def evaluate():
text = input("Text file: ")
print("")
try:
print("Running text evaluation...\n")
with open("./data/" + text, 'r') as textFile:
EvaluateText.evaluate(textFile)
except IOError:
print("File not found. Returning to main menu...\n")
def do_test(tf, vf):
try:
print("\nRunning text evaluation...\n")
with open(tf, 'r') as textFile:
print ("text found")
with open(vf, 'r') as valueFile:
print ("values found")
EvaluateText.evaluate(textFile, valueFile)
except IOError:
print("File not found. Returning to main menu...\n")
def test():
text = input("Text file: ")
values = input("Value file: ")
print("")
try:
print("\nRunning text evaluation...\n")
with open("./data/" + text, 'r') as textFile:
with open("./data/" + values, 'r') as valueFile:
EvaluateText.evaluate(textFile, valueFile)
except IOError:
print("File not found. Returning to main menu...\n")
def button3():
answer = self.MessageBox(window=window)
text = txt_file.get()
values = value_file.get()
if answer=='yes' and text != '':
window.destroy()
try:
with open(text, 'r') as textFile:
with open(values, 'r') as valueFile:
EvaluateText.evaluate(textFile, valueFile)
except IOError:
MsgBox = tkMessageBox.showinfo('Error','File not found. Returning to main menu',parent=window)
def predictEmotion():
varStatusBar.set("")
with open("./data/Priors.csv", "r") as priorFile:
priors = priorFile.readline().strip().split(',')[1:]
priors = [log10(float(x)) for x in priors]
predValues = []
unfound = []
wf = WordFilter.WordFilter()
words = varGuiInput.get()
#print "Input:", words
words = wf.filterWords(words)
#print "Tokens:", words
for word in words:
try:
values = EvaluateText.evaluateWord(word)
except IOError:
varStatusBar.set("WordMap not found. Please train system first.")
raise
if values is not None:
predValues.append(values)
else:
unfound.append(word)
predValues = map(sum, zip(*predValues))
predProb = map(sum, zip(priors, predValues))
predEmotion = EvaluateText.guessEmotion(predProb)
varGuiOutput.set(predEmotion)
#print "Unfound:", unfound
print "Prob:", ', '.join([('%.2f') % x for x in predProb])
max = 10
max = getBarScale(predProb)
str = "Input:", words, "Tokens:", words, "Unfound:", unfound, " ", "Prob:", ', '.join(
[('%.2f') % abs(float("{0:.2f}".format(x)) + max) for x in predProb])
varStatusBar.set(str)
iterable = ([abs(float("{0:.2f}".format(x)) + max) for x in predProb])
emotions = np.fromiter(iterable, float)
objects = ("Empty", "Sadness", "Enthusiasm", "Neutral", "Worry",
"Surprise", "Love", "Fun", "Hate", "Happiness", "Boredom",
"Relief", "Anger")
y_pos = np.arange(len(objects))
fig = plt.figure(figsize=(13, 6))
plt.bar(np.asarray(y_pos, dtype='float'),
emotions,
align='center',
alpha=0.5,
color="blue")
plt.xticks(y_pos, objects)
plt.yticks([])
canvas = FigureCanvasTkAgg(fig, master=guiBox)
canvas.get_tk_widget().grid(row=4, columnspan=2)
canvas.draw()