accuracy 0.1.0

It's pronounced accura-see. For spaCy models.

accuraCy

It's pronounced "accura-see". For spaCy models.

The goal of this project is to generate reports for spaCy models.

what it does

The goal of accuraCy is to offer static reports for spaCy models that help users make better decisions on how the models can be used. At the moment the project supports reports for threshold values for classification.

Here's a preview of what to expect:

There are two kinds of charts.

1. The first kind is a density chart. This chart shows the distribution of confidence scores for a given class. The blue area represents documents that had the tag assigned to the class. The orange area represents documents that didn't.
2. The second kind is a line chart that demonstrates the accuracy, precision and recall values for a given confidence threshold. It's an interactive chart and you can explore the values by hovering over the chart.

install

You can install the latest version from git.

python -m pip install "accuracy @ git+https://github.com/koaning/accuracy.git"

usage

The accuracy project provides a command line interface that can generate reports. The full CLI can also be explored via the --help flag.

> python -m accuracy --help

Usage: python -m accuracy [OPTIONS] COMMAND [ARGS]...

  It's pronounced 'accura-see'. For spaCy models.

Options:
  --help  Show this message and exit.

Commands:
  report   Generate a model report.
  version  Show version number.

accuracy report

The most important command is the report command. You'd typically use it via a command similar to:

> python -m accuracy report training/model-best/ corpus/train.spacy corpus/dev.spacy

Loading model at training/model-best
Running model on training data...
Running model on development data...
Generating Charts ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Done! You can view the report via;

python -m http.server --directory reports PORT 

This will generate a folder, typically called "reports", that contains a full dashboard for the trained spaCy model found in training/model-best.

The CLI has a few configurable settings:

Arguments:
  [MODEL_PATH]  Path to spaCy model
  [TRAIN_PATH]  Path to training data
  [DEV_PATH]    Path to development data
  [FOLDER_OUT]  Output folder for reports  [default: reports]

Options:
  --classes TEXT  Comma-separated string of classes to use
  --help          Show this message and exit.