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main.py
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main.py
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import numpy
import pandas as pd
from tkinter import *
import sys
import math
from pandas.core.frame import DataFrame # Only used for e^x and the factorial
class Team:
def __init__(self, name:str):
self.name = name
# ---- [H/A]X[F/A] -----
# H: Home, A: Away | F: For, A: Against
# ----------------------
self.HM = 0 # Matches
self.AM = 0
self.HP = 0
self.AP = 0
self.HGF = 0 # Goals
self.HGA = 0
self.AGF = 0
self.AGA = 0
self.HSF = 0 # Shots
self.HSA = 0
self.ASF = 0
self.ASA = 0
self.HSTF = 0 # Shots on Target
self.HSTA = 0
self.ASTF = 0
self.ASTA = 0
self.HCF = 0 # Corners
self.HCA = 0
self.ACF = 0
self.ACA = 0
self.HFF = 0 # Faults
self.HFA = 0
self.AFF = 0
self.AFA = 0
self.HYF = 0 # Yellow cards
self.HYA = 0
self.AYF = 0
self.AYA = 0
self.HRF = 0 # Red cars
self.HRA = 0
self.ARF = 0
self.ARA = 0
self.elog = 1.0
self.LG = 0.0
# Convert to dict.
# Return -> {'name': self.name, 'M': self.M,...}
def as_dict(self):
return self.__dict__
# Dynamic parameters
def updateParameters(self):
self.M = self.HM + self.AM
self.Pts = self.HP + self.AP
self.GF = self.HGF + self.AGF
self.GA = self.HGA + self.AGA
self.SF = self.HSF + self.ASF
self.SA = self.HSA + self.ASA
self.STF = self.HSTF + self.ASTF
self.STA = self.HSTA + self.ASTA
self.CF = self.HCF + self.ACF
self.CA = self.HCA + self.ACA
self.FF = self.HFF + self.AFF
self.FA = self.HFA + self.AFA
self.YF = self.HYF + self.AYF
self.YA = self.HYA + self.AYA
self.RF = self.HRF + self.ARF
self.RA = self.HRA + self.ARA
print ("WELCOME! This is my football data analizer.")
print ("--------------------")
print ("This code has been fully developed by enriqueloz88. Despite being an open source project, its use for commercial purposes without my express authorization is not allowed.")
print ("--------------------\n")
print("First, select your league: ")
print(" 1-England - Premier League")
print(" 2-Spain - La Liga")
print(" 3-Italy - Serie A")
print(" 4-France - Ligue 1")
print(" 5-Germany - Bundesliga")
print(" q-Quit the program\n")
league = input("Type an option from the ones above and hit enter: ")
if league=="1":
league = "E0.csv"
elif league=="2":
league = "SP1.csv"
elif league=="3":
league = "I1.csv"
elif league=="4":
league = "F1.csv"
elif league=="5":
league = "D1.csv"
print("\nOkay, now you have to choose the years of the competition, in a XXYY format. For example, if you type 2021, the selected league will be the season 2020-2021, if you type 1213 will be 2012-2012 and if you type 0203 will be 2002-2003")
year = input("Type a year and hit enter: ")
while(len(year) != 4):
print("It seems that you have not entered the year correctly, remember that this must be a 4-digit number")
year = input("Type a year and hit enter: ")
url = 'https://www.football-data.co.uk/mmz4281/' + year + '/' + league
data = pd.read_csv(url)
'''
data = pd.read_csv('input2.csv')
data2 = pd.read_csv('db/2020/SP1.csv')
data = data.merge(data2, how='outer')
data = data.merge(data3, how='outer')
'''
# Delete non-important attributes
eliminate = [0,1,2] # Division, date and time
if 'Referee' in data.columns:
data = data.drop(['Referee'], axis=1)
for x in range(23, len(data.columns)):
eliminate.append(x)
#data.drop(['B365CA', 'B365CH', 'B365CD', '', '', '', ''], axis=1)
data = data.drop(data.columns[eliminate], axis=1) # data.columns is zero-based pd.Index
#Matches
data['P1'] = 0
data['P2'] = 0
data['HM'] = 0
data['AM'] = 0
# For acting as a local -> THX
# Against acting as a local -> THXA
# In favor acting as a visitor -> TAX
# Against acting as a visitor -> TAXA
# Total (home + away) in favor of the team that acts in the current match at home -> TX1
# Total (home + away) against the team that acts at home in the current match -> TXA1
# Total (home + away) in favor of the team that acts in the current match as away -> TX2
# Total (home + away) against the team that acts in the current match as away -> TXA2
# New columns to add to the dataframe. Will have the total stadistics off all teams until
# the day of the match of the row. In the first match will be 0
newAtr = ['THG','THGA','TAG','TAGA','TG1','TGA1','TG2','TGA2','THS','THSA','TAS',
'TASA','TS1','TSA1','TS2','TSA2','THST','THSTA','TAST','TASTA','TST1','TSTA1','TST2',
'TSTA2','THC','THCA','TAC','TACA','TC1','TCA1','TC2','TCA2','THF','THFA','TAF','TAFA',
'TF1','TFA1','TF2','TFA2','THY','THYA','TAY','TAYA','TY1','TYA1','TY2','TYA2','THR',
'THRA','TAR','TARA','TR1','TRA1','TR2','TRA2']
for atr in newAtr:
data[atr] = 0
#Other ratings
data['LG1'] = 0.0 #Last games results. data=data*0.5+POINTS
data['LG2'] = 0.0 #Last games results. data=data*0.5+POINTS
teams = pd.unique(data['HomeTeam'])
teams.sort()
teamsList = []
for team in teams:
t = Team(team) # Create new team object
t.updateParameters()
for x in range(len(data.index)):
if team==data['HomeTeam'].values[x]:
data['P1'].values[x] = t.M
data['HM'].values[x] = t.HM
data['THG'].values[x] = t.HGF
data['THGA'].values[x] = t.HGA
data['TG1'].values[x] = t.GF
data['TGA1'].values[x] = t.GA
data['THS'].values[x] = t.HSF
data['THSA'].values[x] = t.HSA
data['TS1'].values[x] = t.SF
data['TSA1'].values[x] = t.SA
data['THST'].values[x] = t.HSTF
data['THSTA'].values[x] =t.HSTA
data['TST1'].values[x] = t.STF
data['TSTA1'].values[x] =t.STA
data['THC'].values[x] = t.HCF
data['THCA'].values[x] = t.HCA
data['TC1'].values[x] = t.CF
data['TCA1'].values[x] = t.CA
data['THF'].values[x] = t.HFF
data['THFA'].values[x] = t.HFA
data['TF1'].values[x] = t.FF
data['TFA1'].values[x] = t.FA
data['THY'].values[x] = t.HYF
data['THYA'].values[x] = t.HYA
data['TY1'].values[x] = t.YF
data['TYA1'].values[x] = t.YA
data['THR'].values[x] = t.HRF
data['THRA'].values[x] = t.HRA
data['TR1'].values[x] = t.RF
data['TRA1'].values[x] = t.RA
data['LG1'].values[x] = t.LG
t.HM = t.HM + 1
t.HGF = t.HGF + data['FTHG'].values[x]
t.HGA = t.HGA + data['FTAG'].values[x]
t.HSF = t.HSF + data['HS'].values[x]
t.HSA = t.HSA + data['AS'].values[x]
t.HSTF = t.HSTF + data['HST'].values[x]
t.HSTA = t.HSTA + data['AST'].values[x]
t.HCF = t.HCF + data['HC'].values[x]
t.HCA = t.HCA + data['AC'].values[x]
t.HFF = t.HFF + data['HF'].values[x]
t.HFA = t.HFA + data['AF'].values[x]
t.HYF = t.HYF + data['HY'].values[x]
t.HYA = t.HYA + data['AY'].values[x]
t.HRF = t.HRF + data['HR'].values[x]
t.HRA = t.HRA + data['AR'].values[x]
p = -3
if data['FTR'].values[x] == 'H':
t.HP = t.HP + 3
p = 3
elif data['FTR'].values[x] == 'D':
t.HP = t.HP + 1
p = 1
t.LG = t.LG*0.8 + p
t.updateParameters()
if team==data['AwayTeam'].values[x]:
data['P2'].values[x] = t.M
data['AM'].values[x] = t.AM
data['TAG'].values[x] = t.AGF
data['TAGA'].values[x] =t.AGA
data['TG2'].values[x] = t.GF
data['TGA2'].values[x] =t.GA
data['TAS'].values[x] = t.ASF
data['TASA'].values[x] =t.ASA
data['TS2'].values[x] = t.SF
data['TSA2'].values[x] =t.SA
data['TAST'].values[x] =t.ASTF
data['TASTA'].values[x]=t.ASTA
data['TST2'].values[x] =t.STF
data['TSTA2'].values[x]=t.STA
data['TAC'].values[x] = t.ACF
data['TACA'].values[x] =t.ACA
data['TC2'].values[x] = t.CF
data['TCA2'].values[x] =t.CA
data['TAF'].values[x] = t.AFF
data['TAFA'].values[x] =t.AFA
data['TF2'].values[x] = t.FF
data['TFA2'].values[x] =t.FA
data['TAY'].values[x] = t.AYF
data['TAYA'].values[x] =t.AYA
data['TY2'].values[x] = t.YF
data['TYA2'].values[x] =t.YA
data['TAR'].values[x] = t.ARF
data['TARA'].values[x] =t.ARA
data['TR2'].values[x] = t.RF
data['TRA2'].values[x] =t.RA
data['LG2'].values[x] = t.LG
t.AM = t.AM + 1
t.AGF = t.AGF + data['FTAG'].values[x]
t.AGA = t.AGA + data['FTHG'].values[x]
t.ASF = t.ASF + data['AS'].values[x]
t.ASA = t.ASA + data['HS'].values[x]
t.ASTF = t.ASTF + data['AST'].values[x]
t.ASTA = t.ASTA + data['HST'].values[x]
t.ACF = t.ACF + data['AC'].values[x]
t.ACA = t.ACA + data['HC'].values[x]
t.AFF = t.AFF + data['AF'].values[x]
t.AFA = t.AFA + data['HF'].values[x]
t.AYF = t.AYF + data['AY'].values[x]
t.AYA = t.AYA + data['HY'].values[x]
t.ARF = t.ARF + data['AR'].values[x]
t.ARA = t.ARA + data['HR'].values[x]
p = -3
if data['FTR'].values[x] == 'A':
t.AP = t.AP + 3
p = 3
elif data['FTR'].values[x] == 'D':
t.AP = t.AP + 1
p = 1
t.LG = t.LG*0.8 + p
t.updateParameters()
teamsList.append(t)
''' #TeamList to df
df = pd.DataFrame([x.as_dict() for x in teamsList])
print(df)
'''
'''
print ("\n---------------------------------------------------------")
print ("Team")
print ("Goles anotados en casa:" + str(data['THG'].values[len(data)-1]))
'''
writer = pd.ExcelWriter("trainingData/output.xlsx", engine='xlsxwriter')
data.to_excel(writer, index = False, header=True, sheet_name='Sheet1')
data.to_csv("trainingData/output.csv", index = False, header=True)
workbook = writer.book
worksheet = writer.sheets['Sheet1']
worksheet.set_column(2, 80, 5)
worksheet.set_column(0, 1, 12)
writer.save()
#Get the means
for atr in newAtr:
if atr[0]=='T' and atr[1]=='H':
data[atr] = data[atr]/data['HM']
if atr[0]=='T' and atr[1]=='A':
data[atr] = data[atr]/data['AM']
if atr[-1]=='1':
data[atr] = data[atr]/data['P1']
if atr[-1]=='2':
data[atr] = data[atr]/data['P2']
data = data.fillna(0) # Fill NaN with zeros. Case of divide a number by zero
original_data = data
writer = pd.ExcelWriter("trainingData/outputMeans.xlsx", engine='xlsxwriter')
data.to_excel(writer, index = False, header=True, sheet_name='Sheet1')
workbook = writer.book
worksheet = writer.sheets['Sheet1']
worksheet.set_column(2, 80, 5)
worksheet.set_column(0, 1, 12)
writer.save()
'''---------------------------------------------
---------PRINT DIFFERENT STADISTICS-------------
------------------------------------------------'''
def printTable(data, teamsList):
teamsList.sort(key=lambda x: x.Pts, reverse=True)
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format("Pos","Name"," Pts", " M", " GF", " GA", " SF", " SA", " CF", " CA", " FF", " FA"))
print("---------------------------------------------------------------------------------")
for index,team in enumerate(teamsList, start=1):
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format(index,team.name,team.Pts,team.M,team.GF,team.GA,team.SF,team.SA,team.CF,team.CA,team.FF,team.FA))
print("")
def printHomeTable(data, teamList):
teamsList.sort(key=lambda x: x.HP, reverse=True)
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format("Pos","Name"," Pts", " M", " GF", " GA", " SF", " SA", " CF", " CA", " FF", " FA"))
print("---------------------------------------------------------------------------------")
for index,team in enumerate(teamsList, start=1):
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format(index,team.name,team.HP,team.HM,team.HGF,team.HGA,team.HSF,team.HSA,team.HCF,team.HCA,team.HFF,team.HFA))
print("")
def printAwayTable(data, teamList):
teamsList.sort(key=lambda x: x.AP, reverse=True)
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format("Pos","Name"," Pts", " M", " GF", " GA", " SF", " SA", " CF", " CA", " FF", " FA"))
print("---------------------------------------------------------------------------------")
for index,team in enumerate(teamsList, start=1):
print("{0:3} | {1:18}|{2:4} | {3:2} {4:3} {5:3} {6:3} {7:3} {8:3} {9:3} {10:3} {11:3}".format(index,team.name,team.AP,team.AM,team.AGF,team.AGA,team.ASF,team.ASA,team.ACF,team.ACA,team.AFF,team.AFA))
print("")
def printAllTables(data, teamList):
print("HOME TABLE:\n")
printHomeTable(data, teamsList)
print("AWAY TABLE:\n")
printAwayTable(data, teamsList)
print("TOTAL TABLE:\n")
printTable(data, teamsList)
'''---------------------------------------------
---------MAKING THE DIFFERENT MODELS------------
------------------------------------------------'''
noReprGames = 91 # Number of no-representative games. First N games will be deleted
test_size = 0.06 # % of matches used to size the accuracy of the model
# Function to delete the first games of the training files as they are not very representative
def deleteHeadToTrain(data:DataFrame, rows:int):
data = data.drop(range(0,rows), axis=0)
for x in range(len(data.index)):
if data['HM'].values[x]<3 or data['P1'].values[x]<4 or data['AM'].values[x]<3 or data['P2'].values[x]<4:
data.drop(x, axis=0)
return data
# Function to generate a new row (a new match) at the end of the test file, to predict the user desire.
# Return a new df with only one row, that will be concatenated with the test df
def newRowToTest(homeTeam:Team, awayTeam:Team):
newDf = pd.DataFrame(
{
'THG': [homeTeam.HGF/homeTeam.HM],
'THGA': [homeTeam.HGA/homeTeam.HM],
'TG1': [homeTeam.GF/homeTeam.M],
'TGA1': [homeTeam.GA/homeTeam.M],
'TAG': [awayTeam.AGF/awayTeam.AM],
'TAGA': [awayTeam.AGA/awayTeam.AM],
'TG2': [awayTeam.GF/awayTeam.M],
'TGA2': [awayTeam.GA/awayTeam.M],
'THS': [homeTeam.HSF/homeTeam.HM],
'THSA': [homeTeam.HSA/homeTeam.HM],
'TS1': [homeTeam.SF/homeTeam.M],
'TSA1': [homeTeam.SA/homeTeam.M],
'TAS': [awayTeam.ASF/awayTeam.AM],
'TASA': [awayTeam.ASA/awayTeam.AM],
'TS2': [awayTeam.SF/awayTeam.M],
'TSA2': [awayTeam.SA/awayTeam.M],
'THST': [homeTeam.HSTF/homeTeam.HM],
'THSTA': [homeTeam.HSTA/homeTeam.HM],
'TST1': [homeTeam.STF/homeTeam.M],
'TSTA1': [homeTeam.STA/homeTeam.M],
'TAST': [awayTeam.ASTF/awayTeam.AM],
'TASTA': [awayTeam.ASTA/awayTeam.AM],
'TST2': [awayTeam.STF/awayTeam.M],
'TSTA2': [awayTeam.STA/awayTeam.M],
'THC': [homeTeam.HCF/homeTeam.HM],
'THCA': [homeTeam.HCA/homeTeam.HM],
'TC1': [homeTeam.CF/homeTeam.M],
'TCA1': [homeTeam.CA/homeTeam.M],
'TAC': [awayTeam.ACF/awayTeam.AM],
'TACA': [awayTeam.ACA/awayTeam.AM],
'TC2': [awayTeam.CF/awayTeam.M],
'TCA2': [awayTeam.CA/awayTeam.M],
'THF': [homeTeam.HFF/homeTeam.HM],
'THFA': [homeTeam.HFA/homeTeam.HM],
'TF1': [homeTeam.FF/homeTeam.M],
'TFA1': [homeTeam.FA/homeTeam.M],
'TAF': [awayTeam.AFF/awayTeam.AM],
'TAFA': [awayTeam.AFA/awayTeam.AM],
'TF2': [awayTeam.FF/awayTeam.M],
'TFA2': [awayTeam.FA/awayTeam.M],
'THY': [homeTeam.HYF/homeTeam.HM],
'THYA': [homeTeam.HYA/homeTeam.HM],
'TY1': [homeTeam.YF/homeTeam.M],
'TYA1': [homeTeam.YA/homeTeam.M],
'TAY': [awayTeam.AYF/awayTeam.AM],
'TAYA': [awayTeam.AYA/awayTeam.AM],
'TY2': [awayTeam.YF/awayTeam.M],
'TYA2': [awayTeam.YA/awayTeam.M],
'THR': [homeTeam.HRF/homeTeam.HM],
'THRA': [homeTeam.HRA/homeTeam.HM],
'TR1': [homeTeam.RF/homeTeam.M],
'TRA1': [homeTeam.RA/homeTeam.M],
'TAR': [awayTeam.ARF/awayTeam.AM],
'TARA': [awayTeam.ARA/awayTeam.AM],
'TR2': [awayTeam.RF/awayTeam.M],
'TRA2': [awayTeam.RA/awayTeam.M],
'LG1': [homeTeam.LG],
'LG2': [awayTeam.LG]
},
index=[9999],
)
newDf = newDf.fillna(0) # Fill NaN with zeros. Case of divide a number by zero
return newDf
def printTestPredictions(X_test, prediction_test, prediction_test2, Y_test, Y2_test):
#Creating new dataframe to print the predictions
matches_prediction = []
i=0
for row in X_test.index:
if row != 9999:
match_data = []
match_data.append(original_data['HomeTeam'].values[row])
match_data.append(original_data['AwayTeam'].values[row])
match_data.append(prediction_test[i])
match_data.append(prediction_test2[i])
match_data.append(Y_test[i])
match_data.append(Y2_test[i])
match_data = tuple(match_data)
matches_prediction.append(match_data)
i=i+1
print("\nPredictions done to test the model: ")
df_prediction = pd.DataFrame(matches_prediction, columns=['HomeTeam', 'AwayTeam', 'PHG', 'PAG','RHG', 'RAG'])
print (df_prediction)
print("\nHere are the matches used to measure the performance of the model. This is the test file, used in most ML models")
def printBestAttr(X, model):
print("\nBest Attributes:")
feature_list = list(X.columns)
features_imp = pd.Series(model.feature_importances_, index=feature_list).sort_values(ascending=False)
print(features_imp.head())
print("\nWorst Attributes:")
print(features_imp.tail())
# Function that determinate the varibles to predict
# Return list with [Y,Y2,X]. Y and Y2 will be the two variable to predict, dependent on X
def preModel():
data = deleteHeadToTrain(original_data, noReprGames)
#Variables to predict. Dependent variables
Y = data['FTHG'].values
Y = Y.astype('int')
Y2 = data['FTAG'].values
Y2 = Y2.astype('int')
#Indepiendent variables. Can not have data of the actual match
X = data[['THG','TAG','THGA','TAGA','TG1','TG2','TGA1','TGA2','THS','TAS','THSA','TASA','TS1','TS2','TSA1','TSA2',
'THST','TAST','THSTA','TASTA','TST1','TST2','TSTA1','TSTA2','THC','TAC','THCA','TACA','TC1','TC2','TCA1','TCA2',
'THF','TAF','THFA','TAFA','TF1','TF2','TFA1','TFA2','THY','TAY','THYA','TAYA','TY1','TY2','TYA1','TYA2',
'THR','TAR','THRA','TARA','TR1','TR2','TRA1','TRA2','LG1','LG2']]
#X = data[['THG','TAG','THGA','TAGA','TG1','TG2','TGA1','TGA2','ELOG1', 'ELOG2','LG1','LG2']]
return [Y,Y2,X]
# Return the probability of X according to the Poisson Distribution
def poissonDistr(mean:float, X:int):
result = math.exp((-1)*mean) * (mean**X)
result = result / math.factorial(X)
return result
#This is not AI. Thanks to https://www.sbo.net/strategy/football-prediction-model-poisson-distribution/
def teamStrenghtsModel():
total_matches = 0
total_home = 0
total_away = 0
#Select the teams to predict and get the means
for index,team in enumerate(teamsList, start=1):
print(index, team.name)
total_matches = total_matches + team.M
total_home = total_home + team.HGF
total_away = total_away + team.AGF
mean_home = total_home/total_matches
mean_away = total_away/total_matches
homeTeamToPredict = int(input("Select the home team number: "))
awayTeamToPredict = int(input("Select the away team number: "))
homeTeam = teamsList[homeTeamToPredict-1]
awayTeam = teamsList[awayTeamToPredict-1]
#Get the strenghts
homeTeamAtt = (homeTeam.HGF/team.HM) / mean_home
homeTeamDef = (homeTeam.HGA/team.HM) / mean_away
awayTeamAtt = (awayTeam.AGF/team.AM) / mean_away
awayTeamDef = (awayTeam.AGA/team.AM) / mean_home
#Predictions
homePredict = homeTeamAtt*awayTeamDef*mean_home
awayPredict = awayTeamAtt*homeTeamDef*mean_away
print("\nPrediction for","{0:15}".format(str(teamsList[homeTeamToPredict-1].name) + ": "), '{:.2f}'.format(homePredict))
print("Prediction for", "{0:15}".format(str(teamsList[awayTeamToPredict-1].name) + ": "), '{:.2f}'.format(awayPredict))
def randomForest():
variables = preModel()
Y = variables[0]
Y2 = variables[1]
X = variables[2]
X2 = variables[2]
print("\n ---------------------------------")
print("Making random forest.............\n")
#Split data into train and test datasets
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=20)
X2_train, X2_test, Y2_train, Y2_test = train_test_split(X2, Y2, test_size=test_size, random_state=20)
#Add match to predict
for index,team in enumerate(teamsList, start=1):
print(index, team.name)
homeTeamToPredict = int(input("Select the home team number: "))
awayTeamToPredict = int(input("Select the away team number: "))
newDf = newRowToTest(teamsList[homeTeamToPredict-1], teamsList[awayTeamToPredict-1])
X_test = pd.concat([X_test, newDf])
X2_test = pd.concat([X2_test, newDf])
#Get the model
from sklearn.ensemble import RandomForestRegressor
model = RandomForestRegressor(n_estimators=180, min_samples_leaf=2, min_samples_split=3, random_state=20)
model.fit(X_train, Y_train)
prediction_test = model.predict(X_test) #Results of the predictions in a list[]
model.fit(X2_train, Y2_train)
prediction_test2 = model.predict(X2_test)
from sklearn import metrics
# We have to do [:-1] to delete the last row that we introduced manually.
print ("\nMean sq. error for the home team->", '{:.2f}'.format(100*round(metrics.mean_squared_error(Y_test, prediction_test[:-1]),2)), "%")
#print ("Mean abs. error for the home team->", '{:.2f}'.format(100*round(metrics.mean_absolute_error(Y_test, prediction_test),2)), "%")
print ("Mean sq. error for the away team->", '{:.2f}'.format(100*round(metrics.mean_squared_error(Y2_test, prediction_test2[:-1]),2)), "%")
#print ("Mean abs. error for the away team->", '{:.2f}'.format(100*round(metrics.mean_absolute_error(Y2_test, prediction_test2),2)), "%")
print("\nPrediction for","{0:15}".format(str(teamsList[homeTeamToPredict-1].name) + ": "), '{:.2f}'.format(prediction_test[-1]))
print("Prediction for", "{0:15}".format(str(teamsList[awayTeamToPredict-1].name) + ": "), '{:.2f}'.format(prediction_test2[-1]))
sel = '0'
while sel != 'n':
print("\nDo you want to have more data of this model?")
print(" 1-Yes, show me the predictions of the test file")
print(" 2-Yes, show me the more relevant attributes of the tree")
print(" 3-Yes, show me the generated trees")
print(" n-No, quit\n")
sel = input("Type an option from the ones above and hit enter: ")
if sel=='n':
return
sel = int(sel)
if sel==1:
printTestPredictions(X_test, prediction_test, prediction_test2, Y_test, Y2_test)
if sel==2:
printBestAttr(X, model)
if sel==3:
print_decision_rules(model)
# Function to print the trees generates in randomForest
def print_decision_rules(rf):
for tree_idx, est in enumerate(rf.estimators_):
tree = est.tree_
assert tree.value.shape[1] == 1 # no support for multi-output
print('TREE: {}'.format(tree_idx))
iterator = enumerate(zip(tree.children_left, tree.children_right, tree.feature, tree.threshold, tree.value))
for node_idx, data in iterator:
left, right, feature, th, value = data
# left: index of left child (if any)
# right: index of right child (if any)
# feature: index of the feature to check
# th: the threshold to compare against
# value: values associated with classes
# for classifier, value is 0 except the index of the class to return
class_idx = numpy.argmax(value[0])
if left == -1 and right == -1:
print('{} LEAF: return class={}'.format(node_idx, class_idx))
else:
print('{} NODE: if feature[{}] < {} then next={} else next={}'.format(node_idx, feature, th, left, right))
def multilayerPerceptron():
variables = preModel()
Y = variables[0]
Y2 = variables[1]
X = variables[2]
X2 = variables[2]
print("\n ---------------------------------")
print("Making multilayer perceptron.............\n")
#Split data into train and test datasets
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size, random_state=20)
X2_train, X2_test, Y2_train, Y2_test = train_test_split(X2, Y2, test_size=test_size, random_state=20)
#Add match to predict
for index,team in enumerate(teamsList, start=1):
print(index, team.name)
homeTeamToPredict = int(input("Select the home team number: "))
awayTeamToPredict = int(input("Select the away team number: "))
newDf = newRowToTest(teamsList[homeTeamToPredict-1], teamsList[awayTeamToPredict-1])
X_test = pd.concat([X_test, newDf])
X2_test = pd.concat([X2_test, newDf])
#Get the model
from sklearn.neural_network import MLPRegressor
model = MLPRegressor(random_state=20, max_iter=5000, hidden_layer_sizes=100, activation='tanh')
model.fit(X_train, Y_train)
prediction_test = model.predict(X_test) #Results of the predictions in a list[]
model.fit(X2_train, Y2_train)
prediction_test2 = model.predict(X2_test)
from sklearn import metrics
# We have to do [:-1] to delete the last row that we introduced manually.
print ("\nMean sq. error for the home team->", '{:.2f}'.format(100*round(metrics.mean_squared_error(Y_test, prediction_test[:-1]),2)), "%")
#print ("Mean abs. error for the home team->", '{:.2f}'.format(100*round(metrics.mean_absolute_error(Y_test, prediction_test),2)), "%")
print ("Mean sq. error for the away team->", '{:.2f}'.format(100*round(metrics.mean_squared_error(Y2_test, prediction_test2[:-1]),2)), "%")
#print ("Mean abs. error for the away team->", '{:.2f}'.format(100*round(metrics.mean_absolute_error(Y2_test, prediction_test2),2)), "%")
print("\nPrediction for","{0:15}".format(str(teamsList[homeTeamToPredict-1].name) + ": "), '{:.2f}'.format(prediction_test[-1]))
print("Prediction for", "{0:15}".format(str(teamsList[awayTeamToPredict-1].name) + ": "), '{:.2f}'.format(prediction_test2[-1]))
sel = '0'
while sel != 'n':
print("\nDo you want to have more data of this model?")
print(" 1-Yes, show me the predictions of the test file")
print(" n-No, quit\n")
sel = input("Type an option from the ones above and hit enter: ")
if sel=='n':
return
sel = int(sel)
if sel==1:
printTestPredictions(X_test, prediction_test, prediction_test2, Y_test, Y2_test)
if sel==2:
pass
print("Let's start! What do you want to do?")
selContinue = 'y'
while selContinue != 'n':
print(" 1-Print league standings and statistics")
print(" 2-Get some predictions with AI")
print(" q-Quit the program\n")
sel = input("Type an option from the ones above and hit enter: ")
if sel=='q':
sys.exit()
sel = int(sel)
if sel==1:
print("What do you want me to show you?")
print(" 1-Print league clasification")
print(" 2-Print league clasification (Only home matches)")
print(" 3-Print league clasification (Only away matches)")
print(" 123-Print the 3 clasifications of above")
print(" q-Return/Quit\n")
sel2 = input("Type an option from the ones above and hit enter: ")
if sel2 != 'q':
sel2 = int(sel2)
if sel2 == 1:
printTable(original_data, teamsList)
elif sel2 == 2:
printHomeTable(original_data, teamsList)
elif sel2 == 3:
printAwayTable(original_data,teamsList)
elif sel2 == 123:
printAllTables(original_data, teamsList)
elif sel==2:
print("What model do you want to use?")
print(" 1-Random Forest")
print(" 2-Multilayer Perceptron")
print(" 3-Team Strenghts Model")
print(" q-Return/Quit\n")
sel2 = input("Type an option from the ones above and hit enter: ")
if sel2 != 'q':
sel2 = int(sel2)
if sel2 == 1:
randomForest()
elif sel2 == 2:
multilayerPerceptron()
elif sel2 == 3:
teamStrenghtsModel()
selContinue = input("Done! Any other operation (y/n): ")
if selContinue=='n':
print("\nAllright, been a pleasure!\n")
sys.exit()