def apply_classifier(exp,
block,
train_es,
test_es,
classifier_name,
classifier_options=None,
fit_options=None,
base_folder="/tmp",
base_filename="cl"):
"""
@type train_es: ExpressionSet
@type test_es: ExpressionSet
"""
if not classifier_options:
classifier_options = {}
if not fit_options:
fit_options = {}
target_class_column = train_es.pheno_metadata["user_class_title"]
# Unpack data
x_train = train_es.get_assay_data_frame().as_matrix().transpose()
y_train = train_es.get_pheno_data_frame()[target_class_column].as_matrix()
x_test = test_es.get_assay_data_frame().as_matrix().transpose()
y_test = test_es.get_pheno_data_frame()[target_class_column].as_matrix()
# Unfortunately svm can't operate with string labels as a target classes
# so we need to preprocess labels
le = preprocessing.LabelEncoder()
le.fit(y_train)
y_train_fixed = le.transform(y_train)
y_test_fixed = le.transform(y_test)
# Classifier initialization
fabric, apply_func = classifiers_map[classifier_name]
log.debug("Classifier options: %s", classifier_options)
if apply_func is None:
cl = fabric(**classifier_options)
cl.fit(x_train, y_train_fixed, **fit_options)
else:
raise NotImplementedError()
# Applying on test partition
y_test_predicted = cl.predict(x_test)
# Here we build result object
cr = ClassifierResult(base_folder, base_filename)
cr.labels_encode_vector = le.classes_ # Store target class labels
cr.y_true = y_test_fixed
cr.y_predicted = y_test_predicted
cr.classifier = classifier_name
cr.store_model(cl)
return [cr], {}
def apply_classifier(
exp, block,
train_es, test_es,
classifier_name, classifier_options=None, fit_options=None,
base_folder="/tmp", base_filename="cl"
):
"""
@type train_es: ExpressionSet
@type test_es: ExpressionSet
"""
if not classifier_options:
classifier_options = {}
if not fit_options:
fit_options = {}
target_class_column = train_es.pheno_metadata["user_class_title"]
# Unpack data
x_train = train_es.get_assay_data_frame().as_matrix().transpose()
y_train = train_es.get_pheno_data_frame()[target_class_column].as_matrix()
x_test = test_es.get_assay_data_frame().as_matrix().transpose()
y_test = test_es.get_pheno_data_frame()[target_class_column].as_matrix()
# Unfortunately svm can't operate with string labels as a target classes
# so we need to preprocess labels
le = preprocessing.LabelEncoder()
le.fit(y_train)
y_train_fixed = le.transform(y_train)
y_test_fixed = le.transform(y_test)
# Classifier initialization
fabric, apply_func = classifiers_map[classifier_name]
log.debug("Classifier options: %s", classifier_options)
if apply_func is None:
cl = fabric(**classifier_options)
cl.fit(x_train, y_train_fixed, **fit_options)
else:
raise NotImplementedError()
# Applying on test partition
y_test_predicted = cl.predict(x_test)
# Here we build result object
cr = ClassifierResult(base_folder, base_filename)
cr.labels_encode_vector = le.classes_ # Store target class labels
cr.y_true = y_test_fixed
cr.y_predicted = y_test_predicted
cr.classifier = classifier_name
cr.store_model(cl)
return [cr], {}
def apply_classifier(
exp, block,
train_es, test_es,
classifier_name, classifier_options=None, fit_options=None,
base_folder="/tmp", base_filename="cl"
):
"""
@type train_es: ExpressionSet
@type test_es: ExpressionSet
@type exp: Experiment
@type block: GenericBlock
"""
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
if not classifier_options:
classifier_options = {}
if not fit_options:
fit_options = {}
target_class_column = train_es.pheno_metadata["user_class_title"]
tr_es = train_es.get_assay_data_frame()
cols = tr_es.columns
te_es = test_es.get_assay_data_frame()[list(cols)]
# Unpack data
x_train = tr_es.as_matrix()
# x_train = train_es.get_assay_data_frame().as_matrix().transpose()
y_train = train_es.get_pheno_data_frame()[target_class_column].as_matrix()
x_test = te_es.as_matrix()
# x_test = test_es.get_assay_data_frame().as_matrix().transpose()
y_test = test_es.get_pheno_data_frame()[target_class_column].as_matrix()
# Unfortunately svm can't operate with string labels as a target classes
# so we need to preprocess labels
le = preprocessing.LabelEncoder()
le.fit(y_train)
y_train_fixed = le.transform(y_train)
y_test_fixed = le.transform(y_test)
# Classifier initialization
fabric, apply_func = classifiers_map[classifier_name]
# log.debug("Classifier options: %s", classifier_options)
if apply_func is None:
cl = get_classifier(fabric, classifier_options, classifier_name, block)
log.debug("Fitting classifier.")
cl.fit(x_train, y_train_fixed)
log.debug("Finished fitting classifier.")
else:
raise NotImplementedError()
log.debug("Applying on test.")
# Applying on test partition
y_test_predicted = cl.predict(x_test)
log.debug("Building result.")
# Here we build result object
cr = ClassifierResult(base_folder, base_filename)
log.debug("Storing labels.")
cr.labels_encode_vector = le.classes_ # Store target class labels
log.debug("Storing y.")
cr.y_true = y_test_fixed
cr.y_predicted = y_test_predicted
cr.classifier = classifier_name
log.debug("Storing model.")
# TODO Why to store model?
# cr.store_model(cl)
log.debug("Finished apply_classifier.")
return [cr], {}
def apply_classifier(
exp, block,
train_es, test_es,
classifier_name, classifier_options=None, fit_options=None,
base_folder="/tmp", base_filename="cl"
):
"""
@type train_es: ExpressionSet
@type test_es: ExpressionSet
@type exp: Experiment
@type block: GenericBlock
"""
if settings.CELERY_DEBUG:
import sys
sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
import pydevd
pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
if not classifier_options:
classifier_options = {}
if not fit_options:
fit_options = {}
target_class_column = train_es.pheno_metadata["user_class_title"]
tr_es = train_es.get_assay_data_frame()
cols = tr_es.columns
te_es = test_es.get_assay_data_frame()[list(cols)]
# Unpack data
x_train = tr_es.as_matrix()
# x_train = train_es.get_assay_data_frame().as_matrix().transpose()
y_train = train_es.get_pheno_data_frame()[target_class_column].as_matrix()
x_test = te_es.as_matrix()
# x_test = test_es.get_assay_data_frame().as_matrix().transpose()
y_test = test_es.get_pheno_data_frame()[target_class_column].as_matrix()
# Unfortunately svm can't operate with string labels as a target classes
# so we need to preprocess labels
le = preprocessing.LabelEncoder()
le.fit(y_train)
y_train_fixed = le.transform(y_train)
y_test_fixed = le.transform(y_test)
# Classifier initialization
fabric, apply_func = classifiers_map[classifier_name]
# log.debug("Classifier options: %s", classifier_options)
if apply_func is None:
cl = get_classifier(fabric, classifier_options, classifier_name, block)
log.debug("Fitting classifier.")
try:
log.debug(str(x_train))
cl.fit(x_train, y_train_fixed)
except ValueError:
# if settings.CELERY_DEBUG:
# import sys
# sys.path.append('/Migration/skola/phd/projects/miXGENE/mixgene_project/wrappers/pycharm-debug.egg')
# import pydevd
# pydevd.settrace('localhost', port=6901, stdoutToServer=True, stderrToServer=True)
log.debug(str(x_train))
raise
log.debug("Finished fitting classifier.")
else:
raise NotImplementedError()
log.debug("Applying on test.")
# Applying on test partition
y_test_predicted = cl.predict(x_test)
log.debug("Building result.")
# Here we build result object
cr = ClassifierResult(base_folder, base_filename)
log.debug("Storing labels.")
cr.labels_encode_vector = le.classes_ # Store target class labels
log.debug("Storing y.")
cr.y_true = y_test_fixed
cr.y_predicted = y_test_predicted
cr.classifier = classifier_name
log.debug("Storing model.")
# TODO Why to store model?
# cr.store_model(cl)
log.debug("Finished apply_classifier.")
return [cr], {}
