def main():
UsefulFunctions.warning()
# Building Phase
first_X, first_Y = UsefulFunctions.loadVehicleData()
clf_first, first_training_score, first_training_data, first_testing_data, first_graph_data = analyze(
first_X, first_Y)
print(
"Decision Tree Training Score (first) After Cross Validation: {0:.2f}%"
.format(first_training_score * 100))
UsefulFunctions.calc_accuracy(first_training_data[1],
clf_first.predict(first_training_data[0]),
first_testing_data[1],
clf_first.predict(first_testing_data[0]))
second_X, second_Y = UsefulFunctions.loadWineData()
clf_second, second_training_score, second_training_data, second_testing_data, second_graph_data = analyze(
second_X, second_Y)
print(
"Decision Tree Training Score (second) After Cross Validation: {0:.2f}%"
.format(second_training_score * 100))
UsefulFunctions.calc_accuracy(second_training_data[1],
clf_second.predict(second_training_data[0]),
second_testing_data[1],
clf_second.predict(second_testing_data[0]))
def mainCurves():
UsefulFunctions.warning()
first_X, first_Y = UsefulFunctions.loadVehicleData()
first_graph_data = analyzePerNeighbor(first_X, first_Y)
second_X, second_Y = UsefulFunctions.loadWineData()
second_graph_data = analyzePerNeighbor(second_X, second_Y)
graphDataCurves(first_graph_data, second_graph_data)
def main():
wine_X, wine_Y = UsefulFunctions.loadWineData()
clf_wine, wine_training_score, wine_testing_data, wine_graph_data, wine_elapsed_time = analyze(
wine_X, wine_Y)
print(
"Neural Network Tree Training Score (Wine) After Cross Validation: {0}%"
.format(wine_training_score * 100))
print("Neural Network Took (Wine) {0}s to Train".format(wine_elapsed_time))
start = time.time()
results = clf_wine.predict(wine_testing_data[0])
end = time.time() - start
print("Neural Network (Wine) Took {0}s to Test".format(end))
# print(confusion_matrix(wine_testing_data[1], results))
print(wine_graph_data[1])
print("Neural Testing Score for Wine {0}%".format(
cal_accuracy(wine_testing_data[1], results) * 100))
def main():
abaloneX, abaloneY = UsefulFunctions.loadVehicleData()
clf_abalone, abalone_training_score, abalone_testing_data, abalone_graph_data, abalone_elapsed_time = analyze(
abaloneX, abaloneY)
print(
"Neural Network Training Score (Abalone) After Cross Validation: {0}%".
format(abalone_training_score * 100))
print("Neural Network Took (Abalone) {0}s to Train".format(
abalone_elapsed_time))
start = time.time()
results = clf_abalone.predict(abalone_testing_data[0])
end = time.time() - start
print("Neural Network (Abalone) Took {0}s to Test".format(end))
print(confusion_matrix(abalone_testing_data[1], results))
print("Neural Testing Score for Abalone {0}%".format(
cal_accuracy(abalone_testing_data[1], results) * 100))
wine_X, wine_Y = UsefulFunctions.loadWineData()
clf_wine, wine_training_score, wine_testing_data, wine_graph_data, wine_elapsed_time = analyze(
wine_X, wine_Y)
print(
"Neural Network Tree Training Score (Wine) After Cross Validation: {0}%"
.format(wine_training_score * 100))
print("Neural Network Took (Wine) {0}s to Train".format(wine_elapsed_time))
start = time.time()
results = clf_wine.predict(wine_testing_data[0])
end = time.time() - start
print("Neural Network (Wine) Took {0}s to Test".format(end))
print(confusion_matrix(wine_testing_data[1], results))
print("Neural Testing Score for Wine {0}%".format(
cal_accuracy(wine_testing_data[1], results) * 100))
fig = plt.figure(200)
ax1 = plt.subplot(211)
ax1.plot(abalone_graph_data[0], abalone_graph_data[1])
ax1.set_xlabel("Number of Epochs")
ax1.set_ylabel("Cross Validated Accuracy Score")
ax1.set_title("Score vs Number of Epochs for Vehicle Data")
ax2 = plt.subplot(212)
ax2.plot(wine_graph_data[0], wine_graph_data[1])
ax2.set_xlabel("Number of Epochs")
ax2.set_ylabel("Cross Validated Accuracy Score")
ax2.set_title("Score vs Number of Epochs for Wine Data")
fig.tight_layout()
plt.show()
def main():
title = "Learning Curves Vehicle (SVM)"
abalone_X, abalone_Y = UsefulFunctions.loadVehicleData()
abaloneX_train, abaloneX_test, abaloneY_train, abaloneY_test = train_test_split(
abalone_X, abalone_Y, test_size=0.30, random_state=100)
cv = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=42)
# change the kernel here
estimator = SVC(gamma=.001, C=1000.0, kernel='poly')
plt, abalone_elapsed_time = plot_learning_curve(estimator,
title,
abaloneX_train,
abaloneY_train, (0.1, 0.5),
cv=cv,
n_jobs=4)
print("It took SVM (Abalone) {0}s to train".format(abalone_elapsed_time))
estimator.fit(abaloneX_train, abaloneY_train)
t0 = time()
y_pred = estimator.predict(abaloneX_test)
print("SVM (Abalone) Took {0}s to test".format(time() - t0))
print("SVM Accuracy Score (Abalone) was {0}%".format(
accuracy_score(abaloneY_test, y_pred) * 100))
plt.show()
title = "Learning Curves Wine (SVM)"
wine_X, wine_Y = UsefulFunctions.loadWineData()
wineX_train, wineX_test, wineY_train, wineY_test = train_test_split(
wine_X, wine_Y, test_size=0.30, random_state=100)
cv = StratifiedShuffleSplit(n_splits=10, test_size=0.1, random_state=42)
# change the kernel here
estimator = SVC(gamma=.001, C=1000.0, kernel='rbf')
plt, wine_elapsed_time = plot_learning_curve(estimator,
title,
wineX_train,
wineY_train, (0.1, 1.01),
cv=cv,
n_jobs=4)
print("It took SVM (Wine) {0}s to train".format(wine_elapsed_time))
estimator.fit(wineX_train, wineY_train)
t0 = time()
y_pred = estimator.predict(wineX_test)
print("It took SVM (Wine) {0}s to test".format((time() - t0)))
print("SVM Accuracy Score (Wine) was {0}%".format(
accuracy_score(wineY_test, y_pred) * 100))
plt.show()
