dualPredictor 0.0.20

A Python package for simultaneous regression and binary classification for educational analytics.

Empowering Educators with An Open-Source Tool for Simultaneous Grade Prediction and At-risk Student Identification

1. Introduction

The package combines regression analysis with binary classification to forecast student academic outcomes. Designed to simplify the implementation of advanced algorithms, this package allows users to train models, make predictions, and visualize results with just 1 line of code with their dataset. This accessibility benefits educators with varying levels of IT expertise, making sophisticated algorithms readily available. The package is easy to install via GitHub and PyPI:

PyPI Link: https://pypi.org/project/dualPredictor/

Github Repo: https://github.com/098765d/dualPredictor/

Ensuring that educators can integrate advanced analytics into their workflows seamlessly.

• Step 1: Grade Prediction Using the Trained Regressor (Fig 1, Step 1) fit the linear model f(x) using the training data, and grade prediction can be generated from the fitted model

      y\_pred = f(x) = \sum_{j=1}^{M} w_j x_j + b 
  

• Step 2: Determining the Optimal Cut-off (Fig 1, Step 2)

  The goal is to find the cut-off (c) that maximizes the binary classification accuracy. Firstly, the user specifies the metric type used for the model (e.g., Youden index) and denotes the metric function as g(y_true_label, y_pred_label), where:

  \text{optimal\_cut\_off} = \arg\max_c g(y_{\text{true\_label}}, y_{\text{pred\_label}}(c))
  

  This formula searches for the cut-off value that produces the highest value of the metric function g, where:

  • c: The tunned cut-off that determines the y_pred_label
  • y_true_label: True label of the data point based on the default cut-off (e.g., 1 for at-risk, 0 for normal)
  • y_pred_label: Predicted label of the data point based on the tunned cut-off value

• Step 3: Binary Label Prediction: (Fig 1, Step 3)

  • y_pred_label = 1 (at-risk): if y_pred < optimal_cut_off
  • y_pred_label = 0 (normal): if y_pred >= optimal_cut_off

Fig 1: How does dualPredictor provide dual prediction output?

2. The Model Object (Parameters, Methods, and Attributes)

The dualPredictor package aims to simplify complex models for users of all coding levels. It adheres to the syntax of the scikit-learn library and simplifies model training by allowing you to fit the model with just one line of code. The core part of the package is the model object called DualModel, which can be imported from the dualPredictor library.

Table 1: Model Parameters, Methods, and Attributes

Category	Name	Description
Parameters	model_type	Type of regression model to use. For example: - 'lasso' (Lasso regression)
	metric	Metric is used to optimize the cut-off value. For example: - 'youden_index' (Youden's Index)
	default_cut_off	Initial cut-off value used for binary classification. For example: 2.50
Methods	fit(X, y)	- X: The input training data, pandas data frame. - y: The target values (predicted grade). - Returns: Fitted DualModel instance
	predict(X)	- X: The input training data, pandas data frame.
Attributes	alpha_	The value of penalization in Lasso model
	coef_	The coefficients of the model
	intercept_	The intercept value of the model
	feature_names_in_	Names of features during model training
	optimal_cut_off	The optimal cut-off value that maximizes the metric

Example Usage

from dualPredictor import DualModel

# Initialize the model and specify the parameters
model = DualModel(model_type='lasso', metric='youden_index', default_cut_off=2.5)

# Using model methods for training and predicting
# Simplify model training by calling fit method with one line of code
model.fit(X_train, y_train)
grade_predictions, class_predictions = model.predict(X_train)

# Accessing model attributes
print("Alpha (regularization strength):", model.alpha_)
print("Model coefficients:", model.coef_)
print("Model intercept:", model.intercept_)
print("Feature names:", model.feature_names_in_)
print("Optimal cut-off value:", model.optimal_cut_off)

3. Quick Start

3.1 Dependencies Installation

dualPredictor requires the following libraries to be installed:

• NumPy: A fundamental package for scientific computing with Python.
• scikit-learn: A simple and efficient tools for predictive data analysis.
• Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
• Seaborn: A Python data visualization library based on matplotlib that provides a high-level interface for drawing attractive statistical graphics. You can install all the dependencies at once using the following command:

pip install numpy scikit-learn matplotlib seaborn

3.2 Package Installation

You can install the dualPredictor package via PyPI or GitHub (Recommended). Choose one of the following methods:

pip install dualPredictor

pip install git+https://github.com/098765d/dualPredictor.git

3.3 Example Code

After installation, start with:

Step 1. Import the Package: Import the dualPredictor package into your Python environment.

from dualPredictor import DualModel, model_plot

Step 2. Model Initialization: Create a DualModel instance

model = DualModel(model_type='lasso', metric='youden_index', default_cut_off=2.5)

Step 3. Model Fitting: Fit the model to your dataset using the fit method.

model.fit(X_train, y_train)

• X: The input training data (type: pandas DataFrame).
• y: The target values (type: pandas data series).

Step 4. Predictions: Use the model's predict method to generate grade predictions and at-risk classifications.

# example for demo only, model prediction dual output
y_train_pred,y_train_label_pred=model.predict(X_train)

# example of 1st model output = predicted scores (regression result)
y_train_pred
array([3.11893389, 3.06013236, 3.05418893, 3.09776197, 3.14898782,
     2.37679417, 2.99367804, 2.77202421, 2.9603209 , 3.01052573,
     2.99974477, 3.11286716, 3.14708887, 2.78737598, 2.88134869,
     3.07517748, 3.17370297, 3.26615469, 3.2328493 , 2.98423656,
     3.02108518, 2.87746064, 3.03491596, 2.89875586, 3.11079315,
     3.23177653, 3.34291929, 2.57402463, 3.27019917, 3.20073168,
     2.94514418, 3.25307175, 3.19145494, 3.15909904, 3.01481681,
     3.07551728, 2.70973767, 3.07226583, 3.04692613, 2.8883649 ,
     2.63833457, 3.03978663, 3.20974038, 3.13091091, 3.42223703,
     3.07012029, 3.01981077, 3.22368756, 2.69376153, 2.93594929,
     2.91493381, 3.22273808, 2.59310411, 3.00767959, 3.21869359,
     2.86065334, 3.16865551, 3.11258742, 2.87948289, 2.64564212,
     2.88646595, 3.48716006, 3.14482003, 3.15513751, 3.05299286,
     3.20858237, 2.63172024, 2.42824269, 2.88352738, 3.0479989 ,
     2.82405611, 3.16516577, 2.94324523, 3.4453079 , 2.48497569,
     3.00081754, 3.04180887, 3.32979373, 3.12686642, 2.90359338,
     2.95509896, 2.96429385, 3.44471154, 3.20251564, 3.08765075,
     2.5607482 , 3.23986551, 3.19644891, 3.16032825, 2.68092384,
     3.04907167, 2.8159268 , 3.05030088, 3.178372])

# example of 2nd model output = predicted at-risk status (binary label)
y_train_label_pred
array([0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
     0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
     0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
     1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
     0, 1, 0, 0, 0, 0])

• y_train_pred: Predicted grades (regression result).
• y_train_label_pred: Predicted at-risk status (binary label).

Step 5.Visualization: Visualize the model's performance with just one line of code

# Scatter plot for regression analysis 
model_plot.plot_scatter(y_pred, y_true)

# Confusion matrix for binary classification 
model_plot.plot_cm(y_label_true, y_label_pred)

# Model's global explanation: Feature importance plot
model_plot.plot_feature_coefficients(coef=model.coef_, feature_names=model.feature_names_in_)

Fig 2: Visualization Module Sample Outputs

References

[1] Fluss, R., Faraggi, D., & Reiser, B. (2005). Estimation of the Youden Index and its associated cutoff point. Biometrical Journal: Journal of Mathematical Methods in Biosciences, 47(4), 458-472.

[2] Hoerl, A. E., & Kennard, R. W. (1970). Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1), 55-67.

[3] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.

[4] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825-2830.

[5] Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology, 58(1), 267-288.