def create_session_configs(configs_location=None, delete_old_configs=1):
if configs_location is None:
configs_location = ConfigReader.get_configs_location()
if delete_old_configs:
Storage.delete_location(configs_location)
configs = [SessionConfigReader.get_config_template()]
configs = SessionConfigBuilderCustom1.add_all_config_info(configs)
n_configs = len(configs)
SessionLogger.log('Constructed ' + str(n_configs) +
' new session configs from template: \'' +
ConfigReader.get_config_template_id() + '\'.')
config_ids = list()
idx = 0
for conf in configs:
config_id = configs_location + '/' + SessionConfigBuilderCustom1.config_name + str(
idx + 1)
SessionConfigReader.set_config(conf, config_id)
config_ids.append(config_id)
idx = idx + 1
SessionLogger.log('Stored ' + str(n_configs) +
' session configs in \'' + configs_location + '\'.')
return config_ids
def interpret_output(data_frame,
col_name=col_name_class_out,
new_col_name=col_name_result,
storage_level=0,
storage_name='',
log=1):
df = data_frame.copy()
category_list = CategoryListHandler.read_categories()
category_vectors = Vectorizer.get_word_vectors(category_list)
df[new_col_name] = df.apply(lambda x: [
ClassificationInterpreterCustom1.get_highest_similarity(
x[col_name], category_list, category_vectors)
],
axis=1)
log_text = 'Categories have been determined (' + str(len(
df.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
storage_name = storage_name + ClassificationInterpreterCustom1.ext_categorized
Storage.store_pd_frame(df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return df
def add_stopwords(stopwords):
db_type = ConfigReader.get_db_type()
session_id = ConfigReader.get_session_id()
if db_type == StopwordHandler.db_type_fs:
DiskStorageStopwordHandler.add_stopwords(session_id, stopwords)
SessionLogger.log(
str(len(stopwords)) + ' stop words have been added.')
def create_out_vectors(data_frame,
col_name=col_name_categories,
new_col_name=new_col_name_cat_vec,
storage_level=0,
storage_name=''):
classification_interpreter = SessionConfigReader.read_value(
ClassificationInterpreter.classification_interpreter_key)
if classification_interpreter == ClassificationInterpreter.classification_interpreter_custom1:
return ClassificationInterpreterCustom1.create_out_vectors(
data_frame,
col_name=col_name,
new_col_name=new_col_name,
storage_level=storage_level,
storage_name=storage_name)
elif classification_interpreter == ClassificationInterpreter.classification_interpreter_custom2:
return ClassificationInterpreterCustom2.create_out_vectors(
data_frame,
col_name=col_name,
new_col_name=new_col_name,
storage_level=storage_level,
storage_name=storage_name)
else:
SessionLogger.log(
'Tried to create category vectors. Specified ClassificationInterpreter is not supported.',
log_type='error')
return pd.DataFrame()
def import_docs(csv_path=None):
if csv_path is None:
session_folder = os.path.join(TenKGnadImporter.sessions_folder,
SessionConfigReader.get_session_id())
corpus_id = SessionConfigReader.read_value(
TenKGnadImporter.corpus_id_key)
corpus_id = DiskStorageMisc.get_identifier_path(corpus_id)
csv_path = os.path.join(session_folder,
corpus_id + TenKGnadImporter.csv_ext)
df = pd.read_csv(
csv_path,
sep=';',
quotechar='\'',
quoting=csv.QUOTE_MINIMAL,
header=None,
names=[TenKGnadImporter.category_name, TenKGnadImporter.text_name])
category_list = df[TenKGnadImporter.category_name].tolist()
df[TenKGnadImporter.category_name] = df.apply(
lambda x: [x[TenKGnadImporter.category_name]], axis=1)
head, f_name = os.path.split(csv_path)
identifier = f_name.split('.')[0]
Storage.store_pd_frame(df, identifier)
SessionLogger.log('TenKGnad Corpus (' + str(len(df.index)) +
' entries) has been imported into \'' + identifier +
'\' (columns: \'' + TenKGnadImporter.category_name +
'\', \'' + TenKGnadImporter.text_name + '\').')
category_set = set(category_list)
category_list = list(category_set)
CategoryListHandler.set_categories(category_list)
return identifier
def remove_stopwords(data_frame,
custom_stop_words=None,
download_live_stopwords=0,
col_name=col_name,
storage_level=0,
storage_name='',
log=1):
df = data_frame.copy()
stop_words = StopwordHandler.read_stopwords()
if download_live_stopwords:
stop_words = stop_words.union(
StopwordDownloaderNLTK.get_stopwords(store=0))
stop_words = StopWordRemoverCustom.capitalize_words(stop_words)
if custom_stop_words is not None:
stop_words = stop_words.union(custom_stop_words)
df[StopWordRemoverCustom.new_col_name] = df.apply(
lambda x: StopWordRemoverCustom.process_text(
x[col_name], stop_words),
axis=1)
log_text = 'Removed stop words from documents (' + str(len(
df.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
Storage.store_pd_frame(df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + StopWordRemoverCustom.new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return df
def create_out_vectors(data_frame,
col_name=col_name_categories,
new_col_name=new_col_name_cat_vec,
storage_level=0,
storage_name=''):
df = data_frame.copy()
df[ClassificationInterpreterCustom1.one_word_cat] = df.apply(
lambda x: ClassificationInterpreterCustom1.extract_one_word_cat(x[
col_name]),
axis=1)
vectorized_df = Vectorizer.vectorize(
df,
col_name=ClassificationInterpreterCustom1.one_word_cat,
new_col_name=new_col_name,
storage_level=0,
log=0)
vectorized_df = vectorized_df.drop(
columns=[ClassificationInterpreterCustom1.one_word_cat])
vectorized_df[new_col_name] = vectorized_df.apply(
lambda x: (x[new_col_name] + 1) / 2,
axis=1) # adjust to softmax codomain
log_text = 'Category vectors for classifier training have been created (' + str(
len(data_frame.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
storage_name = storage_name + ClassificationInterpreterCustom1.ext_out_vecs
Storage.store_pd_frame(vectorized_df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'
SessionLogger.log(log_text)
return vectorized_df
def remove_stopwords(data_frame,
custom_stop_words=None,
download_live_stopwords=0,
col_name=col_name,
storage_level=0,
storage_name='',
log=1):
stopwordremover = SessionConfigReader.read_value(
StopWordRemover.stopwordremover_key)
if custom_stop_words is None:
custom_stop_words = list()
custom_stop_words.extend(
SessionConfigReader.read_value(StopWordRemover.custom_sw_key))
if stopwordremover == StopWordRemover.stopwordremover_custom:
return StopWordRemoverCustom.remove_stopwords(
data_frame,
custom_stop_words=custom_stop_words,
download_live_stopwords=download_live_stopwords,
col_name=col_name,
storage_level=storage_level,
storage_name=storage_name,
log=log)
else:
if log:
SessionLogger.log(
'Tried to remove stopwords from documents. Specified Stopword Remover not supported.',
log_type='error')
return pd.DataFrame()
def split_train_test(identifier=None, data_frame=None):
if data_frame is None:
data_frame = Storage.load_pd_frame(identifier)
split_ratio = SessionConfigReader.read_value(
TrainTestSplitterCustom1.split_ratio_key)
if split_ratio > 1:
split_ratio = 1
random_state = SessionConfigReader.read_value(
TrainTestSplitterCustom1.random_state_key)
if isinstance(random_state, int):
train = data_frame.sample(frac=split_ratio,
random_state=random_state)
else:
train = data_frame.sample(frac=split_ratio)
test = data_frame.drop(train.index)
if identifier is None:
identifier = SessionConfigReader.read_value(
TrainTestSplitterCustom1.corpus_identifier_key)
train_name = identifier + TrainTestSplitterCustom1.ext_train
test_name = identifier + TrainTestSplitterCustom1.ext_test
Storage.store_pd_frame(train, train_name)
Storage.store_pd_frame(test, test_name)
SessionLogger.log('Split \'' + identifier + '\' (' +
str(len(data_frame.index)) + ' entries) into \'' +
train_name + '\' (' + str(len(train.index)) +
' entries) and \'' + test_name + '\' (' +
str(len(test.index)) + ' entries).')
def interpret_output(data_frame,
col_name=col_name_class_out,
new_col_name=col_name_result,
storage_level=0,
storage_name='',
log=1):
classification_interpreter = SessionConfigReader.read_value(
ClassificationInterpreter.classification_interpreter_key)
if classification_interpreter == ClassificationInterpreter.classification_interpreter_custom1:
return ClassificationInterpreterCustom1.interpret_output(
data_frame,
col_name=col_name,
new_col_name=new_col_name,
storage_level=storage_level,
storage_name=storage_name,
log=log)
elif classification_interpreter == ClassificationInterpreter.classification_interpreter_custom2:
return ClassificationInterpreterCustom2.interpret_output(
data_frame,
col_name=col_name,
new_col_name=new_col_name,
storage_level=storage_level,
storage_name=storage_name,
log=log)
else:
SessionLogger.log(
'Tried to interpret output vectors. Specified ClassificationInterpreter is not supported.',
log_type='error')
return pd.DataFrame()
def preprocess_texts(data_frame, col_name=col_name, new_col_name=new_col_name, storage_level=0, storage_name='', log=1):
preprocessor_type = SessionConfigReader.read_value(TextPreprocessor.preprocessor_key)
if preprocessor_type == TextPreprocessor.preprocessor_custom:
return TextPreprocessorCustom.preprocess_texts(data_frame, col_name=col_name, new_col_name=new_col_name, storage_level=storage_level, storage_name=storage_name, log=log)
else:
if log:
SessionLogger.log('Tried to preprocess texts. Specified Preprocessor is not supported.', log_type='error')
return pd.DataFrame()
def normalize(data_frame, col_name=col_name, storage_level=0, storage_name='', log=1):
lemmatizer = SessionConfigReader.read_value(Lemmatizer.lemmatizer_key)
if lemmatizer == Lemmatizer.lemmatizer_spacy_german:
return LemmatizerSpacyGerman.normalize(data_frame, col_name=col_name, storage_level=storage_level, storage_name=storage_name, log=log)
else:
if log:
SessionLogger.log('Tried to lemmatize documents. Specified Lemmatizer not supported.', log_type='error')
return pd.DataFrame()
def get_stopwords(language=german, store=1, log=1):
nltk.download('stopwords')
sw = set(stopwords.words(language))
log_text = str(len(sw)) + ' stop words downloaded from NLTK.'
if log:
SessionLogger.log(log_text)
if store:
StopwordHandler.set_stopwords(sw)
return sw
def get_model_id():
classifier_type = SessionConfigReader.read_value(
Classifier.classifier_key)
if classifier_type == Classifier.classifier_keras_nn:
return ClassifierKerasNN.get_model_id()
else:
SessionLogger.log(
'Tried to get classifier model id. Specified Classifier is not supported.',
log_type='error')
return ''
def remove_noise(data_frame, col_name=col_name, storage_level=0, storage_name='', log=1):
df = data_frame.copy()
df[NoiseRemoverCustom.new_col_name] = df.apply(lambda x: NoiseRemoverCustom.process_text(x[col_name]), axis=1)
log_text = 'Removed noise from documents (' + str(len(df.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
Storage.store_pd_frame(df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + NoiseRemoverCustom.new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return df
def import_docs():
importer_type = SessionConfigReader.read_value(
CorpusImporter.corpus_importer_key)
if importer_type == CorpusImporter.tenkgnad_importer:
return TenKGnadImporter.import_docs()
else:
SessionLogger.log(
'Tried to import corpus. Specified Corpus Importer is not supported.',
log_type='error')
return ''
def preprocess_texts(data_frame,
col_name=col_name,
new_col_name=col_name_preprocessed,
storage_level=0,
storage_name='',
log=1):
storage_name_ext = storage_name
if storage_name != '':
storage_name_ext = storage_name + TextPreprocessorCustom.ext_noise_removed
noise_removed_df = NoiseRemover.remove_noise(
data_frame,
col_name=col_name,
storage_level=storage_level - 1,
storage_name=storage_name_ext,
log=log)
if storage_name != '':
storage_name_ext = storage_name + TextPreprocessorCustom.ext_stops_removed
stops_removed_df = StopWordRemover.remove_stopwords(
noise_removed_df,
col_name=TextPreprocessorCustom.col_name_noise_removed,
storage_level=storage_level - 1,
storage_name=storage_name_ext,
log=log)
if storage_name != '':
storage_name_ext = storage_name + TextPreprocessorCustom.ext_lemmatized
processed_texts_df = Lemmatizer.normalize(
stops_removed_df,
col_name=TextPreprocessorCustom.col_name_stops_removed,
storage_level=storage_level - 1,
storage_name=storage_name_ext,
log=log)
if storage_level <= 1:
processed_texts_df = processed_texts_df.drop(
columns=[TextPreprocessorCustom.col_name_noise_removed])
processed_texts_df = processed_texts_df.drop(
columns=[TextPreprocessorCustom.col_name_stops_removed])
processed_texts_df = processed_texts_df.rename(
columns={TextPreprocessorCustom.col_name_lemmatized: new_col_name})
log_text = 'Documents have been preprocessed (' + str(
len(data_frame.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
Storage.store_pd_frame(
processed_texts_df,
storage_name + TextPreprocessorCustom.ext_preprocessed)
log_text = log_text + ' Stored in \'' + storage_name + TextPreprocessorCustom.ext_preprocessed + '\' (column: \'' + new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return processed_texts_df
def create_model(data_frame, new_model_id=None, col_name=col_name):
vectorizer_type = SessionConfigReader.read_value(
Vectorizer.vectorizer_key)
if vectorizer_type == Vectorizer.vectorizer_gensim_w2v:
return VectorizerGensimWord2Vec.create_model(
data_frame, new_model_id=new_model_id, col_name=col_name)
else:
SessionLogger.log(
'Tried to create vector model. Specified Vectorizer is not supported.',
log_type='error')
return ''
def split_train_test(identifier=None, data_frame=None):
tt_splitter_type = SessionConfigReader.read_value(
TrainTestSplitter.tt_splitter_key)
if tt_splitter_type == TrainTestSplitter.tt_splitter_custom1:
TrainTestSplitterCustom1.split_train_test(identifier=identifier,
data_frame=data_frame)
else:
SessionLogger.log(
'Tried to split \'' + identifier +
'\' into train and test set. Specified TrainTestSplitter is not supported.',
log_type='error')
def delete_from_folder(path):
if os.path.exists(path):
for filename in os.listdir(path):
file_path = os.path.join(path, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
SessionLogger.log('Failed to delete %s. Reason: %s' %
(file_path, e))
def resume_config_tests_at_idx(idx, run_import=0, run_preprocessing=0, run_vectorization=0):
SessionLogger.log('Resuming config tests at config # ' + str(idx) + ' ...')
if idx > 0:
idx = idx - 1
configs_location = SessionConfigBuilder.get_configs_location()
config_ids = Storage.list_ids(configs_location)
config_ids = SetupRunner.sort_config_list(config_ids)
config_ids_with_dir = list()
for c_id in config_ids:
config_ids_with_dir.append(configs_location + '/' + c_id)
SessionLogger.log('Config ID list has been restored.')
return SetupRunner.run_config_tests(run_import=run_import, run_preprocessing=run_preprocessing, run_vectorization=run_vectorization, config_ids=config_ids_with_dir, resume_at_idx=idx)
def normalize(data_frame,
col_name=col_name,
storage_level=0,
storage_name='',
log=1):
df = data_frame.copy()
df[LemmatizerSpacyGerman.new_col_name] = df.apply(
lambda x: LemmatizerSpacyGerman.process_text(x[col_name]), axis=1)
log_text = 'Documents lemmatized with spacy (' + str(len(
df.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
Storage.store_pd_frame(df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + LemmatizerSpacyGerman.new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return df
def train_model(data_frame,
model_id=None,
fv_col_name=fv_col_name,
cat_v_col_name=cat_v_col_name):
classifier_type = SessionConfigReader.read_value(
Classifier.classifier_key)
if classifier_type == Classifier.classifier_keras_nn:
return ClassifierKerasNN.train_model(data_frame,
model_id=model_id,
fv_col_name=fv_col_name,
cat_v_col_name=cat_v_col_name)
else:
SessionLogger.log(
'Tried to train classifier model. Specified Classifier is not supported.',
log_type='error')
return ''
def remove_noise(data_frame,
col_name=col_name,
storage_level=0,
storage_name='',
log=1):
noiseremover_type = SessionConfigReader.read_value(
NoiseRemover.noiseremover_key)
if noiseremover_type == NoiseRemover.noiseremover_custom:
return NoiseRemoverCustom.remove_noise(data_frame,
col_name=col_name,
storage_level=storage_level,
storage_name=storage_name,
log=log)
else:
if log:
SessionLogger.log(
'Tried to remove noise from documents. Specified Noise Remover not supported.',
log_type='error')
return pd.DataFrame()
def create_out_vectors(data_frame,
col_name=col_name_categories,
new_col_name=new_col_name_cat_vec,
storage_level=0,
storage_name=''):
data_frame[new_col_name] = data_frame.apply(
lambda x: ClassificationInterpreterCustom2.get_cat_vec(x[col_name]
),
axis=1)
log_text = 'Category vectors for classifier training have been created (' + str(
len(data_frame.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
storage_name = storage_name + ClassificationInterpreterCustom2.ext_out_vecs
Storage.store_pd_frame(data_frame, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'
SessionLogger.log(log_text)
return data_frame
def evaluate_output(data_frame,
col_name_categories=col_name_categories,
col_name_outputs=col_name_result):
classification_interpreter = SessionConfigReader.read_value(
ClassificationInterpreter.classification_interpreter_key)
if classification_interpreter == ClassificationInterpreter.classification_interpreter_custom1:
return ClassificationInterpreterCustom1.evaluate_output(
data_frame,
col_name_categories=col_name_categories,
col_name_outputs=col_name_outputs)
elif classification_interpreter == ClassificationInterpreter.classification_interpreter_custom2:
return ClassificationInterpreterCustom2.evaluate_output(
data_frame,
col_name_categories=col_name_categories,
col_name_outputs=col_name_outputs)
else:
SessionLogger.log(
'Tried to evaluate classification. Specified ClassificationInterpreter is not supported.',
log_type='error')
return 0
def run_config_tests(run_import=0, run_preprocessing=0, run_vectorization=0, config_ids=None, resume_at_idx=0):
if config_ids is None:
config_ids = SessionConfigBuilder.create_session_configs()
n_configs = len(config_ids)
idx = resume_at_idx
while idx < len(config_ids):
config_id = config_ids[idx]
ConfigReader.set_session_config_id(config_id)
SetupRunner.run_setup(run_import=run_import, run_preprocessing=run_preprocessing, run_vectorization=run_vectorization, run_classification=0)
res = SetupRunner.run_classification_test()
score = ClassificationInterpreter.evaluate_output(res)
idx = idx + 1
SessionLogger.log('Evaluated config # ' + str(idx) + ' / ' + str(n_configs) + ' . Score: ' + str(score))
EvaluationHandler.sort()
evaluations = EvaluationHandler.load_evaluations()
return evaluations
def classify(data_frame,
model_id=None,
col_name=fv_col_name,
new_col_name=class_out_col_name,
storage_level=0,
storage_name='',
log=1):
df = data_frame.copy()
if model_id is None:
model_id = ClassifierKerasNN.get_model_id()
model = Storage.load_h5_model(model_id)
df[new_col_name] = df.apply(
lambda x: model.predict(np.asarray([x[col_name]]))[0], axis=1)
log_text = 'Classified documents (' + str(len(df.index)) + ' entries).'
if storage_level >= 1 and storage_name != '':
storage_name = storage_name + ClassifierKerasNN.ext_classified
Storage.store_pd_frame(df, storage_name)
log_text = log_text + ' Stored in \'' + storage_name + '\' (column: \'' + new_col_name + '\').'
if log:
SessionLogger.log(log_text)
return df
def classify(data_frame,
model_id=None,
col_name=fv_col_name,
new_col_name=class_out_col_name,
storage_level=0,
storage_name='',
log=1):
classifier_type = SessionConfigReader.read_value(
Classifier.classifier_key)
if classifier_type == Classifier.classifier_keras_nn:
return ClassifierKerasNN.classify(data_frame,
model_id=model_id,
col_name=col_name,
new_col_name=new_col_name,
storage_level=storage_level,
storage_name=storage_name,
log=log)
else:
if log:
SessionLogger.log(
'Tried to classify data. Specified Classifier is not supported.',
log_type='error')
return pd.DataFrame()
def train_model(data_frame,
model_id=None,
fv_col_name=fv_col_name,
cat_v_col_name=cat_v_col_name):
# read config params
config_keys = list()
config_keys.append(ClassifierKerasNN.model_id_key)
config_keys.append(ClassifierKerasNN.epochs_key)
config_keys.append(ClassifierKerasNN.batch_size_key)
config = SessionConfigReader.read_values(config_keys)
if model_id is None:
model_id = config[0]
epochs = config[1]
batch_size = config[2]
# extract vector lists from data frame
doc_vectors = data_frame[fv_col_name].tolist()
cat_vectors = data_frame[cat_v_col_name].tolist()
# load the model
model = Storage.load_h5_model(model_id)
# train the model
model.fit(np.asarray(doc_vectors),
np.asarray(cat_vectors),
epochs=epochs,
batch_size=batch_size)
# store the model
Storage.store_h5_model(model, model_id)
# make log entry
SessionLogger.log('Trained keras neural network \'' + model_id +
'\' with ' + str(len(data_frame.index)) +
' new entries.')
return model_id
def __init__(self, server):
self.sessionLogger = SessionLogger()
self.server = server
self.openSessions = {}
class SessionAdmin:
def __init__(self, server):
self.sessionLogger = SessionLogger()
self.server = server
self.openSessions = {}
def closeAll(self, error):
self.sessionLogger.closeAllSessions(error)
def newPostProcessSession(self, tutorId):
session = PostProcessSession(self.server, self.sessionLogger, tutorId, POST_PROCESS)
self.openSessions[tutorId] = session
session.newSession()
def newOfflineSession(self, tutorId, pupilId):
session = OfflineQuestionSession(self.server, self.sessionLogger, tutorId, OFFLINE_QUESTION)
self.openSessions[tutorId] = session
session.newSession(OFFLINE_QUESTION, pupilId)
def newSession(self, roomId, kind, status, pupilId="NULL"):
if kind == EXTRA_IACLASS:
session = ExtraSession(self.server, self.sessionLogger, roomId, kind)
else:
session = IAPASession(self.server, self.sessionLogger, roomId, kind)
self.openSessions[roomId] = session
session.newSession(status, pupilId)
def closeSession(self, roomId):
session = self.openSessions[roomId]
session.closeSession()
del(self.openSessions[roomId])
def hasOpenSession(self, roomId):
return (roomId in self.openSessions.keys())
def tutorDecide(self, roomId, status=DECIDING, pupilId="NULL"):
session = self.openSessions[roomId]
session.tutorDecide(pupilId)
def tutorEnter(self, roomId, status, pupilId="NULL"):
session = self.openSessions[roomId]
session.tutorEnter(status, pupilId)
def tutorReject(self, roomId, status, pupilId="NULL"):
session = self.openSessions[roomId]
session.tutorReject(status, pupilId)
def tutorEnd(self, roomId, status, pupilId="NULL"):
session = self.openSessions[roomId]
if session.tutorEnd(status, pupilId):
del(self.openSessions[roomId])
def tutorQuit(self, roomId):
session = self.openSessions[roomId]
if session.tutorQuit():
del(self.openSessions[roomId])
def pupilEnter(self, roomId, pupilId):
session = self.openSessions[roomId]
session.pupilEnter(pupilId)
def pupilEnd(self, roomId, status, pupilId="NULL"):
session = self.openSessions[roomId]
if session.pupilEnd(status, pupilId):
del(self.openSessions[roomId])
def changeSessionKind(self, roomId, kind, pupilId=None):
self.closeSession(roomId)
if pupilId != None:
self.newSession(roomId, kind, IACLASS, pupilId)
else:
self.newSession(roomId, kind, WAITING)
def sessionKey(self, roomId):
print self.openSessions
try:
session = self.openSessions[roomId]
return session.sessionKey()
except KeyError:
return None