def iqr_model_test(svm_model, matrix_kernel_test, clipids_test, sv_labels,
target_class=1):
"""
Apply an SVM model on test data
@param svm_model: Loaded SVM model to run with.
@param matrix_kernel_test: n-by-m kernel matrix where n (row) is
|SVs| & m (col) is |test data|
@type matrix_kernel_test: numpy.core.multiarray.ndarray
@param clipids_test: list of clipids ordered
@param target_class: positive class id. Default = 1.
@return: dictionary with 'probs','clipids'
@rtype: dictionary with 'probs' (np.array), 'clipids' (int list)
"""
log = logging.getLogger("SMQTK.iqr_model_test")
weights = svmtools.get_SV_weights_nonlinear_svm(svm_model,
target_class=target_class)
# case when the label of positive data was 2nd in SVM model symbol list
# since platt scaling was parameterized for negative data, swap probs
idx_target = svmtools.get_column_idx_for_class(svm_model, target_class)
if idx_target == 1:
weights *= -1.0
# compute margins
margins = weights[0] * matrix_kernel_test[0]
for i in range(1, matrix_kernel_test.shape[0]):
margins += weights[i] * matrix_kernel_test[i]
if matrix_kernel_test.ndim == 1: # case where there was single test data
margins = np.array([margins]) # make a single number margin into an np array
# compute probs, using platt scaling
log.debug("Computing probabilities via Platt Scaling")
rho = svm_model.rho[0]
probA = svm_model.probA[0]
probB = svm_model.probB[0]
probs = 1.0 / (1.0 + np.exp((margins - rho) * probA + probB))
del margins
# # case when the label of positive data was 2nd in SVM model symbol list
# # since platt scaling was parameterized for negative data, swap probs
# idx_target = svmtools.get_column_idx_for_class(svm_model, target_class)
# if idx_target == 1:
# probs = 1.0 - probs
output = dict()
output['probs'] = probs
output['clipids'] = clipids_test
return output
def iqr_model_test(filepath_model,
matrix_kernel_test,
clipids_test,
target_class=1):
"""
Apply an SVM model on test data
@param filepath_model: a full path to load the learned SVM model
@param matrix_kernel_test: n-by-m kernel maxtrix where n (row) is |SVs| & m (col) is |test data|
@type matrix_kernel_test: 2D numpy.array
@param clipids_test: list of clipids ordered
@param target_class: positive class id. Default = 1.
@return: dictionary with 'probs','clipids'
@rtype: dictionary with 'probs' (np.array), 'clipids' (int list)
"""
model = svmutil.svm_load_model(filepath_model)
weights = svmtools.get_SV_weights_nonlinear_svm(model,
target_class=target_class)
# compute margins
margins = weights[0] * matrix_kernel_test[0]
for i in range(1, matrix_kernel_test.shape[0]):
margins += weights[i] * matrix_kernel_test[i]
if matrix_kernel_test.ndim == 1: # case where there was single test data
margins = np.array([margins
]) # make a single number margin into an np array
# compute probs, using platt scaling
rho = model.rho[0]
probA = model.probA[0]
probB = model.probB[0]
probs = 1.0 / (1.0 + np.exp((margins - rho) * probA + probB))
del margins
# case when the label of positive data was 2nd in SVM model symbol list
# since platt scaling was parameterized for negative data, swap probs
idx_target = svmtools.get_column_idx_for_class(model, target_class)
if idx_target == 1:
probs = 1.0 - probs
output = dict()
output['probs'] = probs
output['clipids'] = clipids_test
return output
def iqr_model_test(svm_model, matrix_kernel_test, clipids_test, target_class=1):
"""
Apply an SVM model on test data
@param svm_model: Loaded SVM model to run with
@param matrix_kernel_test: n-by-m kernel maxtrix where n (row) is |SVs| & m
(col) is |test data|
@type matrix_kernel_test: 2D numpy.array
@param clipids_test: list of clipids ordered
@param target_class: positive class id. Default = 1.
@return: dictionary with 'probs','clipids'
@rtype: dictionary with 'probs' (np.array), 'clipids' (int list)
"""
weights = svmtools.get_SV_weights_nonlinear_svm(svm_model,
target_class=target_class)
# compute margins
margins = weights[0] * matrix_kernel_test[0]
for i in range(1, matrix_kernel_test.shape[0]):
margins += weights[i] * matrix_kernel_test[i]
if matrix_kernel_test.ndim == 1: # case where there was single test data
margins = np.array([margins]) # make a single number margin into an np array
# compute probs, using platt scaling
rho = svm_model.rho[0]
probA = svm_model.probA[0]
probB = svm_model.probB[0]
probs = 1.0 / (1.0 + np.exp((margins - rho) * probA + probB))
del margins
# case when the label of positive data was 2nd in SVM model symbol list
# since platt scaling was parameterized for negative data, swap probs
idx_target = svmtools.get_column_idx_for_class(svm_model, target_class)
if idx_target == 1:
probs = 1.0 - probs
elif idx_target > 1:
raise RuntimeError("Unexpected column index for target class.")
output = dict()
output['probs'] = probs
output['clipids'] = clipids_test
return output
