Local variable importance from a global model
glvi is a Python module for machine learning built on top of Scikit-learn and is distributed under the MIT license.
glvi was developed by Mr. Li for evaluating variable importance heterogeneity through a global model built on a large time-space scope.
glvi 0.1.4 was not supporting Python 2.7 and Python 3.4. glvi 0.1.4 and later require Python 3.5 or newer.
- Python (>= 3.5)
- NumPy (>= 1.11.0)
- SciPy (>= 0.17.0)
- Scikit-learn (>= 0.21.0) User installation
If you already have a working installation of numpy, scipy, pandas and scikit-learn, the easiest way to install glvi is using ``pip`` :: pip install -U glvi User guide
Compute local variable importance based on decrease in node impurity ::
from glvi import todi r_t = todi.lovim(500, max_features=0.3, n_jobs=-1) r_t.fit(train_x, train_y) local_variable_importance = r_t.compute_feature_importance(X,Y,partition_feature = partition_feature, norm=True,n_jobs=-1)
or compute local variable importance based on decrease in accuracy ::
from glvi import meda r_m = meda.lovim(500, max_features=0.3, n_jobs=-1) r_m.fit(train_x, train_y_ local_variable_importance = r_m.compute_feature_importance(X,Y,partition_feature = partition_feature, norm=True,n_jobs=-1)
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
glvi-0.1.7.tar.gz (6.8 kB view hashes)