Wheelodex » Projects » meds-tab

meds-tab

View on PyPI — Reverse Dependencies (0)

0.0.5 meds_tab-0.0.5-py3-none-any.whl

Wheel Details

Project: meds-tab
Version: 0.0.5
Filename: meds_tab-0.0.5-py3-none-any.whl
Download: [link]
Size: 72960
MD5: 3bceafe6a64767ca72ca5ddc5025685d
SHA256: aafa583a995b6132cdf78eaaa432d28738b6b05a98f13f00e30df82789b8338d
Uploaded: 2024-09-10 17:56:51 +0000

dist-info

METADATA · WHEEL · RECORD · top_level.txt · entry_points.txt

METADATA

Metadata-Version: 2.1
Name: meds-tab
Version: 0.0.5
Summary: Scalable Tabularization of MEDS format Time-Series data
Author-Email: Matthew McDermott <mattmcdermott8[at]gmail.com>, Nassim Oufattole <noufattole[at]gmail.com>, Teya Bergamaschi <teyabergamaschi[at]gmail.com>
Project-Url: Homepage, https://github.com/mmcdermott/MEDS_Tabular_AutoML
Project-Url: Issues, https://github.com/mmcdermott/MEDS_Tabular_AutoML/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.11
Requires-Dist: polars (==1.6.0)
Requires-Dist: pyarrow
Requires-Dist: loguru
Requires-Dist: hydra-core (==1.3.2)
Requires-Dist: numpy
Requires-Dist: scipy (<1.14.0)
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: xgboost
Requires-Dist: scikit-learn
Requires-Dist: hydra-optuna-sweeper
Requires-Dist: hydra-joblib-launcher
Requires-Dist: ml-mixins
Requires-Dist: meds (==0.3.3)
Requires-Dist: meds-transforms (==0.0.7)
Requires-Dist: autogluon; python_version == "3.11.*" and extra == "autogluon"
Requires-Dist: pre-commit; extra == "dev"
Requires-Dist: mprofile; extra == "profiling"
Requires-Dist: matplotlib; extra == "profiling"
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-cov; extra == "tests"
Requires-Dist: rootutils; extra == "tests"
Provides-Extra: autogluon
Provides-Extra: dev
Provides-Extra: profiling
Provides-Extra: tests
Description-Content-Type: text/markdown
License-File: LICENSE
[Description omitted; length: 45173 characters]

WHEEL

Wheel-Version: 1.0
Generator: setuptools (74.1.2)
Root-Is-Purelib: true
Tag: py3-none-any

RECORD

Path Digest Size
MEDS_tabular_automl/__init__.py sha256=1mptEzQihbdyqqzMgdns_j5ZGK9gz7hR2bsgA_TnjO4 22
MEDS_tabular_automl/base_model.py sha256=j4v1xPoZij4qccO4d4l2UvcyLu4jSGLeTvPGAAO6r-Y 707
MEDS_tabular_automl/describe_codes.py sha256=g2XB_b5qK4ogC-SoiQg7Acjzv9_70m7FNVPD8PTxgP0 10888
MEDS_tabular_automl/evaluation_callback.py sha256=vdc-S42VQNuDT-htfP-P-S38MpX2AZERDNva8SGzTCM 4128
MEDS_tabular_automl/file_name.py sha256=nGqHsGusomRo2usUjUIGUUUDad-KLx3gQdirpAWFOe4 4803
MEDS_tabular_automl/generate_static_features.py sha256=eGMu6znjeA1sAdoo8WJxFNlyQlmPxMeXx10xut348JE 8677
MEDS_tabular_automl/generate_summarized_reps.py sha256=3siRnbGIj7igyDVh_7pryHuZPjH_pmCxMTx6m2C3rJc 8103
MEDS_tabular_automl/generate_ts_features.py sha256=MEXfctsDi3WQCoDKpVtLNASbmdEiJjXI7BqwkbBakFs 5676
MEDS_tabular_automl/mapper.py sha256=WqxM2Ab9Db-jjsvfLThnvEwpaN_w1DqG0OiltSZHyaU 12585
MEDS_tabular_automl/sklearn_model.py sha256=vSKuB-B3nBCehUvga7wP_pH9cKTMxK_FH4wGBuSm4jY 7191
MEDS_tabular_automl/tabular_dataset.py sha256=L2Rtxe70ubgTP_IpUf7mianIoViD5P4Uo8r9ReduzHc 19662
MEDS_tabular_automl/utils.py sha256=2EuQ7r_OyenoeXRQihy8tQ0ablYI3gCv85QR-TZWIkg 17228
MEDS_tabular_automl/xgboost_model.py sha256=g7c3wlxPmnlX8jkEpZys2rhi04XTFrCBtEKOKtf8mno 7343
MEDS_tabular_automl/configs/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/default.yaml sha256=gtVgGGVVlBjYm59gqOowHQMqCWxWhyxsQfQs6rnbZyg 313
MEDS_tabular_automl/configs/describe_codes.yaml sha256=5nJmgbsC2zJED3rSz2IzIbYlgFyq482QMEOrJL2h0NI 154
MEDS_tabular_automl/configs/launch_model.yaml sha256=0t5chB9IRYs_8o7r0-dZLDyWh8TcLShHVTTMhkWJePU 804
MEDS_tabular_automl/configs/tabularization.yaml sha256=wUs63b2HDRSp0h34bwUdtnNdDSDfOkg0BYT8ZY7CLaY 265
MEDS_tabular_automl/configs/task_specific_caching.yaml sha256=jNE3sqNeCMNIyoF2zCCBjbzXzDJesM4KOVstmFzHbiQ 573
MEDS_tabular_automl/configs/hydra/callbacks/evaluation_callback.yaml sha256=CubHUYFIZ6xFizfNrM8G0PbEd5EdrcsrnGYP-_1S-s4 140
MEDS_tabular_automl/configs/model_launcher/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/autogluon.yaml sha256=bsvD2UZi0oEZaLOf8nR1RqxbMVMr2Ic6okQxRm1SXQ8 71
MEDS_tabular_automl/configs/model_launcher/default.yaml sha256=P7s9EtCKsk6Mdb4ezXM_rXHw4gXiM6dbg5EK--9GFlw 296
MEDS_tabular_automl/configs/model_launcher/knn_classifier.yaml sha256=jdMLMTW-MC5z2v5zVOWQY_vp2_25Sy9aUIew9tbnLgk 840
MEDS_tabular_automl/configs/model_launcher/logistic_regression.yaml sha256=YDsENJpG3_sl3ntSvIsWaP8F7JvnzIeVyJDtw0Ph6Mw 793
MEDS_tabular_automl/configs/model_launcher/random_forest_classifier.yaml sha256=vwSfvtxoK_aU36f6FiYSyURW_3bRBtdGnhROPbAjkUw 1134
MEDS_tabular_automl/configs/model_launcher/sgd_classifier.yaml sha256=xw3W_QFfbw52KVNvQDmY7ykK_jkKIvEVCbNNm34St74 669
MEDS_tabular_automl/configs/model_launcher/xgboost.yaml sha256=uoWyhh70gsIJqDXKJGm_FHHSquu2rvGVTxMzVADat08 981
MEDS_tabular_automl/configs/model_launcher/data_loading_params/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/data_loading_params/default.yaml sha256=sUQJomk4KKv99fDZnDD9QeX9ojCHKmSKjI-QSusMZRA 46
MEDS_tabular_automl/configs/model_launcher/data_processing_params/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/data_processing_params/default.yaml sha256=pbfXmO0Rku8oF25Ec5tmUAVOPGAJJaRi_Z2bX31K8M4 58
MEDS_tabular_automl/configs/model_launcher/data_processing_params/imputer/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/data_processing_params/imputer/default.yaml sha256=GtHbCHsFvVOnizi4U5B3egPFUMqDWaXxT69iIEyPgQo 21
MEDS_tabular_automl/configs/model_launcher/data_processing_params/imputer/mean_imputer.yaml sha256=wQF0uu3z8PmPqSmbEdUeJUoOSYVwA7lamJCil0xxNSI 76
MEDS_tabular_automl/configs/model_launcher/data_processing_params/imputer/median_imputer.yaml sha256=jy_mc7DZSmeQylUJ2OG9XvuRSnma3MalbTZqwORlX0c 78
MEDS_tabular_automl/configs/model_launcher/data_processing_params/imputer/mode_imputer.yaml sha256=LqKhZgnUEicjQCKcsSo8N72hhHiPwUgxeko1G0DZwjc 85
MEDS_tabular_automl/configs/model_launcher/data_processing_params/normalization/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/data_processing_params/normalization/default.yaml sha256=bzQDT1231E_Y_smFpQYWw17xEq2QNdb5U0yvwSo5WKQ 17
MEDS_tabular_automl/configs/model_launcher/data_processing_params/normalization/max_abs_scaler.yaml sha256=fB-i0e_c4swnarUouqqNsKqoS8lP1SACoQe1gFVDUc4 59
MEDS_tabular_automl/configs/model_launcher/data_processing_params/normalization/standard_scaler.yaml sha256=qonpJ4q4MWvEekELrHppHpqKkAZCasnhvRN6USO1ksE 129
MEDS_tabular_automl/configs/model_launcher/path/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/path/default.yaml sha256=L1zLhlSveFNUbbxEU5tE8gmxkCl2F_1SAuBEswRPLcU 353
MEDS_tabular_automl/configs/model_launcher/training_params/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/model_launcher/training_params/default.yaml sha256=yyDZZ2xFpD7nXQj8DKiUb8PyBO3alwx7fco8-S3avMk 100
MEDS_tabular_automl/configs/tabularization/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/configs/tabularization/default.yaml sha256=ImcF5XumxpCw2nX0dbf3-pUGuh3eADbSIQCyfpsw-fI 705
MEDS_tabular_automl/scripts/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
MEDS_tabular_automl/scripts/cache_task.py sha256=ac6sS6doUjqK2lqcomd6VfcvTNLzO39UmXak-cKyeKw 6899
MEDS_tabular_automl/scripts/describe_codes.py sha256=R-aRRa2C8DqhcRmxpa5IM8IeTVjnkOdTdmp_aG5mnjc 3083
MEDS_tabular_automl/scripts/generate_subsets.py sha256=sVHZAKYR5cbyBZjBo-SL0YIFHSc2qickObT8Gc8Q4hM 1267
MEDS_tabular_automl/scripts/launch_autogluon.py sha256=QDy_SpX6vWZ5HpxFQxvxPqyACU6qlgYi5679XKxKDkQ 3407
MEDS_tabular_automl/scripts/launch_model.py sha256=H3Og71mYFs506h4riEHIEKla8JV8sI8D-eumasG_WOo 2541
MEDS_tabular_automl/scripts/tabularize_static.py sha256=VvrCZCDHKGSTH4HXwE5Vpl-MCQrIBU-BTDqz00HWoC0 6934
MEDS_tabular_automl/scripts/tabularize_time_series.py sha256=hiz_ntsw3k8kInTlvvkQ5AY4Mtk1OmJnnc4HHgjpevo 4959
meds_tab-0.0.5.dist-info/LICENSE sha256=SIjdTV3dLVUS16RxHQhWPi_ixO6Y-xZyY8v41QuqHCg 1074
meds_tab-0.0.5.dist-info/METADATA sha256=hCfNJNZJ5uTvUF0q8XjRwexMR6KyjAGWrQTg9N9IMO4 46769
meds_tab-0.0.5.dist-info/WHEEL sha256=cVxcB9AmuTcXqmwrtPhNK88dr7IR_b6qagTj0UvIEbY 91
meds_tab-0.0.5.dist-info/entry_points.txt sha256=-wTOwQ10fkljOXGancL0foKVc-iibLPK8jrN8kcmHV8 590
meds_tab-0.0.5.dist-info/top_level.txt sha256=DTkGQz3ZXRow_yxoIUU3orD8eNgQWFE_llKL5skaBo8 20
meds_tab-0.0.5.dist-info/RECORD — —

top_level.txt

MEDS_tabular_automl

entry_points.txt

[console_scripts]
generate-subsets = MEDS_tabular_automl.scripts.generate_subsets:main
meds-tab-autogluon = MEDS_tabular_automl.scripts.launch_autogluon:main
meds-tab-cache-task = MEDS_tabular_automl.scripts.cache_task:main
meds-tab-describe = MEDS_tabular_automl.scripts.describe_codes:main
meds-tab-model = MEDS_tabular_automl.scripts.launch_model:main
meds-tab-tabularize-static = MEDS_tabular_automl.scripts.tabularize_static:main
meds-tab-tabularize-time-series = MEDS_tabular_automl.scripts.tabularize_time_series:main
meds-tab-xgboost = MEDS_tabular_automl.scripts.launch_model:main