shap

View on PyPIReverse Dependencies (7)

0.29.1 shap-0.29.1-cp37-cp37m-win_amd64.whl
shap-0.29.1-cp36-cp36m-win_amd64.whl
shap-0.29.1-cp36-cp36m-macosx_10_7_x86_64.whl
shap-0.29.1-cp35-cp35m-win_amd64.whl

Wheel Details

Project: shap
Version: 0.29.1
Filename: shap-0.29.1-cp37-cp37m-win_amd64.whl
Download: [link]
Size: 258799
MD5: 7274d2658ad6fd147cc1b03edabf1de3
SHA256: 852b0b87f5e508705a01333d2518f35009773dd3df15d081a244e1df5f024672
Uploaded: 2019-05-15T10:27:54

dist-info

METADATA

Metadata-Version: 2.1
Name: shap
Version: 0.29.1
Summary: A unified approach to explain the output of any machine learning model.
Author: Scott Lundberg
Author-Email: slund1[at]cs.washington.edu
Home-Page: http://github.com/slundberg/shap
License: MIT
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn
Requires-Dist: matplotlib
Requires-Dist: pandas
Requires-Dist: tqdm
Requires-Dist: ipython
Requires-Dist: scikit-image
Description-Content-Type: text/markdown
[Description omitted; length: 324 characters]

WHEEL

Wheel-Version: 1.0
Generator: bdist_wheel (0.33.4)
Root-Is-Purelib: false
Tag: cp37-cp37m-win_amd64

RECORD

Path Digest Size
shap/__init__.py sha256=LoKgliOUv5pm7VQr6kmkXMeacWdko7rAA7Tv9wqR3Xs 742
shap/_cext.cp37-win_amd64.pyd sha256=Hs_4k5A35ybNTr6fpsGQNpxxcZr9fabsYOkkuDTBR9M 45056
shap/common.py sha256=JrOBMWPov58PYlMClHg7zMFzkgffuU6KoWkTA82r6jE 10169
shap/datasets.py sha256=h9POrDGh0aEwVcxqLO5Zi6CF6ggHtuG4H0KX7TvbExw 8830
shap/tree_shap.h sha256=3NglDsVoaVewABO-THfQeQkcYThMAkiNvZoh8NITeF8 58311
shap/benchmark/__init__.py sha256=WmOhJIFf63qJ-0n8V-gjLsqxy5Rx1s0EDs2s7Vb2FuI 933
shap/benchmark/experiments.py sha256=OTDphm63Rk_z5r5XjaIJx0ZEj2Gj4MTzQwn6UYH5RUc 13977
shap/benchmark/measures.py sha256=mycXHqKG6TCadwcPNz-bP3BFMz0FIZHiB9aB0Tr-Yvc 18587
shap/benchmark/methods.py sha256=NDaWN4olkNdG_-itkI6acIDWTfkjIrDnCFy8u7j8HW8 3604
shap/benchmark/metrics.py sha256=nnhcxLnkegjyildsbQxsD5gtwC8bXZ0XRsi4IDCYpXw 31495
shap/benchmark/models.py sha256=NeaRHvmoRQjoHTs-gQOjmwJJm8zHFIuv71CBnW_OvjI 6513
shap/benchmark/plots.py sha256=DDh6s6FWq8QmDDSWq6vYgr4qb3UAKowEzfAPIDyi1wc 23549
shap/explainers/__init__.py sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU 0
shap/explainers/explainer.py sha256=_kElqfFVs_uXlGaKbY21r4a_AhAO7DrswnTgHwziBq8 257
shap/explainers/gradient.py sha256=hwnX8uzTe61LsIrZnqKsRNc7s-iaX3aAw5dEErxx8Xs 24713
shap/explainers/kernel.py sha256=4PTEwIr2f286Wg7yd3dzfh3tAFSVdwxD81e_AdwkV40 30033
shap/explainers/linear.py sha256=mlruyQBc1dP4bAW6Y3jgVcWra5i--IlAsnWWEWOkWsk 10953
shap/explainers/mimic.py sha256=Vi2LvVOsViCjulN6sHk4nxw8KwLF67DhtFqNp6Vsl54 4962
shap/explainers/pytree.py sha256=tR0s29WaJ5ENBQPo3tDs0UIsK6tjpEXMlXWTDGd9SwM 20300
shap/explainers/sampling.py sha256=JCkDENAg_jEV7-VuJJ-vNeyov5AyOnMeRGEcoV3FyLg 7591
shap/explainers/tree.py sha256=kiSgbTzHTFCN1WLth4112o_-KFTb2llh1vxYK7mcgXk 57357
shap/explainers/deep/__init__.py sha256=yOJIv-E-gCPNPQ2doKHD6FzYJ7UdX120pKor3fTYyB0 6613
shap/explainers/deep/deep_pytorch.py sha256=-vGIZ5V0vx4LZaY-PRTzsd8LoNXD2f-NjnOpJ_nPJPo 14854
shap/explainers/deep/deep_tf.py sha256=lRRqCkiTqZh_OFMKA5uIk76DzR8I6VBSgic_MYXnjEw 28206
shap/explainers/other/__init__.py sha256=PKyN3gsu9UNWeV0uxIkxhh3HHAoTRrEuacgzgSoShQA 158
shap/explainers/other/coefficent.py sha256=HCxzU0u3nWG9xKNzve5aixr0rvUY7OV8HLI0uVjmxB4 520
shap/explainers/other/lime.py sha256=7Xkx0s-JXaocseViqIye0IB2y6RpU8ieGqXPAYFWNKs 2531
shap/explainers/other/random.py sha256=hjMhnC8tiqvl5FE0OCKCdIrL4KaJNdYIzxgil5H6bTY 758
shap/explainers/other/treegain.py sha256=3SWGFwZ6kGH9eF4cXHOjvhKu8SG6Fr7GzUCEg7bf_6k 1281
shap/plots/__init__.py sha256=iA5DU9Y41lJzk-nKpcvHCJyYgIZU9JifGcnDdD-yomg 568
shap/plots/colors.py sha256=GFgfgKv_MZlK8qJFHejeaFpf6WTzp4X7XVqku_yH3AQ 4438
shap/plots/dependence.py sha256=XJtgZgqiWRp6voTB8UGHvBSHzgSmoK-vaEhiHzBrE-o 11651
shap/plots/embedding.py sha256=S8QkHMuU4iZvfEa1n_TlXqokolu7ssal7pWgMqsFgPw 2812
shap/plots/force.py sha256=4E0geDEbVMjNvoRO30l5BzpGz9vvgNvnfflVMFKFrnQ 16645
shap/plots/force_matplotlib.py sha256=QffOBLAVAGhVZlRrpwjHCRLhGQO3B5_nIhzzWXv0FSU 14402
shap/plots/image.py sha256=VF6A0whY_tKcLsrN0ZTIusAHxaXXuDWXY4su3Z7E93c 3058
shap/plots/monitoring.py sha256=DTXpfdG98zifVcrIuDtIJn28eHLtXlUBGqmBRX9os38 2634
shap/plots/summary.py sha256=cyE1zJg1ARR_HdAV7GF15vEz8P-Q4q1vCZWnLrzj89c 19281
shap/plots/resources/bundle.js sha256=BKp54Q0uAsmu3rLXDilNO0ifm0Qng1BEMPSpx3HbC6g 385366
shap/plots/resources/logoSmallGray.png sha256=I7UAO6eO-2ghl7qW2AVkpn7LXkQ_8yVSOFh9X8aQqjc 570
shap-0.29.1.dist-info/LICENSE sha256=z1wmMFxjLlRRtu7X4JZKNsTOwRAvDGRn2wY7NIOhkSc 1081
shap-0.29.1.dist-info/METADATA sha256=1WUcG04XrqtYSLIuWHEHX8ujhv-T1fRB7Zhjgon8XMs 821
shap-0.29.1.dist-info/WHEEL sha256=rpJIxsZQulJdxSTRRlB5eKYFBr_5T7UwADMbZS3uepA 106
shap-0.29.1.dist-info/top_level.txt sha256=MjIGqmLADIjgB_iEb34vXFQuCf62i26ItbMzajhG084 5
shap-0.29.1.dist-info/RECORD

top_level.txt

shap