There was no way to guess what were the team in the data. Fixed?

BestPicks.py

@@ -9,7 +9,7 @@
 from Learner import ValueNetwork, maybe_restore_from_checkpoint
 
 CHAMPIONS_SIZE = 150
-PATCHES_SIZE = 150
+PATCHES_SIZE = 149
 
 config = configparser.ConfigParser()
 config.read('config.ini')
@@ -163,6 +163,10 @@ def initUI(self):
         self.yourTeam = QComboBox()
         self.yourTeam.addItems(TEAMS)
         bestPicksLayout.addWidget(self.yourTeam, 0, 1)
+        self.evaluateButton = QPushButton('Wait...')
+        self.evaluateButton.setEnabled(False)
+        self.evaluateButton.clicked.connect(lambda: self.evaluate())
+        bestPicksLayout.addWidget(self.evaluateButton, 0, 2)
         yourRoleLabel = QLabel()
         yourRoleLabel.setText('Your role:')
         bestPicksLayout.addWidget(yourRoleLabel, 1, 0)
@@ -231,6 +235,59 @@ def buildNetwork(self):
 
         self.generateButton.setText('Analyze')
         self.generateButton.setEnabled(True)
+        self.evaluateButton.setText('Evaluate')
+        self.evaluateButton.setEnabled(True)
+
+    def evaluate(self):
+        print('generating for:', str(self.yourRole.currentText()), file=sys.stderr)
+        currentState = OrderedDict([(champ, 'A') for champ in CHAMPIONS_LABEL])
+        bans = [str(self.player1Ban.currentText()), str(self.player2Ban.currentText()), str(self.player3Ban.currentText()),
+                str(self.player4Ban.currentText()), str(self.player5Ban.currentText()), str(self.player6Ban.currentText()),
+                str(self.player7Ban.currentText()), str(self.player8Ban.currentText()), str(self.player9Ban.currentText()),
+                str(self.player10Ban.currentText())]
+        print('bans', bans, file=sys.stderr)
+        picks = [(str(self.player1Pick.currentText()), str(self.player1Role.currentText())),
+                 (str(self.player2Pick.currentText()), str(self.player2Role.currentText())),
+                 (str(self.player3Pick.currentText()), str(self.player3Role.currentText())),
+                 (str(self.player4Pick.currentText()), str(self.player4Role.currentText())),
+                 (str(self.player5Pick.currentText()), str(self.player5Role.currentText())),
+                 (str(self.player6Pick.currentText()), 'Opp'), (str(self.player7Pick.currentText()), 'Opp'),
+                 (str(self.player8Pick.currentText()), 'Opp'), (str(self.player9Pick.currentText()), 'Opp'),
+                 (str(self.player10Pick.currentText()), 'Opp')]
+        print('picks', picks, file=sys.stderr)
+        for ban in bans:
+            if ban [0] != '.':
+                currentState[ban] = 'B'
+        for (pick, role) in picks:
+            if pick[0] != '.' and role[0] in CHAMPIONS_STATUS:
+                currentState[pick] = role[0]
+
+        yourTeam = str(self.yourTeam.currentText())
+        if yourTeam == '...':
+            print('You need to select a team!', file=sys.stderr)
+            return
+        # print(currentState, file=sys.stderr)
+
+        data = []
+
+        row_data = []
+        row_data.extend([1 if currentState[CHAMPIONS_LABEL[k]] == s else 0 for k in range(len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS])
+        row_data.extend([0 for k in range(CHAMPIONS_SIZE - len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS])
+        row_data.extend(PATCH)
+        row_data.append(yourTeam)
+        data.append(row_data)
+
+        batch = [[], []]
+        batch[0] = data
+
+        feed_dict = {
+            self.x: batch[0],
+        }
+        pred_values = self.sess.run(self.y_pred, feed_dict=feed_dict)
+
+        self.results.setRowCount(1)
+        self.results.setItem(0, 0, 'winrate')
+        self.results.setItem(0, 1, '%.2f' % (pred_values[0] * 100))
 
     def generate(self):
         print('generating for:', str(self.yourRole.currentText()), file=sys.stderr)
@@ -284,6 +341,7 @@ def generate(self):
             row_data.extend([1 if state[CHAMPIONS_LABEL[k]] == s else 0 for k in range(len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS])
             row_data.extend([0 for k in range(CHAMPIONS_SIZE - len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS])
             row_data.extend(PATCH)
+            row_data.append(yourTeam)
             data.append(row_data)
 
         batch = [[], []]

DataExtractor.py

@@ -23,6 +23,7 @@
 regions_list = config['REGIONS']
 COLUMNS = [champ for champ in CHAMPIONS_LABEL]
 COLUMNS.append('patch')
+COLUMNS.append('team')
 COLUMNS.append('win')
 COLUMNS.append('file')
 
@@ -83,8 +84,9 @@ def getRoleIndex(lane, role):
 
 for gamePath in gamesPath:
     raw_data = OrderedDict([(champ, []) for champ in CHAMPIONS_LABEL])
-    raw_data['win'] = []
     raw_data['patch'] = []
+    raw_data['team'] = []
+    raw_data['win'] = []
     raw_data['file'] = []
     print(gamePath)
     game = pickle.load(open(gamePath, 'rb'))
@@ -141,8 +143,10 @@ def getRoleIndex(lane, role):
     # Blank, everything is available
     blueState = OrderedDict()
     redState = OrderedDict()
+    blueState['team'] = 0
+    redState['team'] = 1
     blueState['win'] = int(blueWin)
-    redState['win'] = int(blueWin)
+    redState['win'] = int(redWin)
     blueState['patch'] = game_patch
     redState['patch'] = game_patch
     blueState['file'] = os.path.basename(gamePath)
@@ -151,8 +155,8 @@ def getRoleIndex(lane, role):
     redState.update([(champ_name, 'A') for champ_name in CHAMPIONS_LABEL])
     for key, value in blueState.items():
         raw_data[key].append(value)
-    # for key, value in redState.items():
-    #     raw_data[key].append(value)
+    for key, value in redState.items():
+        raw_data[key].append(value)
 
     # Bans
     blueState = dict(blueState)  # don't forget to create a clean copy
@@ -165,8 +169,8 @@ def getRoleIndex(lane, role):
                 break
     for key, value in blueState.items():
         raw_data[key].append(value)
-    # for key, value in redState.items():
-    #     raw_data[key].append(value)
+    for key, value in redState.items():
+        raw_data[key].append(value)
 
     # Smart lane-role
     b_roles = OrderedDict()

DataProcessing.py

@@ -8,8 +8,8 @@
 
 SAVE = 1000
 CHAMPIONS_SIZE = 150
-PATCHES_SIZE = 150
-INPUT_SIZE = CHAMPIONS_SIZE * 8 + PATCHES_SIZE + 1
+PATCHES_SIZE = 149
+INPUT_SIZE = CHAMPIONS_SIZE * 8 + PATCHES_SIZE + 1 + 1  # team color + team win
 config = configparser.ConfigParser()
 config.read('config.ini')
 DATABASE = config['PARAMS']['database']
@@ -27,10 +27,12 @@
 
 names = CHAMPIONS_LABEL[:]
 names.append('patch')
+names.append('team')
 names.append('win')
 names.append('file')
 dtype = {champ: str for champ in CHAMPIONS_LABEL}
 dtype['patch'] = str
+dtype['team'] = int
 dtype['win'] = int
 dtype['file'] = str
 
@@ -60,6 +62,7 @@ def processing(dataFile):
         row_data.extend([1 if row[CHAMPIONS_LABEL[k]] == s else 0 for k in range(len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS])
         row_data.extend([0 for k in range(CHAMPIONS_SIZE - len(CHAMPIONS_LABEL)) for s in CHAMPIONS_STATUS ])
         row_data.extend([1 if row['patch'] == PATCHES[k] else 0 for k in range(PATCHES_SIZE)])
+        row_data.append(row['team'])
         row_data.append(row['win'])
         data.loc[len(data)] = row_data
     if len(data):

Learner.py

@@ -15,8 +15,8 @@
 # 7.14: champions 138, patches: 97
 # current rythm: 6 champions per season, 24 patches per season
 CHAMPIONS_SIZE = 150
-PATCHES_SIZE = 150
-INPUT_SIZE = CHAMPIONS_SIZE * 8 + PATCHES_SIZE
+PATCHES_SIZE = 149
+INPUT_SIZE = CHAMPIONS_SIZE * 8 + PATCHES_SIZE + 1  # team color
 config = configparser.ConfigParser()
 config.read('config.ini')
 DATABASE = config['PARAMS']['database']
@@ -151,7 +151,7 @@ def nextBatchValue(self):
             return batch
         j = min(self.i + self.batchSize, len(self.df))
         batch[0] = self.df.iloc[self.i:j, :-1].values.tolist()
-        batch[1] = self.df.iloc[self.i:j, -1].values.tolist()  # first column is the value
+        batch[1] = self.df.iloc[self.i:j, -1].values.tolist()  # last column is the win value
         if j < len(self.df):
             self.i = j
         else:
@@ -261,5 +261,5 @@ def learn(netType, netArchi, archi_kwargs, batchSize, checkpoint, report, lr):
 if __name__ == '__main__':
     # Testing (production network will be more sopisticated)
     # learn(netType='Value', netArchi='Dense2', archi_kwargs={'NN': 2048, 'training': True}, batchSize=10000, checkpoint=None, report=1, lr=1e-4)
-    learn(netType='Value', netArchi='Dense3', archi_kwargs={'NN': 2048, 'training': True}, batchSize=10000, checkpoint=None, report=1, lr=1e-4)
+    learn(netType='Value', netArchi='Dense3', archi_kwargs={'NN': 2048, 'training': True}, batchSize=1000, checkpoint=None, report=1, lr=1e-4)
     # learn(netType='Value', netArchi='Dense5', archi_kwargs={'NN': 2048, 'training': True}, batchSize=10000, checkpoint=None, report=1, lr=1e-4)