def traverse_movies_OLS(): LBMAP = getLBMap() DMAP = createEmpty() P_ERRORS, ERRORS = [], [] training_data, training_response = [], [] for i in range(len(data)): movie = data[i] m_rev = movie['revenue'] myvector = vectorizeMovie(movie, LBMAP, DMAP) if i > 100: model = LinearRegression() model.fit(training_data, training_response) raw = math.fabs(model.predict(myvector) - m_rev) ERRORS.append(raw) P_ERRORS.append(round(raw/m_rev, 4)) training_data.append(myvector) training_response.append(m_rev) DMAP = update(movie, DMAP) print 'all', avg_float_list(P_ERRORS) print 'all', avg_float_list(ERRORS) print 'all', avg_float_list(ERRORS[3200:])
def traverse_movies_OLS():
LBMAP = getLBMap()
DMAP = createEmpty()
P_ERRORS, ERRORS = [], []
training_data, training_response = [], []
for i in range(len(data)):
movie = data[i]
m_rev = movie['revenue']
myvector = vectorizeMovie(movie, LBMAP, DMAP)
if i > 100:
model = LinearRegression()
model.fit(training_data, training_response)
raw = math.fabs(model.predict(myvector) - m_rev)
ERRORS.append(raw)
P_ERRORS.append(round(raw / m_rev, 4))
training_data.append(myvector)
training_response.append(m_rev)
DMAP = update(movie, DMAP)
print 'all', avg_float_list(P_ERRORS)
print 'all', avg_float_list(ERRORS)
print 'all', avg_float_list(ERRORS[3200:])
def vectorizeMovie(movie, LBMap, DMAP, Discrete=False, Sentiment=False):
OUT = []
for field in ['month', 'mpaa', 'genres']:
OUT += getVector(field, movie[field], LBMap)
OUT += [discretize(movie['runtime'], 90, Discrete)]
OUT += [discretize(movie['budget'], M50, Discrete)]
for entity in movie['stars']:
e_name = entity['name']
v = DMAP['stars'][e_name]
for subfield in ['high', 'avg']:
OUT += [discretize(v[subfield], M100, Discrete)]
for field in ['directors', 'writers', 'production']:
ls_vals = []
for entity in movie[field]:
e_name = entity['name']
ls_vals += [DMAP[field][e_name]['avg']]
OUT += [discretize(avg_float_list(ls_vals), M100, Discrete)]
if Sentiment:
tag = movie['tagline']
if tag:
tblob = TextBlob(tag.strip().encode('ascii', 'ignore'))
if float(tblob.sentiment.polarity) >= 0.0:
OUT += [1]
else: OUT += [0]
else: OUT += [1]
return OUT
def vectorizeMovie(movie, LBMap, DMAP, Discrete=False, Sentiment=False):
OUT = []
for field in ['month', 'mpaa', 'genres']:
OUT += getVector(field, movie[field], LBMap)
OUT += [discretize(movie['runtime'], 90, Discrete)]
OUT += [discretize(movie['budget'], M50, Discrete)]
for entity in movie['stars']:
e_name = entity['name']
v = DMAP['stars'][e_name]
for subfield in ['high', 'avg']:
OUT += [discretize(v[subfield], M100, Discrete)]
for field in ['directors', 'writers', 'production']:
ls_vals = []
for entity in movie[field]:
e_name = entity['name']
ls_vals += [DMAP[field][e_name]['avg']]
OUT += [discretize(avg_float_list(ls_vals), M100, Discrete)]
if Sentiment:
tag = movie['tagline']
if tag:
tblob = TextBlob(tag.strip().encode('ascii', 'ignore'))
if float(tblob.sentiment.polarity) >= 0.0:
OUT += [1]
else:
OUT += [0]
else:
OUT += [1]
return OUT
def graph_regression():
results = {
'Overall': [0, 0, []],
'All Correct': [0, 0, []],
'<100M Correct': [0, 0, []],
'100M+ Correct': [0, 0, []]
}
with open('combined.csv', 'r') as f:
for line in f:
vals = line.split(',')
true, pred, err = vals[0], vals[1], float(vals[2])
all_c, c0_c, c1_c = False, False, False
if true == pred:
all_c = True
if true == '1':
c1_c = True
else:
c0_c = True
for key in results.keys():
if ((key == 'Overall') or (key == 'All Correct' and all_c)
or (key == '<100M Correct' and c0_c)
or (key == '100M+ Correct' and c1_c)):
n, ravg = results[key][0], results[key][1]
new_avg = ((n * ravg) + err) / float(n + 1)
results[key][0], results[key][1] = (n + 1), new_avg
results[key][2] += [new_avg]
else:
results[key][0] += 1
results[key][2] += [results[key][1]]
for key in ['Overall', 'All Correct', '<100M Correct', '100M+ Correct']:
model, scores = key, results[key][2]
print model, avg_float_list(scores)
plt.plot(range(len(scores)), scores, label=model, linewidth=2)
plt.legend()
plt.suptitle('Regression Performance on Correct Classifications')
plt.xlabel('Time Step')
plt.ylabel('Mean Absolute Error')
plt.ylim(10000000, 60000000)
plt.show()
def graph_regression():
results = { 'Overall' : [0, 0, []],
'All Correct' : [0, 0, []],
'<100M Correct' : [0, 0, []],
'100M+ Correct' : [0, 0, []] }
with open('combined.csv', 'r') as f:
for line in f:
vals = line.split(',')
true, pred, err = vals[0], vals[1], float(vals[2])
all_c, c0_c, c1_c = False, False, False
if true == pred:
all_c = True
if true == '1':
c1_c = True
else: c0_c = True
for key in results.keys():
if ((key == 'Overall') or (key == 'All Correct' and all_c)
or (key == '<100M Correct' and c0_c) or (key == '100M+ Correct' and c1_c)):
n, ravg = results[key][0], results[key][1]
new_avg = ((n * ravg) + err) / float(n+1)
results[key][0], results[key][1] = (n + 1), new_avg
results[key][2] += [new_avg]
else:
results[key][0] += 1
results[key][2] += [results[key][1]]
for key in ['Overall', 'All Correct', '<100M Correct', '100M+ Correct']:
model, scores = key, results[key][2]
print model, avg_float_list(scores)
plt.plot(range(len(scores)), scores, label=model, linewidth=2)
plt.legend()
plt.suptitle('Regression Performance on Correct Classifications')
plt.xlabel('Time Step')
plt.ylabel('Mean Absolute Error')
plt.ylim(10000000, 60000000)
plt.show()
