Spatial cross-validation in Python
Project description
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
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