phyto-nas-tsc 0.1.8

Phyto Neural Architecture Search for Time Series Classification

Phyto-NAS-TSC

An evolutionary approach to automatically design optimal neural network architectures for time series classification tasks.

Installation

pip install phyto-nas-tsc

Installation directly from source

git clone https://github.com/carmelyr/Phyto-NAS-T.git cd Phyto-NAS-T pip install -e .

Features

• Evolutionary algorithm for architecture search
• Optimized for time series data (1D signals)
• Optimized for LSTM model
• Tracks optimization history and metrics
• GPU-accelerated training

Quickstart

import numpy as np
from phyto_nas_tsc import fit

# OPTION 1: Use your own data (uncomment)
#X = np.random.randn(100, 1, 10)                     # 100 samples, 1 timestep, 10 features
#y = np.zeros((100, 2))                              # one-hot encoded labels
#y[:50, 0] = 1                                       # first 50 samples = class 0
#y[50:, 1] = 1                                       # next 50 samples = class 1

# OPTION 2: Use built-in dataset                     # let the package load data automatically
# Run optimization
result = fit(
    X=X,                                            # comment out if using built-in data
    y=y,                                            # comment out if using built-in data
    scoring='accuracy',                             # metric to optimize 
    others={
        'population_size': 5,    # required
        'generations': 3,        # required
        'early_stopping': True   # optional
    }
)

print(f"Best Accuracy: {result['accuracy']:.4f}")
print("Best Architecture:")
for param, value in result['architecture'].items():
    print(f"  {param}: {value}")