mirror of
https://github.com/wassname/catalyst.git
synced 2026-06-29 22:32:30 +08:00
Scott Sanderson
121 lines
3.6 KiB
Python
121 lines
3.6 KiB
Python
import numpy as np
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from zipline.pipeline import Classifier
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from zipline.testing import parameter_space
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from zipline.utils.numpy_utils import int64_dtype
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from .base import BasePipelineTestCase
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class ClassifierTestCase(BasePipelineTestCase):
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@parameter_space(mv=[-1, 0, 1, 999])
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def test_isnull(self, mv):
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class C(Classifier):
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dtype = int64_dtype
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missing_value = mv
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inputs = ()
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window_length = 0
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c = C()
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# There's no significance to the values here other than that they
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# contain a mix of missing and non-missing values.
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data = np.array([[-1, 1, 0, 2],
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[3, 0, 1, 0],
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[-5, 0, -1, 0],
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[-3, 1, 2, 2]], dtype=int64_dtype)
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self.check_terms(
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terms={
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'isnull': c.isnull(),
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'notnull': c.notnull()
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},
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expected={
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'isnull': data == mv,
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'notnull': data != mv,
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},
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initial_workspace={c: data},
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mask=self.build_mask(self.ones_mask(shape=data.shape)),
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)
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@parameter_space(compval=[0, 1, 999])
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def test_eq(self, compval):
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class C(Classifier):
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dtype = int64_dtype
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missing_value = -1
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inputs = ()
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window_length = 0
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c = C()
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# There's no significance to the values here other than that they
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# contain a mix of the comparison value and other values.
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data = np.array([[-1, 1, 0, 2],
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[3, 0, 1, 0],
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[-5, 0, -1, 0],
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[-3, 1, 2, 2]], dtype=int64_dtype)
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self.check_terms(
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terms={
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'eq': c.eq(compval),
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},
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expected={
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'eq': (data == compval),
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},
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initial_workspace={c: data},
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mask=self.build_mask(self.ones_mask(shape=data.shape)),
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)
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@parameter_space(missing=[-1, 0, 1])
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def test_disallow_comparison_to_missing_value(self, missing):
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class C(Classifier):
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dtype = int64_dtype
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missing_value = missing
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inputs = ()
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window_length = 0
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with self.assertRaises(ValueError) as e:
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C().eq(missing)
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errmsg = str(e.exception)
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self.assertEqual(
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errmsg,
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"Comparison against self.missing_value ({v}) in C.eq().\n"
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"Missing values have NaN semantics, so the requested comparison"
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" would always produce False.\n"
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"Use the isnull() method to check for missing values.".format(
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v=missing,
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),
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)
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@parameter_space(compval=[0, 1, 999], missing=[-1, 0, 999])
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def test_not_equal(self, compval, missing):
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class C(Classifier):
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dtype = int64_dtype
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missing_value = missing
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inputs = ()
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window_length = 0
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c = C()
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# There's no significance to the values here other than that they
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# contain a mix of the comparison value and other values.
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data = np.array([[-1, 1, 0, 2],
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[3, 0, 1, 0],
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[-5, 0, -1, 0],
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[-3, 1, 2, 2]], dtype=int64_dtype)
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self.check_terms(
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terms={
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'ne': c != compval,
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},
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expected={
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'ne': (data != compval) & (data != C.missing_value),
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},
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initial_workspace={c: data},
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mask=self.build_mask(self.ones_mask(shape=data.shape)),
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)
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