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mihaic merged 2 commits into from
May 5, 2020
Merged

Unpin scikit-learn and set gamma to auto in SVC tests #461

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1c9cc37
Unpin scikit-learn and set scale to auto in SVC tests
ariekahn May 5, 2020
7aa591a
Update SVC gamma in examples
ariekahn May 5, 2020
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10 changes: 5 additions & 5 deletions examples/fcma/classification.py
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Expand Up @@ -32,7 +32,7 @@

def example_of_aggregating_sim_matrix(raw_data, labels, num_subjects, num_epochs_per_subj):
# aggregate the kernel matrix to save memory
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
clf = Classifier(svm_clf, num_processed_voxels=1000, epochs_per_subj=num_epochs_per_subj)
rearranged_data = raw_data[num_epochs_per_subj:] + raw_data[0:num_epochs_per_subj]
rearranged_labels = labels[num_epochs_per_subj:] + labels[0:num_epochs_per_subj]
Expand All @@ -54,7 +54,7 @@ def example_of_aggregating_sim_matrix(raw_data, labels, num_subjects, num_epochs

def example_of_cross_validation_with_detailed_info(raw_data, labels, num_subjects, num_epochs_per_subj):
# no shrinking, set C=1
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
#logit_clf = LogisticRegression()
clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
# doing leave-one-subject-out cross validation
Expand Down Expand Up @@ -86,7 +86,7 @@ def example_of_cross_validation_using_model_selection(raw_data, labels, num_subj
# for self-correlation, i.e. correlation between the same data matrix.

# no shrinking, set C=1
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
#logit_clf = LogisticRegression()
clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
# doing leave-one-subject-out cross validation
Expand All @@ -104,7 +104,7 @@ def example_of_cross_validation_using_model_selection(raw_data, labels, num_subj

def example_of_correlating_two_components(raw_data, raw_data2, labels, num_subjects, num_epochs_per_subj):
# aggregate the kernel matrix to save memory
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
clf = Classifier(svm_clf, epochs_per_subj=num_epochs_per_subj)
num_training_samples=num_epochs_per_subj*(num_subjects-1)
clf.fit(list(zip(raw_data[0:num_training_samples], raw_data2[0:num_training_samples])),
Expand All @@ -127,7 +127,7 @@ def example_of_correlating_two_components(raw_data, raw_data2, labels, num_subje
def example_of_correlating_two_components_aggregating_sim_matrix(raw_data, raw_data2, labels,
num_subjects, num_epochs_per_subj):
# aggregate the kernel matrix to save memory
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
clf = Classifier(svm_clf, num_processed_voxels=1000, epochs_per_subj=num_epochs_per_subj)
num_training_samples=num_epochs_per_subj*(num_subjects-1)
clf.fit(list(zip(raw_data, raw_data2)), labels,
Expand Down
2 changes: 1 addition & 1 deletion examples/fcma/mvpa_classification.py
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Expand Up @@ -57,7 +57,7 @@
# transpose data to facilitate training and prediction
processed_data = processed_data.T

clf = svm.SVC(kernel='linear', shrinking=False, C=1)
clf = svm.SVC(kernel='linear', shrinking=False, C=1, gamma='auto')
# doing leave-one-subject-out cross validation
for i in range(num_subjects):
leave_start = i * num_epochs_per_subj
Expand Down
2 changes: 1 addition & 1 deletion examples/fcma/mvpa_voxel_selection.py
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Expand Up @@ -79,7 +79,7 @@
# create a Searchlight object
sl = Searchlight(sl_rad=1)
mvs = MVPAVoxelSelector(data, mask, labels, num_subjs, sl)
clf = svm.SVC(kernel='linear', shrinking=False, C=1)
clf = svm.SVC(kernel='linear', shrinking=False, C=1, gamma='auto')
# only rank 0 has meaningful return values
score_volume, results = mvs.run(clf)
# this output is just for result checking
Expand Down
2 changes: 1 addition & 1 deletion examples/fcma/voxel_selection.py
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Expand Up @@ -77,7 +77,7 @@
# if providing two masks, just append raw_data2 as the last input argument
#vs = VoxelSelector(labels, epochs_per_subj, num_subjs, raw_data, raw_data2=raw_data2)
# for cross validation, use SVM with precomputed kernel
clf = svm.SVC(kernel='precomputed', shrinking=False, C=10)
clf = svm.SVC(kernel='precomputed', shrinking=False, C=10, gamma='auto')
results = vs.run(clf)
# this output is just for result checking
if MPI.COMM_WORLD.Get_rank()==0:
Expand Down
6 changes: 3 additions & 3 deletions examples/funcalign/srm_image_prediction_example.ipynb
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Expand Up @@ -252,7 +252,7 @@
" train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]\n",
"\n",
" # Train a Nu-SVM classifier using scikit learn\n",
" classifier = NuSVC(nu=0.5, kernel='linear')\n",
" classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')\n",
" classifier = classifier.fit(train_data.T, train_labels)\n",
"\n",
" # Predict on the test data\n",
Expand Down Expand Up @@ -393,7 +393,7 @@
" train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]\n",
"\n",
" # Train a Nu-SVM classifier using scikit learn\n",
" classifier = NuSVC(nu=0.5, kernel='linear')\n",
" classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')\n",
" classifier = classifier.fit(train_data.T, train_labels)\n",
"\n",
" # Predict on the test data\n",
Expand Down Expand Up @@ -448,4 +448,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
}
}
4 changes: 2 additions & 2 deletions examples/funcalign/srm_image_prediction_example.py
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Expand Up @@ -110,7 +110,7 @@ def plot_confusion_matrix(cm, title="Confusion Matrix"):
train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]

# Train a Nu-SVM classifier using scikit learn
classifier = NuSVC(nu=0.5, kernel='linear')
classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')
classifier = classifier.fit(train_data.T, train_labels)

# Predict on the test data
Expand Down Expand Up @@ -153,7 +153,7 @@ def plot_confusion_matrix(cm, title="Confusion Matrix"):
train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]

# Train a Nu-SVM classifier using scikit learn
classifier = NuSVC(nu=0.5, kernel='linear')
classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')
classifier = classifier.fit(train_data.T, train_labels)

# Predict on the test data
Expand Down
2 changes: 1 addition & 1 deletion examples/funcalign/srm_image_prediction_example_distributed.py
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Expand Up @@ -128,7 +128,7 @@ def plot_confusion_matrix(cm, title="Confusion Matrix"):
train_data[:, start_index:end_index] = image_data_shared[train_subjects[train_subject]]

# Train a Nu-SVM classifier using scikit learn
classifier = NuSVC(nu=0.5, kernel='linear')
classifier = NuSVC(nu=0.5, kernel='linear', gamma='auto')
classifier = classifier.fit(train_data.T, train_labels)

# Predict on the test data
Expand Down
2 changes: 1 addition & 1 deletion examples/searchlight/example_searchlight.py
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Expand Up @@ -78,7 +78,7 @@
def sfn(l, msk, myrad, bcast_var):
import sklearn.svm
import sklearn.model_selection
classifier = sklearn.svm.SVC()
classifier = sklearn.svm.SVC(gamma='auto')
data = l[0][msk,:].T
return np.mean(sklearn.model_selection.cross_val_score(classifier, data, bcast_var,n_jobs=1))

Expand Down
2 changes: 1 addition & 1 deletion examples/utils/fmrisim_multivariate_example.ipynb
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Expand Up @@ -9040,7 +9040,7 @@
"X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n",
" input_mat, input_labels, test_size=0.2, random_state=0)\n",
"\n",
"clf = sklearn.svm.SVC(kernel='linear', C=1).fit(X_train, y_train)\n",
"clf = sklearn.svm.SVC(kernel='linear', C=1, gamma='auto').fit(X_train, y_train)\n",
"\n",
"score = clf.score(X_test, y_test)\n",
"print('Classification accuracy between condition A and B: %0.3f' % score)"
Expand Down
2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -128,7 +128,7 @@ def finalize_options(self):
'nitime',
# https://github.com/numpy/numpy/issues/14189
'numpy!=1.17.*',
'scikit-learn[alldeps]>=0.18,<0.22',
'scikit-learn[alldeps]>=0.18',
# See https://github.com/scipy/scipy/pull/8082
'scipy!=1.0.0',
'statsmodels',
Expand Down
4 changes: 2 additions & 2 deletions tests/fcma/test_classification.py
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Expand Up @@ -46,7 +46,7 @@ def test_classification():
# 5 subjects, 4 epochs per subject
epochs_per_subj = 4
# svm
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
training_data = fake_raw_data[0:12]
clf = Classifier(svm_clf, epochs_per_subj=epochs_per_subj)
clf.fit(list(zip(training_data, training_data)), labels[0:12])
Expand Down Expand Up @@ -134,7 +134,7 @@ def test_classification_with_two_components():
# 5 subjects, 4 epochs per subject
epochs_per_subj = 4
# svm
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
svm_clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
training_data = fake_raw_data[0: 12]
training_data2 = fake_raw_data2[0: 12]
clf = Classifier(svm_clf, epochs_per_subj=epochs_per_subj)
Expand Down
2 changes: 1 addition & 1 deletion tests/fcma/test_mvpa_voxel_selection.py
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Expand Up @@ -34,7 +34,7 @@ def test_mvpa_voxel_selection():
mvs = MVPAVoxelSelector(data, mask, labels, 2, sl)
# for cross validation, use SVM with precomputed kernel

clf = svm.SVC(kernel='rbf', C=10)
clf = svm.SVC(kernel='rbf', C=10, gamma='auto')
result_volume, results = mvs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = []
Expand Down
4 changes: 2 additions & 2 deletions tests/fcma/test_voxel_selection.py
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Expand Up @@ -58,7 +58,7 @@ def test_voxel_selection():
'within-subject normalization does not provide correct results'
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
Expand Down Expand Up @@ -89,7 +89,7 @@ def test_voxel_selection_with_two_masks():
raw_data2=fake_raw_data2, voxel_unit=1)
# for cross validation, use SVM with precomputed kernel
# no shrinking, set C=1
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1)
clf = svm.SVC(kernel='precomputed', shrinking=False, C=1, gamma='auto')
results = vs.run(clf)
if MPI.COMM_WORLD.Get_rank() == 0:
output = [None] * len(results)
Expand Down