Description:

glvi 0.1.7

glvi
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)
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)

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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