from typing import List

import networkx as nx
import numpy as np
from gensim.models.word2vec import Word2Vec

from karateclub.estimator import Estimator
from karateclub.utils.walker import BiasedRandomWalker


class Node2Vec(Estimator):
    r"""An implementation of `"Node2Vec" <https://cs.stanford.edu/~jure/pubs/node2vec-kdd16.pdf>`_
    from the KDD '16 paper "node2vec: Scalable Feature Learning for Networks".
    The procedure uses biased second order random walks to approximate the pointwise mutual information
    matrix obtained by pooling normalized adjacency matrix powers. This matrix
    is decomposed by an approximate factorization technique.

    Args:
        walk_number (int): Number of random walks. Default is 10.
        walk_length (int): Length of random walks. Default is 80.
        p (float): Return parameter (1/p transition probability) to move towards from previous node.
        q (float): In-out parameter (1/q transition probability) to move away from previous node.
        dimensions (int): Dimensionality of embedding. Default is 128.
        workers (int): Number of cores. Default is 4.
        window_size (int): Matrix power order. Default is 5.
        epochs (int): Number of epochs. Default is 1.
        use_hierarchical_softmax (bool): Whether to use hierarchical softmax or negative sampling to train the model. Default is True.
        number_of_negative_samples (int): Number of negative nodes to sample (usually between 5-20). If set to 0, no negative sampling is used. Default is 5.
        learning_rate (float): HogWild! learning rate. Default is 0.05.
        min_count (int): Minimal count of node occurrences. Default is 1.
        seed (int): Random seed value. Default is 42.
    """
    _embedding: List[np.ndarray]

    def __init__(
        self,
        walk_number: int = 10,
        walk_length: int = 80,
        p: float = 1.0,
        q: float = 1.0,
        dimensions: int = 128,
        workers: int = 4,
        window_size: int = 5,
        epochs: int = 1,
        use_hierarchical_softmax: bool = True,
        number_of_negative_samples: int = 5,
        learning_rate: float = 0.05,
        min_count: int = 1,
        seed: int = 42,
    ):
        super(Node2Vec, self).__init__()

        self.walk_number = walk_number
        self.walk_length = walk_length
        self.p = p
        self.q = q
        self.dimensions = dimensions
        self.workers = workers
        self.window_size = window_size
        self.epochs = epochs
        self.use_hierarchical_softmax = use_hierarchical_softmax
        self.number_of_negative_samples = number_of_negative_samples
        self.learning_rate = learning_rate
        self.min_count = min_count
        self.seed = seed

    def fit(self, graph: nx.classes.graph.Graph):
        """
        Fitting a DeepWalk model.

        Arg types:
            * **graph** *(NetworkX graph)* - The graph to be embedded.
        """
        self._set_seed()
        graph = self._check_graph(graph)
        walker = BiasedRandomWalker(self.walk_length, self.walk_number, self.p, self.q)
        walker.do_walks(graph)

        model = Word2Vec(
            walker.walks,
            hs=1 if self.use_hierarchical_softmax else 0,
            negative=self.number_of_negative_samples,
            alpha=self.learning_rate,
            epochs=self.epochs,
            vector_size=self.dimensions,
            window=self.window_size,
            min_count=self.min_count,
            workers=self.workers,
            seed=self.seed,
        )

        n_nodes = graph.number_of_nodes()
        self._embedding = [model.wv[str(n)] for n in range(n_nodes)]

    def get_embedding(self) -> np.array:
        r"""Getting the node embedding.

        Return types:
            * **embedding** *(Numpy array)* - The embedding of nodes.
        """
        return np.array(self._embedding)
