import pandas as pd
import numpy as np

# Load user-permission assignments
# Format: user_id, permission_id (one row per assignment)
assignments = pd.read_csv("user_permissions.csv")

# Create binary user-permission matrix (UPA matrix)
upa_matrix = assignments.pivot_table(
    index="user_id",
    columns="permission_id",
    aggfunc="size",
    fill_value=0
)
upa_matrix = (upa_matrix > 0).astype(int)

print(f"Users: {upa_matrix.shape[0]}")
print(f"Permissions: {upa_matrix.shape[1]}")
print(f"Assignments: {assignments.shape[0]}")
print(f"Density: {upa_matrix.values.sum() / upa_matrix.size:.2%}")

from sklearn.cluster import AgglomerativeClustering
from sklearn.metrics import silhouette_score

def find_optimal_clusters(matrix, max_k=50):
    """Find optimal number of roles using silhouette analysis."""
    scores = []
    for k in range(2, min(max_k, matrix.shape[0])):
        clustering = AgglomerativeClustering(
            n_clusters=k, metric="jaccard", linkage="average"
        )
        labels = clustering.fit_predict(matrix)
        score = silhouette_score(matrix, labels, metric="jaccard")
        scores.append((k, score))

    optimal_k = max(scores, key=lambda x: x[1])[0]
    return optimal_k, scores

def mine_roles_clustering(upa_matrix, n_clusters):
    """Mine roles using hierarchical clustering on Jaccard distance."""
    clustering = AgglomerativeClustering(
        n_clusters=n_clusters, metric="jaccard", linkage="average"
    )
    user_matrix = upa_matrix.values
    labels = clustering.fit_predict(user_matrix)

    roles = {}
    for cluster_id in range(n_clusters):
        cluster_users = upa_matrix.index[labels == cluster_id]
        cluster_permissions = upa_matrix.loc[cluster_users]

        # Core role = permissions held by >80% of cluster members
        permission_frequency = cluster_permissions.mean()
        core_permissions = permission_frequency[permission_frequency >= 0.8].index.tolist()

        roles[f"Role_{cluster_id}"] = {
            "permissions": core_permissions,
            "user_count": len(cluster_users),
            "users": cluster_users.tolist(),
            "coverage": permission_frequency[permission_frequency >= 0.8].mean()
        }

    return roles, labels

def mine_roles_fca(upa_matrix, min_support=3):
    """Mine roles using Formal Concept Analysis (frequent closed itemsets)."""
    from itertools import combinations

    users = upa_matrix.index.tolist()
    permissions = upa_matrix.columns.tolist()

    concepts = []

    # Find all maximal permission sets shared by at least min_support users
    for size in range(len(permissions), 0, -1):
        for perm_combo in combinations(permissions, size):
            perm_set = set(perm_combo)
            # Find users who have ALL permissions in this set
            matching_users = []
            for user in users:
                user_perms = set(upa_matrix.columns[upa_matrix.loc[user] == 1])
                if perm_set.issubset(user_perms):
                    matching_users.append(user)

            if len(matching_users) >= min_support:
                # Check if this is a closed concept (no superset with same extent)
                is_closed = True
                for concept in concepts:
                    if set(matching_users) == set(concept["users"]) and \
                       perm_set.issubset(set(concept["permissions"])):
                        is_closed = False
                        break

                if is_closed:
                    concepts.append({
                        "permissions": list(perm_set),
                        "users": matching_users,
                        "support": len(matching_users)
                    })

        if len(concepts) > 100:  # Limit for performance
            break

    return concepts

def evaluate_role_set(roles, upa_matrix):
    """Evaluate the quality of a mined role set."""
    total_assignments = upa_matrix.values.sum()
    covered_assignments = 0
    extra_assignments = 0

    for role_name, role_data in roles.items():
        role_perms = set(role_data["permissions"])
        for user in role_data["users"]:
            user_perms = set(upa_matrix.columns[upa_matrix.loc[user] == 1])
            covered = role_perms.intersection(user_perms)
            extra = role_perms - user_perms
            covered_assignments += len(covered)
            extra_assignments += len(extra)

    metrics = {
        "total_roles": len(roles),
        "total_assignments": total_assignments,
        "covered_assignments": covered_assignments,
        "coverage_rate": covered_assignments / total_assignments if total_assignments else 0,
        "extra_permissions": extra_assignments,
        "deviation_rate": extra_assignments / (covered_assignments + extra_assignments) if (covered_assignments + extra_assignments) else 0,
        "avg_role_size": np.mean([len(r["permissions"]) for r in roles.values()]),
        "avg_users_per_role": np.mean([r["user_count"] for r in roles.values()]),
    }
    return metrics