I have a csv file containing evaluation data from my Deep Learning models. The table contains one row per sample. Each row has the columns "predicted\_class" and "gt\_class". Please visualize this table in a confusion matrix using the Vega visualization library
To visualize the evaluation data in a confusion matrix using the Vega visualization library, you'll first need to prepare your data, and then create the appropriate Vega specification for the confusion matrix. Below are the steps to achieve this:

### Step 1: Prepare Your Data

Assuming you have a CSV file with columns `predicted_class` and `gt_class`, you'll need to compute the confusion matrix from this data. You can do this using Python and pandas. Here's a sample code snippet to help you create the confusion matrix:

```python
import pandas as pd

# Load your CSV file
data = pd.read_csv('path_to_your_file.csv')

# Create a confusion matrix
confusion_matrix = pd.crosstab(data['gt_class'], data['predicted_class'], rownames=['Actual'], colnames=['Predicted'], margins=False)

# Reset index to use in Vega
confusion_matrix.reset_index(inplace=True)
confusion_matrix = confusion_matrix.melt(id_vars='Actual', var_name='Predicted', value_name='Count')

# Save the confusion matrix as a CSV for Vega
confusion_matrix.to_csv('confusion_matrix.csv', index=False)
```

### Step 2: Create Vega Specification

Next, you can create a Vega specification to visualize the confusion matrix. Here's an example Vega specification that assumes your confusion matrix is stored in `confusion_matrix.csv`.

```json
{
  "$schema": "https://vega.github.io/schema/vega/v5
I have a csv file containing evaluation data from my Deep Learning models. The table contains one row per sample. Each row has the columns "predicted\_class" and "gt\_class". Please visualize this table in a confusion matrix using the Vega visualization library.

To visualize the confusion matrix using Vega, you can follow these steps:

1. Install Vega and Vega-Lite libraries by running the following command:

```bash
pip install vega vega_datasets
```

2. Create a Python script named `confusion_matrix.py` and paste the following code:

```python
import csv
import vega_datasets
import vega_lite as vl

# Load the CSV file
data = []
with open('evaluation_data.csv', newline='') as f:
    reader = csv.DictReader(f)
    data = [row for row in reader]

# Create a confusion matrix data structure
confusion_matrix = [[0 for _ in range(len(set(data, key='gt_class')))] for _ in range(len(set(data, key='predicted_class')))]

for row in data:
    predicted_class = row['predicted_class']
    gt_class = row['gt_class']
    confusion_matrix[int(predicted_class)][int(gt_class)] += 1

# Define the Vega-Lite specification
spec = vl.Mark(
    "bar",
    encoding={
        "x