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pyimpute

(★ 129)

Spatial classification and regression using Scikit-learn and Rasterio

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- - cca.r
  - cca_example.png
  - example_output.txt
- example_eval.py
- example_points.py
- example_raster_sampling.py
- example_certainty.png
- example_responses.png
- memory.png
- time.png
- - __init__.py
  - _main.py
- - dtmean8_12.img
  - gt_dem.img
  - int_cnl_euc.img
  - p_n.img
  - pmean_all.img
  - points.geojson
  - responses.tif
  - tmax8.img
  - tmean_all.img
- __init__.py
- conftest.py
- test_impute.py
.gitignore
.travis.yml
example.png
LICENSE.txt
MANIFEST.in
README.md
release.sh
requirements.txt
setup.cfg
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/perrygeo/pyimpute

Downloads the entire project code from GitHub to your computer.

cd pyimpute

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install pyimpute

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. Python

Easy

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install pyimpute

Installs the package published on PyPI directly — no need to clone the source.

pip install -e .

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

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![travis](https://travis-ci.org/perrygeo/pyimpute.svg)

## 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](https://raw.githubusercontent.com/perrygeo/pyimpute/master/example.png)

## 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](https://github.com/perrygeo/python-impute/blob/master/examples/) and [the wiki](https://github.com/perrygeo/pyimpute/wiki)