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Python module for geospatial prediction using scikit-learn and rasterio

pyimpute provides high-level python functions for bridging the gap between spatial data formats and machine learning software to facilitate supervised classification and regression on geospatial data. This allows you to create landscape-scale predictions based on sparse observations.

The observations, known as the training data, consists of:

response variables: what we are trying to predict
explanatory variables: variables which explain the spatial patterns of responses

The target data consists of explanatory variables represented by raster datasets. There are no response variables available for the target data; the goal is to predict a raster surface of responses. The responses can either be discrete (classification) or continuous (regression).

example

Pyimpute Functions

load_training_vector: Load training data where responses are vector data (explanatory variables are always raster)
load_training_raster: Load training data where responses are raster data
stratified_sample_raster: Random sampling of raster cells based on discrete classes
evaluate_clf: Performs cross-validation and prints metrics to help tune your scikit-learn classifiers.
load_targets: Loads target raster data into data structures required by scikit-learn
impute: takes target data and your scikit-learn classifier and makes predictions, outputing GeoTiffs

These functions don't really provide any ground-breaking new functionality, they merely saves lots of tedious data wrangling that would otherwise bog your analysis down in low-level details. In other words, pyimpute provides a high-level python workflow for spatial prediction, making it easier to:

explore new variables more easily
frequently update predictions with new information (e.g. new Landsat imagery as it becomes available)
bring the technique to other disciplines and geographies

Basic example

Here's what a pyimpute workflow might look like. In this example, we have two explanatory variables as rasters (temperature and precipitation) and a geojson with point observations of habitat suitability for a plant species. Our goal is to predict habitat suitability across the entire region based only on the explanatory variables.

from pyimpute import load_training_vector, load_targets, impute, evaluate_clf
from sklearn.ensemble import RandomForestClassifier

Load some training data

explanatory_rasters = ['temperature.tif', 'precipitation.tif']
response_data = 'point_observations.geojson'

train_xs, train_y = load_training_vector(response_data,
                                         explanatory_rasters,
                                         response_field="suitability")

Train a scikit-learn classifier

clf = RandomForestClassifier(n_estimators=10, n_jobs=1)
clf.fit(train_xs, train_y)

Evalute the classifier using several validation metrics, manually inspecting the output

evaluate_clf(clf, train_xs, train_y)

Load target raster data

target_xs, raster_info = load_targets(explanatory_rasters)

Make predictions, outputing geotiffs

impute(target_xs, clf, raster_info, outdir='/tmp',
        linechunk=400, class_prob=True, certainty=True)

assert os.path.exists("/tmp/responses.tif")
assert os.path.exists("/tmp/certainty.tif")
assert os.path.exists("/tmp/probability_0.tif")
assert os.path.exists("/tmp/probability_1.tif")

Installation

Assuming you have libgdal and the scipy system dependencies installed, you can install with pip

pip install pyimpute

Alternatively, install from the source code

git clone https://github.com/perrygeo/pyimpute.git
cd pyimpute
pip install -e .

See the .travis.yml file for a working example on Ubuntu systems.

Other resources

For an overview, watch my presentation at FOSS4G 2014: <a href="http://vimeo.com/106235287">Spatial-Temporal Prediction of Climate Change Impacts using pyimpute, scikit-learn and GDAL — Matthew Perry</a>

Also, check out the examples and the wiki