mindforge-ml 0.1.7

An open-source machine learning library for research and production, offering unified tools across deep learning, NLP, computer vision, reinforcement learning, and predictive analytics.

MindForge ML – Hypertension Anomaly Detection

MindForge is an open-source ML library offering simple, consistent, and extensible tools for building and experimenting with models. Starting with unsupervised learning and anomaly detection, it aims to expand into deep learning, NLP, and predictive analytics, making ML more accessible, modular, and production-ready.

mindforge provides simple unsupervised ML tools for anomaly detection, clustering, and visualization. The core component is an AutoEncoder wrapped in Unsupervisedmodel, with utilities for clustering (KMeans), dimensionality reduction (PCA/t-SNE), and visualization.

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🚀 Model (from mindforge_ml.unsupervised.model)

Importing the Model

from mindforge_ml.unsupervised import AutoEncoder

Importing the Model

from mindforge_ml.unsupervised.model import Unsupervisedmodel

Training

model = Unsupervisedmodel(input_dim=X.shape[1])
model.fit(X_scaled, epochs=20, batch_size=32)

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🔑 Core Methods

1. transform(X) → Latent Features

• Input: Scaled data X
• Why scaled? Scaling ensures features with larger magnitudes don’t dominate training.
• What it does: Encodes the data into a compressed latent representation (low-dimensional).
• Use case: Great for clustering or dimensionality reduction.

latent = model.transform(X_scaled)

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2. reconstruct(X) → Reconstructed Data

• Input: Latent features
• Why latent? The decoder learns to rebuild the original input from compressed representations.
• What it does: Produces a reconstruction close to the original scaled input.

reconstructed = model.reconstruct(X_scaled)

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3. anomaly_scores(X) → Reconstruction Errors

• Input: Scaled data X
• Why scaled? Because reconstruction error depends on feature magnitudes; scaling avoids bias.
• What it does: Calculates per-sample mean squared error (original vs reconstruction).
• Use case: High scores indicate anomalies (e.g., hypertension cases).

scores = model.anomaly_scores(X_scaled)

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4. Save / Load Model

# Save model
model.save("hypertension_model.pth")

# Load model
model.load("hypertension_model.pth")

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🔧 Utils (from mindforge_ml.utils)

Utility functions help prepare data, cluster, reduce dimensions, and detect anomalies.

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1. scale_data(X)

Standardizes features by removing the mean and scaling to unit variance.

• Why? Autoencoders, PCA, and clustering methods are sensitive to feature magnitudes. Scaling prevents one feature from dominating.
• What it returns: Numpy array of scaled values.

from mindforge_ml.utils import scale_data
X_scaled = scale_data(X)

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2. cluster_kmeans(X, n_clusters=3, random_state=42)

Clusters data using KMeans.

• Input:

  • Usually latent features from the model (model.transform(X_scaled)).

  • Can also take scaled raw data or reconstructed data depending on the use case.

    • Latent features → clustering compressed representations (recommended).
    • Scaled input → clustering original data (baseline).
    • Reconstructed data → clustering the autoencoder’s “understanding” of the data.

• n_clusters: Number of groups to form (default=3).

• random_state: Ensures reproducibility.

clusters = cluster_kmeans(latent, n_clusters=2)

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3. reduce_pca(X, n_components=2)

Dimensionality reduction using Principal Component Analysis (PCA).

• n_components:

  • 2 (default) → for easy 2D visualization.
  • 3 → for 3D visualization.
  • Higher numbers (10, 50, …) → for preprocessing before clustering.

• Input: Typically latent features, but any scaled data can be reduced.

X_pca = reduce_pca(latent, n_components=2)

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4. reduce_tsne(X, n_components=2, random_state=42, perplexity=30, lr=200)

Dimensionality reduction using t-SNE (t-distributed stochastic neighbor embedding).

• Parameters:

  • n_components: Output dimensions (2D or 3D).
  • perplexity: Approximate number of nearest neighbors. Default = 30.
  • learning_rate (lr): Step size; default = 200.

• Input: Usually latent features, since t-SNE works best on compressed, meaningful data.

• Considerations:

  • t-SNE is more computationally expensive than PCA.
  • Use PCA first to reduce dimensions (e.g., 50) before t-SNE if the dataset is large.

X_tsne = reduce_tsne(latent, n_components=2, perplexity=30)

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5. detect_anomalies(errors, threshold=None)

Identifies anomalies based on reconstruction error.

• errors: Output of model.anomaly_scores(X_scaled).

• threshold:

  • If None: threshold = mean + 2 * std (default heuristic).
  • You can set your own cutoff depending on your domain.

• Returns:

  • anomalies: Boolean mask (True = anomaly).
  • threshold: Value used for detection.

anomalies, threshold = detect_anomalies(scores)

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📊 Visualization (from mindforge_ml.visualization)

This module provides functions to visualize training progress, clusters, and anomaly detection.

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1. plot_losses(train_losses, val_losses=None)

Plots training (and optionally validation) loss over epochs.

• Inputs:

  • train_losses: List of training loss values per epoch.
  • val_losses: (Optional) List of validation losses per epoch.

from mindforge_ml.visualization import plot_losses

plot_losses(train_losses, val_losses)

✅ Helps monitor overfitting (when validation diverges from training).

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2. plot_clusters(X_2d, clusters, method="PCA", cmap="viridis")

Visualizes clusters in 2D space.

• Inputs:

  • X_2d: Data reduced to 2D (via reduce_pca or reduce_tsne).
  • clusters: Cluster labels (from cluster_kmeans).
  • method: String label for plot title (“PCA” or “t-SNE”).
  • cmap: Colormap (default = "viridis").

from mindforge_ml.visualization import plot_clusters

X_pca = reduce_pca(latent, n_components=2)
clusters = cluster_kmeans(latent, n_clusters=3)
plot_clusters(X_pca, clusters, method="PCA")

💡 Works with both PCA and t-SNE outputs — just set method accordingly.

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3. plot_anomalies(errors, anomalies, threshold)

Visualizes the reconstruction error distribution and highlights anomalies.

• Inputs:

  • errors: Reconstruction errors (from model.anomaly_scores).
  • anomalies: Boolean mask (True = anomaly). Optional — can be passed as None.
  • threshold: Cutoff value for anomaly detection.

• Behavior:

  • Plots error histogram.
  • Draws a red vertical line for threshold.
  • If anomalies is provided, highlights them in orange.

from mindforge_ml.visualization import plot_anomalies

errors = model.anomaly_scores(X_scaled)
anomalies, threshold = detect_anomalies(errors)
plot_anomalies(errors, anomalies, threshold)

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📌 Note:

• You should always scale your input before training (scale_data).
• Visualization is most meaningful when applied to latent features and anomaly scores.

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GET DEMO DATASET FOR USAGE

from mindforge_ml.datasets.loader import load_hypertension_data

df = load_hypertension_data() print(df.head())

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MindForge Transformer Model

🚀 Features

• Unified API for building ML models
• NLP utilities for tokenization and sequence-to-sequence tasks
• Visualization tools to track training metrics (loss, accuracy)
• Supports CPU and GPU with automatic device selection
• Extensible modules for datasets, utils, and supervised learning

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📦 Installation

pip install mindforge-ml

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📂 Package Structure

MindForge modules are organized into sub-packages for clarity:

from mindforge_ml.datasets.loader import seq2seqdataset
from mindforge_ml.utils import tokenize, smart_tokenizer, ml_vocab_size, pad_token_id
from mindforge_ml.visualization import plot_losses, plot_accuracy
from mindforge_ml.supervised.model import MFTransformerSeq2Seq

• datasets.loader → dataset utilities (e.g., seq2seqdataset)
• utils → tokenization helpers, vocab size, padding utilities
• visualization → plotting training loss and accuracy
• supervised.model → supervised models (e.g., custom transformer MFTransformerSeq2Seq)

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🛠️ Usage Example

1. Import Dependencies

import torch
from transformers import AutoTokenizer

from mindforge_ml.datasets.loader import seq2seqdataset
from mindforge_ml.utils import tokenize, smart_tokenizer, ml_vocab_size, pad_token_id
from mindforge_ml.visualization import plot_losses, plot_accuracy
from mindforge_ml.supervised.model import MFTransformerSeq2Seq

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2. Load Tokenizer and Data

tokenizer = AutoTokenizer.from_pretrained("t5-base")

# Example dataset (pairs of input/output text)
queries = ["What is a boy?", "Translate English to French: Hello world"]
targets = ["A male child", "Bonjour le monde"]

# Convert to tokenized tensors
input_ids, attention_mask, labels = seq2seqdataset(queries, targets, tokenizer)

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3. Initialize Model

device = "cuda" if torch.cuda.is_available() else "cpu"
model = MFTransformerSeq2Seq(vocab_size=ml_vocab_size(), device=device)

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4. Train Model

losses = model.fit(input_ids, attention_mask, labels, epochs=10, batch_size=2)

# Visualize training progress
plot_losses(losses)

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5. Make Predictions

query = "What is a boy?"
prediction = model.predict(query, max_len=20)
print("Prediction:", prediction)

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6. Visualize Accuracy

# If accuracy tracking is enabled during training
accuracies = [0.45, 0.52, 0.63, 0.71, 0.80]  # example
plot_accuracy(accuracies)

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⚡ Roadmap

Planned features for upcoming releases:

• 🔹 Computer Vision (CNNs, image datasets, augmentation)
• 🔹 Reinforcement Learning (agents, environments)
• 🔹 Advanced NLP (transformer variants, embeddings, pretraining)
• 🔹 Time Series & Predictive Analytics
• 🔹 Model Deployment & Export

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📜 License

MindForge is released under the MIT License, encouraging open collaboration and community contributions.

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🤝 Contributing

Contributions are welcome! Please submit issues, feature requests, or pull requests on GitHub.