From 11816dc7533d43bd1b1cf96d6a28483195ca4035 Mon Sep 17 00:00:00 2001
From: bbauvin <baptiste.bauvin@centrale-marseille.fr>
Date: Thu, 19 Oct 2017 11:19:40 -0400
Subject: [PATCH] Modified splitting code to keep the same balance on the
classes
---
Code/MonoMultiViewClassifiers/ExecClassif.py | 3 +--
.../utils/execution.py | 23 +++++++++++-------
Code/Tests/Test_utils/test_execution.py | 24 +++++++++++++++++--
3 files changed, 38 insertions(+), 12 deletions(-)
diff --git a/Code/MonoMultiViewClassifiers/ExecClassif.py b/Code/MonoMultiViewClassifiers/ExecClassif.py
index 8758ddf7..68ff67b4 100644
--- a/Code/MonoMultiViewClassifiers/ExecClassif.py
+++ b/Code/MonoMultiViewClassifiers/ExecClassif.py
@@ -296,8 +296,7 @@ def execClassif(arguments):
args.CL_classes)
datasetLength = DATASET.get("Metadata").attrs["datasetLength"]
- indices = np.arange(datasetLength)
- classificationIndices = execution.genSplits(statsIter, indices, DATASET, args.CL_split, statsIterRandomStates)
+ classificationIndices = execution.genSplits(statsIter, datasetLength, DATASET, args.CL_split, statsIterRandomStates)
kFolds = execution.genKFolds(statsIter, args.CL_nbFolds, statsIterRandomStates)
diff --git a/Code/MonoMultiViewClassifiers/utils/execution.py b/Code/MonoMultiViewClassifiers/utils/execution.py
index b3761ee7..d4ebb75d 100644
--- a/Code/MonoMultiViewClassifiers/utils/execution.py
+++ b/Code/MonoMultiViewClassifiers/utils/execution.py
@@ -197,6 +197,7 @@ def initRandomState(randomStateArg, directory):
cPickle.dump(randomState, handle)
return randomState
+
def initLogFile(args):
resultDirectory = "../Results/" + args.name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "/"
logFileName = time.strftime("%Y%m%d-%H%M%S") + "-" + ''.join(args.CL_type) + "-" + "_".join(
@@ -224,20 +225,26 @@ def initLogFile(args):
return resultDirectory
-def genSplits(statsIter, indices, DATASET, splitRatio, statsIterRandomStates):
+def genSplits(statsIter, datasetlength, DATASET, splitRatio, statsIterRandomStates):
+ indices = np.arange(datasetlength)
if statsIter > 1:
splits = []
for randomState in statsIterRandomStates:
- trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices,
- DATASET.get("Labels").value,
- test_size=splitRatio,
- random_state=randomState)
+ foldsObj = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, random_state=randomState, test_size=splitRatio)
+ folds = foldsObj.split(indices, DATASET.get("Labels").value)
+ for fold in folds:
+ train_fold, test_fold = fold
+ trainIndices = indices[train_fold]
+ testIndices = indices[test_fold]
splits.append([trainIndices, testIndices])
return splits
else:
- trainIndices, testIndices, a, b = sklearn.model_selection.train_test_split(indices, DATASET.get("Labels").value,
- test_size=splitRatio,
- random_state=statsIterRandomStates)
+ foldsObj = sklearn.model_selection.StratifiedShuffleSplit(n_splits=1, random_state=statsIterRandomStates, test_size=splitRatio)
+ folds = foldsObj.split(indices, DATASET.get("Labels").value)
+ for fold in folds:
+ train_fold, test_fold = fold
+ trainIndices = indices[train_fold]
+ testIndices = indices[test_fold]
return trainIndices, testIndices
diff --git a/Code/Tests/Test_utils/test_execution.py b/Code/Tests/Test_utils/test_execution.py
index d88d9e32..3a5d5ef0 100644
--- a/Code/Tests/Test_utils/test_execution.py
+++ b/Code/Tests/Test_utils/test_execution.py
@@ -4,6 +4,8 @@ import os
import h5py
import numpy as np
+from sklearn.model_selection import StratifiedShuffleSplit
+
from MonoMultiViewClassifiers.utils import execution
@@ -43,5 +45,23 @@ class Test_initLogFile(unittest.TestCase):
class Test_genSplits(unittest.TestCase):
- def test_genSplits_no_iter(self):
- pass
\ No newline at end of file
+ def test_genSplits_no_iter_ratio(self):
+ X_indices = np.random.randint(0,500,50)
+ labels = np.zeros(500)
+ labels[X_indices[:10]] = 1
+ foldsObj = StratifiedShuffleSplit(n_splits=1, random_state=42, test_size=0.2)
+ folds = foldsObj.split(X_indices, labels[X_indices])
+ for fold in folds:
+ train_fold, test_fold = fold
+ train_indices = X_indices[train_fold]
+ test_indices = X_indices[test_fold]
+ self.assertEqual(len(train_indices), 0.8*50)
+ self.assertEqual(len(test_indices), 0.2*50)
+ for index in test_indices:
+ self.assertIn(index, X_indices)
+ for index in train_indices:
+ self.assertIn(index, X_indices)
+ self.assertGreater(len(np.where(labels[train_indices]==0)[0]), 0)
+ self.assertGreater(len(np.where(labels[test_indices]==0)[0]), 0)
+ self.assertGreater(len(np.where(labels[train_indices]==1)[0]), 0)
+ self.assertGreater(len(np.where(labels[test_indices]==1)[0]), 0)
--
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