def train():
model_id = int(request.form["model_id"])
model = db.session.query(EmbeddingModel).get(
model_id) # type: EmbeddingModel
if model is None:
raise ValueError("No embedding model found by id {0}".format(model_id))
separator = request.form["separator"]
text_column = int(request.form["text_column"])
tag_column = int(request.form["tag_column"])
positive_tag = request.form["positive_tag"]
negative_tag = request.form["negative_tag"]
uploaded_files = []
for filename, file_stream in request.files.items():
file = File()
file.separator = separator
file.text_column = text_column
file.tag_column = tag_column
file.positive_tag = positive_tag
file.negative_tag = negative_tag
uploaded_files.append(file_logic.save(file, file_stream))
extend_vocabulary = True if request.form[
"extend_vocabulary"] == "true" else False
model = emb_model_logic.init_training(model_id,
[file.id for file in uploaded_files],
extend_vocabulary)
return jsonify(model.to_dict())
def train():
net_id = int(request.form["net_id"])
net = net_logic.get(net_id) # type: Net
separator = request.form["separator"]
text_column = int(request.form["text_column"])
tag_column = int(request.form["tag_column"])
positive_tag = request.form["positive_tag"]
negative_tag = request.form["negative_tag"]
uploaded_files = []
for filename, file_stream in request.files.items():
file = File()
file.separator = separator
file.text_column = text_column
file.tag_column = tag_column
file.positive_tag = positive_tag
file.negative_tag = negative_tag
uploaded_files.append(file_logic.save(file, file_stream))
extend_vocabulary = True if request.form[
"extend_vocabulary"] == "true" else False
net = net_logic.init_training(net.id, [file.id for file in uploaded_files],
extend_vocabulary)
net_info = net.to_dict(_hide=[])
return jsonify(net_info)
def add_files(text_set_id):
text_set_id = int(text_set_id)
text_set = text_set_logic.get(text_set_id)
uploaded_files = []
for filename, file_stream in request.files.items():
fileindex = re.search("file\\[(?P<index>\d+)\\]", filename).group('index')
text_column = request.form.get("text_column[{}]".format(fileindex), '')
text_column = int(text_column) if text_column else 1
separator = request.form.get("separator[{}]".format(fileindex), '')
separator = separator if separator else ','
file = File()
file.separator = separator
file.text_column = text_column
uploaded_files.append(file_logic.save(file, file_stream))
text_set_logic.add_files_to_text_set(text_set_id, [file.id for file in uploaded_files])
return jsonify(text_set.to_dict(_hide=[]))
def extend():
vocabulary_id = request.form["vocabulary_id"]
separator = request.form["file"]["separator"]
text_column = request.form["file"]["text_column"]
tag_column = request.form["file"]["tag_column"]
positive_tag = request.form["file"]["positive_tag"]
negative_tag = request.form["file"]["negative_tag"]
uploaded_files = []
for filename, file_stream in request.files.items():
file = File()
file.separator = separator
file.text_column = text_column
file.tag_column = tag_column
file.positive_tag = positive_tag
file.negative_tag = negative_tag
uploaded_files.append(file_logic.save(file, file_stream))
vocabulary = vocabulary_logic.init_extending(
vocabulary_id, [file.id for file in uploaded_files])
return jsonify(vocabulary.to_dict())