forked from s452662/SystemyRozmyte
148 lines
3.9 KiB
Python
148 lines
3.9 KiB
Python
import simpful
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from data_filters import *
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import pandas as pd
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'''
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def kategoryzuj_strzaly(ilosc_strzalow):
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FS = FuzzySystem()
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TLV = AutoTriangle(3, terms=['mało', 'średnio', 'dużo'], universe_of_discourse=[0, 25])
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FS.add_linguistic_variable("strzaly", TLV)
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shots1 = TriangleFuzzySet(0, 0, 6, term='mało')
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shots2 = TriangleFuzzySet(6, 12, 18, term='średnio')
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shots3 = TriangleFuzzySet(12, 25, 25, term='dużo')
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FS.add_linguistic_variable("bilans", LinguisticVariable([shots1, shots2, shots3], universe_of_discourse=[0, 25]))
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FS.add_rules(["IF strzaly IS mało THEN bilans IS mało",
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"IF strzaly IS średnio THEN bilans IS średnio",
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"IF strzaly IS dużo THEN bilans IS dużo"])
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FS.set_variable("strzaly", ilosc_strzalow)
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bilans = FS.inference()
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if bilans['bilans'] >= 12:
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return 2
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elif bilans['bilans'] <= 6:
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return 0
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else:
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return 1
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def kategorie_strzalow(druzyna, sezon, data, df):
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ostatnie_spotkania = last5Matches(sezon, druzyna, data, df)
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shots = []
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for index, spotkanie in ostatnie_spotkania.iterrows():
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if spotkanie['home_team'] == druzyna:
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ilosc_strzalow = spotkanie['home_shots']
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else:
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ilosc_strzalow = spotkanie['away_shots']
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kategoria = kategoryzuj_strzaly(ilosc_strzalow)
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shots.append(kategoria)
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ostatnie_spotkania['cat_shots'] = shots
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return ostatnie_spotkania
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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):
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if teamHome:
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data_5 = last5Matches(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 = last5Matches(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_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):
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if teamHome:
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data_5 = last5Matches(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 = last5Matches(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 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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