inference.py

import numpy as np
import pandas as pd
import skops.io as sio
import shap
import plotly.graph_objects as go
import os
import sys
import warnings

warnings.filterwarnings("ignore", category=UserWarning, module="sklearn")

# ---------------------------------------------------------------------------
# Compatibility patch — inject _RemainderColsList if the installed sklearn
# version does not have it (added in sklearn 1.4+). This allows .skops files
# saved with a newer sklearn to load correctly on older environments.
# ---------------------------------------------------------------------------
import sklearn.compose._column_transformer as _ct
if not hasattr(_ct, "_RemainderColsList"):
    class _RemainderColsList(list):
        """Minimal shim for sklearn._RemainderColsList (missing in this env)."""
        def __init__(self, lst=None, future_dtype=None):
            super().__init__(lst or [])
            self.future_dtype = future_dtype
    _ct._RemainderColsList = _RemainderColsList
    import sklearn.compose
    sklearn.compose._RemainderColsList = _RemainderColsList


# ---------------------------------------------------------------------------
# Column / feature definitions
# ---------------------------------------------------------------------------

NUM_COLUMNS = ["AGE", "NACS2YR"]
CATEG_COLUMNS = [
    "AGEGPFF",
    "SEX",
    "KPS",
    "DONORF",
    "GRAFTYPE",
    "CONDGRPF",
    "CONDGRP_FINAL",
    "ATGF",
    "GVHD_FINAL",
    "HLA_FINAL",
    "RCMVPR",
    "EXCHTFPR",
    "VOC2YPR",
    "VOCFRQPR",
    "SCATXRSN",
]

FEATURE_NAMES = NUM_COLUMNS + CATEG_COLUMNS

OUTCOMES                = ["DEAD", "GF", "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI", "DWOGF"]
CLASSIFICATION_OUTCOMES = OUTCOMES

REPORTING_OUTCOMES = [
    "OS", "EFS", "GF", "DEAD",
    "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI",
]

OUTCOME_DESCRIPTIONS = {
    "OS":       "Overall Survival",
    "EFS":      "Event-Free Survival",
    "DEAD":     "Total Mortality",
    "GF":       "Graft Failure",
    "AGVHD":    "Acute Graft-versus-Host Disease",
    "CGVHD":    "Chronic Graft-versus-Host Disease",
    "VOCPSHI":  "Vaso-Occlusive Crisis Post-HCT",
    "STROKEHI": "Stroke Post-HCT",
}

SHAP_OUTCOMES = ["DEAD", "GF", "AGVHD", "CGVHD", "VOCPSHI", "STROKEHI", "OS", "EFS"]

MODEL_DIR           = "."
CONSENSUS_THRESHOLD = 0.5
DEFAULT_N_BOOT_CI   = 500


# ---------------------------------------------------------------------------
# Model loading — skops
# ---------------------------------------------------------------------------

def _load_skops_model(fname):
    try:
        untrusted = sio.get_untrusted_types(file=fname)
        return sio.load(fname, trusted=untrusted)
    except Exception as e:
        print(f"Error loading '{fname}': {e}")
        sys.exit(1)


preprocessor = _load_skops_model(os.path.join(MODEL_DIR, "preprocessor.skops"))

classification_model_data = {}
for _o in CLASSIFICATION_OUTCOMES:
    _path = os.path.join(MODEL_DIR, f"ensemble_model_{_o}.skops")
    if os.path.exists(_path):
        classification_model_data[_o] = _load_skops_model(_path)
    else:
        print(f"Warning: Model for {_o} not found at {_path}. Skipping.")

classification_models = {o: d["models"] for o, d in classification_model_data.items()}
betas                 = {o: d["beta"]   for o, d in classification_model_data.items()}
priors                = {o: d["prior"]  for o, d in classification_model_data.items()}
consensus_thresholds  = {
    o: d.get("consensus_threshold", CONSENSUS_THRESHOLD)
    for o, d in classification_model_data.items()
}

# Calibrators — isotonic only; supports both old and new key names
calibrators = {}
for _o, _d in classification_model_data.items():
    _cal      = None
    _cal_type = _d.get("calibrator_type", None)

    if "calibrator" in _d and _d["calibrator"] is not None:
        if _cal_type is None or _cal_type == "isotonic":
            _cal = _d["calibrator"]
        else:
            print(
                f"Warning: outcome '{_o}' has calibrator_type='{_cal_type}'. "
                "Skipping non-isotonic calibrator (isotonic-only policy)."
            )
    elif "isotonic_calibrator" in _d and _d["isotonic_calibrator"] is not None:
        _cal = _d["isotonic_calibrator"]

    calibrators[_o] = _cal

# Alias expected by app.py
isotonic_calibrators = calibrators

oof_probs_calibrated = {
    o: d.get("oof_probs_calibrated") for o, d in classification_model_data.items()
}

ohe                     = preprocessor.named_transformers_["cat"]
ohe_feature_names       = ohe.get_feature_names_out(CATEG_COLUMNS)
processed_feature_names = np.concatenate([NUM_COLUMNS, ohe_feature_names])


# ---------------------------------------------------------------------------
# SHAP background data
# ---------------------------------------------------------------------------

np.random.seed(23)
_n_background = 500

_background_data = {
    "AGE":           np.random.uniform(5, 50, _n_background),
    "NACS2YR":       np.random.randint(0, 5, _n_background),
    "AGEGPFF":       np.random.choice(["<=10", "11-17", "18-29", "30-49", ">=50"], _n_background),
    "SEX":           np.random.choice(["Male", "Female"], _n_background),
    "KPS":           np.random.choice(["<90", "≥ 90"], _n_background),
    "DONORF":        np.random.choice([
                         "HLA identical sibling", "HLA mismatch relative",
                         "Matched unrelated donor",
                         "Mismatched unrelated donor or cord blood",
                     ], _n_background),
    "GRAFTYPE":      np.random.choice(["Bone marrow", "Peripheral blood", "Cord blood"], _n_background),
    "CONDGRPF":      np.random.choice(["MAC", "RIC", "NMA"], _n_background),
    "CONDGRP_FINAL": np.random.choice(["TBI/Cy", "Bu/Cy", "Flu/Bu", "Flu/Mel"], _n_background),
    "ATGF":          np.random.choice(["ATG", "Alemtuzumab", "None"], _n_background),
    "GVHD_FINAL":    np.random.choice(["CNI + MMF", "CNI + MTX", "Post-CY + siro +- MMF"], _n_background),
    "HLA_FINAL":     np.random.choice(["8/8", "7/8", "≤ 6/8"], _n_background),
    "RCMVPR":        np.random.choice(["Negative", "Positive"], _n_background),
    "EXCHTFPR":      np.random.choice(["No", "Yes"], _n_background),
    "VOC2YPR":       np.random.choice(["No", "Yes"], _n_background),
    "VOCFRQPR":      np.random.choice(["< 3/yr", "≥ 3/yr"], _n_background),
    "SCATXRSN":      np.random.choice([
                         "CNS event", "Acute chest Syndrome",
                         "Recurrent vaso-occlusive pain", "Recurrent priapism",
                         "Excessive transfusion requirements/iron overload",
                         "Cardio-pulmonary", "Chronic transfusion", "Asymptomatic",
                         "Renal insufficiency", "Splenic sequestration",
                         "Avascular necrosis", "Hodgkin lymphoma",
                     ], _n_background),
}

_background_df = pd.DataFrame(_background_data)[FEATURE_NAMES]
_X_background  = preprocessor.transform(_background_df)
shap_background = shap.maskers.Independent(_X_background)


# ---------------------------------------------------------------------------
# Calibration helpers
# ---------------------------------------------------------------------------

def calibrate_probabilities_undersampling(p_s, beta):
    p_s         = np.asarray(p_s, dtype=float)
    numerator   = beta * p_s
    denominator = np.maximum((beta - 1.0) * p_s + 1.0, 1e-10)
    return np.clip(numerator / denominator, 0.0, 1.0)


def predict_consensus_signed_voting(ensemble_models, X_test, threshold=0.5):
    individual_probas = np.array(
        [m.predict_proba(X_test)[:, 1] for m in ensemble_models]
    )
    binary_preds    = (individual_probas >= threshold).astype(int)
    signed_votes    = np.where(binary_preds == 1, 1, -1)
    avg_signed_vote = np.mean(signed_votes, axis=0)
    consensus_pred  = (avg_signed_vote > 0).astype(int)
    avg_proba       = np.mean(individual_probas, axis=0)
    return consensus_pred, avg_proba, avg_signed_vote, individual_probas.flatten()


def predict_consensus_majority(ensemble_models, X_test, threshold=0.5):
    individual_probas = np.array(
        [m.predict_proba(X_test)[:, 1] for m in ensemble_models]
    )
    avg_proba = np.mean(individual_probas, axis=0)
    return avg_proba, individual_probas.flatten()


# ---------------------------------------------------------------------------
# Bootstrap CI
# ---------------------------------------------------------------------------

def bootstrap_ci_from_oof(
    point_estimate: float,
    oof_probs: np.ndarray,
    n_boot: int = DEFAULT_N_BOOT_CI,
    confidence: float = 0.95,
    random_state: int = 42,
) -> tuple:
    if oof_probs is None or len(oof_probs) == 0:
        return float(point_estimate), float(point_estimate)

    oof_probs  = np.asarray(oof_probs, dtype=float)
    rng        = np.random.RandomState(random_state)
    grand_mean = np.mean(oof_probs)
    n          = len(oof_probs)

    boot_means = np.array([
        np.mean(rng.choice(oof_probs, size=n, replace=True))
        for _ in range(n_boot)
    ])

    shift      = point_estimate - grand_mean
    boot_means = boot_means + shift

    alpha = 1.0 - confidence
    lo = float(np.clip(np.percentile(boot_means, 100 * alpha / 2),       0.0, 1.0))
    hi = float(np.clip(np.percentile(boot_means, 100 * (1 - alpha / 2)), 0.0, 1.0))
    return lo, hi


# ---------------------------------------------------------------------------
# Calibration dispatch
# ---------------------------------------------------------------------------

def _calibrate_point(outcome: str, raw_prob: float, use_calibration: bool) -> float:
    beta   = betas[outcome]
    p_beta = float(calibrate_probabilities_undersampling([raw_prob], beta)[0])

    if not use_calibration:
        return p_beta

    cal = calibrators.get(outcome)
    if cal is None:
        return p_beta

    return float(cal.transform([p_beta])[0])


# ---------------------------------------------------------------------------
# Main prediction functions
# ---------------------------------------------------------------------------

def predict_all_outcomes(
    user_inputs,
    use_calibration: bool = True,
    use_signed_voting: bool = True,
    n_boot_ci: int = DEFAULT_N_BOOT_CI,
):
    if isinstance(user_inputs, dict):
        input_df = pd.DataFrame([user_inputs])
    else:
        input_df = pd.DataFrame([user_inputs], columns=FEATURE_NAMES)

    input_df = input_df[FEATURE_NAMES]
    X        = preprocessor.transform(input_df)

    probs, intervals = {}, {}

    for o in CLASSIFICATION_OUTCOMES:
        if o not in classification_models:
            continue

        threshold = consensus_thresholds.get(o, CONSENSUS_THRESHOLD)

        if use_signed_voting:
            _, uncalib_arr, _, _ = predict_consensus_signed_voting(
                classification_models[o], X, threshold
            )
        else:
            uncalib_arr, _ = predict_consensus_majority(
                classification_models[o], X, threshold
            )

        raw_prob   = float(uncalib_arr[0])
        event_prob = _calibrate_point(o, raw_prob, use_calibration)

        lo, hi = bootstrap_ci_from_oof(
            point_estimate=event_prob,
            oof_probs=oof_probs_calibrated.get(o),
            n_boot=n_boot_ci,
        )

        probs[o]     = event_prob
        intervals[o] = (lo, hi)

    # OS = 1 - P(DEAD)
    if "DEAD" in probs:
        p_dead      = probs["DEAD"]
        probs["OS"] = float(1.0 - p_dead)

        dead_lo, dead_hi = intervals["DEAD"]
        intervals["OS"]  = (
            float(np.clip(1.0 - dead_hi, 0, 1)),
            float(np.clip(1.0 - dead_lo, 0, 1)),
        )

    # EFS = 1 - P(DWOGF) - P(GF)
    if "DWOGF" in probs and "GF" in probs:
        p_dwogf      = probs["DWOGF"]
        p_gf         = probs["GF"]
        probs["EFS"] = float(np.clip(1.0 - p_dwogf - p_gf, 0.0, 1.0))

        oof_dwogf = oof_probs_calibrated.get("DWOGF")
        oof_gf    = oof_probs_calibrated.get("GF")

        if oof_dwogf is not None and oof_gf is not None:
            oof_dwogf = np.asarray(oof_dwogf, dtype=float)
            oof_gf    = np.asarray(oof_gf,    dtype=float)
            n_min     = min(len(oof_dwogf), len(oof_gf))
            oof_dwogf = oof_dwogf[:n_min]
            oof_gf    = oof_gf[:n_min]

            rng         = np.random.RandomState(42)
            grand_dwogf = np.mean(oof_dwogf)
            grand_gf    = np.mean(oof_gf)
            shift_dwogf = p_dwogf - grand_dwogf
            shift_gf    = p_gf    - grand_gf

            efs_boot = np.array([
                np.clip(
                    1.0
                    - (np.mean(rng.choice(oof_dwogf, size=n_min, replace=True)) + shift_dwogf)
                    - (np.mean(rng.choice(oof_gf,    size=n_min, replace=True)) + shift_gf),
                    0.0, 1.0,
                )
                for _ in range(n_boot_ci)
            ])
            efs_lo           = float(np.percentile(efs_boot, 2.5))
            efs_hi           = float(np.percentile(efs_boot, 97.5))
            intervals["EFS"] = (efs_lo, efs_hi)
        else:
            intervals["EFS"] = (probs["EFS"], probs["EFS"])

    return probs, intervals


def predict_with_comparison(user_inputs, n_boot_ci: int = DEFAULT_N_BOOT_CI):
    cal_probs,   cal_intervals   = predict_all_outcomes(user_inputs, True,  True, n_boot_ci)
    uncal_probs, uncal_intervals = predict_all_outcomes(user_inputs, False, True, n_boot_ci)
    return (cal_probs, cal_intervals), (uncal_probs, uncal_intervals)


# ---------------------------------------------------------------------------
# SHAP helpers
# ---------------------------------------------------------------------------

def _get_shap_values_for_model_outcome(user_inputs, model_outcome, invert, X_proc):
    """Return per-model SHAP values (shape: n_models × n_processed_features)."""
    all_model_shap_vals = []
    for rf_model in classification_models[model_outcome]:
        explainer = shap.TreeExplainer(rf_model, model_output="probability", data=shap_background)
        shap_vals = explainer.shap_values(X_proc)

        if isinstance(shap_vals, list):
            shap_vals = shap_vals[1]
        elif shap_vals.ndim == 3 and shap_vals.shape[2] == 2:
            shap_vals = shap_vals[:, :, 1]

        sv = shap_vals[0]
        if invert:
            sv = -sv
        all_model_shap_vals.append(sv)

    return np.array(all_model_shap_vals)


def compute_shap_values_with_direction(user_inputs, outcome, max_display=10):
    if isinstance(user_inputs, dict):
        input_df = pd.DataFrame([user_inputs])
    else:
        input_df = pd.DataFrame([user_inputs], columns=FEATURE_NAMES)

    X_proc = preprocessor.transform(input_df)

    processed_to_orig = {f: f for f in NUM_COLUMNS}
    for pf in ohe_feature_names:
        processed_to_orig[pf] = pf.split("_", 1)[0]

    if outcome == "OS":
        raw_shap = _get_shap_values_for_model_outcome(user_inputs, "DEAD", invert=True, X_proc=X_proc)
    elif outcome == "EFS":
        shap_dwogf = _get_shap_values_for_model_outcome(user_inputs, "DWOGF", invert=True, X_proc=X_proc)
        shap_gf    = _get_shap_values_for_model_outcome(user_inputs, "GF",    invert=True, X_proc=X_proc)
        raw_shap   = np.concatenate([shap_dwogf, shap_gf], axis=0)
    else:
        raw_shap = _get_shap_values_for_model_outcome(user_inputs, outcome, invert=False, X_proc=X_proc)

    unique_orig_features = list(dict.fromkeys(processed_to_orig.values()))
    n_models             = len(raw_shap)

    model_shap_by_orig = np.zeros((n_models, len(unique_orig_features)))
    for model_idx in range(n_models):
        agg_by_orig = {}
        for i, pf in enumerate(processed_feature_names):
            orig = processed_to_orig[pf]
            agg_by_orig.setdefault(orig, 0.0)
            agg_by_orig[orig] += raw_shap[model_idx, i]
        for feat_idx, feat_name in enumerate(unique_orig_features):
            model_shap_by_orig[model_idx, feat_idx] = agg_by_orig.get(feat_name, 0.0)

    mean_shap_vals = np.mean(model_shap_by_orig, axis=0)

    rng                  = np.random.RandomState(42)
    bootstrap_shap_means = np.array([
        np.mean(model_shap_by_orig[rng.choice(n_models, size=n_models, replace=True)], axis=0)
        for _ in range(DEFAULT_N_BOOT_CI)
    ])
    shap_ci_low  = np.percentile(bootstrap_shap_means, 2.5,  axis=0)
    shap_ci_high = np.percentile(bootstrap_shap_means, 97.5, axis=0)

    order = np.argsort(-np.abs(mean_shap_vals))

    top_feat_names = []
    for i in order[:max_display]:
        feat_name = unique_orig_features[i]
        if feat_name in user_inputs:
            val = user_inputs[feat_name]
            if isinstance(val, float) and val != int(val):
                display_name = f"{feat_name} = {val:.2f}"
            elif isinstance(val, (int, float)):
                display_name = f"{feat_name} = {int(val)}"
            else:
                val_str = str(val)
                if len(val_str) > 20:
                    val_str = val_str[:17] + "..."
                display_name = f"{feat_name} = {val_str}"
        else:
            display_name = feat_name
        top_feat_names.append(display_name)

    top_feat_names = top_feat_names[::-1]
    top_shap_vals  = mean_shap_vals[order][:max_display][::-1]
    top_ci_low     = shap_ci_low[order][:max_display][::-1]
    top_ci_high    = shap_ci_high[order][:max_display][::-1]

    return top_feat_names, top_shap_vals, top_ci_low, top_ci_high


def create_shap_plot(user_inputs, outcome, max_display=10):
    feat_names, shap_vals, ci_low, ci_high = compute_shap_values_with_direction(
        user_inputs, outcome, max_display
    )

    colors      = ["blue" if v >= 0 else "red" for v in shap_vals]
    error_minus = shap_vals - ci_low
    error_plus  = ci_high - shap_vals

    fig = go.Figure()
    fig.add_trace(go.Bar(
        y=feat_names,
        x=shap_vals,
        orientation="h",
        marker=dict(color=colors),
        showlegend=False,
        error_x=dict(
            type="data",
            symmetric=False,
            array=error_plus,
            arrayminus=error_minus,
            color="gray",
            thickness=1.5,
            width=4,
        ),
    ))
    fig.add_vline(x=0, line_width=1, line_color="black")

    fig.update_layout(
        title=dict(
            text=OUTCOME_DESCRIPTIONS.get(outcome, outcome),
            x=0.5, xanchor="center",
            font=dict(size=14, color="black"),
        ),
        xaxis_title="SHAP value",
        yaxis_title="",
        height=400,
        margin=dict(l=120, r=60, t=50, b=50),
        plot_bgcolor="white",
        paper_bgcolor="white",
        xaxis=dict(showgrid=True, gridcolor="lightgray", zeroline=True,
                   zerolinecolor="black", zerolinewidth=1),
        yaxis=dict(showgrid=False),
    )
    return fig


def create_all_shap_plots(user_inputs, max_display=10):
    return {o: create_shap_plot(user_inputs, o, max_display) for o in SHAP_OUTCOMES}



def icon_array(probability, outcome):
    outcome_labels = {
        "DEAD":     ("Death",           "Overall Survival"),
        "GF":       ("Graft Failure",   "No Graft Failure"),
        "AGVHD":    ("AGVHD",           "No AGVHD"),
        "CGVHD":    ("CGVHD",           "No CGVHD"),
        "VOCPSHI":  ("VOC Post-HCT",    "No VOC Post-HCT"),
        "STROKEHI": ("Stroke Post-HCT", "No Stroke Post-HCT"),
    }

    event_label, no_event_label = outcome_labels.get(outcome, ("Event", "No Event"))
    n_total    = 100
    n_event    = round(probability * n_total)
    n_no_event = n_total - n_event
    cols, rows = 10, 10

    shapes = []
    icon_idx = 0

    for row in range(rows - 1, -1, -1):   # top → bottom
        for col in range(cols):            # left → right
            color = "#ff6b6b" if icon_idx < n_event else "#4ecdc4"
            x0 = col * 1.2
            y0 = row * 1.6

            # --- head (circle) ---
            cx, cy_head, hr = x0 + 0.5, y0 + 1.35, 0.22
            shapes.append(dict(
                type="circle", xref="x", yref="y",
                x0=cx - hr, y0=cy_head - hr,
                x1=cx + hr, y1=cy_head + hr,
                fillcolor=color, line=dict(color=color, width=0),
            ))

            # --- body (pentagon-ish path) ---
            shapes.append(dict(
                type="path", xref="x", yref="y",
                path=(
                    f"M {x0+0.18},{y0+1.10} "
                    f"L {x0+0.82},{y0+1.10} "
                    f"L {x0+0.90},{y0+0.55} "
                    f"L {x0+0.60},{y0+0.55} "
                    f"L {x0+0.60},{y0+0.0} "
                    f"L {x0+0.40},{y0+0.0} "
                    f"L {x0+0.40},{y0+0.55} "
                    f"L {x0+0.10},{y0+0.55} Z"
                ),
                fillcolor=color, line=dict(color=color, width=0),
            ))
            icon_idx += 1

    fig = go.Figure()
    fig.update_layout(
        title=dict(
            text=(
                f"<b>{OUTCOME_DESCRIPTIONS.get(outcome, outcome)}</b><br>"
                f"<span style='font-size:12px;color:#ff6b6b'>■ {event_label}: {n_event}%</span>"
                f"&nbsp;&nbsp;"
                f"<span style='font-size:12px;color:#4ecdc4'>■ {no_event_label}: {n_no_event}%</span>"
            ),
            x=0.5, xanchor="center",
            font=dict(size=14, color="black"),
        ),
        shapes=shapes,
        xaxis=dict(
            range=[-0.3, cols * 1.2 + 0.1],
            showgrid=False, zeroline=False, showticklabels=False,
        ),
        yaxis=dict(
            range=[-0.3, rows * 1.6 + 0.3],
            showgrid=False, zeroline=False, showticklabels=False,
            scaleanchor="x", scaleratio=1,
        ),
        height=460,
        width=430,
        margin=dict(l=10, r=10, t=90, b=10),
        plot_bgcolor="white",
        paper_bgcolor="white",
    )
    return fig