Mercurial > repos > goeckslab > image_learner
diff plotly_plots.py @ 8:85e6f4b2ad18 draft default tip
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
| author | goeckslab |
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| date | Thu, 14 Aug 2025 14:53:10 +0000 |
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| children |
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--- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/plotly_plots.py Thu Aug 14 14:53:10 2025 +0000 @@ -0,0 +1,148 @@ +import json +from typing import Dict, List, Optional + +import numpy as np +import plotly.graph_objects as go +import plotly.io as pio + + +def build_classification_plots( + test_stats_path: str, + training_stats_path: Optional[str] = None, +) -> List[Dict[str, str]]: + """ + Read Ludwig’s test_statistics.json and build three interactive Plotly panels: + - Confusion Matrix + - ROC-AUC + - Classification Report Heatmap + + Returns a list of dicts, each with: + { + "title": <plot title>, + "html": <HTML fragment for embedding> + } + """ + # --- Load test stats --- + with open(test_stats_path, "r") as f: + test_stats = json.load(f) + label_stats = test_stats["label"] + + # common sizing + cell = 40 + n_classes = len(label_stats["confusion_matrix"]) + side_px = max(cell * n_classes + 200, 600) + common_cfg = {"displayModeBar": True, "scrollZoom": True} + + plots: List[Dict[str, str]] = [] + + # 0) Confusion Matrix + cm = np.array(label_stats["confusion_matrix"], dtype=int) + labels = label_stats.get("labels", [str(i) for i in range(cm.shape[0])]) + total = cm.sum() + + fig_cm = go.Figure( + go.Heatmap( + z=cm, + x=labels, + y=labels, + colorscale="Blues", + showscale=True, + colorbar=dict(title="Count"), + ) + ) + fig_cm.update_traces(xgap=2, ygap=2) + fig_cm.update_layout( + title=dict(text="Confusion Matrix", x=0.5), + xaxis_title="Predicted", + yaxis_title="Observed", + yaxis_autorange="reversed", + width=side_px, + height=side_px, + margin=dict(t=100, l=80, r=80, b=80), + ) + + # annotate counts and percentages + mval = cm.max() if cm.size else 0 + thresh = mval / 2 + for i in range(cm.shape[0]): + for j in range(cm.shape[1]): + v = cm[i, j] + pct = (v / total * 100) if total > 0 else 0 + color = "white" if v > thresh else "black" + fig_cm.add_annotation( + x=labels[j], + y=labels[i], + text=f"<b>{v}</b>", + showarrow=False, + font=dict(color=color, size=14), + xanchor="center", + yanchor="bottom", + yshift=2, + ) + fig_cm.add_annotation( + x=labels[j], + y=labels[i], + text=f"{pct:.1f}%", + showarrow=False, + font=dict(color=color, size=13), + xanchor="center", + yanchor="top", + yshift=-2, + ) + + plots.append({ + "title": "Confusion Matrix", + "html": pio.to_html( + fig_cm, + full_html=False, + include_plotlyjs="cdn", + config=common_cfg + ) + }) + + # 2) Classification Report Heatmap + pcs = label_stats.get("per_class_stats", {}) + if pcs: + classes = list(pcs.keys()) + metrics = ["precision", "recall", "f1_score"] + z, txt = [], [] + for c in classes: + row, trow = [], [] + for m in metrics: + val = pcs[c].get(m, 0) + row.append(val) + trow.append(f"{val:.2f}") + z.append(row) + txt.append(trow) + + fig_cr = go.Figure( + go.Heatmap( + z=z, + x=metrics, + y=[str(c) for c in classes], + text=txt, + texttemplate="%{text}", + colorscale="Reds", + showscale=True, + colorbar=dict(title="Value"), + ) + ) + fig_cr.update_layout( + title="Classification Report", + xaxis_title="", + yaxis_title="Class", + width=side_px, + height=side_px, + margin=dict(t=80, l=80, r=80, b=80), + ) + plots.append({ + "title": "Classification Report", + "html": pio.to_html( + fig_cr, + full_html=False, + include_plotlyjs=False, + config=common_cfg + ) + }) + + return plots