Tagging the alfa version

NaelsonDouglas · Jul 23, 2020 · 29be49b · 29be49b
1 parent c6bfe48
commit 29be49b
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Showing 5 changed files with 55 additions and 20 deletions.
diff --git a/src/backend/classifier.py b/src/backend/classifier.py
@@ -5,30 +5,40 @@
 import pandas as pd
 
 class Classifier:
-        def __init__(self):
-                self.tables_manager = Tables()
+        def __init__(self,is_dummy):
+                self.tables_manager = Tables(is_dummy)                
                 self.data = self.tables_manager.get_all_matches().dropna()
                 logging.info(self.data.head())
-                self.x = self.data[['R1','R2','R3','R4','R5','D1','D2','D3','D4','D5']].values
-                self.y = self.data[['radiant_win']].values
+                if is_dummy:
+                        self.x = self.data[range(1,130)].values                        
+                        self.y = self.data[['win']].values
+                else:
+                        self.x = self.data[['R1','R2','R3','R4','R5','D1','D2','D3','D4','D5']].values
+                        self.y = self.data[['radiant_win']].values
+                        #self.x = self.data[['R1','R2','R3','R4','R5']].values                
+
+
+                logging.info(self.x)
+                logging.info(self.y)
                 self.test_size=0.3
                 self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.x, self.y, test_size=self.test_size)
                 self.classifier = None #To be filled by child class.
 
         def train(self):
                 self.classifier.fit(self.x_train, self.y_train.ravel())
 
-        def results(self):
-                self.result = self.classifier.predict(self.x_test)
+        def results(self):                
+                self.result = self.classifier.predict(self.x_test)                
                 self.confusion_matrix = pd.crosstab(self.y_test.ravel(),self.result, rownames=['Real'], colnames=['Predito'], margins=True)
+                print(self.confusion_matrix)                
                 self.fscore = f1_score(self.y_test.ravel(),self.result,average='macro')
-                self.overview = {'F-score': self.fscore, 'Confusion-matrix': self.confusion_matrix}                
+                self.overview = {'F-score': self.fscore, 'Confusion-matrix': self.confusion_matrix}                                
                 self.print_results()
                 return self.overview
 
         def print_results(self):    
-                print('Training dataset size: '+str(len(self.data.index)))
-                print('Training dataset percentage: '+str(100*self.test_size)+"%")            
+                print('Dataset size: '+str(len(self.data.index)))
+                print('Training percentage: '+str(100*self.test_size)+"%")            
                 print('_____________________')                
                 print('Confusion-matrix: \n'+str(self.overview['Confusion-matrix']))
                 print('_____________________')

diff --git a/src/backend/knn.py b/src/backend/knn.py
@@ -2,13 +2,14 @@
 from classifier import Classifier
 from sklearn.neighbors import KNeighborsClassifier
 
-class Knn(Classifier):]
+class Knn(Classifier):
         def __init__(self,k):
-                super().__init__()
+                super().__init__(False)
                 self.k = k
                 self.classifier = KNeighborsClassifier(n_neighbors=self.k)
                 self.train()
                 self.results()
+                self.dummy = True
 
         def print_results(self):
                 print('=====================')

diff --git a/src/backend/main.py b/src/backend/main.py
@@ -4,7 +4,10 @@
 from tables_manager import Tables
 import logging
 from knn import Knn
+from random_forest import RandomForest
 import pandas as pd
 
-tables_manager = Tables()
-knn = Knn(7)
+#tables_manager = Tables()
+#print(tables_manager.get_all_matches())
+#knn = Knn(7)
+random_forest = RandomForest()
diff --git a/src/backend/tables_manager.py b/src/backend/tables_manager.py
@@ -6,14 +6,35 @@
 import json
 
 class Tables:
-        def __init__(self):
-                self.context  = Context()                
+        def __init__(self,is_dummy = False):
+                self.context  = Context()
+                self.dummy = is_dummy
 
         def get_all_matches(self):
                 matches_collection = self.context.db['Matches']
-                matches_cursor = matches_collection.find({})
-                p = list(matches_cursor)
-                result = pd.DataFrame(p)
+                matches_cursor = matches_collection.find({})                
+                cursor_list = list(matches_cursor)
+                result = None
+                if self.dummy:
+                        dummy_cursor = []
+                        heroes_range = ['_id', 'win']
+                        heroes_range = heroes_range+list(range(1,130))
+                        for match in cursor_list:                                
+                                radiant_row = dict.fromkeys(heroes_range,0)
+                                dire_row = dict.fromkeys(heroes_range,0)
+                                radiant_row['_id'] = match['_id']
+                                dire_row['_id'] = match['_id']                                
+                                for r in  ['R1','R2','R3','R4','R5']:                                       
+                                        radiant_row[match[r]] = 1                                        
+                                        radiant_row['win'] = match['radiant_win']
+                                for d in  ['D1','D2','D3','D4','D5']:
+                                        dire_row[match[d]] = 1
+                                        dire_row['win'] = not match['radiant_win']
+                                dummy_cursor.append(dire_row)
+                                dummy_cursor.append(radiant_row)
+                        result = pd.DataFrame(dummy_cursor)
+                else:
+                        result = pd.DataFrame(cursor_list)
                 #result['teams'] = result['teams'].map(lambda x :np.concatenate((x[0],x[1])))
                 #result['radiant_win'] = result['radiant_win'] * 1                
                 return result               

diff --git a/src/configs.json b/src/configs.json
@@ -5,7 +5,7 @@
                 "local":{
                         "openDotaApiBaseUrl": "https://api.opendota.com/api",
                         "steamBaseUrl": "https://api.steampowered.com/IDOTA2Match_570",
-                        "steamApiKey": "NULL",
+                        "steamApiKey": "null",
                         "imagesBaseUrl": "http://cdn.dota2.com",
                         "databasePort": 27017,
                         "databaseIP": "localhost",
@@ -17,4 +17,4 @@
                         "databasePort": 27017
                         }
         }        
-}
+}