122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
import pandas as pd
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from fuzzy import *
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def generateTrainingData(dataframe):
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columns = ['season','date','home_team','away_team','result_full','home_passes','away_passes',
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'home_possession','away_possession','home_shots','away_shots']
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return dataframe[columns]
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def generateFuzzyLogicData(dataframe):
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columns = ['season','date','home_team','away_team','result_full','c_home_form_5m','c_away_form_5m',#,'c_home_passes','c_away_passes',
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# 'c_home_possession','c_away_possession','c_home_shots','c_away_shots',
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'c_home_diff_5m', 'c_away_diff_5m', 'c_home_aggression_5m',
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'c_away_aggression_5m', 'c_home_aggression_season', 'c_away_aggression_season',
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'c_home_form_season','c_away_form_season',
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'c_home_diff_season', 'c_away_diff_season']
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return dataframe[columns]
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def last5Matches(season, teamA, data, df):
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# Wybierz rekordy dla danej pary drużyn i sezonu
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subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
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# Filtruj dane, aby zawierały te przed daną datą
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before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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# Posortuj wg daty w odwrotnej kolejności
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before_given_date = before_given_date.sort_values(by='date', ascending=False)
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# Wybierz 5 ostatnich przed daną datą
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last_before_date = before_given_date.head(5)
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return last_before_date
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def last5MatchesBtwTeams(teamA, teamB, data, df):
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subset = df[(((df['home_team'] == teamA) | (df['away_team'] == teamA)) & ((df['home_team'] == teamB) | (df['away_team'] == teamB)))]
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before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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before_given_date = before_given_date.sort_values(by='date', ascending=False)
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last_before_date = before_given_date.head(5)
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return last_before_date
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def seasonMatches(season, teamA, data, df):
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# Wybierz rekordy dla danej pary drużyn i sezonu
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subset = df[((df['season'] == season) & ((df['home_team'] == teamA) | (df['away_team'] == teamA)))]
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# Filtruj dane, aby zawierały te przed daną datą
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before_given_date = subset[pd.to_datetime(subset['date']) < pd.to_datetime(data)]
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# Posortuj wg daty w odwrotnej kolejności
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before_given_date = before_given_date.sort_values(by='date', ascending=False)
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return before_given_date
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def getResult(score,teamHome):
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x,y = score.split('-')
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x = int(x)
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y = int(y)
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if (x > y and teamHome == True) or (x < y and teamHome == False):
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return "win"
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elif x == y:
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return "draw"
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else:
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return "loss"
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def calculateAggression(matches, team):
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aggression = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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yellow_cards = row['home_yellow_cards']
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red_cards = row['home_red_cards']
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else:
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yellow_cards = row['away_yellow_cards']
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red_cards = row['away_red_cards']
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aggression_result = calculateFuzzyAggression(yellow_cards, red_cards)
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#print(aggression_result['aggression'])
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aggression = aggression + aggression_result['aggression']
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if matches.shape[0] != 0:
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aggression_avg = aggression / matches.shape[0]
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else:
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aggression_avg = 0
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return aggression_avg
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def calculatePoints(matches, team):
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points = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x = getResult(row['result_full'], teamHome)
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#print(x)
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if x == "win":
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points = points + 3
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elif x == "draw":
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points = points + 1
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if matches.shape[0] != 0:
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points_avg = points / matches.shape[0]
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else:
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points_avg = 0
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return points_avg
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def calculateGoalDifference(matches, team):
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goal_diff = 0
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for index, row in matches.iterrows():
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if team == row['home_team']:
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teamHome = True
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else:
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teamHome = False
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x,y = row['result_full'].split('-')
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x = int(x)
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y = int(y)
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if teamHome:
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goal_diff = goal_diff + (x-y)
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else:
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goal_diff = goal_diff + (y-x)
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return goal_diff
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