def std_svm(path):
"""
function that runs the best svm five times and print their accuracy
:param path: path to the preprocessed data. If the provided preprocessed data is used:
Data/preprocessed_data.csv
"""
data = GetData.load_preprocessed_data(path)
data = data.drop(
['avg_home_win_odds', 'avg_draw_odds', 'avg_away_win_odds'], axis=1)
input_vector, output_vector = GetData.convert_df_to_lists(data, False)
svm_accs = []
for i in range(5):
random_state = random.randint(1, 10)
x_train, x_test, y_train, y_test = train_test_split(
input_vector,
output_vector,
test_size=0.25,
random_state=random_state)
print('SVM running...')
svm_acc = SVM.support_vector_machine(x_train, x_test, y_train, y_test,
10**7, 10**-4)
svm_accs.append(svm_acc)
print('svm accuracies:', svm_accs)
def run_best_ann_and_svm(data):
"""
function that runs the best svm and neural network (the shallow neural network with the relu activation function)
and prints their confusion matrices and accuracies. The difference in probabilities for the neural network is also
printed.
:param data: dataframe containing the preprocessed data
"""
state = random.randint(1, 10)
input_vector, output_vector = GetData.convert_df_to_lists(data, False)
x_train, x_test, y_train, y_test = train_test_split(input_vector,
output_vector,
test_size=0.25,
random_state=state)
print('SVM running...')
svm_acc = SVM.support_vector_machine(x_train, x_test, y_train, y_test,
10**7, 10**-4)
y_train = GetData.convert_output_vector_to_nn_format(y_train)
y_test = GetData.convert_output_vector_to_nn_format(y_test)
print('Neural Network running...')
print(
'The confusion matrix and probability differences will be printed after the NN has trained'
)
shallow_nn_relu_acc = ShallowNeuralNetworks.shallow_neural_network_relu(
x_train, x_test, y_train, y_test, 10)
model_names = ['SVM', 'Shallow NN With relu']
accuracies = [svm_acc, shallow_nn_relu_acc]
ModelAccuracyPlot.present_results(model_names, accuracies)
def main():
data = GetData.load_preprocessed_data('/Users/vegardhaneberg/PycharmProjects/SoccerGroupProject/Data/preprocessed_data.csv')
data = data.drop(['avg_home_win_odds', 'avg_draw_odds', 'avg_away_win_odds'], axis=1)
input_vector, output_vector = GetData.convert_df_to_lists(data, False)
x_train, x_test, y_train, y_test = train_test_split(input_vector, output_vector, test_size=0.25, random_state=1)
y_train = GetData.convert_output_vector_to_nn_format(y_train)
y_test = GetData.convert_output_vector_to_nn_format(y_test)
two_layer_neural_network_tanh(x_train, x_test, y_train, y_test, 10)
def run_all_models_from_preprocessed_data():
data = GetData.load_preprocessed_data()
data = data.drop(
['avg_home_win_odds', 'avg_draw_odds', 'avg_away_win_odds'], axis=1)
input_vector, output_vector = GetData.convert_df_to_lists(data, False)
random_state = random.randint(1, 10)
#run_all_shallow(input_vector, output_vector, random_state)
#run_all_two_layer(input_vector, output_vector, random_state)
run_all_deep(input_vector, output_vector, random_state)
def run_all_two_layer(input_vector, output_vector, random_state):
"""
function that runs all neural networks with two hidden layers, both with and without pca and prints the results
:param input_vector: a list with lists that contains the input to the models
:param output_vector: a list that contains the match results on the format 0, 1 or 2
:param random_state: the state for the train test split
"""
x_train, x_test, y_train, y_test = train_test_split(
input_vector, output_vector, test_size=0.25, random_state=random_state)
y_train = GetData.convert_output_vector_to_nn_format(y_train)
y_test = GetData.convert_output_vector_to_nn_format(y_test)
two_layer_nn_relu_acc = TwoLayerNeuralNetwork.two_layer_neural_network_relu(
x_train, x_test, y_train, y_test, 10)
two_layer_nn_tanh_acc = TwoLayerNeuralNetwork.two_layer_neural_network_tanh(
x_train, x_test, y_train, y_test, 10)
two_layer_nn_sigmoid_acc = TwoLayerNeuralNetwork.two_layer_neural_network_sigmoid(
x_train, x_test, y_train, y_test, 10)
df = GetData.create_df_from_two_lists([input_vector, output_vector])
pca_df = PCA.pca(df, 'match_result', 2)
list_data = GetData.convert_df_to_lists(pca_df, False)
x_train, x_test, y_train, y_test = train_test_split(list_data[0],
list_data[1],
test_size=0.25,
random_state=1)
y_train = GetData.convert_output_vector_to_nn_format(y_train)
y_test = GetData.convert_output_vector_to_nn_format(y_test)
two_layer_nn_relu_pca_acc = TwoLayerNeuralNetwork.two_layer_neural_network_relu(
x_train, x_test, y_train, y_test, 2)
two_layer_nn_tanh_pca_acc = TwoLayerNeuralNetwork.two_layer_neural_network_tanh(
x_train, x_test, y_train, y_test, 2)
two_layer_nn_sigmoid_pca_acc = TwoLayerNeuralNetwork.two_layer_neural_network_sigmoid(
x_train, x_test, y_train, y_test, 2)
model_names = [
'Two Hidden Layer NN With relu', 'Two Hidden Layer NN With tanh',
'Two Hidden Layer NN With Sigmoid',
'Two Hidden Layer NN With relu and PCA',
'Two Hidden Layer NN With tanh and PCA',
'Two Hidden Layer NN With Sigmoid and PCA'
]
accuracies = [
two_layer_nn_relu_acc, two_layer_nn_tanh_acc, two_layer_nn_sigmoid_acc,
two_layer_nn_relu_pca_acc, two_layer_nn_tanh_pca_acc,
two_layer_nn_sigmoid_pca_acc
]
ModelAccuracyPlot.present_results(model_names, accuracies)
def std_best_models(path):
"""
function that runs the best three neural networks five times and print their accuracy
:param path: path to the preprocessed data. If the provided preprocessed data is used:
Data/preprocessed_data.csv
"""
data = GetData.load_preprocessed_data(path)
data = data.drop(
['avg_home_win_odds', 'avg_draw_odds', 'avg_away_win_odds'], axis=1)
input_vector, output_vector = GetData.convert_df_to_lists(data, False)
shallow_tanh_accs = []
deep_relu_accs = []
deep_tanh_accs = []
for i in range(5):
random_state = random.randint(1, 10)
x_train, x_test, y_train, y_test = train_test_split(
input_vector,
output_vector,
test_size=0.25,
random_state=random_state)
y_train = GetData.convert_output_vector_to_nn_format(y_train)
y_test = GetData.convert_output_vector_to_nn_format(y_test)
shallow_nn_relu_acc = ShallowNeuralNetworks.shallow_neural_network_relu(
x_train, x_test, y_train, y_test, 10)
deep_nn_relu_acc = DeepNeuralNetworks.deep_neural_network_relu(
x_train, x_test, y_train, y_test, 10)
deep_nn_tanh_acc = DeepNeuralNetworks.deep_neural_network_tanh(
x_train, x_test, y_train, y_test, 10)
shallow_tanh_accs.append(shallow_nn_relu_acc)
deep_relu_accs.append(deep_nn_relu_acc)
deep_tanh_accs.append(deep_nn_tanh_acc)
print('Two layer:', shallow_tanh_accs)
print('Deep relu:', deep_relu_accs)
print('Deep tanh:', deep_tanh_accs)