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FLAML
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Fix typos, upgrade yarn packages, add some improvements (#1290) 

* Fix typos, upgrade yarn packages, add some improvements

* Fix joblib 1.4.0 breaks joblib-spark

* Fix xgboost test error

* Pin xgboost<2.0.0

* Try update prophet to 1.5.1

* Update github workflow

* Revert prophet version

* Update github workflow

* Update install libomp

* Fix test errors

* Fix test errors

* Add retry to test and coverage

* Revert "Add retry to test and coverage"

This reverts commit ce13097cd5.

* Increase test budget

* Add more data to test_models, try fixing ValueError: Found array with 0 sample(s) (shape=(0, 252)) while a minimum of 1 is required.
This commit is contained in:
Li Jiang 2024-07-19 21:40:04 +08:00 коммит произвёл GitHub
Родитель 165d7467f9
Коммит d8129b9211
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Идентификатор ключа GPG: B5690EEEBB952194
18 изменённых файлов: 103 добавлений и 80 удалений
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20
.github/workflows/python-package.yml поставляемый
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@ -32,17 +32,15 @@ jobs:
os: [ubuntu-latest, macos-latest, windows-2019]
python-version: ["3.8", "3.9", "3.10"]
steps:
- uses: actions/checkout@v3
- uses: actions/checkout@v4
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v4
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: On mac + python 3.10, install libomp to facilitate lgbm and xgboost install
if: matrix.os == 'macOS-latest' && matrix.python-version == '3.10'
- name: On mac, install libomp to facilitate lgbm and xgboost install
if: matrix.os == 'macOS-latest'
run: |
# remove libomp version constraint after xgboost works with libomp>11.1.0 on python 3.10
wget https://raw.githubusercontent.com/Homebrew/homebrew-core/679923b4eb48a8dc7ecc1f05d06063cd79b3fc00/Formula/libomp.rb -O $(find $(brew --repository) -name libomp.rb)
brew unlink libomp
brew update
brew install libomp
export CC=/usr/bin/clang
export CXX=/usr/bin/clang++
@ -71,16 +69,16 @@ jobs:
pip install -e .[ray]
# use macOS to test xgboost 1, but macOS also supports xgboost 2
pip install "xgboost<2"
- name: If linux or mac, install prophet on python < 3.9
if: (matrix.os == 'macOS-latest' || matrix.os == 'ubuntu-latest') && matrix.python-version != '3.9' && matrix.python-version != '3.10'
- name: If linux, install prophet on python < 3.9
if: matrix.os == 'ubuntu-latest' && matrix.python-version < '3.9'
run: |
pip install -e .[forecast]
- name: Install vw on python < 3.10
if: matrix.python-version != '3.10'
run: |
pip install -e .[vw]
- name: Uninstall pyspark on (python 3.9) or (python 3.8 + windows)
if: matrix.python-version == '3.9' || (matrix.python-version == '3.8' && matrix.os == 'windows-2019')
- name: Uninstall pyspark on (python 3.9) or windows
if: matrix.python-version == '3.9' || matrix.os == 'windows-2019'
run: |
# Uninstall pyspark to test env without pyspark
pip uninstall -y pyspark

1
.gitignore поставляемый
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@ -165,3 +165,4 @@ flaml/tune/spark/mylearner.py
# local config files
*.config.local
patch.diff

2
Dockerfile
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@ -1,5 +1,5 @@
# basic setup
FROM python:3.7
FROM mcr.microsoft.com/devcontainers/python:3.8
RUN apt-get update && apt-get -y update
RUN apt-get install -y sudo git npm

14
flaml/automl/automl.py
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@ -212,9 +212,9 @@ class AutoML(BaseEstimator):
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
hyperparameter configurations for the corresponding estimators.
The value can be a single hyperparameter configuration dict or a list
of hyperparameter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
@ -1348,9 +1348,9 @@ class AutoML(BaseEstimator):
- if "data:path" use data-dependent defaults which are stored at path;
- if "static", use data-independent defaults.
If dict, keys are the name of the estimators, and values are the starting
hyperparamter configurations for the corresponding estimators.
The value can be a single hyperparamter configuration dict or a list
of hyperparamter configuration dicts.
hyperparameter configurations for the corresponding estimators.
The value can be a single hyperparameter configuration dict or a list
of hyperparameter configuration dicts.
In the following code example, we get starting_points from the
`automl` object and use them in the `new_automl` object.
e.g.,
@ -2326,7 +2326,7 @@ class AutoML(BaseEstimator):
)
time_used = time.time() - start_run_time
better = False
if analysis.trials:
if analysis.trials and analysis.trials[-1].last_result:
result = analysis.trials[-1].last_result
search_state.update(result, time_used=time_used)
if self._estimator_index is None:

8
flaml/automl/model.py
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@ -73,6 +73,14 @@ try:
except ImportError:
LGBMClassifier = LGBMRegressor = LGBMRanker = None
xgb_callback = False
try:
from xgboost.callback import TrainingCallback
xgb_callback = True
except ImportError: # for xgboost<1.3
TrainingCallback = object
logger = logging.getLogger("flaml.automl")
# FREE_MEM_RATIO = 0.2

4
flaml/tune/searcher/online_searcher.py
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@ -66,7 +66,7 @@ class ChampionFrontierSearcher(BaseSearcher):
POLY_EXPANSION_ADDITION_NUM = 1
# the order of polynomial expansions to add based on the given seed interactions
EXPANSION_ORDER = 2
# the number of new challengers with new numerical hyperparamter configs
# the number of new challengers with new numerical hyperparameter configs
NUMERICAL_NUM = 2
# In order to use CFO, a loss name and loss values of configs are need
@ -80,7 +80,7 @@ class ChampionFrontierSearcher(BaseSearcher):
CFO_SEARCHER_METRIC_NAME = "pseudo_loss"
CFO_SEARCHER_LARGE_LOSS = 1e6
# the random seed used in generating numerical hyperparamter configs (when CFO is not used)
# the random seed used in generating numerical hyperparameter configs (when CFO is not used)
NUM_RANDOM_SEED = 111
CHAMPION_TRIAL_NAME = "champion_trial"

6
notebook/autogen_agentchat_RetrieveChat.ipynb
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@ -2604,9 +2604,9 @@
" - if \"data:path\" use data-dependent defaults which are stored at path;\n",
" - if \"static\", use data-independent defaults.\n",
" If dict, keys are the name of the estimators, and values are the starting\n",
" hyperparamter configurations for the corresponding estimators.\n",
" The value can be a single hyperparamter configuration dict or a list\n",
" of hyperparamter configuration dicts.\n",
" hyperparameter configurations for the corresponding estimators.\n",
" The value can be a single hyperparameter configuration dict or a list\n",
" of hyperparameter configuration dicts.\n",
" In the following code example, we get starting_points from the\n",
" `automl` object and use them in the `new_automl` object.\n",
" e.g.,\n",

2
notebook/autovw.ipynb
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@ -240,7 +240,7 @@
"from flaml import AutoVW\n",
"\n",
"'''create an AutoVW instance for tuning namespace interactions'''\n",
"# configure both hyperparamters to tune, e.g., 'interactions', and fixed arguments about the online learner,\n",
"# configure both hyperparameters to tune, e.g., 'interactions', and fixed arguments about the online learner,\n",
"# e.g., 'quiet' in the search_space argument.\n",
"autovw_ni = AutoVW(max_live_model_num=5, search_space={'interactions': AutoVW.AUTOMATIC, 'quiet': ''})\n",
"\n",

2
notebook/tune_synapseml.ipynb
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@ -1032,7 +1032,7 @@
},
"source": [
"## 5. Check results\n",
"In this step, we retrain the model using the \"best\" hyperparamters on the full training dataset, and use the test dataset to compare evaluation metrics for the initial and \"best\" model."
"In this step, we retrain the model using the \"best\" hyperparameters on the full training dataset, and use the test dataset to compare evaluation metrics for the initial and \"best\" model."
]
},
{

11
setup.py
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@ -37,7 +37,7 @@ setuptools.setup(
extras_require={
"automl": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"xgboost>=0.90,<2.0.0",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
@ -48,10 +48,11 @@ setuptools.setup(
"spark": [
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
"joblib<=1.3.2",
],
"test": [
"lightgbm>=2.3.1",
"xgboost>=0.90",
"xgboost>=0.90,<2.0.0",
"scipy>=1.4.1",
"pandas>=1.1.4",
"scikit-learn>=0.24",
@ -61,7 +62,8 @@ setuptools.setup(
"pre-commit",
"torch",
"torchvision",
"catboost>=0.26,<1.2",
"catboost>=0.26,<1.2; python_version<'3.11'",
"catboost>=0.26; python_version>='3.11'",
"rgf-python",
"optuna==2.8.0",
"openml",
@ -74,10 +76,11 @@ setuptools.setup(
"rouge_score",
"hcrystalball==0.1.10",
"seqeval",
"pytorch-forecasting>=0.9.0,<=0.10.1",
"pytorch-forecasting>=0.9.0,<=0.10.1; python_version<'3.11'",
"mlflow",
"pyspark>=3.2.0",
"joblibspark>=0.5.0",
"joblib<=1.3.2",
"nbconvert",
"nbformat",
"ipykernel",

5
test/automl/test_classification.py
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@ -295,7 +295,10 @@ class TestClassification(unittest.TestCase):
import sys
current_xgboost_version = xgb.__version__
subprocess.check_call([sys.executable, "-m", "pip", "install", "xgboost==1.3.3", "--user"])
try:
subprocess.check_call([sys.executable, "-m", "pip", "install", "xgboost==1.3.3", "--user"])
except subprocess.CalledProcessError:
return
automl = AutoML()
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
print(automl.feature_names_in_)

2
test/automl/test_constraints.py
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@ -23,7 +23,7 @@ def test_metric_constraints():
"log_type": "all",
"retrain_full": "budget",
"keep_search_state": True,
"time_budget": 2,
"time_budget": 5,
"pred_time_limit": 5.1e-05,
}

8
test/automl/test_forecast.py
Просмотреть файл

@ -94,7 +94,7 @@ def test_forecast_automl(budget=10, estimators_when_no_prophet=["arima", "sarima
def test_models(budget=3):
n = 100
n = 200
X = pd.DataFrame(
{
"A": pd.date_range(start="1900-01-01", periods=n, freq="D"),
@ -109,14 +109,14 @@ def test_models(budget=3):
continue # TFT is covered by its own test
automl = AutoML()
automl.fit(
X_train=X[:72], # a single column of timestamp
y_train=y[:72], # value for each timestamp
X_train=X[:144], # a single column of timestamp
y_train=y[:144], # value for each timestamp
estimator_list=[est],
period=12, # time horizon to forecast, e.g., 12 months
task="ts_forecast",
time_budget=budget, # time budget in seconds
)
automl.predict(X[72:])
automl.predict(X[144:])
def test_numpy():

5
test/automl/test_notebook_example.py
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@ -1,5 +1,6 @@
import sys
import pytest
from minio.error import ServerError
from openml.exceptions import OpenMLServerException
from requests.exceptions import ChunkedEncodingError, SSLError
@ -108,6 +109,10 @@ def test_automl(budget=5, dataset_format="dataframe", hpo_method=None):
automl.fit(X_train=X_train, y_train=y_train, ensemble=True, **settings)
@pytest.mark.skipif(
sys.platform in ["win32"] and sys.version.startswith("3.9"),
reason="do not run if windows and python 3.9",
)
def test_automl_array():
test_automl(5, "array", "bs")

2
test/automl/test_training_log.py
Просмотреть файл

@ -98,6 +98,8 @@ class TestTrainingLog(unittest.TestCase):
print("IsADirectoryError happens as expected in linux.")
except PermissionError:
print("PermissionError happens as expected in windows.")
except FileExistsError:
print("FileExistsError happens as expected in MacOS.")
def test_each_estimator(self):
try:

17
test/test_autovw.py
Просмотреть файл

@ -91,11 +91,14 @@ def shuffle_data(X, y, seed):
def get_oml_to_vw(did, max_ns_num, ds_dir=VW_DS_DIR):
success = False
print("-----getting oml dataset-------", did)
ds = openml.datasets.get_dataset(did)
target_attribute = ds.default_target_attribute
# if target_attribute is None and did in OML_target_attribute_dict:
# target_attribute = OML_target_attribute_dict[did]
try:
ds = openml.datasets.get_dataset(did)
target_attribute = ds.default_target_attribute
# if target_attribute is None and did in OML_target_attribute_dict:
# target_attribute = OML_target_attribute_dict[did]
except SSLError as e:
print(e)
return
print("target=ds.default_target_attribute", target_attribute)
data = ds.get_data(target=target_attribute, dataset_format="array")
X, y = data[0], data[1] # return X: pd DataFrame, y: pd series
@ -349,8 +352,8 @@ def get_vw_tuning_problem(tuning_hp="NamesapceInteraction"):
@pytest.mark.skipif(
"3.10" in sys.version,
reason="do not run on py 3.10",
"3.10" in sys.version or "3.11" in sys.version,
reason="do not run on py >= 3.10",
)
class TestAutoVW(unittest.TestCase):
def test_vw_oml_problem_and_vanilla_vw(self):

2
test/tune/test_searcher.py
Просмотреть файл

@ -310,7 +310,7 @@ def test_searchers():
print(searcher.suggest("t1"))
from flaml import tune
tune.run(lambda x: 1, config={}, use_ray=use_ray, log_file_name="logs/searcher.log")
tune.run(lambda x: 1, config={}, mode="max", use_ray=use_ray, log_file_name="logs/searcher.log")
searcher = BlendSearch(space=config, cost_attr="cost", cost_budget=10, metric="m", mode="min")
analysis = tune.run(lambda x: {"cost": 2, "m": x["b"]}, search_alg=searcher, num_samples=10)
assert len(analysis.trials) == 5

72
website/yarn.lock
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@ -145,12 +145,12 @@
dependencies:
"@babel/highlight" "^7.18.6"
"@babel/code-frame@^7.22.13":
version "7.22.13"
resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.22.13.tgz#e3c1c099402598483b7a8c46a721d1038803755e"
integrity sha512-XktuhWlJ5g+3TJXc5upd9Ks1HutSArik6jf2eAjYFyIOf4ej3RN+184cZbzDvbPnuTJIUhPKKJE3cIsYTiAT3w==
"@babel/code-frame@^7.22.13", "@babel/code-frame@^7.23.5":
version "7.23.5"
resolved "https://registry.yarnpkg.com/@babel/code-frame/-/code-frame-7.23.5.tgz#9009b69a8c602293476ad598ff53e4562e15c244"
integrity sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==
dependencies:
"@babel/highlight" "^7.22.13"
"@babel/highlight" "^7.23.4"
chalk "^2.4.2"
"@babel/compat-data@^7.17.7", "@babel/compat-data@^7.20.0", "@babel/compat-data@^7.20.1":
@ -210,12 +210,12 @@
"@jridgewell/gen-mapping" "^0.3.2"
jsesc "^2.5.1"
"@babel/generator@^7.23.0":
version "7.23.0"
resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.23.0.tgz#df5c386e2218be505b34837acbcb874d7a983420"
integrity sha512-lN85QRR+5IbYrMWM6Y4pE/noaQtg4pNiqeNGX60eqOfo6gtEj6uw/JagelB8vVztSd7R6M5n1+PQkDbHbBRU4g==
"@babel/generator@^7.23.6":
version "7.23.6"
resolved "https://registry.yarnpkg.com/@babel/generator/-/generator-7.23.6.tgz#9e1fca4811c77a10580d17d26b57b036133f3c2e"
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dependencies:
"@babel/types" "^7.23.0"
"@babel/types" "^7.23.6"
"@jridgewell/gen-mapping" "^0.3.2"
"@jridgewell/trace-mapping" "^0.3.17"
jsesc "^2.5.1"
@ -424,10 +424,10 @@
resolved "https://registry.npmmirror.com/@babel/helper-string-parser/-/helper-string-parser-7.19.4.tgz#38d3acb654b4701a9b77fb0615a96f775c3a9e63"
integrity sha512-nHtDoQcuqFmwYNYPz3Rah5ph2p8PFeFCsZk9A/48dPc/rGocJ5J3hAAZ7pb76VWX3fZKu+uEr/FhH5jLx7umrw==
"@babel/helper-string-parser@^7.22.5":
version "7.22.5"
resolved "https://registry.yarnpkg.com/@babel/helper-string-parser/-/helper-string-parser-7.22.5.tgz#533f36457a25814cf1df6488523ad547d784a99f"
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"@babel/helper-string-parser@^7.23.4":
version "7.23.4"
resolved "https://registry.yarnpkg.com/@babel/helper-string-parser/-/helper-string-parser-7.23.4.tgz#9478c707febcbbe1ddb38a3d91a2e054ae622d83"
integrity sha512-803gmbQdqwdf4olxrX4AJyFBV/RTr3rSmOj0rKwesmzlfhYNDEs+/iOcznzpNWlJlIlTJC2QfPFcHB6DlzdVLQ==
"@babel/helper-validator-identifier@^7.18.6", "@babel/helper-validator-identifier@^7.19.1":
version "7.19.1"
@ -472,10 +472,10 @@
chalk "^2.0.0"
js-tokens "^4.0.0"
"@babel/highlight@^7.22.13":
version "7.22.20"
resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.22.20.tgz#4ca92b71d80554b01427815e06f2df965b9c1f54"
integrity sha512-dkdMCN3py0+ksCgYmGG8jKeGA/8Tk+gJwSYYlFGxG5lmhfKNoAy004YpLxpS1W2J8m/EK2Ew+yOs9pVRwO89mg==
"@babel/highlight@^7.23.4":
version "7.23.4"
resolved "https://registry.yarnpkg.com/@babel/highlight/-/highlight-7.23.4.tgz#edaadf4d8232e1a961432db785091207ead0621b"
integrity sha512-acGdbYSfp2WheJoJm/EBBBLh/ID8KDc64ISZ9DYtBmC8/Q204PZJLHyzeB5qMzJ5trcOkybd78M4x2KWsUq++A==
dependencies:
"@babel/helper-validator-identifier" "^7.22.20"
chalk "^2.4.2"
@ -486,10 +486,10 @@
resolved "https://registry.npmmirror.com/@babel/parser/-/parser-7.20.2.tgz#9aeb9b92f64412b5f81064d46f6a1ac0881337f4"
integrity sha512-afk318kh2uKbo7BEj2QtEi8HVCGrwHUffrYDy7dgVcSa2j9lY3LDjPzcyGdpX7xgm35aWqvciZJ4WKmdF/SxYg==
"@babel/parser@^7.22.15", "@babel/parser@^7.23.0":
version "7.23.0"
resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.23.0.tgz#da950e622420bf96ca0d0f2909cdddac3acd8719"
integrity sha512-vvPKKdMemU85V9WE/l5wZEmImpCtLqbnTvqDS2U1fJ96KrxoW7KrXhNsNCblQlg8Ck4b85yxdTyelsMUgFUXiw==
"@babel/parser@^7.22.15", "@babel/parser@^7.23.6":
version "7.23.6"
resolved "https://registry.yarnpkg.com/@babel/parser/-/parser-7.23.6.tgz#ba1c9e512bda72a47e285ae42aff9d2a635a9e3b"
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"@babel/plugin-bugfix-safari-id-destructuring-collision-in-function-expression@^7.18.6":
version "7.18.6"
@ -1229,19 +1229,19 @@
"@babel/types" "^7.22.15"
"@babel/traverse@^7.12.13", "@babel/traverse@^7.12.9", "@babel/traverse@^7.19.0", "@babel/traverse@^7.19.1", "@babel/traverse@^7.20.1":
version "7.23.2"
resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.23.2.tgz#329c7a06735e144a506bdb2cad0268b7f46f4ad8"
integrity sha512-azpe59SQ48qG6nu2CzcMLbxUudtN+dOM9kDbUqGq3HXUJRlo7i8fvPoxQUzYgLZ4cMVmuZgm8vvBpNeRhd6XSw==
version "7.23.6"
resolved "https://registry.yarnpkg.com/@babel/traverse/-/traverse-7.23.6.tgz#b53526a2367a0dd6edc423637f3d2d0f2521abc5"
integrity sha512-czastdK1e8YByZqezMPFiZ8ahwVMh/ESl9vPgvgdB9AmFMGP5jfpFax74AQgl5zj4XHzqeYAg2l8PuUeRS1MgQ==
dependencies:
"@babel/code-frame" "^7.22.13"
"@babel/generator" "^7.23.0"
"@babel/code-frame" "^7.23.5"
"@babel/generator" "^7.23.6"
"@babel/helper-environment-visitor" "^7.22.20"
"@babel/helper-function-name" "^7.23.0"
"@babel/helper-hoist-variables" "^7.22.5"
"@babel/helper-split-export-declaration" "^7.22.6"
"@babel/parser" "^7.23.0"
"@babel/types" "^7.23.0"
debug "^4.1.0"
"@babel/parser" "^7.23.6"
"@babel/types" "^7.23.6"
debug "^4.3.1"
globals "^11.1.0"
"@babel/types@^7.12.6", "@babel/types@^7.12.7", "@babel/types@^7.18.10", "@babel/types@^7.18.6", "@babel/types@^7.18.9", "@babel/types@^7.19.0", "@babel/types@^7.20.0", "@babel/types@^7.20.2", "@babel/types@^7.4.4":
@ -1253,12 +1253,12 @@
"@babel/helper-validator-identifier" "^7.19.1"
to-fast-properties "^2.0.0"
"@babel/types@^7.22.15", "@babel/types@^7.22.5", "@babel/types@^7.23.0":
version "7.23.0"
resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.23.0.tgz#8c1f020c9df0e737e4e247c0619f58c68458aaeb"
integrity sha512-0oIyUfKoI3mSqMvsxBdclDwxXKXAUA8v/apZbc+iSyARYou1o8ZGDxbUYyLFoW2arqS2jDGqJuZvv1d/io1axg==
"@babel/types@^7.22.15", "@babel/types@^7.22.5", "@babel/types@^7.23.0", "@babel/types@^7.23.6":
version "7.23.6"
resolved "https://registry.yarnpkg.com/@babel/types/-/types-7.23.6.tgz#be33fdb151e1f5a56877d704492c240fc71c7ccd"
integrity sha512-+uarb83brBzPKN38NX1MkB6vb6+mwvR6amUulqAE7ccQw1pEl+bCia9TbdG1lsnFP7lZySvUn37CHyXQdfTwzg==
dependencies:
"@babel/helper-string-parser" "^7.22.5"
"@babel/helper-string-parser" "^7.23.4"
"@babel/helper-validator-identifier" "^7.22.20"
to-fast-properties "^2.0.0"
@ -3520,7 +3520,7 @@ debug@2.6.9, debug@^2.6.0:
dependencies:
ms "2.0.0"
debug@4, debug@^4.1.0, debug@^4.1.1, debug@^4.2.0:
debug@4, debug@^4.1.0, debug@^4.1.1, debug@^4.2.0, debug@^4.3.1:
version "4.3.4"
resolved "https://registry.npmmirror.com/debug/-/debug-4.3.4.tgz#1319f6579357f2338d3337d2cdd4914bb5dcc865"
integrity sha512-PRWFHuSU3eDtQJPvnNY7Jcket1j0t5OuOsFzPPzsekD52Zl8qUfFIPEiswXqIvHWGVHOgX+7G/vCNNhehwxfkQ==