autofe-grass 0.0.3

Automated Feature Engineering framework

AutoFEAT

Framework for automated feature generation based on statistics and AI tools

Installation

pip install autofe-vsu-project

Quick Start

import pandas as pd
from autofe import GroupAggregationFeatures, StatisticalFeatureGenerator

# Load your data
df = pd.read_csv('your_data.csv')
X_train, X_test, y_train, y_test = train_test_split(X, y)

# Generate group-based features
group_features = GroupAggregationFeatures(
    numeric_cols=['age', 'fare'],
    group_cols=['pclass', 'sex'],
    aggs=['mean', 'std'],
    add_deviation=True
)
X_train_grouped = group_features.fit_transform(X_train)

# Generate statistical features
stat_features = StatisticalFeatureGenerator(
    numeric_cols=['age', 'fare'],
    unary=['log', 'sqrt'],
    pairwise=['diff']
)
X_train_stats = stat_features.fit_transform(X_train_grouped, y_train)

Key Features

• Group Aggregations - Mean, std, min, max, sum, count by categories
• Statistical Transforms - Log, sqrt, ratio, difference between features
• Sklearn-Compatible - Works with Pipeline, GridSearchCV

🔧 Requirements

• Python >= 3.8
• numpy >= 1.19.0
• pandas >= 1.2.0
• scikit-learn >= 0.24.0