# Copyright 2019-2022 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.
__all__ = [
"n_records",
"n_relevant",
"n_irrelevant",
"n_unlabeled",
"n_missing_title",
"n_missing_abstract",
"title_length",
"abstract_length",
"n_keywords",
"n_duplicates",
]
import numpy as np
[docs]
def n_records(data):
"""Return the number of records.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
return len(data)
[docs]
def n_relevant(data):
"""Return the number of relevant records.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.labels is not None:
return len(np.where(data.labels == 1)[0])
return None
[docs]
def n_irrelevant(data):
"""Return the number of irrelevant records.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.labels is None:
return None
return len(np.where(data.labels == 0)[0])
[docs]
def n_unlabeled(data):
"""Return the number of unlabeled records.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.labels is None:
return None
return len(data.labels) - n_relevant(data) - n_irrelevant(data)
[docs]
def n_missing_title(data):
"""Return the number of records with missing titles.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
n_missing = 0
if data.title is None:
return None, None
if data.labels is None:
n_missing_included = None
else:
n_missing_included = 0
for i in range(len(data.title)):
if len(data.title[i]) == 0:
n_missing += 1
if data.labels is not None and data.labels[i] == 1:
n_missing_included += 1
return n_missing, n_missing_included
[docs]
def n_missing_abstract(data):
"""Return the number of records with missing abstracts.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
n_missing = 0
if data.abstract is None:
return None, None
if data.labels is None:
n_missing_included = None
else:
n_missing_included = 0
for i in range(len(data.abstract)):
if len(data.abstract[i]) == 0:
n_missing += 1
if data.labels is not None and data.labels[i] == 1:
n_missing_included += 1
return n_missing, n_missing_included
[docs]
def title_length(data):
"""Return the average length of the titles.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.title is None:
return None
avg_len = 0
for i in range(len(data.title)):
avg_len += len(data.title[i])
return avg_len / len(data.title)
[docs]
def abstract_length(data):
"""Return the average length of the abstracts.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.abstract is None:
return None
avg_len = 0
for i in range(len(data.abstract)):
avg_len += len(data.abstract[i])
return avg_len / len(data.abstract)
[docs]
def n_keywords(data):
"""Return the number of keywords.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
Return
------
int:
The statistic
"""
if data.keywords is None:
return None
return np.average([len(keywords) for keywords in data.keywords])
[docs]
def n_duplicates(data, pid="doi"):
"""Number of duplicates.
Duplicate detection can be a very challenging task. Multiple
algorithms can be used and results can be vary.
Arguments
---------
data: asreview.Dataset
An Dataset object with the records.
pid: string
Which persistent identifier (PID) to use for deduplication.
Default is 'doi'.
Return
------
int:
Number of duplicates
"""
return int(data.duplicated(pid).sum())