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Local variable importance from a global model

Project description


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.

glvi requires:

  • 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), 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), train_y_
local_variable_importance = r_m.compute_feature_importance(X,Y,partition_feature = partition_feature, norm=True,n_jobs=-1)

Project details

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Files for glvi, version 0.1.7
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