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conftest.py
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conftest.py
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import pytest
import scipy.sparse
import numpy as np
from rasa.utils.tensorflow.model_data import RasaModelData, FeatureArray
@pytest.fixture
async def model_data() -> RasaModelData:
return RasaModelData(
label_key="label",
label_sub_key="ids",
data={
"text": {
"sentence": [
FeatureArray(
np.array(
[
np.random.rand(5, 14),
np.random.rand(2, 14),
np.random.rand(3, 14),
np.random.rand(1, 14),
np.random.rand(3, 14),
],
dtype=object,
),
number_of_dimensions=3,
),
FeatureArray(
np.array(
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(5, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(2, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(1, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
],
dtype=object,
),
number_of_dimensions=3,
),
]
},
"action_text": {
"sequence": [
FeatureArray(
np.array(
[
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(5, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(2, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(1, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
],
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(5, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(2, 10))
),
],
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(5, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(1, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
],
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
)
],
[
scipy.sparse.csr_matrix(
np.random.randint(5, size=(3, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(1, 10))
),
scipy.sparse.csr_matrix(
np.random.randint(5, size=(7, 10))
),
],
],
dtype=object,
),
number_of_dimensions=4,
),
FeatureArray(
np.array(
[
[
np.random.rand(5, 14),
np.random.rand(2, 14),
np.random.rand(3, 14),
np.random.rand(1, 14),
np.random.rand(3, 14),
],
[np.random.rand(5, 14), np.random.rand(2, 14)],
[
np.random.rand(5, 14),
np.random.rand(1, 14),
np.random.rand(3, 14),
],
[np.random.rand(3, 14)],
[
np.random.rand(3, 14),
np.random.rand(1, 14),
np.random.rand(7, 14),
],
],
dtype=object,
),
number_of_dimensions=4,
),
]
},
"dialogue": {
"sentence": [
FeatureArray(
np.array(
[
np.random.randint(2, size=(5, 10)),
np.random.randint(2, size=(2, 10)),
np.random.randint(2, size=(3, 10)),
np.random.randint(2, size=(1, 10)),
np.random.randint(2, size=(3, 10)),
],
dtype=object,
),
number_of_dimensions=3,
)
]
},
"label": {
"ids": [FeatureArray(np.array([0, 1, 0, 1, 1]), number_of_dimensions=1)]
},
"entities": {
"tag_ids": [
FeatureArray(
np.array(
[
np.array([[0], [1], [1], [0], [2]]),
np.array([[2], [0]]),
np.array([[0], [1], [1]]),
np.array([[0], [1]]),
np.array([[0], [0], [0]]),
],
dtype=object,
),
number_of_dimensions=3,
)
]
},
},
)