mingyk/test-model

Linear Regression Model

This repository hosts a simple linear regression model. The model provides two primary functions:

1. Retrieve Coefficients and Intercept: Given column names, the model will return its coefficients and intercept.
2. Predict Values from CSV: Given column names and a CSV file with data, the model will use the data to predict values.

Model Files

• linear_regression_model.joblib: The trained linear regression model, saved with joblib.
• model.py: Contains the model class and custom Hugging Face pipeline for processing inputs and returning outputs.

Usage

1. Retrieve Coefficients and Intercept

Send a JSON payload with just column names to retrieve the model’s coefficients and intercept.

Input JSON Example:

{
  "inputs": {
    "columns": ["feature1", "feature2", "feature3"]
  }
}

Response JSON Example:

{
  "coefficients": {"feature1": 0.5, "feature2": -1.2, "feature3": 2.3},
  "intercept": 0.1
}

2. Predict Values from CSV

Send a request with column names and a CSV file containing data for prediction. The model will use the data in the specified columns to make predictions.

• Columns: A JSON list of strings specifying the column names used in the model.
• CSV File: A file containing the data rows, uploaded as binary content.

Python Example Using requests:

import requests

url = "https://api-inference.huggingface.co/models/your-username/linear-regression-model"
headers = {"Authorization": "Bearer YOUR_HUGGINGFACE_API_TOKEN"}

# Define the columns and open the CSV file in binary mode
columns = ["feature1", "feature2", "feature3"]
files = {
    "inputs": ("data.csv", open("path/to/your/data.csv", "rb")),
    "columns": (None, str(columns))  # Send columns as JSON string
}

response = requests.post(url, headers=headers, files=files)
print(response.json())

curl Example:

curl -X POST "https://api-inference.huggingface.co/models/your-username/linear-regression-model" \
     -H "Authorization: Bearer YOUR_HUGGINGFACE_API_TOKEN" \
     -F "columns=['feature1', 'feature2', 'feature3']" \
     -F "inputs=@path/to/your/data.csv"

Response JSON Example:

{
  "predictions": [10.5, 15.8, 12.3]
}

3. File Structure

The main files in this repository:

• linear_regression_model.joblib: Contains the trained linear regression model.
• model.py: Model and pipeline definitions, handling CSV input and returning predictions or coefficients.
• README.md: This file, explaining model functionality and usage.

Notes

1. Ensure your CSV file includes the specified columns for accurate predictions.
2. The CSV file is temporarily saved and used for predictions. It will not be stored permanently.
3. Your API token is required to authenticate requests.

This setup allows users to choose between retrieving coefficients or making predictions based on CSV data for more flexibility.