import numpy as np
import matplotlib.pyplot as plt
from library.analysis import testSets, generators


testSets.append("Average")

kScore = "cohens kappa score"
f1Score = "f1 score"

ignoreSet = ["yeast_me2"]

gans = [g[0] for g in generators]
algs = {"LR", "GB", "KNN"}

dataset  = [
    "folding_abalone9-18",
    "folding_abalone_17_vs_7_8_9_10",
    "folding_car-vgood",
    "folding_car_good",
    "folding_flare-F",
    "folding_hypothyroid",
    "folding_kddcup-guess_passwd_vs_satan",
    "folding_kr-vs-k-three_vs_eleven",
    "folding_kr-vs-k-zero-one_vs_draw",
    "folding_shuttle-2_vs_5",
    "folding_winequality-red-4",
    "folding_yeast4",
    "folding_yeast5",
    "folding_yeast6",
    "folding_ozone_level",
    "folding_yeast_me2",
    "Average"
    ]

knn_ProWRAS_f1 = [0.384,0.347,0.818,0.641,0.301,0.553,1.0,0.94,0.9,1.0,0.141,0.308,0.714,0.545,0.556,0.339,0.538]
knn_ProWRAS_k = [0.35,0.328,0.81,0.622,0.263,0.528,1.0,0.938,0.896,1.0,0.093,0.268,0.704,0.531,0.526,0.305,0.515]

lr_ProWRAS_f1 = [0.488,0.315,0.407,0.103,0.341,0.446,0.99,0.928,0.853,1.0,0.158,0.308,0.591,0.326,0.347,0.295,0.472]
lr_ProWRAS_k = [0.446,0.287,0.371,0.033,0.3,0.407,0.99,0.926,0.847,1.0,0.119,0.268,0.574,0.3,0.319,0.254,0.441]

gb_ProWRAS_f1 = [0.385,0.335,0.959,0.863,0.320,0.803,0.998,0.995,0.969,1.0,0.156,0.335,0.735,0.514,0.329,0.225,0.600]
gb_ProWRAS_k = [0.341,0.310,0.957,0.857,0.291,0.794,0.998,0.995,0.967,1.0,0.115,0.303,0.726,0.501,0.303,0.328,0.589]


ProWrasPaper = "ProWRAS-paper"

statistic = { ProWrasPaper: {} }
for (n, f1, k) in zip(dataset, lr_ProWRAS_f1, lr_ProWRAS_k):
    if n in ignoreSet:
        continue
    
    if n not in statistic[ProWrasPaper]:
        statistic[ProWrasPaper][n] = {}
    
    statistic[ProWrasPaper][n]["LR"] = { kScore: k, f1Score: f1 }


for (n, f1, k) in zip(dataset, gb_ProWRAS_f1, gb_ProWRAS_k):
    if n in ignoreSet:
        continue
    
    if n not in statistic[ProWrasPaper]:
        statistic[ProWrasPaper][n] = {}
    
    statistic[ProWrasPaper][n]["GB"] = { kScore: k, f1Score: f1 }

    
for (n, f1, k) in zip(dataset, knn_ProWRAS_f1, knn_ProWRAS_k):
    if n in ignoreSet:
        continue
    
    if n not in statistic[ProWrasPaper]:
        statistic[ProWrasPaper][n] = {}
    
    statistic[ProWrasPaper][n]["KNN"] = { kScore: k, f1Score: f1 }
    

dataset = list(filter(lambda n: n not in ignoreSet, dataset))


def loadDiagnoseData(ganType, datasetName):
    fileName = f"data_result/{ganType}/{datasetName}.csv"
    r = {}
    try:
        with open(fileName) as f:
            newBlock = True
            n = ""
            for line in f:
                line = line.strip()
                if newBlock:
                    n = line
                    newBlock = False
                elif line == "---":
                    newBlock = True
                else:
                    parts = line.split(";")
                    if parts[0] == "avg":
                        r[n] = { f1Score: float(parts[5]), kScore: float(parts[6]) }
    except FileNotFoundError as e:
        print(f"Missing file: {fileName}")
    return r



for gan in gans:
    if gan not in statistic:
        statistic[gan] = {}
    
    for ds in testSets:
        if ds != "Average":
            statistic[gan][ds] = loadDiagnoseData(gan, ds)


ks = set()

for gan in statistic.keys():
    f1 = { n: 0.0 for n in algs }
    k =  { n: 0.0 for n in algs }
    c = 0

    for ds in statistic[gan].keys():
        ks.add(ds)
        if ds != "Average":
            c += 1
            for n in algs:
                if n in statistic[gan][ds].keys():
                    f1[n] += statistic[gan][ds][n][f1Score]
                    k[n] += statistic[gan][ds][n][kScore]

    avg = {}
    for n in algs:
        avg[n] = { f1Score: f1[n] / c, kScore: k[n] / c }
    statistic[gan]["Average"] = avg

print(ks)


def cleanupName(name):
    return name.replace("folding_", "").replace("imblearn_", "").replace("kaggle_", "")



def showDiagnose(algo, score):
    def gr(n):
        if n in resultList:
            return resultList[n][algo].data[score]
        else:
            return 0.0
    
    print(f"{algo}: {score}")
    
    gans = list(statistic.keys())

    w = 0.8 / len(gans)
    bar = list(range(len(testSets)))
    plt.figure(figsize=(20, 18))
    for g in gans:
        values = [
                 (statistic[g][d][algo][score] if algo in statistic[g][d].keys() else 0.0) if d in statistic[g] else 0.0
                 for d in testSets
                 ]
        plt.barh(bar, values, w, label=g)
        
        
        bar = [i - w for i in bar]

    plt.xlabel("Dataset")
    plt.ylabel(score)
    plt.yticks(range(len(testSets)), [cleanupName(name) for name in testSets])
    #plt.yticks(rotation="vertical")
    plt.legend()
    plt.savefig(f"data_result/statistic-{algo}-{score}.pdf")
    plt.show()


def showDiagnoseAverage(score, onlyOneBar=False):
    def gr(n):
        if n in resultList:
            return resultList[n][algo].data[score]
        else:
            return 0.0
    
    print(f"Average: {score}")
    
    gans = list(statistic.keys())

    w = 0.8 / len(gans)
    if onlyOneBar:
        barType = "O"
        bar = range(len(algs))
    else:
        barType = "M"
        bar = [0.8 + i - w for i in range(len(algs)) ]
    plt.figure(figsize=(20, 18))
    for g in gans:
        values = [
                 (statistic[g]["Average"][algo][score] if algo in statistic[g]["Average"].keys() else 0.0)
                 for algo in algs
                 ]
        plt.barh(bar, values, w, label=g)
        
        if not onlyOneBar:
            bar = [i - w for i in bar]

    plt.xlabel("Dataset")
    plt.ylabel(score)
    plt.yticks(range(len(algs)), algs)
    plt.legend()
    plt.savefig(f"data_result/statistic-Average-{score}-{barType}.pdf")
    plt.show()


for a in algs:
    showDiagnose(a, f1Score)
    showDiagnose(a, kScore)
    
showDiagnoseAverage(f1Score)
showDiagnoseAverage(kScore)
showDiagnoseAverage(kScore, True)