Spatial Leave-One-SubGroup-Out (SLOSGO)

This example shows how to make a Spatial Leave-One-SubGroup-Out.

Import librairies

import numpy as np
from museotoolbox.cross_validation import SpatialLeaveOneSubGroupOut
from museotoolbox import datasets,processing

Load HistoricalMap dataset

_,centroid = datasets.load_historical_data(low_res=True,centroid=True)
raster,vector = datasets.load_historical_data(low_res=True)

field = 'Class'

Extract label (‘Class’ field) and groups (‘uniquefid’ field) Compute distanceMatrix with centroid (one point per group)

X,y,groups = processing.extract_ROI(raster,vector,field,'uniquefid')
distance_matrix,distance_label = processing.get_distance_matrix(raster,centroid,'uniquefid')

Create CV

n_splits will be the number of the least populated class

SLOSGO = SpatialLeaveOneSubGroupOut(distance_thresold=100,distance_matrix=distance_matrix,
                                   distance_label=distance_label,random_state=12)

Note

Split is made to generate each fold

SLOSGO.get_n_splits(X,y,groups)
for tr,vl in SLOSGO.split(X,y,groups):
    print(np.unique(groups[vl]))
    print(np.unique(groups[tr]))

SLOSGO.save_to_vector(vector,'Class','uniquefid','/tmp/slosgo.gpkg')

Out:

[ 7  9 11 12 16]
[ 1  2  3  4  5  6  8 10 13 14 15]
[ 2  5 13 14 17]
[ 1  4  6  7  9 11 12 15]
Warning : This function generates vector files according to your vector.
        The number of features may differ from the number of pixels used in classification.
        If you want to save every ROI pixels in the vector, please use processing.sample_extraction before.

[['/tmp/slosgo_train_0.gpkg', '/tmp/slosgo_valid_0.gpkg'], ['/tmp/slosgo_train_1.gpkg', '/tmp/slosgo_valid_1.gpkg']]

Draw image

from __drawCVmethods import plotMethod
plotMethod('SLOO-group')

Out:

/home/docs/checkouts/readthedocs.org/user_builds/museotoolbox/checkouts/latest/examples/cross_validation/__drawCVmethods.py:35: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  ax = f.add_subplot(111)