stree_datasets/analysis_mysql.py

import argparse
from typing import Tuple
from experimentation.Sets import Datasets
from experimentation.Utils import TextColor
from experimentation.Database import MySQL

report_csv = "report.csv"
models_tree = [
    "stree",
    "wodt",
    "j48svm",
    "oc1",
    "cart",
    "baseRaF",
    "baseRoF",
    "baseRRoF",
]
models_ensemble = ["odte", "adaBoost", "bagging", "TBRaF", "TBRoF", "TBRRoF"]
title = "Best model results"
lengths = (30, 9, 11, 11, 11, 11, 11, 11, 11, 11)


def parse_arguments() -> Tuple[str, str, str, bool, bool]:
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "-e",
        "--experiment",
        type=str,
        choices=["gridsearch", "crossval", "any"],
        required=False,
        default="gridsearch",
    )
    ap.add_argument(
        "-m",
        "--model",
        type=str,
        choices=["tree", "ensemble"],
        required=False,
        default="tree",
    )
    ap.add_argument(
        "-c",
        "--csv-output",
        type=bool,
        required=False,
        default=False,
    )
    args = ap.parse_args()
    return (args.experiment, args.model, args.csv_output)


def report_header_content(title, experiment, model_type):
    length = sum(lengths) + len(lengths) - 1
    output = "\n" + "*" * length + "\n"
    titles_length = len(title) + len(experiment) + len(model_type) + 21
    num = (length - titles_length) // 2 - 3
    num2 = length - titles_length - 7 - 2 * num
    output += (
        "*"
        + " " * num
        + f"{title} - Experiment: {experiment} - Models: {model_type}"
        + " " * (num + num2)
        + "*\n"
    )
    output += "*" * length + "\n\n"
    lines = ""
    for item, data in enumerate(fields):
        output += f"{fields[item]:{lengths[item]}} "
        lines += "=" * lengths[item] + " "
    output += f"\n{lines}"
    return output


def report_header(title, experiment, model_type):
    print(
        TextColor.HEADER
        + report_header_content(title, experiment, model_type)
        + TextColor.ENDC
    )


def report_line(line):
    output = f"{line['dataset']:{lengths[0] + 5}s} "
    data = models.copy()
    data.insert(0, "reference")
    for key, model in enumerate(data):
        output += f"{line[model]:{lengths[key + 1]}s} "
    return output


def report_footer(agg):
    print(
        TextColor.GREEN
        + f"we have better results {agg['better']['items']:2d} times"
    )
    print(
        TextColor.RED
        + f"we have worse  results {agg['worse']['items']:2d} times"
    )
    color = TextColor.LINE1
    for item in models:
        print(
            color + f"{item:10s} used {agg[item]['items']:2d} times ", end=""
        )
        print(
            color + f"better {agg[item]['better']:2d} times ",
            end="",
        )
        print(color + f"worse {agg[item]['worse']:2d} times ")
        color = (
            TextColor.LINE2 if color == TextColor.LINE1 else TextColor.LINE1
        )


(experiment, model_type, csv_output) = parse_arguments()
dbh = MySQL()
database = dbh.get_connection()
dt = Datasets(False, False, "tanveer")
fields = ("Dataset", "Reference")
models = models_tree if model_type == "tree" else models_ensemble
for item in models:
    fields += (f"{item}",)
report_header(title, experiment, model_type)
color = TextColor.LINE1
agg = {}
for item in [
    "better",
    "worse",
] + models:
    agg[item] = {}
    agg[item]["items"] = 0
    agg[item]["better"] = 0
    agg[item]["worse"] = 0
if csv_output:
    f = open(report_csv, "w")
    print("dataset, classifier, accuracy", file=f)
for dataset in dt:
    find_one = False
    # Look for max accuracy for any given dataset
    line = {"dataset": color + dataset[0]}
    record = dbh.find_best(dataset[0], models, experiment)
    max_accuracy = 0.0 if record is None else record[5]
    for model in models:
        record = dbh.find_best(dataset[0], model, experiment)
        if record is None:
            line[model] = color + "-" * 9 + "  "
        else:
            reference = record[13]
            accuracy = record[5]
            find_one = True
            agg[model]["items"] += 1
            if accuracy > reference:
                sign = "+"
                agg["better"]["items"] += 1
                agg[model]["better"] += 1
            else:
                sign = "-"
                agg["worse"]["items"] += 1
                agg[model]["worse"] += 1
            item = f"{accuracy:9.7} {sign}"
            line["reference"] = f"{reference:9.7}"
            line[model] = (
                TextColor.GREEN + TextColor.BOLD + item + TextColor.ENDC
                if accuracy == max_accuracy
                else color + item
            )
            if csv_output:
                print(f"{dataset[0]}, {model}, {accuracy}", file=f)
    if not find_one:
        print(TextColor.FAIL + f"*No results found for {dataset[0]}")
    else:
        color = (
            TextColor.LINE2 if color == TextColor.LINE1 else TextColor.LINE1
        )
        print(report_line(line))
report_footer(agg)
if csv_output:
    f.close()
    print(f"{report_csv} file generated")
dbh.close()