forked from s452662/SystemyRozmyte
157 lines
5.4 KiB
Python
157 lines
5.4 KiB
Python
import pandas as pd
|
|
from fuzzy import *
|
|
|
|
|
|
def save_to_csv(filename, dataframe):
|
|
dataframe.to_csv(filename, mode='a', index=False, header=not pd.DataFrame().append(dataframe).empty)
|
|
|
|
def split_to_parts(dataframe, part_size):
|
|
for i in range(0, len(dataframe), part_size):
|
|
yield dataframe.iloc[i:i + part_size]
|
|
|
|
def przetwarzaj_co_50_rekordow(plik_wejsciowy, plik_wyjsciowy):
|
|
dataframe_wejsciowe = pd.read_csv(plik_wejsciowy)
|
|
|
|
|
|
def generateTrainingData(dataframe):
|
|
columns = ['season','date','home_team','away_team','result_full','home_passes','away_passes',
|
|
'home_possession','away_possession','home_shots','away_shots']
|
|
return dataframe[columns]
|
|
|
|
|
|
def generateFuzzyLogicData(dataframe):
|
|
columns = ['season','date','home_team','away_team','result_full','c_home_form_5m','c_away_form_5m',#,'c_home_passes','c_away_passes',
|
|
# 'c_home_possession','c_away_possession','c_home_shots','c_away_shots',
|
|
'c_home_diff_5m', 'c_away_diff_5m',"c_home_form_5s",
|
|
'c_away_form_5s','c_home_diff_5s','c_away_diff_5s'
|
|
, 'c_home_aggression_5m',
|
|
'c_away_aggression_5m', 'c_away_shots_5m','c_away_shots_5m',
|
|
'c_away_shots_5btw', 'c_away_shots_5btw', 'c_away_defence_5m',
|
|
'c_away_defence_5m', 'c_away_defence_5btw', 'c_away_defence_5btw',
|
|
'c_home_passing_5m', 'c_away_passing_5m', 'c_home_passing_5btw',
|
|
'c_away_passing_5btw', 'c_away_aggression_5btw', 'c_away_aggression_5btw'
|
|
|
|
|
|
#'c_home_aggression_season', 'c_away_aggression_season',
|
|
# 'c_home_form_season','c_away_form_season',
|
|
# 'c_home_diff_season', 'c_away_diff_season'
|
|
]
|
|
return dataframe[columns]
|
|
|
|
|
|
def last5Matches(season, teamA, data, df):
|
|
|
|
subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
|
|
|
|
|
|
before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
|
|
|
|
|
|
before_given_date = before_given_date.sort_values(by='date', ascending=False)
|
|
|
|
|
|
last_before_date = before_given_date.head(5)
|
|
|
|
return last_before_date, "_5m"
|
|
|
|
def last5MatchesBtwTeams(teamA, teamB, data, df):
|
|
subset = df[(((df['home_team'] == teamA) | (df['away_team'] == teamA)) & ((df['home_team'] == teamB) | (df['away_team'] == teamB)))]
|
|
before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
|
|
before_given_date = before_given_date.sort_values(by='date', ascending=False)
|
|
last_before_date = before_given_date.head(5)
|
|
|
|
return last_before_date, "_5btw"
|
|
|
|
def seasonMatches(season, teamA, data, df):
|
|
# Wybierz rekordy dla danej pary drużyn i sezonu
|
|
subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
|
|
|
|
# Filtruj dane, aby zawierały te przed daną datą
|
|
before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
|
|
|
|
# Posortuj wg daty w odwrotnej kolejności
|
|
before_given_date = before_given_date.sort_values(by='date', ascending=False)
|
|
|
|
return before_given_date, "_s"
|
|
|
|
def getResult(score,teamHome):
|
|
x,y = score.split('-')
|
|
x = int(x)
|
|
y = int(y)
|
|
|
|
if (x > y and teamHome == True) or (x < y and teamHome == False):
|
|
return "win"
|
|
elif x == y:
|
|
return "draw"
|
|
else:
|
|
return "loss"
|
|
|
|
# def calculateAggression(matches, team):
|
|
# aggression = 0
|
|
# for index, row in matches.iterrows():
|
|
# if team == row['home_team']:
|
|
# yellow_cards = row['home_yellow_cards']
|
|
# red_cards = row['home_red_cards']
|
|
# else:
|
|
# yellow_cards = row['away_yellow_cards']
|
|
# red_cards = row['away_red_cards']
|
|
# aggression_result = calculateFuzzyAggression(yellow_cards, red_cards)
|
|
# #print(aggression_result['aggression'])
|
|
# aggression = aggression + aggression_result['aggression']
|
|
# if matches.shape[0] != 0:
|
|
# aggression_avg = aggression / matches.shape[0]
|
|
# else:
|
|
# aggression_avg = 0
|
|
# return aggression_avg
|
|
|
|
def calculatePoints(matches, team):
|
|
points = 0
|
|
for index, row in matches.iterrows():
|
|
if team == row['home_team']:
|
|
teamHome = True
|
|
else:
|
|
teamHome = False
|
|
x = getResult(row['result_full'], teamHome)
|
|
#print(x)
|
|
if x == "win":
|
|
points = points + 3
|
|
elif x == "draw":
|
|
points = points + 1
|
|
if matches.shape[0] != 0:
|
|
points_avg = points / matches.shape[0]
|
|
else:
|
|
points_avg = 0
|
|
return points_avg
|
|
|
|
|
|
def calculateGoalDifference(matches, team):
|
|
goal_diff = 0
|
|
for index, row in matches.iterrows():
|
|
if team == row['home_team']:
|
|
teamHome = True
|
|
else:
|
|
teamHome = False
|
|
x,y = row['result_full'].split('-')
|
|
x = int(x)
|
|
y = int(y)
|
|
if teamHome:
|
|
goal_diff = goal_diff + (x-y)
|
|
else:
|
|
goal_diff = goal_diff + (y-x)
|
|
return goal_diff
|
|
|
|
def calculateColumn(matches, team, column_name):
|
|
result = 0
|
|
for index, row in matches.iterrows():
|
|
if team == row['home_team']:
|
|
column = row[column_name]
|
|
else:
|
|
column = row[column_name]
|
|
result = result + column
|
|
if matches.shape[0] != 0:
|
|
result_avg = result / matches.shape[0]
|
|
else:
|
|
result_avg = 0
|
|
return result_avg
|
|
|