feat(plotnine): implement line-loss-training

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

@@ -0,0 +1,82 @@
+""" pyplots.ai
+line-loss-training: Training Loss Curve
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-31
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    annotate,
+    element_line,
+    element_text,
+    geom_line,
+    geom_point,
+    geom_vline,
+    ggplot,
+    labs,
+    scale_color_manual,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Simulated training history with typical loss curve behavior
+np.random.seed(42)
+epochs = np.arange(1, 51)
+
+# Training loss: starts high, decreases with diminishing returns
+train_loss = 2.5 * np.exp(-0.08 * epochs) + 0.15 + np.random.normal(0, 0.02, len(epochs))
+
+# Validation loss: follows training initially, then diverges (overfitting)
+val_loss = 2.5 * np.exp(-0.06 * epochs) + 0.25 + np.random.normal(0, 0.03, len(epochs))
+# Add uptick after epoch 30 to show clear overfitting
+val_loss[30:] += np.linspace(0, 0.25, 20)
+
+# Find optimal stopping point (minimum validation loss)
+optimal_epoch = epochs[np.argmin(val_loss)]
+
+# Create long-format DataFrame for plotnine
+df = pd.DataFrame(
+    {
+        "Epoch": np.concatenate([epochs, epochs]),
+        "Loss": np.concatenate([train_loss, val_loss]),
+        "Type": ["Training Loss"] * len(epochs) + ["Validation Loss"] * len(epochs),
+    }
+)
+
+# Plot
+plot = (
+    ggplot(df, aes(x="Epoch", y="Loss", color="Type"))
+    + geom_line(size=1.5, alpha=0.9)
+    + geom_point(size=3, alpha=0.7)
+    + geom_vline(xintercept=optimal_epoch, linetype="dashed", color="#555555", size=0.8, alpha=0.7)
+    + annotate(
+        "text",
+        x=optimal_epoch + 1,
+        y=max(val_loss) * 0.85,
+        label=f"Best: {optimal_epoch}",
+        size=12,
+        ha="left",
+        color="#555555",
+    )
+    + scale_color_manual(values={"Training Loss": "#306998", "Validation Loss": "#FFD43B"})
+    + labs(title="line-loss-training · plotnine · pyplots.ai", x="Epoch", y="Cross-Entropy Loss", color="")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        text=element_text(size=14),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        plot_title=element_text(size=24, weight="bold"),
+        legend_text=element_text(size=16),
+        legend_position="top",
+        legend_direction="horizontal",
+        panel_grid_major=element_line(color="#cccccc", size=0.5, alpha=0.3),
+        panel_grid_minor=element_line(color="#eeeeee", size=0.3, alpha=0.2),
+    )
+)
+
+# Save
+plot.save("plot.png", dpi=300, verbose=False)

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

@@ -0,0 +1,28 @@
+library: plotnine
+specification_id: line-loss-training
+created: '2025-12-31T00:35:43Z'
+updated: '2025-12-31T00:38:02Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20608693967
+issue: 2860
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/line-loss-training/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/line-loss-training/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent visualization of training dynamics with clear overfitting pattern visible
+    after epoch 30
+  - Proper use of plotnine grammar of graphics with clean, readable code structure
+  - Smart annotation showing optimal stopping point with vertical dashed line
+  - Well-chosen color scheme (blue/yellow) that is colorblind-accessible
+  - Perfect title format and comprehensive axis labeling with loss function specified
+  weaknesses:
+  - Grid line visibility is inconsistent - some lines appear more prominent than others
+    despite same alpha settings
+  - 'The Best: 46 annotation could be positioned better - currently placed at a fixed
+    y-position that may not scale well with different data'
+  - Yellow color for validation loss could benefit from slightly darker shade for
+    better contrast against white background