Add fuzzylogic aggression metric
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Halal37
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651b3562da
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@ -1,6 +1,7 @@
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import pandas as pd
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from simpful import *
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FS = FuzzySystem()
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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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@ -10,10 +11,61 @@ def generateTrainingData(dataframe):
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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_form_season','c_away_form_season',
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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 calculateFuzzyAggression(yellow_cards, red_cards):
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FS.set_crisp_output_value("low", 0.0)
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FS.set_crisp_output_value("average", 0.5)
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FS.set_crisp_output_value("high", 1.0)
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Yellow_cards1 = TriangleFuzzySet(0, 2, 3, term="low")
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Yellow_cards2 = TriangleFuzzySet(2, 3, 4, term="average")
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Yellow_cards3 = TriangleFuzzySet(3, 4, 4, term="high")
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FS.add_linguistic_variable("yellow_cards", LinguisticVariable([Yellow_cards1, Yellow_cards2, Yellow_cards3], universe_of_discourse=[0, 10]))
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Red_cards1 = TriangleFuzzySet(0, 0, 1, term="low")
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Red_cards2 = TriangleFuzzySet(0, 1, 2, term="average")
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Red_cards3 = TriangleFuzzySet(1, 2, 2, term="high")
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FS.add_linguistic_variable("red_cards", LinguisticVariable([Red_cards1, Red_cards2, Red_cards3], universe_of_discourse=[0, 4]))
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# Pass_domination1 = TriangleFuzzySet(2,2,6, term="low")
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# Pass_domination2 = TriangleFuzzySet(3,5,7, term="average")
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# Pass_domination3 = TriangleFuzzySet(4,8,8, term="high")
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# FS.add_linguistic_variable("passes_domination", LinguisticVariable([Pass_domination1, Pass_domination2, Pass_domination3], universe_of_discourse=[0,10]))
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FS.add_rules([
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"IF (yellow_cards IS low) AND (red_cards IS low) THEN (aggression IS low)",
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"IF (yellow_cards IS high) AND (red_cards IS high) THEN (aggression IS high)",
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"IF (yellow_cards IS average) AND (red_cards IS average) THEN (aggression IS average)",
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"IF (yellow_cards IS low) AND (red_cards IS high) THEN (aggression IS high)",
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"IF (yellow_cards IS high ) AND (red_cards IS low) THEN (aggression IS high)",
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"IF (yellow_cards IS average) AND (red_cards IS high) THEN (aggression IS high)",
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"IF (yellow_cards IS high) AND (red_cards IS average) THEN (aggression IS high)",
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"IF (yellow_cards IS low) AND (red_cards IS average) THEN (aggression IS low)",
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"IF (yellow_cards IS average) AND (red_cards IS low) THEN (aggression IS average)"
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])
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FS.set_variable("yellow_cards", yellow_cards)
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FS.set_variable("red_cards", red_cards)
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aggression = FS.inference()
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return aggression
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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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@ -53,7 +105,24 @@ def getResult(score,teamHome):
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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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7
main.py
7
main.py
@ -23,7 +23,6 @@ from sklearn.metrics import classification_report
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if __name__ == "__main__":
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df = pd.read_csv('df_full_premierleague.csv')
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result = last5Matches('10/11', 'Stoke City', '2010-10-02', df)
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#print(result.to_markdown())
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#print(result)
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@ -50,13 +49,17 @@ if __name__ == "__main__":
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df = add_column(df, get_method(df, True, categorize_diff, last5Matches), "c_home_diff_5m")#categorize_diff
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df = add_column(df, get_method(df, False, categorize_diff,last5Matches), "c_away_diff_5m")
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df = add_column(df, get_method(df, True, categorize_aggression, last5Matches), "c_home_aggression_5m")#categorize_diff
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df = add_column(df, get_method(df, False, categorize_aggression,last5Matches), "c_away_aggression_5m")
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df = add_column(df, get_method(df, True, categorize_points, seasonMatches), "c_home_form_season")
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df = add_column(df, get_method(df, False, categorize_points, seasonMatches), "c_away_form_season")
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df = add_column(df, get_method(df, True, categorize_diff, seasonMatches), "c_home_diff_season")#categorize_diff
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df = add_column(df, get_method(df, False, categorize_diff,seasonMatches), "c_away_diff_season")
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df = add_column(df, get_method(df, True, categorize_aggression, seasonMatches), "c_home_aggression_season")#categorize_diff
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df = add_column(df, get_method(df, False, categorize_aggression,seasonMatches), "c_away_aggression_season")
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df = generateFuzzyLogicData(df)
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label_encoder = LabelEncoder()
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13
rules.py
13
rules.py
@ -190,7 +190,18 @@ def categorize_diff(data, row, teamHome, matches_type):
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else:
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return 1
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def categorize_aggression(data, row, teamHome, matches_type):
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if teamHome:
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data_5 = matches_type(row['season'], row['home_team'], row['date'], data)
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diff = calculateAggression(data_5,row['home_team'])
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else:
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data_5 = matches_type(row['season'], row['away_team'], row['date'], data)
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diff = calculateAggression(data_5,row['away_team'])
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return diff
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# if diff <=0:
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# return 0
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# else:
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# return 1
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def get_diff_home(data):
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points = []
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for index, row in data.iterrows():
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