asreview-insights 1.0rc3

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 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).

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. 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 (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.

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

    "asreviewVersion": "1.0rc2+14.gac96c1a",
    "apiVersion": "1.0rc1+3.g19a776d.dirty",
    "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
                    ]
                ]
            }
        ]
    }
}

Each available metric 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 output 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

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

{
    "asreviewVersion": "1.0rc2+14.gac96c1a",
    "apiVersion": "1.0rc1+3.g19a776d.dirty",
    "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
                    ]
                ]
            }
        ]
    }
}

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.