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asreviewinsights 1.1.2

ASReview Insights

This official extension to ASReview
LAB extends the software with tools for
plotting and extracting the statistical results of
several performance metrics. The extension is
especially useful in combination with the simulation
functionality
of ASReview LAB.
❣️ ASReview Insights is the successor to
ASReview-visualization.
ASReview insights is available for ASReview
LAB version 1 or later.
Use ASReview visualization for versions 0.x.
Installation
ASReview Insights can be installed from PyPI:
pip install asreview-insights

After installation, check if the asreview-insights package is listed as an
extension. Use the following command:
asreview --help

It should list the 'plot' subcommand and the 'metrics' subcommand.
Performance metrics
The ASReview Insights extension is useful for measuring the performance of
active learning models on collections of binary labeled text. The extension
can be used after performing a simulation study that involves mimicking the
screening process with a specific model. As it is already known which records
are labeled relevant, the simulation can automatically reenact the screening
process as if a screener were using active learning. The performance of one or
multiple models can be measured by different metrics and the
ASReview Insights extension can plot or compute the values for such metrics
from ASReview project files.
The recall is the proportion of relevant records that have been found at a
certain point during the screening phase. It is sometimes also called the
proportion of Relevant Record Found (RRF) after screening an X% of the total
records. For example, the RRF@10 is the recall (i.e., the proportion of the
total number of relevant records) at screening 10% of the total number of
records available in the dataset.
A variation is the Extra Relevant records Found (ERF), which is the proportion
of relevant records found after correcting for the number of relevant records
found via random screening (assuming a uniform distribution of relevant
records).
The Work Saved over Sampling (WSS) is a measure of "the work saved over and
above the work saved by simple sampling for a given level of recall" (Cohen
et al., 2006. It is defined as the
proportion of records a screener does not have to screen compared to
random reading after providing the prior knowledge used to train the first
iteration of the model. The WSS is typically measured at a recall of .95
(WSS@95), reflecting the proportion of records saved by using active learning
at the cost of failing to identify .05 of relevant publications.
The following plot illustrates the differences between the metrics Recall
(y-axis), WSS (blue line), and ERF (red line). The dataset contains 1.000
hypothetical records with labels. The stepped line on the diagonal is the
naive labeling approach (screening randomly sorted records).

Both recall and WSS are sensitive to the position of the cutoff value and the
distribution of the data. Moreover, the WSS makes assumptions about the
acceptable recall level whereas this level might depend on the research
question at hand. Therefore, Ferdinands et al.
(2020) proposed two new metrics: (1)
the Time to Discover a relevant record as the fraction of records needed
to screen to detect this record (TD); and (2) the Average Time to Discover
(ATD) as an indicator of how many records need to be screened on average to
find all relevant records in the dataset.
Basic usage
The ASReview Insights package extends ASReview LAB with two new subcommands
(see asreview --help): plot and metrics. The plots
and metrics are derived from an ASReview project file. The ASReview file
(extension .asreview) can be
exported
from ASReview LAB after a
simulation,
or it is generated from running a simulation via the command
line.
For example, an ASReview can be generated with:
asreview simulate benchmark:van_de_schoot_2017 -s sim_van_de_schoot_2017.asreview --init_seed 535

To use the most basic options of the ASReview Insights extension, run
asreview plot recall YOUR_ASREVIEW_FILE.asreview

where recall is the type of the plot, or
asreview metrics sim_van_de_schoot_2017.asreview

More options are described in the sections below. All options can be
obtained via asreview plot --help or asreview metrics --help.
Plot
Plot types
recall
The recall is an important metric to study the performance of active learning
algorithms in the context of information retrieval. ASReview Insights
offers a straightforward command line interface to plot a "recall curve". The
recall curve is the recall at any moment in the active learning process.
To plot the recall curve, you need a ASReview file (extension .asreview).To
plot the recall, use this syntax (Replace YOUR_ASREVIEW_FILE.asreview by
your ASReview file name.):
asreview plot recall YOUR_ASREVIEW_FILE.asreview

The following plot is the result of simulating the van_de_schoot_2017 in
the benchmark platform (command asreview simulate benchmark:van_de_schoot_2017 -s sim_van_de_schoot_2017.asreview).

On the vertical axis, you find the recall (i.e, the proportion of the relevant
records) after every labeling decision. The horizontal axis shows the
proportion of total number of records in the dataset. The steeper the recall
curve, the higher the performance of active learning when comparted to random
screening. The recall curve can also be used to estimate stopping criteria, see
the discussions in #557 and #1115.
asreview plot recall YOUR_ASREVIEW_FILE.asreview

wss
The Work Saved over Sampling (WSS) metric is an useful metric to study the
performance of active learning alorithms compared with a naive (random order)
approach at a given level of recall. ASReview Insights offers a
straightforward command line interface to plot the WSS at any level of recall.
To plot the WSS curve, you need a ASReview file (extension .asreview). To
plot the WSS, use this syntax (Replace YOUR_ASREVIEW_FILE.asreview by your
ASReview file name.):
asreview plot wss YOUR_ASREVIEW_FILE.asreview

The following plot is the result of simulating the van_de_schoot_2017 in
the benchmark platform (command asreview simulate benchmark:van_de_schoot_2017 -s sim_van_de_schoot_2017.asreview).

On the vertical axis, you find the WSS after every labeling decision. The
recall is displayed on the horizontal axis. As shown in the figure, the
WSS is linearly related to the recall.
erf
The Extra Relevant Records found is a derivative of the recall and presents
the proportion of relevant records found after correcting for the number of
relevant records found via random screening (assuming a uniform distribution
of relevant records).
To plot the WSS curve, you need a ASReview file (extension .asreview). To
plot the WSS, use this syntax (Replace YOUR_ASREVIEW_FILE.asreview by your
ASReview file name.):
asreview plot erf YOUR_ASREVIEW_FILE.asreview

The following plot is the result of simulating the van_de_schoot_2017 in
the benchmark platform (command asreview simulate benchmark:van_de_schoot_2017 -s sim_van_de_schoot_2017.asreview).

On the vertical axis, you find the ERF after every labeling decision. The
horizontal axis shows the proportion of total number of records in the
dataset. The steep increase of the ERF in the beginning of the process is
related to the steep recall curve.
Plotting CLI
Optional arguments for the command line are --priors to include prior
knowledge, --x_absolute and --y_absolute to use absolute axes.
See asreview plot -h for all command line arguments.
Plotting API
To make use of the more advanced features, you can make use of the Python API.
The advantage is that you can tweak every single element of the plot in the
way you like. The following examples show how the Python API can be used. They
make use of matplotlib extensively. See the Introduction to
Matplotlib
for examples on using the API.
The following example show how to plot the recall with the API and save the
result. The plot is saved using the matplotlib API.
import matplotlib.pyplot as plt

from asreview import open_state
from asreviewcontrib.insights.plot import plot_recall

with open_state("example.asreview") as s:

fig, ax = plt.subplots()

plot_recall(ax, s)

fig.savefig("example.png")

Other options are plot_wss and plot_erf.
Example: Customize plot
It's straightforward to customize the plots if you are familiar with
matplotlib. The following example shows how to update the title of the plot.
import matplotlib.pyplot as plt

from asreview import open_state
from asreviewcontrib.insights.plot import plot_wss

with open_state("example.asreview") as s:

fig, ax = plt.subplots()
plot_wss(ax, s)

plt.title("WSS with custom title")

fig.savefig("example_custom_title.png")

Example: Prior knowledge
It's possible to include prior knowledge to your plot. By default, prior
knowledge is excluded from the plot.
import matplotlib.pyplot as plt

from asreview import open_state
from asreviewcontrib.insights.plot import plot_wss

with open_state("example.asreview") as s:

fig, ax = plt.subplots()
plot_wss(ax, s, priors=True)

Example: Relative versus absolute axes
By default, all axes in ASReview Insights are relative. The API can be used to
change this behavior. The arguments are identical for each plot function.
import matplotlib.pyplot as plt

from asreview import open_state
from asreviewcontrib.insights.plot import plot_wss

with open_state("example.asreview") as s:

fig, ax = plt.subplots()
plot_wss(ax, s, x_absolute=True, y_absolute=True)

fig.savefig("example_absolute_axis.png")

Example: Multiple curves in one plot
It is possible to have multiple curves in one plot by using the API,
and add a legend.
import matplotlib.pyplot as plt

from asreview import open_state
from asreviewcontrib.insights.plot import plot_recall

fig, ax = plt.subplots()

with open_state("tests/asreview_files/sim_van_de_schoot_2017_1.asreview") as s1:
plot_recall(ax, s1)

with open_state("tests/asreview_files/"
"sim_van_de_schoot_2017_logistic.asreview") as s2:
plot_recall(ax, s2)

ax.lines[0].set_label("Naive Bayes")
ax.lines[2].set_label("Logistic")
ax.legend()

fig.savefig("docs/example_multiple_lines.png")

metrics
The metrics subcommand in ASReview Insights can be used to compute metrics
at given values. The easiest way to get compute metrics for a ASReview project
file is with the following command don the command line:
asreview metrics sim_van_de_schoot_2017.asreview

which results in
"asreviewVersion": "1.0",
"apiVersion": "1.0",
"data": {
"items": [
{
"id": "recall",
"title": "Recall",
"value": [
[
0.1,
1.0
],
[
0.25,
1.0
],
[
0.5,
1.0
],
[
0.75,
1.0
],
[
0.9,
1.0
]
]
},
{
"id": "wss",
"title": "Work Saved over Sampling",
"value": [
[
0.95,
0.8913851624373686
]
]
},
{
"id": "erf",
"title": "Extra Relevant record Found",
"value": [
[
0.1,
0.9047619047619048
]
]
},
{
"id": "atd",
"title": "Average time to discovery",
"value": 101.71428571428571
},
{
"id": "td",
"title": "Time to discovery",
"value": [
[
3898,
22
],
[
284,
23
],
[
592,
25
],
...
[
2382,
184
],
[
5479,
224
],
[
3316,
575
]
]
}
]
}
}

Each available item has two values. The first value is the value at which the
metric is computed. In the plots above, this is the x-axis. The second value
is the results of the metric. Some metrics are computed for multiple values.

Metric
Description pos. 1
Description pos. 2
Default

recall
Labels
Recall
0.1, 0.25, 0.5, 0.75, 0.9

wss
Recall
Work Saved over Sampling at recall
0.95

erf
Labels
ERF
0.10

atd
Average time to discovery (in label actions)
-
-

td
Row number (starting at 0)
Number of records labeled
-

Override default values
It is possible to override the default values of asreview metrics. See
asreview metrics -h for more information or see the example below.
asreview metrics sim_van_de_schoot_2017.asreview --wss 0.9 0.95

{
"asreviewVersion": "1.0",
"apiVersion": "1.0",
"data": {
"items": [
{
"id": "recall",
"title": "Recall",
"value": [
[
0.1,
1.0
],
[
0.25,
1.0
],
[
0.5,
1.0
],
[
0.75,
1.0
],
[
0.9,
1.0
]
]
},
{
"id": "wss",
"title": "Work Saved over Sampling",
"value": [
[
0.9,
0.8474220139001132
],
[
0.95,
0.8913851624373686
]
]
},
{
"id": "erf",
"title": "Extra Relevant record Found",
"value": [
[
0.1,
0.9047619047619048
]
]
},
{
"id": "atd",
"title": "Average time to discovery",
"value": 101.71428571428571
},
{
"id": "td",
"title": "Time to discovery",
"value": [
[
3898,
22
],
[
284,
23
],
[
592,
25
],
...
[
2382,
184
],
[
5479,
224
],
[
3316,
575
]
]
}
]
}
}

Save metrics to file
Metrics can be saved to a file in the JSON format. Use the flag -o or
--output.
asreview metrics sim_van_de_schoot_2017.asreview -o my_file.json

Metrics CLI
Optional arguments for the command line are --priors to include prior
knowledge, --x_absolute and --y_absolute to use absolute axes.
See asreview metrics -h for all command line arguments.
Metrics API
Metrics are easily accesible with the ASReview Insights API.
Compute the recall after reading half of the dataset.
from asreview import open_state
from asreviewcontrib.insights.metrics import recall

with open_state("example.asreview") as s:

print(recall(s, 0.5))

Other metrics are available like wss and erf.
Example: Prior knowledge
It's possible to include prior knowledge to your metric. By default, prior
knowledge is excluded from the metric.
from asreview import open_state
from asreviewcontrib.insights.metrics import recall

with open_state("example.asreview") as s:

print(recall(s, 0.5, priors=True))

License
This extension is published under the MIT license.
Contact
This extension is part of the ASReview project (asreview.ai). It is maintained by the
maintainers of ASReview LAB. See ASReview
LAB for contact information and more
resources.