# Copyright 2019 The ASReview Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import numpy as np
from asreview.init_sampling import sample_prior_knowledge
from asreview.review import BaseReview
[docs]class ReviewSimulate(BaseReview):
"""Automated Systematic Review in simulation mode."""
name = "simulate"
def __init__(self,
as_data,
*args,
n_prior_included=0,
n_prior_excluded=0,
prior_idx=None,
init_seed=None,
**kwargs):
self.n_prior_included = n_prior_included
self.n_prior_excluded = n_prior_excluded
if prior_idx is not None and len(prior_idx) != 0:
start_idx = prior_idx
else:
labels = as_data.labels
labeled_idx = np.where((labels == 0) | (labels == 1))[0]
if len(labeled_idx) != len(labels):
logging.warning("Simulating partial review, ignoring unlabeled"
f" papers (n={len(labels)-len(labeled_idx)}.")
as_data = as_data.slice(labeled_idx)
labels = as_data.labels
print(labels)
start_idx = as_data.prior_data_idx
if len(start_idx) == 0 and n_prior_included + n_prior_excluded > 0:
start_idx = sample_prior_knowledge(
labels, n_prior_included, n_prior_excluded,
random_state=init_seed)
super(ReviewSimulate, self).__init__(
as_data, *args, start_idx=start_idx, **kwargs)
def _get_labels(self, ind):
"""Get the labels directly from memory.
Arguments
---------
ind: list, np.array
A list with indices
Returns
-------
list, np.array
The corresponding true labels for each indice.
"""
return self.y[ind, ]