# utils/causal_algorithms.py
import networkx as nx
import pandas as pd
import numpy as np
from causallearn.search.ConstraintBased.PC import pc
# from causallearn.search.ScoreBased.GES import ges # Example import for GES
# from notears import notears_linear # Example import for NOTEARS

class CausalDiscoveryAlgorithms:
    def pc_algorithm(self, df, alpha=0.05):
        """
        Run PC algorithm to learn causal graph.
        Returns a directed graph's adjacency matrix.
        Requires numerical data.
        """
        data_array = df.to_numpy()
        cg = pc(data_array, alpha=alpha, indep_test="fisherz")
        adj_matrix = cg.G.graph
        return adj_matrix

    def ges_algorithm(self, df):
        """
        Placeholder for GES (Greedy Equivalence Search) algorithm.
        Returns a directed graph's adjacency matrix.
        You would implement or integrate the GES algorithm here.
        """
        # Example: G, edges = ges(data_array)
        # For now, returning a simplified correlation-based graph for demonstration
        print("GES algorithm is a placeholder. Using a simplified correlation-based graph.")
        G = nx.DiGraph()
        nodes = df.columns
        G.add_nodes_from(nodes)
        corr_matrix = df.corr().abs()
        threshold = 0.3
        for i, col1 in enumerate(nodes):
            for col2 in nodes[i+1:]:
                if corr_matrix.loc[col1, col2] > threshold:
                    if np.random.rand() > 0.5:
                        G.add_edge(col1, col2)
                    else:
                        G.add_edge(col2, col1)
        return nx.to_numpy_array(G) # Convert to adjacency matrix

    def notears_algorithm(self, df):
        """
        Placeholder for NOTEARS algorithm.
        Returns a directed graph's adjacency matrix.
        You would implement or integrate the NOTEARS algorithm here.
        """
        # Example: W_est = notears_linear(data_array)
        print("NOTEARS algorithm is a placeholder. Using a simplified correlation-based graph.")
        G = nx.DiGraph()
        nodes = df.columns
        G.add_nodes_from(nodes)
        corr_matrix = df.corr().abs()
        threshold = 0.3
        for i, col1 in enumerate(nodes):
            for col2 in nodes[i+1:]:
                if corr_matrix.loc[col1, col2] > threshold:
                    if np.random.rand() > 0.5:
                        G.add_edge(col1, col2)
                    else:
                        G.add_edge(col2, col1)
        return nx.to_numpy_array(G) # Convert to adjacency matrix