def fast_predict(sc, file_input, file_output, sports_model, politics_model,
technology_model):
"""
Predicts using the provided models
"""
tweets = sc.textFile(file_input).map(parse_json).filter(
lambda x: is_valid(x) and is_english(x))
try:
print("Reading stored classification model")
sports = pickle.load(open(sports_model, 'rb'))
politics = pickle.load(open(politics_model, 'rb'))
technology = pickle.load(open(technology_model, 'rb'))
def predict_labels(tweet):
x = parse(tweet)
labels = []
if sports.predict(x) > 0.0:
labels.append("sports")
if politics.predict(x) > 0.0:
labels.append("politics")
if technology.predict(x):
labels.append("technology")
return labels
print("Computing predictions")
predictions = tweets.map(lambda t: (t, predict_labels(t)))
filtered_predictions = predictions.filter(lambda t: len(t[1]) == 1)
filtered_predictions.map(prediction_string).saveAsTextFile(file_output)
print("Done!")
except Exception as e:
print("Error:")
print(e)
def test(sc, file_positive, files_negative, file_model):
"""
Tests a classification model using positive samples in file_positive and
negative samples in file_negative. It prints the results to standard output
:param sc: The spark context
:type sc: SparkContext
:param file_positive: The file with tweets to predict
:type file_positive: str
:param files_negative: The files with tweets to reject
:type files_negative: list[str]
:param file_model: The file where the model is located
:type file_model: str
"""
tweets_positive = sc.textFile(file_positive).map(parse_json).filter(lambda x: is_valid(x) and is_english(x)).cache()
list_negatives = [sc.textFile(file_negative).map(parse_json).filter(lambda x: is_valid(x) and is_english(x)) for file_negative in files_negative]
tweets_negative = list_negatives[0]
for ln in list_negatives[1:]:
tweets_negative = tweets_negative.union(ln)
try:
print("Reading stored classification model")
model = pickle.load(open(file_model, 'rb'))
print("Computing predictions")
threshold = 0.0
total_positive = tweets_positive.count()
total_negative = tweets_negative.count()
true_positives = tweets_positive.filter(lambda x: model.predict(parse(x)) > threshold).count()
true_negatives = tweets_negative.filter(lambda x: model.predict(parse(x)) <= threshold).count()
false_negatives = total_positive - true_positives
false_positives = total_negative - true_negatives
print("Results for %s:" % file_model)
print(" Total positives: %d" % total_positive)
print(" Total negatives: %d" % total_negative)
print(" False positives: %d" % false_positives)
print(" False negatives: %d" % false_negatives)
precision = 0.0
recall = 0.0
try:
precision = float(true_positives) / float(true_positives + false_positives)
recall = float(true_positives) / float(true_positives + false_negatives)
except:
pass
print(" Precision: %f" % precision)
print(" Recall: %f" % recall)
print("Done!")
except Exception as e:
print("Error:")
print(e)
def fast_predict(sc, file_input, file_output, sports_model, politics_model, technology_model):
"""
Predicts using the provided models
"""
tweets = sc.textFile(file_input).map(parse_json).filter(lambda x: is_valid(x) and is_english(x))
try:
print("Reading stored classification model")
sports = pickle.load(open(sports_model, 'rb'))
politics = pickle.load(open(politics_model, 'rb'))
technology = pickle.load(open(technology_model, 'rb'))
def predict_labels(tweet):
x = parse(tweet)
labels = []
if sports.predict(x) > 0.0:
labels.append("sports")
if politics.predict(x) > 0.0:
labels.append("politics")
if technology.predict(x):
labels.append("technology")
return labels
print("Computing predictions")
predictions = tweets.map(lambda t: (t, predict_labels(t)))
filtered_predictions = predictions.filter(lambda t: len(t[1]) == 1)
filtered_predictions.map(prediction_string).saveAsTextFile(file_output)
print("Done!")
except Exception as e:
print("Error:")
print(e)