comparison image_learner_cli.py @ 10:b0d893d04d4c draft default tip

planemo upload for repository https://github.com/goeckslab/gleam.git commit 1594d503179f28987720594eb49b48a15486f073

9:9e912fce264c

        "early_stop",
        "threshold",
    ]

    rows = []

    for key in display_keys:
        val = config.get(key, None)
        if key == "threshold":
            if output_type != "binary":
                continue

                        )
                    else:
                        val_str = val
            else:
                val_str = val if val is not None else "N/A"
            if val_str == "N/A" and key not in [
                "task_type"
            ]:  # Skip if N/A for non-essential
                continue
        rows.append(
            f"<tr>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left;'>"
            f"{key.replace('_', ' ').title()}</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center;'>"
            f"{val_str}</td>"
            f"</tr>"
        )
    aug_cfg = config.get("augmentation")
    if aug_cfg:
        types = [str(a.get("type", "")) for a in aug_cfg]
        aug_val = ", ".join(types)
        rows.append(
            f"<tr><td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left;'>Augmentation</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center;'>{aug_val}</td></tr>"
        )
    if split_info:
        rows.append(
            f"<tr><td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left;'>Data Split</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center;'>{split_info}</td></tr>"
        )
    html = f"""
        <h2 style="text-align: center;">Model and Training Summary</h2>
        <div style="display: flex; justify-content: center;">
          <table style="border-collapse: collapse; width: 100%; table-layout: fixed;">

        base_viz_dir = exp_dir / "visualizations"
        train_viz_dir = base_viz_dir / "train"
        test_viz_dir = base_viz_dir / "test"

        html = get_html_template()
        html += f"<h1>{title}</h1>"

        metrics_html = ""
        train_val_metrics_html = ""
        test_metrics_html = ""

                config, split_info, training_progress, output_type
            )
        except Exception as e:
            logger.warning(f"Could not load config for HTML report: {e}")

        def render_img_section(
            title: str, dir_path: Path, output_type: str = None
        ) -> str:
            if not dir_path.exists():
                return f"<h2>{title}</h2><p><em>Directory not found.</em></p>"
            # collect every PNG
            imgs = list(dir_path.glob("*.png"))
            # --- EXCLUDE Ludwig's base confusion matrix and any top-N confusion_matrix files ---
            imgs = [
                img
                for img in imgs
                if not (
                    img.name == "confusion_matrix.png"
                    or img.name.startswith("confusion_matrix__label_top")
                    or img.name == "roc_curves.png"
                )
            ]
            if not imgs:
                return f"<h2>{title}</h2><p><em>No plots found.</em></p>"
            if output_type == "binary":
                order = [
                    "roc_curves_from_prediction_statistics.png",
                    "compare_performance_label.png",
                    "confusion_matrix_entropy__label_top2.png",
                    # ...you can tweak ordering as needed
                ]
                img_names = {img.name: img for img in imgs}
                ordered = [img_names[n] for n in order if n in img_names]
                others = sorted(img for img in imgs if img.name not in order)
                imgs = ordered + others

                unwanted = {
                    "compare_classifiers_multiclass_multimetric__label_best10.png",
                    "compare_classifiers_multiclass_multimetric__label_top10.png",
                    "compare_classifiers_multiclass_multimetric__label_worst10.png",
                }
                display_order = [
                    "roc_curves.png",
                    "compare_performance_label.png",
                    "compare_classifiers_performance_from_prob.png",
                    "confusion_matrix_entropy__label_top10.png",
                ]
                # filter and order
                valid_imgs = [img for img in imgs if img.name not in unwanted]
                img_map = {img.name: img for img in valid_imgs}
                ordered = [img_map[n] for n in display_order if n in img_map]
                others = sorted(
                    img for img in valid_imgs if img.name not in display_order
                )
                imgs = ordered + others
            else:
                # regression: just sort whatever's left
                imgs = sorted(imgs)
            # render each remaining PNG
            html = ""
            for img in imgs:
                b64 = encode_image_to_base64(str(img))
                img_title = img.stem.replace("_", " ").title()
                html += (
                    f"<h2 style='text-align: center;'>{img_title}</h2>"
                    f'<div class="plot" style="margin-bottom:20px;text-align:center;">'
                    f'<img src="data:image/png;base64,{b64}" '
                    f'style="max-width:90%;max-height:600px;border:1px solid #ddd;" />'
                    f"</div>"
                )
            return html

        tab1_content = config_html + metrics_html
        tab2_content = train_val_metrics_html + render_img_section(
            "Training and Validation Visualizations", train_viz_dir
        )
        # --- Predictions vs Ground Truth table ---
        preds_section = ""
        parquet_path = exp_dir / PREDICTIONS_PARQUET_FILE_NAME
        if output_type == "regression" and parquet_path.exists():
            try:
                # 1) load predictions from Parquet

                df_table.columns = [LABEL_COLUMN_NAME, "prediction"]
                # 4) render as HTML
                preds_html = df_table.to_html(index=False, classes="predictions-table")
                preds_section = (
                    "<h2 style='text-align: center;'>Ground Truth vs. Predictions</h2>"
                    "<div style='overflow-y:auto; max-height:400px; overflow-x:auto; margin-bottom:20px;'>"
                    + preds_html
                    + "</div>"
                )
            except Exception as e:
                logger.warning(f"Could not build Predictions vs GT table: {e}")
        tab3_content = test_metrics_html + preds_section
        if output_type in ("binary", "category"):
            training_stats_path = exp_dir / "training_statistics.json"
            interactive_plots = build_classification_plots(
                str(test_stats_path),
                str(training_stats_path),
            )
            for plot in interactive_plots:
                # 2) inject the static "roc_curves_from_prediction_statistics.png"
                if plot["title"] == "ROC-AUC":
                    static_img = (
                        test_viz_dir / "roc_curves_from_prediction_statistics.png"
                    )
                    if static_img.exists():
                        b64 = encode_image_to_base64(str(static_img))
                        tab3_content += (
                            "<h2 style='text-align: center;'>"
                            "Roc Curves From Prediction Statistics"
                            "</h2>"
                            f'<div class="plot" style="margin-bottom:20px;text-align:center;">'
                            f'<img src="data:image/png;base64,{b64}" '
                            f'style="max-width:90%;max-height:600px;border:1px solid #ddd;" />'
                            "</div>"
                        )
                # always render the plotly panels exactly as before
                tab3_content += (
                    f"<h2 style='text-align: center;'>{plot['title']}</h2>"
                    + plot["html"]
                )
            tab3_content += render_img_section(
                "Test Visualizations", test_viz_dir, output_type
            )
        # assemble the tabs and help modal
        tabbed_html = build_tabbed_html(tab1_content, tab2_content, tab3_content)
        modal_html = get_metrics_help_modal()
        html += tabbed_html + modal_html + get_html_closing()

        try:

10:b0d893d04d4c

        "early_stop",
        "threshold",
    ]

    rows = []
    for key in display_keys:
        val = config.get(key, None)
        if key == "threshold":
            if output_type != "binary":
                continue

                        )
                    else:
                        val_str = val
            else:
                val_str = val if val is not None else "N/A"
            if val_str == "N/A" and key not in ["task_type"]:
                continue
        rows.append(
            f"<tr>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>"
            f"{key.replace('_', ' ').title()}</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>"
            f"{val_str}</td>"
            f"</tr>"
        )
    aug_cfg = config.get("augmentation")
    if aug_cfg:
        types = [str(a.get("type", "")) for a in aug_cfg]
        aug_val = ", ".join(types)
        rows.append(
            f"<tr><td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>Augmentation</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>{aug_val}</td></tr>"
        )
    if split_info:
        rows.append(
            f"<tr><td style='padding: 6px 12px; border: 1px solid #ccc; text-align: left; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>Data Split</td>"
            f"<td style='padding: 6px 12px; border: 1px solid #ccc; text-align: center; "
            f"white-space: normal; word-break: break-word; overflow-wrap: anywhere;'>{split_info}</td></tr>"
        )
    html = f"""
        <h2 style="text-align: center;">Model and Training Summary</h2>
        <div style="display: flex; justify-content: center;">
          <table style="border-collapse: collapse; width: 100%; table-layout: fixed;">

        base_viz_dir = exp_dir / "visualizations"
        train_viz_dir = base_viz_dir / "train"
        test_viz_dir = base_viz_dir / "test"

        html = get_html_template()

        # Extra CSS & JS: center Plotly and enable CSV download for predictions table
        html += """
<style>
  /* Center Plotly figures (both wrapper and native classes) */
  .plotly-center { display: flex; justify-content: center; }
  .plotly-center .plotly-graph-div, .plotly-center .js-plotly-plot { margin: 0 auto !important; }
  .js-plotly-plot, .plotly-graph-div { margin-left: auto !important; margin-right: auto !important; }

  /* Download button for predictions table */
  .download-btn {
    padding: 8px 12px;
    border: 1px solid #4CAF50;
    background: #4CAF50;
    color: white;
    border-radius: 6px;
    cursor: pointer;
  }
  .download-btn:hover { filter: brightness(0.95); }
  .preds-controls {
    display: flex;
    justify-content: flex-end;
    gap: 8px;
    margin: 8px 0;
  }
</style>
<script>
  function tableToCSV(table){
    const rows = Array.from(table.querySelectorAll('tr'));
    return rows.map(row =>
      Array.from(row.querySelectorAll('th,td')).map(cell => {
        let text = cell.innerText.replace(/\\r?\\n|\\r/g,' ').trim();
        if (text.includes('"') || text.includes(',')) {
          text = '"' + text.replace(/"/g,'""') + '"';
        }
        return text;
      }).join(',')
    ).join('\\n');
  }
  document.addEventListener('DOMContentLoaded', function(){
    const btn = document.getElementById('downloadPredsCsv');
    if(btn){
      btn.addEventListener('click', function(){
        const tbl = document.querySelector('.predictions-table');
        if(!tbl){ alert('Predictions table not found.'); return; }
        const csv = tableToCSV(tbl);
        const blob = new Blob([csv], {type: 'text/csv;charset=utf-8;'});
        const url = URL.createObjectURL(blob);
        const a = document.createElement('a');
        a.href = url;
        a.download = 'ground_truth_vs_predictions.csv';
        document.body.appendChild(a);
        a.click();
        document.body.removeChild(a);
        URL.revokeObjectURL(url);
      });
    }
  });
</script>
"""
        html += f"<h1>{title}</h1>"

        metrics_html = ""
        train_val_metrics_html = ""
        test_metrics_html = ""

                config, split_info, training_progress, output_type
            )
        except Exception as e:
            logger.warning(f"Could not load config for HTML report: {e}")

        # ---------- image rendering with exclusions ----------
        def render_img_section(
            title: str,
            dir_path: Path,
            output_type: str = None,
            exclude_names: Optional[set] = None,
        ) -> str:
            if not dir_path.exists():
                return f"<h2>{title}</h2><p><em>Directory not found.</em></p>"

            exclude_names = exclude_names or set()

            imgs = list(dir_path.glob("*.png"))

            default_exclude = {"confusion_matrix.png", "roc_curves.png"}

            imgs = [
                img
                for img in imgs
                if img.name not in default_exclude
                and img.name not in exclude_names
                and not img.name.startswith("confusion_matrix__label_top")
            ]

            if not imgs:
                return f"<h2>{title}</h2><p><em>No plots found.</em></p>"

            if output_type == "binary":
                order = [
                    "roc_curves_from_prediction_statistics.png",
                    "compare_performance_label.png",
                    "confusion_matrix_entropy__label_top2.png",
                ]
                img_names = {img.name: img for img in imgs}
                ordered = [img_names[n] for n in order if n in img_names]
                others = sorted(img for img in imgs if img.name not in order)
                imgs = ordered + others

                unwanted = {
                    "compare_classifiers_multiclass_multimetric__label_best10.png",
                    "compare_classifiers_multiclass_multimetric__label_top10.png",
                    "compare_classifiers_multiclass_multimetric__label_worst10.png",
                }
                valid_imgs = [img for img in imgs if img.name not in unwanted]
                display_order = [
                    "roc_curves.png",
                    "compare_performance_label.png",
                    "compare_classifiers_performance_from_prob.png",
                    "confusion_matrix_entropy__label_top10.png",
                ]
                img_map = {img.name: img for img in valid_imgs}
                ordered = [img_map[n] for n in display_order if n in img_map]
                others = sorted(
                    img for img in valid_imgs if img.name not in display_order
                )
                imgs = ordered + others
            else:
                imgs = sorted(imgs)

            html_section = ""
            for img in imgs:
                b64 = encode_image_to_base64(str(img))
                img_title = img.stem.replace("_", " ").title()
                html_section += (
                    f"<h2 style='text-align: center;'>{img_title}</h2>"
                    f'<div class="plot" style="margin-bottom:20px;text-align:center;">'
                    f'<img src="data:image/png;base64,{b64}" '
                    f'style="max-width:90%;max-height:600px;border:1px solid #ddd;" />'
                    f"</div>"
                )
            return html_section

        tab1_content = config_html + metrics_html

        tab2_content = train_val_metrics_html + render_img_section(
            "Training and Validation Visualizations",
            train_viz_dir,
            output_type,
            exclude_names={
                "compare_classifiers_performance_from_prob.png",
                "roc_curves_from_prediction_statistics.png",
                "precision_recall_curves_from_prediction_statistics.png",
                "precision_recall_curve.png",
            },
        )

        # --- Predictions vs Ground Truth table (REGRESSION ONLY) ---
        preds_section = ""
        parquet_path = exp_dir / PREDICTIONS_PARQUET_FILE_NAME
        if output_type == "regression" and parquet_path.exists():
            try:
                # 1) load predictions from Parquet

                df_table.columns = [LABEL_COLUMN_NAME, "prediction"]
                # 4) render as HTML
                preds_html = df_table.to_html(index=False, classes="predictions-table")
                preds_section = (
                    "<h2 style='text-align: center;'>Ground Truth vs. Predictions</h2>"
                    "<div class='preds-controls'>"
                    "<button id='downloadPredsCsv' class='download-btn'>Download CSV</button>"
                    "</div>"
                    "<div class='scroll-rows-30' style='overflow-x:auto; overflow-y:auto; max-height:900px; margin-bottom:20px;'>"
                    + preds_html
                    + "</div>"
                )
            except Exception as e:
                logger.warning(f"Could not build Predictions vs GT table: {e}")

        tab3_content = test_metrics_html + preds_section

        # Classification-only interactive Plotly panels (centered)
        if output_type in ("binary", "category"):
            training_stats_path = exp_dir / "training_statistics.json"
            interactive_plots = build_classification_plots(
                str(test_stats_path),
                str(training_stats_path),
            )
            for plot in interactive_plots:
                tab3_content += (
                    f"<h2 style='text-align: center;'>{plot['title']}</h2>"
                    f"<div class='plotly-center'>{plot['html']}</div>"
                )

        # Add static TEST PNGs (with default dedupe/exclusions)
        tab3_content += render_img_section(
            "Test Visualizations", test_viz_dir, output_type
        )

        tabbed_html = build_tabbed_html(tab1_content, tab2_content, tab3_content)
        modal_html = get_metrics_help_modal()
        html += tabbed_html + modal_html + get_html_closing()

        try: