DTAnalyze
Functions to estimate importance of features in determining predictions for individual samples (aka "feature activations"). Fast nogil implementation in Cython.
Example Usage
import numpy as np
from sklearn.ensemble import RandomForestRegressor
from DTAnalyze.Activation import GetActivations
A = np.random.rand(256, 3)
Y = (2 * (A[:, 0] > 0.5) - (A[:, 1] < 0.5) -
(A[:, 2] > 0.5) + np.random.normal(0, 0.1, size=256))
rfr = RandomForestRegressor(n_jobs=4).fit(A, Y)
L1 = GetActivations(rfr, A)
Install
python setup.py build_ext
Then copy build artifact into DTAnalyze (sub) folder and put that folder somewhere in your path.
1 maintainer
Release files for DTAnalyze 1.0
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Source distribution (sdist)
| File | Size | Uploaded | |
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| DTAnalyze-1.0.tar.gz | 127.0 kB | Details |
Release files / DTAnalyze-1.0.tar.gz
| Download URL | DTAnalyze-1.0.tar.gz |
|---|---|
| Size | 127.0 kB |
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