pycpidr

Python library for computing propositional idea density.

Table of Contents

• Introduction
• What is Idea Density?
• Installation
• Usage
  • CPIDR
  • DEPID
• Requirements
• Development Setup
• Running Tests
• CPIDR Parity with CPIDR 3.2
• References
• Citing
• Contributing
• License

Introduction

pycpidr is a Python library which determines the propositional idea density of an English text automatically. This project aims to make this functionality more accessible to Python developers and researchers. pycpidr provides two ways of computing idea density:

• CPIDR. The CPIDR implementation in pycpidr is a direct port of the Computerized Propositional Idea Density Rater (CPIDR) 3.2 (Brown et al., 2008) [1]
• DEPID. This library implements the DEPID algorithm described by Sirts et al (2017) [2]

Here's a quick example of how to use pycpidr:

from pycpidr import cpidr, depid

text = "The quick brown fox jumps over the lazy dog."
cpidr_word_count, proposition_count, cpidr_density, word_list = cpidr(text)
depid_density, depid_word_count, dependencies = depid(text)

print(f"CPIDR density: {cpidr_density:.3f}")
print(f"DEPID density: {depid_density:.3f}")

What is Idea Density?

Idea density, also known as propositional density, is a measure of the amount of information conveyed relative to the number of words used. It's calculated by dividing the number of expressed propositions by the number of words. This metric has applications in various fields, including linguistics, cognitive science, and healthcare research.

Installation

Using pip

1. Install the package

pip install pycpidr

2. Download the required spaCy model:

python -m spacy download en_core_web_sm

Using poetry

poetry add pycpidr
python -m spacy download en_core_web_sm

Usage

CPIDR

Here's a simple example of how to use CPIDR:

from pycpidr import cpidr

text = "The quick brown fox jumps over the lazy dog."
word_count, proposition_count, density, word_list = cpidr(text)

print(f"Word count: {word_count}")
print(f"Proposition count: {proposition_count}")
print(f"Idea density: {density:.3f}")

# Analyzing speech
speech_text = "Um, you know, I think that, like, the weather is nice today."
word_count, proposition_count, density, word_list = cpidr(speech_text, speech_mode=True)

print(f"Speech mode - Idea density: {density:.3f}")

# Detailed word analysis
for word in word_list.items:
    if word.is_word:
        print(f"Token: {word.token}, Tag: {word.tag}, Is proposition: {word.is_proposition}")

Speech Mode

pycpidr CPIDR mode supports a speech mode that handles common speech patterns and fillers differently from written text. When analyzing transcripts or spoken language, use the speech_mode=True parameter for more accurate results.

DEPID

Here's an example of how to use the DEPID functionality:

from pycpidr import depid

text = "The quick brown fox jumps over the lazy dog."
density, word_count, dependencies = depid(text)
print(f"Word count: {word_count}")
print(f"Idea density: {density:.3f}")
print("Dependencies:")
for dep in dependencies:
    print(f"Token: {dep[0]}, Dependency: {dep[1]}, Head: {dep[2]}")

DEPID-R

DEPID-R counts distinct dependencies.

from pycpidr import depid

text = "This is a test of DEPID-R. This is a test of DEPID-R"
density, word_count, dependencies = depid(text, is_depid_r=True)

print(f"DEPID-R idea density: {density:.3f}")

Using custom filters

pycpidr DEPID mode supports custom filtering of sentences and tokens. By default, pycpidr uses filters described by (Sirts et al., 2017):

• Sentence filter.
  • Filter out sentences with "I" or "You" as the subject of the sentence (i.e. if the "I" or "You" token dependency is "nsubj" and it's head dependency is the root).
  • Note: Sirts et al (2017) also filters out vague sentences using SpeciTeller. That is a filter which pycpidr does not yet implement.
• Token filters:
  • Filter out "det" dependencies if the token is "a", "an" or "the".
  • Filter out "nsubj" dependencies if the token is "it" or "this".
  • Filter out all "cc" dependencies.

This example demonstrates how to apply your own custom filters to modify the analysis. The sentence_filters and token_filters parameters allow you to customize the DEPID algorithm to suit your specific needs.

def custom_sentence_filter(sent):
    return len(sent) > 3
def custom_token_filter(token):
    return token.pos_ != "DET"
text_with_filters = "I run. The quick brown fox jumps over the lazy dog."
density, word_count, dependencies = depid(text_with_filters,
sentence_filters=[custom_sentence_filter],
token_filters=[custom_token_filter])
print(f"\nWith custom filters - Idea density: {density:.3f}")

Requirements

• Python 3.10+
• spaCy 3.7.5+

Development Setup

To set up the development environment:

1. Clone the repository
2. Install Poetry if you haven't already: pip install poetry
3. Install project dependencies: poetry install
4. Install the required spaCy model: poetry run python -m spacy download en_core_web_sm
5. Activate the virtual environment: poetry shell

Running Tests

To run the tests, use pytest:

pytest tests/

CPIDR Parity with CPIDR 3.2

Because this port uses spaCy as a part-of-speech tagger instead of the original program's MontyLingua, there is a very slight difference in the reported idea density. This port includes unit tests containing 847 words of text. This project: 434 propositions. 0.512 idea density CPIDR 3.2: 436 propositions. 0.515 idea density

For more information about the original CPIDR 3.2, please visit CASPR's official page.

References

[1] Brown, C., Snodgrass, T., Kemper, S. J., Herman, R., & Covington, M. A. (2008). Automatic measurement of propositional idea density from part-of-speech tagging. Behavior research methods, 40(2), 540-545.

[2] Sirts, K., Piguet, O., & Johnson, M. (2017). Idea density for predicting Alzheimer's disease from transcribed speech. arXiv preprint arXiv:1706.04473.

Citing

If you use this project in your research, you may cite it as:

Jason Robison. (2024). pycpidr (0.2.0) [Source code]. GitHub. https://github.com/jrrobison1/pycpidr

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Please ensure that your code passes all tests and follows the project's coding style.

License

This project is licensed under the GNU General Public License v2.0. See the LICENSE file for details.

pycpidr's CPIDR implementation is a port of the original CPIDR 3.2, which was released under GPL v2. This project maintains the same license to comply with the terms of the original software.