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dreamteam.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Apr 20 02:58:47 2019
+
+@author: ManishChalana
+"""
+
+import numpy as np
+import collections
+import pandas as pd
+
+
+
+class Team:
+    def __init__(self, players, teams):
+        self.players = players
+        self.team1 = teams[0]
+        self.team2 = teams[1]
+        numPlayers = collections.Counter([player['team'] for player in self.players])
+        assert list(numPlayers.values())==[11,11],  "Invalid combination! Number of players in " + self.team1 + ": " + str(numPlayers[self.team1]) + "," + self.team2 + ": " + str(numPlayers[self.team2])
+        
+        
+class Dream11(Team):
+    
+    def __init__(self, players, teams):
+        super().__init__(players, teams)
+        self.teamParams = {'min':4, 'max':7}
+        self.maxPoints = 100
+        self.combinations = [[1,3,2,5], [1,3,3,4], [1,4,1,5], [1,4,2,4], [1,4,3,3], [1,5,1,4], [1,5,2,3]]
+#        self.combinationsProb = [0.14]*6 + [0.16] 
+#        self.combinationsProb = [0.1,0.2,0.05,0.2,0.3,0.05,0.1]
+        self.playerNames = [p['name'] for p in players]
+        self.playerPointMap = {p['name']:p['points'] for p in self.players}
+        self.playerTeamMap = {p['name']:p['team'] for p in self.players}
+        self.playerTypeMap = {p['name']:p['type'] for p in self.players}
+        self.playerTypeCount = collections.Counter(self.playerTypeMap.values())
+                
+    @staticmethod
+    def getSoftmaxProbabilities(points, toneDownMultiplier=1):
+        if len(points) == 0:
+            return []
+        points = [toneDownMultiplier*point for point in points]
+        denom = np.sum([np.exp(point) for point in points])
+        softMax = [np.exp(point)/denom for point in points]
+        return softMax
+    
+    
+    def sample(self, players, req): 
+        numStars = np.sum([p['star'] for p in players])
+        
+        availableStars = [p['name'] for p in players if p['star']==1]
+        availableStarsPoints = [p['points'] for p in players if p['star']==1]
+        availableStarsSoftMax = Dream11.getSoftmaxProbabilities(availableStarsPoints)
+        
+        availableNonStars = [p['name'] for p in players if not(p['star']==1)]
+        availableNonStarsPoints = [p['points'] for p in players if not(p['star']==1)]
+        availableNonStarsSoftMax = Dream11.getSoftmaxProbabilities(availableNonStarsPoints)
+    
+        if numStars >= req:
+            return ([] if len(availableStars)==0 else list(np.random.choice(availableStars, size = req, p=availableStarsSoftMax, replace=False)))
+        else:
+            return availableStars + list(np.random.choice(availableNonStars, size = req - numStars, p=availableNonStarsSoftMax, replace=False))      
+    
+    def calculateTeamVector(self, team):
+        teamVector = [player['points'] if (player['name'] in team) else 0 for player in self.players]
+        return teamVector
+
+    def checkUniqueTeamCriteria(self, sampledTeam, selectedTeams):
+        return (np.sum([set(team)==set(sampledTeam) for team in selectedTeams]) == 0)
+    
+    def checkMinPlayersTeamCriteria(self, teamDemography):
+        return (teamDemography[self.team1]>=self.teamParams['min']) and (teamDemography[self.team2]>=self.teamParams['min']) 
+    
+    def checkMaxPlayersTeamCriteria(self, teamDemography):
+        return (teamDemography[self.team1]<=self.teamParams['max']) and (teamDemography[self.team2]<=self.teamParams['max'])
+    
+    def checkMaxTeamPointsCriteria(self, sampledTeam):
+        totalPoints = np.sum([self.playerPointMap[player] for player in sampledTeam])
+        return (totalPoints <= self.maxPoints)
+        
+    def checkSimilarityIndexCriteria(self, sampledTeam, selectedTeams, selected, thresh=24):
+        
+        similarityIndexCriteria = False
+        similarityIndices = []
+        
+        if selected>0:
+            similarityIndices = [np.sqrt(np.sum(np.square(np.array(self.calculateTeamVector(sampledTeam)) - np.array(self.calculateTeamVector(team))))) for team in selectedTeams]
+            if np.min(similarityIndices) > thresh:
+                similarityIndexCriteria = True
+        else:
+            similarityIndexCriteria = True
+        return similarityIndices, similarityIndexCriteria
+    
+    def chooseCaptain(self, sampledTeam, captain):
+        [c, vc] = ['', '']
+        if captain:
+            sampledTeamSoftmax = [self.playerPointMap[name] for name in sampledTeam]
+            [c, vc] = np.random.choice(sampledTeam, size=2, replace=False, p=Dream11.getSoftmaxProbabilities(sampledTeamSoftmax, toneDownMultiplier=0.75))   
+            
+        return [c, vc]
+
+    def updateSelectedTeams(self, sampledTeam, selectedTeams, selected, captain):
+        #Finding the numbers of wks, bats, ar and bowls in the sampled team
+        teamDemography = collections.Counter([self.playerTeamMap[player] for player in sampledTeam])
+        
+        # Finding the total number of points spent in selecting the sampled team and ensuring that it is less than maximum available points
+        pointsCriteria = self.checkMaxTeamPointsCriteria(sampledTeam)
+        
+        # Ensuring that sampled team is not same as one of the previously selected teams
+        uniqueTeamCriteria = self.checkUniqueTeamCriteria(sampledTeam, selectedTeams)
+        
+        # Making sure that minimum required players are selected in sampled team from each of the two parent teams
+        minPlayersCriteria = self.checkMinPlayersTeamCriteria(teamDemography)
+        
+        # Making sure that maximum required players are selected in sampled team from each of the two parent teams
+        maxPlayersCriteria = self.checkMaxPlayersTeamCriteria(teamDemography)
+        
+        # Calculating the similarity index of the sampled team with the already selected teams to ensure dissimilarity
+        similarityIndices, similarityIndexCriteria = self.checkSimilarityIndexCriteria(sampledTeam, selectedTeams, selected)
+        
+        # Choosing a captain and vice captain
+        [c, vc] = self.chooseCaptain(sampledTeam, captain)
+        
+        if  pointsCriteria and uniqueTeamCriteria and minPlayersCriteria and maxPlayersCriteria and similarityIndexCriteria:                
+            
+            # When all criterias are fulfilled by the team, append the sampled team to list of already selected teams
+            selectedTeams.append(sampledTeam)                                
+            selected = selected + 1
+            
+            self.printTeamDetails(sampledTeam, selected, c, vc, similarityIndices)
+            
+            # Finding the attributes of the selected team
+            
+        return selectedTeams, selected
+    
+    
+    def printTeamDetails(self, sampledTeam, index, c, vc, similarityIndices):
+        
+        selectedPlayersNames = [name.upper() for name in sampledTeam]
+        selectedPlayersPoints = [[p for p in self.players if p['name']==name][0]['points'] for name in sampledTeam]
+        selectedPlayersTeams = [[p for p in self.players if p['name']==name][0]['team'].upper() for name in sampledTeam]
+        selectedPlayersTypes = [[p for p in self.players if p['name']==name][0]['type'] for name in sampledTeam]
+        selectedPlayersCVC = ['c' if name==c else 'vc' if name==vc else ' ' for name in sampledTeam ]
+        selectedPlayersTypesCounter = collections.Counter(selectedPlayersTypes)
+        selectedPlayersTeamsCounter = collections.Counter(selectedPlayersTeams)
+            
+        # Defining a dataframe containing the details of sampled team
+        teamDf = pd.DataFrame({'Player': selectedPlayersNames, 'C/VC': selectedPlayersCVC, 'Points':selectedPlayersPoints, 'Team':selectedPlayersTeams, 'Type':selectedPlayersTypes}, index=range(1,12))[['Player', 'C/VC', 'Team', 'Type', 'Points']]
+        teamDf = pd.concat([teamDf[teamDf['Type']==playType].sort_values(['Points'], ascending=False) for playType in ['wk', 'bat', 'ar', 'bowl']])
+        teamDf.index = range(1,12)
+            
+        # Printing the team on console
+        print("------------ Team", index, "------------\n")
+        print(teamDf)
+        print("\nTotal points invested:", np.sum(teamDf['Points']))
+        print("Wicket-keepers:", selectedPlayersTypesCounter['wk'], ", Batsman:", selectedPlayersTypesCounter['bat'], ", All-rounders:", selectedPlayersTypesCounter['ar'], " Bowlers:", selectedPlayersTypesCounter['bowl'])
+        print(self.team1.upper(), ":", selectedPlayersTeamsCounter[self.team1.upper()], self.team2.upper(), ":", selectedPlayersTeamsCounter[self.team2.upper()])
+        print("Similarity indices:", similarityIndices)
+        print('\n')
+        
+    
+    
+    
+    def generateTeams(self, numTeams, captain=False):
+        selectedTeams = []
+        selected = 0
+        
+        while selected < numTeams:
+            # Choose a random combination of number of wicket-keepers, batsman, all-rounders and bowlers
+            while True:
+                [numWks, numBats, numArs, numBowls] = self.combinations[np.random.choice(len(self.combinations), size=1)[0]]
+                if (numWks <= self.playerTypeCount['wk']) and (numBats <=self.playerTypeCount['bat']) and (numArs <=self.playerTypeCount['ar']) and (numBowls<=self.playerTypeCount['bowl']):
+                    break
+            
+            # Choose Keepers
+            wks = self.sample([p for p in self.players if p['type']=='wk'], numWks)
+            
+            # Choose batsman
+            bats = self.sample([p for p in self.players if p['type']=='bat'], numBats)
+            
+            # Choose all-rounders
+            ars = self.sample([p for p in self.players if p['type']=='ar'], numArs)
+            
+            # Choose bowlers
+            bowls = self.sample([p for p in self.players if p['type']=='bowl'], numBowls)
+            
+            # Chosen team
+            sampledTeam = wks + bats + ars + bowls
+            
+            # Update selected teams list based on whether the sampled team fulfills all eligibility criteria
+            selectedTeams, selected = self.updateSelectedTeams(sampledTeam, selectedTeams, selected, captain)
+                            
+            
+                
+                
+                
+                
+        
+    
+    

main.py

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+
+
+
+
+
+bat = 'bat'
+bowl = 'bowl'
+wk = 'wk'
+ar = 'ar'
+
+
+t1 = 'srh'
+t2 = 'kkr'
+numOfTeams = 8
+
+# ('player name', 'player role', 'player points in dream11 app', 'player team', 'star player i.e. identifier to denote whether must be included in the team or not (if star =1, player will be included in the team)')
+
+p1 = {'name': 'J Bairstow', 'type':wk, 'points':10 , 'team': t1, 'star':1}#
+p2 = {'name': 'D Warner', 'type':bat, 'points': 11, 'team': t1, 'star':1}#
+p3 = {'name': 'Y Pathan', 'type':bat, 'points': 8, 'team': t1, 'star':0}#
+p4 = {'name': 'M Pandey', 'type':bat, 'points': 8.5, 'team': t1, 'star':0}#
+p5 = {'name': 'V Shankar', 'type':ar, 'points': 8.5, 'team': t1, 'star':0}#
+p6 = {'name': 'S Al Hasan', 'type':ar, 'points': 8.5, 'team': t1, 'star':0}#
+p7 = {'name': 'D Hooda', 'type':bat, 'points': 8, 'team': t1, 'star':0}#
+p8 = {'name': 'Rashid Khan', 'type':bowl, 'points': 9, 'team': t1, 'star':0}#
+p9 = {'name': 'Bhuvi', 'type':bowl, 'points': 8.5, 'team': t1, 'star':0}#
+p10 = {'name': 'S Sharma', 'type':bowl, 'points': 8.5, 'team': t1, 'star':0}#
+p11 = {'name': 'S Kaul', 'type':bowl, 'points': 8.5, 'team': t1, 'star':0}#
+
+
+p12 = {'name': 'C lynn', 'type':bat, 'points': 9.5, 'team': t2, 'star':0}#
+p13 = {'name': 'N Rana', 'type':bat, 'points': 9, 'team': t2, 'star':0}#
+p14 = {'name': 'R Uthappa', 'type':bat, 'points': 9, 'team': t2, 'star':0}#
+p15 = {'name': 'D Karthik', 'type':wk, 'points': 9, 'team': t2, 'star':0}#
+p16 = {'name': 'S Gill', 'type':bat, 'points': 8, 'team': t2, 'star':0}#
+p17 = {'name': 'S Narine', 'type':ar, 'points': 9, 'team': t2, 'star':0}#
+p18 = {'name': 'A Russell', 'type':ar, 'points': 10.5, 'team': t2, 'star':1}#
+p19 = {'name': 'P Chawla', 'type':bowl, 'points': 8.5, 'team': t2, 'star':0}#
+p20 = {'name': 'K Yadav', 'type':bowl, 'points': 8.5, 'team': t2, 'star':0}#
+p21 = {'name': 'L Ferguson', 'type':bowl, 'points': 8.5, 'team': t2, 'star':0}#
+p22 = {'name': 'P Krishna', 'type':bowl, 'points': 8, 'team': t2, 'star':0}#    
+
+players = [p1, p2, p3, p4, p5, p6, p7, p8, p9, p10, p11,
+           p12, p13, p14, p15, p16, p17, p18, p19, p20, p21, p22]
+teams = [t1, t2]
+
+from dreamteam import Dream11
+
+
+if __name__ == '__main__':        
+    Dream11(players, teams).generateTeams(numOfTeams, captain=True) 
+
+    
+
+
+