Generalized Omni-dimensional Development

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visualization [2025/05/30 13:57] – [Workflow of the Method] eagleeyenebulavisualization [2025/05/30 13:59] (current) – [Best Practices] eagleeyenebula
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 ===== Usage Examples ===== ===== Usage Examples =====
  
-Below are practical examples of how to use the `plot_metrics()` method for visualizing training metrics.+Below are practical examples of how to use the **plot_metrics()** method for visualizing training metrics.
  
 ==== Example 1: Visualizing Accuracy and Loss ==== ==== Example 1: Visualizing Accuracy and Loss ====
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 <code> <code>
-```python+python
 from visualization import Visualization from visualization import Visualization
  
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 # Plot metrics # Plot metrics
 Visualization.plot_metrics(metrics) Visualization.plot_metrics(metrics)
-```+
 </code> </code>
  
 **Expected Output**: **Expected Output**:
-- A plot with two lines:+* A plot with two lines:
   1. Accuracy improving over epochs.   1. Accuracy improving over epochs.
   2. Loss decreasing over epochs.   2. Loss decreasing over epochs.
-- X-axis labeled as "Epochs". +* X-axis labeled as "Epochs". 
-- Y-axis labeled as "Values".+* Y-axis labeled as "Values".
  
 ==== Example 2: Single Metric Plot ==== ==== Example 2: Single Metric Plot ====
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 <code> <code>
-```python+python
 # Only validation loss # Only validation loss
 metrics = { metrics = {
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 Visualization.plot_metrics(metrics) Visualization.plot_metrics(metrics)
-```+
 </code> </code>
  
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 <code> <code>
-```python+python
 # Extended visualization # Extended visualization
 metrics = { metrics = {
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 plt.grid(color="gray", linestyle="--", linewidth=0.5) plt.grid(color="gray", linestyle="--", linewidth=0.5)
 plt.show() plt.show()
-```+
 </code> </code>
  
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 <code> <code>
-```python+python
 # Invalid metrics format to simulate failure # Invalid metrics format to simulate failure
 metrics = { metrics = {
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 Visualization.plot_metrics(metrics) Visualization.plot_metrics(metrics)
-```+
 </code> </code>
  
 **Expected Log**: **Expected Log**:
 <code> <code>
-```+
 `ERROR:root:Failed to plot metrics: object of type 'NoneType' has no len() ` `ERROR:root:Failed to plot metrics: object of type 'NoneType' has no len() `
-``` +
 </code> </code>
  
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 <code> <code>
-```python+python
 for key, values in metrics.items(): for key, values in metrics.items():
     plt.plot(values, label=key, linestyle="--", marker="o", color="red")     plt.plot(values, label=key, linestyle="--", marker="o", color="red")
-```+
 </code> </code>
  
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 <code> <code>
-```python+python
 plt.savefig("metrics_plot.png", dpi=300) plt.savefig("metrics_plot.png", dpi=300)
 logging.info(f"Metrics plot saved to metrics_plot.png") logging.info(f"Metrics plot saved to metrics_plot.png")
-```+
 </code> </code>
  
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 Embed the plotted visualizations into a web dashboard using tools such as **Dash** or **Flask**: Embed the plotted visualizations into a web dashboard using tools such as **Dash** or **Flask**:
 <code> <code>
-```python+python
 import io import io
 from flask import Flask, Response from flask import Flask, Response
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 if __name__ == "__main__": if __name__ == "__main__":
     app.run(debug=True)     app.run(debug=True)
-```+
 </code> </code>
  
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 1. **Label Your Plots**: 1. **Label Your Plots**:
-   Always provide meaningful titles, axis labels, and legends to improve readability.+   * Always provide meaningful titles, axis labels, and legends to improve readability.
  
 2. **Validate Data Inputs**: 2. **Validate Data Inputs**:
-   Ensure that all metric keys and values are properly structured before invoking `plot_metrics()`.+   * Ensure that all metric keys and values are properly structured before invoking `plot_metrics()`.
  
 3. **Modular Designs**: 3. **Modular Designs**:
-   Extend and customize utility functions without modifying the core `Visualization` class directly.+   * Extend and customize utility functions without modifying the core `Visualization` class directly.
  
 4. **Optimize for Scaling**: 4. **Optimize for Scaling**:
-   For handling large datasets, reduce the number of points or smooth values to improve performance.+   * For handling large datasets, reduce the number of points or smooth values to improve performance.
  
 ===== Conclusion ===== ===== Conclusion =====
visualization.1748613432.txt.gz · Last modified: 2025/05/30 13:57 by eagleeyenebula