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 sptiotemporal heterogeneity of variable imporance through a global model

built on a large time-space scope.

glvi inherits from the RandomForestRegressor in scikit-learn. If you want to use glvi to estimate local variable importance,

model fitting is necessary. But at present, using a fitted random forest model from RandomForestRegressor is not supported. To

accelerate the process, parallel is provided which is same with the parallel in scikit-learn. The parallel process can be

implemented by the parameter of n_jobs which just is consistent with the scikit-learn.

glvi 0.1.5 and later was not supporting Python 2.7 and Python 3.4. glvi 0.1.5 and later require Python 3.5 or newer.

glvi requires:

• Python (>= 3.5)
• NumPy (>= 1.11.0)
• SciPy (>= 0.17.0)
• Pandas (>= 0.24.0)
• Joblib (>= 0.11.0)
• Scikit-learn (>= 0.20.0)

User installation

Install from github is available but is not recommended. 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

Or build from source for Windows

python setup.py install

For Linux

pip install --verbose

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)

Development

To acquire lower computation cost, we also developed a another package called forest-gis using Cython to accelerate the process. Please refer to : https://github.com/PowderL/Tree-based-machine-learning-for-gis.