class TestNumpyXMatrix:
@given(data(), shape_matrix())
def test_input_as_tuples(self, data, shape):
X = data.draw(numpy_X_matrices(shape))
assert X.shape == shape
@given(data())
def test_input_as_strategy(self, data):
data.draw(numpy_X_matrices(shape_matrix()))
@given(data())
def test_error_shape_0_smaller_shape_1(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_matrices([10, 20]))
@given(data(), shape_matrix(), ordered_pair(32, 47))
def test_min_max_values(self, data, shape, min_max_values):
min_value, max_value = min_max_values
X = data.draw(numpy_X_matrices(shape, min_value=min_value, max_value=max_value))
assert X.min() >= min_value
assert X.max() <= max_value
@given(data(), shape_matrix())
def test_no_nan(self, data, shape):
X = data.draw(numpy_X_matrices(shape, allow_nan=False, allow_infinity=True))
assert not np.isnan(X).any()
@given(data(), shape_matrix())
def test_no_infinity(self, data, shape):
X = data.draw(numpy_X_matrices(shape, allow_nan=True, allow_infinity=False))
assert not np.isinf(X).any()
class TestNumpyXyMatrices:
@given(data(), shape_X_y_matrices())
def test_input_as_tuples(self, data, shape_X_y):
X, y = data.draw(numpy_X_y_matrices(shape_X_y))
assert X.shape == shape_X_y[0]
assert y.shape == shape_X_y[1]
assert len(y.shape) == 1
@given(data(), shape_X_y_matrices(y_as_vector=False))
def test_input_as_tuples_y_matrix(self, data, shape_X_y):
X, y = data.draw(numpy_X_y_matrices(shape_X_y))
assert X.shape == shape_X_y[0]
assert y.shape == shape_X_y[1]
assert len(y.shape) == 2
@given(data())
def test_input_as_strategy(self, data):
data.draw(numpy_X_y_matrices(shape_X_y_matrices()))
@given(data())
def test_error_shape_0_smaller_shape_1(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_y_matrices([[10, 20], [10, 1]]))
@given(data())
def test_error_shape_0_different(self, data):
with pytest.raises(ValueError):
data.draw(numpy_X_y_matrices([[10, 5], [4, 1]]))
@given(data(), shape_X_y_matrices(), ordered_pair(32, 47))
def test_min_max_values(self, data, shape_X_y, min_max_values):
min_value, max_value = min_max_values
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, min_value=min_value, max_value=max_value)
)
assert X.min() >= min_value
assert y.min() >= min_value
assert X.max() <= max_value
assert y.max() <= max_value
@given(data(), shape_X_y_matrices())
def test_no_nan(self, data, shape_X_y):
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, allow_nan=False, allow_infinity=True)
)
assert not np.isnan(X).any()
assert not np.isnan(y).any()
@given(data(), shape_X_y_matrices())
def test_no_infinity(self, data, shape_X_y):
X, y = data.draw(
numpy_X_y_matrices(shape_X_y, allow_nan=True, allow_infinity=False)
)
assert not np.isinf(X).any()
assert not np.isinf(y).any()
def test_ordered_pair_min_equal_max(data, value):
with pytest.raises(ValueError):
data.draw(ordered_pair(value, value))
from typing import Tuple
import pytest
from hypothesis import given
from hypothesis.strategies import integers, data
from gtime.utils.hypothesis.general_strategies import (
ordered_pair,
shape_matrix,
shape_X_y_matrices,
)
@given(ordered_pair(0, 10))
def test_ordered_pair(pair: Tuple[int, int]):
assert pair[0] < pair[1]
@given(ordered_pair(27, 132))
def test_ordered_pair_values(pair: Tuple[int, int]):
assert pair[0] >= 27
assert pair[1] <= 132
@given(data=data(), value=integers(0, 10))
def test_ordered_pair_min_equal_max(data, value):
with pytest.raises(ValueError):
data.draw(ordered_pair(value, value))
@given(data=data(), shape_0=ordered_pair(10, 100), shape_1=ordered_pair(1, 8))