from itertools import takewhile

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.neighbors import NearestNeighbors
from tqdm import tqdm

from sc.scorers import ratio


def find_centers(X, y):
    c = []
    for label in range(y.max() + 1):
        c.append(X[y == label].mean(axis=0).tolist())
    return np.array(c)


def prune_mask(mask, ind):
    new_mask = []
    k, i = 0, 0
    for bit in mask:
        if bit:
            if k == ind[i]:
                i += 1
                new_mask.append(True)
            else:
                new_mask.append(False)
            k += 1
        else:
            new_mask.append(True)
    return np.array(new_mask)


def score_ratio(transform, *, cluster=None,
                inside_cluster=False, start_from_center=False, only_centroids=False,
                max_c=100, dist_c=1., centroid_cap=10, leaf_size=30,
                dataset="data/step-12768-submissions-with-ratio.csv", nrows=None,
                show_dist_plot=True):
    data = pd.read_csv(dataset, nrows=nrows)
    X = transform.fit_transform(data["code"]).toarray()
    data = data.iloc[transform.named_steps["pre"].icorrect]

    if cluster is not None:
        y = cluster.fit_predict(X).astype(int)
    else:
        y = -np.ones(X.shape[0], dtype=int)

    centers = find_centers(X, y)

    if only_centroids:
        for label in range(y.max() + 1):
            train = (data["status"] == "correct") & (y == label)
            if sum(train) != 0:
                nn = NearestNeighbors(n_neighbors=min(centroid_cap, sum(train)), n_jobs=-1, leaf_size=leaf_size)
                nn.fit(X[train])
                ind = nn.kneighbors(centers[label][np.newaxis, :], return_distance=False)
                mask = prune_mask(train, ind[0])
                data, X, y = data[mask], X[mask], y[mask]

    if not inside_cluster:
        y = -np.ones(X.shape[0], dtype=int)

    diff_ratios = []
    for label in tqdm(range(y.min(), y.max() + 1)):
        train = (data["status"] == "correct")
        if label != -1:
            train &= (y == label)
        test = (data["status"] != "correct") & (y == label)
        if sum(train) == 0 or sum(test) == 0:
            est_ratios = np.zeros(sum(test))
        else:
            nn = NearestNeighbors(n_neighbors=min(max_c, sum(train)), n_jobs=-1, leaf_size=leaf_size)
            nn.fit(X[train])
            est_ratios = []
            train_codes = data[train]["code"].values
            test_codes = tqdm(data[test]["code"].values)
            if start_from_center and label != -1:
                dist, ind = nn.kneighbors(centers[label][np.newaxis, :])
                dist, ind = dist[0], ind[0]
                est_codes = list(map(lambda t: train_codes[t[1]],
                                     takewhile(lambda t: t[0] <= dist_c, zip(dist, ind))))
                for code in test_codes:
                    est_ratios.append(ratio(code, est_codes))
            else:
                dist, ind = nn.kneighbors(X[test])
                for code, est_dist, est_ind in zip(test_codes, dist, ind):
                    est_codes = map(lambda t: train_codes[t[1]],
                                    takewhile(lambda t: t[0] <= dist_c, zip(est_dist, est_ind)))
                    est_ratios.append(ratio(code, est_codes))
        diff_ratios.extend(data[test]["best_ratio"] - est_ratios)

    diff_ratios = np.array(diff_ratios)
    if show_dist_plot:
        sns.distplot(diff_ratios)
        plt.show()
    return diff_ratios.mean(), diff_ratios.var()

matplotlib.pyplot.show