"""
Position-Aware Trajectory Model

Incorporates the finding that early flips hurt more than late flips.
Tests various position-weighted strategies.
"""

import json
import numpy as np
from pathlib import Path
from sklearn.model_selection import train_test_split
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import accuracy_score, roc_auc_score, brier_score_loss
from scipy.optimize import minimize
import warnings
warnings.filterwarnings('ignore')


def parse_trajectory(bitstring, n_meas, expect='0'):
    if len(bitstring) < n_meas + 1:
        return None

    final = bitstring[0]
    intermediate = bitstring[1:n_meas+1]

    n_flips = intermediate.count('1')
    flip_positions = [i for i, b in enumerate(intermediate) if b == '1']

    correct = 1 if final == expect else 0

    return {
        'n_flips': n_flips,
        'n_meas': n_meas,
        'flip_positions': flip_positions,
        'correct': correct,
        'intermediate': intermediate,
    }


def build_position_features(traj):
    """
    Build rich position-aware features.
    """
    n_flips = traj['n_flips']
    n_meas = traj['n_meas']
    positions = traj['flip_positions']

    features = {
        'n_flips': n_flips,
        'n_meas': n_meas,
        'flip_rate': n_flips / n_meas if n_meas > 0 else 0,
    }

    for i in range(16):
        features[f'pos_{i}'] = 1 if i in positions else 0

    if n_meas > 0:
        q1 = n_meas // 4
        q2 = n_meas // 2
        q3 = 3 * n_meas // 4

        features['flips_q1'] = sum(1 for p in positions if p < q1)
        features['flips_q2'] = sum(1 for p in positions if q1 <= p < q2)
        features['flips_q3'] = sum(1 for p in positions if q2 <= p < q3)
        features['flips_q4'] = sum(1 for p in positions if p >= q3)
    else:
        features['flips_q1'] = 0
        features['flips_q2'] = 0
        features['flips_q3'] = 0
        features['flips_q4'] = 0

    if positions:
        features['first_flip_pos'] = min(positions)
        features['last_flip_pos'] = max(positions)
        features['flip_span'] = max(positions) - min(positions)
        features['mean_flip_pos'] = np.mean(positions)
    else:
        features['first_flip_pos'] = n_meas
        features['last_flip_pos'] = -1
        features['flip_span'] = 0
        features['mean_flip_pos'] = n_meas / 2

    if n_meas > 0 and positions:
        features['weighted_flip_sum'] = sum((n_meas - p) / n_meas for p in positions)
    else:
        features['weighted_flip_sum'] = 0

    features['has_pos0_flip'] = 1 if 0 in positions else 0
    features['has_pos1_flip'] = 1 if 1 in positions else 0

    if n_flips >= 2 and len(positions) >= 2:
        sorted_pos = sorted(positions)
        gaps = [sorted_pos[i+1] - sorted_pos[i] for i in range(len(sorted_pos)-1)]
        features['mean_gap'] = np.mean(gaps)
        features['consecutive_flips'] = sum(1 for g in gaps if g == 1)
    else:
        features['mean_gap'] = 0
        features['consecutive_flips'] = 0

    return features


def load_data_with_positions():
    """Load data with rich position features."""
    samples = []

    results_dir = Path('D:/qiskit-zenodragging/results')

    traj_file = results_dir / 'trajectory_estimation' / 'trajectory_estimation.json'
    if traj_file.exists():
        with open(traj_file) as f:
            data = json.load(f)

        for circuit_name, circuit_data in data['data'].items():
            n_meas = circuit_data['n_meas']

            if 'freeze' in circuit_name or 'x_freeze' in circuit_name:
                expect = '0'
            elif 'drag' in circuit_name:
                expect = '1'
            else:
                expect = '0'

            for bs in circuit_data['bitstrings']:
                traj = parse_trajectory(bs, n_meas, expect)
                if traj:
                    features = build_position_features(traj)
                    features['circuit'] = circuit_name
                    features['correct'] = traj['correct']
                    features['flip_positions'] = traj['flip_positions']
                    samples.append(features)

    vqe_file = results_dir / 'vqe_trajectory_validation' / 'vqe_trajectory_validation.json'
    if vqe_file.exists():
        with open(vqe_file) as f:
            data = json.load(f)

        for result_name, result_data in data.get('results', {}).items():
            if 'bitstrings' not in result_data:
                continue
            if result_data.get('type') != 'zeno':
                continue

            n_meas = 8
            true_z = result_data.get('true_z', 0)
            expect = '0' if true_z >= 0 else '1'

            for bs in result_data['bitstrings']:
                traj = parse_trajectory(bs, n_meas, expect)
                if traj:
                    features = build_position_features(traj)
                    features['circuit'] = result_name
                    features['correct'] = traj['correct']
                    features['flip_positions'] = traj['flip_positions']
                    samples.append(features)

    return samples


def position_weighted_score(positions, n_meas, weights):
    """
    Compute position-weighted penalty score.
    weights[i] = penalty for flip at position i
    """
    if not positions:
        return 0
    return sum(weights[min(p, len(weights)-1)] for p in positions)


def evaluate_weighting_fn(samples, weight_fn):
    """Evaluate a weighting function on samples."""
    total_weight = 0
    weighted_correct = 0

    for s in samples:
        w = weight_fn(s)
        total_weight += w
        weighted_correct += w * s['correct']

    if total_weight == 0:
        return {'fidelity': 0, 'utilization': 0, 'effective_yield': 0}

    fidelity = weighted_correct / total_weight
    utilization = total_weight / len(samples)
    effective_yield = fidelity * utilization

    return {
        'fidelity': fidelity,
        'utilization': utilization,
        'effective_yield': effective_yield,
    }


def optimize_position_weights(train_samples, n_positions=8):
    """
    Learn optimal position weights via optimization.
    """
    def objective(params):
        decay = params[0]
        pos_weights = params[1:n_positions+1]

        def weight_fn(s):
            n_flips = s['n_flips']
            positions = s['flip_positions']
            n_meas = s['n_meas']

            if n_flips == 0:
                return 1.0

            base_weight = np.exp(-decay * n_flips)

            pos_penalty = 0
            for p in positions:
                if p < n_positions:
                    pos_penalty += pos_weights[p]
                else:
                    pos_penalty += pos_weights[-1]

            return base_weight * np.exp(-pos_penalty)

        result = evaluate_weighting_fn(train_samples, weight_fn)
        return -result['effective_yield']

    x0 = [0.5] + [0.1] * n_positions

    bounds = [(0.01, 2.0)] + [(0.0, 1.0)] * n_positions

    result = minimize(objective, x0, method='L-BFGS-B', bounds=bounds)

    return result.x


def main():
    print("=" * 70)
    print("POSITION-AWARE TRAJECTORY MODEL")
    print("=" * 70)

    print("\n[1/6] Loading data with position features...")
    samples = load_data_with_positions()
    print(f"  Samples: {len(samples)}")

    feature_cols = [k for k in samples[0].keys()
                    if k not in ['circuit', 'correct', 'flip_positions']]
    print(f"  Features: {len(feature_cols)}")

    print("\n[2/6] Train/test split...")
    train_samples, test_samples = train_test_split(
        samples, test_size=0.2, random_state=42,
        stratify=[s['correct'] for s in samples]
    )
    print(f"  Train: {len(train_samples)}, Test: {len(test_samples)}")

    print("\n[3/6] Analyzing position impact...")

    position_fidelity = {}
    for pos in range(16):
        with_flip = [s for s in samples if pos in s['flip_positions']]
        without_flip = [s for s in samples if pos not in s['flip_positions']]

        if with_flip and without_flip:
            fid_with = np.mean([s['correct'] for s in with_flip])
            fid_without = np.mean([s['correct'] for s in without_flip])
            impact = fid_without - fid_with
            position_fidelity[pos] = {
                'with_flip': fid_with,
                'without_flip': fid_without,
                'impact': impact,
                'n_with': len(with_flip),
            }

    print("\n  Position impact on fidelity:")
    print("  Pos | With Flip | Without | Impact | N")
    print("  " + "-" * 45)
    for pos in sorted(position_fidelity.keys()):
        pf = position_fidelity[pos]
        if pf['n_with'] > 100:
            print(f"   {pos:2d} |   {pf['with_flip']:5.1%}   |  {pf['without_flip']:5.1%}  | {pf['impact']:+5.1%} | {pf['n_with']}")

    print("\n[4/6] Optimizing position weights...")
    optimal_params = optimize_position_weights(train_samples, n_positions=8)
    optimal_decay = optimal_params[0]
    optimal_pos_weights = optimal_params[1:9]

    print(f"\n  Optimal decay: {optimal_decay:.3f}")
    print("  Optimal position weights:")
    for i, w in enumerate(optimal_pos_weights):
        print(f"    pos_{i}: {w:.3f}")

    print("\n[5/6] Training gradient boosting model...")

    X_train = np.array([[s[f] for f in feature_cols] for s in train_samples])
    y_train = np.array([s['correct'] for s in train_samples])
    X_test = np.array([[s[f] for f in feature_cols] for s in test_samples])
    y_test = np.array([s['correct'] for s in test_samples])

    model = GradientBoostingClassifier(n_estimators=200, max_depth=6, random_state=42)
    model.fit(X_train, y_train)

    y_proba = model.predict_proba(X_test)[:, 1]

    print(f"  Accuracy: {accuracy_score(y_test, (y_proba > 0.5).astype(int)):.3f}")
    print(f"  AUC: {roc_auc_score(y_test, y_proba):.3f}")

    print("\n[6/6] Comparing all strategies...")

    def hard_weight(s):
        return 1.0 if s['n_flips'] == 0 else 0.0

    def soft_k2_weight(s):
        return 1.0 if s['n_flips'] <= 2 else 0.0

    def exp_weight(s):
        return np.exp(-s['n_flips'])

    def optimal_position_weight(s):
        if s['n_flips'] == 0:
            return 1.0
        base = np.exp(-optimal_decay * s['n_flips'])
        pos_penalty = sum(optimal_pos_weights[min(p, 7)] for p in s['flip_positions'])
        return base * np.exp(-pos_penalty)

    def linear_position_weight(s):
        if s['n_flips'] == 0:
            return 1.0
        n_meas = s['n_meas']
        penalty = sum((n_meas - p) / n_meas for p in s['flip_positions'])
        return np.exp(-0.5 * penalty)

    def early_penalty_weight(s):
        if s['n_flips'] == 0:
            return 1.0
        early_flips = sum(1 for p in s['flip_positions'] if p < s['n_meas'] // 2)
        late_flips = s['n_flips'] - early_flips
        return np.exp(-1.5 * early_flips - 0.3 * late_flips)

    def threshold_position_weight(s):
        if s['n_flips'] == 0:
            return 1.0
        if s['n_flips'] > 3:
            return 0.0
        if 0 in s['flip_positions']:
            if s['n_flips'] > 1:
                return 0.0
            return 0.5
        if s['n_flips'] <= 2:
            return 1.0
        return 0.3

    for i, s in enumerate(test_samples):
        s['model_proba'] = y_proba[i]

    def model_weight(s):
        return s['model_proba']

    strategies = {
        'Hard (k=0)': hard_weight,
        'Soft (k<=2)': soft_k2_weight,
        'Exp(-n)': exp_weight,
        'Learned Position': optimal_position_weight,
        'Linear Position': linear_position_weight,
        'Early Penalty': early_penalty_weight,
        'Threshold+Position': threshold_position_weight,
        'GBM Model': model_weight,
    }

    print("\n" + "=" * 70)
    print("RESULTS")
    print("=" * 70)

    print("\n{:<20} | {:>10} | {:>10} | {:>12}".format(
        "Strategy", "Fidelity", "Util.", "Eff. Yield"))
    print("-" * 58)

    results = {}
    for name, weight_fn in strategies.items():
        r = evaluate_weighting_fn(test_samples, weight_fn)
        results[name] = r
        print("{:<20} | {:>9.1%} | {:>9.1%} | {:>11.1%}".format(
            name, r['fidelity'], r['utilization'], r['effective_yield']))

    best_name = max(results.keys(), key=lambda k: results[k]['effective_yield'])
    best_yield = results[best_name]['effective_yield']
    exp_yield = results['Exp(-n)']['effective_yield']

    print("\n" + "=" * 70)
    print("SUMMARY")
    print("=" * 70)

    print(f"\nBest strategy: {best_name}")
    print(f"  Effective yield: {best_yield:.1%}")

    improvement = (best_yield - exp_yield) / exp_yield * 100
    print(f"\nImprovement over Exp(-n): {improvement:+.1f}%")

    soft_yield = results['Soft (k<=2)']['effective_yield']
    improvement_soft = (best_yield - soft_yield) / soft_yield * 100
    print(f"Improvement over Soft (k<=2): {improvement_soft:+.1f}%")

    print("\n" + "-" * 70)
    print("KEY INSIGHT")
    print("-" * 70)
    print("""
Position 0 (first measurement) has outsized impact.
A flip at position 0 corrupts ALL subsequent evolution.
A flip at position N-1 only affects the final readout.

Optimal strategy: threshold + position awareness
- Accept k<=2 flips IF none at position 0
- Heavily penalize position 0 flips
- Lightly penalize late flips
""")

    output = {
        'dataset_size': len(samples),
        'position_impact': {str(k): v for k, v in position_fidelity.items()},
        'optimal_decay': float(optimal_decay),
        'optimal_position_weights': [float(w) for w in optimal_pos_weights],
        'results': {name: {k: float(v) for k, v in r.items()} for name, r in results.items()},
        'best_strategy': best_name,
        'improvement_over_exp': improvement,
    }

    out_file = Path('D:/kishka/data/position_aware_model_results.json')
    with open(out_file, 'w') as f:
        json.dump(output, f, indent=2)

    print(f"\nSaved: {out_file}")

    return output


if __name__ == "__main__":
    main()