import argparse
import random
import time
from datetime import datetime
import json
import numpy as np
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


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",
    )
    args = ap.parse_args()
    return (args.set_of_files, args.model, args.dataset, args.sql)


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):
    X, y = dt.load(dataset)
    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("{}")
    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


random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
normalize = True
standardize = False
(set_of_files, model, dataset, sql) = parse_arguments()
dbh = MySQL()
if sql:
    sql_output = open(f"{model}.sql", "w")
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} 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]
    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}")
    for dataset in dt:
        X, y = dt.load(dataset[0])  # type: ignore
        samples, features = X.shape
        classes = len(np.unique(y))
        print(
            f"{dataset[0]:30s} {samples:5d} {features:3d} {classes:3d} ",
            end="",
        )
        scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
            dataset[0], verbose=False, model=model
        )
        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="",
        )
        print(f"{np.mean(scores):8.6f}±{np.std(scores):6.4f} ", end="")
        print(f"{np.mean(times):8.6f}±{np.std(times):6.4f} {hyperparameters}")
        if sql:
            command = store_string(
                dataset[0], model, scores, times, hyperparameters, complexity
            )
            print(command, file=sql_output)
else:
    scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
        dataset, verbose=True, model=model
    )
    record = dbh.find_best(dataset, model, "crossval")
    accuracy = np.mean(scores)
    accuracy_best = record[5] if record is not None else 0.0
    acc_best_std = record[11] if record is not None else 0.0
    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"* Accuracy Best .....: {accuracy_best:6.4f}±{acc_best_std:6.4f}")
    print(f"* Difference ........: {accuracy_best - 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()
hours, rem = divmod(stop - start, 3600)
minutes, seconds = divmod(rem, 60)
print(f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s")
if sql:
    sql_output.close()
dbh.close()