SystemyRozmyte/rules.py

import simpful
from data_filters import *
import pandas as pd
'''
def kategoryzuj_strzaly(ilosc_strzalow):
    FS = FuzzySystem()
    TLV = AutoTriangle(3, terms=['mało', 'średnio', 'dużo'], universe_of_discourse=[0, 25])
    FS.add_linguistic_variable("strzaly", TLV)

    shots1 = TriangleFuzzySet(0, 0, 6, term='mało')
    shots2 = TriangleFuzzySet(6, 12, 18, term='średnio')
    shots3 = TriangleFuzzySet(12, 25, 25, term='dużo')
    FS.add_linguistic_variable("bilans", LinguisticVariable([shots1, shots2, shots3], universe_of_discourse=[0, 25]))

    FS.add_rules(["IF strzaly IS mało THEN bilans IS mało",
                    "IF strzaly IS średnio THEN bilans IS średnio",
                    "IF strzaly IS dużo THEN bilans IS dużo"])

    FS.set_variable("strzaly", ilosc_strzalow)
    bilans = FS.inference()
    if bilans['bilans'] >= 12:
        return 2
    elif bilans['bilans'] <= 6:
        return 0
    else:
        return 1


def kategorie_strzalow(druzyna, sezon, data, df):
    ostatnie_spotkania = last5Matches(sezon, druzyna, data, df)

    shots = []
    for index, spotkanie in ostatnie_spotkania.iterrows():
        if spotkanie['home_team'] == druzyna:
            ilosc_strzalow = spotkanie['home_shots']
        else:
            ilosc_strzalow = spotkanie['away_shots']

        kategoria = kategoryzuj_strzaly(ilosc_strzalow)
        shots.append(kategoria)
    ostatnie_spotkania['cat_shots'] = shots

    return ostatnie_spotkania

    '''

def categorize_shots(shots):
    if shots >= 12:
        return 2
    elif shots <= 6:
        return 0
    else:
        return 1

def categorize_passes(pass_count):
    if pass_count < 400:
        return 0  #słabo
    elif 400 <= pass_count <= 500:
        return 1  #średnio
    else:
        return 2  #dużo
        
def categorize_possesion(shots):
    if shots >= 56:
        return 2
    elif shots <= 40:
        return 0
    else:
        return 1
    
def categorize_points(data, row, teamHome):
    if teamHome:
        data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
        points = calculatePoints(data_5,row['home_team'])
    else:
        data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
        points = calculatePoints(data_5,row['away_team'])
    if points <=1:
        return 0
    elif points >=2:
        return 2
    else:
        return 1


def get_points_home(data):
    points = []
    for index, row in data.iterrows():
        points.append(categorize_points(data, row, True))
    return points




def get_points_away(data):
    points = []
    for index, row in data.iterrows():
        points.append(categorize_points(data, row, False))
    return points



def categorize_diff(data, row, teamHome):
    if teamHome:
        data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
        diff = calculateGoalDifference(data_5,row['home_team'])
    else:
        data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
        diff = calculateGoalDifference(data_5,row['away_team'])
    if diff <=0:
        return 0
    else:
        return 1


def get_diff_home(data):
    points = []
    for index, row in data.iterrows():
        points.append(categorize_diff(data, row, True))
    return points




def get_diff_away(data):
    points = []
    for index, row in data.iterrows():
        points.append(categorize_diff(data, row, False))
    return points




def add_column(data_frame, transform_function, new_column, existing_column=None):
    if existing_column != None:
        new_column_values = data_frame[existing_column].apply(transform_function)
        data_frame[new_column] = new_column_values
    else:
        new_column_values = transform_function
        data_frame[new_column] = new_column_values
    return data_frame

def get_result_list(df, home_team):
    results = []
    for score in df['result_full']:
        results.append(getResult(score,home_team))
    return results