maxwailab 1.3.9

Bootstrap-based model stability and supervised binning toolkit

Bootstrap ML diagnostics + statistical inference + Spark

A lightweight toolkit for statistically robust model diagnostics using bootstrap resampling, with both in-memory and distributed (PySpark) support.

The library provides utilities for:

• supervised tree binning
• bootstrap-based feature selection
• model stability analysis
• hyperparameter sensitivity analysis
• statistical feature diagnostics (e.g., logistic relevance, missing analysis)
• scalable data diagnostics with PySpark

The toolkit focuses on reducing overfitting and improving model interpretability by leveraging bootstrap distributions and statistical inference rather than single-point estimates.

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Installation

Option	Command
Core (minimal dependencies)	pip install maxwailab
Survival Module Only	pip install maxwailab[survival]
PySpark Module Only	pip install maxwailab[pyspark]
Everything (core + all optional)	pip install maxwailab[all]
Core from GitHub	pip install git+https://github.com/MaxWienandts/maxwailab.git
GitHub with survival extras	pip install "git+https://github.com/MaxWienandts/maxwailab.git#egg=maxwailab[survival]"
GitHub with PySpark extras	pip install "git+https://github.com/MaxWienandts/maxwailab.git#egg=maxwailab[pyspark]"

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Core Philosophy

Most ML workflows rely on single train/validation splits.

This library instead uses bootstrap resampling to estimate:

• performance distributions
• feature selection stability
• hyperparameter robustness

Benefits:

• reduces variance from a single split
• identifies unstable variables
• provides confidence intervals for model performance

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Workflow Overview

Typical modeling workflow using this library:

1️⃣ Supervised binning (optional)

tree_supervised_binning
bootstrap_tree_binning_auc_analysis
plot_target_mean_by_binned_variable


2️⃣ Feature selection

bootstrap_lightgbm_forward_selection
bootstrap_model_variable_comparison_paired
bootstrap_survival_forward_selection
survival_bootstrap_model_comparison
    
3️⃣ Diagnostics

performance_forward_selection_boxplot
variable_frequency_forward_selection


4️⃣ Extract best variables

top_k_forward_selection_variables_by_frequency_usage
top_k_variables_by_forward_selection_boxplot


5️⃣ Hyperparameter analysis

lightgbm_hyperparameter_auc_curve_bootstrap


6️⃣ PySpark Data Diagnostics
pyspark_missing_values_table
pyspark_minmax_value
pyspark_compare_columns
pyspark_value_counts_spark
pyspark_missing_by_group
pyspark_logistic_feature_significance

---

LightGBM Classification Example

import maxwailab

# Forward selection with bootstrap
result_bootstrap = maxwailab.bootstrap_lightgbm_forward_selection(
    df=data,
    target="target",
    n_bootstrap=30,
    n_max_variables=15,
    metric_to_optimize="auc_roc",
    hyperparameters=lgb_params
)

# Analyze performance stability
maxwailab.performance_forward_selection_boxplot(result_bootstrap["auc_roc"], "AUC")

# Variable selection stability
maxwailab.variable_frequency_forward_selection(result_bootstrap["variables"], n_bootstraps=30)

# Extract best variables
top_vars = maxwailab.top_k_forward_selection_variables_by_frequency_usage(result_bootstrap["variables"], n_bootstraps=30, k=10)
# Or
top_vars = maxwailab.top_k_variables_by_forward_selection_boxplot(result_bootstrap["variables"], n_bootstraps=30, k=10)

Paired Bootstrap Comparison (LightGBM)

Compare two models: baseline vs modified (adding/removing variables):

comparison = maxwailab.bootstrap_model_variable_comparison_paired_lgbm(
    df_train=df_train,
    base_variables=["var1", "var2"],
    variables_to_add=["var3"],
    variables_to_remove=["var2"],
    target_col="target",
    n_bootstrap=100,
    metric="auc",
    hyperparameters=lgb_params
)

Generates:

• Validation performance distributions
• Paired bootstrap difference distribution
• Statistical summary (mean, 95% CI, probability of improvement)

Tree-based Supervised Binning

from maxwailab import tree_supervised_binning

tree_supervised_binning(df=data, feature="age", target="target", max_leaf_nodes=5)

# Bootstrap binning stability
bootstrap_tree_binning_auc_analysis(df_train, df_val, feature="age", target="target")
Hyperparameter Sensitivity Analysis
lightgbm_hyperparameter_auc_curve_bootstrap(
    X_train, y_train, X_val, y_val,
    hyperparameters=lgb_params,
    hyperparameter_name="num_leaves",
    hyperparameter_values=[5,10,20,40],
    n_bootstrap=50
)

Analyze target behavior across variable ranges

# Define bins (no need for -inf / +inf)
bins = [0, 18, 30, 50, 80]

summary = plot_target_mean_by_binned_variable(
    df=data,
    target="target",
    variable="age",
    bins=bins
)

• Visualizes target mean per bin
• Displays observation count and percentage
• Useful for feature understanding and pre-binning analysis

Survival Analysis Workflows

Bootstrap Forward Selection for Survival Models

result_survival = maxwailab.bootstrap_survival_forward_selection(
    df_train=df_train,
    duration_col="duration",
    event_col="event",
    start_month_col="start_month",
    model_type="cox_breslow",
    n_bootstrap=50,
    n_max_variables=10,
    metric_to_optimize="c_index",
    hyperparameters=cox_params
)

# Analyze performance stability
maxwailab.performance_forward_selection_boxplot(result_survival["auc_roc"], "AUC")

# Variable selection stability
maxwailab.variable_frequency_forward_selection(result_survival["variables"], n_bootstraps=30)

# Extract best variables
top_vars = maxwailab.top_k_forward_selection_variables_by_frequency_usage(result_survival["variables"], n_bootstraps=30, k=10)
# Or
top_vars = maxwailab.top_k_variables_by_forward_selection_boxplot(result_survival["variables"], n_bootstraps=30, k=10)

Paired Bootstrap Comparison for Survival Models

comparison_surv = maxwailab.bootstrap_model_variable_comparison_paired(
    df_train=df_train,
    model_type="cox_breslow",
    base_variables=["var1", "var2"],
    variables_to_add=["var3"],
    variables_to_remove=["var2"],
    n_bootstrap=50,
    metric="c_index"
)

Generates:

• Baseline vs Modified model performance distribution
• Paired difference plot
• Statistical inference summary

Compare Multiple Survival Models

models_dict = {
    "Cox": CoxModel(),
    "AFT": AFTModel()
}

comparison_multi = maxwailab.survival_bootstrap_model_comparison(
    df_train=df_train,
    models_dict=models_dict,
    feature_cols=["var1", "var2", "var3"],
    n_bootstrap=50
)

Outputs:

• Bootstrap distributions per model
• Ranking summary

---

🔍 Additional Utilities

The examples above cover the core functionality of the library.
maxwailab also includes several additional utilities for:

• PySpark-based data diagnostics
• statistical feature analysis
• extended bootstrap evaluations
• survival modeling workflows

For a complete list of available functions and usage examples, refer to the notebooks/ directory in the repository, which contains practical, end-to-end implementations.

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Module Structure

maxwailab
│
├── binning
│   ├── tree_supervised_binning
│   ├── bootstrap_tree_binning_auc_analysis
│   ├── plot_target_mean_by_binned_variable
│   ├── pandas_one_hot_encode,
│   └── pandas_round_number_strings,
│
├── feature_selection
│   ├── bootstrap_lightgbm_forward_selection
│   ├── performance_forward_selection_boxplot
│   ├── variable_frequency_forward_selection
│   ├── top_k_forward_selection_variables_by_frequency_usage
│   ├── top_k_variables_by_forward_selection_boxplot
│   └── bootstrap_model_variable_comparison_paired_lgbm
│
├── hyperparameter_analysis
│   └── lightgbm_hyperparameter_auc_curve_bootstrap
│
├── survival_feature_selection
│   ├── bootstrap_survival_forward_selection
│   ├── bootstrap_model_variable_comparison_paired
│   └── survival_bootstrap_model_comparison
│
├── pyspark_basic_functions
│   ├── pyspark_missing_values_table
│   ├── pyspark_minmax_value
│   ├── pyspark_compare_columns
│   ├── pyspark_value_counts_spark
│   ├── pyspark_missing_by_group
│   ├── pyspark_logistic_feature_significance
│   ├── pyspark_one_hot_encode,
│   ├── pyspark_print_shape,
│   └── pyspark_round_number_strings,

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When to Use This Library

This library is particularly useful for:

• credit risk models
• tabular ML problems
• high-stakes predictive modeling
• interpretable ML workflows
• Using large-scale datasets with PySpark

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License

MIT License