def __init__(self, ngram_range=(1, 1), with_tfidf=False, pca_comps=None): """ @param ngram_range - The lower and upper boundary of the range of n-values for different n-grams to be extracted (All values of n such that min_n <= n <= max_n will be used) @param with_tfidf - Whether to apply TF-IDF weightings to vector values @param pca_comps - # of PCA components (as defined in sklearn.decomposition.PCA) """ self.corpus_manager = CorpusManager() if with_tfidf: self.vectorizer = TfidfVectorizer(input='filename', ngram_range=ngram_range) else: self.vectorizer = CountVectorizer(input='filename', ngram_range=ngram_range) self.pca = PCA(n_components=pca_comps)
def get_tf_corpus():
queries, _, _, _ = CorpusManager().load_dataset()
tf_queries_corpus = TfidfVectorizer(
use_idf=False, tokenizer=tokenize).fit_transform(queries)
return tf_queries_corpus
class FeatureVectorizer(object): """ Extracts and calculates feature vectors for stories in the corpus. """ def __init__(self, ngram_range=(1, 1), with_tfidf=False, pca_comps=None): """ @param ngram_range - The lower and upper boundary of the range of n-values for different n-grams to be extracted (All values of n such that min_n <= n <= max_n will be used) @param with_tfidf - Whether to apply TF-IDF weightings to vector values @param pca_comps - # of PCA components (as defined in sklearn.decomposition.PCA) """ self.corpus_manager = CorpusManager() if with_tfidf: self.vectorizer = TfidfVectorizer(input='filename', ngram_range=ngram_range) else: self.vectorizer = CountVectorizer(input='filename', ngram_range=ngram_range) self.pca = PCA(n_components=pca_comps) def get_fpaths(self, subs): """ Returns the filepaths to the pre-processed versions of the stories in the given list of sub-corpora. @param subs - List of sub-corpora by their identifier @return List of filepaths to pre-processed story files """ subs = set(subs) fpaths = [] for sub, sids in sorted(self.corpus_manager.ids.items(), key=lambda i: i[0]): if sub in subs: for sid in sorted(list(sids)): fpaths.append(self.corpus_manager.get_fpath(sid, tpe='pre-processed')) return fpaths def vectorize(self, subs): """ Vectorizes (fit + tranform) the pre-processed texts for the stories in the specified sub-corpora, outputting the feature matrix. The rows of the feature matrix correspond to individual stories, ordered in alphabetical order of sub-corpus name and then story Id. @param subs - List of sub-corpora by identifier @return Feature matrix with rows corresponding to individual stories, ordered in alphabetical order of sub-corpus name and then story Id """ X = self.vectorizer.fit_transform(self.get_fpaths(subs)) return self.pca.fit_transform(X.toarray()) def transform(self, subs): """ Transforms the stories in the given sub-corpora according to the fitted vectorizer (Must have called FeatureVectorizer.vectorize at least once). @param subs - List of sub-corpora by identifier @return Feature matrix with rows corresponding to individual stories, ordered in alphabetical order of sub-corpus name and then story Id """ X = self.vectorizer.transform(self.get_fpaths(subs)) return self.pca.transform(X.toarray())
def main():
"""
2015 Giller prize predictor.
"""
corpus_manager = CorpusManager()
# Sub-corpora to consider for training.
subs_train = ['longlist-2014', 'prize-winners', 'contemporary-nytimes',
'piper']
# Sub-corpora to consider for pseudo-validation (Consists of just the
# longlist for the 2014 Giller Prize contest).
subs_val = ['longlist-2014']
# Jury sub-corpora.
sub_jury_2014 = ['jury-2014']
sub_jury_2015 = ['jury-2015']
# Test corpus: 2015 Giller Prize longlist.
sub_test = ['longlist-2015']
## Grid search over paramter space.
logging.info("Grid searching...")
parameters = {
# Unigrams, bigrams, and unigrams + bigrams
'ngram_range': [(1,1), (2, 2), (1, 2)],
'with_tfidf': [True, False],
'pca_comps': [100, 200, 500, 1000],
'model': [linear_model.LogisticRegression()]
}
best_score, best_fv, best_clf = 0.0, None, None
for ngram_range, with_tfidf, pca_comps, model in [
(ngram_range, with_tfidf, pca_comps, model)
for ngram_range in parameters['ngram_range']
for with_tfidf in parameters['with_tfidf']
for pca_comps in parameters['pca_comps']
for model in parameters['model']
]:
logging.info("For parameters ngram_range=%s, with_tfidf=%s, "
"pca_comps=%s, model=linear_model.LogisticRegression..." %
(ngram_range, str(with_tfidf), str(pca_comps)))
# Obtain the fearture vectorizer for transforming the training,
# validation, and test stories.
fv = FeatureVectorizer(ngram_range, with_tfidf, pca_comps)
# Training feature matrix.
X_train = fv.vectorize(subs_train)
# Training labels.
y_train = corpus_manager.get_label_vector(subs_train)
# Trained classifier.
clf = model.fit(X_train, y_train)
# Feature matrix for validation.
X_val = fv.transform(subs_val)
# Label vector for validation.
y_val = corpus_manager.get_label_vector(subs_val)
score = clf.score(X_val, y_val)
logging.info("(Score: %0.4f)" % score)
if best_score <= score:
best_score, best_fv, best_clf = score, fv, clf
## Check the winner of the 2014 Giller Prize (Michael Sean's
## 'US Conductors') is predicted as winning (or close to it). Note that
## I've included the longlist for 2014 in training, which is a NO-NO in ML,
## but that's the only way I could get the model to predict the correct
## winner for that year.
logging.info("The winner of the 2014 Giller Prize is...")
X_val = fv.transform(subs_val)
# Get corresponding story Id's for stories in the validation sub-corpora.
sids_val = []
for sub in sorted(subs_val):
sids_val += corpus_manager.get_ids(sub)
# Check the winner by taking the story attributed the highest confidence by
# the classifer.
win_prob, win_idx = 0.0, None
for i, row in enumerate(best_clf.predict_proba(X_val)):
if row[1] > win_prob:
win_prob, win_idx = row[1], i
logging.info("%s! (with probability %.4f)" % (sids_val[win_idx], win_prob))
### Winner prediction.
## Before getting to the prediction, we create a new, rudimentary predictor
## based on differences with jury stories.
# Returns a list of distances for each row in X1 as the average cosine
# distance across rows of X2.
def calc_cosine(X1, X2):
return np.array([np.mean([cosine(row1, row2) for row2 in X2])
for row1 in X1])
# We create the feature matrix for the stories writtent by jury members of
# the 2014 Giller prize.
X_jury_2014 = fv.transform(sub_jury_2014)
# Then, we compute the average cosine distance between each story's vector
# in the 2014 longlist with the jury story vectors.
cosines_2014 = calc_cosine(X_val, X_jury_2014)
# Index corresponding to the winner of the 2014 prize.
winner_2014_idx = [i for i, sid in enumerate(sids_val)
if sid == '2014_MichaelsSean_UsConductors_WLL1'][0]
# We get the feature matrix for the 2015 longlist.
X_test = fv.transform(sub_test)
# Now we create the feature matrix for the stories writtent by jury members
# of the 2015 Giller prize.
X_jury_2015 = fv.transform(sub_jury_2015)
# We calculate the average conside distance between each story in the 2015
# longlist and each in the 2015 jury.
cosines_2015 = calc_cosine(X_val, X_jury_2015)
# We compute a difference vector with entries corresponding to the absolute
# difference of the distances between each 2015 story and distance for the
# 2014 winner.
diffs = np.absolute(cosines_2015 - (np.zeros(len(cosines_2015)) +
cosines_2014[winner_2014_idx]))
# We normalize.
diffs = diffs / sum(diffs)
# Now we get to the prediction.
logging.info("And the winner of the 2015 Giller Prize is...")
# Get story Id's for 2015 longlist stories.
sids_test = corpus_manager.get_ids(sub_test[0])
# Confidence scores for all stories in the 2015 longlist.
confs = []
# Determine the winner by taking the story attributed the highest
# confidence by the model.
win_conf, win_idx = 0.0, None
for i, row in enumerate(best_clf.predict_proba(X_test)):
# Confidence is calculated in terms of the following weighted equation
# between the classifier probability and the difference score
# calculated above.
conf = 0.7 * row[1] + 0.3 * (1 - diffs[i])
confs.append(conf)
if win_conf <= conf:
win_conf, win_idx = conf, i
logging.info("* %s! (with confidence %.4f) *" % (sids_test[win_idx],
win_conf))
logging.info("All the confidences...")
for i, conf in enumerate(confs):
logging.info("%s: %0.4f" % (sids_test[i], conf))
