class ViralityPrediction:
SCORE_PLOT_FILENAME = "hashtags_score.png"
# If CLASSIFICATION is set to True, classification is used, otherwise regression
CLASSIFICATION = True
# K defines the number of results in the Top-K virality predictions
K = 10
def __init__(self,
normalize=False,
balance=False,
tweet_threshold=0,
score=False,
dump_model=True):
"""
Import or train the regression model
"""
self.model = RegressionModel()
if not self.model.load():
training_set, testing_set = RegressionModel.load_datasets(
balance=balance, viral_threshold=tweet_threshold)
if ViralityPrediction.CLASSIFICATION == True:
training_set = self.model.normaliseFeats(training_set)
testing_set = self.model.normaliseFeats(testing_set)
self.model.trainClassifier(training_set, normalize=normalize)
if score:
self.model.scoreClassifier(testing_set)
else:
self.model.trainRegression(training_set, normalize=normalize)
if score:
self.model.scoreRegression(testing_set)
if dump_model:
self.model.dump()
def predict(self, hashtags, hashtag_threshold=None):
"""
Return a dictionary containing for each hashtag its virality prediction
- hashtags: dictionary hashtag -> array of tweets features
- hashtag_threshold: if defined, the dictionary value will be a boolean
set at true if the hashtag goes viral, false otherwise.
If not defined, the value will be the number of retweets
Features are [followers_count, friends_count, listed_count,
statuses_count, hashtags_count, media_count, user_mention_count,
url_count, verified_account, is_a_retweet, tweet_length]
"""
values = {}
for key, value in hashtags.iteritems():
if ViralityPrediction.CLASSIFICATION:
tweets_values = self.model.predictClassifier(value)
else:
tweets_values = self.model.predictRegression(value)
hashtag_value = sum(tweets_values)
if hashtag_threshold is not None:
if hashtag_value >= hashtag_threshold:
values[key] = 1
else:
values[key] = 0
else:
# Round to the nearest 10 below the current value
values[key] = max(0,
int(math.floor(hashtag_value / 10.0)) * 10)
return values
def score(self,
expected,
predicted,
labels=None,
showPlot=True,
savePlot=False):
if showPlot or savePlot:
x = np.arange(len(expected))
width = 0.8
ticks = x + x * width
fig = plt.figure()
ax = fig.add_subplot(111)
bar1 = ax.bar(ticks, expected, color='green')
bar2 = ax.bar(ticks + width, predicted, color='blue')
ax.set_xlim(-width, (ticks + width)[-1] + 2 * width)
ax.set_ylim(0, max(max(expected), max(predicted)) * 1.05)
ax.set_xticks(ticks + width)
if labels is not None:
xtickNames = ax.set_xticklabels(labels)
plt.setp(xtickNames, rotation=45, fontsize=10)
ax.set_title('Expected and predicted retweet count per hashtag')
ax.legend((bar1[0], bar2[0]), ('Expected', 'Predicted'))
if savePlot:
plt.savefig(DataAnalyser.PLOT_DIR +
ViralityPrediction.SCORE_PLOT_FILENAME,
format='png')
if showPlot:
plt.show()
return np.mean(predicted - expected)**2
class ViralityPrediction:
SCORE_PLOT_FILENAME = "hashtags_score.png"
# If CLASSIFICATION is set to True, classification is used, otherwise regression
CLASSIFICATION = True
# K defines the number of results in the Top-K virality predictions
K = 10
def __init__(self, normalize=False, balance=False, tweet_threshold=0, score=False, dump_model=True):
"""
Import or train the regression model
"""
self.model = RegressionModel()
if not self.model.load():
training_set, testing_set = RegressionModel.load_datasets(
balance=balance, viral_threshold=tweet_threshold)
if ViralityPrediction.CLASSIFICATION == True:
training_set = self.model.normaliseFeats(training_set)
testing_set = self.model.normaliseFeats(testing_set)
self.model.trainClassifier(training_set, normalize=normalize)
if score:
self.model.scoreClassifier(testing_set)
else:
self.model.trainRegression(training_set, normalize=normalize)
if score:
self.model.scoreRegression(testing_set)
if dump_model:
self.model.dump()
def predict(self, hashtags, hashtag_threshold=None):
"""
Return a dictionary containing for each hashtag its virality prediction
- hashtags: dictionary hashtag -> array of tweets features
- hashtag_threshold: if defined, the dictionary value will be a boolean
set at true if the hashtag goes viral, false otherwise.
If not defined, the value will be the number of retweets
Features are [followers_count, friends_count, listed_count,
statuses_count, hashtags_count, media_count, user_mention_count,
url_count, verified_account, is_a_retweet, tweet_length]
"""
values = {}
for key, value in hashtags.iteritems():
if ViralityPrediction.CLASSIFICATION:
tweets_values = self.model.predictClassifier(value)
else:
tweets_values = self.model.predictRegression(value)
hashtag_value = sum(tweets_values)
if hashtag_threshold is not None:
if hashtag_value >= hashtag_threshold:
values[key] = 1
else:
values[key] = 0
else:
# Round to the nearest 10 below the current value
values[key] = max(0, int(math.floor(hashtag_value / 10.0)) * 10)
return values
def score(self, expected, predicted, labels=None, showPlot=True, savePlot=False):
if showPlot or savePlot:
x = np.arange(len(expected))
width = 0.8
ticks = x + x * width
fig = plt.figure()
ax = fig.add_subplot(111)
bar1 = ax.bar(ticks, expected, color='green')
bar2 = ax.bar(ticks + width, predicted, color='blue')
ax.set_xlim(-width, (ticks + width)[-1] + 2 * width)
ax.set_ylim(0, max(max(expected), max(predicted)) * 1.05)
ax.set_xticks(ticks + width)
if labels is not None:
xtickNames = ax.set_xticklabels(labels)
plt.setp(xtickNames, rotation=45, fontsize=10)
ax.set_title('Expected and predicted retweet count per hashtag')
ax.legend((bar1[0], bar2[0]), ('Expected', 'Predicted'))
if savePlot:
plt.savefig(DataAnalyser.PLOT_DIR + ViralityPrediction.SCORE_PLOT_FILENAME, format='png')
if showPlot:
plt.show()
return np.mean(predicted - expected) ** 2
