# pycpidr
[](https://pypi.org/project/pycpidr/) [](https://github.com/jrrobison1/pycpidr/actions/workflows/unit_tests.yml) [](https://pepy.tech/project/pycpidr)
Python library for computing propositional idea density.
## Table of Contents
- [Introduction](#introduction)
- [What is Idea Density?](#what-is-idea-density)
- [Installation](#installation)
- [Usage](#usage)
- [CPIDR](#cpidr)
- [DEPID](#depid)
- [Requirements](#requirements)
- [Development Setup](#development-setup)
- [Running Tests](#running-tests)
- [CPIDR Parity with CPIDR 3.2](#cpidr-parity-with-cpidr-32)
- [References](#references)
- [Citing](#citing)
- [Contributing](#contributing)
- [License](#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:
```python
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
```bash
pip install pycpidr
```
2. Download the required spaCy model:
```bash
python -m spacy download en_core_web_sm
```
### Using poetry
```bash
poetry add pycpidr
python -m spacy download en_core_web_sm
```
## Usage
### CPIDR
Here's a simple example of how to use CPIDR:
```python
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:
```python
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.
```python
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.
```python
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:
```bash
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](http://ai1.ai.uga.edu/caspr/).
## 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](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.
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"description": "# pycpidr\n[](https://pypi.org/project/pycpidr/) [](https://github.com/jrrobison1/pycpidr/actions/workflows/unit_tests.yml) [](https://pepy.tech/project/pycpidr)\n\nPython library for computing propositional idea density.\n\n## Table of Contents\n- [Introduction](#introduction)\n- [What is Idea Density?](#what-is-idea-density)\n- [Installation](#installation)\n- [Usage](#usage)\n - [CPIDR](#cpidr)\n - [DEPID](#depid)\n- [Requirements](#requirements)\n- [Development Setup](#development-setup)\n- [Running Tests](#running-tests)\n- [CPIDR Parity with CPIDR 3.2](#cpidr-parity-with-cpidr-32)\n- [References](#references)\n- [Citing](#citing)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\npycpidr 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:\n- 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]\n- DEPID. This library implements the DEPID algorithm described by (Sirts et al., 2017) [2]\n\nHere's a quick example of how to use pycpidr:\n```python\nfrom pycpidr import cpidr, depid\n\ntext = \"The quick brown fox jumps over the lazy dog.\"\ncpidr_word_count, proposition_count, cpidr_density, word_list = cpidr(text)\ndepid_density, depid_word_count, dependencies = depid(text)\n\nprint(f\"CPIDR density: {cpidr_density:.3f}\")\nprint(f\"DEPID density: {depid_density:.3f}\")\n```\n\n## What is Idea Density?\n\nIdea 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.\n\n## Installation\n\n### Using pip\n1. Install the package\n```bash\npip install pycpidr\n```\n\n2. Download the required spaCy model:\n```bash\npython -m spacy download en_core_web_sm\n```\n\n### Using poetry\n\n```bash\npoetry add pycpidr\npython -m spacy download en_core_web_sm\n```\n\n## Usage\n### CPIDR\nHere's a simple example of how to use CPIDR:\n\n```python\nfrom pycpidr import cpidr\n\ntext = \"The quick brown fox jumps over the lazy dog.\"\nword_count, proposition_count, density, word_list = cpidr(text)\n\nprint(f\"Word count: {word_count}\")\nprint(f\"Proposition count: {proposition_count}\")\nprint(f\"Idea density: {density:.3f}\")\n\n# Analyzing speech\nspeech_text = \"Um, you know, I think that, like, the weather is nice today.\"\nword_count, proposition_count, density, word_list = cpidr(speech_text, speech_mode=True)\n\nprint(f\"Speech mode - Idea density: {density:.3f}\")\n\n# Detailed word analysis\nfor word in word_list.items:\n if word.is_word:\n print(f\"Token: {word.token}, Tag: {word.tag}, Is proposition: {word.is_proposition}\")\n```\n\n#### Speech Mode\n\npycpidr 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.\n\n### DEPID\nHere's an example of how to use the DEPID functionality:\n```python\nfrom pycpidr import depid\n\ntext = \"The quick brown fox jumps over the lazy dog.\"\ndensity, word_count, dependencies = depid(text)\nprint(f\"Word count: {word_count}\")\nprint(f\"Idea density: {density:.3f}\")\nprint(\"Dependencies:\")\nfor dep in dependencies:\n print(f\"Token: {dep[0]}, Dependency: {dep[1]}, Head: {dep[2]}\")\n```\n\n#### DEPID-R\nDEPID-R counts _distinct_ dependencies.\n\n```python\nfrom pycpidr import depid\n\ntext = \"This is a test of DEPID-R. This is a test of DEPID-R\"\ndensity, word_count, dependencies = depid(text, is_depid_r=True)\n\nprint(f\"DEPID-R idea density: {density:.3f}\")\n```\n\n#### Using custom filters\npycpidr DEPID mode supports custom filtering of sentences and tokens. By default, pycpidr uses filters described by (Sirts et al., 2017):\n- Sentence filter. \n - 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). \n - Note: Sirts et al (2017) also filters out vague sentences using SpeciTeller. That is a filter which pycpidr does not yet implement.\n- Token filters:\n - Filter out \"det\" dependencies if the token is \"a\", \"an\" or \"the\".\n - Filter out \"nsubj\" dependencies if the token is \"it\" or \"this\".\n - Filter out all \"cc\" dependencies.\n\nThis 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.\n```python\ndef custom_sentence_filter(sent):\n return len(sent) > 3\ndef custom_token_filter(token):\n return token.pos_ != \"DET\"\ntext_with_filters = \"I run. The quick brown fox jumps over the lazy dog.\"\ndensity, word_count, dependencies = depid(text_with_filters,\nsentence_filters=[custom_sentence_filter],\ntoken_filters=[custom_token_filter])\nprint(f\"\\nWith custom filters - Idea density: {density:.3f}\")\n```\n\n\n## Requirements\n\n- Python 3.10+\n- spaCy 3.7.5+\n\n## Development Setup\n\nTo set up the development environment:\n\n1. Clone the repository\n2. Install Poetry if you haven't already: `pip install poetry`\n3. Install project dependencies: `poetry install`\n4. Install the required spaCy model: `poetry run python -m spacy download en_core_web_sm`\n5. Activate the virtual environment: `poetry shell`\n\n## Running Tests\n\nTo run the tests, use pytest:\n\n```bash\npytest tests/\n```\n\n## CPIDR Parity with CPIDR 3.2\nBecause 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.\nThis project: 434 propositions. 0.512 idea density\nCPIDR 3.2: 436 propositions. 0.515 idea density\n\nFor more information about the original CPIDR 3.2, please visit [CASPR's official page](http://ai1.ai.uga.edu/caspr/).\n\n## References\n[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.\n\n[2] Sirts, K., Piguet, O., & Johnson, M. (2017). Idea density for predicting Alzheimer's disease from transcribed speech. arXiv preprint arXiv:1706.04473.\n\n## Citing\nIf you use this project in your research, you may cite it as: \n\nJason Robison. (2024). *pycpidr* (0.2.0) [Source code]. GitHub. https://github.com/jrrobison1/pycpidr\n\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. 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