In-class Practice: LR Protein Classification

Environment Setup:

1. Python Environment: Ensure you have Python installed (preferably Python 3.x).
2. Dependencies: Install required dependencies using pip:

pip install numpy pandas scikit-learn biopython

Code Flow:

1. Data Preprocessing:

• The code preprocesses the data, loading protein structure diagrams and sequence information.
• If the --ent flag is provided, the data is loaded from a file using a feature engineering function feature_extraction() from fea.py. Otherwise, it loads from pre-existing files.
• The code reads a CAST file containing protein sequence information and a Numpy array containing diagrams.

2. Model Initialization:

• You should implement the LRModel class and LRFromScratch class.
• For LRModel class, perhaps you can explore different initialization settings,like Regularization parameter C.
• For the LRFromScratch class, you should implement LR model with gradient descent optimization by your own code. This means you should not use torch or any other deep learning library.

3. Training and Evaluation:

• The code trains the selected model on the training data and evaluates its performance on both training and test datasets.
• It partitions the dataset into training and testing sets for each task.
• The model's accuracy is printed for each dataset.

Filling in the Blanks:

1. LRModel Class:

• Fill in the initialization, training, and evaluation methods for the Logistic Regression model.

2. LRFromScratch Class:

• Fill in the initialization, training, and evaluation methods for the Logistic Regression model of your own implementation.

3. Data processing:

• Read a CAST file containing protein sequence information and a Numpy array containing diagrams.Process them in training form.

Running the Script:

• Execute the script main.py from the command line.
• You can provide arguments to customize data loading method.

Experimental Requirements:

1. Complete Implementation of Protein Classification (Data Loading and LRModel) - 1.5 points

   • Ensure the code effectively reads and preprocesses protein structure data and sequences.
   • Implement LR model from sklearn library.

2. Complete Implementation of LRFromScratch - 1.5 points

   • Implement LRFromScratch model with methods of gradient descent.
   • Pay attention to the loss function format you choose, since the labels here are in {0,1}.

Total Score: 3 points

Other exploration:

1. Analysis and Discussion on the Impact of Regularization Coefficients and other initialization settings
   • Analyze how varying the regularization coefficient (C) affects the model's performance and generalization.
   • Except Regularization Coefficients,choose 1 or 2 other settings you want to explore.
2. Feature Engineering
   • Provide insights on extracting useful features from protein structure data or utilizing feature selection methods to reduce dimensionality.

Submission:

Submit both the code and the running results in a single zip archive named "学号_姓名_课堂练习1.zip".

ddl: 2025/3/24 23:59