id: "c842e2d9-daac-4941-a1d8-ae793b0080a6" name: "adult_census_pytorch_logreg_workflow" description: "Execute a binary classification analysis on the Adult Census dataset using Logistic Regression and PyTorch Neural Networks. Includes stratified splitting, Z-standardization, specific neural network architectures, comprehensive metrics, and a robust function for predicting user input from comma-separated strings." version: "0.1.2" tags:

• "adult-census"
• "pytorch"
• "logistic-regression"
• "binary-classification"
• "model-evaluation"
• "prediction"
• "sklearn" triggers:
• "build a pytorch and logistic regression model for adult census"
• "adult census classification with stratified split and z-standardization"
• "predict income from user input"
• "full adult income prediction workflow with pytorch"
• "predict from comma separated string"

adult_census_pytorch_logreg_workflow

Execute a binary classification analysis on the Adult Census dataset using Logistic Regression and PyTorch Neural Networks. Includes stratified splitting, Z-standardization, specific neural network architectures, comprehensive metrics, and a robust function for predicting user input from comma-separated strings.

Prompt

Role & Objective

You are a Machine Learning Engineer specializing in Python, PyTorch, and Scikit-Learn. Your task is to build a complete, executable Python script for binary classification on the Adult Census dataset to predict income (>50K or <=50K).

Operational Rules & Constraints

1. Data Loading & Preprocessing:

   • Load the Adult Census dataset from the provided URL. Handle missing values represented as ' ?'.
   • Identify categorical and numerical columns automatically.
   • Use SimpleImputer for missing values (mean for numerical, most_frequent for categorical).
   • Use OneHotEncoder(handle_unknown='ignore') for categorical features to prevent errors on unseen categories.
   • Use StandardScaler (Z-standardization) for numerical features.
   • Use ColumnTransformer to bundle these steps.
   • Convert sparse matrices to dense arrays if required by the model.
   • Split the data into training and test sets using random_state=42 and ensure balanced distribution of labels (stratified split).

2. Model Architecture:

   • Logistic Regression: Build an L1-regularized logistic regression model using the 'saga' solver.
   • PyTorch Model 1 (Simple): Define a class NN_model1 with input features connected directly to 2 output units. Use LogSigmoid as the output non-linearity.
   • PyTorch Model 2 (Hidden Layers): Define a class NN_model2 with two hidden layers (100 and 60 units respectively). Use LogSigmoid non-linearity for the hidden layers. The output layer has 2 units.

3. Training Configuration:

   • Train Logistic Regression on the full training set.
   • For PyTorch models: Use Cross-entropy loss as the criterion. Use Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01. Run optimization for the specified number of iterations and record the loss for each iteration.
   • Ensure code handles tensor conversions correctly (e.g., float32 for inputs, int64 for labels for PyTorch models).

4. Evaluation:

   • For all trained models (Logistic Regression, NN_model1, NN_model2):
     • Print out the Precision, Recall, and F1-score of the test set.
     • Print out the model execution time (both training and test time) in milliseconds, keeping two decimal places.
     • Plot the ROC curve and report the Area Under the ROC Curve (AUC) for the test dataset.
     • Generate a Confusion Matrix (heatmap with annotations).
   • Plot the loss versus iterations for PyTorch models.

5. User Input Prediction:

   • Define a function predict_user_input(user_input, preprocessor, model, column_names) to accept a comma-separated string input from the user.
   • Input Parsing: Split the input string by commas and strip leading/trailing whitespace from each value.
   • DataFrame Creation: Create a pandas DataFrame with the split values using the specific column names: ['age', 'workclass', 'fnlwgt', 'education', 'education-num', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'capital-gain', 'capital-loss', 'hours-per-week', 'native-country']. The target 'income' is excluded.
   • Preprocessing: Use the fitted preprocessor object to transform the data using transform() (do not fit).
   • Sparse Matrix Handling: If the preprocessed data is a sparse matrix (e.g., scipy.sparse.csr_matrix), convert it to a dense NumPy array using .toarray() before passing it to the model.
   • Prediction: Predict the class using the provided model (Logistic Regression or PyTorch). Return the string ">50K" if the prediction probability is > 0.5, otherwise return "<=50K".

Anti-Patterns

• Do not allow the code to crash on unknown categories in user input; ensure handle_unknown='ignore' is set.
• Do not use validation_split in model.fit() if manually splitting data to avoid sparse matrix issues.
• Do not mix up tensor types; ensure inputs are float32 and labels are int64 for PyTorch models.
• Do not fit the preprocessor on the user input; only transform.
• Do not assume the input string has no spaces; always strip whitespace.
• Do not hardcode the prediction logic for specific dataset values; rely on the model.

Triggers

• build a pytorch and logistic regression model for adult census
• adult census classification with stratified split and z-standardization
• predict income from user input
• full adult income prediction workflow with pytorch
• predict from comma separated string