mango-autoencoder 1.0.1a2

Library with a collection of useful classes and methods to DRY

Mango Autoencoder <<<<<<< HEAD ================ ======= ================= >>>>>>> master

A Python library for anomaly detection in time series using neural autoencoders.

Description

Mango Autoencoder is a specialized tool for time series analysis that uses neural autoencoder networks to detect anomalies and reconstruct data. It is designed to be highly configurable and easy to use, with advanced data processing capabilities.

Key Features

Flexible Neural Architectures - Supports LSTM, GRU, and RNN

Anomaly Detection - Automatic identification of anomalous patterns in time series

Data Reconstruction - Ability to reconstruct missing or corrupted data

Advanced Processing - Normalization, imputation, and handling of missing values

Integrated Visualization - Plotting tools for result analysis

Bidirectional Configuration - Support for bidirectional encoders and decoders

Mask Handling - Intelligent data processing with custom masks

New Data Reconstruction - Reconstruct unknown data with iterative improvement

Installation

Using uv:

# Create virtual environment with Python 3.11
uv venv --python 3.11
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

uv add mango-autoencoder

Using pip:

pip install mango-autoencoder

Dependencies

• Python >= 3.10

• TensorFlow >= 2.18.0

• Pandas >= 2.0.3

• Polars >= 1.31.0

• Scikit-learn >= 1.6.1

• Plotly >= 6.2.0

Basic Usage

from mango_autoencoder import AutoEncoder
import numpy as np

# Create autoencoder instance
autoencoder = AutoEncoder()

# Configure and train the model
autoencoder.build_and_train(
    context_window=10,
    data=time_series_data,
    time_step_to_check=[0, 1, 2],
    feature_to_check=[0, 1],
    hidden_dim=64,
    form="lstm",
    epochs=100
)

# Reconstruct data
reconstruction = autoencoder.reconstruct()

Advanced Usage: Reconstructing New Data

The reconstruct_new_data method allows you to reconstruct unknown data using a trained model. This is particularly useful for:

• Missing Data Imputation: Fill in missing values in time series

• Data Quality Improvement: Correct corrupted or noisy data

• Iterative Refinement: Improve reconstruction quality through multiple iterations

Example Usage

from pathlib import Path
from mango_autoencoder import AutoEncoder

# Load a trained model
model = AutoEncoder.load_from_pickle("path/to/model.pkl")

# Set up output directory
reconstruct_output_dir = Path("autoencoder_output/reconstruction")
reconstruct_output_dir.mkdir(parents=True, exist_ok=True)

# Perform reconstruction on new data
reconstructed_results = model.reconstruct_new_data(
    id_columns="source_file",
    data=data,
    iterations=3,
    save_path=str(reconstruct_output_dir),
    reconstruction_diagnostic=True
)

Parameters

• ``data``: Input data (numpy array, pandas DataFrame, or polars DataFrame)

• ``iterations``: Number of reconstruction iterations (default: 1) - Higher iterations can improve reconstruction quality for data with many missing values - Each iteration uses the previous reconstruction to improve the next one

• ``id_columns``: Column(s) that define IDs to process reconstruction separately - Useful when data contains multiple time series (e.g., different sensors, locations) - Can be a string, integer, or list of strings/integers

• ``save_path``: Path to save reconstruction results and diagnostics

• ``reconstruction_diagnostic``: If True, generates error analysis and visualization files

How It Works

1. Data Validation: Checks that the new data has the same features as the training data

2. ID Processing: Separates data by ID columns if specified

3. Iterative Reconstruction: - For each iteration, the model reconstructs the data - Missing values (NaN) are filled with reconstructed values - The process repeats to improve reconstruction quality

4. Result Generation: Returns reconstructed data and optionally saves diagnostic files

Output Files

Training Phase

When you train a model with build_and_train(), the following files are created in the specified save_path:

Model Files

• ``models/model.pkl``: Main model file containing the trained Keras model and training parameters

• ``models/{epoch}.pkl``: Checkpoint files saved every checkpoint epochs (e.g., 10.pkl, 20.pkl)

Visualization Files

• ``loss_history.html``: Interactive plot showing training and validation loss over epochs

Reconstruction Files (if reconstruction_diagnostic=True) <<<<<<< HEAD ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ======= ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>>>>>> master

• ``actual_vs_reconstructed.html``: Interactive plot comparing original vs reconstructed data

• ``reconstruction_error.csv``: Detailed reconstruction error data

• ``reconstruction_error_summary.csv``: Summary statistics of reconstruction errors

• ``reconstruction_error_boxplot.html``: Box plot visualization of reconstruction errors by feature and data split

Reconstruction Phase (reconstruct_new_data)

When using reconstruct_new_data(), the following files are created in the specified save_path:

Reconstruction Results

• ``reconstruct_new_data/{id}_reconstruction_results.csv``: Reconstructed data for each ID (or “global” if no IDs)

Diagnostic Files (if reconstruction_diagnostic=True) <<<<<<< HEAD ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ ======= ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ >>>>>>> master

• ``reconstruct_new_data/{id}_reconstruction_error.csv``: Reconstruction error data for each ID

• ``reconstruct_new_data/{id}_reconstruction_error_summary.csv``: Summary statistics for each ID

• ``reconstruct_new_data/{id}_reconstruction_error_boxplot.html``: Box plot of reconstruction errors for each ID

File Structure Example

autoencoder_output/
├── models/
│   ├── model.pkl
│   ├── 10.pkl
│   ├── 20.pkl
│   └── ...
├── loss_history.html
├── actual_vs_reconstructed.html
├── reconstruction_error.csv
├── reconstruction_error_summary.csv
├── reconstruction_error_boxplot.html
└── reconstruct_new_data/
    ├── global_reconstruction_results.csv
    ├── global_reconstruction_error.csv
    ├── global_reconstruction_error_summary.csv
    └── global_reconstruction_error_boxplot.html

Project Structure

mango_autoencoder/
├── mango_autoencoder/
│   ├── autoencoder.py          # Main autoencoder class
│   ├── modules/
│   │   ├── encoder.py          # Encoding module
│   │   ├── decoder.py          # Decoding module
│   │   └── anomaly_detector.py # Anomaly detector
│   ├── utils/
│   │   ├── processing.py       # Processing utilities
│   │   ├── plots.py           # Visualization tools
│   │   └── sequences.py       # Sequence processing
│   ├── tests/                  # Unit tests
│   │   └── test_autoencoder.py # Autoencoder tests
│   └── logging/                # Logging utilities
├── pyproject.toml             # Project configuration
└── uv.lock                    # Dependency lock file

Documentation

For detailed documentation, visit the Mango Documentation.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For questions, issues, or contributions, please contact:

• Email: [e-mail]

• Create an issue on the repository

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