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---
title: DictionaryLearningAndAnthropic
emoji: 📈
colorFrom: blue
colorTo: purple
sdk: streamlit
sdk_version: 1.35.0
app_file: app.py
pinned: false
license: mit
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
Inspired by this video which discusses how Anthropic is studying how their model works using Dictionary Learning.
https://youtu.be/AYTYk-kmXuI?t=168
Importing Libraries:
streamlit for creating the app.
Scikit-learn (sklearn.decomposition.DictionaryLearning and datasets.load_digits) for dictionary learning and loading the dataset.
matplotlib for plotting.
numpy for numerical operations.
Streamlit App Initialization:
The app sets a title and a description with st.title and st.write.
Loading Data:
Digit data is loaded using load_digits(). This dataset contains 8x8 images of digits (0-9).
Sample Image Display:
A slider is created with st.slider to select an index for displaying a sample image from the dataset using st.image.
Selecting Number of Components:
Another slider allows users to choose the number of dictionary components (n_components) for dictionary learning.
Performing Dictionary Learning:
DictionaryLearning is initialized with the selected number of components and fitted to the data.
The dictionary elements learned are stored in dictionary.
Displaying Dictionary Components:
The learned dictionary components are displayed as images using Matplotlib. Each component is reshaped into an 8x8 image.
Displaying Sparsity:
Sparsity is calculated as the mean of zero entries in the transformed data (X_transformed) and displayed with st.write.
Assistant: Sure! The requirements.txt file is used to specify the dependencies needed for your Python project. For the Streamlit application demonstrating dictionary learning, you'll need the following libraries:
streamlit to create the interactive web app.
scikit-learn for performing dictionary learning and loading the dataset.
matplotlib for plotting the images.
numpy for numerical operations.
Here is a requirements.txt file that includes all these dependencies:
streamlit>=1.0.0
scikit-learn>=0.24.0
matplotlib>=3.0.0
numpy>=1.19.0
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