freamon 0.3.31

Advanced feature engineering, analysis, modeling and optimization for data science

Freamon: Feature-Rich EDA, Analytics, and Modeling Toolkit

Freamon is a comprehensive Python toolkit for exploratory data analysis, feature engineering, and model development with a focus on practical data science workflows.

Features

• Exploratory Data Analysis: Automatic EDA with comprehensive reporting in HTML, Markdown, and Jupyter notebooks
• Feature Engineering: Advanced feature engineering for numeric, categorical, and text data
• Deduplication: Multiple deduplication methods with index tracking to map results back to original data
• Topic Modeling: Optimized text analysis with NMF and LDA, supporting large datasets up to 100K documents
• Automated Modeling: Intelligent end-to-end modeling workflow for text, tabular, and time series data
• Modeling: Custom model implementations with feature importance and model interpretation
• Pipeline: Scikit-learn compatible pipeline with additional features
• Drift Analysis: Tools for detecting and analyzing data drift
• Word Embeddings: Integration with various word embedding techniques
• Visualization: Publication-quality visualizations with proper handling of all special characters
• Performance Optimization: Multiprocessing support and intelligent sampling for large dataset analysis

Installation

pip install freamon

Quick Start

from freamon.eda import EDAAnalyzer

# Create an analyzer instance
analyzer = EDAAnalyzer(df, target_column='target')

# Run the analysis
analyzer.run_full_analysis()

# Generate a report
analyzer.generate_report('eda_report.html')

# Or a markdown report for version control
analyzer.generate_report('eda_report.md', format='markdown')

Key Components

Automated Modeling Flow

Perform end-to-end modeling with automatic handling of text and time series features:

from freamon import auto_model

# Simple interface - just provide a dataframe, target, and optional date column
results = auto_model(
    df=train_df,
    target_column='target',
    date_column='date',  # Optional for time series
    model_type='lightgbm',
    problem_type='classification',
    text_columns=['text_column'],  # Will be auto-detected if not provided
    categorical_columns=['category_column']  # Will be auto-detected if not provided
)

# Access the trained model and results
model = results['model']
feature_importance = results['feature_importance']
text_topics = results['text_topics']
cv_metrics = results['metrics']

# Make predictions on new data
predictions = results['autoflow'].predict(test_df)

EDA Module

The EDA module provides comprehensive data analysis:

from freamon.eda import EDAAnalyzer

analyzer = EDAAnalyzer(df, target_column='target')
analyzer.run_full_analysis()

# Generate different types of reports
analyzer.generate_report('report.html')  # HTML report
analyzer.generate_report('report.md', format='markdown')  # Markdown report
analyzer.generate_report('report.md', format='markdown', convert_to_html=True)  # Both formats

Deduplication with Tracking

Perform deduplication while maintaining the ability to map results back to the original dataset:

from freamon.deduplication.exact_deduplication import hash_deduplication
from examples.deduplication_tracking_example import IndexTracker

# Initialize tracker with original dataframe
tracker = IndexTracker().initialize_from_df(df)

# Perform deduplication
deduped_df = hash_deduplication(df['text_column'])

# Update tracking
kept_indices = deduped_df.index.tolist()
tracker.update_from_kept_indices(kept_indices)

# Map results back to original dataset
full_results = tracker.create_full_result_df(
    results_df, original_df, fill_value={'predicted': None}
)

Pipeline with Deduplication

Create ML pipelines that include deduplication steps:

from freamon.pipeline.pipeline import Pipeline
from examples.pipeline_with_deduplication_tracking import (
    IndexTrackingPipeline, HashDeduplicationStep
)

# Create pipeline with deduplication
pipeline = IndexTrackingPipeline(steps=[
    TextPreprocessingStep(text_column='text'),
    HashDeduplicationStep(text_column='processed_text'),
    ModelTrainingStep()
])

# Run pipeline and track indices
processed_data = pipeline.fit_transform(df)

# Map results back to original indices
mapped_results = pipeline.create_full_result_df(
    'model_training', results_df, fill_value={'predicted': 'unknown'}
)

Documentation

For more detailed information, refer to the examples directory and the following resources:

• Deduplication Tracking
• Markdown Report Generation
• LSH Deduplication

License

MIT License