Essential Python scripts for intermediate machine learning practitioners
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introduction
As a machine learning engineer, you probably enjoy working on interesting tasks like experimenting with model architectures, fine-tuning hyperparameters, and analyzing results. But how much of your day is actually spent on uninteresting tasks like preprocessing data, managing experiment configurations, debugging model performance issues, and tracking which hyperparameters worked best across dozens of training runs?
To be honest, it’s probably eating up a good portion of your productive time. Machine learning practitioners spend countless hours on repetitive tasks like handling missing values, normalizing features, setting cross-validation folds, and logging experiments, when they could be focusing on actually building better models.
This article describes five Python scripts specifically designed to tackle repetitive machine learning pipeline tasks that consume experiment time. Let’s get started!
🔗 The code can be found on GitHub. See the README file for requirements, getting started, usage examples, and more.
1. Automated feature engineering pipeline
Problem: Every new dataset requires the same tedious preprocessing steps. Manually check for missing values, encode categorical variables, scale numeric features, handle outliers, and design domain-specific features. Switching between projects always results in rewriting similar preprocessing logic with slightly different requirements.
Script behavior: Scripts automatically handle common feature engineering tasks through configurable pipelines. Detect feature types, apply appropriate transformations, generate engineered features based on predefined strategies, handle missing data, and create consistent preprocessing pipelines that can be saved and reused across projects. It also provides a detailed report on applied transformations and post-engineering feature significance.
structure: The script automatically profiles the dataset to detect numeric, categorical, date/time, and text columns. Apply the appropriate transformation to each type.
- Robust scaling or standardization of numeric variables,
- Targeted or one-hot encoding of categorical variables, and
- Circular encoding of date and time features.
The script uses iterative imputation for missing values and uses IQR or isolated forestgenerates polynomial features and interaction terms for a numeric sequence.
⏩ Get automated feature engineering pipeline scripts
2. Hyperparameter optimization manager
Problem: I’m performing a grid search or random search for hyperparameter tuning, but managing all the configurations, keeping track of the combinations I tried, and analyzing the results is a pain. You’ll probably have a Jupyter notebook full of hyperparameter dictionaries, manual logs showing what worked, and no systematic way to compare runs. I’m not sure if it can be further improved when I find the right parameters. Starting over means losing track of what you’ve already considered.
Script behavior: Provides a unified interface for optimizing hyperparameters using multiple strategies. Grid search, random search, Bayesian optimization, sequential halving method. Automatically record all experiments including parameters, metrics, and metadata. Generate an optimization report showing parameter importance, convergence plots, and optimal configurations. Supports early stopping and resource allocation to avoid wasting compute on improper configurations.
structure: The script wraps various optimization libraries — scikit-learn, Optuna, Scikit optimization — into a unified interface. Allocate computational resources using continuous halving or hyperband to eliminate bad configurations early. All trials are recorded in a database or JSON file with parameters, cross-validation scores, training times, and timestamps. The script calculates parameter importance using: Functional analysis of variance It also generates visualizations showing convergence, parameter distribution, and parameter-performance correlation. You can query and filter results to analyze specific parameter ranges or restart optimization from a previous run.
⏩ Get the hyperparameter optimization manager script
3. Model performance debugger
Problem: The model’s performance has suddenly decreased or is not performing as expected on certain data segments. Manually slice the data by different features, calculate metrics for each slice, check the predictive distribution, and look for data drift. This is a time-consuming process and there is no systematic approach. You may miss important issues hidden in the interactions of specific subgroups or features.
Script behavior: Perform comprehensive model debugging by analyzing performance across data segments, detecting problematic slices where the model is underperforming, identifying feature and prediction drift, checking for label leakage and data quality issues, and generating detailed diagnostic reports with actionable insights. It also compares the current model’s performance to baseline metrics to detect degradation over time.
structure: The script performs slice-based analysis by automatically partitioning the data along each feature dimension and calculating metrics for each slice.
- Use statistical tests to identify segments whose performance is significantly worse than overall performance.
- For drift detection, compare the feature distribution between training and test data using: Kolmogorov-Smirnov test or population stability index.
The script also performs automated feature importance analysis to identify potential label leaks by checking for features of questionable importance. All findings are summarized in a visualized and interactive report.
⏩ Get the model performance debugger script
4. Cross-validation strategy manager
Problem: Different datasets require different cross-validation strategies.
- Time series data requires time-based partitioning.
- Unbalanced datasets require stratified partitioning.
- Grouped data requires group-aware partitioning.
Implement these strategies manually for each project, write custom code to avoid data leakage, and verify that the split makes sense. This is an error-prone and iterative task, especially when multiple partitioning strategies need to be compared to see which one gives the most reliable performance estimate.
Script behavior: Provides preconfigured cross-validation strategies for different data types and machine learning projects. Automatically discover appropriate splitting strategies based on data characteristics, prevent data leakage across folds, generate stratified splits for unbalanced data, process time series with proper temporal ordering, and support grouped/clustered data splitting. Validate split quality and provide metrics on fold distribution and balance.
structure: The script analyzes the characteristics of the dataset and determines an appropriate partitioning strategy.
- For temporal data, create expanding or rolling window splits that take temporal order into account.
- For unbalanced datasets, use stratified splits to maintain class proportions across folds.
- Specifying a group column keeps all samples of the same group together in the same fold.
This script validates the split by checking for data leakage (future information in the time series training set), group contamination, and class distribution imbalance. Provides a split iterator compatible with scikit-learn. crossval score and Grid search CV.
⏩ Get the cross-validation strategy manager script
5. Experiment Tracker
Problem: I’ve run dozens of experiments with different models, features, and hyperparameters, and it’s chaotic to keep track of them all. Notebooks are scattered across directories, naming conventions are inconsistent, and there’s no easy way to compare results. If you’re asked “Which model performed best?” or “What features did you try?” you’ll have to sift through your files to reconstruct your experiment history. It is very difficult to reproduce past results because we do not know exactly what code and data were used.
Script behavior: The Experiment Tracker script provides lightweight experiment tracking that logs all model training runs with parameters, metrics, feature sets, data versions, and code versions. Capture model artifacts, training configurations, and environment details. Generate comparison tables and visualizations between experiments. Supports tagging and organizing experiments by project or purpose. Make your experiments fully reproducible by recording everything you need to reproduce your results.
structure: The script creates a structured directory for each experiment containing all metadata in JSON format. This does the following:
- Capture the model’s hyperparameters by introspecting the model objects.
- Log all metrics passed by the user and save the model artifacts using: job rib or picklesand
- Records environment information (Python version, package version).
The script stores all experiments in a queryable format, allowing easy filtering and comparison. Generate a pandas DataFrame for tabular comparisons and visualizations for comparing metrics across experiments. The tracking database is: SQLite Work locally or integrate with remote storage as needed.
⏩ Get the experiment tracker script
summary
These five scripts focus on key operational challenges that machine learning practitioners regularly encounter. Here’s a quick summary of how these scripts work.
- An automated feature engineering pipeline consistently handles iterative preprocessing and feature creation.
- Hyperparameter optimization manager systematically explores parameter space and tracks all experiments
- Model Performance Debugger identifies performance issues and automatically diagnoses model failures
- Cross-validation strategy manager ensures proper validation without data leakage for various data types
- Experiment Tracker organizes all your machine learning experiments and makes your results reproducible
Writing Python scripts to solve the most common problems can be a useful and interesting exercise. If you want, you can later switch to tools like MLflow or Weights & Biases to track your experiments. Have fun experimenting!
