148 lines
3.9 KiB
Python
148 lines
3.9 KiB
Python
import simpful
|
|
from data_filters import *
|
|
import pandas as pd
|
|
'''
|
|
def kategoryzuj_strzaly(ilosc_strzalow):
|
|
FS = FuzzySystem()
|
|
TLV = AutoTriangle(3, terms=['mało', 'średnio', 'dużo'], universe_of_discourse=[0, 25])
|
|
FS.add_linguistic_variable("strzaly", TLV)
|
|
|
|
shots1 = TriangleFuzzySet(0, 0, 6, term='mało')
|
|
shots2 = TriangleFuzzySet(6, 12, 18, term='średnio')
|
|
shots3 = TriangleFuzzySet(12, 25, 25, term='dużo')
|
|
FS.add_linguistic_variable("bilans", LinguisticVariable([shots1, shots2, shots3], universe_of_discourse=[0, 25]))
|
|
|
|
FS.add_rules(["IF strzaly IS mało THEN bilans IS mało",
|
|
"IF strzaly IS średnio THEN bilans IS średnio",
|
|
"IF strzaly IS dużo THEN bilans IS dużo"])
|
|
|
|
FS.set_variable("strzaly", ilosc_strzalow)
|
|
bilans = FS.inference()
|
|
if bilans['bilans'] >= 12:
|
|
return 2
|
|
elif bilans['bilans'] <= 6:
|
|
return 0
|
|
else:
|
|
return 1
|
|
|
|
|
|
def kategorie_strzalow(druzyna, sezon, data, df):
|
|
ostatnie_spotkania = last5Matches(sezon, druzyna, data, df)
|
|
|
|
shots = []
|
|
for index, spotkanie in ostatnie_spotkania.iterrows():
|
|
if spotkanie['home_team'] == druzyna:
|
|
ilosc_strzalow = spotkanie['home_shots']
|
|
else:
|
|
ilosc_strzalow = spotkanie['away_shots']
|
|
|
|
kategoria = kategoryzuj_strzaly(ilosc_strzalow)
|
|
shots.append(kategoria)
|
|
ostatnie_spotkania['cat_shots'] = shots
|
|
|
|
return ostatnie_spotkania
|
|
|
|
'''
|
|
|
|
def categorize_shots(shots):
|
|
if shots >= 12:
|
|
return 2
|
|
elif shots <= 6:
|
|
return 0
|
|
else:
|
|
return 1
|
|
|
|
def categorize_passes(pass_count):
|
|
if pass_count < 400:
|
|
return 0 #słabo
|
|
elif 400 <= pass_count <= 500:
|
|
return 1 #średnio
|
|
else:
|
|
return 2 #dużo
|
|
|
|
def categorize_possesion(shots):
|
|
if shots >= 56:
|
|
return 2
|
|
elif shots <= 40:
|
|
return 0
|
|
else:
|
|
return 1
|
|
|
|
def categorize_points(data, row, teamHome):
|
|
if teamHome:
|
|
data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
|
|
points = calculatePoints(data_5,row['home_team'])
|
|
else:
|
|
data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
|
|
points = calculatePoints(data_5,row['away_team'])
|
|
if points <=1:
|
|
return 0
|
|
elif points >=2:
|
|
return 2
|
|
else:
|
|
return 1
|
|
|
|
|
|
def get_points_home(data):
|
|
points = []
|
|
for index, row in data.iterrows():
|
|
points.append(categorize_points(data, row, True))
|
|
return points
|
|
|
|
|
|
|
|
|
|
def get_points_away(data):
|
|
points = []
|
|
for index, row in data.iterrows():
|
|
points.append(categorize_points(data, row, False))
|
|
return points
|
|
|
|
|
|
|
|
def categorize_diff(data, row, teamHome):
|
|
if teamHome:
|
|
data_5 = last5Matches(row['season'], row['home_team'], row['date'], data)
|
|
diff = calculateGoalDifference(data_5,row['home_team'])
|
|
else:
|
|
data_5 = last5Matches(row['season'], row['away_team'], row['date'], data)
|
|
diff = calculateGoalDifference(data_5,row['away_team'])
|
|
if diff <=0:
|
|
return 0
|
|
else:
|
|
return 1
|
|
|
|
|
|
def get_diff_home(data):
|
|
points = []
|
|
for index, row in data.iterrows():
|
|
points.append(categorize_diff(data, row, True))
|
|
return points
|
|
|
|
|
|
|
|
|
|
def get_diff_away(data):
|
|
points = []
|
|
for index, row in data.iterrows():
|
|
points.append(categorize_diff(data, row, False))
|
|
return points
|
|
|
|
|
|
|
|
|
|
def add_column(data_frame, transform_function, new_column, existing_column=None):
|
|
if existing_column != None:
|
|
new_column_values = data_frame[existing_column].apply(transform_function)
|
|
data_frame[new_column] = new_column_values
|
|
else:
|
|
new_column_values = transform_function
|
|
data_frame[new_column] = new_column_values
|
|
return data_frame
|
|
|
|
def get_result_list(df, home_team):
|
|
results = []
|
|
for score in df['result_full']:
|
|
results.append(getResult(score,home_team))
|
|
return results
|