asreview-insights 1.0rc1

Insight tools for the ASReview project

asreview-insights

This package is currently under development. See ASReview-visualization for stable version compatible with ASReview LAB <=0.19.x.

❣️ ASReview-insights is the successor to ASReview-visualization. ASReview insights is available for version 1 or later. Use ASReview visualization for versions 0.x.

This official extension to ASReview LAB extends asreview with tools for plotting and extraction of metrics and statistics. The extension is especially useful in combination with the simulation feature of ASReview LAB.

Installation

ASReview-insights can be installed from PyPI:

pip install --dev 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 'stats' subcommand.

Active learning performance

The ASReview-insights extension is useful to measure the performance of active learning models on collections of binary labeled text. The performance can be measured with widely used metrics like Recall and Work Saved over Sampling (WSS). The ASReview-insights extension can plot or compute metrics from ASReview project files, ideally as the result of a simulation. See LINK for more information on simulating with ASReview LAB.

The following plot (with generated, fake data) explains the different metrics Recall, WSS, and ERF. The dataset contains 1000 records. The stepped line on the diagonal is the naive labeling approach (screening randomly sorted records).

Plots

The plots in this section are derived from an ASReview (v1.0) file generated from

asreview simulate benchmark:van_de_schoot_2017 -s sim_van_de_schoot_2017.asreview --init_seed 535

Plot types

Recall

The recall is an important metric to study the performance of active learning algorithms in the context of information retrieval. The 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). The file can be exported from the ASReview LAB user interface, or is the result of a simulation. 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 after every labeling decision. The number of labeling actions on the horizontal axis is equal to the number of label actions and can't exceed the number of records in the dataset.

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. The ASReview Insights offers a straightforward command line interface to plot the wss at any recall.

To plot the WSS curve, you need a ASReview file (extension .asreview). The file can be exported from the ASReview LAB user interface, or is the result of a simulation. 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.

ERF

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 number of labeling actions on the horizontal axis is equal to the number of label actions and can't exceed the number of records in the dataset.

Very sparse datasets

Very sparse or small datasets can provide good explanation on interesting details of the plotting subcommands in this extension. Important details are for example the handling of prior knowledge and the computation of the recall prediction in case of random screening.

The following plot shows the result of a collection of 4 records with 3 relevant items (inclusions). The relevant items are found in the following order:

[1, 1, 0, 1, 0]

The black line is an estimate of the recall after every screened record in a naive manner (also refered to as 'random').

Recall (est) when screening 1 = (3 relevant records / 4 records left)  / 3 = 0.25
Recall (est) when screening 2 = (1/4) * (3 relevant records / 3 records left)  / 3 +
                                (3/4) * (2 relevant records / 3 records left)  / 3 = (1/4 + 3/4*2/3) / 3 = 0.25

The Work Saved over Sampling (WSS) is the difference between the recall of the simulation and the theoretical recall of random screening.

The following graph shows the recall versus the WSS. This comparison is important because it is the fundamental of the WSS@95% metric used in the literature about Active Learning for systematic reviewing.

Plotting CLI

See asreview plot -h for all command line arguments.

% asreview plot -h
usage: asreview plot [-h] [--priors] [--no-priors] [--x_absolute] [--y_absolute] [-V] [-o OUTPUT]
                     type asreview_files [asreview_files ...]

positional arguments:
  type                  Plot type. Default 'recall'.
  asreview_files        A (list of) ASReview files.

optional arguments:
  -h, --help            show this help message and exit
  --priors              Include records used as prior knowledge in the plot.
  --no-priors           Exclude records used as prior knowledge in the plot. Default.
  --x_absolute          Make use of absolute coordinates on the x-axis.
  --y_absolute          Make use of absolute coordinates on the y-axis.
  -V, --version         show program's version number and exit
  -o OUTPUT, --output OUTPUT
                        Save the plot to a file. File formats are detected by the matplotlib library, check there to see available
                        formats.

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 in ASReview-insights can be used to extract metrics at given values. The easiest way to get metrics on a ASReview project file is with the following command don the command line:

asreview stats sim_van_de_schoot_2017.asreview

which results in

{
    "metrics": {
        "recall": [
            {
                "x": 0.1,
                "y": 1.0
            },
            {
                "x": 0.25,
                "y": 1.0
            },
            {
                "x": 0.5,
                "y": 1.0
            },
            {
                "x": 0.75,
                "y": 1.0
            },
            {
                "x": 0.9,
                "y": 1.0
            }
        ],
        "wss": [
            {
                "x": 0.95,
                "y": 0.9107806691449815
            }
        ],
        "erf": [
            {
                "x": 0.95,
                "y": 0.047619047619047616
            }
        ]
    }
}

Each available metric has x and y values. The x value is the value at which the metric is computed. In the plots above, this is the x-axis. The y value is the output of the metric. Some metrics are computed for multiple values.

Metric	X description	Y description	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	0.95

Override default values

It is possible to override the default values of asreview stats. See asreview stats -h for more information or see the example below.

asreview stats sim_van_de_schoot_2017.asreview --wss 0.9 0.95

{
    "metrics": {
        "recall": [
            {
                "x": 0.1,
                "y": 1.0
            },
            {
                "x": 0.25,
                "y": 1.0
            },
            {
                "x": 0.5,
                "y": 1.0
            },
            {
                "x": 0.75,
                "y": 1.0
            },
            {
                "x": 0.9,
                "y": 1.0
            }
        ],
        "wss": [
            {
                "x": 0.9,
                "y": 0.8692419589461775
            },
            {
                "x": 0.95,
                "y": 0.9107806691449815
            }
        ],
        "erf": [
            {
                "x": 0.95,
                "y": 0.047619047619047616
            }
        ]
    }
}

Save metrics to file

Metrics can be saved to a file in the JSON format. Use the flag -o or --output.

asreview stats sim_van_de_schoot_2017.asreview -o my_file.json

Metrics CLI

See asreview stats -h for all command line arguments.

% asreview stats -h
usage: asreview stats [-h] [-V] [--recall recall [recall ...]] [--wss wss [wss ...]] [--erf erf [erf ...]] [--priors] [--no-priors]
                      [--x_absolute] [--y_absolute] [-o OUTPUT]
                      asreview_files [asreview_files ...]

positional arguments:
  asreview_files        A combination of data directories or files.

optional arguments:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  --recall recall [recall ...]
                        A (list of) values to compute the recall at.
  --wss wss [wss ...]   A (list of) values to compute the wss at.
  --erf erf [erf ...]   A (list of) values to compute the erf at.
  --priors              Include records used as prior knowledge in the metrics.
  --no-priors           Exclude records used as prior knowledge in the metrics. Default.
  --x_absolute          Make use of absolute coordinates on the x-axis.
  --y_absolute          Make use of absolute coordinates on the y-axis.
  -o OUTPUT, --output OUTPUT
                        Save the statistics and metrics to a JSON file.

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.