feat(plotly): implement line-loss-training (#2903)

## Implementation: `line-loss-training` - plotly

Implements the **plotly** version of `line-loss-training`.

**File:** `plots/line-loss-training/implementations/plotly.py`

**Parent Issue:** #2860

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20608572889)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/line-loss-training/implementations/plotly.py

@@ -0,0 +1,105 @@
+""" pyplots.ai
+line-loss-training: Training Loss Curve
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+
+
+# Data - Simulated neural network training history
+np.random.seed(42)
+epochs = np.arange(1, 101)
+
+# Training loss: starts high, decreases with noise, eventually plateaus
+train_loss = 2.5 * np.exp(-0.05 * epochs) + 0.15 + np.random.normal(0, 0.02, len(epochs))
+train_loss = np.maximum(train_loss, 0.1)  # Ensure positive
+
+# Validation loss: follows training initially, then diverges (overfitting after epoch ~60)
+val_loss = 2.5 * np.exp(-0.045 * epochs) + 0.25 + np.random.normal(0, 0.03, len(epochs))
+# Add overfitting effect: validation loss starts increasing after epoch 60
+overfitting_effect = np.where(epochs > 60, 0.008 * (epochs - 60), 0)
+val_loss = val_loss + overfitting_effect
+val_loss = np.maximum(val_loss, 0.15)
+
+# Find minimum validation loss epoch for annotation
+min_val_epoch = epochs[np.argmin(val_loss)]
+min_val_loss = np.min(val_loss)
+
+# Create figure
+fig = go.Figure()
+
+# Training loss curve
+fig.add_trace(
+    go.Scatter(
+        x=epochs,
+        y=train_loss,
+        mode="lines",
+        name="Training Loss",
+        line=dict(color="#306998", width=3),
+        hovertemplate="Epoch %{x}<br>Training Loss: %{y:.4f}<extra></extra>",
+    )
+)
+
+# Validation loss curve
+fig.add_trace(
+    go.Scatter(
+        x=epochs,
+        y=val_loss,
+        mode="lines",
+        name="Validation Loss",
+        line=dict(color="#FFD43B", width=3),
+        hovertemplate="Epoch %{x}<br>Validation Loss: %{y:.4f}<extra></extra>",
+    )
+)
+
+# Mark minimum validation loss point
+fig.add_trace(
+    go.Scatter(
+        x=[min_val_epoch],
+        y=[min_val_loss],
+        mode="markers+text",
+        name="Best Epoch",
+        marker=dict(color="#E74C3C", size=16, symbol="star"),
+        text=[f"Best: Epoch {min_val_epoch}"],
+        textposition="top center",
+        textfont=dict(size=16, color="#E74C3C"),
+        hovertemplate="Best Epoch: %{x}<br>Min Val Loss: %{y:.4f}<extra></extra>",
+    )
+)
+
+# Update layout
+fig.update_layout(
+    title=dict(text="line-loss-training · plotly · pyplots.ai", font=dict(size=28), x=0.5, xanchor="center"),
+    xaxis=dict(
+        title=dict(text="Epoch", font=dict(size=22)),
+        tickfont=dict(size=18),
+        gridcolor="rgba(128, 128, 128, 0.3)",
+        gridwidth=1,
+        showgrid=True,
+        range=[0, 105],
+    ),
+    yaxis=dict(
+        title=dict(text="Cross-Entropy Loss", font=dict(size=22)),
+        tickfont=dict(size=18),
+        gridcolor="rgba(128, 128, 128, 0.3)",
+        gridwidth=1,
+        showgrid=True,
+    ),
+    legend=dict(
+        font=dict(size=18),
+        x=0.75,
+        y=0.95,
+        bgcolor="rgba(255, 255, 255, 0.8)",
+        bordercolor="rgba(128, 128, 128, 0.3)",
+        borderwidth=1,
+    ),
+    template="plotly_white",
+    margin=dict(l=100, r=80, t=100, b=100),
+    plot_bgcolor="white",
+)
+
+# Save as PNG and HTML
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/line-loss-training/metadata/plotly.yaml

@@ -0,0 +1,28 @@
+library: plotly
+specification_id: line-loss-training
+created: '2025-12-31T00:05:54Z'
+updated: '2025-12-31T00:10:32Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608572889
+issue: 2860
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-loss-training/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-loss-training/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/line-loss-training/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of overfitting visualization with validation loss diverging
+    after epoch 60
+  - Clean, readable code with proper seed for reproducibility
+  - Good use of Plotly hover templates showing epoch and loss values on hover
+  - Appropriate color scheme (blue/yellow) with good accessibility
+  - Best epoch marker with star symbol effectively highlights the optimal stopping
+    point
+  - HTML output enabled for full interactivity
+  weaknesses:
+  - Legend background slightly blends with grid lines in upper right area
+  - Star marker for best epoch could be larger (size 16 is adequate but not prominent)
+  - Could utilize Plotly-specific features like vertical line annotation at best epoch
+    or shaded overfitting region