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https://github.com/wassname/catalyst.git
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Scott Sanderson
53d3b0855b
Classifiers are computations that represent grouping keys. They can be used in conjuction with normalization functions like ``zscore`` or ``demean`` to perform normalizations over subsets of a dataset. Notable changes: - Added ``demean()`` and ``zscore()`` methods to ``Factor``. - Added a classifier versions of ``Latest`` and ``CustomTermMixin``. The .latest attribute of int64 dataset columns no produces a classifier by default. - Added ``Everything``, a classifier that maps all data to the same value. - Added ``zipline.lib.normalize``, which implements a naive, pure-Python grouped normalize function. This will likely be moved to Cython in a subsequent PR.
46 lines
1.6 KiB
Python
46 lines
1.6 KiB
Python
import numpy as np
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def naive_grouped_rowwise_apply(data, group_labels, func, out=None):
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"""
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Simple implementation of grouped row-wise function application.
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Parameters
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----------
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data : ndarray[ndim=2]
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Input array over which to apply a grouped function.
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group_labels : ndarray[ndim=2, dtype=int64]
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Labels to use to bucket inputs from array.
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Should be the same shape as array.
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func : function[ndarray[ndim=1]] -> function[ndarray[ndim=1]]
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Function to apply to pieces of each row in array.
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out : ndarray, optional
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Array into which to write output. If not supplied, a new array of the
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same shape as ``data`` is allocated and returned.
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Example
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-------
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>>> data = np.array([[1., 2., 3.],
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... [2., 3., 4.],
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... [5., 6., 7.]])
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>>> labels = np.array([[0, 0, 1],
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... [0, 1, 0],
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... [1, 0, 2]])
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>>> naive_grouped_rowwise_apply(data, labels, lambda row: row - row.min())
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array([[ 0., 1., 0.],
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[ 0., 0., 2.],
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[ 0., 0., 0.]])
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>>> naive_grouped_rowwise_apply(data, labels, lambda row: row / row.sum())
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array([[ 0.33333333, 0.66666667, 1. ],
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[ 0.33333333, 1. , 0.66666667],
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[ 1. , 1. , 1. ]])
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"""
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if out is None:
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out = np.empty_like(data)
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for (row, label_row, out_row) in zip(data, group_labels, out):
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for label in np.unique(label_row):
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locs = (label_row == label)
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out_row[locs] = func(row[locs])
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return out
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