From 705a3e4a5b8a94d5ca5cfa2317a5e55bd09dfcb7 Mon Sep 17 00:00:00 2001
From: Raffaele Gaetano <raffaele.gaetano@cirad.fr>
Date: Thu, 1 Jun 2023 10:22:23 +0200
Subject: [PATCH] ENH: ensuring robust stratified group k-fold generation.
---
Learning/ObjectBased.py | 55 ++++++++++++++++++++++++++++++++++++-----
1 file changed, 49 insertions(+), 6 deletions(-)
diff --git a/Learning/ObjectBased.py b/Learning/ObjectBased.py
index 737b013..17c70e3 100644
--- a/Learning/ObjectBased.py
+++ b/Learning/ObjectBased.py
@@ -1,6 +1,9 @@
import glob
+
+import numpy as np
+
from OBIA.OBIABase import *
-from sklearn.model_selection import StratifiedGroupKFold
+from sklearn.model_selection import StratifiedGroupKFold, GroupShuffleSplit
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, accuracy_score, cohen_kappa_score, precision_recall_fscore_support
@@ -20,19 +23,46 @@ class ObjectBasedClassifier:
'Y': Y,
'groups': G,
'perc2': p2,
- 'perc98': p98
+ 'perc98': p98,
+ 'folds': []
}
return
def gen_k_folds(self, k):
- self.training_base['folds'] = []
sgk = StratifiedGroupKFold(n_splits=k, shuffle=True)
for tr_i, ts_i in sgk.split(self.training_base['X'],
self.training_base['Y'],
self.training_base['groups']):
self.training_base['folds'].append((tr_i, ts_i))
+ # check if all classes are in all splits
+ n_classes = len(np.unique(self.training_base['Y']))
+ ok = True
+ for f in self.training_base['folds']:
+ ok &= (len(np.unique(self.training_base['Y'][f[0]])) == n_classes and
+ len(np.unique(self.training_base['Y'][f[1]])) == n_classes)
+ if not ok:
+ self.training_base['folds'] = []
+ raise Exception("Not all classes are present in each fold/split.\n"
+ "Please check that you have enough groups (e.g. 2 x n_folds) per class.")
return
+ def gen_hold_out(self, test_train_ratio=0.5, n_splits=1):
+ gss = GroupShuffleSplit(n_splits=n_splits, test_size=test_train_ratio)
+ for tr_i, ts_i in gss.split(self.training_base['X'],
+ self.training_base['Y'],
+ self.training_base['groups']):
+ self.training_base['folds'].append((tr_i, ts_i))
+ # check if all classes are in all splits
+ n_classes = len(np.unique(self.training_base['Y']))
+ ok = True
+ for f in self.training_base['folds']:
+ ok &= (len(np.unique(self.training_base['Y'][f[0]])) == n_classes and
+ len(np.unique(self.training_base['Y'][f[1]])) == n_classes)
+ if not ok:
+ self.training_base['folds'] = []
+ raise Exception("Not all classes are present in each split.\n"
+ "Please check that you have enough groups (e.g. 2 x (1/test_train_ratio)) per class.")
+
def train_RF(self, n_estimators):
assert('folds' in self.training_base.keys())
models = []
@@ -50,14 +80,27 @@ class ObjectBasedClassifier:
'p_r_f1': precision_recall_fscore_support(y_true, y_pred, zero_division=0)
}
)
- '''
summary = {
'accuracy_mean': np.mean([x['accuracy'] for x in results]),
'accuracy_std': np.std([x['accuracy'] for x in results]),
'kappa_mean': np.mean([x['kappa'] for x in results]),
'kappa_std': np.std([x['kappa'] for x in results]),
+ 'prec_mean': np.mean([x['p_r_f1'][0] for x in results], axis=0),
+ 'prec_std': np.std([x['p_r_f1'][0] for x in results], axis=0),
+ 'rec_mean': np.mean([x['p_r_f1'][1] for x in results], axis=0),
+ 'rec_std': np.std([x['p_r_f1'][1] for x in results], axis=0),
'f1_mean': np.mean([x['p_r_f1'][2] for x in results], axis=0),
'f1_std': np.std([x['p_r_f1'][2] for x in results], axis=0)
}
- '''
- return models, results
+ return models, summary, results
+
+#TEST CODE
+def run_test():
+ obc = ObjectBasedClassifier('/DATA/Moringa_Sample/Parakou/output/segmentation/segmentation.tif',
+ '/DATA/Moringa_Sample/Parakou/input/REF/ref.shp',
+ ['/DATA/Moringa_Sample/Parakou/output/S2_processed/T31PDL/*/*FEAT.tif'],
+ ['/DATA/Moringa_Sample/Parakou/input/THR/THR_SPOT6.tif'])
+ obc.gen_k_folds(5)
+ m,s,r = obc.train_RF(100)
+ print(s)
+ return
--
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