237 lines
7.5 KiB
Python
237 lines
7.5 KiB
Python
import simpful
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from data_filters import *
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import pandas as pd
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FS = FuzzySystem()
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# Dominacja OK
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# Jakość strzałów - Witek
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# Agresesywnosc (zolte + czerwone kartki) - Wojtek
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# odbiory i wslizgi (xDef) - Michał, ekspert od xDef
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# statystyki z calego sezonu - Wojtek OK
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# 5 ostatnich spotkan miedzy druzynami - Witek
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def categorize_fuzzy_passes(passes,possession):
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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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Pass1 = TriangleFuzzySet(300,300,500, term="low")
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Pass2 = TriangleFuzzySet(300,450,600, term="average")
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Pass3 = TriangleFuzzySet(400,600,600, term="high")
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FS.add_linguistic_variable("passes", LinguisticVariable([Pass1, Pass2, Pass3], universe_of_discourse=[0,1000]))
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Poss1 = TriangleFuzzySet(30,30,45, term="low")
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Poss2 = TriangleFuzzySet(40,50,60, term="average")
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Poss3 = TriangleFuzzySet(55,70,70, term="high")
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FS.add_linguistic_variable("possession", LinguisticVariable([Poss1, Poss2, Poss3], universe_of_discourse=[0,100]))
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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 (passes IS low) AND (possession IS low) THEN (pass_domination IS low)",
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"IF (passes IS high) AND (possession IS high) THEN (pass_domination IS high)",
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"IF (passes IS average) AND (possession IS average) THEN (pass_domination IS average)",
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"IF (passes IS low) AND (possession IS high) THEN (pass_domination IS average)",
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"IF (passes IS high ) AND (possession IS low) THEN (pass_domination IS average)",
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"IF (passes IS average) AND (possession IS high) THEN (pass_domination IS high)",
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"IF (passes IS high) AND (possession IS average) THEN (pass_domination IS high)",
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"IF (passes IS low) AND (possession IS average) THEN (pass_domination IS low)",
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"IF (passes IS average) AND (possession IS low) THEN (pass_domination IS average)"
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])
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FS.set_variable("passes", passes)
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FS.set_variable("possession", possession)
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pass_domination = FS.inference()
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return pass_domination
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def categorize_fuzzy_shots(shots_overall, shots_on_target):
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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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Shot_ov1 = TriangleFuzzySet(0,0,5, term="low") #pozmieniać przedziały (nakładają się)
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Shot_ov2 = TriangleFuzzySet(5,10,15, term="medium")
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Shot_ov3 = TriangleFuzzySet(15,25,25, term="high")
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FS.add_linguistic_variable("shots_overall", LinguisticVariable([Shot_ov1, Shot_ov2, Shot_ov3], universe_of_discourse=[0,35]))
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Shot_ont1 = TriangleFuzzySet(0,0,2, term="low")
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Shot_ont2 = TriangleFuzzySet(2,4,6, term="medium")
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Shot_ont3 = TriangleFuzzySet(6,10,10, term="high")
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FS.add_linguistic_variable("shots_on_target", LinguisticVariable([Shot_ont1, Shot_ont2, Shot_ont3], universe_of_discourse=[0,15]))
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#Qual_of_shots1 = TriangleFuzzySet(0,0,0.3, term="low")
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#Qual_of_shots2 = TriangleFuzzySet(0.2,0.5,0.8, term="medium")
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#Qual_of_shots3 = TriangleFuzzySet(0.7,1,1, term="high")
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#FS.add_linguistic_variable("expected_goals", LinguisticVariable([Qual_of_shots1, Qual_of_shots2, Qual_of_shots3], universe_of_discourse=[0,1]))
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FS.add_rules([
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"IF (shots_overall IS low) AND (shots_on_target IS low) THEN (quality_of_shots IS low)",
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"IF (shots_overall IS high) AND (shots_on_target IS high) THEN (quality_of_shots IS high)",
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"IF (shots_overall IS average) AND (shots_on_target IS average) THEN (quality_of_shots IS average)",
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"IF (shots_overall IS low) AND (shots_on_target IS high) THEN (quality_of_shots IS high)",
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"IF (shots_overall IS high ) AND (shots_on_target IS low) THEN (quality_of_shots IS low)",
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"IF (shots_overall IS average) AND (shots_on_target IS high) THEN (quality_of_shots IS high)",
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"IF (shots_overall IS high) AND (shots_on_target IS average) THEN (quality_of_shots IS average)",
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"IF (shots_overall IS low) AND (shots_on_target IS average) THEN (quality_of_shots IS average)",
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"IF (shots_overall IS average) AND (shots_on_target IS low) THEN (quality_of_shots IS low)"
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])
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def categorize_shots(shots):
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if shots >= 12:
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return 2
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elif shots <= 6:
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return 0
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else:
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return 1
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def categorize_passes(pass_count):
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if pass_count < 400:
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return 0 #słabo
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elif 400 <= pass_count <= 500:
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return 1 #średnio
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else:
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return 2 #dużo
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def categorize_possesion(shots):
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if shots >= 56:
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return 2
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elif shots <= 40:
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return 0
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else:
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return 1
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def categorize_points(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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points = calculatePoints(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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points = calculatePoints(data_5,row['away_team'])
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if points <=1:
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return 0
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elif points >=2:
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return 2
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else:
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return 1
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def get_method(data, home_away, method, matches_type):
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values = []
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for index, row in data.iterrows():
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values.append(method(data, row, home_away, matches_type))
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return values
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def get_points_home(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_points(data, row, True))
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return points
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def get_points_away(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_points(data, row, False))
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return points
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def categorize_diff(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 = calculateGoalDifference(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 = calculateGoalDifference(data_5,row['away_team'])
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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 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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points.append(categorize_diff(data, row, True))
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return points
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def get_diff_away(data):
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points = []
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for index, row in data.iterrows():
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points.append(categorize_diff(data, row, False))
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return points
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def add_column(data_frame, transform_function, new_column, existing_column=None):
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if existing_column != None:
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new_column_values = data_frame[existing_column].apply(transform_function)
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data_frame[new_column] = new_column_values
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else:
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new_column_values = transform_function
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data_frame[new_column] = new_column_values
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return data_frame
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def get_result_list(df, home_team):
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results = []
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for score in df['result_full']:
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results.append(getResult(score,home_team))
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return results
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