def handle(self, dictionary_id, output_folder, **options):
if not dictionary_id:
raise CommandError("Dictionary id is required.")
try:
dictionary_id = int(dictionary_id)
except ValueError:
raise CommandError("Dictionary id must be a number.")
if not output_folder:
raise CommandError("Output folder path is required.")
num_pairs = options.get('num_pairs')
#num_conditions = options.get('num_conditions')
#num_stages = options.get('num_stages')
experiment_name = options.get('experiment_name')
# make sure the folder exists
check_or_create_dir(output_folder)
output_filename = "%s/user_accounts.log" % output_folder
with open(output_filename, "w") as output:
with transaction.atomic(savepoint=False):
# create an experiment
experiment = experiment_models.Experiment(
name=experiment_name,
saved_path_root=output_folder,
dictionary_id=dictionary_id)
experiment.save()
experiment.initialize_controlled_experiment( #num_conditions=num_conditions,
#num_stages=num_stages,
num_pairs=num_pairs,
output=output)
def handle(self, dictionary_id, output_folder, **options):
if not dictionary_id:
raise CommandError("Dictionary id is required.")
try:
dictionary_id = int(dictionary_id)
except ValueError:
raise CommandError("Dictionary id must be a number.")
if not output_folder:
raise CommandError("Output folder path is required.")
num_pairs = options.get('num_pairs')
#num_conditions = options.get('num_conditions')
#num_stages = options.get('num_stages')
experiment_name = options.get('experiment_name')
# make sure the folder exists
check_or_create_dir(output_folder)
output_filename = "%s/user_accounts.log" % output_folder
with open(output_filename, "w") as output:
with transaction.atomic(savepoint=False):
# create an experiment
experiment = experiment_models.Experiment(name=experiment_name,
saved_path_root=output_folder,
dictionary_id=dictionary_id)
experiment.save()
experiment.initialize_controlled_experiment(#num_conditions=num_conditions,
#num_stages=num_stages,
num_pairs=num_pairs,
output=output)
def handle(self, dictionary_id, output_folder, **options):
if not dictionary_id:
raise CommandError("Dictionary id is required.")
try:
dictionary_id = int(dictionary_id)
except ValueError:
raise CommandError("Dictionary id must be a number.")
if not output_folder:
raise CommandError("Output folder path is required.")
num_pairs = options.get("num_pairs")
num_conditions = options.get("num_conditions")
num_stages = options.get("num_stages")
experiment_name = options.get("experiment_name")
# make sure the folder exists
check_or_create_dir(output_folder)
output_filename = "%s/user_accounts.log" % output_folder
with open(output_filename, "w") as output:
# create an experiment
experiment = experiment_models.Experiment(name=experiment_name, dictionary_id=dictionary_id)
experiment.save()
experiment.initialize_experiment(
num_conditions=num_conditions, num_stages=num_stages, num_pairs=num_pairs, output=output
)
def handle(self, dataset_id, save_path, **options):
action = options.get('action')
tweet_parser_path = options.get('tweet_parser_path')
if not dataset_id:
raise CommandError("Dataset id is required.")
try:
dataset_id = int(dataset_id)
except ValueError:
raise CommandError("Dataset id must be a number.")
if not save_path:
raise CommandError("File save path is required.")
check_or_create_dir(save_path)
if action == 'all' or action == 'dump':
from msgvis.apps.enhance.tasks import dump_tweets
print "Dumping messages..."
dump_tweets(dataset_id, save_path)
if action == 'all' or action == 'parse':
from msgvis.apps.enhance.tasks import parse_tweets
output_path = "%s/parsed_tweets" %save_path
check_or_create_dir(output_path)
print "\n=========="
print "Parsing messages..."
parse_tweets(tweet_parser_path, save_path, output_path)
if action == 'all' or action == 'lemmatize':
from msgvis.apps.enhance.tasks import lemmatize_tweets
input_path = "%s/parsed_tweets" %save_path
output_path = "%s/converted_tweets" %save_path
check_or_create_dir(output_path)
print "\n=========="
print "Lemmatizing messages..."
lemmatize_tweets(input_path, output_path)
def handle(self, dataset_id, save_path, **options):
action = options.get('action')
tweet_parser_path = options.get('tweet_parser_path')
if not dataset_id:
raise CommandError("Dataset id is required.")
try:
dataset_id = int(dataset_id)
except ValueError:
raise CommandError("Dataset id must be a number.")
if not save_path:
raise CommandError("File save path is required.")
check_or_create_dir(save_path)
if action == 'all' or action == 'dump':
from msgvis.apps.enhance.tasks import dump_tweets
print "Dumping messages..."
dump_tweets(dataset_id, save_path)
if action == 'all' or action == 'parse':
from msgvis.apps.enhance.tasks import parse_tweets
output_path = "%s/parsed_tweets" % save_path
check_or_create_dir(output_path)
print "\n=========="
print "Parsing messages..."
parse_tweets(tweet_parser_path, save_path, output_path)
if action == 'all' or action == 'lemmatize':
from msgvis.apps.enhance.tasks import lemmatize_tweets
input_path = "%s/parsed_tweets" % save_path
output_path = "%s/converted_tweets" % save_path
check_or_create_dir(output_path)
print "\n=========="
print "Lemmatizing messages..."
lemmatize_tweets(input_path, output_path)
def process_stage(self, use_tfidf=False):
experiment = self.assignment.experiment
dictionary = experiment.dictionary
try:
for source in self.assignment.pair.users.all():
sources = ["system", source]
features = list(dictionary.get_feature_list(sources))
messages = self.selected_messages.all()
master_messages = dictionary.dataset.get_master_message_set().all()
feature_index_map = {}
for idx, feature in enumerate(features):
feature_index_map[feature.index] = idx
model_save_path = "%s/%s_stage%d/" % (experiment.saved_path_root, source.username, self.order)
check_or_create_dir(model_save_path)
X, y, code_map_inverse = coding_utils.get_formatted_data(
dictionary=dictionary,
source=source,
messages=messages,
feature_index_map=feature_index_map,
feature_num=len(features),
use_tfidf=use_tfidf,
master_messages=master_messages,
)
lin_clf = coding_utils.train_model(X, y, model_save_path=model_save_path)
svm_model = SVMModel(source=source, source_stage=self, saved_path=model_save_path)
svm_model.save()
weights = []
for code_index, code_id in code_map_inverse.iteritems():
for feature_index, feature in enumerate(features):
try:
if lin_clf.coef_.shape[0] == 1:
weight = lin_clf.coef_[0][feature_index]
else:
weight = lin_clf.coef_[code_index][feature_index]
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
model_weight = SVMModelWeight(
svm_model=svm_model, code_id=code_id, feature=feature, weight=weight
)
weights.append(model_weight)
SVMModelWeight.objects.bulk_create(weights)
try:
next_stage = self.get_next_stage()
except IndexError:
pass
else:
next_message_set = next_stage.message_set.messages.all()
code_assignments = []
next_X = coding_utils.get_formatted_X(
messages=next_message_set,
dictionary=dictionary,
source=source,
feature_index_map=feature_index_map,
feature_num=len(features),
use_tfidf=use_tfidf,
)
predict_y, prob = coding_utils.get_prediction(lin_clf, next_X)
for idx, message in enumerate(next_message_set):
code_index = predict_y[idx]
code_id = code_map_inverse[code_index]
try:
if lin_clf.coef_.shape[0] == 1:
if code_index == 1:
probability = prob[idx]
else:
probability = 1 - prob[idx]
else:
probability = prob[idx, code_index]
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
code_assignment = coding_models.CodeAssignment(
message=message,
source=source,
code_id=code_id,
is_user_labeled=False,
probability=probability,
)
code_assignments.append(code_assignment)
coding_models.CodeAssignment.objects.bulk_create(code_assignments)
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
def process_stage(self, use_tfidf=False):
experiment = self.assignment.experiment
dictionary = experiment.dictionary
try:
for source in self.assignment.pair.users.all():
sources = ["system", source]
features = list(dictionary.get_feature_list(sources))
messages = self.selected_messages.all()
master_messages = dictionary.dataset.get_master_message_set(
).all()
feature_index_map = {}
for idx, feature in enumerate(features):
feature_index_map[feature.index] = idx
model_save_path = "%s/%s_stage%d/" % (
experiment.saved_path_root, source.username, self.order)
check_or_create_dir(model_save_path)
X, y, code_map_inverse = coding_utils.get_formatted_data(
dictionary=dictionary,
source=source,
messages=messages,
feature_index_map=feature_index_map,
feature_num=len(features),
use_tfidf=use_tfidf,
master_messages=master_messages)
lin_clf = coding_utils.train_model(
X, y, model_save_path=model_save_path)
svm_model = SVMModel(source=source,
source_stage=self,
saved_path=model_save_path)
svm_model.save()
weights = []
for code_index, code_id in code_map_inverse.iteritems():
for feature_index, feature in enumerate(features):
try:
if lin_clf.coef_.shape[0] == 1:
weight = lin_clf.coef_[0][feature_index]
else:
weight = lin_clf.coef_[code_index][
feature_index]
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
model_weight = SVMModelWeight(svm_model=svm_model,
code_id=code_id,
feature=feature,
weight=weight)
weights.append(model_weight)
SVMModelWeight.objects.bulk_create(weights)
try:
next_stage = self.get_next_stage()
except IndexError:
pass
else:
next_message_set = next_stage.message_set.messages.all()
code_assignments = []
next_X = coding_utils.get_formatted_X(
messages=next_message_set,
dictionary=dictionary,
source=source,
feature_index_map=feature_index_map,
feature_num=len(features),
use_tfidf=use_tfidf)
predict_y, prob = coding_utils.get_prediction(
lin_clf, next_X)
for idx, message in enumerate(next_message_set):
code_index = predict_y[idx]
code_id = code_map_inverse[code_index]
try:
if lin_clf.coef_.shape[0] == 1:
if code_index == 1:
probability = prob[idx]
else:
probability = 1 - prob[idx]
else:
probability = prob[idx, code_index]
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
code_assignment = coding_models.CodeAssignment(
message=message,
source=source,
code_id=code_id,
is_user_labeled=False,
probability=probability)
code_assignments.append(code_assignment)
coding_models.CodeAssignment.objects.bulk_create(
code_assignments)
except:
import traceback
traceback.print_exc()
import pdb
pdb.set_trace()
