Updated with latest versions of scripts and other files

runBNN.py

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+from keras.models import Sequential
+from keras.layers import Dense
+from keras.callbacks import CSVLogger
+from keras.utils import np_utils
+from keras import optimizers
+from keras import layers
+from keras.models import load_model
+import numpy
+import pandas as pd
+import os
+
+
+if os.path.exists("history.csv"):
+	os.remove("history.csv")#delete old history file
+
+
+# fix random seed for reproducibility
+numpy.random.seed(7)
+
+# Defining column names for dataset
+X_COLUMN_NAMES = ['APM','SelectByHotkeys','AssignToHotkeys','UniqueHotkeys','MinimapAttacks','MinimapRightClicks','NumberOfPACs','GapBetweenPACs','ActionLatency','ActionsInPAC','TotalMapExplored','WorkersMade','UniqueUnitsMade','ComplexUnitsMade','ComplexAbilitiesUsed']
+Y_COLUMN_NAMES = ['LeagueIndex']
+
+# Import training dataset
+#training_dataset_X = pd.read_csv('SkillCraft1_Dataset_modified.csv', names=X_COLUMN_NAMES, header=None)#using whole dataset forcross validation
+training_dataset_X = pd.read_csv('trainX.csv', names=X_COLUMN_NAMES, header=None)
+print(training_dataset_X.head())
+
+train_x = training_dataset_X.iloc[:,:].values
+
+
+
+training_dataset_Y = pd.read_csv('trainY.csv', names=Y_COLUMN_NAMES, header=None)
+training_dataset_Y.loc[:,'LeagueIndex'] = training_dataset_Y.loc[:,'LeagueIndex'] - 1;
+print("First rows of Training dataset Y: \n\n",training_dataset_Y.head(),"\n*********************************\n")
+print("Training Dataset Y summary: \n\n",training_dataset_Y.describe(),"\n*********************************\n")
+
+
+train_y = training_dataset_Y.iloc[:,:].values
+
+# Encoding training dataset
+encoding_train_y = np_utils.to_categorical(train_y)
+
+# Import testing dataset
+test_dataset_X = pd.read_csv('testX.csv', names=X_COLUMN_NAMES, header=None)
+test_x = test_dataset_X.iloc[:, :].values
+
+test_dataset_Y = pd.read_csv('testY.csv', names=Y_COLUMN_NAMES, header=None)
+test_dataset_Y.loc[:,'LeagueIndex'] = test_dataset_Y.loc[:,'LeagueIndex'] - 1;
+
+test_y = test_dataset_Y.iloc[:, :].values
+
+# Encoding training dataset
+encoding_test_y = np_utils.to_categorical(test_y)
+
+activation = layers.LeakyReLU(alpha=0.2)
+
+# Creating a model
+model = Sequential()
+model.add(Dense(15, input_dim=15))
+model.add(Dense(35, activation='sigmoid'))
+#model.add(Dense(1, activation='sigmoid'))
+model.add(Dense(8, activation='softmax')) # when league indices have been converted to 0-7 instead of 1-8
+#model.add(Dense(9, activation='softmax'))
+#model.add(Dense(1, activation='linear')) #for non-softmax andnon-encoded to categorical
+
+print(model.summary())
+
+
+#Choosing optimizer
+sgdOptimizer = optimizers.SGD(lr=0.0025, momentum=0.2, decay=0.0, nesterov=False)
+
+# Compiling model
+#model.compile(loss='categorical_crossentropy', optimizer = sgdOptimizer, metrics=['accuracy'])
+model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
+
+csv_logger = CSVLogger('history.csv', append=True, separator=',')
+
+# Training a model
+history = model.fit(train_x, encoding_train_y, epochs=100, batch_size=60, validation_split = 0.25,callbacks=[csv_logger])
+
+model.save('arslan.h5');
+
+#model.fit(train_x, train_y, epochs=50, batch_size=10)
+
+fileModel = load_model('arslan.h5');
+
+# evaluate the model
+scores = fileModel.evaluate(test_x, encoding_test_y)
+#scores = model.evaluate(test_x, test_y)
+
+print("\nAccuracy: %.2f%%" % (scores[1]*100))
+
+#model_predictions = fileModel.predict(test_x);
+
+#print("\n\n",model_predictions[0:5])
+

runBNN_CV.py

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+from keras.models import Sequential
+from keras.layers import Dense
+from keras.callbacks import CSVLogger
+from keras.utils import np_utils
+from keras import optimizers
+from keras import layers
+from sklearn.cross_validation import StratifiedKFold
+import numpy
+import pandas as pd
+import os
+
+
+
+
+#missing values are question marks in this data set
+missingValues = ["?"]
+
+#read csv into dataframe 
+df = pd.read_csv("SkillCraft1_Dataset_modified.csv", na_values = missingValues)
+
+df.loc[:,'LeagueIndex'] = df.loc[:,'LeagueIndex'] - 1;
+
+# fix random seed for reproducibility
+numpy.random.seed(7)
+
+# Defining column names for dataset
+X_COLUMN_NAMES = ['APM','SelectByHotkeys','AssignToHotkeys','UniqueHotkeys','MinimapAttacks','MinimapRightClicks','NumberOfPACs','GapBetweenPACs','ActionLatency','ActionsInPAC','TotalMapExplored','WorkersMade','UniqueUnitsMade','ComplexUnitsMade','ComplexAbilitiesUsed']
+Y_COLUMN_NAMES = ['LeagueIndex']
+
+
+total_x = df.iloc[:,1:16].values
+total_y = df.iloc[:,0].values
+
+print(total_y)
+#print(total_x)
+
+
+n_folds = 10
+#skf = StratifiedKFold(n_splits=n_folds)
+#skf.get_n_splits(total_x, total_y)
+
+labels = [0,1,2,3,4,5,6,7]
+
+skf = StratifiedKFold(total_y, n_folds=n_folds, shuffle=True)
+
+sumOfAccs = 0;
+
+for i, (train, test) in enumerate(skf):
+
+	train_x = total_x[train]
+	test_x = total_x[test]
+
+	# Encoding training dataset
+	encoding_train_y = np_utils.to_categorical(total_y[train])
+
+	# Encoding training dataset
+	encoding_test_y = np_utils.to_categorical(total_y[test])
+
+	activation = layers.LeakyReLU(alpha=0.2)
+
+	# Creating a model
+	model = Sequential()
+	model.add(Dense(15, input_dim=15))
+	model.add(Dense(35, activation='sigmoid'))
+	#model.add(Dense(1, activation='sigmoid'))
+	model.add(Dense(8, activation='softmax')) # when league indices have been converted to 0-7 instead of 1-8
+	#model.add(Dense(9, activation='softmax'))
+	#model.add(Dense(1, activation='linear')) #for non-softmax andnon-encoded to categorical
+
+
+
+	print(model.summary())
+
+
+	#Choosing optimizer
+	sgdOptimizer = optimizers.SGD(lr=0.0025, momentum=0.2, decay=0.0, nesterov=False)
+
+	# Compiling model
+	#model.compile(loss='categorical_crossentropy', optimizer = sgdOptimizer, metrics=['accuracy'])
+	model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
+
+	csv_logger = CSVLogger('history' + str(i) + '.csv', append=True, separator=',')
+
+	# Training a model
+	history = model.fit(train_x, encoding_train_y, epochs=100, batch_size=60, validation_split = 0.25,callbacks=[csv_logger])
+
+
+	#model.fit(train_x, train_y, epochs=50, batch_size=10)
+
+	# evaluate the model
+	scores = model.evaluate(test_x, encoding_test_y)
+	#scores = model.evaluate(test_x, test_y)
+
+	sumOfAccs += scores[1];
+
+	print("\nAccuracy: %.2f%%" % (scores[1]*100))
+
+
+print("\nAccuracy overall: %.2f%%" % ((sumOfAccs / n_folds)*100))

runBNN_halveRanks.py

@@ -0,0 +1,101 @@
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.callbacks import CSVLogger
+from keras.utils import np_utils
+from keras import optimizers
+from keras import layers
+import numpy
+import pandas as pd
+import os
+
+if os.path.exists("history.csv"):
+	os.remove("history.csv")#delete old history file
+
+# fix random seed for reproducibility
+numpy.random.seed(7)
+
+# Defining column names for dataset
+X_COLUMN_NAMES = ['APM','SelectByHotkeys','AssignToHotkeys','UniqueHotkeys','MinimapAttacks','MinimapRightClicks','NumberOfPACs','GapBetweenPACs','ActionLatency','ActionsInPAC','TotalMapExplored','WorkersMade','UniqueUnitsMade','ComplexUnitsMade','ComplexAbilitiesUsed']
+Y_COLUMN_NAMES = ['LeagueIndex']
+
+# Import training dataset
+#training_dataset_X = pd.read_csv('SkillCraft1_Dataset_modified.csv', names=X_COLUMN_NAMES, header=None)#using whole dataset forcross validation
+training_dataset_X = pd.read_csv('trainX.csv', names=X_COLUMN_NAMES, header=None)
+print(training_dataset_X.head())
+
+train_x = training_dataset_X.iloc[:,:].values
+
+
+
+training_dataset_Y = pd.read_csv('trainY.csv', names=Y_COLUMN_NAMES, header=None)
+training_dataset_Y.loc[:,'LeagueIndex'] = training_dataset_Y.loc[:,'LeagueIndex'] - 1;
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 1,'LeagueIndex'] = 0; # map (0,1) to 0
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 2,'LeagueIndex'] = 1; # map (2,3) to 1
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 3,'LeagueIndex'] = 1; # map (2,3) to 1
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 4,'LeagueIndex'] = 2; # map (4,5) to 2
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 5,'LeagueIndex'] = 2; # map (4,5) to 2
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 6,'LeagueIndex'] = 3; # map (6,7) to 3
+training_dataset_Y.loc[training_dataset_Y['LeagueIndex'] == 7,'LeagueIndex'] = 3; # map (6,7) to 3
+print("First rows of Training dataset Y: \n\n",training_dataset_Y.head(),"\n*********************************\n")
+print("Training Dataset Y summary: \n\n",training_dataset_Y.describe(),"\n*********************************\n")
+
+
+train_y = training_dataset_Y.iloc[:,:].values
+
+# Encoding training dataset
+encoding_train_y = np_utils.to_categorical(train_y)
+
+# Import testing dataset
+test_dataset_X = pd.read_csv('testX.csv', names=X_COLUMN_NAMES, header=None)
+test_x = test_dataset_X.iloc[:, :].values
+
+test_dataset_Y = pd.read_csv('testY.csv', names=Y_COLUMN_NAMES, header=None)
+test_dataset_Y.loc[:,'LeagueIndex'] = test_dataset_Y.loc[:,'LeagueIndex'] - 1;
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 1,'LeagueIndex'] = 0; # map (0,1) to 0
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 2,'LeagueIndex'] = 1; # map (2,3) to 1
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 3,'LeagueIndex'] = 1; # map (2,3) to 1
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 4,'LeagueIndex'] = 2; # map (4,5) to 2
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 5,'LeagueIndex'] = 2; # map (4,5) to 2
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 6,'LeagueIndex'] = 3; # map (6,7) to 3
+test_dataset_Y.loc[test_dataset_Y['LeagueIndex'] == 7,'LeagueIndex'] = 3; # map (6,7) to 3
+
+
+test_y = test_dataset_Y.iloc[:, :].values
+
+# Encoding training dataset
+encoding_test_y = np_utils.to_categorical(test_y)
+
+activation = layers.LeakyReLU(alpha=0.2)
+
+# Creating a model
+model = Sequential()
+model.add(Dense(15, input_dim=15))
+model.add(Dense(35, activation='sigmoid'))
+#model.add(Dense(1, activation='sigmoid'))
+model.add(Dense(4, activation='softmax')) # when league indices have been converted to 0-3 instead of 1-8
+#model.add(Dense(9, activation='softmax'))
+#model.add(Dense(1, activation='linear')) #for non-softmax andnon-encoded to categorical
+
+print(model.summary())
+
+
+#Choosing optimizer
+sgdOptimizer = optimizers.SGD(lr=0.0025, momentum=0.25, decay=0.0, nesterov=False)
+
+# Compiling model
+#model.compile(loss='categorical_crossentropy', optimizer = sgdOptimizer, metrics=['accuracy'])
+model.compile(loss='categorical_crossentropy', optimizer = 'adam', metrics=['accuracy'])
+
+csv_logger = CSVLogger('history.csv', append=True, separator=',')
+
+# Training a model
+history = model.fit(train_x, encoding_train_y, epochs=100, batch_size=40, validation_split = 0.25,callbacks=[csv_logger])
+
+
+#model.fit(train_x, train_y, epochs=50, batch_size=10)
+
+# evaluate the model
+scores = model.evaluate(test_x, encoding_test_y)
+#scores = model.evaluate(test_x, test_y)
+
+print("\nAccuracy: %.2f%%" % (scores[1]*100))

runMLR.py

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+import pandas as pd
+import numpy as np
+import sklearn
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+
+
+
+
+#missing values are question marks in this data set
+missingValues = ["?"]
+
+#read csv into dataframe 
+df = pd.read_csv("SkillCraft1_Dataset_modified.csv", na_values = missingValues)
+
+print("*****************************************\nCorrelation coefficients: \n")
+
+print(df.corr())
+
+
+print("****************************************\n")
+
+print(list(df))
+
+print("****************************************\n")
+
+print("Getting X and Y arrays...")
+
+X = df[['APM','SelectByHotkeys','AssignToHotkeys','UniqueHotkeys','MinimapAttacks','MinimapRightClicks','NumberOfPACs','GapBetweenPACs','ActionLatency','ActionsInPAC','TotalMapExplored','WorkersMade','UniqueUnitsMade','ComplexUnitsMade','ComplexAbilitiesUsed']]
+
+Y = df['LeagueIndex']
+
+print("Splitting into Training and Testing arrays....")
+
+trainX , testX , trainY, testY = train_test_split(X , Y , test_size=0.2)
+
+print("Outputting trainging and testing datasets to files...")
+
+with open('trainX.csv', 'w') as FOUT:
+    np.savetxt(FOUT, trainX, delimiter = ',')
+
+with open('testX.csv', 'w') as FOUT:
+    np.savetxt(FOUT, testX, delimiter = ',')
+
+with open('trainY.csv', 'w') as FOUT:
+    np.savetxt(FOUT, trainY, delimiter = ',')
+
+with open('testY.csv', 'w') as FOUT:
+    np.savetxt(FOUT, testY, delimiter = ',')
+
+print("Creating and Training model...")
+
+lm = LinearRegression()
+
+model = lm.fit(trainX , trainY)
+
+
+print("Visualizing predictions...")
+
+predictions = lm.predict(testX)
+
+with open('predictionsMLR.txt', 'w') as FOUT:
+    np.savetxt(FOUT, predictions,delimiter = ',')
+
+plt.scatter(testY,predictions)
+
+
+
+print("Score: ", model.score(testX, testY))
+
+plt.title('SkillCraft1 Multiple Linear Regression Predictions')
+plt.xlabel('Expected LeagueIndex')
+plt.ylabel('Predicted LeagueIndex')
+plt.show()
+
+