import argparse
import random
import time
from datetime import datetime
import json
import numpy as np
import xlsxwriter
from sklearn.tree import DecisionTreeClassifier
from stree import Stree
from sklearn.model_selection import KFold, cross_validate
from experimentation.Sets import Datasets
from experimentation.Database import MySQL
from wodt import TreeClassifier
from experimentation.Utils import TextColor
from mdlp import MDLP


CHECK_MARK = "\N{heavy check mark}"
EXCLAMATION_MARK = "\N{heavy exclamation mark symbol}"
BLACK_STAR = "\N{black star}"


def parse_arguments():
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "-S",
        "--set-of-files",
        type=str,
        choices=["aaai", "tanveer"],
        required=False,
        default="tanveer",
    )
    ap.add_argument(
        "-m",
        "--model",
        type=str,
        required=False,
        default="stree_default",
        help="model name, default stree_default",
    )
    ap.add_argument(
        "-d",
        "--dataset",
        type=str,
        required=True,
        help="dataset to process, all for everyone",
    )
    ap.add_argument(
        "-s",
        "--sql",
        default=False,
        type=bool,
        required=False,
        help="generate report_score.sql",
    )
    ap.add_argument(
        "-n",
        "--normalize",
        type=int,
        required=True,
    )
    ap.add_argument(
        "-x",
        "--excel",
        type=str,
        default="",
        required=False,
        help="generate excel file",
    )
    ap.add_argument(
        "-di",
        "--discretize",
        type=bool,
        default=False,
        required=False,
        help="Discretize datasets",
    )
    ap.add_argument(
        "-p", "--parameters", type=str, required=False, default="{}"
    )
    args = ap.parse_args()
    return (
        args.set_of_files,
        args.model,
        args.dataset,
        args.sql,
        bool(args.normalize),
        args.parameters,
        args.excel,
        args.discretize,
    )


def get_classifier(model, random_state, hyperparameters):
    if model == "stree" or model == "stree_default":
        clf = Stree(random_state=random_state)
        clf.set_params(**hyperparameters)
    if model == "wodt":
        clf = TreeClassifier(random_state=random_state)
    if model == "cart":
        clf = DecisionTreeClassifier(random_state=random_state)
    return clf


def process_dataset(dataset, verbose, model, params):
    X, y = dt.load(dataset)
    if discretize:
        mdlp = MDLP(random_state=1)
        X = mdlp.fit_transform(X, y)
    scores = []
    times = []
    nodes = []
    leaves = []
    depths = []
    if verbose:
        print(
            f"* Processing dataset [{dataset}] from Set: {set_of_files} with "
            f"{model}"
        )
        print(f"X.shape: {X.shape}")
        print(f"{X[:4]}")
        print(f"Random seeds: {random_seeds}")
    hyperparameters = json.loads(params)
    if model == "stree":
        # Get the optimized parameters
        record = dbh.find_best(dataset, model, "gridsearch")
        hyperparameters = json.loads(record[8] if record[8] != "" else "{}")
        hyperparameters.pop("random_state", None)
    for random_state in random_seeds:
        random.seed(random_state)
        np.random.seed(random_state)
        kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
        clf = get_classifier(model, random_state, hyperparameters)
        res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
        scores.append(res["test_score"])
        times.append(res["fit_time"])
        for result_item in res["estimator"]:
            if model == "cart":
                nodes_item = result_item.tree_.node_count
                depth_item = result_item.tree_.max_depth
                leaves_item = result_item.get_n_leaves()
            else:
                nodes_item, leaves_item = result_item.nodes_leaves()
                depth_item = result_item.depth_
            nodes.append(nodes_item)
            leaves.append(leaves_item)
            depths.append(depth_item)
        if verbose:
            print(
                f"Random seed: {random_state:5d} Accuracy: "
                f"{res['test_score'].mean():6.4f}±"
                f"{res['test_score'].std():6.4f} "
                f"{res['fit_time'].mean():5.3f}s"
            )
    return scores, times, json.dumps(hyperparameters), nodes, leaves, depths


def store_string(
    dataset, model, accuracy, time_spent, hyperparameters, complexity
):
    attributes = [
        "date",
        "time",
        "type",
        "accuracy",
        "accuracy_std",
        "dataset",
        "classifier",
        "norm",
        "stand",
        "time_spent",
        "time_spent_std",
        "parameters",
        "nodes",
        "leaves",
        "depth",
    ]
    command_insert = (
        "replace into results ("
        + ",".join(attributes)
        + ") values("
        + ("'%s'," * len(attributes))[:-1]
        + ");"
    )
    now = datetime.now()
    date = now.strftime("%Y-%m-%d")
    time = now.strftime("%H:%M:%S")
    nodes, leaves, depth = complexity.values()
    values = (
        date,
        time,
        "crossval",
        np.mean(accuracy),
        np.std(accuracy),
        dataset,
        model,
        1,
        0,
        np.mean(time_spent),
        np.std(time_spent),
        hyperparameters,
        nodes,
        leaves,
        depth,
    )
    result = command_insert % values
    return result


def excel_write_line(
    book,
    sheet,
    name,
    samples,
    features,
    classes,
    accuracy,
    times,
    hyperparameters,
    complexity,
    status,
):
    try:
        excel_write_line.row += 1
    except AttributeError:
        excel_write_line.row = 4
    size_n = 14
    decimal = book.add_format({"num_format": "0.000000", "font_size": size_n})
    integer = book.add_format({"num_format": "#,###", "font_size": size_n})
    normal = book.add_format({"font_size": size_n})
    col = 0
    status, _ = excel_status(status)
    sheet.write(excel_write_line.row, col, name, normal)
    sheet.write(excel_write_line.row, col + 1, samples, integer)
    sheet.write(excel_write_line.row, col + 2, features, normal)
    sheet.write(excel_write_line.row, col + 3, classes, normal)
    sheet.write(excel_write_line.row, col + 4, complexity["nodes"], normal)
    sheet.write(excel_write_line.row, col + 5, complexity["leaves"], normal)
    sheet.write(excel_write_line.row, col + 6, complexity["depth"], normal)
    sheet.write(excel_write_line.row, col + 7, accuracy, decimal)
    sheet.write(excel_write_line.row, col + 8, status, normal)
    sheet.write(excel_write_line.row, col + 9, np.mean(times), decimal)
    sheet.write(excel_write_line.row, col + 10, hyperparameters, normal)


def excel_write_header(book, sheet):
    header = book.add_format()
    header.set_font_size(18)
    subheader = book.add_format()
    subheader.set_font_size(16)
    sheet.write(
        0,
        0,
        f"Process all datasets set with {model}: {set_of_files} "
        f"norm: {normalize} std: {standardize} discretize: {discretize} "
        f"store in: {model}",
        header,
    )
    sheet.write(
        1,
        0,
        "5 Fold Cross Validation with 10 random seeds",
        subheader,
    )
    sheet.write(1, 5, f"{random_seeds}", subheader)
    header_cols = [
        ("Dataset", 30),
        ("Samples", 10),
        ("Variables", 7),
        ("Classes", 7),
        ("Nodes", 7),
        ("Leaves", 7),
        ("Depth", 7),
        ("Accuracy", 10),
        ("Stat", 3),
        ("Time", 10),
        ("Parameters", 50),
    ]
    bold = book.add_format({"bold": True, "font_size": 14})
    i = 0
    for item, length in header_cols:
        sheet.write(3, i, item, bold)
        sheet.set_column(i, i, length)
        i += 1


def excel_status(status):
    if status == TextColor.GREEN + CHECK_MARK + TextColor.ENDC:
        return EXCLAMATION_MARK, "Accuracy better than stree optimized"
    elif status == TextColor.RED + BLACK_STAR + TextColor.ENDC:
        return BLACK_STAR, "Best accuracy of al models"
    elif status != " ":
        return CHECK_MARK, "Accuracy better than original stree_default"
    return " ", ""


def excel_write_totals(book, sheet, totals, start):
    i = 2
    bold = book.add_format({"bold": True, "font_size": 16})
    for key, total in totals.items():
        status, text = excel_status(key)
        sheet.write(excel_write_line.row + i, 1, status, bold)
        sheet.write(excel_write_line.row + i, 2, total, bold)
        sheet.write(excel_write_line.row + i, 3, text, bold)
        i += 1
    time_spent = get_time(start, time.time())
    sheet.write(excel_write_line.row + i + 1, 0, time_spent, bold)


def compute_status(dbh, name, model, accuracy):
    n_dig = 6
    ac_round = round(accuracy, n_dig)
    better_default = CHECK_MARK
    better_stree = TextColor.GREEN + CHECK_MARK + TextColor.ENDC
    best = TextColor.RED + BLACK_STAR + TextColor.ENDC
    best_default, _ = get_best_score(dbh, name, model)
    best_stree, _ = get_best_score(dbh, name, "stree")
    best_all, _ = get_best_score(dbh, name, models_tree)
    status = better_default if ac_round > round(best_default, n_dig) else " "
    status = better_stree if ac_round > round(best_stree, n_dig) else status
    status = best if ac_round > round(best_all, n_dig) else status
    return status


def get_best_score(dbh, name, model):
    record = dbh.find_best(name, model, "crossval")
    accuracy = record[5] if record is not None else 0.0
    acc_std = record[11] if record is not None else 0.0
    return accuracy, acc_std


def get_time(start, stop):
    hours, rem = divmod(stop - start, 3600)
    minutes, seconds = divmod(rem, 60)
    return f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s"


random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
models_tree = [
    "stree",
    "stree_default",
    "wodt",
    "j48svm",
    "oc1",
    "cart",
    "baseRaF",
]
standardize = False
(
    set_of_files,
    model,
    dataset,
    sql,
    normalize,
    parameters,
    excel,
    discretize,
) = parse_arguments()
# parameters = '{"splitter":"cfs","max_features":"auto"}'
dbh = MySQL()
if sql:
    sql_output = open(f"{model}.sql", "w")
if excel != "":
    file_name = f"{excel}.xlsx"
    excel_wb = xlsxwriter.Workbook(file_name)
    excel_ws = excel_wb.add_worksheet(model)
    excel_write_header(excel_wb, excel_ws)
database = dbh.get_connection()
dt = Datasets(normalize, standardize, set_of_files)
start = time.time()
if dataset == "all":
    print(
        f"* Process all datasets set with {model}: {set_of_files} "
        f"norm: {normalize} std: {standardize} discretize: {discretize}"
        f" store in: {model}"
    )
    print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}\n")
    header_cols = [
        "Dataset",
        "Samp",
        "Var",
        "Cls",
        "Nodes",
        "Leaves",
        "Depth",
        "Accuracy",
        "Time",
        "Parameters",
    ]
    header_lengths = [30, 5, 3, 3, 7, 7, 7, 15, 15, 10]
    parameters = json.dumps(json.loads(parameters))
    if parameters != "{}" and len(parameters) > 10:
        header_lengths.pop()
        header_lengths.append(len(parameters))
    line_col = ""
    for field, underscore in zip(header_cols, header_lengths):
        print(f"{field:{underscore}s} ", end="")
        line_col += "=" * underscore + " "
    print(f"\n{line_col}")
    totals = {}
    accuracy_total = 0.0
    for dataset in dt:
        name = dataset[0]
        X, y = dt.load(name)  # type: ignore
        samples, features = X.shape
        classes = len(np.unique(y))
        print(
            f"{name:30s} {samples:5d} {features:3d} {classes:3d} ",
            end="",
        )
        scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
            dataset[0], verbose=False, model=model, params=parameters
        )
        complexity = dict(
            nodes=float(np.mean(nodes)),
            leaves=float(np.mean(leaves)),
            depth=float(np.mean(depth)),
        )
        nodes_item, leaves_item, depth_item = complexity.values()
        print(
            f"{nodes_item:7.2f} {leaves_item:7.2f} {depth_item:7.2f} ",
            end="",
        )
        accuracy = np.mean(scores)
        accuracy_total += accuracy
        status = (
            compute_status(dbh, name, model, accuracy)
            if model == "stree_default"
            else " "
        )
        if status != " ":
            if status not in totals:
                totals[status] = 1
            else:
                totals[status] += 1
        print(f"{accuracy:8.6f}±{np.std(scores):6.4f}{status}", end="")
        print(f"{np.mean(times):8.6f}±{np.std(times):6.4f} {hyperparameters}")
        if sql:
            command = store_string(
                name, model, scores, times, hyperparameters, complexity
            )
            print(command, file=sql_output)
        if excel != "":
            excel_write_line(
                excel_wb,
                excel_ws,
                name,
                samples,
                features,
                classes,
                accuracy,
                times,
                hyperparameters,
                complexity,
                status,
            )
    for key, value in totals.items():
        print(f"{key} .....: {value:2d}")
    print(
        f"** Accuracy compared to stree_default (liblinear-ovr) .: "
        f"{accuracy_total/40.282203:7.4f}"
    )
    if excel != "":
        excel_write_totals(excel_wb, excel_ws, totals, start)
        excel_wb.close()
else:
    scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
        dataset, verbose=True, model=model, params=parameters
    )
    best_accuracy, acc_best_std = get_best_score(dbh, dataset, model)
    accuracy = np.mean(scores)
    print(f"* Normalize/Standard.: {normalize} / {standardize}")
    print(
        f"* Accuracy Computed .: {accuracy:6.4f}±{np.std(scores):6.4f} "
        f"{np.mean(times):5.3f}s"
    )
    print(f"* Best Accuracy model: {best_accuracy:6.4f}±{acc_best_std:6.4f}")
    print(f"* Difference ........: {best_accuracy - accuracy:6.4f}")
    best_accuracy, acc_best_std = get_best_score(dbh, dataset, models_tree)
    print(f"* Best Accuracy .....: {best_accuracy:6.4f}±{acc_best_std:6.4f}")
    print(f"* Difference ........: {best_accuracy - accuracy:6.4f}")
    print(
        f"* Nodes/Leaves/Depth : {np.mean(nodes):.2f} {np.mean(leaves):.2f} "
        f"{np.mean(depth):.2f} "
    )
    print(f"- Hyperparameters ...: {hyperparameters}")
stop = time.time()
time_spent = get_time(start, time.time())
print(f"{time_spent}")
if sql:
    sql_output.close()
dbh.close()