def api_get_project_info(project_id): # noqa: F401
"""Get info on the article"""
logging.info("get project info")
try:
# read the file with project info
with open(get_project_file_path(project_id), "r") as fp:
project_info = json.load(fp)
# check if there is a dataset
try:
get_data_file_path(project_id)
project_info["projectHasDataset"] = True
except Exception:
project_info["projectHasDataset"] = False
# check if there is a prior knowledge (check if there is a model set),
# if this is the case, the reviewer past the prior knowledge screen.
project_info["projectHasPriorKnowledge"] = \
get_kwargs_path(project_id).exists()
# check if there is a prior knowledge (check if there is a model set),
# if this is the case, the reviewer past the prior knowledge screen.
project_info["projectHasAlgorithms"] = \
get_kwargs_path(project_id).exists()
# backwards support <0.10
if "projectInitReady" not in project_info:
if project_info["projectHasPriorKnowledge"]:
project_info["projectInitReady"] = True
else:
project_info["projectInitReady"] = False
response = jsonify(project_info)
except FileNotFoundError as err:
logging.error(err)
response = jsonify(message="Project not found.")
return response, 400
except Exception as err:
logging.error(err)
response = jsonify(message="Internal Server Error.")
return response, 500
return response
def remove_dataset_to_project(project_id, file_name):
"""Remove dataset from project
"""
project_file_path = get_project_file_path(project_id)
fp_lock = get_lock_path(project_id)
with SQLiteLock(fp_lock,
blocking=True,
lock_name="active",
project_id=project_id):
# open the projects file
with open(project_file_path, "r") as f_read:
project_dict = json.load(f_read)
# remove the path from the project file
data_fn = project_dict["dataset_path"]
del project_dict["dataset_path"]
with open(project_file_path, "w") as f_write:
json.dump(project_dict, f_write)
# files to remove
data_path = get_data_file_path(project_id, data_fn)
pool_path = get_pool_path(project_id)
labeled_path = get_labeled_path(project_id)
os.remove(str(data_path))
os.remove(str(pool_path))
os.remove(str(labeled_path))
def read_data(project_id, use_cache=True, save_cache=True):
"""Get ASReviewData object from file.
Parameters
----------
project_id: str, iterable
The project identifier.
use_cache: bool
Use the pickle file if available.
save_cache: bool
Save the file to a pickle file if not available.
Returns
-------
ASReviewData:
The data object for internal use in ASReview.
"""
# use cache file
if use_cache:
try:
return _read_data_from_cache(project_id)
except CacheDataError:
pass
# load from file
fp_data = get_data_file_path(project_id)
data_obj = ASReviewData.from_file(fp_data)
# save a pickle version
if save_cache:
_write_data_to_cache(project_id, data_obj)
return data_obj
def api_get_project_data(project_id): # noqa: F401
"""Get info on the article"""
if not is_project(project_id):
response = jsonify(message="Project not found.")
return response, 404
try:
filename = get_data_file_path(project_id).stem
# get statistics of the dataset
statistics = get_data_statistics(project_id)
statistics["filename"] = filename
except FileNotFoundError as err:
print(err)
statistics = {"filename": None}
except Exception as err:
print(err)
message = f"Failed to get file. {err}"
return jsonify(message=message), 400
response = jsonify(statistics)
response.headers.add('Access-Control-Allow-Origin', '*')
return response
def api_get_project_info(project_id): # noqa: F401
"""Get info on the article"""
try:
# read the file with project info
with open(get_project_file_path(project_id), "r") as fp:
project_info = json.load(fp)
# check if there is a dataset
try:
get_data_file_path(project_id)
project_info["projectHasDataset"] = True
except Exception:
project_info["projectHasDataset"] = False
# check if there is a prior knowledge (check if there is a model set),
# if this is the case, the reviewer past the prior knowledge screen.
project_info["projectHasPriorKnowledge"] = \
get_kwargs_path(project_id).exists()
# check if there is a prior knowledge (check if there is a model set),
# if this is the case, the reviewer past the prior knowledge screen.
project_info["projectHasAlgorithms"] = \
get_kwargs_path(project_id).exists()
# backwards support <0.10
if "projectInitReady" not in project_info:
if project_info["projectHasPriorKnowledge"]:
project_info["projectInitReady"] = True
else:
project_info["projectInitReady"] = False
except FileNotFoundError:
raise ProjectNotFoundError()
return jsonify(project_info)
def read_data(project_id, save_tmp=True):
"""Get ASReviewData object from file.
Parameters
----------
project_id: str, iterable
The project identifier.
save_tmp: bool
Save the file to a pickle file if not available.
Returns
-------
ASReviewData:
The data object for internal use in ASReview.
"""
fp_data = get_data_file_path(project_id)
fp_data_pickle = Path(fp_data).with_suffix(fp_data.suffix + ".pickle")
try:
# get the pickle data
with open(fp_data_pickle, 'rb') as f_pickle_read:
data_obj = pickle.load(f_pickle_read)
return data_obj
except FileNotFoundError:
# file not available
data_obj = ASReviewData.from_file(fp_data)
except pickle.PickleError:
# problem loading pickle file
# remove the pickle file
os.remove(fp_data_pickle)
data_obj = ASReviewData.from_file(fp_data)
# save a pickle version
if save_tmp:
logging.info("Store a copy of the data in a pickle file.")
with open(fp_data_pickle, 'wb') as f_pickle:
pickle.dump(data_obj, f_pickle)
return data_obj
def read_data(project_id):
"""Get ASReviewData object of the dataset"""
dataset = get_data_file_path(project_id)
return ASReviewData.from_file(dataset)
def _get_cache_data_path(project_id):
fp_data = get_data_file_path(project_id)
return get_data_file_path(project_id) \
.with_suffix(fp_data.suffix + ".pickle")
def train_model(project_id, label_method=None):
"""Add the new labels to the review and do the modeling.
It uses a lock to ensure only one model is running at the same time.
Old results directories are deleted after 4 iterations.
It has one argument on the CLI, which is the base project directory.
"""
logging.info(f"Project {project_id} - Train a new model for project")
# get file locations
asr_kwargs_file = get_kwargs_path(project_id)
lock_file = get_lock_path(project_id)
# Lock so that only one training run is running at the same time.
# It doesn't lock the flask server/client.
with SQLiteLock(lock_file,
blocking=False,
lock_name="training",
project_id=project_id) as lock:
# If the lock is not acquired, another training instance is running.
if not lock.locked():
logging.info("Project {project_id} - "
"Cannot acquire lock, other instance running.")
return
# Lock the current state. We want to have a consistent active state.
# This does communicate with the flask backend; it prevents writing and
# reading to the same files at the same time.
with SQLiteLock(lock_file,
blocking=True,
lock_name="active",
project_id=project_id) as lock:
# Get the all labels since last run. If no new labels, quit.
new_label_history = read_label_history(project_id)
data_fp = str(get_data_file_path(project_id))
as_data = read_data(project_id)
state_file = get_state_path(project_id)
# collect command line arguments and pass them to the reviewer
with open(asr_kwargs_file, "r") as fp:
asr_kwargs = json.load(fp)
asr_kwargs['state_file'] = str(state_file)
reviewer = get_reviewer(dataset=data_fp, mode="minimal", **asr_kwargs)
with open_state(state_file) as state:
old_label_history = get_label_train_history(state)
diff_history = get_diff_history(new_label_history, old_label_history)
if len(diff_history) == 0:
logging.info(
"Project {project_id} - No new labels since last run.")
return
query_idx = np.array([x[0] for x in diff_history], dtype=int)
inclusions = np.array([x[1] for x in diff_history], dtype=int)
# Classify the new labels, train and store the results.
with open_state(state_file) as state:
reviewer.classify(query_idx,
inclusions,
state,
method=label_method)
reviewer.train()
reviewer.log_probabilities(state)
new_query_idx = reviewer.query(reviewer.n_pool()).tolist()
reviewer.log_current_query(state)
proba = state.pred_proba.tolist()
with SQLiteLock(lock_file,
blocking=True,
lock_name="active",
project_id=project_id) as lock:
current_pool = read_pool(project_id)
in_current_pool = np.zeros(len(as_data))
in_current_pool[current_pool] = 1
new_pool = [x for x in new_query_idx if in_current_pool[x]]
write_pool(project_id, new_pool)
write_proba(project_id, proba)
def train_model(project_id, label_method=None):
"""Add the new labels to the review and do the modeling.
It uses a lock to ensure only one model is running at the same time.
Old results directories are deleted after 4 iterations.
It has one argument on the CLI, which is the base project directory.
"""
logging.info(f"Project {project_id} - Train a new model for project")
# get file locations
asr_kwargs_file = get_kwargs_path(project_id)
lock_file = get_lock_path(project_id)
# Lock so that only one training run is running at the same time.
# It doesn't lock the flask server/client.
with SQLiteLock(
lock_file, blocking=False, lock_name="training",
project_id=project_id) as lock:
# If the lock is not acquired, another training instance is running.
if not lock.locked():
logging.info("Project {project_id} - "
"Cannot acquire lock, other instance running.")
return
# Lock the current state. We want to have a consistent active state.
# This does communicate with the flask backend; it prevents writing and
# reading to the same files at the same time.
with SQLiteLock(
lock_file,
blocking=True,
lock_name="active",
project_id=project_id) as lock:
# Get the all labels since last run. If no new labels, quit.
new_label_history = read_label_history(project_id)
data_fp = str(get_data_file_path(project_id))
as_data = read_data(project_id)
state_file = get_state_path(project_id)
# collect command line arguments and pass them to the reviewer
with open(asr_kwargs_file, "r") as fp:
asr_kwargs = json.load(fp)
try:
del asr_kwargs["abstract_only"]
except KeyError:
pass
asr_kwargs['state_file'] = str(state_file)
reviewer = get_reviewer(dataset=data_fp, mode="minimal", **asr_kwargs)
with open_state(state_file) as state:
old_label_history = _get_label_train_history(state)
diff_history = _get_diff_history(new_label_history, old_label_history)
if len(diff_history) == 0:
logging.info(
"Project {project_id} - No new labels since last run.")
return
query_record_ids = np.array([x[0] for x in diff_history], dtype=int)
inclusions = np.array([x[1] for x in diff_history], dtype=int)
query_idx = convert_id_to_idx(as_data, query_record_ids)
# Classify the new labels, train and store the results.
with open_state(state_file) as state:
reviewer.classify(
query_idx, inclusions, state, method=label_method)
reviewer.train()
reviewer.log_probabilities(state)
new_query_idx = reviewer.query(reviewer.n_pool()).tolist()
reviewer.log_current_query(state)
# write the proba to a pandas dataframe with record_ids as index
proba = pd.DataFrame(
{"proba": state.pred_proba.tolist()},
index=pd.Index(as_data.record_ids, name="record_id")
)
# update the pool and output the proba's
# important: pool is sorted on query
with SQLiteLock(
lock_file,
blocking=True,
lock_name="active",
project_id=project_id) as lock:
# read the pool
current_pool = read_pool(project_id)
# diff pool and new_query_ind
current_pool_idx = convert_id_to_idx(as_data, current_pool)
current_pool_idx = frozenset(current_pool_idx)
new_pool_idx = [x for x in new_query_idx if x in current_pool_idx]
# convert new_pool_idx back to record_ids
new_pool = convert_idx_to_id(as_data, new_pool_idx)
# write the pool and proba
write_pool(project_id, new_pool)
write_proba(project_id, proba)
