def main():
nbOutput = 4
file = IOHelper().checkArg(sys.argv)
if (len(file) < 2):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
featuresId = range(7, 19)
# nbInput = len(featuresId)
X = generateDataset(d, featuresId)
X, nbInput = d.featureExpand(d, X)
X = d.featureRescale(d, X)
allWeight, AllOutput = csvToArray(file[1])
allclassifier = MultiClassifier(nbInput, AllOutput)
allclassifier.initWeight(allWeight)
with open('houses.csv', 'w') as file:
file.write("Index,Hogwarts House\n")
for i, d in enumerate(X):
name = allclassifier.predict(d)
file.write(str(i) + "," + name + "\n")
def main():
file = IOHelper().checkArg(sys.argv)
if (len(file) < 1):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
fig, axes = plt.subplots(figsize=(18, 10))
fig.tight_layout()
start = 6
width = 13
widthStart = 0
widthEnd = widthStart + width
ystart = start
for i in range(width):
drawOneSub(d, start, ystart, range(widthStart, widthEnd))
widthStart += width
widthEnd += width
start += 1
print("")
# plt.title(d.getName(index))
plt.savefig('scatter_plot.png')
plt.show()
def main():
file = IOHelper().checkArg(sys.argv)
if (len(file) < 1):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
fig, axes = plt.subplots(figsize=(18, 10))
fig.tight_layout()
for index in range(6, 19):
mean = d.mean(d.getFeature(index))
std = d.standardDeviation(d.getFeature(index), mean)
if mean >= -10 and mean <= 10 and std >= -10 and std <= 10:
addFeatureOnSubplot(index, d.getFeature(index), d.getName(index),
1)
elif mean >= -500 and mean <= 500 and std >= -500 and std <= 500:
addFeatureOnSubplot(index, d.getFeature(index), d.getName(index),
2)
elif mean >= -2000 and mean <= 2000 and std >= -2000 and std <= 2000:
addFeatureOnSubplot(index, d.getFeature(index), d.getName(index),
3)
else:
addFeatureOnSubplot(index, d.getFeature(index), d.getName(index),
4)
# plt.title(d.getName(index))
plt.savefig('scatter_plot.png')
plt.show()
def main():
file = IOHelper().checkArg(sys.argv)
if (len(file) < 1):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
d.printFeatureHeader()
d.printAllFeature()
def main():
nbInput = 0
epoch = 30
file = IOHelper().checkArg(sys.argv)
if (len(file) < 2):
print("Missing file")
exit(1)
featuresId = range(7, 19)
### train
d = Dataset()
d.loadFile(file[0])
# nbInput = len(featuresId)
X, Y = generateDataset(d, featuresId)
X, nbInput = d.featureExpand(d, X)
X = d.featureRescale(d, X)
### test
d_test = Dataset()
d_test.loadFile(file[1])
X_test, Y_test = generateDataset(d_test, featuresId)
X_test, nbInput = d_test.featureExpand(d_test, X_test)
X_test = d_test.featureRescale(d_test, X_test)
houseArray = d.getFeature(1, uniq=True)
allclassifier = MultiClassifier(nbInput, houseArray)
lr = 10.0
oldLoss = 9e+9
allclassifier.setLr(lr)
# allclassifier.printInfo()
for j in range(epoch):
loss = allclassifier.train(X, Y)
allLoss = loss.sum()
if abs(allLoss) > abs(oldLoss) and lr > 0.000000001:
lr /= 10
print("DECREASE TO " + str(lr))
allclassifier.setLr(lr)
oldLoss = allLoss
allclassifier.saveWeight()
# y_true, y_pred = generatePrediction(allclassifier, X, Y)
y_true, y_pred = generatePrediction(allclassifier, X_test, Y_test)
# print(y_true)
# print(y_pred)
acc = accuracy_score(y_true, y_pred) * 100
print("epoch: {0:<15.5g} Loss1: {1:<15.5g} Loss2: {2:<15.5g} Loss3: {3:<15.5g} Loss4: {4:<15.5g} LOSS: {5:<15.5g} Accuracy: {6:<g}%" \
.format(j, loss[0], loss[1], loss[2], loss[3], allLoss, acc))
def main():
file = IOHelper().checkArg(sys.argv)
if (len(file) < 1):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
for index in range(6, 19):
x1 = d.getFeature(index, 1, 'Gryffindor')
x2 = d.getFeature(index, 1, 'Hufflepuff')
x3 = d.getFeature(index, 1, 'Ravenclaw')
x4 = d.getFeature(index, 1, 'Slytherin')
x1s = sorted(d.getFeature(index, 1, 'Gryffindor'))
x2s = sorted(d.getFeature(index, 1, 'Hufflepuff'))
x3s = sorted(d.getFeature(index, 1, 'Ravenclaw'))
x4s = sorted(d.getFeature(index, 1, 'Slytherin'))
ax = plt.subplot(1, 1, 1)
plt.tight_layout()
ax.set_xlim([-10, len(x1) + 10])
plt.scatter(np.arange(len(x1)),
x1,
c='b',
s=10,
alpha=0.3,
label='Gryffindor')
plt.scatter(np.arange(len(x2)),
x2,
c='g',
s=10,
alpha=0.3,
label='Hufflepuff')
plt.scatter(np.arange(len(x3)),
x3,
c='c',
s=10,
alpha=0.3,
label='Ravenclaw')
plt.scatter(np.arange(len(x4)),
x4,
c='r',
s=10,
alpha=0.3,
label='Slytherin')
plt.scatter(np.arange(len(x1)), x1s, c='b', s=10, alpha=0.3)
plt.scatter(np.arange(len(x2)), x2s, c='g', s=10, alpha=0.3)
plt.scatter(np.arange(len(x3)), x3s, c='c', s=10, alpha=0.3)
plt.scatter(np.arange(len(x4)), x4s, c='r', s=10, alpha=0.3)
plt.title(d.getName(index))
plt.ylabel('Worst <---> Best')
plt.xlabel('Evaluation')
plt.legend()
plt.tight_layout()
# plt.set_xlim([-10, len(x1) + 10])
plt.show()
def main():
file = IOHelper().checkArg(sys.argv)
if (len(file) < 1):
print("Missing file")
exit(1)
d = Dataset()
d.loadFile(file[0])
print(d.getName(1))
HOUSE = {}
for house in d.getFeature(1, uniq=True):
HOUSE[house] = {}
HOUSE[house]['1moy'] = []
HOUSE[house]['2min'] = []
HOUSE[house]['3q25'] = []
HOUSE[house]['4q50'] = []
HOUSE[house]['5q75'] = []
HOUSE[house]['6max'] = []
HOUSE['name'] = []
print(house)
d.printFeatureHeader()
index = 6
while index <= 18:
nom = d.getName(index)
nb = d.count(d.getFeature(index, 1, house))
moy = d.mean(d.getFeature(index, 1, house))
std = d.standardDeviation(d.getFeature(index, 1, house), moy)
min1 = d.min(d.getFeature(index, 1, house))
q25, q50, q75 = d.quartile(d.getFeature(index, 1, house))
max1 = d.max(d.getFeature(index, 1, house))
print("{0:<40s} {1:<15.5g} {2:<15.5g} {3:<15.5g} {4:<15.5g} {5:<15.5g} {6:<15.5g} {7:<15.5g} {8:<15.5g}" \
.format(nom, nb, std, moy, min1, q25, q50, q75, max1))
index += 1
HOUSE[house]['1moy'].append(moy)
HOUSE[house]['2min'].append(min1)
HOUSE[house]['3q25'].append(q25)
HOUSE[house]['4q50'].append(q50)
HOUSE[house]['5q75'].append(q75)
HOUSE[house]['6max'].append(max1)
HOUSE['name'].append(nom)
print("")
x = np.arange(len(HOUSE['Gryffindor']['1moy']))
width = 0.035
w = 0
for i in sorted(HOUSE['Gryffindor']):
# for i in sorted(HOUSE['Gryffindor'].iterkeys()):
if w == 0:
plt.bar(x + (width * (w + 1)),
HOUSE['Gryffindor'][i],
width,
color='b',
label='Gryffindor')
plt.bar(x + (width * (w + 2)),
HOUSE['Hufflepuff'][i],
width,
color='r',
label='Hufflepuff')
plt.bar(x + (width * (w + 3)),
HOUSE['Ravenclaw'][i],
width,
color='g',
label='Ravenclaw')
plt.bar(x + (width * (w + 4)),
HOUSE['Slytherin'][i],
width,
color='c',
label='Slytherin')
else:
plt.bar(x + (width * (w + 1)),
HOUSE['Gryffindor'][i],
width,
color='b')
plt.bar(x + (width * (w + 2)),
HOUSE['Hufflepuff'][i],
width,
color='r')
plt.bar(x + (width * (w + 3)),
HOUSE['Ravenclaw'][i],
width,
color='g')
plt.bar(x + (width * (w + 4)),
HOUSE['Slytherin'][i],
width,
color='c')
w += 4
plt.tight_layout()
plt.xticks(x, HOUSE['name'], rotation='vertical')
plt.xlabel('Cours')
plt.ylabel('Evaluation')
plt.legend()
plt.show()
