226 lines
7.5 KiB
Python
226 lines
7.5 KiB
Python
import pytest
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from quacc.data import ExClassManager as ECM, ExtendedCollection
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import numpy as np
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import scipy.sparse as sp
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class TestExClassManager:
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@pytest.mark.parametrize(
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"true_class,pred_class,result",
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[
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(0, 0, 0),
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(0, 1, 1),
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(1, 0, 2),
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(1, 1, 3),
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],
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)
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def test_get_ex(self, true_class, pred_class, result):
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ncl = 2
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assert ECM.get_ex(ncl, true_class, pred_class) == result
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@pytest.mark.parametrize(
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"ex_class,result",
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[
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(0, 0),
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(1, 1),
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(2, 0),
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(3, 1),
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],
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)
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def test_get_pred(self, ex_class, result):
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ncl = 2
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assert ECM.get_pred(ncl, ex_class) == result
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@pytest.mark.parametrize(
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"ex_class,result",
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[
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(0, 0),
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(1, 0),
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(2, 1),
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(3, 1),
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],
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)
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def test_get_true(self, ex_class, result):
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ncl = 2
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assert ECM.get_true(ncl, ex_class) == result
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class TestExtendedCollection:
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@pytest.mark.parametrize(
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"instances,result",
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[
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(
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np.asarray(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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[np.asarray([1, 3]), np.asarray([0, 2])],
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),
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(
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sp.csr_matrix(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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[np.asarray([1, 3]), np.asarray([0, 2])],
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),
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(
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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[np.asarray([], dtype=int), np.asarray([0, 1])],
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),
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(
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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[np.asarray([], dtype=int), np.asarray([0, 1])],
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),
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(
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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[np.asarray([0, 1]), np.asarray([], dtype=int)],
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),
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(
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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[np.asarray([0, 1]), np.asarray([], dtype=int)],
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),
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],
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)
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def test__split_index_by_pred(self, instances, result):
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ncl = 2
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assert all(
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np.array_equal(a, b)
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for (a, b) in zip(
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ExtendedCollection._split_index_by_pred(ncl, instances),
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result,
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)
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)
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@pytest.mark.parametrize(
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"instances,s_inst,norms",
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[
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(
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np.asarray(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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[
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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],
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[0.5, 0.5],
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),
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(
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sp.csr_matrix(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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[
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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],
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[0.5, 0.5],
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),
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(
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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[
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([], dtype=int),
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],
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[1.0, 0.0],
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),
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(
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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[
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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sp.csr_matrix([], dtype=int),
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],
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[1.0, 0.0],
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),
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(
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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[
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np.asarray([], dtype=int),
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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],
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[0.0, 1.0],
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),
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(
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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[
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sp.csr_matrix([], dtype=int),
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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],
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[0.0, 1.0],
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),
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],
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)
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def test_split_inst_by_pred(self, instances, s_inst, norms):
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ncl = 2
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_s_inst, _norms = ExtendedCollection.split_inst_by_pred(ncl, instances)
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if isinstance(s_inst, np.ndarray):
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assert all(np.array_equal(a, b) for (a, b) in zip(_s_inst, s_inst))
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if isinstance(s_inst, sp.csr_matrix):
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assert all((a != b).nnz == 0 for (a, b) in zip(_s_inst, s_inst))
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assert all(a == b for (a, b) in zip(_norms, norms))
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@pytest.mark.parametrize(
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"instances,labels,inst0,lbl0,inst1,lbl1",
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[
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(
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np.asarray(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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np.asarray([3, 0, 1, 2]),
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 1]),
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([1, 0]),
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),
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(
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sp.csr_matrix(
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[[0, 0.3, 0.7], [1, 0.54, 0.46], [2, 0.28, 0.72], [3, 0.6, 0.4]]
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),
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np.asarray([3, 0, 1, 2]),
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 1]),
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([1, 0]),
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),
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(
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([3, 1]),
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np.asarray([], dtype=int),
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np.asarray([], dtype=int),
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np.asarray([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([1, 0]),
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),
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(
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([3, 1]),
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sp.csr_matrix(np.empty((0, 0), dtype=int)),
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np.asarray([], dtype=int),
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sp.csr_matrix([[0, 0.3, 0.7], [2, 0.28, 0.72]]),
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np.asarray([1, 0]),
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),
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(
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 2]),
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np.asarray([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 1]),
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np.asarray([], dtype=int),
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np.asarray([], dtype=int),
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),
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(
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 2]),
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sp.csr_matrix([[1, 0.54, 0.46], [3, 0.6, 0.4]]),
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np.asarray([0, 1]),
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sp.csr_matrix(np.empty((0, 0), dtype=int)),
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np.asarray([], dtype=int),
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),
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],
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)
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def test_split_by_pred(self, instances, labels, inst0, lbl0, inst1, lbl1):
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ec = ExtendedCollection(instances, labels, classes=range(0, 4))
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[ec0, ec1] = ec.split_by_pred()
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if isinstance(instances, np.ndarray):
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assert np.array_equal(ec0.X, inst0)
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assert np.array_equal(ec1.X, inst1)
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if isinstance(instances, sp.csr_matrix):
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assert (ec0.X != inst0).nnz == 0
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assert (ec1.X != inst1).nnz == 0
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assert np.array_equal(ec0.y, lbl0)
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assert np.array_equal(ec1.y, lbl1)
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