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TST: add test for 1d array overwrite

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This commit is contained in:
Maya Tydykov
2016-09-27 09:54:40 -04:00
parent e53d7fc9b2
commit 2975f9b2fd
5 changed files with 261 additions and 120 deletions
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tests/pipeline/test_adjusted_array.py
+101
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@@ -22,6 +22,7 @@ from zipline.lib.adjustment import (
Datetime64Overwrite,
Float64Multiply,
Float64Overwrite,
Float641DArrayOverwrite,
ObjectOverwrite,
)
from zipline.lib.adjusted_array import AdjustedArray, NOMASK
@@ -304,6 +305,105 @@ def _gen_overwrite_adjustment_cases(name,
)
def _gen_overwrite_1d_array_adjustment_case():
"""
Generate test cases for overwrite adjustments.
The algorithm used here is the same as the one used above for
multiplicative adjustments. The only difference is the semantics of how
the adjustments are expected to modify the arrays.
This is parameterized on `make_input` and `make_expected_output` functions,
which take 2-D lists of values and transform them into desired input/output
arrays. We do this so that we can easily test both vanilla numpy ndarrays
and our own LabelArray class for strings.
"""
adjustments = {}
buffer_as_of = [None] * 6
baseline = as_dtype(float64_dtype, [[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
buffer_as_of[0] = as_dtype(float64_dtype, [[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
# Note that row indices are inclusive!
adjustments[1] = [
Float641DArrayOverwrite(array([0]),
array([0]),
array([0]),
array([0]),
as_dtype(float64_dtype, array([1])))
]
buffer_as_of[1] = as_dtype(float64_dtype, [[1, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
# No adjustment at index 2.
buffer_as_of[2] = buffer_as_of[1]
adjustments[3] = [
Float641DArrayOverwrite(array([0, 2, 1]),
array([1, 2, 2]),
array([0, 0, 1]),
array([0, 0, 1]),
as_dtype(float64_dtype, array([4, 1, 3])))
]
buffer_as_of[3] = as_dtype(float64_dtype, [[4, 2, 2],
[4, 3, 2],
[1, 3, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
adjustments[4] = [
Float641DArrayOverwrite(array([0]),
array([3]),
array([2]),
array([2]),
as_dtype(float64_dtype, array([5])))
]
buffer_as_of[4] = as_dtype(float64_dtype, [[4, 2, 5],
[4, 3, 5],
[1, 3, 5],
[2, 2, 5],
[2, 2, 2],
[2, 2, 2]])
adjustments[5] = [
Float641DArrayOverwrite(array([0, 2]),
array([4, 2]),
array([1, 2]),
array([1, 2]),
as_dtype(float64_dtype, array([6, 7]))),
]
buffer_as_of[5] = as_dtype(float64_dtype, [[4, 6, 5],
[4, 6, 5],
[1, 6, 7],
[2, 6, 5],
[2, 6, 2],
[2, 2, 2]])
return _gen_expectations(
baseline,
default_missing_value_for_dtype(float64_dtype),
adjustments,
buffer_as_of,
nrows=6,
)
def _gen_expectations(baseline,
missing_value,
adjustments,
@@ -442,6 +542,7 @@ class AdjustedArrayTestCase(TestCase):
datetime64ns_dtype,
),
),
_gen_overwrite_1d_array_adjustment_case(),
# There are six cases here:
# Using np.bytes/np.unicode/object arrays as inputs.
# Passing np.bytes/np.unicode/object arrays to LabelArray,