ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values #469

Yuang-Deng · 2022-08-11T06:42:18Z

NOTE: ISSUES ARE NOT FOR CODE HELP - Ask for Help at https://stackoverflow.com

Your issue may already be reported!
Also, please search on the issue tracker before creating one.

I'm submitting a ...
- bug report
- feature request
- support request => Please do not submit support request here, see note at the top of this template.

Issue Description

When Issue Happens
Steps To Reproduce
1.Auto-PyTorch from [email protected]:automl/Auto-PyTorch.git
1.config space from [email protected]:automl/ConfigSpace.git
1.run examples/APT-TS/APT_task.py with electricity

Expected Behavior

Current Behavior

Possible Solution

Your Code

import numpy as np

from autoPyTorch.api.time_series_forecasting import TimeSeriesForecastingTask
import pandas as pds
from datetime import datetime
import warnings
import os
import copy
from pathlib import Path

import argparse

import csv
import shutil

from autoPyTorch.datasets.resampling_strategy import (
    CrossValTypes,
    HoldoutValTypes,
)

import data_loader
from constant import VALUE_COL_NAME, TIME_COL_NAME, SEASONALITY_MAP, FREQUENCY_MAP, DATASETS


def compute_loss(forecast_horizon, seasonality, final_forecasts, test_series_list, train_series_list):
    epsilon = 0.1

    MASE = []
    sMAPE = []
    msMAPE = []
    MAE = []
    RMSE = []

    sqrt_forecast_horizon = np.sqrt(forecast_horizon)

    idx = 0

    for f, y, y_data in zip(final_forecasts, test_series_list, train_series_list):

        M = len(y_data)

        diff_abs = np.abs(f - y)

        if M == seasonality:
            mase_denominator = 0
        else:
            mase_denominator_coefficient = forecast_horizon / (M - seasonality)
            mase_denominator = mase_denominator_coefficient * \
                               np.sum(np.abs(y_data[seasonality:] - y_data[:-seasonality]))

            abs_loss = np.sum(diff_abs)
            mase = abs_loss / mase_denominator

        if mase_denominator == 0:
            mase_denominator_coefficient = forecast_horizon / (M - 1)
            mase_denominator = mase_denominator_coefficient * \
                               np.sum(np.abs(y_data[1:] - y_data[:-1]))
            mase = abs_loss / mase_denominator

        if np.isnan(mase) or np.isinf(mase):
            # see the R file
            pass
        else:
            MASE.append(mase)

        smape = 2 * diff_abs / (np.abs(y) + np.abs(f))
        smape[diff_abs == 0] = 0
        smape = np.sum(smape) / forecast_horizon
        sMAPE.append(smape)

        msmape = np.sum(2 * diff_abs / (np.maximum(np.abs(y) + np.abs(f) + epsilon, epsilon + 0.5))) / forecast_horizon
        msMAPE.append(msmape)

        mae = abs_loss / forecast_horizon
        MAE.append(mae)

        rmse = np.linalg.norm(f - y) / sqrt_forecast_horizon
        RMSE.append(rmse)


        idx += 1
    res = {}

    res['Mean MASE'] = np.mean(MASE)

    res['Median MASE'] = np.median(MASE)

    res['Mean sMAPE'] = np.mean(sMAPE)
    res['Median sMAPE'] = np.median(sMAPE)

    res['Mean mSMAPE'] = np.mean(msMAPE)
    res['Median mSMAPE'] = np.median(msMAPE)

    res['Mean MAE'] = np.mean(MAE)
    res['Median MAE'] = np.median(MAE)

    res['Mean RMSE'] = np.mean(RMSE)
    res['Median RMSE'] = np.median(RMSE)


    return res


def main(working_dir="/home/$USER/tmp/tmp",
         dataset_name='nn5_daily',
         budget_type='dataset_size',
         res_dir="/home/ubuntu/autopytorch/Auto-PyTorch/work_dirs/tsf_res",
         validation='holdout',
         seed=1):
    file_name, external_forecast_horizon, integer_conversion = DATASETS[dataset_name]

    dataset_path = Path("/home/ubuntu/autopytorch/tsf_data") / dataset_name / file_name

    df, frequency, forecast_horizon, contain_missing_values, contain_equal_length = \
        data_loader.convert_tsf_to_dataframe(str(dataset_path))

    # If the forecast horizon is not given within the .tsf file, then it should be provided as a function input
    if forecast_horizon is None:
        if external_forecast_horizon is None:
            raise Exception("Please provide the required prediction steps")
        else:
            forecast_horizon = external_forecast_horizon

    train_series_list = []
    test_series_list = []

    X_train = []
    y_train = []

    X_test = []
    y_test = []

    if frequency is not None:
        freq = FREQUENCY_MAP[frequency]
        seasonality = SEASONALITY_MAP[frequency]
    else:
        freq = "1Y"
        seasonality = 1

    shortest_sequence = np.inf
    train_start_time_list = []

    for index, row in df.iterrows():
        if TIME_COL_NAME in df.columns:
            train_start_time = row[TIME_COL_NAME]
        else:
            train_start_time = datetime.strptime('1900-01-01 00-00-00',
                                                 '%Y-%m-%d %H-%M-%S')  # Adding a dummy timestamp, if the timestamps are not available in the dataset or consider_time is False
        train_start_time_list.append(pds.Timestamp(train_start_time, freq=freq))

        series_data = row[VALUE_COL_NAME].to_numpy()
        # Creating training and test series. Test series will be only used during evaluation
        train_series_data = series_data[:len(series_data) - forecast_horizon]
        test_series_data = series_data[(len(series_data) - forecast_horizon): len(series_data)]

        y_test.append(series_data[-forecast_horizon:])

        train_series_list.append(train_series_data)
        test_series_list.append(test_series_data)

        shortest_sequence = min(len(train_series_data), shortest_sequence)

    if validation == 'cv':
        n_splits = 3
        while shortest_sequence - forecast_horizon - forecast_horizon * n_splits <= 0:
            n_splits -= 1

        if n_splits >= 2:
            resampling_strategy = CrossValTypes.time_series_cross_validation
            resampling_strategy_args = {'num_splits': n_splits}

        else:
            warnings.warn('The dataset is not suitable for cross validation, we will try holdout instead')
            validation = 'holdout'
    elif validation == 'holdout_ts':
        resampling_strategy = CrossValTypes.time_series_ts_cross_validation
        resampling_strategy_args = None
    if validation == 'holdout':
        resampling_strategy = HoldoutValTypes.time_series_hold_out_validation
        resampling_strategy_args = None


    X_train = copy.deepcopy(train_series_list)
    y_train = copy.deepcopy(train_series_list)

    X_test = copy.deepcopy(X_train)

    path = Path(working_dir) / 'APT_run'
    path_log = str(path / dataset_name / budget_type / f'{seed}' / "log")
    path_pred = str(path / dataset_name / budget_type / f'{seed}' / "output")

    # Remove intermediate files
    try:
        shutil.rmtree(path_log)
        shutil.rmtree(path_pred)
    except OSError as e:
        print("Error: %s - %s." % (e.filename, e.strerror))

    smac_source_dir = Path(path_log) / "smac3-output"


    api = TimeSeriesForecastingTask(
        #delete_tmp_folder_after_terminate=False,
        #delete_output_folder_after_terminate=False,
        seed=seed,
        ensemble_size=20,
        resampling_strategy=resampling_strategy,
        resampling_strategy_args=resampling_strategy_args,
        temporary_directory=path_log,
        output_directory=path_pred,
    )

    api.set_pipeline_config(device="cuda",
                            torch_num_threads=8,
                            early_stopping=20)
    if budget_type == "random_search":
        budget_kwargs = {'budget_type': 'random_search',
                         'max_budget': None,
                         'min_budget': None}

    elif budget_type != 'full_budget':
        from autoPyTorch.constants import FORECASTING_BUDGET_TYPE
        if budget_type not in FORECASTING_BUDGET_TYPE and budget_type != 'epochs':
            raise NotImplementedError('Unknown Budget Type!')
        budget_kwargs = {'budget_type': budget_type,
                         'max_budget': 50 if budget_type == 'epochs' else 1.0,
                         'min_budget': 5 if budget_type == 'epochs' else 0.1}
    else:
        budget_kwargs = {'budget_type': 'epochs',
                         'max_budget': 50,
                         'min_budget': 50}

    api.search(
        X_train=None,
        y_train=copy.deepcopy(y_train),
        optimize_metric='mean_MASE_forecasting',
        n_prediction_steps=forecast_horizon,
        **budget_kwargs,
        freq=freq,
        # start_times_train=train_start_time_list,
        memory_limit=32 * 1024,
        normalize_y=False,
        total_walltime_limit=600,
        min_num_test_instances=1000,
    )

    from autoPyTorch.data.time_series_forecasting_validator import TimeSeriesForecastingInputValidator

    res_dir = Path(res_dir)

    res_dir_task = res_dir / dataset_name / budget_type / f'{seed}'

    smac_res_path = res_dir_task / 'smac3-output'

    if not os.path.exists(str(res_dir_task)):
        os.makedirs(str(res_dir_task))

    try:
        shutil.rmtree(smac_res_path)
    except OSError as e:
        print("Error: %s - %s." % (e.filename, e.strerror))

    try:
        shutil.copytree(str(smac_source_dir), smac_res_path)
    except OSError as e:  # python >2.5
        print("Error: %s - %s." % (e.filename, e.strerror))


    refit_dataset = api.dataset.create_refit_set()

    train_pred_seq = []
    test_sets = api.dataset.generate_test_seqs()

    # try:
    #     api.refit(refit_dataset, 0)

    #     # pred = api.predict(test_sets)

    # except Exception as e:
    #     print(e)
    #     exit()
    
    api.refit(refit_dataset, 0)
    
    pred = api.predict(test_sets)


    if integer_conversion:
        final_forecasts = np.round(pred)
    else:
        final_forecasts = pred

    if frequency is not None:
        freq = FREQUENCY_MAP[frequency]
        seasonality = SEASONALITY_MAP[frequency]
    else:
        freq = "1Y"
        seasonality = 1

    if isinstance(seasonality, list):
        seasonality = min(seasonality)  # Use to calculate MASE
    seasonality = int(seasonality)

    res = compute_loss(forecast_horizon, seasonality, pred, y_test, train_series_data)
    print(res)


    # write the forecasting results to a file
    forecast_file_path = res_dir_task / f"{dataset_name}_{budget_type}_results.txt"

    with open(forecast_file_path, "w") as output:
        writer = csv.writer(output, lineterminator='\n')
        writer.writerows(final_forecasts)

    # Write training dataset and the actual results into separate files, which are then used for error calculations
    # We do not use the built-in evaluation method in GluonTS as some of the error measures we use are not implemented in that
    temp_dataset_path = res_dir_task / f"{dataset_name}_dataset.txt"
    temp_results_path = res_dir_task / f"{dataset_name}_ground_truth.txt"

    # with open(str(temp_dataset_path), "w") as output_dataset:
    #    writer = csv.writer(output_dataset, lineterminator='\n')
    #    writer.writerows(train_series_list)

    with open(str(temp_results_path), "w") as output_results:
        writer = csv.writer(output_results, lineterminator='\n')
        writer.writerows(test_series_list)


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='APT_Task')
    parser.add_argument('--dataset_name', type=str, default="electricity_hourly", help='dataset name')
    parser.add_argument("--budget_type", default="epochs", type=str, help='budget type')
    parser.add_argument("--working_dir", default="/home/ubuntu/autopytorch/Auto-PyTorch/work_dirs", type=str,
                        help="directory where datasets and tmp files are stored")
    parser.add_argument('--validation', type=str, default="holdout", help='type of validation')
    parser.add_argument('--seed', type=int, default="10", help='random seed')

    args = parser.parse_args()

    dataset_name = args.dataset_name
    budget_type = args.budget_type
    working_dir = args.working_dir
    validation = args.validation
    seed = args.seed

    main(working_dir=working_dir, dataset_name=dataset_name, budget_type=budget_type, validation=validation, seed=seed)

Error message

Traceback (most recent call last):
  File "/home/ubuntu/.pyenv/versions/3.8.9/lib/python3.8/runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "/home/ubuntu/.pyenv/versions/3.8.9/lib/python3.8/runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/__main__.py", line 39, in <module>
    cli.main()
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 430, in main
    run()
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/adapter/../../debugpy/launcher/../../debugpy/../debugpy/server/cli.py", line 284, in run_file
    runpy.run_path(target, run_name="__main__")
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 321, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 135, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "/home/ubuntu/.vscode-server/extensions/ms-python.python-2022.12.0/pythonFiles/lib/python/debugpy/_vendored/pydevd/_pydevd_bundle/pydevd_runpy.py", line 124, in _run_code
    exec(code, run_globals)
  File "./examples/APT-TS/APT_task.py", line 346, in <module>
    main(working_dir=working_dir, dataset_name=dataset_name, budget_type=budget_type, validation=validation, seed=seed)
  File "./examples/APT-TS/APT_task.py", line 283, in main
    api.refit(refit_dataset, 0)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/api/base_task.py", line 1441, in refit
    fit_and_suppress_warnings(self._logger, model, X, y=None)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/evaluation/abstract_evaluator.py", line 338, in fit_and_suppress_warnings
    pipeline.fit(X, y)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/base_pipeline.py", line 155, in fit
    self.fit_estimator(X, y, **fit_params)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/base_pipeline.py", line 174, in fit_estimator
    self._final_estimator.fit(X, y, **fit_params)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/training/trainer/__init__.py", line 211, in fit
    self._fit(
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/training/trainer/__init__.py", line 310, in _fit
    train_loss, train_metrics = self.choice.train_epoch(
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/training/trainer/forecasting_trainer/forecasting_base_trainer.py", line 106, in train_epoch
    loss, outputs = self.train_step(data, targets)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/training/trainer/forecasting_trainer/forecasting_base_trainer.py", line 206, in train_step
    outputs = self.model(past_targets=past_target,
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/setup/network/forecasting_architecture.py", line 602, in forward
    output = self.head(decoder_output)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1130, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/autoPyTorch/pipeline/components/setup/network_head/forecasting_network_head/distribution.py", line 95, in forward
    return self.dist_cls(*self.domain_map(*params_unbounded))
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/distributions/studentT.py", line 50, in __init__
    self._chi2 = Chi2(self.df)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/distributions/chi2.py", line 22, in __init__
    super(Chi2, self).__init__(0.5 * df, 0.5, validate_args=validate_args)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/distributions/gamma.py", line 52, in __init__
    super(Gamma, self).__init__(batch_shape, validate_args=validate_args)
  File "/home/ubuntu/autopytorch/Auto-PyTorch2/.venv/lib/python3.8/site-packages/torch/distributions/distribution.py", line 55, in __init__
    raise ValueError(
ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values:
tensor([[[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]],

        [[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]],

        [[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]],

        ...,

        [[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]],

        [[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]],

        [[nan],
         [nan],
         [nan],
         ...,
         [nan],
         [nan],
         [nan]]], grad_fn=<MulBackward0>)

Your Local environment

Operating System, version
Ubuntu 20.04.4 LTS
Python, version
3.8.9
Outputs of pip freeze or conda list

absl-py==1.2.0
aiohttp==3.8.1
aiosignal==1.2.0
alembic==1.8.1
async-timeout==4.0.2
attrs==22.1.0
autopage==0.5.1
-e [email protected]:automl/Auto-PyTorch.git@c138dff1909464110f4d35dfb9c016e724a8c25a#egg=autoPyTorch
cachetools==5.2.0
catboost==1.0.6
certifi==2022.6.15
charset-normalizer==2.1.0
click==8.1.3
cliff==3.10.1
cloudpickle==2.1.0
cmaes==0.8.2
cmd2==2.4.2
colorlog==6.6.0
-e [email protected]:automl/ConfigSpace.git@e681dc9fa32cf113fe4a658bf0c36306f32376d2#egg=ConfigSpace
convertdate==2.4.0
cycler==0.11.0
Cython==0.29.32
dask==2022.8.0
Deprecated==1.2.13
distributed==2022.8.0
emcee==3.1.2
flaky==3.7.0
fonttools==4.34.4
frozenlist==1.3.1
fsspec==2022.7.1
gluonts==0.10.2
google-auth==2.10.0
google-auth-oauthlib==0.4.6
graphviz==0.20.1
greenlet==1.1.2
grpcio==1.47.0
HeapDict==1.0.1
hijri-converter==2.2.4
holidays==0.14.2
idna==3.3
imageio==2.21.0
imgaug==0.4.0
importlib-metadata==4.12.0
importlib-resources==5.9.0
Jinja2==3.1.2
joblib==1.1.0
kiwisolver==1.4.4
korean-lunar-calendar==0.2.1
lightgbm==3.3.2
llvmlite==0.39.0
locket==1.0.0
lockfile==0.12.2
Mako==1.2.1
Markdown==3.4.1
MarkupSafe==2.1.1
matplotlib==3.5.2
msgpack==1.0.4
multidict==6.0.2
networkx==2.8.5
numba==0.56.0
numpy==1.22.4
oauthlib==3.2.0
opencv-python==4.6.0.66
optuna==2.10.1
packaging==21.3
pandas==1.4.3
partd==1.2.0
patsy==0.5.2
pbr==5.9.0
Pillow==9.2.0
plotly==5.9.0
prettytable==3.3.0
protobuf==3.19.4
psutil==5.9.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pydantic==1.9.1
pyDeprecate==0.3.2
PyMeeus==0.5.11
pynisher==0.6.4
pyparsing==3.0.9
pyperclip==1.8.2
pyrfr==0.8.3
python-dateutil==2.8.2
pytorch-forecasting==0.10.2
pytorch-lightning==1.7.0
pytz==2022.1
PyWavelets==1.3.0
PyYAML==6.0
regex==2022.7.25
requests==2.28.1
requests-oauthlib==1.3.1
rsa==4.9
scikit-image==0.19.3
scikit-learn==0.24.2
scipy==1.8.1
Shapely==1.8.2
six==1.16.0
sktime==0.13.0
smac==1.4.0
sortedcontainers==2.4.0
SQLAlchemy==1.4.39
statsmodels==0.13.2
stevedore==4.0.0
tabulate==0.8.10
tblib==1.7.0
tenacity==8.0.1
tensorboard==2.9.1
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.1
threadpoolctl==3.1.0
tifffile==2022.8.3
toolz==0.12.0
torch==1.12.1
torchmetrics==0.9.3
torchvision==0.13.1
tornado==6.1
tqdm==4.64.0
typing-extensions==4.3.0
urllib3==1.26.11
wcwidth==0.2.5
Werkzeug==2.2.1
wrapt==1.14.1
yarl==1.8.1
zict==2.2.0
zipp==3.8.1

Make sure to add all the information needed to understand the bug so that someone can help.
If the info is missing, we'll add the 'Needs more information' label and close the issue until there is enough information.

The text was updated successfully, but these errors were encountered:

dengdifan · 2022-08-11T11:14:21Z

Hi,
this error happens because one hyperparameter configuration passed the HPO process (only trained on the training set) but failed during the refit process (when the model is trained on the training+validation set). Currently, our optimizer does not consider catching the exception within refit. We will fix this ASAP and incorporate it into the next release.
Before that, you could simply remove that line called refit() (though this might slightly weaken the final performance).

Yuang-Deng · 2022-08-12T02:20:32Z

ok, thanks for your reply.

ravinkohli · 2022-08-23T16:49:12Z

Hi, we have fixed this issue in the latest release. You can install it using pip install autoPyTorch==0.2.1. I am closing this issue for now.

ravinkohli self-assigned this Aug 12, 2022

ravinkohli mentioned this issue Aug 12, 2022

[Enhancement] Make refit() method run in dask #268

Closed

ravinkohli added the bug Something isn't working label Aug 12, 2022

ravinkohli mentioned this issue Aug 16, 2022

[ADD] Robustly refit models in final ensemble in parallel #471

Merged

10 tasks

ravinkohli linked a pull request Aug 16, 2022 that will close this issue

[ADD] Robustly refit models in final ensemble in parallel #471

Merged

10 tasks

ravinkohli closed this as completed Aug 23, 2022

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values #469

ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values #469

Yuang-Deng commented Aug 11, 2022

dengdifan commented Aug 11, 2022

Yuang-Deng commented Aug 12, 2022

ravinkohli commented Aug 23, 2022

ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values #469

ValueError: Expected parameter df (Tensor of shape (32, 168, 1)) of distribution Chi2() to satisfy the constraint GreaterThan(lower_bound=0.0), but found invalid values #469

Comments

Yuang-Deng commented Aug 11, 2022

Issue Description

Expected Behavior

Current Behavior

Possible Solution

Your Code

Error message

Your Local environment

dengdifan commented Aug 11, 2022

Yuang-Deng commented Aug 12, 2022

ravinkohli commented Aug 23, 2022