unslml 0.1.2

Simple AutoML library for classification and regression

UNSLML

A state-of-the-art, robust, and highly accurate AutoML and Machine Learning Library in Python.

unslml automatically detects task types (classification or regression), performs stateful feature engineering, filters extreme outliers, conducts hyperparameter searches, and provides simple one-line model saving and loading.

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🌟 Key Features

• Auto-Task Detection: Automatically detects whether your target is a classification or regression task based on target column datatypes.
• Smart Numeric Text Parser: Automatically extracts numerical values from string columns that represent measurements or values (e.g., "1200 sqft" -> 1200.0, "42 Lac" -> 4,200,000.0, "1.40 Cr" -> 14,000,000.0).
• Robust Outlier Filtering: Automatically identifies and filters extreme target outliers in regression (e.g. data entry typos) to prevent metric skew.
• Stateful Preprocessing: Saves imputations and categorical mapping encodings during training to ensure identical transformation on test/prediction sets.
• Auto-Hyperparameter Tuning: Performs grid search cross-validation across multiple standard estimators (Logistic/Linear Regression, Decision Trees, Random Forests, KNN).
• Smart Performance Scaling: Sub-samples extremely large datasets during the parameter search phase to run in seconds rather than hours.
• Pipeline Serialization (Save & Load): Prompts you to save the entire pipeline state to a .pkl file at the end of training, which can be loaded back with a single line of code.

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🚀 Installation

Install the library directly from PyPI using pip:

pip install unslml

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💻 How to Use

1. Training & Auto-Saving a Pipeline

Create a script (e.g., train.py) to fit the model. The fitting process automatically runs preprocessing, tunes multiple models, reports evaluation scores, and prompts you to save the best model:

from unslml import AutoML

# Initialize AutoML pipeline
ml = AutoML()

# Fit model (auto-detects task type, handles preprocessing & fits best model)
ml.fit(
    file="house_prices.csv",
    target="Price (in rupees)"
)
# Prompt: "Enter the file path to save the best model (default: best_model.pkl): "

2. Loading & Predicting on Unseen Data

You can load the saved .pkl file (which contains the best model, categorical mappings, and median values) and predict on raw, unprocessed pandas DataFrames:

import pandas as pd
from unslml import AutoML

# Load the entire trained pipeline
ml_loaded = AutoML.load("best_model.pkl")

# New raw sample data to predict
new_houses = pd.DataFrame({
    'location': ['location_name'],
    'Bathroom': [2],
    'Balcony': [1.0],
    'facing': ['North'],
    'Furnishing': ['Semi-Furnished'],
    'Transaction': ['Resale']
})

# Make predictions directly (preprocessing is applied automatically)
predictions = ml_loaded.predict(new_houses)
print("Predicted Prices:", predictions)