added tests for data.py
This commit is contained in:
Lorenzo Volpi
parent
1d5507889b
commit
de48da638a
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@ -2,184 +2,286 @@ import numpy as np
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import pytest
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import scipy.sparse as sp
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from quacc.data import ExtendedCollection
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from quacc.data import (
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ExtendedCollection,
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ExtendedData,
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ExtendedLabels,
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ExtendedPrev,
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ExtensionPolicy,
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)
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class TestExtendedCollection:
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@pytest.mark.ext
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@pytest.mark.extpol
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class TestExtendedPolicy:
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@pytest.mark.parametrize(
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"instances,result",
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"extpol,nbcl,result",
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[
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(ExtensionPolicy(), 2, np.array([0, 1, 2, 3])),
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(ExtensionPolicy(collapse_false=True), 2, np.array([0, 1, 2])),
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(ExtensionPolicy(), 3, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])),
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(ExtensionPolicy(collapse_false=True), 3, np.array([0, 1, 2, 3])),
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],
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)
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def test_qclasses(self, extpol, nbcl, result):
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assert (result == extpol.qclasses(nbcl)).all()
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@pytest.mark.parametrize(
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"extpol,nbcl,result",
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[
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(ExtensionPolicy(), 2, np.array([0, 1, 2, 3])),
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(ExtensionPolicy(collapse_false=True), 2, np.array([0, 1, 2, 3])),
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(ExtensionPolicy(), 3, np.array([0, 1, 2, 3, 4, 5, 6, 7, 8])),
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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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ExtensionPolicy(collapse_false=True),
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3,
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np.array([0, 1, 2, 3, 4, 5, 6, 7, 8]),
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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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def test_eclasses(self, extpol, nbcl, result):
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assert (result == extpol.eclasses(nbcl)).all()
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@pytest.mark.parametrize(
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"instances,s_inst,norms",
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"extpol,nbcl,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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ExtensionPolicy(),
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2,
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(
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np.array([0, 0, 1, 1]),
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np.array([0, 1, 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, 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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ExtensionPolicy(collapse_false=True),
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2,
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(
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np.array([0, 1, 0]),
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np.array([0, 1, 1]),
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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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ExtensionPolicy(),
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3,
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(
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np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
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np.array([0, 1, 2, 0, 1, 2, 0, 1, 2]),
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),
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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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ExtensionPolicy(collapse_false=True),
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3,
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(
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np.array([0, 1, 2, 0]),
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np.array([0, 1, 2, 1]),
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),
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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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def test_matrix_idx(self, extpol, nbcl, result):
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_midx = extpol.matrix_idx(nbcl)
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assert len(_midx) == len(result)
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assert all((idx == r).all() for idx, r in zip(_midx, result))
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@pytest.mark.parametrize(
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"instances,labels,inst0,lbl0,inst1,lbl1",
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"extpol,nbcl,true,pred,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([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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ExtensionPolicy(),
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2,
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np.array([1, 0, 1, 1, 0, 0]),
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np.array([1, 0, 0, 1, 1, 0]),
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np.array([3, 0, 2, 3, 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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ExtensionPolicy(collapse_false=True),
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2,
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np.array([1, 0, 1, 1, 0, 0]),
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np.array([1, 0, 0, 1, 1, 0]),
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np.array([1, 0, 2, 1, 2, 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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ExtensionPolicy(),
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3,
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np.array([1, 2, 0, 1, 0, 2, 0, 1, 2]),
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np.array([1, 0, 0, 0, 1, 1, 2, 2, 2]),
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np.array([4, 6, 0, 3, 1, 7, 2, 5, 8]),
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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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ExtensionPolicy(collapse_false=True),
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3,
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np.array([1, 2, 0, 1, 0, 2, 0, 1, 2]),
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np.array([1, 0, 0, 0, 1, 1, 2, 2, 2]),
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np.array([1, 3, 0, 3, 3, 3, 3, 3, 2]),
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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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def test_ext_lbl(self, extpol, nbcl, true, pred, result):
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vfun = extpol.ext_lbl(nbcl)
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assert (vfun(true, pred) == result).all()
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@pytest.mark.ext
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@pytest.mark.extd
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class TestExtendedData:
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@pytest.mark.parametrize(
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"pred_proba,result",
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[
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(
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np.array([[0.3, 0.7], [0.54, 0.46], [0.28, 0.72], [0.6, 0.4]]),
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[np.array([1, 3]), np.array([0, 2])],
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),
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(
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np.array([[0.3, 0.7], [0.28, 0.72]]),
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[np.array([]), np.array([0, 1])],
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),
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(
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np.array([[0.54, 0.46], [0.6, 0.4]]),
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[np.array([0, 1]), np.array([])],
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),
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(
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np.array(
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[
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[0.25, 0.4, 0.35],
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[0.24, 0.3, 0.46],
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[0.61, 0.28, 0.11],
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[0.4, 0.1, 0.5],
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]
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),
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[np.array([2]), np.array([0]), np.array([1, 3])],
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),
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],
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)
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def test__split_index_by_pred(self, monkeypatch, pred_proba, result):
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def mockinit(self, pred_proba):
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self.pred_proba_ = pred_proba
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monkeypatch.setattr(ExtendedData, "__init__", mockinit)
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ed = ExtendedData(pred_proba)
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_split_index = ed._ExtendedData__split_index_by_pred()
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assert len(_split_index) == len(result)
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assert all((a == b).all() for (a, b) in zip(_split_index, result))
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@pytest.mark.ext
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@pytest.mark.extl
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class TestExtendedLabels:
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@pytest.mark.parametrize(
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"true,pred,nbcl,extpol,result",
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[
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(
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np.array([1, 0, 0, 1, 1]),
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np.array([1, 1, 0, 0, 1]),
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2,
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ExtensionPolicy(),
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np.array([3, 1, 0, 2, 3]),
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),
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(
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np.array([1, 0, 0, 1, 1]),
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np.array([1, 1, 0, 0, 1]),
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2,
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ExtensionPolicy(collapse_false=True),
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np.array([1, 2, 0, 2, 1]),
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),
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],
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)
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def test_y(self, true, pred, nbcl, extpol, result):
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el = ExtendedLabels(true, pred, nbcl, extpol)
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assert (el.y == result).all()
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@pytest.mark.ext
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@pytest.mark.extp
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class TestExtendedPrev:
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@pytest.mark.parametrize(
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"flat,nbcl,extpol,q_classes,result",
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[
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(
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np.array([0.2, 0, 0.8, 0]),
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2,
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ExtensionPolicy(),
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[0, 1, 2, 3],
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np.array([0.2, 0, 0.8, 0]),
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),
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(
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np.array([0.2, 0.8]),
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2,
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ExtensionPolicy(),
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[0, 3],
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np.array([0.2, 0, 0, 0.8]),
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),
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(
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np.array([0.2, 0.8]),
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2,
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ExtensionPolicy(collapse_false=True),
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[0, 2],
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np.array([0.2, 0, 0.8]),
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),
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(
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np.array([0.1, 0.1, 0.6, 0.2]),
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3,
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ExtensionPolicy(),
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[0, 1, 3, 5],
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np.array([0.1, 0.1, 0, 0.6, 0, 0.2, 0, 0, 0]),
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),
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(
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np.array([0.1, 0.1, 0.6]),
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3,
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ExtensionPolicy(collapse_false=True),
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[0, 1, 2],
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np.array([0.1, 0.1, 0.6, 0]),
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),
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],
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)
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def test__check_q_classes(self, monkeypatch, flat, nbcl, extpol, q_classes, result):
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def mockinit(self, flat, nbcl, extpol):
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self.flat = flat
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self.nbcl = nbcl
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self.extpol = extpol
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monkeypatch.setattr(ExtendedPrev, "__init__", mockinit)
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ep = ExtendedPrev(flat, nbcl, extpol)
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ep._ExtendedPrev__check_q_classes(q_classes)
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assert (ep.flat == result).all()
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@pytest.mark.parametrize(
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"flat,nbcl,extpol,result",
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[
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(
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np.array([0.05, 0.1, 0.6, 0.25]),
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2,
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ExtensionPolicy(),
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np.array([[0.05, 0.1], [0.6, 0.25]]),
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),
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(
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np.array([0.05, 0.1, 0.85]),
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2,
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ExtensionPolicy(collapse_false=True),
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np.array([[0.05, 0.85], [0, 0.1]]),
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),
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(
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np.array([0.05, 0.1, 0.2, 0.15, 0.04, 0.06, 0.15, 0.14, 0.1]),
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3,
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ExtensionPolicy(),
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np.array([[0.05, 0.1, 0.2], [0.15, 0.04, 0.06], [0.15, 0.14, 0.1]]),
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),
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(
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np.array([0.05, 0.2, 0.65, 0.1]),
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3,
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ExtensionPolicy(collapse_false=True),
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np.array([[0.05, 0.1, 0], [0, 0.2, 0], [0, 0, 0.65]]),
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),
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],
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)
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def test__build_matrix(self, monkeypatch, flat, nbcl, extpol, result):
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def mockinit(self, flat, nbcl, extpol):
|
||||
self.flat = flat
|
||||
self.nbcl = nbcl
|
||||
self.extpol = extpol
|
||||
|
||||
monkeypatch.setattr(ExtendedPrev, "__init__", mockinit)
|
||||
ep = ExtendedPrev(flat, nbcl, extpol)
|
||||
_matrix = ep._ExtendedPrev__build_matrix()
|
||||
assert _matrix.shape == result.shape
|
||||
assert (_matrix == result).all()
|
||||
|
|
|
|||
Loading…
Reference in New Issue