# freqIG

![Attribution Visualization](https://raw.githubusercontent.com/abrhm11/freqIG/main/freqIG_attributions.png)

*This is a basic example of this method, the code is given below in 'Examples'.*


## Overview

This repository contains the implementation of **freqIG**, a method based on the principle of **FLEX (Frequency Layer Explanation)** [1], designed to explain the predictions of deep neural networks (DNNs) for time-series classification tasks. freqIG combines **Integrated Gradients (IG)** with a frequency-domain transform (via the **Real Fast Fourier Transform (RFFT)**) to provide frequency-based attribution scores.

The method is generally useful for understanding how different frequency components of a time-series input influence the predictions of a DNN, thus enhancing model interpretability.

*For details and an application of this method, see [1]: "Using EEG Frequency Attributions to Explain the Classifications of a Deep Neural Network for Sleep Staging" (Paul Gräve et al.). ~ soon to be published, if not already available*

---

## Features

- **RFFT Transformation**: Input time-series data are transformed into the frequency domain using the RFFT.
- **iRFFT Transformation**: The inverse RFFT (iRFFT) is implemented as the first layer in the DNN to process frequency-domain inputs.
- **Integrated Gradients Attribution**: Captum's IG method is used to compute relevance scores for frequency bands, providing insights into the features contributing to the model's predictions.

---

## Definition (FLEX principle)
Let F be our model (DNN) and x be our input (time-series data). Then with  
`F̄ = F ∘ iRFFT` and `x̄ = RFFT(x)` we get

$$
FLEXᵢ(F, x) = IGᵢ(F̄, x̄)
$$

where  
`FLEX(F, x) = (FLEX₁(F, x), ..., FLEXₙ(F, x))` with `x ∈ ℝⁿ`.


---

## Installation

### Requirements
- Python 3.8+
- Required libraries:
  - `numpy`
  - `torch`
  - `captum`
- Optional libraries (for model conversion features):
  - `onnx`
  - `tf2onnx`
  - `onnx2pytorch`

Install the base package with required dependencies:

```bash
pip install freqig
```

To enable model conversion support (e.g., from ONNX or Keras), install with the optional extras:

```bash
pip install freqig[convert]
```

You can also install only specific optional dependencies if needed:

```bash
pip install freqig[onnx2pytorch]
pip install freqig[onnx]
pip install freqig[tf2onnx]
```

---

# Documentation

## freqIG.attribute
Compute frequency-based attribution scores for a model predicting on time-series data.

```bash
freqIG.attribute(
    input: Union[np.ndarray, list, torch.Tensor],
    model: Any,
    target: Optional[int] = None,
    baseline: Optional[Union[np.ndarray, list, torch.Tensor]] = None,
    n_steps: int = 50,
    segment: Optional[Union[np.ndarray, list, torch.Tensor]] = None,
    start_idx: Optional[int] = None,
    additional_forward_args: Optional[Any] = None
) -> np.ndarray
```

### Parameters
- **input** : array-like or torch.Tensor  
  The input time-series data.

- **model** : callable  
  The (frequency-domain) model to explain.

- **target** : int, optional  
  Index of the class to explain. If None, explains the model's predicted class.

- **baseline** : array-like or torch.Tensor, optional  
  Baseline input for Integrated Gradients. Defaults to zero input.

- **n_steps** : int, default=50  
  Number of steps in the IG path.

- **segment** : array-like or torch.Tensor, optional  
  Segment of the input for localized attribution.

- **start_idx** : int, optional  
  Start index of the segment within the original input.

- **additional_forward_args** : Any, optional  
  Additional arguments passed to the model during attribution.

### Returns

- **np.ndarray**  
  Array containing the frequency attribution scores.

### Raises

- **ValueError**  
  If `segment` is provided but `start_idx` is missing, or if the segment exceeds the bounds of the input.
- **ValueError**  
  If `baseline` is provided but its shape does not match the input.

### Notes

This function applies Integrated Gradients in the frequency domain to provide frequency-wise attributions for any model acting on time-series data, following the FLEX [1] principle.

### References

[1] Using EEG Frequency Attributions to Explain the Classifications of a Deep Neural Network for Sleep Staging  
Paul Gräve, T. Steinbrinker, F. Ehrlich, P. Hempel, P. Zaschke, D. Krefting, N. Spicher; 2025.

### Examples

```bash
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import numpy as np
from scripts.freqIG import attribute
import torch
import matplotlib.pyplot as plt

# Set seeds for reproducibility
np.random.seed(42)
torch.manual_seed(42)

# Define sampling rate in Hz and signal length:
fs = 128                # Sampling frequency, e.g. 128 Hz
n_samples = 100
n_features = 256         # Number of samples per time series

# Frequency axis in Hz:
freqs = np.fft.rfftfreq(n_features, d=1/fs)

# --- Select target frequency in Hz ---
possible_freqs_hz = np.arange(1, min(10, int(fs // 2)))  # Valid Hz, up to Nyquist
target_freq_hz = np.random.choice(possible_freqs_hz)
# Find closest matching index on the FFT axis:
target_freq_idx = np.argmin(np.abs(freqs - target_freq_hz))
target_freq = freqs[target_freq_idx]
print(f"Target frequency: {target_freq:.1f} Hz @ Index {target_freq_idx}")

# --- Generate data ---
X = []
y = []
t = np.arange(n_features) / fs  # Time axis in seconds

for i in range(n_samples):
    label = np.random.randint(0, 2)
    base = 5 * np.random.randn(n_features)
    if label == 1:
        phase = np.random.uniform(0, 2*np.pi)
        amplitude = np.random.uniform(10, 30)
        base += amplitude * np.sin(2 * np.pi * target_freq * t + phase)
    X.append(base)
    y.append(label)

X = np.stack(X)
y = np.array(y)

X_torch = torch.tensor(X, dtype=torch.float32)
y_torch = torch.tensor(y, dtype=torch.long)

class SimpleCNN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv1d(1, 8, kernel_size=5, padding=2)
        self.relu1 = torch.nn.ReLU()
        self.conv2 = torch.nn.Conv1d(8, 16, kernel_size=3, padding=1)
        self.relu2 = torch.nn.ReLU()
        self.pool = torch.nn.AdaptiveAvgPool1d(1)
        self.fc = torch.nn.Linear(16, 2)
    def forward(self, x):
        if x.dim() == 2:
            x = x.unsqueeze(1)  # [batch, 1, time]
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool(x)       # [batch, channels, 1]
        x = x.squeeze(-1)      # [batch, channels]
        return self.fc(x)

model = SimpleCNN()

# --- Training ---
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
model.train()
for epoch in range(150):
    optimizer.zero_grad()
    outputs = model(X_torch)
    loss = criterion(outputs, y_torch)
    loss.backward()
    optimizer.step()
model.eval()

# 1. Compute accuracy
with torch.no_grad():
    logits = model(X_torch)
    preds = torch.argmax(logits, dim=1).cpu().numpy()
    accuracy = np.mean(preds == y)
print(f"Model accuracy: {accuracy:.3f}")

# The first class 1 sample that is correctly classified by the model is used as an example
with torch.no_grad():
    logits = model(X_torch)
    preds = torch.argmax(logits, dim=1).cpu().numpy()

idx_candidates = np.flatnonzero((y == 1) & (preds == 1))
if len(idx_candidates) == 0:
    raise ValueError("No correctly classified class 1 samples found.")
idx = idx_candidates[0]
sample = X[idx:idx+1]

attr_scores = attribute(
    input=sample,
    model=model,
    target=1,        # Class 1 == "has the target frequency"
    n_steps=50
)

# --- Attribution visualization (as dictionary) ---
freq_axis = np.fft.rfftfreq(n_features, d=1)
attr_dict = {freq: score for freq, score in zip(freq_axis, attr_scores)}

# -----------------------------------------------------------------------------
# 2. Plot one example from class 0 and one from class 1
fig, axs = plt.subplots(2, 1, figsize=(8, 4), gridspec_kw={'height_ratios': [1, 1.8]})

ex0 = np.where(y == 0)[0][0]
ex1 = np.where(y == 1)[0][0]

# Plot 1 – Time series example
axs[0].plot(np.arange(n_features), X[ex0], label="Noise")
axs[0].plot(np.arange(n_features), X[ex1], label=f"Sine Wave [{target_freq_hz} Hz] + Noise")
axs[0].set_title("Example input time series")
axs[0].set_xlabel("Time step")
axs[0].set_ylabel("Amplitude")
axs[0].legend(loc='upper right')

# Plot 2 – Frequency attributions
bar_width = 0.8
axs[1].bar(freqs, attr_scores, width=bar_width, color='tab:orange')
axs[1].set_xlabel("Frequency [Hz]")
axs[1].set_ylabel("Attribution [AU]")
axs[1].set_title("Frequency attributions for a random 'Sine Wave' sample")

plt.tight_layout()
plt.savefig("freqIG_attributions.png")
print("Plots saved as: freqIG_attributions.png")
```