134 lines
3.9 KiB
Plaintext
134 lines
3.9 KiB
Plaintext
Metadata-Version: 2.1
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Name: slicer
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Version: 0.0.8
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Summary: A small package for big slicing.
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Home-page: https://github.com/interpretml/slicer
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Author: InterpretML
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Author-email: interpret@microsoft.com
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Classifier: Programming Language :: Python :: 3.8
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Development Status :: 3 - Alpha
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Classifier: License :: OSI Approved :: MIT License
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Classifier: Operating System :: OS Independent
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Requires-Python: >=3.6
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Description-Content-Type: text/markdown
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License-File: LICENSE
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# slicer [alpha]
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*(Equal Contribution) Samuel Jenkins & Harsha Nori & Scott Lundberg*
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**slicer** wraps tensor-like objects and provides a uniform slicing interface via `__getitem__`.
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<br/>
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It supports many data types including:
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[numpy](https://github.com/numpy/numpy) |
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[pandas](https://github.com/pandas-dev/pandas) |
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[scipy](https://docs.scipy.org/doc/scipy/reference/sparse.html) |
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[pytorch](https://github.com/pytorch/pytorch) |
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[list](https://github.com/python/cpython) |
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[tuple](https://github.com/python/cpython) |
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[dict](https://github.com/python/cpython)
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And enables upgraded slicing functionality on its objects:
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```python
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# Handles non-integer indexes for slicing.
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S(df)[:, ["Age", "Income"]]
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# Handles nested slicing in one call.
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S(nested_list)[..., :5]
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```
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It can also simultaneously slice many objects at once:
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```python
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# Gets first elements of both objects.
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S(first=df, second=ar)[0, :]
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```
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This package has **0** dependencies. Not even one.
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## Installation
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Python 3.6+ | Linux, Mac, Windows
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```sh
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pip install slicer
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```
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## Getting Started
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Basic anonymous slicing:
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```python
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from slicer import Slicer as S
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li = [[1, 2, 3], [4, 5, 6]]
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S(li)[:, 0:2].o
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# [[1, 2], [4, 5]]
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di = {'x': [1, 2, 3], 'y': [4, 5, 6]}
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S(di)[:, 0:2].o
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# {'x': [1, 2], 'y': [4, 5]}
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```
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Basic named slicing:
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```python
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import pandas as pd
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import numpy as np
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df = pd.DataFrame({'A': [1, 3], 'B': [2, 4]})
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ar = np.array([[5, 6], [7, 8]])
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sliced = S(first=df, second=ar)[0, :]
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sliced.first
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# A 1
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# B 2
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# Name: 0, dtype: int64
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sliced.second
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# array([5, 6])
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```
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Real example:
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```python
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from slicer import Slicer as S
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from slicer import Alias as A
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data = [[1, 2], [3, 4]]
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values = [[5, 6], [7, 8]]
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identifiers = ["id1", "id1"]
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instance_names = ["r1", "r2"]
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feature_names = ["f1", "f2"]
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full_name = "A"
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slicer = S(
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data=data,
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values=values,
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# Aliases are objects that also function as slicing keys.
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# A(obj, dim) where dim informs what dimension it can be sliced on.
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identifiers=A(identifiers, 0),
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instance_names=A(instance_names, 0),
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feature_names=A(feature_names, 1),
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full_name=full_name,
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)
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sliced = slicer[:, 1] # Tensor-like parallel slicing on all objects
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assert sliced.data == [2, 4]
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assert sliced.instance_names == ["r1", "r2"]
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assert sliced.feature_names == "f2"
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assert sliced.values == [6, 8]
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sliced = slicer["r1", "f2"] # Example use of aliasing
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assert sliced.data == 2
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assert sliced.feature_names == "f2"
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assert sliced.instance_names == "r1"
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assert sliced.values == 6
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```
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## Contact us
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Raise an issue on GitHub, or contact us at interpret@microsoft.com
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