Source code

Note

Mapping Learning is written in Python and uses major Open Source libraries, like scikit-learn (Machine Learning algorithms), numpy and pandas to manipulate scientific data and Gdal to handle geographic data.

Modules

Mapping Learning consists of 4 modules. The first 3 modules allow you to start from your files and to obtain predictions (based on machine learning), and vice versa. The fourth (app) is the “conductor”, who drives the other parts of the code.

1. maplearn.ml : machine learning processing (and preprocessing)
2. maplearn.datahandler : to get/export a dataset usable in machine learning and the corresponding files
3. maplearn.filehandler : read/write a file
4. maplearn.app : application modules (configuration, …)

• maplearn.ml package
  • Machine Learning
  • Submodules
  • maplearn.ml.classification module
  • maplearn.ml.clustering module
  • maplearn.ml.confusion module
  • maplearn.ml.distance module
  • maplearn.ml.reduction module
  • maplearn.ml.regression module
  • maplearn.ml.machine module
• maplearn.datahandler package
  • Submodules
  • maplearn.datahandler.packdata module
    • Machine Learning dataset
  • maplearn.datahandler.labels module
  • maplearn.datahandler.loader module
  • maplearn.datahandler.writer module
  • maplearn.datahandler.signature module
  • maplearn.datahandler.plotter module
• maplearn.filehandler package
  • Submodules
  • maplearn.filehandler.csv module
  • maplearn.filehandler.excel module
  • maplearn.filehandler.imagegeo module
  • maplearn.filehandler.shapefile module
  • maplearn.filehandler.filehandler module
• maplearn.app package
  • Submodules
  • maplearn.app.config module
    • Input/Output [io]
    • Preprocessing [preprocess]
    • Processing [process]
    • Metadata [metadata]
  • maplearn.app.main module
  • maplearn.app.reporting module