Mercurial > repos > goeckslab > image_learner
annotate plotly_plots.py @ 11:c5150cceab47 draft default tip
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
author | goeckslab |
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date | Sat, 18 Oct 2025 03:17:09 +0000 |
parents | 85e6f4b2ad18 |
children |
rev | line source |
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85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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changeset
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1 import json |
11
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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2 from pathlib import Path |
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85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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3 from typing import Dict, List, Optional |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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4 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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5 import numpy as np |
11
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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6 import pandas as pd |
8
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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7 import plotly.graph_objects as go |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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8 import plotly.io as pio |
11
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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9 from constants import LABEL_COLUMN_NAME, SPLIT_COLUMN_NAME |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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10 from sklearn.metrics import auc, roc_curve |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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11 from sklearn.preprocessing import label_binarize |
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85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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12 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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13 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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14 def build_classification_plots( |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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15 test_stats_path: str, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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16 training_stats_path: Optional[str] = None, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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17 ) -> List[Dict[str, str]]: |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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18 """ |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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19 Read Ludwig’s test_statistics.json and build three interactive Plotly panels: |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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20 - Confusion Matrix |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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21 - ROC-AUC |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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22 - Classification Report Heatmap |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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23 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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24 Returns a list of dicts, each with: |
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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25 { |
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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26 "title": <plot title>, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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27 "html": <HTML fragment for embedding> |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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28 } |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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29 """ |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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30 # --- Load test stats --- |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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31 with open(test_stats_path, "r") as f: |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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32 test_stats = json.load(f) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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33 label_stats = test_stats["label"] |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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34 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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35 # common sizing |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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36 cell = 40 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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37 n_classes = len(label_stats["confusion_matrix"]) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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38 side_px = max(cell * n_classes + 200, 600) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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39 common_cfg = {"displayModeBar": True, "scrollZoom": True} |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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40 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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41 plots: List[Dict[str, str]] = [] |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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42 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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43 # 0) Confusion Matrix |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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44 cm = np.array(label_stats["confusion_matrix"], dtype=int) |
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c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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45 # Try to get actual class names from per_class_stats keys (which contain the real labels) |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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46 pcs = label_stats.get("per_class_stats", {}) |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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47 if pcs: |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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48 labels = list(pcs.keys()) |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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49 else: |
c5150cceab47
planemo upload for repository https://github.com/goeckslab/gleam.git commit 0fe927b618cd4dfc87af7baaa827034cc6813225
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50 labels = label_stats.get("labels", [str(i) for i in range(cm.shape[0])]) |
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85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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51 total = cm.sum() |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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52 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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53 fig_cm = go.Figure( |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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54 go.Heatmap( |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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55 z=cm, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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56 x=labels, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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57 y=labels, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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58 colorscale="Blues", |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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59 showscale=True, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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60 colorbar=dict(title="Count"), |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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61 ) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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62 ) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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63 fig_cm.update_traces(xgap=2, ygap=2) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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64 fig_cm.update_layout( |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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65 title=dict(text="Confusion Matrix", x=0.5), |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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66 xaxis_title="Predicted", |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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67 yaxis_title="Observed", |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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68 yaxis_autorange="reversed", |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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69 width=side_px, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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70 height=side_px, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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71 margin=dict(t=100, l=80, r=80, b=80), |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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72 ) |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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73 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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74 # annotate counts and percentages |
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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75 mval = cm.max() if cm.size else 0 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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76 thresh = mval / 2 |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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77 for i in range(cm.shape[0]): |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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78 for j in range(cm.shape[1]): |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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79 v = cm[i, j] |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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80 pct = (v / total * 100) if total > 0 else 0 |
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planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
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81 color = "white" if v > thresh else "black" |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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82 fig_cm.add_annotation( |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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83 x=labels[j], |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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84 y=labels[i], |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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85 text=f"<b>{v}</b>", |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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86 showarrow=False, |
85e6f4b2ad18
planemo upload for repository https://github.com/goeckslab/gleam.git commit 8a42eb9b33df7e1df5ad5153b380e20b910a05b6
goeckslab
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87 font=dict(color=color, size=14), |
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88 xanchor="center", |
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89 yanchor="bottom", |
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90 yshift=2, |
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91 ) |
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92 fig_cm.add_annotation( |
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93 x=labels[j], |
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94 y=labels[i], |
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95 text=f"{pct:.1f}%", |
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96 showarrow=False, |
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97 font=dict(color=color, size=13), |
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98 xanchor="center", |
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99 yanchor="top", |
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100 yshift=-2, |
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101 ) |
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102 |
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103 plots.append({ |
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104 "title": "Confusion Matrix", |
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105 "html": pio.to_html( |
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106 fig_cm, |
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107 full_html=False, |
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108 include_plotlyjs="cdn", |
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109 config=common_cfg |
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110 ) |
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111 }) |
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112 |
11
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113 # 1) ROC-AUC Curves (Multi-class) |
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114 roc_plot = _build_roc_auc_plot(test_stats_path, labels, common_cfg) |
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115 if roc_plot: |
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116 plots.append(roc_plot) |
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117 |
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118 # 2) Classification Report Heatmap |
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119 pcs = label_stats.get("per_class_stats", {}) |
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120 if pcs: |
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121 classes = list(pcs.keys()) |
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122 metrics = ["precision", "recall", "f1_score"] |
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123 z, txt = [], [] |
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124 for c in classes: |
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125 row, trow = [], [] |
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126 for m in metrics: |
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127 val = pcs[c].get(m, 0) |
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128 row.append(val) |
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129 trow.append(f"{val:.2f}") |
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130 z.append(row) |
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131 txt.append(trow) |
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132 |
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133 fig_cr = go.Figure( |
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134 go.Heatmap( |
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135 z=z, |
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136 x=metrics, |
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137 y=[str(c) for c in classes], |
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138 text=txt, |
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139 texttemplate="%{text}", |
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140 colorscale="Reds", |
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141 showscale=True, |
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142 colorbar=dict(title="Value"), |
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143 ) |
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144 ) |
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145 fig_cr.update_layout( |
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146 title="Classification Report", |
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147 xaxis_title="", |
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148 yaxis_title="Class", |
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149 width=side_px, |
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150 height=side_px, |
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151 margin=dict(t=80, l=80, r=80, b=80), |
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152 ) |
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153 plots.append({ |
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154 "title": "Classification Report", |
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155 "html": pio.to_html( |
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156 fig_cr, |
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157 full_html=False, |
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158 include_plotlyjs=False, |
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159 config=common_cfg |
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160 ) |
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161 }) |
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162 |
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163 return plots |
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164 |
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165 |
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166 def _build_roc_auc_plot(test_stats_path: str, class_labels: List[str], config: dict) -> Optional[Dict[str, str]]: |
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167 """ |
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168 Build an interactive ROC-AUC curve plot for multi-class classification. |
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169 Following sklearn's ROC example with micro-average and per-class curves. |
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170 |
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171 Args: |
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172 test_stats_path: Path to test_statistics.json |
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173 class_labels: List of class label names |
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174 config: Plotly config dict |
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175 |
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176 Returns: |
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177 Dict with title and HTML, or None if data unavailable |
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178 """ |
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179 try: |
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180 # Get the experiment directory from test_stats_path |
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181 exp_dir = Path(test_stats_path).parent |
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182 |
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183 # Load predictions with probabilities |
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184 predictions_path = exp_dir / "predictions.csv" |
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185 if not predictions_path.exists(): |
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186 return None |
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187 |
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188 df_pred = pd.read_csv(predictions_path) |
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189 |
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190 if SPLIT_COLUMN_NAME in df_pred.columns: |
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191 split_series = df_pred[SPLIT_COLUMN_NAME].astype(str).str.lower() |
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192 test_mask = split_series.isin({"2", "test", "testing"}) |
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193 if test_mask.any(): |
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194 df_pred = df_pred[test_mask].reset_index(drop=True) |
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195 |
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196 if df_pred.empty: |
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197 return None |
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198 |
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199 # Extract probability columns (label_probabilities_0, label_probabilities_1, etc.) |
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200 # or label_probabilities_<class_name> for string labels |
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201 prob_cols = [col for col in df_pred.columns if col.startswith('label_probabilities_') and col != 'label_probabilities'] |
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202 |
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203 # Sort by class number if numeric, otherwise keep alphabetical order |
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204 if prob_cols and prob_cols[0].split('_')[-1].isdigit(): |
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205 prob_cols.sort(key=lambda x: int(x.split('_')[-1])) |
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206 else: |
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207 prob_cols.sort() # Alphabetical sort for string class names |
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208 |
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209 if not prob_cols: |
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210 return None |
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211 |
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212 # Get probabilities matrix (n_samples x n_classes) |
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213 y_score = df_pred[prob_cols].values |
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214 n_classes = len(prob_cols) |
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215 |
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216 y_true = None |
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217 candidate_cols = [ |
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218 LABEL_COLUMN_NAME, |
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219 f"{LABEL_COLUMN_NAME}_ground_truth", |
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220 f"{LABEL_COLUMN_NAME}__ground_truth", |
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221 f"{LABEL_COLUMN_NAME}_target", |
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222 f"{LABEL_COLUMN_NAME}__target", |
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223 ] |
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224 candidate_cols.extend( |
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225 [ |
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226 col |
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227 for col in df_pred.columns |
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228 if (col.startswith(f"{LABEL_COLUMN_NAME}_") or col.startswith(f"{LABEL_COLUMN_NAME}__")) |
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229 and "probabilities" not in col |
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230 and "predictions" not in col |
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231 ] |
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232 ) |
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233 for col in candidate_cols: |
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234 if col in df_pred.columns and col not in prob_cols: |
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235 y_true = df_pred[col].values |
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236 break |
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237 |
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238 if y_true is None: |
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239 desc_path = exp_dir / "description.json" |
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240 if desc_path.exists(): |
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241 try: |
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242 with open(desc_path, 'r') as f: |
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243 desc = json.load(f) |
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244 dataset_path = desc.get('dataset', '') |
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245 if dataset_path and Path(dataset_path).exists(): |
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246 df_orig = pd.read_csv(dataset_path) |
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247 if SPLIT_COLUMN_NAME in df_orig.columns: |
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248 df_orig = df_orig[df_orig[SPLIT_COLUMN_NAME] == 2].reset_index(drop=True) |
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249 if LABEL_COLUMN_NAME in df_orig.columns: |
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250 y_true = df_orig[LABEL_COLUMN_NAME].values |
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251 if len(y_true) != len(df_pred): |
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252 print( |
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253 f"Warning: Test set size mismatch. Truncating to {len(df_pred)} samples for ROC plot." |
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254 ) |
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255 y_true = y_true[:len(df_pred)] |
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256 else: |
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257 print("Warning: Original dataset referenced in description.json is unavailable.") |
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258 except Exception as exc: # pragma: no cover - defensive |
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259 print(f"Warning: Failed to recover labels from dataset: {exc}") |
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260 |
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261 if y_true is None or len(y_true) == 0: |
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262 print("Warning: Unable to locate ground-truth labels for ROC plot.") |
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263 return None |
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264 |
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265 if len(y_true) != len(y_score): |
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266 limit = min(len(y_true), len(y_score)) |
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267 if limit == 0: |
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268 return None |
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269 print(f"Warning: Aligning prediction and label lengths to {limit} samples for ROC plot.") |
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270 y_true = y_true[:limit] |
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271 y_score = y_score[:limit] |
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272 |
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273 # Get actual class names from probability column names |
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274 actual_classes = [col.replace('label_probabilities_', '') for col in prob_cols] |
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275 display_classes = class_labels if len(class_labels) == n_classes else actual_classes |
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276 |
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277 # Binarize the output following sklearn example |
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278 # Use actual class names if they're strings, otherwise use range |
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279 if isinstance(y_true[0], str): |
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280 y_test = label_binarize(y_true, classes=actual_classes) |
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281 else: |
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282 y_test = label_binarize(y_true, classes=list(range(n_classes))) |
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283 |
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284 # Handle binary classification case |
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285 if y_test.ndim != 2: |
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286 y_test = np.atleast_2d(y_test) |
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287 |
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288 if n_classes == 2: |
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289 if y_test.shape[1] == 1: |
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290 y_test = np.hstack([1 - y_test, y_test]) |
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291 elif y_test.shape[1] != 2: |
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292 print("Warning: Unexpected label binarization shape for binary ROC plot.") |
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293 return None |
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294 elif y_test.shape[1] != n_classes: |
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295 print("Warning: Label binarization did not produce expected class dimension; skipping ROC plot.") |
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296 return None |
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297 |
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298 # Compute ROC curve and ROC area for each class (following sklearn example) |
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299 fpr = dict() |
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300 tpr = dict() |
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301 roc_auc = dict() |
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302 |
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303 for i in range(n_classes): |
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304 if np.sum(y_test[:, i]) > 0: # Check if class exists in test set |
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305 fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) |
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306 roc_auc[i] = auc(fpr[i], tpr[i]) |
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307 |
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308 # Compute micro-average ROC curve and ROC area (sklearn example) |
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309 fpr["micro"], tpr["micro"], _ = roc_curve(y_test.ravel(), y_score.ravel()) |
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310 roc_auc["micro"] = auc(fpr["micro"], tpr["micro"]) |
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311 |
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312 # Create ROC curve plot |
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313 fig_roc = go.Figure() |
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314 |
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315 # Colors for different classes |
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316 colors = [ |
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317 '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', |
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318 '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf' |
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319 ] |
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320 |
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321 # Plot micro-average ROC curve first (most important) |
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322 fig_roc.add_trace(go.Scatter( |
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323 x=fpr["micro"], |
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324 y=tpr["micro"], |
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325 mode='lines', |
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326 name=f'Micro-average ROC (AUC = {roc_auc["micro"]:.3f})', |
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327 line=dict(color='deeppink', width=3, dash='dot'), |
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328 hovertemplate=('<b>Micro-average ROC</b><br>' |
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329 'FPR: %{x:.3f}<br>' |
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330 'TPR: %{y:.3f}<br>' |
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331 f'AUC: {roc_auc["micro"]:.3f}<extra></extra>') |
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332 )) |
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333 |
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334 # Plot ROC curve for each class |
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335 for i in range(n_classes): |
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336 if i in roc_auc: # Only plot if class exists in test set |
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337 class_name = display_classes[i] if i < len(display_classes) else f"Class {i}" |
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338 color = colors[i % len(colors)] |
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339 |
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340 fig_roc.add_trace(go.Scatter( |
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341 x=fpr[i], |
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342 y=tpr[i], |
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343 mode='lines', |
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344 name=f'{class_name} (AUC = {roc_auc[i]:.3f})', |
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345 line=dict(color=color, width=2), |
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346 hovertemplate=(f'<b>{class_name}</b><br>' |
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347 'FPR: %{x:.3f}<br>' |
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348 'TPR: %{y:.3f}<br>' |
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349 f'AUC: {roc_auc[i]:.3f}<extra></extra>') |
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350 )) |
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351 |
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352 # Add diagonal line (random classifier) |
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353 fig_roc.add_trace(go.Scatter( |
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354 x=[0, 1], |
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355 y=[0, 1], |
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356 mode='lines', |
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357 name='Random Classifier', |
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358 line=dict(color='gray', width=1, dash='dash'), |
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359 hovertemplate='Random Classifier<br>AUC = 0.500<extra></extra>' |
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360 )) |
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361 |
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362 # Calculate macro-average AUC |
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363 class_aucs = [roc_auc[i] for i in range(n_classes) if i in roc_auc] |
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364 if class_aucs: |
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365 macro_auc = np.mean(class_aucs) |
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366 title_text = f"ROC Curves (Micro-avg = {roc_auc['micro']:.3f}, Macro-avg = {macro_auc:.3f})" |
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367 else: |
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368 title_text = f"ROC Curves (Micro-avg = {roc_auc['micro']:.3f})" |
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369 |
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370 fig_roc.update_layout( |
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371 title=dict(text=title_text, x=0.5), |
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372 xaxis_title="False Positive Rate", |
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373 yaxis_title="True Positive Rate", |
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374 width=700, |
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375 height=600, |
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376 margin=dict(t=80, l=80, r=80, b=80), |
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377 legend=dict( |
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378 x=0.6, |
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379 y=0.1, |
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380 bgcolor="rgba(255,255,255,0.9)", |
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381 bordercolor="rgba(0,0,0,0.2)", |
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382 borderwidth=1 |
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383 ), |
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384 hovermode='closest' |
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385 ) |
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386 |
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387 # Set equal aspect ratio and proper range |
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388 fig_roc.update_xaxes(range=[0, 1.0]) |
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389 fig_roc.update_yaxes(range=[0, 1.05]) |
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390 |
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391 return { |
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392 "title": "ROC-AUC Curves", |
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393 "html": pio.to_html( |
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394 fig_roc, |
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395 full_html=False, |
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396 include_plotlyjs=False, |
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397 config=config |
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398 ) |
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399 } |
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400 |
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401 except Exception as e: |
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402 print(f"Error building ROC-AUC plot: {e}") |
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403 return None |