def orig_train_adjective_phase_classifier(path, adjective, all_features):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective)) + ".pkl"
newpath = os.path.join(path, "trained_adjectives")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s" % adjective
train_X = []
for phase in phases:
train_set = all_features[adjective][phase]['train']
train_X.append(train_set['features'])
train_Y = train_set['labels']
object_ids = train_set['object_ids']
train_X = np.concatenate(train_X, axis=1)
print "Training adjective %s" % adjective
if True:
trained_clf, scaler = utilities.train_svm_gridsearch(
train_X=train_X,
train_Y=train_Y,
verbose=True,
object_ids=object_ids,
n_jobs=6,
scale=True)
else:
trained_clf = utilities.train_gradient_boost(
train_X=train_X,
train_Y=train_Y,
object_ids=object_ids,
)
dataset = all_features[adjective]
dataset['adjective'] = adjective
dataset['classifier'] = trained_clf
dataset['scaler'] = scaler
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
def orig_train_adjective_phase_classifier(path, adjective, all_features):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective))+".pkl"
newpath = os.path.join(path, "trained_adjectives")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s" % adjective
train_X = []
for phase in phases:
train_set = all_features[adjective][phase]['train']
train_X.append(train_set['features'])
train_Y = train_set['labels']
object_ids = train_set['object_ids']
train_X = np.concatenate(train_X, axis=1)
print "Training adjective %s" % adjective
if True:
trained_clf,scaler = utilities.train_svm_gridsearch(train_X = train_X,
train_Y = train_Y,
verbose=True,
object_ids = object_ids,
n_jobs = 6,
scale = True
)
else:
trained_clf = utilities.train_gradient_boost(train_X = train_X,
train_Y = train_Y,
object_ids = object_ids,
)
dataset = all_features[adjective]
dataset['adjective'] = adjective
dataset['classifier'] = trained_clf
dataset['scaler'] = scaler
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
def orig_train_adjective_phase_classifier(path, adjective, all_features):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective)) + ".pkl"
newpath = os.path.join(path, "trained_adjectives_univ_deep_search")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s" % adjective
train_X = []
for phase in phases:
train_set = all_features[adjective][phase]["train"]
train_X.append(train_set["features"])
train_Y = train_set["labels"]
object_ids = train_set["object_ids"]
train_X = np.concatenate(train_X, axis=1)
"""
# Scale the data
scaler = preprocessing.StandardScaler().fit(train_X)
train_X = scaler.transform(train_X)
all_features[adjective]['scaler'] = scaler
all_features[adjective]['train'] = train_X # store off scaled
# Remove features!
all_features[adjective]['tree_features'] = remove_feature_tree_based(train_X,train_Y)
print np.shape(train_X)
train_X = all_features[adjective]['tree_features'][1]; # transformed features
print np.shape(train_X)
print "Training adjective %s" % adjective
"""
if sum(train_Y) < 180:
trained_clf, scaler = utilities.train_univariate_selection(
train_X, train_Y, verbose=True, object_ids=object_ids, n_jobs=6, scale=True
)
print trained_clf
else:
trained_clf, scaler = utilities.train_svm_gridsearch(
train_X=train_X, train_Y=train_Y, verbose=True, object_ids=object_ids, n_jobs=6, scale=True
)
dataset = all_features[adjective]
dataset["scaler"] = scaler
dataset["adjective"] = adjective
dataset["classifier"] = trained_clf
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
def orig_train_adjective_phase_classifier(path, adjective, all_features):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective)) + ".pkl"
newpath = os.path.join(path, "trained_adjectives_tree")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s" % adjective
train_X = []
for phase in phases:
train_set = all_features[adjective][phase]['train']
train_X.append(train_set['features'])
train_Y = train_set['labels']
object_ids = train_set['object_ids']
train_X = np.concatenate(train_X, axis=1)
# Scale the data
scaler = preprocessing.StandardScaler().fit(train_X)
train_X = scaler.transform(train_X)
all_features[adjective]['scaler'] = scaler
all_features[adjective]['train'] = train_X # store off scaled
# Remove features!
all_features[adjective]['tree_features'] = remove_feature_tree_based(
train_X, train_Y)
print np.shape(train_X)
train_X = all_features[adjective]['tree_features'][1]
# transformed features
print np.shape(train_X)
print "Training adjective %s" % adjective
'''
trained_clf, scaler = utilities.train_univariate_selection(train_X,train_Y,
verbose=True,
object_ids = object_ids,
n_jobs = 6,
scale = True
)
all_features[adjective][phase]['scaler'] = scaler
all_features[adjective][phase]['univ_select'] = trained_clf
print trained_clf
'''
if True:
trained_clf, scaler = utilities.train_svm_gridsearch(
train_X=train_X,
train_Y=train_Y,
verbose=True,
object_ids=object_ids,
n_jobs=6,
scale=False)
else:
trained_clf = utilities.train_gradient_boost(
train_X=train_X,
train_Y=train_Y,
object_ids=object_ids,
)
dataset = all_features[adjective]
dataset['adjective'] = adjective
dataset['classifier'] = trained_clf
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
def original_train_adjective_phase_classifier(path1, path2, adjective, phase, all_features1, all_features2, boost):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective, phase))+".pkl"
newpath = os.path.join(path1, "trained_adjective_phase_merge")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s and phase %s" % (adjective, phase)
# First set of features
train_set = all_features1[adjective][phase]['train']
train_X1 = train_set['features']
train_Y = train_set['labels']
object_ids = train_set['object_ids']
# test set
test_set1 = all_features1[adjective][phase]['test']
test_X1 = test_set1['features']
test_set2 = all_features2[adjective][phase]['test']
test_X2 = test_set2['features']
test_X = np.concatenate((test_X1,test_X2),axis=1)
all_features1[adjective][phase]['test'] = test_X
# Second set of features
train_set2 = all_features2[adjective][phase]['train']
train_X2 = train_set2['features']
# Merge the two for a nx51 vector
train_X = np.concatenate((train_X1, train_X2), axis=1)
print "Training adjective %s and phase %s" %(adjective, phase)
if not boost:
trained_clf, scaler = utilities.train_svm_gridsearch(train_X = train_X,
train_Y = train_Y,
verbose=True,
object_ids = object_ids,
n_jobs = 6,
scale = True
)
else:
trained_clf, scaler = utilities.train_gradient_boost(train_X = train_X,
train_Y = train_Y,
object_ids = object_ids,
verbose = True,
n_jobs = 6,
scale = True
)
dataset = all_features1[adjective][phase]
dataset['adjective'] = adjective
dataset['phase'] = phase
dataset['classifier'] = trained_clf
dataset['scaler'] = scaler
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
def original_train_adjective_phase_classifier(path1, path2, adjective, phase,
all_features1, all_features2,
boost):
"""
Example function on how to access all of the features
stored in adjective_phase_set
"""
# File name
dataset_file_name = "_".join(("trained", adjective, phase)) + ".pkl"
newpath = os.path.join(path1, "trained_adjective_phase_merge")
path_name = os.path.join(newpath, dataset_file_name)
if os.path.exists(path_name):
print "File %s already exists, skipping it." % path_name
return
print "Creating adjective %s and phase %s" % (adjective, phase)
# First set of features
train_set = all_features1[adjective][phase]['train']
train_X1 = train_set['features']
train_Y = train_set['labels']
object_ids = train_set['object_ids']
# test set
test_set1 = all_features1[adjective][phase]['test']
test_X1 = test_set1['features']
test_set2 = all_features2[adjective][phase]['test']
test_X2 = test_set2['features']
test_X = np.concatenate((test_X1, test_X2), axis=1)
all_features1[adjective][phase]['test'] = test_X
# Second set of features
train_set2 = all_features2[adjective][phase]['train']
train_X2 = train_set2['features']
# Merge the two for a nx51 vector
train_X = np.concatenate((train_X1, train_X2), axis=1)
print "Training adjective %s and phase %s" % (adjective, phase)
if not boost:
trained_clf, scaler = utilities.train_svm_gridsearch(
train_X=train_X,
train_Y=train_Y,
verbose=True,
object_ids=object_ids,
n_jobs=6,
scale=True)
else:
trained_clf, scaler = utilities.train_gradient_boost(
train_X=train_X,
train_Y=train_Y,
object_ids=object_ids,
verbose=True,
n_jobs=6,
scale=True)
dataset = all_features1[adjective][phase]
dataset['adjective'] = adjective
dataset['phase'] = phase
dataset['classifier'] = trained_clf
dataset['scaler'] = scaler
print "Saving trained_classifier"
# Save the results in the folder
with open(path_name, "w") as f:
print "Saving file: ", path_name
cPickle.dump(dataset, f, protocol=cPickle.HIGHEST_PROTOCOL)
