def test_grid_from_X():
# tests for _grid_from_X: sanity check for output, and for shapes.
# Make sure that the grid is a cartesian product of the input (it will use
# the unique values instead of the percentiles)
percentiles = (.05, .95)
grid_resolution = 100
X = np.asarray([[1, 2], [3, 4]])
grid, axes = _grid_from_X(X, percentiles, grid_resolution)
assert_array_equal(grid, [[1, 2], [1, 4], [3, 2], [3, 4]])
assert_array_equal(axes, X.T)
# test shapes of returned objects depending on the number of unique values
# for a feature.
rng = np.random.RandomState(0)
grid_resolution = 15
# n_unique_values > grid_resolution
X = rng.normal(size=(20, 2))
grid, axes = _grid_from_X(X, percentiles, grid_resolution=grid_resolution)
assert grid.shape == (grid_resolution * grid_resolution, X.shape[1])
assert np.asarray(axes).shape == (2, grid_resolution)
# n_unique_values < grid_resolution, will use actual values
n_unique_values = 12
X[n_unique_values - 1:, 0] = 12345
rng.shuffle(X) # just to make sure the order is irrelevant
grid, axes = _grid_from_X(X, percentiles, grid_resolution=grid_resolution)
assert grid.shape == (n_unique_values * grid_resolution, X.shape[1])
# axes is a list of arrays of different shapes
assert axes[0].shape == (n_unique_values, )
assert axes[1].shape == (grid_resolution, )
def test_grid_from_X():
# tests for _grid_from_X: sanity check for output, and for shapes.
# Make sure that the grid is a cartesian product of the input (it will use
# the unique values instead of the percentiles)
percentiles = (.05, .95)
grid_resolution = 100
X = np.asarray([[1, 2],
[3, 4]])
grid, axes = _grid_from_X(X, percentiles, grid_resolution)
assert_array_equal(grid, [[1, 2],
[1, 4],
[3, 2],
[3, 4]])
assert_array_equal(axes, X.T)
# test shapes of returned objects depending on the number of unique values
# for a feature.
rng = np.random.RandomState(0)
grid_resolution = 15
# n_unique_values > grid_resolution
X = rng.normal(size=(20, 2))
grid, axes = _grid_from_X(X, percentiles, grid_resolution=grid_resolution)
assert grid.shape == (grid_resolution * grid_resolution, X.shape[1])
assert np.asarray(axes).shape == (2, grid_resolution)
# n_unique_values < grid_resolution, will use actual values
n_unique_values = 12
X[n_unique_values - 1:, 0] = 12345
rng.shuffle(X) # just to make sure the order is irrelevant
grid, axes = _grid_from_X(X, percentiles, grid_resolution=grid_resolution)
assert grid.shape == (n_unique_values * grid_resolution, X.shape[1])
# axes is a list of arrays of different shapes
assert axes[0].shape == (n_unique_values,)
assert axes[1].shape == (grid_resolution,)
def partial_dependce_compute(model,
X,
features,
percentiles=[0, 1],
grid_resolution=100,
use_unique=True):
grid, values = _grid_from_X(X[:, features],
percentiles,
grid_resolution,
use_unique=use_unique)
prediction_method = lambda X: model.predict_proba(X)
averaged_predictions = get_averaged_predictions(prediction_method, X, grid,
features)
averaged_predictions = averaged_predictions.reshape(
-1, *[val.shape[0] for val in values])
averaged_predictions = averaged_predictions.T
return averaged_predictions, values
def test_grid_from_X_error(grid_resolution, percentiles, err_msg):
X = np.asarray([[1, 2], [3, 4]])
with pytest.raises(ValueError, match=err_msg):
_grid_from_X(
X, grid_resolution=grid_resolution, percentiles=percentiles
)
def test_grid_from_X_error(grid_resolution, percentiles, err_msg):
X = np.asarray([[1, 2], [3, 4]])
with pytest.raises(ValueError, match=err_msg):
_grid_from_X(X,
grid_resolution=grid_resolution,
percentiles=percentiles)
