spacv: spatial cross-validation in Python

spacv is a small Python 3 (3.6 and above) package for cross-validation of models that assess generalization performance to datasets with spatial dependence. spacv provides a familiar sklearn-like API to expose a suite of tools useful for points-based spatial prediction tasks. See the notebook spacv_guide.ipynb for usage.

Dependencies

• numpy
• matplotlib
• pandas
• geopandas
• shapely
• scikit-learn
• scipy

Installation and usage

To install use pip:

$ pip install spacv

Then build quick spatial cross-validation workflows with sklearn as:

import spacv
import geopandas as gpd
from sklearn.model_selection import cross_val_score
from sklearn.svm import SVC

df = gpd.read_file('data/baltim.geojson')

XYs = df['geometry']
X = df[['NROOM', 'BMENT', 'NBATH', 'PRICE', 'LOTSZ', 'SQFT']]
y = df['PATIO']

# Build fold indices as a generator
skcv = spacv.SKCV(n_splits=4, buffer_radius=10).split(XYs)

svc = SVC()

cross_val_score(svc,       # Model 
                X,         # Features
                y,         # Labels
                cv = skcv) # Fold indices