## 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).
## 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,
Train a scikit-learn classifier
clf = RandomForestClassifier(n_estimators=10, n_jobs=1)
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)
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
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](https://github.com/perrygeo/python-impute/blob/master/examples/) and [the wiki](https://github.com/perrygeo/pyimpute/wiki)
TODO: Brief introduction on what you do with files - including link to relevant help section.