code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def test_get_all_accuracy_metrics_returns(get_test_set):
"""Test if correct accuracy metrics are returned."""
y_pred, y_std, y_true = get_test_set
met_dict = get_all_accuracy_metrics(y_pred, y_true)
met_keys = met_dict.keys()
assert len(met_keys) == 6
met_str_list = ["mae", "rmse", "mdae", "mar... | Test if correct accuracy metrics are returned. | test_get_all_accuracy_metrics_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_get_all_average_calibration_returns(get_test_set):
"""Test if correct average calibration metrics are returned."""
n_bins = 20
met_dict = get_all_average_calibration(*get_test_set, n_bins)
met_keys = met_dict.keys()
assert len(met_keys) == 3
met_str_list = ["rms_cal", "ma_cal", "miscal... | Test if correct average calibration metrics are returned. | test_get_all_average_calibration_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_get_all_adversarial_group_calibration_returns(get_test_set):
"""Test if correct adversarial group calibration metrics are returned."""
n_bins = 20
met_dict = get_all_adversarial_group_calibration(*get_test_set, n_bins)
met_keys = met_dict.keys()
assert len(met_keys) == 2
met_str_list =... | Test if correct adversarial group calibration metrics are returned. | test_get_all_adversarial_group_calibration_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_get_all_sharpness_metrics_returns(get_test_set):
"""Test if correct sharpness metrics are returned."""
y_pred, y_std, y_true = get_test_set
met_dict = get_all_sharpness_metrics(y_std)
met_keys = met_dict.keys()
assert len(met_keys) == 1
assert "sharp" in met_keys | Test if correct sharpness metrics are returned. | test_get_all_sharpness_metrics_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_get_all_scoring_rule_metrics_returns(get_test_set):
"""Test if correct scoring rule metrics are returned."""
resolution = 99
scaled = True
met_dict = get_all_scoring_rule_metrics(*get_test_set, resolution, scaled)
met_keys = met_dict.keys()
assert len(met_keys) == 4
met_str_list = ... | Test if correct scoring rule metrics are returned. | test_get_all_scoring_rule_metrics_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_get_all_metrics_returns(get_test_set):
"""Test if correct metrics are returned by get_all_metrics function."""
met_dict = get_all_metrics(*get_test_set)
met_keys = met_dict.keys()
assert len(met_keys) == 5
met_str_list = [
"accuracy",
"avg_calibration",
"adv_group_c... | Test if correct metrics are returned by get_all_metrics function. | test_get_all_metrics_returns | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics.py | MIT |
def test_prediction_error_metric_fields(get_test_set):
"""Test if prediction error metrics have correct fields."""
y_pred, y_std, y_true = get_test_set
met_dict = prediction_error_metrics(y_pred, y_true)
met_keys = met_dict.keys()
assert len(met_keys) == 6
met_str_list = ["mae", "rmse", "mdae",... | Test if prediction error metrics have correct fields. | test_prediction_error_metric_fields | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_accuracy.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_accuracy.py | MIT |
def test_prediction_error_metric_values(get_test_set):
"""Test if prediction error metrics have correct values."""
y_pred, y_std, y_true = get_test_set
met_dict = prediction_error_metrics(y_pred, y_true)
print(met_dict)
assert met_dict["mae"] > 0.21 and met_dict["mae"] < 0.22
assert met_dict["rm... | Test if prediction error metrics have correct values. | test_prediction_error_metric_values | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_accuracy.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_accuracy.py | MIT |
def test_sharpness_on_test_set(supply_test_set):
"""Test sharpness on the test set for some dummy values."""
_, test_std, _ = supply_test_set
assert np.abs(sharpness(test_std) - 0.648074069840786) < 1e-6 | Test sharpness on the test set for some dummy values. | test_sharpness_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_root_mean_squared_calibration_error_on_test_set(supply_test_set):
"""Test root mean squared calibration error on some dummy values."""
test_rmsce_nonvectorized_interval = root_mean_squared_calibration_error(
*supply_test_set,
num_bins=100,
vectorized=False,
recal_model=N... | Test root mean squared calibration error on some dummy values. | test_root_mean_squared_calibration_error_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_mean_absolute_calibration_error_on_test_set(supply_test_set):
"""Test mean absolute calibration error on some dummy values."""
test_mace_nonvectorized_interval = mean_absolute_calibration_error(
*supply_test_set,
num_bins=100,
vectorized=False,
recal_model=None,
... | Test mean absolute calibration error on some dummy values. | test_mean_absolute_calibration_error_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_adversarial_group_calibration_on_test_set(supply_test_set):
"""Test adversarial group calibration on test set for some dummy values."""
test_out_interval = adversarial_group_calibration(
*supply_test_set,
cali_type="mean_abs",
prop_type="interval",
num_bins=100,
... | Test adversarial group calibration on test set for some dummy values. | test_adversarial_group_calibration_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_miscalibration_area_on_test_set(supply_test_set):
"""Test miscalibration area on some dummy values."""
test_miscal_area_nonvectorized_interval = miscalibration_area(
*supply_test_set,
num_bins=100,
vectorized=False,
recal_model=None,
prop_type="interval"
)
... | Test miscalibration area on some dummy values. | test_miscalibration_area_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_vectorization_for_proportion_list_on_test_set(supply_test_set):
"""Test vectorization in get_proportion_lists on the test set for some dummy values."""
(
test_exp_props_nonvec_interval,
test_obs_props_nonvec_interval,
) = get_proportion_lists(
*supply_test_set, num_bins=100,... | Test vectorization in get_proportion_lists on the test set for some dummy values. | test_vectorization_for_proportion_list_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_get_proportion_lists_vectorized_on_test_set(supply_test_set):
"""Test get_proportion_lists_vectorized on the test set for some dummy values."""
(
test_exp_props_interval,
test_obs_props_interval,
) = get_proportion_lists_vectorized(
*supply_test_set, num_bins=100, recal_mode... | Test get_proportion_lists_vectorized on the test set for some dummy values. | test_get_proportion_lists_vectorized_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_get_proportion_lists_on_test_set(supply_test_set):
"""Test get_proportion_lists on the test set for some dummy values."""
test_exp_props_interval, test_obs_props_interval = get_proportion_lists(
*supply_test_set, num_bins=100, recal_model=None, prop_type="interval"
)
assert len(test_exp... | Test get_proportion_lists on the test set for some dummy values. | test_get_proportion_lists_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_get_proportion_in_interval_on_test_set(supply_test_set):
"""Test get_proportion_in_interval on the test set for some dummy values."""
test_quantile_value_list = [
(0.0, 0.0),
(0.25, 0.0),
(0.5, 0.0),
(0.75, 0.3333333333333333),
(1.0, 1.0),
]
for test_q, t... | Test get_proportion_in_interval on the test set for some dummy values. | test_get_proportion_in_interval_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_get_prediction_interval_on_test_set(supply_test_set):
"""Test get_prediction_interval on the test set for some dummy values."""
test_quantile_value_list = [
(
0.01,
np.array([1.00125335, 2.00626673, 3.01253347]),
np.array([0.99874665, 1.99373327, 2.98746653])... | Test get_prediction_interval on the test set for some dummy values. | test_get_prediction_interval_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_calibration.py | MIT |
def test_nll_gaussian_on_one_pt():
"""Sanity check by testing one point at mean of gaussian."""
y_pred = np.array([0])
y_true = np.array([0])
y_std = np.array([1 / np.sqrt(2 * np.pi)])
assert np.abs(nll_gaussian(y_pred, y_std, y_true)) < 1e-6 | Sanity check by testing one point at mean of gaussian. | test_nll_gaussian_on_one_pt | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_scoring_rule.py | MIT |
def test_check_score_on_one_pt():
"""Sanity check to show that check score is minimized (i.e. 0) if data
occurs at the exact requested quantile."""
y_pred = np.array([0])
y_true = np.array([1])
y_std = np.array([1])
score = check_score(
y_pred=y_pred,
y_std=y_std,
y_true=... | Sanity check to show that check score is minimized (i.e. 0) if data
occurs at the exact requested quantile. | test_check_score_on_one_pt | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_scoring_rule.py | MIT |
def test_interval_score_on_one_pt():
"""Sanity check on interval score. For one point in the center of the
distribution and intervals one standard deviation and two standard
deviations away, should return ((1 std) * 2 + (2 std) * 2) / 2 = 3.
"""
y_pred = np.array([0])
y_true = np.array([0])
... | Sanity check on interval score. For one point in the center of the
distribution and intervals one standard deviation and two standard
deviations away, should return ((1 std) * 2 + (2 std) * 2) / 2 = 3.
| test_interval_score_on_one_pt | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_metrics_scoring_rule.py | MIT |
def test_recal_model_mace_criterion_on_test_set(supply_test_set):
"""
Test recalibration on mean absolute calibration error on the test set
for some dummy values.
"""
test_mace = mean_absolute_calibration_error(
*supply_test_set, num_bins=100, vectorized=True, recal_model=None
)
test... |
Test recalibration on mean absolute calibration error on the test set
for some dummy values.
| test_recal_model_mace_criterion_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_recal_model_rmce_criterion_on_test_set(supply_test_set):
"""
Test recalibration on root mean squared calibration error on the test set
for some dummy values.
"""
test_rmsce = root_mean_squared_calibration_error(
*supply_test_set, num_bins=100, vectorized=True, recal_model=None
)... |
Test recalibration on root mean squared calibration error on the test set
for some dummy values.
| test_recal_model_rmce_criterion_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_recal_model_miscal_area_criterion_on_test_set(supply_test_set):
"""
Test recalibration on miscalibration area on the test set
for some dummy values.
"""
test_miscal_area = miscalibration_area(
*supply_test_set, num_bins=100, vectorized=True, recal_model=None
)
test_exp_props... |
Test recalibration on miscalibration area on the test set
for some dummy values.
| test_recal_model_miscal_area_criterion_on_test_set | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_optimize_recalibration_ratio_mace_criterion(supply_test_set):
"""
Test standard deviation recalibration on mean absolute calibration error
on the test set for some dummy values.
"""
random.seed(0)
np.random.seed(seed=0)
y_pred, y_std, y_true = supply_test_set
ma_cal_ratio = opt... |
Test standard deviation recalibration on mean absolute calibration error
on the test set for some dummy values.
| test_optimize_recalibration_ratio_mace_criterion | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_optimize_recalibration_ratio_rmce_criterion(supply_test_set):
"""
Test standard deviation recalibration on root mean squared calibration error
on the test set for some dummy values.
"""
random.seed(0)
np.random.seed(seed=0)
y_pred, y_std, y_true = supply_test_set
rms_cal_ratio ... |
Test standard deviation recalibration on root mean squared calibration error
on the test set for some dummy values.
| test_optimize_recalibration_ratio_rmce_criterion | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_optimize_recalibration_ratio_miscal_area_criterion(supply_test_set):
"""
Test standard deviation recalibration on miscalibration area
on the test set for some dummy values.
"""
random.seed(0)
np.random.seed(seed=0)
y_pred, y_std, y_true = supply_test_set
miscal_ratio = optimize... |
Test standard deviation recalibration on miscalibration area
on the test set for some dummy values.
| test_optimize_recalibration_ratio_miscal_area_criterion | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_get_std_recalibrator(supply_test_set):
"""
Test get_std_recalibration on the test set for some dummy values.
"""
random.seed(0)
np.random.seed(seed=0)
y_pred, y_std, y_true = supply_test_set
test_quantile_prop_list = [
(0.01, 0.00, 0.00),
(0.25, 0.06, 0.00),
... |
Test get_std_recalibration on the test set for some dummy values.
| test_get_std_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_recalibration.py | MIT |
def test_filter_subset(get_test_set):
"""Test if filter_subset returns correct number of subset elements."""
y_pred, y_std, y_true, _ = get_test_set
_test_n_subset = 2
[y_pred, y_std, y_true] = filter_subset([y_pred, y_std, y_true], _test_n_subset)
assert len(y_pred) == _test_n_subset
assert len... | Test if filter_subset returns correct number of subset elements. | test_filter_subset | python | uncertainty-toolbox/uncertainty-toolbox | tests/test_viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/tests/test_viz.py | MIT |
def synthetic_arange_random(
num_points: int = 10,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Dataset of evenly spaced points and identity function (with some randomization).
This function returns predictions and predictive uncertainties (given as standard
deviations) from some hypo... | Dataset of evenly spaced points and identity function (with some randomization).
This function returns predictions and predictive uncertainties (given as standard
deviations) from some hypothetical uncertainty model, along with true input x and
output y data points.
Args:
num_points: The numbe... | synthetic_arange_random | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/data.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/data.py | MIT |
def synthetic_sine_heteroscedastic(
n_points: int = 10,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
"""Return samples from "synthetic sine" heteroscedastic noisy function.
This returns a synthetic dataset which can be used to train and assess a predictive
uncertainty model.
Args:
... | Return samples from "synthetic sine" heteroscedastic noisy function.
This returns a synthetic dataset which can be used to train and assess a predictive
uncertainty model.
Args:
n_points: The number of data points in the set.
Returns:
- Predicted output points y.
- Predictive ... | synthetic_sine_heteroscedastic | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/data.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/data.py | MIT |
def get_all_accuracy_metrics(
y_pred: np.ndarray,
y_true: np.ndarray,
verbose: bool = True,
) -> Dict[str, float]:
"""Compute all accuracy metrics.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
... | Compute all accuracy metrics.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
verbose: Activate verbose mode.
Returns:
The evaluations for all accuracy related metrics.
| get_all_accuracy_metrics | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def get_all_average_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int,
verbose: bool = True,
) -> Dict[str, float]:
"""Compute all metrics for average calibration.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_s... | Compute all metrics for average calibration.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of he predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
num_bins: The number of bin... | get_all_average_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def get_all_adversarial_group_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int,
verbose: bool = True,
) -> Dict[str, Dict[str, np.ndarray]]:
"""Compute all metrics for adversarial group calibration.
Args:
y_pred: 1D array of the predicted means f... | Compute all metrics for adversarial group calibration.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of he predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
num_bins: The num... | get_all_adversarial_group_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def get_all_sharpness_metrics(
y_std: np.ndarray,
verbose: bool = True,
) -> Dict[str, float]:
"""Compute all sharpness metrics
Args:
y_std: 1D array of he predicted standard deviations for the held out dataset.
verbose: Activate verbose mode.
Returns:
The evaluations for a... | Compute all sharpness metrics
Args:
y_std: 1D array of he predicted standard deviations for the held out dataset.
verbose: Activate verbose mode.
Returns:
The evaluations for all sharpness metrics.
| get_all_sharpness_metrics | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def get_all_scoring_rule_metrics(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
resolution: int,
scaled: bool,
verbose: bool = True,
) -> Dict[str, float]:
"""Compute all scoring rule metrics
Args:
y_pred: 1D array of the predicted means for the held out dataset.
... | Compute all scoring rule metrics
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of he predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
resolution: The number of quantiles to ... | get_all_scoring_rule_metrics | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def get_all_metrics(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
resolution: int = 99,
scaled: bool = True,
verbose: bool = True,
) -> Dict[str, Any]:
"""Compute all metrics.
Args:
y_pred: 1D array of the predicted means for the held out d... | Compute all metrics.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of he predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
num_bins: The number of bins to use for discretizat... | get_all_metrics | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics.py | MIT |
def prediction_error_metrics(
y_pred: np.ndarray,
y_true: np.ndarray,
) -> Dict[str, float]:
"""Get all prediction error metrics.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
Returns:
A ... | Get all prediction error metrics.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
Returns:
A dictionary with Mean average error ('mae'), Root mean squared
error ('rmse'), Median absolute error ... | prediction_error_metrics | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_accuracy.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_accuracy.py | MIT |
def sharpness(y_std: np.ndarray) -> float:
"""Return sharpness (a single measure of the overall confidence).
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
Returns:
A single scalar which quantifies the average of the standard deviations.
"""
# ... | Return sharpness (a single measure of the overall confidence).
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
Returns:
A single scalar which quantifies the average of the standard deviations.
| sharpness | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def root_mean_squared_calibration_error(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
vectorized: bool = False,
recal_model: IsotonicRegression = None,
prop_type: str = "interval",
) -> float:
"""Root mean squared calibration error.
Args:
y... | Root mean squared calibration error.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
num_bins: number of discretization... | root_mean_squared_calibration_error | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def mean_absolute_calibration_error(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
vectorized: bool = False,
recal_model: IsotonicRegression = None,
prop_type: str = "interval",
) -> float:
"""Mean absolute calibration error; identical to ECE.
Args:... | Mean absolute calibration error; identical to ECE.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
num_bins: number of ... | mean_absolute_calibration_error | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def adversarial_group_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
cali_type: str,
prop_type: str = "interval",
num_bins: int = 100,
num_group_bins: int = 10,
draw_with_replacement: bool = False,
num_trials: int = 10,
num_group_draws: int = 10,
verb... | Adversarial group calibration.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
cali_type: type of calibration error to ... | adversarial_group_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def miscalibration_area_from_proportions(
exp_proportions: np.ndarray, obs_proportions: np.ndarray
) -> float:
"""Miscalibration area from expected and observed proportions lists.
This function returns the same output as `miscalibration_area` directly from a list
of expected proportions (the proportion... | Miscalibration area from expected and observed proportions lists.
This function returns the same output as `miscalibration_area` directly from a list
of expected proportions (the proportion of data that you expect to observe within
prediction intervals) and a list of observed proportions (the proportion da... | miscalibration_area_from_proportions | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_lists_vectorized(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
recal_model: Any = None,
prop_type: str = "interval",
) -> Tuple[np.ndarray, np.ndarray]:
"""Arrays of expected and observed proportions
Returns the expected proportions ... | Arrays of expected and observed proportions
Returns the expected proportions and observed proportion of points falling into
intervals corresponding to a range of quantiles.
Computations here are vectorized for faster execution, but this function is
not suited when there are memory constraints.
Arg... | get_proportion_lists_vectorized | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_lists(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
num_bins: int = 100,
recal_model: IsotonicRegression = None,
prop_type: str = "interval",
) -> Tuple[np.ndarray, np.ndarray]:
"""Arrays of expected and observed proportions
Return arrays of expected and... | Arrays of expected and observed proportions
Return arrays of expected and observed proportions of points falling into
intervals corresponding to a range of quantiles.
Computations here are not vectorized, in case there are memory constraints.
Args:
y_pred: 1D array of the predicted means for t... | get_proportion_lists | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_in_interval(
y_pred: np.ndarray, y_std: np.ndarray, y_true: np.ndarray, quantile: float
) -> float:
"""For a specified quantile, return the proportion of points falling into
an interval corresponding to that quantile.
Args:
y_pred: 1D array of the predicted means for the held... | For a specified quantile, return the proportion of points falling into
an interval corresponding to that quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of t... | get_proportion_in_interval | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_proportion_under_quantile(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
quantile: float,
) -> float:
"""Get the proportion of data that are below the predicted quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array... | Get the proportion of data that are below the predicted quantile.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
quant... | get_proportion_under_quantile | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_prediction_interval(
y_pred: np.ndarray,
y_std: np.ndarray,
quantile: np.ndarray,
recal_model: Optional[IsotonicRegression] = None,
) -> Namespace:
"""Return the centered predictional interval corresponding to a quantile.
For a specified quantile level q (must be a float, or a singleton... | Return the centered predictional interval corresponding to a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the centered prediction interval corresponding
to the pair of quantiles at levels (0.5-q/2) and (0.5+q/2),
i.e. interval that has nominal coverage equal to q... | get_prediction_interval | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def get_quantile(
y_pred: np.ndarray,
y_std: np.ndarray,
quantile: np.ndarray,
recal_model: Optional[IsotonicRegression] = None,
) -> float:
"""Return the value corresponding with a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the quantile prediction,... | Return the value corresponding with a quantile.
For a specified quantile level q (must be a float, or a singleton),
return the quantile prediction,
i.e. bound that has nominal coverage below the bound equal to q.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y... | get_quantile | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_calibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_calibration.py | MIT |
def nll_gaussian(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
) -> float:
"""Negative log likelihood for a gaussian.
The negative log likelihood for held out data (y_true) given predictive
uncertainty with mean (y_pred) and standard-deviation (y_std).
... | Negative log likelihood for a gaussian.
The negative log likelihood for held out data (y_true) given predictive
uncertainty with mean (y_pred) and standard-deviation (y_std).
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard d... | nll_gaussian | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def crps_gaussian(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
) -> float:
"""The negatively oriented continuous ranked probability score for Gaussians.
Computes CRPS for held out data (y_true) given predictive uncertainty with mean
(y_pred) and standard-... | The negatively oriented continuous ranked probability score for Gaussians.
Computes CRPS for held out data (y_true) given predictive uncertainty with mean
(y_pred) and standard-deviation (y_std). Each test point is given equal weight
in the overall score over the test set.
Negatively oriented means a ... | crps_gaussian | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def check_score(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
start_q: float = 0.01,
end_q: float = 0.99,
resolution: int = 99,
) -> float:
"""The negatively oriented check score.
Computes the negatively oriented check score for held out data (y_tr... | The negatively oriented check score.
Computes the negatively oriented check score for held out data (y_true)
given predictive uncertainty with mean (y_pred) and standard-deviation (y_std).
Each test point and each quantile is given equal weight in the overall score
over the test set and list of quantil... | check_score | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def interval_score(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
scaled: bool = True,
start_p: float = 0.01,
end_p: float = 0.99,
resolution: int = 99,
) -> float:
"""The negatively oriented interval score.
Compute the negatively oriented interval score for held out da... | The negatively oriented interval score.
Compute the negatively oriented interval score for held out data (y_true)
given predictive uncertainty with mean (y_pred) and standard-deviation
(y_std). Each test point and each percentile is given equal weight in the
overall score over the test set and list of ... | interval_score | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/metrics_scoring_rule.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/metrics_scoring_rule.py | MIT |
def get_q_idx(exp_props: np.ndarray, q: float) -> int:
"""Utility function which outputs the array index of an element.
Gets the (approximate) index of a specified probability value, q, in the expected proportions array.
Used as a utility function in isotonic regression recalibration.
Args:
ex... | Utility function which outputs the array index of an element.
Gets the (approximate) index of a specified probability value, q, in the expected proportions array.
Used as a utility function in isotonic regression recalibration.
Args:
exp_props: 1D array of expected probabilities.
q: a spec... | get_q_idx | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def iso_recal(
exp_props: np.ndarray,
obs_props: np.ndarray,
) -> IsotonicRegression:
"""Recalibration algorithm based on isotonic regression.
Fits and outputs an isotonic recalibration model that maps observed
probabilities to expected probabilities. This mapping provides
the necessary adjustm... | Recalibration algorithm based on isotonic regression.
Fits and outputs an isotonic recalibration model that maps observed
probabilities to expected probabilities. This mapping provides
the necessary adjustments to produce better calibrated outputs.
Args:
exp_props: 1D array of expected probabi... | iso_recal | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def optimize_recalibration_ratio(
y_mean: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
criterion: str = "ma_cal",
optimizer_bounds: Tuple[float, float] = (1e-2, 1e2),
) -> float:
"""Scale factor which uniformly recalibrates predicted standard deviations.
Searches via black-box optimiz... | Scale factor which uniformly recalibrates predicted standard deviations.
Searches via black-box optimization the standard deviation scale factor (opt_ratio)
which produces the best recalibration, i.e. updated standard deviation
can be written as opt_ratio * y_std.
Args:
y_mean: 1D array of the... | optimize_recalibration_ratio | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_std_recalibrator(
y_mean: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
criterion: str = "ma_cal",
optimizer_bounds: Tuple[float, float] = (1e-2, 1e2),
) -> Callable[[np.ndarray], np.ndarray]:
"""Standard deviation recalibrator.
Computes the standard deviation recalibration rat... | Standard deviation recalibrator.
Computes the standard deviation recalibration ratio and returns a function
which takes in an array of uncalibrated standard deviations and returns
an array of recalibrated standard deviations.
Args:
y_mean: 1D array of the predicted means for the recalibration ... | get_std_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_quantile_recalibrator(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
) -> Callable[[np.ndarray, np.ndarray, Union[float, np.ndarray]], np.ndarray]:
"""Quantile recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and s... | Quantile recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and standard deviation predictions and a specified
quantile level, and returns the recalibrated quantile.
Args:
y_pred: 1D array of the predicted means for the recalibration da... | get_quantile_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def get_interval_recalibrator(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
) -> Callable[[np.ndarray, np.ndarray, Union[float, np.ndarray]], np.ndarray]:
"""Prediction interval recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the... | Prediction interval recalibrator.
Fits an isotonic regression recalibration model and returns a function
which takes in the mean and standard deviation predictions and a specified
centered interval coverage level, and returns the recalibrated interval.
Args:
y_pred: 1D array of the predicted m... | get_interval_recalibrator | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/recalibration.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/recalibration.py | MIT |
def assert_is_flat_same_shape(*args: Any) -> Union[bool, NoReturn]:
"""Check if inputs are all same-length 1d numpy.ndarray.
Args:
args: the numpy arrays to check.
Returns:
True if all arrays are flat and the same shape, or else raises assertion error.
"""
assert len(args) > 0
... | Check if inputs are all same-length 1d numpy.ndarray.
Args:
args: the numpy arrays to check.
Returns:
True if all arrays are flat and the same shape, or else raises assertion error.
| assert_is_flat_same_shape | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def assert_is_positive(*args: Any) -> Union[bool, NoReturn]:
"""Assert that all numpy arrays are positive.
Args:
args: the numpy arrays to check.
Returns:
True if all elements in all arrays are positive values, or else raises assertion error.
"""
assert len(args) > 0
for arr in... | Assert that all numpy arrays are positive.
Args:
args: the numpy arrays to check.
Returns:
True if all elements in all arrays are positive values, or else raises assertion error.
| assert_is_positive | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def trapezoid_area(
xl: np.ndarray,
al: np.ndarray,
bl: np.ndarray,
xr: np.ndarray,
ar: np.ndarray,
br: np.ndarray,
absolute: bool = True,
) -> Numeric:
"""
Calculate the area of a vertical-sided trapezoid, formed connecting the following points:
(xl, al) - (xl, bl) - (xr, br... |
Calculate the area of a vertical-sided trapezoid, formed connecting the following points:
(xl, al) - (xl, bl) - (xr, br) - (xr, ar) - (xl, al)
This function considers the case that the edges of the trapezoid might cross,
and explicitly accounts for this.
Args:
xl: The x coordinate of ... | trapezoid_area | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def intersection(
p1: Tuple[Numeric, Numeric],
p2: Tuple[Numeric, Numeric],
p3: Tuple[Numeric, Numeric],
p4: Tuple[Numeric, Numeric],
) -> Tuple[Numeric, Numeric]:
"""
Calculate the intersection of two lines between four points, as defined in
https://en.wikipedia.org/wiki/Line%E2%80%93line_i... |
Calculate the intersection of two lines between four points, as defined in
https://en.wikipedia.org/wiki/Line%E2%80%93line_intersection.
This is an array option and works can be used to calculate the intersections of
entire arrays of points at the same time.
Args:
p1: The point (x1, y1), ... | intersection | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/utils.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/utils.py | MIT |
def plot_xy(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
x: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
xlims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
leg_loc: Union[int, str] = 3,
... | Plot one-dimensional inputs with associated predicted values, predictive
uncertainties, and true values.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true la... | plot_xy | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_intervals(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot predictio... | Plot predictions and predictive intervals versus true values.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true labels in the held out dataset.
n_subset:... | plot_intervals | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_intervals_ordered(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ylims: Union[Tuple[float, float], None] = None,
num_stds_confidence_bound: int = 2,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot p... | Plot predictions and predictive intervals versus true values, with points ordered
by true value along x-axis.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the tr... | plot_intervals_ordered | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
curve_label: Union[str, None] = None,
vectorized: bool = True,
exp_props: Union[np.ndarray, None] = None,
obs_props: Union[np.ndarray, None] = None,
ax: Union[matplotli... | Plot the observed proportion vs prediction proportion of outputs falling into a
range of intervals, and display miscalibration area.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_t... | plot_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_adversarial_group_calibration(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
cali_type: str = "mean_abs",
curve_label: Union[str, None] = None,
group_size: Union[np.ndarray, None] = None,
score_mean: Union[np.ndarray, None] = None,... | Plot adversarial group calibration plots by varying group size from 0% to 100% of
dataset size and recording the worst calibration occurred for each group size.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the h... | plot_adversarial_group_calibration | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_sharpness(
y_std: np.ndarray,
n_subset: Union[int, None] = None,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot sharpness of the predictive uncertainties.
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
... | Plot sharpness of the predictive uncertainties.
Args:
y_std: 1D array of the predicted standard deviations for the held out dataset.
n_subset: Number of points to plot after filtering.
ax: matplotlib.axes.Axes object.
Returns:
matplotlib.axes.Axes object with plot added.
| plot_sharpness | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def plot_residuals_vs_stds(
y_pred: np.ndarray,
y_std: np.ndarray,
y_true: np.ndarray,
n_subset: Union[int, None] = None,
ax: Union[matplotlib.axes.Axes, None] = None,
) -> matplotlib.axes.Axes:
"""Plot absolute value of the prediction residuals versus standard deviations of the
predictive u... | Plot absolute value of the prediction residuals versus standard deviations of the
predictive uncertainties.
Args:
y_pred: 1D array of the predicted means for the held out dataset.
y_std: 1D array of the predicted standard deviations for the held out dataset.
y_true: 1D array of the true... | plot_residuals_vs_stds | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def filter_subset(input_list: List[List[Any]], n_subset: int) -> List[List[Any]]:
"""Keep only n_subset random indices from all lists given in input_list.
Args:
input_list: list of lists.
n_subset: Number of points to plot after filtering.
Returns:
List of all input lists with size... | Keep only n_subset random indices from all lists given in input_list.
Args:
input_list: list of lists.
n_subset: Number of points to plot after filtering.
Returns:
List of all input lists with sizes reduced to n_subset.
| filter_subset | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def set_style(style_str: str = "default") -> NoReturn:
"""Set the matplotlib plotting style.
Args:
style_str: string for style file.
"""
if style_str == "default":
plt.style.use((pathlib.Path(__file__).parent / "matplotlibrc").resolve()) | Set the matplotlib plotting style.
Args:
style_str: string for style file.
| set_style | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def save_figure(
file_name: str = "figure",
ext_list: Union[list, str, None] = None,
white_background: bool = True,
) -> NoReturn:
"""Save matplotlib figure for all extensions in ext_list.
Args:
file_name: name of saved image file.
ext_list: list of strings (or single string) denoti... | Save matplotlib figure for all extensions in ext_list.
Args:
file_name: name of saved image file.
ext_list: list of strings (or single string) denoting file type.
white_background: set background of image to white if True.
| save_figure | python | uncertainty-toolbox/uncertainty-toolbox | uncertainty_toolbox/viz.py | https://github.com/uncertainty-toolbox/uncertainty-toolbox/blob/master/uncertainty_toolbox/viz.py | MIT |
def image_transform(self, images, lm):
"""
param:
images: -- PIL image
lm: -- numpy array
"""
W,H = images.size
if np.mean(lm) == -1:
lm = (self.lm3d_std[:, :2]+1)/2.
lm = np.concatenate(
[lm[:... |
param:
images: -- PIL image
lm: -- numpy array
| image_transform | python | OpenTalker/video-retalking | third_part/face3d/coeff_detector.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/coeff_detector.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
self.opt = opt
# self.root = opt.dataroot
self.current_epoch = 0 | Initialize the class; save the options in the class
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/base_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/base_dataset.py | Apache-2.0 |
def default_flist_reader(flist):
"""
flist format: impath label\nimpath label\n ...(same to caffe's filelist)
"""
imlist = []
with open(flist, 'r') as rf:
for line in rf.readlines():
impath = line.strip()
imlist.append(impath)
return imlist |
flist format: impath label
impath label
...(same to caffe's filelist)
| default_flist_reader | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
BaseDataset.__init__(self, opt)
self.lm3d_std = load_lm3d(opt.bfm_folder)
... | Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
img (tensor) -- an image in the input domain
... | Return a data point and its metadata information.
Parameters:
index (int) -- a random integer for data indexing
Returns a dictionary that contains A, B, A_paths and B_paths
img (tensor) -- an image in the input domain
msk (tensor) -- its correspondi... | __getitem__ | python | OpenTalker/video-retalking | third_part/face3d/data/flist_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/flist_dataset.py | Apache-2.0 |
def modify_commandline_options(parser, is_train):
"""Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add tra... | Add new dataset-specific options, and rewrite default values for existing options.
Parameters:
parser -- original option parser
is_train (bool) -- whether training phase or test phase. You can use this flag to add training-specific or test-specific options.
Returns:
... | modify_commandline_options | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths an... | Initialize this dataset class.
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
A few things can be done here.
- save the options (have been done in BaseDataset)
- get image paths and meta information of the dataset.
... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def __getitem__(self, index):
"""Return a data point and its metadata information.
Parameters:
index -- a random integer for data indexing
Returns:
a dictionary of data with their names. It usually contains the data itself and its metadata information.
Step 1: ... | Return a data point and its metadata information.
Parameters:
index -- a random integer for data indexing
Returns:
a dictionary of data with their names. It usually contains the data itself and its metadata information.
Step 1: get a random image path: e.g., path = sel... | __getitem__ | python | OpenTalker/video-retalking | third_part/face3d/data/template_dataset.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/template_dataset.py | Apache-2.0 |
def find_dataset_using_name(dataset_name):
"""Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
"""
dataset_filename = "data." + dataset_name + "_datase... | Import the module "data/[dataset_name]_dataset.py".
In the file, the class called DatasetNameDataset() will
be instantiated. It has to be a subclass of BaseDataset,
and it is case-insensitive.
| find_dataset_using_name | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def create_dataset(opt, rank=0):
"""Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
... | Create a dataset given the option.
This function wraps the class CustomDatasetDataLoader.
This is the main interface between this package and 'train.py'/'test.py'
Example:
>>> from data import create_dataset
>>> dataset = create_dataset(opt)
| create_dataset | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def __init__(self, opt, rank=0):
"""Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
"""
self.opt = opt
dataset_class = find_dataset_using_name(opt.dataset_mode)
self.dataset = data... | Initialize this class
Step 1: create a dataset instance given the name [dataset_mode]
Step 2: create a multi-threaded data loader.
| __init__ | python | OpenTalker/video-retalking | third_part/face3d/data/__init__.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/data/__init__.py | Apache-2.0 |
def __init__(self, opt):
"""Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should f... | Initialize the BaseModel class.
Parameters:
opt (Option class)-- stores all the experiment flags; needs to be a subclass of BaseOptions
When creating your custom class, you need to implement your own initialization.
In this fucntion, you should first call <BaseModel.__init__(self, ... | __init__ | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def setup(self, opt):
"""Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
"""
if self.isTrain:
self.schedulers = [networks.get_scheduler(optimizer, opt) for optimiz... | Load and print networks; create schedulers
Parameters:
opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions
| setup | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def update_learning_rate(self):
"""Update learning rates for all the networks; called at the end of every epoch"""
for scheduler in self.schedulers:
if self.opt.lr_policy == 'plateau':
scheduler.step(self.metric)
else:
scheduler.step()
lr ... | Update learning rates for all the networks; called at the end of every epoch | update_learning_rate | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def get_current_visuals(self):
"""Return visualization images. train.py will display these images with visdom, and save the images to a HTML"""
visual_ret = OrderedDict()
for name in self.visual_names:
if isinstance(name, str):
visual_ret[name] = getattr(self, name)[:... | Return visualization images. train.py will display these images with visdom, and save the images to a HTML | get_current_visuals | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def get_current_losses(self):
"""Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
errors_ret = OrderedDict()
for name in self.loss_names:
if isinstance(name, str):
errors_ret[name] = float(getattr(self, 'loss_... | Return traning losses / errors. train.py will print out these errors on console, and save them to a file | get_current_losses | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def save_networks(self, epoch):
"""Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
if not os.path.isdir(self.save_dir):
os.makedirs(self.save_dir)
save_filename = 'epo... | Save all the networks to the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
| save_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def __patch_instance_norm_state_dict(self, state_dict, module, keys, i=0):
"""Fix InstanceNorm checkpoints incompatibility (prior to 0.4)"""
key = keys[i]
if i + 1 == len(keys): # at the end, pointing to a parameter/buffer
if module.__class__.__name__.startswith('InstanceNorm') and ... | Fix InstanceNorm checkpoints incompatibility (prior to 0.4) | __patch_instance_norm_state_dict | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def load_networks(self, epoch):
"""Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
"""
if self.opt.isTrain and self.opt.pretrained_name is not None:
load_dir = os.path.join(self.... | Load all the networks from the disk.
Parameters:
epoch (int) -- current epoch; used in the file name '%s_net_%s.pth' % (epoch, name)
| load_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def print_networks(self, verbose):
"""Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
"""
print('---------- Networks initialized -------------')
for nam... | Print the total number of parameters in the network and (if verbose) network architecture
Parameters:
verbose (bool) -- if verbose: print the network architecture
| print_networks | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def set_requires_grad(self, nets, requires_grad=False):
"""Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
"""
... | Set requies_grad=Fasle for all the networks to avoid unnecessary computations
Parameters:
nets (network list) -- a list of networks
requires_grad (bool) -- whether the networks require gradients or not
| set_requires_grad | python | OpenTalker/video-retalking | third_part/face3d/models/base_model.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/base_model.py | Apache-2.0 |
def compute_shape(self, id_coeff, exp_coeff):
"""
Return:
face_shape -- torch.tensor, size (B, N, 3)
Parameters:
id_coeff -- torch.tensor, size (B, 80), identity coeffs
exp_coeff -- torch.tensor, size (B, 64), expression coeffs
""... |
Return:
face_shape -- torch.tensor, size (B, N, 3)
Parameters:
id_coeff -- torch.tensor, size (B, 80), identity coeffs
exp_coeff -- torch.tensor, size (B, 64), expression coeffs
| compute_shape | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_texture(self, tex_coeff, normalize=True):
"""
Return:
face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
Parameters:
tex_coeff -- torch.tensor, size (B, 80)
"""
batch_size = tex_coeff.shape[0]
face_tex... |
Return:
face_texture -- torch.tensor, size (B, N, 3), in RGB order, range (0, 1.)
Parameters:
tex_coeff -- torch.tensor, size (B, 80)
| compute_texture | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_norm(self, face_shape):
"""
Return:
vertex_norm -- torch.tensor, size (B, N, 3)
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
v1 = face_shape[:, self.face_buf[:, 0]]
v2 = face_shape[:, self.face_buf[:, 1]]
... |
Return:
vertex_norm -- torch.tensor, size (B, N, 3)
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
| compute_norm | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_color(self, face_texture, face_norm, gamma):
"""
Return:
face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
Parameters:
face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
face_norm -- torch.tens... |
Return:
face_color -- torch.tensor, size (B, N, 3), range (0, 1.)
Parameters:
face_texture -- torch.tensor, size (B, N, 3), from texture model, range (0, 1.)
face_norm -- torch.tensor, size (B, N, 3), rotated face normal
gamma ... | compute_color | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def compute_rotation(self, angles):
"""
Return:
rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
Parameters:
angles -- torch.tensor, size (B, 3), radian
"""
batch_size = angles.shape[0]
ones = torch.ones([batch_size, 1])... |
Return:
rot -- torch.tensor, size (B, 3, 3) pts @ trans_mat
Parameters:
angles -- torch.tensor, size (B, 3), radian
| compute_rotation | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
def to_image(self, face_shape):
"""
Return:
face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
"""
# to image_plane
face_proj = face_shape @ self.pe... |
Return:
face_proj -- torch.tensor, size (B, N, 2), y direction is opposite to v direction
Parameters:
face_shape -- torch.tensor, size (B, N, 3)
| to_image | python | OpenTalker/video-retalking | third_part/face3d/models/bfm.py | https://github.com/OpenTalker/video-retalking/blob/master/third_part/face3d/models/bfm.py | Apache-2.0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.