import os
from operator import itemgetter
import math
import json
import abc
import shutil
import subprocess
import xlsxwriter
from Experiments import Datasets, BestResults
from Utils import Folders, Files, Symbols, BEST_ACCURACY_STREE, TextColor


class BaseReport(abc.ABC):
    def __init__(self, file_name, best_file=False):
        self.file_name = file_name
        if not os.path.isfile(file_name):
            if not os.path.isfile(os.path.join(Folders.results, file_name)):
                raise FileNotFoundError(f"{file_name} does not exists!")
            else:
                file_name = os.path.join(Folders.results, file_name)
        with open(file_name) as f:
            self.data = json.load(f)
        self.best_acc_file = best_file
        self.lines = self.data if best_file else self.data["results"]

    def _get_accuracy(self, item):
        return self.data[item][0] if self.best_acc_file else item["score"]

    def report(self):
        self.header()
        accuracy_total = 0.0
        for result in self.lines:
            self.print_line(result)
            accuracy_total += self._get_accuracy(result)
        self.footer(accuracy_total)

    def _load_best_results(self, score, model):
        best = BestResults(score, model, Datasets())
        self.best_results = best.load({})

    def _compute_status(self, dataset, accuracy: float):
        best = self.best_results[dataset][0]
        status = " "
        if accuracy == best:
            status = Symbols.equal_best
        elif accuracy > best:
            status = Symbols.better_best
        if status != " ":
            if status not in self._compare_totals:
                self._compare_totals[status] = 1
            else:
                self._compare_totals[status] += 1
        return status

    @staticmethod
    def _status_meaning(status):
        meaning = {
            Symbols.equal_best: "Equal to best",
            Symbols.better_best: "Better than best",
        }
        return meaning[status]

    @abc.abstractmethod
    def header(self) -> None:
        pass

    @abc.abstractmethod
    def print_line(self, result) -> None:
        pass

    @abc.abstractmethod
    def footer(self, accuracy: float) -> None:
        pass


class Report(BaseReport):
    header_lengths = [30, 5, 3, 3, 7, 7, 7, 15, 16, 15]
    header_cols = [
        "Dataset",
        "Samp",
        "Var",
        "Cls",
        "Nodes",
        "Leaves",
        "Depth",
        "Score",
        "Time",
        "Hyperparameters",
    ]

    def __init__(self, file_name: str, compare: bool = False):
        super().__init__(file_name)
        self.nline = 0
        self.compare = compare

    def header_line(self, text: str) -> None:
        print(TextColor.LINE1, end="")
        length = sum(self.header_lengths) + len(self.header_lengths) - 3
        if text == "*":
            print("*" * (length + 2))
        else:
            print(f"*{text:{length}s}*")

    def print_line(self, result) -> None:
        self.nline += 1
        text_color = (
            TextColor.LINE1 if self.nline % 2 == 0 else TextColor.LINE2
        )
        print(text_color, end="")
        hl = self.header_lengths
        i = 0
        print(f"{result['dataset']:{hl[i]}s} ", end="")
        i += 1
        print(f"{result['samples']:{hl[i]},d} ", end="")
        i += 1
        print(f"{result['features']:{hl[i]}d} ", end="")
        i += 1
        print(f"{result['classes']:{hl[i]}d} ", end="")
        i += 1
        print(f"{result['nodes']:{hl[i]}.2f} ", end="")
        i += 1
        print(f"{result['leaves']:{hl[i]}.2f} ", end="")
        i += 1
        print(f"{result['depth']:{hl[i]}.2f} ", end="")
        i += 1
        if self.compare:
            status = self._compute_status(result["dataset"], result["score"])
        else:
            status = " "
        print(
            f"{result['score']:8.6f}±{result['score_std']:6.4f}{status}",
            end="",
        )
        i += 1
        print(
            f"{result['time']:9.6f}±{result['time_std']:6.4f} ",
            end="",
        )
        i += 1
        print(f"{str(result['hyperparameters']):{hl[i]}s} ")

    def header(self) -> None:
        if self.compare:
            self._load_best_results(
                self.data["score_name"], self.data["model"]
            )
            self._compare_totals = {}
        self.header_line("*")
        self.header_line(
            f" Report {self.data['model']} ver. {self.data['version']}"
            f" with {self.data['folds']} Folds "
            f"cross validation and {len(self.data['seeds'])} random seeds. "
            f"{self.data['date']} {self.data['time']}"
        )
        self.header_line(f" {self.data['title']}")
        self.header_line(
            f" Random seeds: {self.data['seeds']} Stratified: "
            f"{self.data['stratified']}"
        )
        hours = self.data["duration"] / 3600
        self.header_line(
            f" Execution took {self.data['duration']:7.2f} seconds, "
            f" {hours:5.2f} hours, on {self.data['platform']}"
        )
        self.header_line(f" Score is {self.data['score_name']}")
        self.header_line("*")
        print("")
        line_col = ""
        for field, underscore in zip(self.header_cols, self.header_lengths):
            print(f"{field:{underscore}s} ", end="")
            line_col += "=" * underscore + " "
        print(f"\n{line_col}")

    def footer(self, accuracy: float) -> None:
        self.header_line("*")
        if self.compare:
            for key, value in self._compare_totals.items():
                self.header_line(
                    f" {key} {self._status_meaning(key)} .....: {value:2d}"
                )
        self.header_line(
            f" Accuracy compared to stree_default (liblinear-ovr) .: "
            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
        )
        self.header_line("*")


class ReportBest(BaseReport):
    header_lengths = [30, 8, 76, 45]
    header_cols = [
        "Dataset",
        "Score",
        "File/Message",
        "Hyperparameters",
    ]

    def __init__(self, score, model, best, grid):
        name = (
            Files.best_results(score, model)
            if best
            else Files.grid_output(score, model)
        )
        self.best = best
        self.grid = grid
        file_name = os.path.join(Folders.results, name)
        super().__init__(file_name, best_file=True)
        self.compare = False
        self.score_name = score
        self.model = model

    def header_line(self, text: str) -> None:
        length = sum(self.header_lengths) + len(self.header_lengths) - 3
        if text == "*":
            print("*" * (length + 2))
        else:
            print(f"*{text:{length}s}*")

    def print_line(self, result):
        hl = self.header_lengths
        print(f"{result:{hl[0]}s} ", end="")
        print(
            f"{self.data[result][0]:8.6f} ",
            end="",
        )
        print(
            f"{self.data[result][2]:{hl[2]}s} ",
            end="",
        )
        print(f"{str(self.data[result][1]):{hl[1]}s} ")

    def header(self):
        self.header_line("*")
        kind = "Best" if self.best else "Grid"
        self.header_line(
            f" Report {kind} {self.score_name} Scores with {self.model} "
            "in any platform"
        )
        self.header_line("*")
        print("")
        line_col = ""
        for field, underscore in zip(self.header_cols, self.header_lengths):
            print(f"{field:{underscore}s} ", end="")
            line_col += "=" * underscore + " "
        print(f"\n{line_col}")

    def footer(self, accuracy):
        self.header_line("*")
        if self.compare:
            for key, value in self._compare_totals.items():
                self.header_line(
                    f" {key} {self._status_meaning(key)} .....: {value:2d}"
                )
        self.header_line(
            f" Scores compared to stree_default accuracy (liblinear-ovr) .: "
            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
        )
        self.header_line("*")


class Excel(BaseReport):
    row = 6
    # alternate lines colors
    color1 = "#DCE6F1"
    color2 = "#FDE9D9"
    color3 = "#B1A0C7"

    def __init__(self, file_name, compare=False, book=None):
        super().__init__(file_name)
        self.compare = compare
        if self.compare:
            self._load_best_results(
                self.data["score_name"], self.data["model"]
            )
            self._compare_totals = {}
        if book is None:
            self.excel_file_name = self.file_name.replace(".json", ".xlsx")
            self.book = xlsxwriter.Workbook(self.excel_file_name)
            self.set_book_properties()
            self.close = True
        else:
            self.book = book
            self.close = False
        self.sheet = self.book.add_worksheet(self.data["model"])
        self.max_hyper_width = 0
        self.col_hyperparams = 0

    @staticmethod
    def set_properties(book, title):
        book.set_properties(
            {
                "title": title,
                "subject": "Machine learning results",
                "author": "Ricardo Montañana Gómez",
                "manager": "Dr. J. A. Gámez, Dr. J. M. Puerta",
                "company": "UCLM",
                "comments": "Created with Python and XlsxWriter",
            }
        )

    def set_book_properties(self):
        self.set_properties(self.book, self.get_title())

    def get_title(self):
        return (
            f" Report {self.data['model']} ver. {self.data['version']}"
            f" with {self.data['folds']} Folds "
            f"cross validation and {len(self.data['seeds'])} random seeds. "
            f"{self.data['date']} {self.data['time']}"
        )

    def get_file_name(self):
        return self.excel_file_name

    def header(self):
        merge_format = self.book.add_format(
            {
                "border": 1,
                "bold": 1,
                "align": "center",
                "valign": "vcenter",
                "font_size": 18,
                "bg_color": self.color3,
            }
        )
        merge_format_subheader = self.book.add_format(
            {
                "border": 1,
                "bold": 1,
                "align": "center",
                "valign": "vcenter",
                "font_size": 16,
                "bg_color": self.color1,
            }
        )
        merge_format_subheader_left = self.book.add_format(
            {
                "border": 1,
                "bold": 1,
                "align": "left",
                "valign": "vcenter",
                "font_size": 12,
                "bg_color": self.color1,
            }
        )
        header_text = self.get_title()
        self.sheet.merge_range(0, 0, 0, 11, header_text, merge_format)
        self.sheet.merge_range(
            1, 0, 1, 11, f" {self.data['title']}", merge_format_subheader
        )
        self.sheet.merge_range(
            2,
            0,
            3,
            0,
            f" Score is {self.data['score_name']}",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            2,
            1,
            3,
            3,
            " Execution time",
            merge_format_subheader,
        )
        hours = self.data["duration"] / 3600
        self.sheet.merge_range(
            2,
            4,
            2,
            5,
            f"{self.data['duration']:7,.2f} s",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            3,
            4,
            3,
            5,
            f" {hours:5.2f} h",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            2,
            6,
            3,
            6,
            " ",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            2,
            7,
            3,
            7,
            "Platform",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            2,
            8,
            3,
            8,
            f"{self.data['platform']}",
            merge_format_subheader,
        )
        self.sheet.merge_range(
            2,
            9,
            2,
            11,
            f"Random seeds: {self.data['seeds']}",
            merge_format_subheader_left,
        )
        self.sheet.merge_range(
            3,
            9,
            3,
            11,
            f"Stratified: {self.data['stratified']}",
            merge_format_subheader_left,
        )
        header_cols = [
            ("Dataset", 30),
            ("Samples", 10),
            ("Features", 7),
            ("Classes", 7),
            ("Nodes", 7),
            ("Leaves", 7),
            ("Depth", 7),
            ("Score", 12),
            ("Score Std.", 12),
            ("Time", 12),
            ("Time Std.", 12),
            ("Hyperparameters", 50),
        ]
        if self.compare:
            header_cols.insert(8, ("Stat", 3))
        bold = self.book.add_format(
            {
                "bold": True,
                "font_size": 14,
                "bg_color": self.color3,
                "border": 1,
            }
        )
        i = 0
        for item, length in header_cols:
            self.sheet.write(5, i, item, bold)
            self.sheet.set_column(i, i, length)
            i += 1

    def print_line(self, result):
        size_n = 14
        decimal = self.book.add_format(
            {"num_format": "0.000000", "font_size": size_n, "border": 1}
        )
        integer = self.book.add_format(
            {"num_format": "#,###", "font_size": size_n, "border": 1}
        )
        normal = self.book.add_format({"font_size": size_n, "border": 1})
        col = 0
        if self.row % 2 == 0:
            normal.set_bg_color(self.color1)
            decimal.set_bg_color(self.color1)
            integer.set_bg_color(self.color1)
        else:
            normal.set_bg_color(self.color2)
            decimal.set_bg_color(self.color2)
            integer.set_bg_color(self.color2)
        self.sheet.write(self.row, col, result["dataset"], normal)
        self.sheet.write(self.row, col + 1, result["samples"], integer)
        self.sheet.write(self.row, col + 2, result["features"], normal)
        self.sheet.write(self.row, col + 3, result["classes"], normal)
        self.sheet.write(self.row, col + 4, result["nodes"], normal)
        self.sheet.write(self.row, col + 5, result["leaves"], normal)
        self.sheet.write(self.row, col + 6, result["depth"], normal)
        self.sheet.write(self.row, col + 7, result["score"], decimal)
        if self.compare:
            status = self._compute_status(result["dataset"], result["score"])
            self.sheet.write(self.row, col + 8, status, normal)
            col = 9
        else:
            col = 8
        self.sheet.write(self.row, col, result["score_std"], decimal)
        self.sheet.write(self.row, col + 1, result["time"], decimal)
        self.sheet.write(self.row, col + 2, result["time_std"], decimal)
        self.sheet.write(
            self.row, col + 3, str(result["hyperparameters"]), normal
        )
        self.col_hyperparams = col + 3
        self.max_hyper_width = max(
            self.max_hyper_width, len(str(result["hyperparameters"]))
        )
        self.row += 1

    def footer(self, accuracy):
        if self.compare:
            self.row += 2
            bold = self.book.add_format({"bold": True, "font_size": 16})
            for key, total in self._compare_totals.items():
                self.sheet.write(self.row, 1, key, bold)
                self.sheet.write(self.row, 2, total, bold)
                self.sheet.write(self.row, 3, self._status_meaning(key), bold)
                self.row += 1
        message = (
            f"** Accuracy compared to stree_default (liblinear-ovr) .: "
            f"{accuracy/BEST_ACCURACY_STREE:7.4f}"
        )
        bold = self.book.add_format({"bold": True, "font_size": 14})
        # set width of the hyperparams column with the maximum width
        self.sheet.set_column(
            self.col_hyperparams,
            self.col_hyperparams,
            max(self.max_hyper_width + 1, 23),
        )
        self.sheet.write(self.row + 1, 0, message, bold)
        for c in range(self.row + 2):
            self.sheet.set_row(c, 20)
        self.sheet.set_row(0, 25)
        self.sheet.freeze_panes(6, 1)
        self.sheet.hide_gridlines()
        if self.close:
            self.book.close()


class SQL(BaseReport):
    table_name = "results"

    def header(self):
        file_name = self.file_name.replace(".json", ".sql")
        self.file = open(file_name, "w")

    def print_line(self, result):
        attributes = [
            "date",
            "time",
            "type",
            "title",
            "stratified",
            "score_name",
            "score",
            "score_std",
            "dataset",
            "classifier",
            "version",
            "norm",
            "stand",
            "time_spent",
            "time_spent_std",
            "parameters",
            "nodes",
            "leaves",
            "depth",
            "platform",
            "nfolds",
            "seeds",
        ]
        command_insert = (
            f"replace into {self.table_name} ("
            + ",".join(attributes)
            + ") values("
            + ("'%s'," * len(attributes))[:-1]
            + ");\n"
        )
        values = (
            self.data["date"],
            self.data["time"],
            "crossval",
            self.data["title"],
            "1" if self.data["stratified"] else "0",
            self.data["score_name"],
            result["score"],
            result["score_std"],
            result["dataset"],
            self.data["model"],
            self.data["version"],
            0,
            1,
            result["time"],
            result["time_std"],
            str(result["hyperparameters"]).replace("'", '"'),
            result["nodes"],
            result["leaves"],
            result["depth"],
            self.data["platform"],
            self.data["folds"],
            str(self.data["seeds"]),
        )
        self.file.write(command_insert % values)

    def footer(self, accuracy):
        self.file.close()


class Benchmark:
    def __init__(self, score):
        self._score = score
        self._results = []
        self._models = []
        self._report = {}
        self._datasets = set()

    def get_result_file_name(self):
        return os.path.join(Folders.results, Files.exreport(self._score))

    def compile_results(self):
        summary = Summary()
        summary.acquire(given_score=self._score)
        self._models = summary.get_models()
        for model in self._models:
            best = summary.best_result(
                criterion="model", value=model, score=self._score
            )
            file_name = os.path.join(Folders.results, best["file"])
            with open(file_name) as fi:
                experiment = json.load(fi)
                for result in experiment["results"]:
                    dataset = result["dataset"]
                    record = {
                        "model": model,
                        "dataset": dataset,
                        "score": result["score"],
                        "score_std": result["score_std"],
                        "file_name": file_name,
                    }
                    self._results.append(record)
                    if model not in self._report:
                        self._report[model] = {}
                    self._report[model][dataset] = record
                    self._datasets.add(dataset)
        self._datasets = sorted(self._datasets)

    def save_results(self):
        # build Files.exreport
        result_file_name = self.get_result_file_name()
        with open(result_file_name, "w") as f:
            f.write(f"classifier, dataset, {self._score}, stdev, file_name\n")
            for record in self._results:
                f.write(
                    f"{record['model']}, {record['dataset']}, "
                    f"{record['score']}, {record['score_std']}, "
                    f"{record['file_name']}\n"
                )

    def exreport(self):
        def end_message(message, file):
            length = 100
            print("*" * length)
            print(message)
            print("*" * length)
            with open(os.path.join(Folders.results, file)) as f:
                data = f.read().splitlines()
                for line in data:
                    print(line)

        # Remove previous results
        try:
            shutil.rmtree(Folders.report)
            os.remove(Files.exreport_pdf)
        except FileNotFoundError:
            pass
        except OSError as os_error:
            print("Error: %s : %s" % (Folders.report, os_error.strerror))
        # Compute Friedman & Holm Tests
        fout = open(
            os.path.join(Folders.results, Files.exreport_output(self._score)),
            "w",
        )
        ferr = open(
            os.path.join(Folders.results, Files.exreport_err(self._score)), "w"
        )
        result = subprocess.run(
            [
                "Rscript",
                os.path.join(Folders.src, Files.benchmark_r),
                self._score,
            ],
            stdout=fout,
            stderr=ferr,
        )
        fout.close()
        ferr.close()
        if result.returncode != 0:
            end_message(
                "Error computing benchmark", Files.exreport_err(self._score)
            )
        else:
            end_message("Benchmark Ok", Files.exreport_output(self._score))
        Files.open(Files.exreport_pdf)

    def report(self):
        print(f"{'Dataset':30s} ", end="")
        lines = "=" * 30 + " "
        for model in self._models:
            print(f"{model:^13s} ", end="")
            lines += "=" * 13 + " "
        print(f"\n{lines}")
        for dataset in self._datasets:
            print(f"{dataset:30s} ", end="")
            for model in self._models:
                result = self._report[model][dataset]
                print(f"{float(result['score']):.5f}±", end="")
                print(f"{float(result['score_std']):.3f} ", end="")
            print("")
        d_name = next(iter(self._datasets))
        print(f"\n{'Model':30s} {'File Name':75s} Score")
        print("=" * 30 + " " + "=" * 75 + " ========")
        for model in self._models:
            file_name = self._report[model][d_name]["file_name"]
            report = StubReport(file_name)
            report.report()
            print(f"{model:^30s} {file_name:75s} {report.score:8.5f}")

    def get_excel_file_name(self):
        return os.path.join(
            Folders.exreport, Files.exreport_excel(self._score)
        )

    def excel(self):
        book = xlsxwriter.Workbook(self.get_excel_file_name())
        Excel.set_properties(book, "Experimentation summary")
        sheet = book.add_worksheet("Benchmark")
        normal = book.add_format({"font_size": 14, "border": 1})
        decimal = book.add_format(
            {"num_format": "0.000000", "font_size": 14, "border": 1}
        )
        decimal_total = book.add_format(
            {
                "num_format": "0.000000",
                "font_size": 14,
                "border": 1,
                "bold": True,
                "bg_color": Excel.color3,
            }
        )
        two_decimal_total = book.add_format(
            {
                "num_format": "0.00",
                "font_size": 14,
                "border": 1,
                "bold": True,
                "bg_color": Excel.color3,
            }
        )
        merge_format_header = book.add_format(
            {
                "border": 1,
                "bold": 1,
                "align": "center",
                "valign": "vcenter",
                "font_size": 14,
                "bg_color": Excel.color1,
            }
        )
        merge_format = book.add_format(
            {
                "border": 1,
                "bold": 1,
                "align": "center",
                "valign": "vcenter",
                "font_size": 14,
                "bg_color": Excel.color3,
            }
        )
        merge_format_normal = book.add_format(
            {
                "border": 1,
                "valign": "vcenter",
                "font_size": 14,
            }
        )
        row = row_init = 4

        def header():
            nonlocal row
            sheet.merge_range(
                0, 0, 1, 0, "Benchmark of Models", merge_format_header
            )
            sheet.merge_range(
                0, 1, 1, 2, f"Score is {self._score}", merge_format_header
            )
            sheet.set_row(1, 20)
            # Set columns width
            sheet.set_column(0, 0, 40)
            for column in range(2 * len(self._results)):
                sheet.set_column(column + 1, column + 1, 15)
            # Set report header
            # Merge 2 rows
            sheet.merge_range(row, 0, row + 1, 0, "Dataset", merge_format)
            column = 1
            for model in self._models:
                # Merge 3 columns
                sheet.merge_range(
                    row, column, row, column + 2, model, merge_format
                )
                column += 3
            row += 1
            column = 1
            for _ in range(len(self._models)):
                sheet.write(row, column, "Score", merge_format)
                sheet.write(row, column + 1, "Stdev", merge_format)
                sheet.write(row, column + 2, "Rank", merge_format)
                column += 3

        def body():
            nonlocal row
            for dataset in self._datasets:
                row += 1
                normal = book.add_format({"font_size": 14, "border": 1})
                decimal = book.add_format(
                    {
                        "num_format": "0.000000",
                        "font_size": 14,
                        "border": 1,
                    }
                )
                if row % 2 == 0:
                    normal.set_bg_color(Excel.color1)
                    decimal.set_bg_color(Excel.color1)
                else:
                    normal.set_bg_color(Excel.color2)
                    decimal.set_bg_color(Excel.color2)
                sheet.write(row, 0, f"{dataset:30s}", normal)
                column = 1
                range_cells = ""
                for col in range(0, len(self._models) * 3, 3):
                    range_cells += chr(ord("B") + col) + str(row + 1) + ","
                range_cells = range_cells[:-1]
                for model in self._models:
                    sheet.write(
                        row,
                        column,
                        float(self._report[model][dataset]["score"]),
                        decimal,
                    )
                    column += 1
                    sheet.write(
                        row,
                        column,
                        float(self._report[model][dataset]["score_std"]),
                        decimal,
                    )
                    column += 1
                    cell_target = chr(ord("B") + column - 3) + str(row + 1)
                    sheet.write_formula(
                        row,
                        column,
                        f"=rank({cell_target},({range_cells}))",
                        normal,
                    )
                    column += 1

        def footer():
            nonlocal row
            for c in range(row_init, row + 2):
                sheet.set_row(c, 20)
            # Write totals
            row += 1
            sheet.write(row, 0, "Total", merge_format)
            for col in range(0, len(self._models) * 3, 3):
                range_metric = (
                    f"{chr(ord('B') + col )}7:{chr(ord('B') + col )}{row}"
                )
                sheet.write_formula(
                    row,
                    col + 1,
                    f"=sum({range_metric})/{BEST_ACCURACY_STREE}",
                    decimal_total,
                )
                range_rank = (
                    f"{chr(ord('B') + col + 2)}7:"
                    f"{chr(ord('B') + col + 2)}{row}"
                )
                sheet.write_formula(
                    row,
                    col + 3,
                    f"=average({range_rank})",
                    two_decimal_total,
                )
            row += 1

        def models_files():
            nonlocal row
            row += 2
            # Set report header
            # Merge 2 rows
            sheet.merge_range(row, 0, row + 1, 0, "Model", merge_format)
            sheet.merge_range(row, 1, row + 1, 5, "File", merge_format)
            sheet.merge_range(row, 6, row + 1, 6, "Score", merge_format)
            row += 1
            d_name = next(iter(self._datasets))
            for model in self._models:
                file_name = self._report[model][d_name]["file_name"]
                report = StubReport(file_name)
                report.report()
                row += 1
                sheet.write(
                    row,
                    0,
                    model,
                    normal,
                )
                sheet.merge_range(
                    row, 1, row, 5, file_name, merge_format_normal
                )
                sheet.write(
                    row,
                    6,
                    report.score,
                    decimal,
                )
                k = Excel(file_name=file_name, book=book)
                k.report()
            sheet.freeze_panes(6, 1)
            sheet.hide_gridlines()

        def exreport_output():
            file_name = os.path.join(
                Folders.results, Files.exreport_output(self._score)
            )
            sheet = book.add_worksheet("Exreport")
            normal = book.add_format(
                {
                    "font_size": 14,
                    "border": 1,
                    "font_color": "blue",
                    "font_name": "Courier",
                    "bold": True,
                }
            )
            with open(file_name) as f:
                lines = f.read().splitlines()
            row = 0
            for line in lines:
                sheet.write(row, 0, line, normal)
                row += 1

        header()
        body()
        footer()
        models_files()
        exreport_output()
        book.close()


class StubReport(BaseReport):
    def __init__(self, file_name):
        super().__init__(file_name=file_name, best_file=False)

    def print_line(self, line) -> None:
        pass

    def header(self) -> None:
        self.title = self.data["title"]
        self.duration = self.data["duration"]

    def footer(self, accuracy: float) -> None:
        self.accuracy = accuracy
        self.score = accuracy / BEST_ACCURACY_STREE


class Summary:
    def __init__(self, hidden=False) -> None:
        self.results = Files().get_all_results(hidden=hidden)
        self.data = []
        self.datasets = {}
        self.models = set()
        self.hidden = hidden

    def get_models(self):
        return sorted(self.models)

    def acquire(self, given_score="any") -> None:
        """Get all results"""
        for result in self.results:
            (
                score,
                model,
                platform,
                date,
                time,
                stratified,
            ) = Files().split_file_name(result)
            if given_score in ("any", score):
                self.models.add(model)
                report = StubReport(
                    os.path.join(
                        Folders.hidden_results
                        if self.hidden
                        else Folders.results,
                        result,
                    )
                )
                report.report()
                entry = dict(
                    score=score,
                    model=model,
                    title=report.title,
                    platform=platform,
                    date=date,
                    time=time,
                    stratified=stratified,
                    file=result,
                    metric=report.score,
                    duration=report.duration,
                )
                self.datasets[result] = report.lines
                self.data.append(entry)

    def list_results(
        self,
        score=None,
        model=None,
        input_data=None,
        sort_key="date",
        number=0,
    ) -> None:
        """Print the list of results"""
        data = self.data.copy() if input_data is None else input_data
        if score:
            data = [x for x in data if x["score"] == score]
        if model:
            data = [x for x in data if x["model"] == model]
        keys = (
            itemgetter(sort_key, "time")
            if sort_key == "date"
            else itemgetter(sort_key, "date", "time")
        )
        data = sorted(data, key=keys, reverse=True)
        if number > 0:
            data = data[:number]
        max_file = max(len(x["file"]) for x in data)
        max_title = max(len(x["title"]) for x in data)
        if self.hidden:
            color1 = TextColor.GREEN
            color2 = TextColor.YELLOW
        else:
            color1 = TextColor.LINE1
            color2 = TextColor.LINE2
        print(color1, end="")
        print(
            f"{'Date':10s} {'File':{max_file}s} {'Score':7s} {'Time(h)':7s} "
            f"{'Title':s}"
        )
        print(
            "=" * 10
            + " "
            + "=" * max_file
            + " "
            + "=" * 8
            + " "
            + "=" * 7
            + " "
            + "=" * max_title
        )
        print(
            "\n".join(
                [
                    (color2 if n % 2 == 0 else color1)
                    + f"{x['date']} {x['file']:{max_file}s} "
                    f"{x['metric']:8.5f} "
                    f"{x['duration']/3600:7.3f} "
                    f"{x['title']}"
                    for n, x in enumerate(data)
                ]
            )
        )

    def show_result(self, data: dict, title: str = "") -> None:
        def whites(n: int) -> str:
            return " " * n + "*"

        if data == {}:
            print(f"** {title} has No data **")
            return
        file_name = data["file"]
        metric = data["metric"]
        result = StubReport(os.path.join(Folders.results, file_name))
        length = 81
        print("*" * length)
        if title != "":
            print(f"*{title:^{length - 2}s}*")
            print("*" + "-" * (length - 2) + "*")
        print("*" + whites(length - 2))
        print(f"* {result.data['title']:^{length - 4}} *")
        print("*" + whites(length - 2))
        print(
            f"* Model: {result.data['model']:15s} "
            f"Ver. {result.data['version']:10s} "
            f"Score: {result.data['score_name']:10s} "
            f"Metric: {metric:10.7f}" + whites(length - 78)
        )
        print("*" + whites(length - 2))
        print(
            f"* Date : {result.data['date']:15s} Time: "
            f"{result.data['time']:18s} Time Spent: "
            f"{result.data['duration']:9,.2f} secs." + whites(length - 78)
        )
        seeds = str(result.data["seeds"])
        seeds_len = len(seeds)
        print(
            f"* Seeds: {seeds:{seeds_len}s} Platform: "
            f"{result.data['platform']:17s} " + whites(length - 79)
        )
        print(
            f"* Stratified: {str(result.data['stratified']):15s}"
            + whites(length - 30)
        )
        print(f"* {file_name:60s}" + whites(length - 63))
        print("*" + whites(length - 2))
        print("*" * length)

    def best_results(self, criterion=None, value=None, score="accuracy", n=10):
        # First filter the same score results (accuracy, f1, ...)
        haystack = [x for x in self.data if x["score"] == score]
        haystack = (
            haystack
            if criterion is None or value is None
            else [x for x in haystack if x[criterion] == value]
        )
        return (
            sorted(
                haystack,
                key=lambda x: -1.0 if math.isnan(x["metric"]) else x["metric"],
                reverse=True,
            )[:n]
            if len(haystack) > 0
            else {}
        )

    def best_result(
        self, criterion=None, value=None, score="accuracy"
    ) -> dict:
        return self.best_results(criterion, value, score)[0]

    def best_results_datasets(self, score="accuracy") -> dict:
        """Get the best results for each dataset"""
        dt = Datasets()
        best_results = {}
        for dataset in dt:
            best_results[dataset] = (1, "", "", "")
        haystack = [x for x in self.data if x["score"] == score]
        # Search for the best results for each dataset
        for entry in haystack:
            for dataset in self.datasets[entry["file"]]:
                if dataset["score"] < best_results[dataset["dataset"]][0]:
                    best_results[dataset["dataset"]] = (
                        dataset["score"],
                        dataset["hyperparameters"],
                        entry["file"],
                        entry["title"],
                    )
        return best_results

    def show_top(self, score="accuracy", n=10):
        self.list_results(
            score=score,
            input_data=self.best_results(score=score, n=n),
            sort_key="metric",
        )