mlprimer 1.0.8

Complete ML pipeline for rapid model selection, tuning, and export.

MLPrimer

MLPrimer is an all-in-one Python package for rapid machine learning experimentation and deployment.
It automates the full ML workflow—from exploratory data analysis to model selection and hyperparameter tuning—behind a consistent, task-aware API.

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Overview

MLPrimer orchestrates the complete ML pipeline with a single function call.

from mlprimer.bootstrap import run_pipeline

result = run_pipeline(df, target_var="target")
best_model_name, best_model = result.get_best_tuned_model()
predictions = best_model.predict(X_new)

Pipeline Stages

1. Statistics – Feature profiling, normality testing, distribution analysis
2. Correlation – Association testing (Pearson, Spearman, Chi-Square, ANOVA, t-tests)
3. Preprocessing – Task-aware splitting, class balancing (SMOTE), target transforms
4. Model Selection – 25+ models trained and ranked automatically
5. Tuning – Hyperparameter optimization for the top-N models

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Key Features

Data Analysis

• Comprehensive statistics: mean, median, std, skewness, normality tests
• Association testing: Pearson, Spearman, Chi-Square, Point-Biserial, ANOVA
• Automatic target type inference (binary, multiclass, regression, count)

Preprocessing

• Task-aware splitting (stratified, grouped, time-series)
• Class balancing: SMOTE, under-sampling, class weights
• Target transformations: Yeo-Johnson, Box-Cox, log1p
• Sample weighting for imbalanced or heteroscedastic targets

Model Selection (25+ Models)

• Classification: Logistic Regression, Random Forest, SVC, XGBoost, LightGBM, CatBoost, MLP
• Regression: ElasticNet, Ridge, Lasso, SVR, Random Forest, XGBoost, LightGBM, CatBoost
• Robust Models: Huber, RANSAC, Theil-Sen
• Neural Networks: MLP Classifier / Regressor

Hyperparameter Tuning

• Grid Search, Randomized Search, Successive Halving, Bayesian Optimization
• Task-aware cross-validation
• Multi-metric tracking
• Predefined parameter grids for supported models

Export & Deployment

• Persist best or top-N models with metadata
• Auto-generated inference and tuning scripts
• Export feature importance, associations, and rankings

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Installation

From PyPI (recommended)

pip install mlprimer

From Source

git clone https://github.com/emrec/mlprimer.git
cd mlprimer
pip install -e .

Optional Dependencies

# Bayesian optimization
pip install mlprimer[bayesian]

# Boosting models (XGBoost, LightGBM, CatBoost)
pip install mlprimer[advanced]

# All optional features
pip install mlprimer[all]

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Quick Start

Complete Pipeline

import pandas as pd
from mlprimer.bootstrap import run_pipeline

df = pd.read_csv("data.csv")

result = run_pipeline(
    df,
    target_var="target",
    tune_top_n=5,
    verbose=2
)

best_model_name, best_model = result.get_best_tuned_model()
predictions = best_model.predict(X_test)

Step-by-Step Workflow

from mlprimer.statistics import calculate_summary
from mlprimer.correlation import check_association
from mlprimer.preprocessing import infer_target_type, split_data, apply_smote_classification
from mlprimer.model_selection import run_full_model_selection
from mlprimer.tuning import tune_random

feature_summary = calculate_summary(df)
associations = check_association(df, target_var="target")

target_type = infer_target_type(df["target"])
X_train, X_test, y_train, y_test = split_data(
    df.drop("target", axis=1),
    df["target"],
    task="classification"
)

X_train, y_train = apply_smote_classification(X_train, y_train)

results = run_full_model_selection(
    X_train, y_train, X_test, y_test, target_type=target_type
)

best_model = results["best_model"]
tuned = tune_random(
    best_model,
    X_train,
    y_train,
    {"n_estimators": [100, 200], "max_depth": [10, 20]},
    task="binary"
)

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Exporting Models

from mlprimer.bootstrap import (
    export_best_model,
    export_models,
    create_inference_script
)

export_best_model(result, "best_model.pkl")
export_models(result, "models/", include_top_n=5)
create_inference_script(result, "inference.py")

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Core Modules

statistics.py

• calculate_summary(df)
• check_normal(x)

correlation.py

• check_association(df, target_var)
• analyze_associations(df, target_var)

preprocessing.py

• infer_target_type(y)
• export_target_distribution(y, task)
• split_data(X, y, task, stratify=None, groups=None, time_series=False)
• apply_smote_classification(X, y)
• apply_target_transform(y, method)
• make_sample_weights_regression(y)

model_selection.py

• run_full_model_selection(...)
• suggest_models(target_type)
• train_models(X_train, y_train, task)
• evaluate_models(models, X_test, y_test, task)

tuning.py

• tune_grid
• tune_random
• tune_halving_grid
• tune_halving_random
• tune_bayesian
• tune_with_calibration

bootstrap.py

• run_pipeline
• export_results
• export_best_model
• export_models
• create_fine_tuning_script
• create_inference_script
• create_comparison_script

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Supported Tasks

Task	Target Type	Models	Metrics
Binary Classification	2 classes	15+	F1, AUC, Precision, Recall
Multiclass	3+ classes	15+	F1-weighted, Balanced Accuracy
Regression	Continuous	14+	MAE, RMSE, R²
Count	Non-negative ints	14+	MAE, RMSE

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Architecture

bootstrap.py
 ├─ statistics.py
 ├─ correlation.py
 ├─ preprocessing.py
 ├─ model_selection.py
 └─ tuning.py

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Requirements

• Python ≥ 3.8
• pandas ≥ 1.0
• numpy ≥ 1.18
• scikit-learn ≥ 0.24
• scipy ≥ 1.5
• imbalanced-learn ≥ 0.8

Optional

• xgboost
• lightgbm
• catboost
• scikit-optimize

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License

MIT License

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Author

Emre Can Konca

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Acknowledgments

Built on top of scikit-learn, XGBoost, LightGBM, CatBoost, and the broader open-source ML ecosystem.