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TST: add test for datetime array and update test

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TST: fix quarter normalization test

TST: change test name

BUG: remove arg

BUG: look at dict keys

TST: add test for windowing

MAINT: raise ValueError instead of asserting

TST: add assertion to check windowing

TST: parametrize test over number of quarters forward/back.

BUG: fix adjustment calculation logic for quarter crossovers.

TST: add test for previous quarter windows

BUG: fix bugs in calculating previous windows

BUG: fix missing value for datetime

TST: add test case for missing quarter
This commit is contained in:
Maya Tydykov
2016-09-27 09:54:41 -04:00
parent 199c775fbf
commit ebbe85b79a
7 changed files with 552 additions and 226 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tests/pipeline/test_adjusted_array.py
+82 -61
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@@ -20,6 +20,7 @@ from toolz import curry
from zipline.errors import WindowLengthNotPositive, WindowLengthTooLong
from zipline.lib.adjustment import (
Datetime64Overwrite,
Datetime641DArrayOverwrite,
Float64Multiply,
Float64Overwrite,
Float641DArrayOverwrite,
@@ -305,7 +306,11 @@ def _gen_overwrite_adjustment_cases(name,
)
def _gen_overwrite_1d_array_adjustment_case():
def _gen_overwrite_1d_array_adjustment_case(name,
make_input,
make_expected_output,
dtype,
missing_value):
"""
Generate test cases for overwrite adjustments.
@@ -314,90 +319,91 @@ def _gen_overwrite_1d_array_adjustment_case():
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
which take 1-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.
"""
adjustment_type = {
float64_dtype: Float641DArrayOverwrite,
datetime64ns_dtype: Datetime641DArrayOverwrite,
}[dtype]
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]])
baseline = make_input([[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]])
buffer_as_of[0] = make_expected_output([[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
vals1 = [1]
# Note that row indices are inclusive!
adjustments[1] = [
Float641DArrayOverwrite(array([0]),
array([0]),
array([0]),
array([0]),
as_dtype(float64_dtype, array([1])))
adjustment_type(
0, 0, 0, 0,
array([coerce_to_dtype(dtype, val) for val in vals1])
)
]
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]])
buffer_as_of[1] = make_input([[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]
vals3 = [4, 4, 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])))
adjustment_type(
0, 2, 0, 0,
array([coerce_to_dtype(dtype, val) for val in vals3])
)
]
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]])
buffer_as_of[3] = make_input([[4, 2, 2],
[4, 2, 2],
[1, 2, 2],
[2, 2, 2],
[2, 2, 2],
[2, 2, 2]])
vals4 = [5] * 4
adjustments[4] = [
Float641DArrayOverwrite(array([0]),
array([3]),
array([2]),
array([2]),
as_dtype(float64_dtype, array([5])))
adjustment_type(
0, 3, 2, 2,
array([coerce_to_dtype(dtype, val) for val in vals4]))
]
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]])
buffer_as_of[4] = make_input([[4, 2, 5],
[4, 2, 5],
[1, 2, 5],
[2, 2, 5],
[2, 2, 2],
[2, 2, 2]])
vals5 = range(1, 6)
adjustments[5] = [
Float641DArrayOverwrite(array([0, 2]),
array([4, 2]),
array([1, 2]),
array([1, 2]),
as_dtype(float64_dtype, array([6, 7]))),
adjustment_type(
0, 4, 1, 1,
array([coerce_to_dtype(dtype, val) for val in vals5])),
]
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]])
buffer_as_of[5] = make_input([[4, 1, 5],
[4, 2, 5],
[1, 3, 5],
[2, 4, 5],
[2, 5, 2],
[2, 2, 2]])
return _gen_expectations(
baseline,
default_missing_value_for_dtype(float64_dtype),
missing_value,
adjustments,
buffer_as_of,
nrows=6,
@@ -542,7 +548,22 @@ class AdjustedArrayTestCase(TestCase):
datetime64ns_dtype,
),
),
_gen_overwrite_1d_array_adjustment_case(),
_gen_overwrite_1d_array_adjustment_case(
'float',
make_input=as_dtype(float64_dtype),
make_expected_output=as_dtype(float64_dtype),
dtype=float64_dtype,
missing_value=default_missing_value_for_dtype(float64_dtype),
),
_gen_overwrite_1d_array_adjustment_case(
'datetime',
make_input=as_dtype(datetime64ns_dtype),
make_expected_output=as_dtype(datetime64ns_dtype),
dtype=datetime64ns_dtype,
missing_value=default_missing_value_for_dtype(
datetime64ns_dtype,
),
),
# There are six cases here:
# Using np.bytes/np.unicode/object arrays as inputs.
# Passing np.bytes/np.unicode/object arrays to LabelArray,