ictonyx 0.3.3
Ictonyx: Iteration Comparison Testing Over N-runs: Yield eXamination
License: MIT
Requires Python: >=3.10
Keywords: machine-learning, variability, statistics, model-analysis

# Ictonyx

**Iteration Comparison Testing Over N-runs: Yield eXamination**

A Python framework for assessing machine learning model variability and performing rigorous statistical comparisons.

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## The Problem

Machine learning models are typically trained once on a given data set. We use a variety of metrics to assess and compare these models - but generally, on one training run.

But training a model involves stochastic factors: random initialization, data shuffling, dropout. Training the same model on the same data will often produce different predicted values and different assessment metrics. Our model parameters aren't constants; they are random variables, and we should treat them that way.

Ictonyx runs a model multiple times and provides complete distributions for model metrics, along with measures of center and dispersion. This allows formal statistical inferences to be made about the models being trained.

## Installation

```bash
pip install ictonyx
```

Requires Python 3.10+. Current release: **0.3.3**
([changelog](CHANGELOG.md) · [PyPI](https://pypi.org/project/ictonyx/))

## Quick Start: sklearn

```python
import ictonyx as ix
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

results = ix.variability_study(
    model=RandomForestClassifier,
    data=df,
    target_column='target',
    runs=20
)

print(results.summarize())
```

Output:
```
Study Summary:
  Successful runs: 20/20
  Val accuracy: 0.8800 (SD = 0.0267)
```

## Comparing Two Models

```python
import ictonyx as ix
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.datasets import load_iris
import pandas as pd

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

comparison = ix.compare_models(
    models=[RandomForestClassifier, GradientBoostingClassifier],
    data=df,
    target_column='target',
    runs=20
)

print(comparison['overall_test'].get_summary())
```

Output:
```
Kruskal-Wallis: 26.000, p=0.0000 ***, eta-squared=0.658, 95% CI [0.0312, 0.1088]
```

## Intelligent Statistical Analysis

When you compare models, Ictonyx doesn't just run a single test. It builds a complete statistical argument:

1. **Assumption checking** — tests both groups for normality (Shapiro-Wilk) and equal variance (Levene's test)
2. **Automatic test selection** — routes to Student's t-test, Welch's t-test, or Mann-Whitney U based on what the data actually supports
3. **Effect sizes** — reports Cohen's d or rank-biserial correlation so you know if the difference is practically meaningful, not just statistically significant
4. **Bootstrap confidence intervals** — computes BCa (bias-corrected and accelerated) 95% CIs for both the mean difference and the effect size, giving you a plausible range for the true performance gap
5. **Multiple comparison correction** — applies Bonferroni, Holm, or Benjamini-Hochberg when comparing 3+ models

All of this happens automatically inside `compare_models()` and `compare_two_models()`. No configuration needed.

## Deep Learning: MNIST Example with Visualization

The library was originally built for TensorFlow/Keras workflows. This example trains a CNN 10 times and plots the variability:

```python
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential, layers, Input

import ictonyx as ix
from ictonyx import (
    ModelConfig,
    KerasModelWrapper,
    ArraysDataHandler,
    run_variability_study,
    plot_variability_summary
)

# Load MNIST
(X_train, y_train), _ = tf.keras.datasets.mnist.load_data()
X_train = X_train.astype('float32') / 255.0
X_train = X_train[..., np.newaxis]

# Use subset for speed
X = X_train[:5000]
y = y_train[:5000]

def create_cnn(config: ModelConfig) -> KerasModelWrapper:
    model = Sequential([
        Input(shape=(28, 28, 1)),
        layers.Conv2D(32, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Flatten(),
        layers.Dense(64, activation='relu'),
        layers.Dense(10, activation='softmax')
    ])
    model.compile(
        optimizer='adam',
        loss='sparse_categorical_crossentropy',
        metrics=['accuracy']
    )
    return KerasModelWrapper(model)

config = ModelConfig({'epochs': 10, 'batch_size': 64, 'verbose': 0})
data_handler = ArraysDataHandler(X, y)

results = run_variability_study(
    model_builder=create_cnn,
    data_handler=data_handler,
    model_config=config,
    num_runs=10
)

print(results.summarize())

plot_variability_summary(
    all_runs_metrics_list=results.all_runs_metrics,
    final_metrics_series=results.final_metrics['val_accuracy'],
    metric='accuracy'
)
```

Output:

```
Study Summary:
  Successful runs: 10/10
  val_accuracy: 0.9592 (SD = 0.0050)
  Seed: 42
Variability Study Results
==============================
Successful runs: 10
Seed: 42
val_accuracy:
  Mean: 0.9592
  Std:  0.0050
  Min:  0.9520
  Max:  0.9700
```
![plot](images/variability_plot_1.png)

## GPU Memory Isolation

Training Keras models in a loop leaks GPU memory. Ictonyx can run each training session in an isolated subprocess:

```python
results = run_variability_study(
    model_builder=create_cnn,
    data_handler=data_handler,
    model_config=config,
    num_runs=10,
    use_process_isolation=True,
    gpu_memory_limit=4096
)
```

## Features

- **Variability studies** — run N training iterations, get full metric distributions with mean, SD, min, max
- **Statistical model comparison** — assumption-driven test selection, effect sizes, bootstrap confidence intervals, multiple comparison correction
- **Framework support** — scikit-learn and TensorFlow/Keras, with model wrappers that handle the training loop
- **GPU memory isolation** — subprocess-based training to prevent memory leaks in repeated Keras runs
- **Visualization** — training curves, metric distributions, forest plots, comparison boxplots
- **High-level API** — `variability_study()` and `compare_models()` for single-function usage

## License

MIT