feat(plotly): implement precision-recall (#2314)

## Implementation: `precision-recall` - plotly

Implements the **plotly** version of `precision-recall`.

**File:** `plots/precision-recall/implementations/plotly.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20526594839)*

---------

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

Author: github-actions[bot]

plots/precision-recall/implementations/plotly.py

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+""" pyplots.ai
+precision-recall: Precision-Recall Curve
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-26
+"""
+
+import numpy as np
+import plotly.graph_objects as go
+from sklearn.metrics import average_precision_score, precision_recall_curve
+
+
+# Data - Simulate a binary classification scenario (fraud detection)
+np.random.seed(42)
+n_samples = 1000
+
+# Imbalanced dataset: 10% positive class (fraud cases)
+y_true = np.zeros(n_samples, dtype=int)
+y_true[:100] = 1
+np.random.shuffle(y_true)
+
+# Generate prediction scores - good classifier with some noise
+y_scores = np.where(
+    y_true == 1,
+    np.random.beta(5, 2, n_samples),  # Higher scores for positive class
+    np.random.beta(2, 5, n_samples),  # Lower scores for negative class
+)
+
+# Calculate precision-recall curve
+precision, recall, thresholds = precision_recall_curve(y_true, y_scores)
+average_precision = average_precision_score(y_true, y_scores)
+
+# Calculate baseline (random classifier performance)
+positive_class_ratio = np.mean(y_true)
+
+# Create figure
+fig = go.Figure()
+
+# Add precision-recall curve (stepped style for accuracy)
+fig.add_trace(
+    go.Scatter(
+        x=recall,
+        y=precision,
+        mode="lines",
+        name=f"Classifier (AP = {average_precision:.3f})",
+        line={"color": "#306998", "width": 4, "shape": "hv"},
+        fill="tozeroy",
+        fillcolor="rgba(48, 105, 152, 0.15)",
+    )
+)
+
+# Add baseline reference line (random classifier)
+fig.add_trace(
+    go.Scatter(
+        x=[0, 1],
+        y=[positive_class_ratio, positive_class_ratio],
+        mode="lines",
+        name=f"Random Baseline ({positive_class_ratio:.2f})",
+        line={"color": "#FFD43B", "width": 3, "dash": "dash"},
+    )
+)
+
+# Add iso-F1 curves
+f1_values = [0.2, 0.4, 0.6, 0.8]
+for f1 in f1_values:
+    # Iso-F1: precision = f1 * recall / (2 * recall - f1) for valid recall range
+    x_iso = np.linspace(f1 / 2 + 0.01, 1, 100)  # Start above f1/2 to avoid division issues
+    y_iso = f1 * x_iso / (2 * x_iso - f1)
+    # Only keep valid values within [0, 1] range
+    mask = (y_iso > 0) & (y_iso <= 1)
+    fig.add_trace(
+        go.Scatter(
+            x=x_iso[mask],
+            y=y_iso[mask],
+            mode="lines",
+            name=f"F1 = {f1}",
+            line={"color": "gray", "width": 1.5, "dash": "dot"},
+            opacity=0.5,
+            showlegend=True if f1 == 0.2 else False,
+            legendgroup="iso-f1",
+        )
+    )
+
+# Update layout for 4800x2700 px
+fig.update_layout(
+    title={"text": "precision-recall · plotly · pyplots.ai", "font": {"size": 32}, "x": 0.5, "xanchor": "center"},
+    xaxis={
+        "title": {"text": "Recall (Sensitivity)", "font": {"size": 24}},
+        "tickfont": {"size": 18},
+        "range": [0, 1.02],
+        "showgrid": True,
+        "gridcolor": "rgba(0, 0, 0, 0.1)",
+        "gridwidth": 1,
+        "zeroline": False,
+    },
+    yaxis={
+        "title": {"text": "Precision (Positive Predictive Value)", "font": {"size": 24}},
+        "tickfont": {"size": 18},
+        "range": [0, 1.05],
+        "showgrid": True,
+        "gridcolor": "rgba(0, 0, 0, 0.1)",
+        "gridwidth": 1,
+        "zeroline": False,
+    },
+    legend={
+        "font": {"size": 18},
+        "x": 0.02,
+        "y": 0.02,
+        "xanchor": "left",
+        "yanchor": "bottom",
+        "bgcolor": "rgba(255, 255, 255, 0.9)",
+        "bordercolor": "rgba(0, 0, 0, 0.3)",
+        "borderwidth": 1,
+    },
+    template="plotly_white",
+    margin={"l": 100, "r": 60, "t": 100, "b": 100},
+)
+
+# Add annotation for iso-F1 curves
+fig.add_annotation(
+    x=0.92, y=0.92, text="Iso-F1 curves", font={"size": 16, "color": "gray"}, showarrow=False, xanchor="right"
+)
+
+# Save outputs
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/precision-recall/metadata/plotly.yaml

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+library: plotly
+specification_id: precision-recall
+created: '2025-12-26T17:39:47Z'
+updated: '2025-12-26T17:49:47Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20526594839
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/precision-recall/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/precision-recall/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/precision-recall/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent implementation of stepped line style (shape=hv) which accurately represents
+    threshold-based changes
+  - 'Comprehensive features: includes AP score, baseline reference, and iso-F1 curves
+    as suggested in spec'
+  - Clean, professional color scheme with good accessibility (blue/yellow instead
+    of red/green)
+  - Well-structured code following KISS principles with realistic fraud detection
+    scenario
+  - Area fill under curve provides good visual representation of Average Precision
+  weaknesses:
+  - Legend shows only F1 = 0.2 for iso-F1 curves while 4 curves (0.2, 0.4, 0.6, 0.8)
+    are visible - should either show all or use a different approach
+  - No custom hover template to show threshold values or detailed information at each
+    point on the curve
+  - Plotlys distinctive interactivity could be better leveraged with click-to-select
+    threshold functionality