> a = np.array([], dtype=np.complex128)
>>> np.issubdtype(a.dtype, np.inexact)
True

Perhaps add a test for this case (ignore me if you already did, by the time I'm writing this, I didn't finished the review yet), just to make sure it doesn't crash or anything...

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That's correct. I wanted to support complex numbers and there is a unit test for it.

not_close_mask = ~np.isclose(
obtained_column.values,
expected_column.values,
Expand All @@ -138,7 +137,7 @@ def _check_fn(self, obtained_filename, expected_filename):
diff_ids = np.where(not_close_mask)[0]
diff_obtained_data = obtained_column[diff_ids]
diff_expected_data = expected_column[diff_ids]
if data_type == bool:
if obtained_column.values.dtype == bool:
diffs = np.logical_xor(obtained_column, expected_column)[diff_ids]
else:
diffs = np.abs(obtained_column - expected_column)[diff_ids]
Expand Down Expand Up @@ -199,7 +198,7 @@ def check(
will ignore embed_data completely, being useful if a reference file is located
in the session data dir for example.

:param dict tolerances: dict mapping keys from the data_dict to tolerance settings for the
:param dict tolerances: dict mapping keys from the data_frame to tolerance settings for the
given data. Example::

tolerances={'U': Tolerance(atol=1e-2)}
Expand All @@ -223,7 +222,7 @@ def check(
__tracebackhide__ = True

assert type(data_frame) is pd.DataFrame, (
"Only pandas DataFrames are supported on on dataframe_regression fixture.\n"
"Only pandas DataFrames are supported on dataframe_regression fixture.\n"
"Object with type '%s' was given." % (str(type(data_frame)),)
)

Expand All @@ -235,7 +234,7 @@ def check(
# Rejected: timedelta, datetime, objects, zero-terminated bytes, unicode strings and raw data
assert array.dtype not in ["m", "M", "O", "S", "a", "U", "V"], (
"Only numeric data is supported on dataframe_regression fixture.\n"
"Array with type '%s' was given.\n" % (str(array.dtype),)
"Array with type '%s' was given." % (str(array.dtype),)
)

if tolerances is None:
Expand Down