def calc_lda(tf_matrix, n_topics=10, max_iter=20):
lda = decomposition.LatentDirichletAllocation(n_topics=n_topics,
max_iter=max_iter,
learning_method='online',
learning_offset=50.)
doctopic = lda.fit_transform(tf_matrix)
return doctopic, lda
def summary():
if 'show_id' in request.args:
showrun = request.args.get('show_id')
states = State.query.filter_by(showrun=int(showrun)).order_by(
State.created_date.desc()).all()
texts = [s.text for s in states]
else:
texts = []
show_ids = request.args.get('show_ids')
show_ids = [int(x) for x in show_ids.split(',')]
for show_id in show_ids:
states = State.query.filter_by(showrun=show_id).order_by(
State.created_date.desc()).all()
t = [s.text for s in states]
texts.extend(t)
vectorizer = TfidfVectorizer(stop_words='english',
min_df=1,
tokenizer=tokenize_nltk)
dtm = vectorizer.fit_transform(texts).toarray()
vocab = np.array(vectorizer.get_feature_names())
#Define Topic Model: LatentDirichletAllocation (LDA)
clf = decomposition.LatentDirichletAllocation(n_topics=5, random_state=3)
num_top_words = 3
doctopic = clf.fit_transform(dtm)
topic_words = []
for topic in clf.components_:
word_idx = np.argsort(topic)[::-1][0:num_top_words]
topic_words.append([vocab[i] for i in word_idx])
merged = list(set(itertools.chain.from_iterable(
topic_words))) # use set for unique items
return jsonify({'data': {'topics': merged}})
def runLDA(train, test, dev, numComponents, applianceName):
# Function that run the Latent Dirichlet Allocation
# It takes in a train,test and dev dataset that comprises frequency distributions
# numComponents specifies the number of clusters or topics for the model
# It returns the perplexity score of the dev set, and the clusters assignements on the train,dev and test set
model = skld.LatentDirichletAllocation(n_components=numComponents,
verbose=0)
model.fit(train)
predictions = model.transform(test)
test_classification = np.argmax(predictions, 1)
test_classification = test_classification.astype(int)
predictions2 = model.transform(dev)
dev_classification = np.argmax(predictions2, 1)
dev_classification = dev_classification.astype(int)
predictions3 = model.transform(train)
train_classification = np.argmax(predictions3, 1)
train_classification = train_classification.astype(int)
if numComponents > 10:
nRows = 3
else:
nRows = 2
perplex = model.perplexity(dev)
#compare_distributions(test,dev_classification,numComponents,nRows,applianceName)
#compare_distributions(dev, dev_classification, numComponents, nRows, applianceName)
return perplex, train_classification, dev_classification, test_classification
def latent_dirichlet():
'''
Fits a latent dirichlet allocation model to the corpus of organization
descriptions and/or self-reported IRS designations. Then prints the top
10 words in each component. Does NOT return the model for further use.
'''
n_top_words = 10
connect = sql.connect("myform/with_coords")
db = connect.cursor()
query = '''SELECT text_dump, pp_text FROM mcp'''
corpus = []
for all_text in db.execute(query).fetchall():
writing = ""
for alpha in all_text:
if alpha:
writing += alpha + " "
corpus.append(writing)
new_stops = ["chicago", "illinois", "founded", "year"]
vect = sktext.CountVectorizer(stop_words = STOP_WORDS + new_stops)
dtm = vect.fit_transform(corpus)
model = skdecomp.LatentDirichletAllocation(
n_components=N_CLUST,
max_iter=5,
learning_method='online',
learning_offset=50)
model.fit(dtm)
tf_feature_names = vect.get_feature_names()
print_top_words(model, tf_feature_names, n_top_words)
def __init__(self, config):
self.topics_name = 'X_topics'
self.name = 'lda_model'
self.model = decomposition.LatentDirichletAllocation(
n_components=config['n_components'],
learning_method=config['learning_method'],
max_iter=config['max_iter'])
def train_lda_model(x_train_count, count_vector):
""" train a LDA Model """
lda_model = decomposition.LatentDirichletAllocation(
n_components=20, learning_method='online', max_iter=20)
x_topics = lda_model.fit_transform(x_train_count)
topic_word = lda_model.components_
vocab = count_vector.get_feature_names()
return x_topics, topic_word, vocab
def modeling(docs, alpha, beta, num_words, num_topics):
learning = sk.LatentDirichletAllocation(num_topics,
alpha,
beta,
learning_method='batch',
max_iter=5000)
learning.fit(docs)
return (learning.transform(docs), learning.components_)
def Linear_discriminant_analysis(self, source):
min_max_scaler = preprocessing.MinMaxScaler()
data_source = min_max_scaler.fit_transform(source)
pca = decomposition.LatentDirichletAllocation(n_components=2)
#居然LDA没有？？？
result = {}
result['data'] = pca.fit_transform(data_source)
result['params'] = 0
return result
def lda(
n_topics: int,
name: Optional[str] = "lda",
) -> TopicModelingOperation:
model = skdecomp.LatentDirichletAllocation(n_components=n_topics)
return TopicModelingOperation(
model=model,
name=name,
)
def extract_topics_lda(data_samples, preprocessor, n_features, n_topics, n_top_words, n_gram_range=(1,1), more_stopwords=None):
lda = decomposer.LatentDirichletAllocation(n_topics=n_topics, max_iter=5,
learning_method='online', learning_offset=50.,
random_state=0)
topics_words = _extract_topics_decomposer(data_samples, preprocessor, lda, n_features, n_topics, n_top_words, n_gram_range, more_stopwords)
return topics_words
def run_latent_dirichlet_allocation(X=None):
lda = decomposition.LatentDirichletAllocation()
if X is None:
X = users_as_real_vectors(users)
p = lda.fit_transform(X)
mask = (p[:, 0] * p[:, 0] + p[:, 1] * p[:, 1] < 12)
p = p[mask]
plt.figure()
plt.scatter(p[:, 0], p[:, 1])
plt.show()
def extract_topics(data_samples, lang, n_features, n_topics, n_top_words, more_stopwords=None):
n_gram_range = (2, 2)
t0 = time()
preprocessor = None
if lang in ["en", "english"]:
preprocessor = TokenHandler.EnTokenHandler(stemming=True, stopword=True)
if lang in ["tr", "turkish"]:
preprocessor = TokenHandler.TrTokenHandler(stopword=True, more_stopwords=more_stopwords,
stemming=False,
remove_numbers=True,
deasciify=True, remove_punkt=True)
'''
tf_vectorizer = txtfeatext.CountVectorizer(tokenizer=preprocessor,
ngram_range=(1, 2),
max_features=n_features)
tf_matrix = tf_vectorizer.fit_transform(data_samples)
'''
tfidf_vectorizer = txtfeatext.TfidfVectorizer(tokenizer=preprocessor,
ngram_range=n_gram_range,
max_features=n_features)
tfidf_matrix = tfidf_vectorizer.fit_transform(data_samples)
t1 = time()
print("1- Vectorizing took ", (t1-t0), "sec.")
# apply NMF
'''
print("Applying NMF on tf*idf weighted terms, n_samples=%d and n_features=%d..."
% (n_samples, n_features))
'''
nmf = decomposer.NMF(n_components=n_topics, random_state=1, alpha=.1, l1_ratio=.5).fit(tfidf_matrix)
print("\nTopics in NMF model:")
print_topic_words(nmf, tfidf_vectorizer, n_top_words)
#nmf_topics = get_topic_words(model, vectorizer, n_top_words)
t2 = time()
print("NMF took ", t2 - t1, "sec.")
#print("Applying LDA on tf weighted terms, n_samples=%d and n_features=%d..." % (n_samples, n_features))
lda = decomposer.LatentDirichletAllocation(n_topics=n_topics, max_iter=5,
learning_method='online', learning_offset=50.,
random_state=0)
lda.fit(tfidf_matrix)
print("\nTopics in LDA model:")
print_topic_words(lda, tfidf_vectorizer, n_top_words)
t3 = time()
print("LDA took ", t3 - t2, "sec.")
'''
def perform_lda(max_df, min_df, topics, ngram):
vectorizer = CountVectorizer(stop_words='english',
max_df=max_df,
min_df=min_df,
ngram_range=ngram)
matrixX = vectorizer.fit_transform(wordX)
lda = d.LatentDirichletAllocation(n_components=topics,
max_iter=10,
verbose=1)
lda.fit(matrixX)
return [lda, vectorizer, max_df, min_df, topics, ngram]
def LDA(num_topics, num_top_words, deck):
vectorizer = CountVectorizer(tokenizer=word_tokenize)
X = vectorizer.fit_transform(deck)
X_vocab = np.array(vectorizer.get_feature_names())
lda = decomposition.LatentDirichletAllocation(n_topics=num_topics,learning_method='online')
lda.fit_transform(X)
lda_topic_words = []
for topic in lda.components_:
word_idx = np.argsort(topic)[::-1][0:num_top_words]
lda_topic_words.append([X_vocab[i] for i in word_idx])
lda_topic_words
def topic_modelling(flag): #function for tokenization, training etc
pd.set_option('display.max_colwidth', -1)
X_train, x_test = train_test_split(reviews_datasets,
test_size=0.9,
random_state=111)
# printx_test,typex_test))
vectorizer_tf = TfidfVectorizer(tokenizer=tokenize,
stop_words='english',
max_df=0.75,
min_df=50,
max_features=10000,
use_idf=False,
norm=None)
tf_vectors = vectorizer_tf.fit_transform(X_train.text)
if (flag == 1):
lda = decomposition.LatentDirichletAllocation(n_components=10,
max_iter=3,
learning_method='online',
learning_offset=50,
n_jobs=-1,
random_state=111)
with open("lda_model.pk", "wb") as f:
pickle.dump(lda, f)
else:
with open("lda_model.pk", "rb") as f:
lda = pickle.load(f)
W1 = lda.fit_transform(tf_vectors)
H1 = lda.components_
num_words = 15
vocab = np.array(vectorizer_tf.get_feature_names())
top_words = lambda t: [vocab[i] for i in np.argsort(t)[:-num_words - 1:-1]]
topic_words = ([top_words(t) for t in H1])
topics = [' '.join(t) for t in topic_words]
colnames = ["Topic" + str(i) for i in range(lda.n_components)]
docnames = ["Doc" + str(i) for i in range(len(X_train.text))]
df_doc_topic = pd.DataFrame(np.round(W1, 2),
columns=colnames,
index=docnames)
df_doc_topic = pd.DataFrame(np.round(W1, 2),
columns=colnames,
index=docnames)
topic_important = np.argmax(df_doc_topic.values, axis=1)
df_doc_topic['most_matched_topic'] = topic_important
print("Log Likelihood: ", lda.score(tf_vectors))
print("Perplexity: ", lda.perplexity(tf_vectors))
return lda, vectorizer_tf, topics
def _topics_extraction_with_lda(X):
tf_vectorizer = TfidfVectorizer(min_df=2,
ngram_range=(1, 2),
stop_words=get_stop_words('nl'))
data = tf_vectorizer.fit_transform(X)
#print('Before LDA, sample size: {}'.format(data.shape))
best_params_ = {
'n_topics': 4,
'max_iter': 100,
'n_jobs': -1,
'learning_method': 'batch'
}
lda = decomposition.LatentDirichletAllocation(**best_params_)
data = lda.fit_transform(data)
#print('After LDA, sample size: {}'.format(data.shape))
return data
def construct_model ( self ):
'''
Learn an 10 topic LDA model on the wine descriptions provided in
the database.
'''
df = self.df
tf_vectorizer = CountVectorizer(max_df=0.95, min_df=2,
max_features=2000,
stop_words='english')
tf_names = tf_vectorizer.fit_transform ( df['description'].dropna() )
model = decomposition.LatentDirichletAllocation ( )
model.fit ( tf_names )
self.model = model
self.tf_vectorizer = tf_vectorizer
def get_top_k_latent_semantics(k, model, gesture_word_matrix):
if model == "PCA":
model = decomposition.PCA(n_components=top_k_input)
if model == "SVD":
model = decomposition.TruncatedSVD(n_components=top_k_input)
if model == "NMF":
model = decomposition.NMF(n_components=top_k_input, max_iter=10000)
if model == "LDA":
model = decomposition.LatentDirichletAllocation(n_components=top_k_input)
model.fit(gesture_word_matrix)
# take the top-k latent semantics
top_k_matrix = model.components_
return top_k_matrix
def get_lda_df():
lda = decomp.LatentDirichletAllocation(
n_topics=data_config.lda_num_topics,
doc_topic_prior=1./200,
topic_word_prior=1./200,
)
print "getting sample 1"
lda_w_2_idx, lda_sample = models.create_lda_sample(p=.1)
print "partial fit 1"
lda.partial_fit(lda_sample)
print "getting sample 2"
_, lda_sample = models.create_lda_sample(p=.1)
print "partial fit 2"
lda.partial_fit(lda_sample)
lda_mean = {}
lda_mean_mean = {}
#lda_max = {}
print "labeling corpus"
for day, comments in corpus.get_day_preprocessed_comments(p=.25, include_oos=True):
print day
coo_dict = coll.defaultdict(int)
for doc_idx, sent_tokens in enumerate(comments):
for sent in sent_tokens:
for token in sent:
if token not in lda_w_2_idx:
continue
word_idx = lda_w_2_idx[token]
coo_dict[doc_idx, word_idx] += 1
lda_day = lda.transform(util.d_to_sparse(coo_dict, shape=(doc_idx+1, lda.components_.shape[1])))
lda_day_mean = (lda_day.T / lda_day.sum(axis=1)).T
lda_mean[day] = lda_day.mean(axis=0)
lda_mean_mean[day] = lda_day_mean.mean(axis=0)
#lda_max[day] = lda_day.max(axis=0)
lda_idx_2_w = {idx: w for w, idx in lda_w_2_idx.viewitems()}
days, lda_reps = zip(*sorted(lda_mean.items(), key=lambda x:x[0]))
df_lda_mean = pd.DataFrame(map(tuple, lda_reps), index=days)
days, lda_reps = zip(*sorted(lda_mean_mean.items(), key=lambda x:x[0]))
df_lda_mean_mean = pd.DataFrame(map(tuple, lda_reps), index=days)
days, lda_reps = zip(*sorted(lda_max.items(), key=lambda x:x[0]))
df_lda_max = pd.DataFrame(map(tuple, lda_reps), index=days)
return lda, lda_w_2_idx, pd.DataFrame(map(tuple, lda_reps), index=days)
def Dirichlet(documents, vectorizer):
dtm = vectorizer.fit_transform(documents).toarray()
lda = decomposition.LatentDirichletAllocation(n_topics=num_topics,
max_iter=50,
learning_method='online',
learning_offset=50.,
random_state=0,
verbose=1,
evaluate_every=1,
doc_topic_prior=0.2,
topic_word_prior=0.6)
lda.fit(dtm)
print_top_words(lda, vectorizer)
return lda
def generate_topic(data, num_topic=10):
vector = CountVectorizer()
vector.fit(data)
vocab = vector.vocabulary_
vector = CountVectorizer(stop_words="english", vocabulary=vocab.keys())
X = vector.fit_transform(data)
lda = decomposition.LatentDirichletAllocation(n_topics=num_topic,
learning_method="online")
for day in range(X.shape[0] - 1, -1, -1):
lda.partial_fit(X[day, :])
doc_topic = lda.transform(X[day, :])
alpha = sum(doc_topic) / len(doc_topic)
eta = sum(doc_topic) / len(doc_topic)
lda.set_params(doc_topic_prior=alpha, topic_word_prior=eta)
doc_topic = lda.transform(X)
doc_topic = pandas.DataFrame(doc_topic)
return doc_topic
def fit_and_predict_LDA(num_topics, num_top_words, vocab, dtm_train, dtm_test):
"""
Fit the LDA topic modeling to the training document term matrix.
Using the generated topics, map test document term matrix to
document to topic matrix. Also return topic words.
Parameters
----------
num_topics: int
number of topics NMF decomposition should generate
num_top_words: int
number of topic words stored in topic_words list
vocab: set
set of unique terms in the reviews
dtm_train: scipy sparse matrix
Data for training (matrix with features, e.g. BOW or tf-idf)
dtm_test: scipy sparse matrix
Data for testing and used for 'prediction' (matrix with features, e.g. BOW or tf-idf)
Returns
-------
Tuple(numpy.ndarray, set)
Returns doctopic, topic_words as a tuple
"""
lda = decomposition.LatentDirichletAllocation(n_topics=num_topics, random_state=1)
lda.fit(dtm_train)
doctopic = lda.transform(dtm_test)
#scale the document-component matrix such that the component values associated with each document sum to one
doctopic = doctopic / np.sum(doctopic, axis=1, keepdims=True)
topic_words = []
for topic in lda.components_: # components is the topic-term matrix
word_idx = np.argsort(topic)[::-1][0:num_top_words]
topic_words.append([vocab[i] for i in word_idx])
print_top_5_topics(doctopic, len(lda.components_), topic_words)
# for t in range(len(topic_words)):
# print("Topic {}: {}".format(t+1, ' '.join(topic_words[t][:10])))
# I just hard coded the type of tokenization, because I didn't want to over-complicate the arguments to this function
pickle.dump((doctopic, topic_words), open("pickles/lda-np-"+str(num_topics)+"-doctopic-topic_words.p", "wb"))
return (doctopic, topic_words)
def process_lda():
# inputs
file_lines = []
lines = []
with codecs.open(sys.argv[1], encoding='UTF-8') as f:
file_lines = f.read().splitlines()
#lines = cleanup(file_lines)
lines = file_lines
# params for LDA
n_feats = 250 #1000
n_topics = 20
n_top_words = 50
# getting a custom stop-words list
en_stop_words = []
en_stop_words = stop_words()
# use tf (raw term count) features for LDA
tf_vectorizer = text.CountVectorizer(
max_df=0.95,
min_df=2,
#max_features=n_feats, stop_words='english')
max_features=n_feats,
stop_words=en_stop_words)
tf = tf_vectorizer.fit_transform(lines)
tf_feature_names = tf_vectorizer.get_feature_names()
# fit an LDA model to the tf feats of the textual data
lda = decomposition.LatentDirichletAllocation(max_iter=10,
learning_method='online',
learning_offset=50.,
random_state=0,
verbose=1).fit(tf)
# outputs
with open(
sys.argv[2] + "/lda." + sys.argv[1].split("/")[-1:].pop() + ".out",
"w") as f:
messages = print_top_words(lda, tf_feature_names, n_top_words)
for m in messages:
f.write("{}\n".format(m.encode('utf-8')))
def extract_topic(str_arg, num_topics=1, num_top_words=3):
vectorizer = text.CountVectorizer(input='content',
analyzer='word',
lowercase=True,
stop_words='english')
dtm = vectorizer.fit_transform(str_arg.split())
vocab = np.array(vectorizer.get_feature_names())
#clf = decomposition.NMF(n_components=num_topics, random_state=1) ## topic extraction
clf = decomposition.LatentDirichletAllocation(n_components=num_topics,
learning_method='online')
clf.fit_transform(dtm)
topic_words = []
for topic in clf.components_:
word_idx = np.argsort(topic)[::-1][
0:num_top_words] ##[::-1] reverses the list
topic_words.append([vocab[i] for i in word_idx])
return topic_words
def lda(df,
cat=[
'Name',
'Platform',
'Genre',
'Developer',
'Year_of_Release',
'Rating',
]):
for v in tqdm(itertools.permutations(cat, 2)):
# co-occurence matrix
n_comp = 3
if (v[0] in ['Publisher', 'Name', 'Developer']) & (
v[1] in ['Publisher', 'Name', 'Developer', 'Rating']):
continue
if (v[0] == 'Platform_Genre') & (v[1] in ['Platform', 'Genre']):
continue
if (v[1] == 'Platform_Genre') & (v[0] in ['Platform', 'Genre']):
continue
if f'lda1_{v[0]}_{v[1]}' not in df.columns.values.tolist():
print(f'{v[0]} vs {v[1]}')
agg_df = pd.crosstab(df[v[0]], df[v[1]])
# lda
trans = decomposition.LatentDirichletAllocation(
n_components=n_comp, random_state=42)
trans2 = decomposition.NMF(n_components=n_comp,
max_iter=8000,
random_state=42)
trans3 = decomposition.PCA(n_components=n_comp, random_state=42)
lda_df = add_transformed(agg_df, trans, v, method='lda')
nmf_df = add_transformed(agg_df, trans2, v, method='nmf')
pca_df = add_transformed(agg_df, trans3, v, method='pca')
# merge
df = df.merge(lda_df, how='left', on=v[0])
df = df.merge(nmf_df, how='left', on=v[0])
df = df.merge(pca_df, how='left', on=v[0])
return df
def get_lda_features(features, xtrain_count):
# create a count vectorizer object
count_vect = CountVectorizer(analyzer='word', token_pattern=r'\w{1,}')
count_vect.fit(features)
# train a LDA Model
lda_model = decomposition.LatentDirichletAllocation(
n_components=20, learning_method='online', max_iter=20)
X_topics = lda_model.fit_transform(xtrain_count)
topic_word = lda_model.components_
vocab = count_vect.get_feature_names()
# view the topic models
n_top_words = 10
topic_summaries = []
for i, topic_dist in enumerate(topic_word):
topic_words = numpy.array(vocab)[numpy.argsort(
topic_dist)][:-(n_top_words + 1):-1]
topic_summaries.append(' '.join(topic_words))
return topic_summaries
def clustervis_pipelines(visdim):
return {
'PCA': Pipeline([
('sca', preprocessing.MaxAbsScaler()),
('clu', decomposition.PCA(n_components=visdim)),
('sca2', preprocessing.MaxAbsScaler()),
]),
'NMF': Pipeline([
('sca', preprocessing.MaxAbsScaler()),
('clu', decomposition.NMF(n_components=visdim, random_state=1, alpha=.1, l1_ratio=.5)),
('sca2', preprocessing.MaxAbsScaler()),
]),
'LDA': Pipeline([
('sca', preprocessing.MaxAbsScaler()),
('clu', decomposition.LatentDirichletAllocation(n_components=visdim, learning_method='online')),
('sca2', preprocessing.MaxAbsScaler()),
]),
'SVD': Pipeline([
('sca', preprocessing.MaxAbsScaler()),
('clu', decomposition.TruncatedSVD(n_components=visdim)),
('sca2', preprocessing.MaxAbsScaler()),
])
}
def perform(self):
lda = skd.LatentDirichletAllocation(n_components=self.n_components, random_state=self.random_state)
transform = lda.fit_transform(self.data)
return transform
terms = vectorizer.get_feature_names()
for i in range(true_k):
print("Cluster %d:" % i),
for ind in order_centroids[i, :10]:
print(' %s' % terms[ind]),
print
print("\n")
print("Prediction")
prediction = model.predict(valid_x)
print(prediction)'''
# train a LDA Model
lda_model = decomposition.LatentDirichletAllocation(n_components=20, learning_method='online', max_iter=20)
X_topics = lda_model.fit_transform(xtrain_count)
topic_word = lda_model.components_
vocab = count_vect.get_feature_names()
# view the topic models
n_top_words = 10
topic_summaries = []
for i, topic_dist in enumerate(topic_word):
topic_words = numpy.array(vocab)[numpy.argsort(topic_dist)][:-(n_top_words+1):-1]
topic_summaries.append(' '.join(topic_words))
def train_model(classifier, feature_vector_train, label, feature_vector_valid, is_neural_net=False):
# fit the training dataset on the classifier
classifier.fit(feature_vector_train, label)
#dtm = extract_tfidf_dtm(documents, advanced_parsing.extract_np_tokens)
count_vect = pickle.load(open("pickles/np-30000-count-vect.p", "rb"))
tfidf_transformer = pickle.load(open("pickles/np-30000-tfidf-trans.p", "rb"))
dtm = pickle.load(open("pickles/np-30000-dtm.p", "rb"))
import numpy as np # a conventional alias
import sklearn.feature_extraction.text as text
from sklearn import decomposition
num_topics = 60
num_top_words = 20
lda = decomposition.LatentDirichletAllocation(n_topics=num_topics,
random_state=1)
# this next step may take some time
doctopic = nmf.fit_transform(dtm)
doctopic = doctopic / np.sum(doctopic, axis=1, keepdims=True)
# print words associated with topics
topic_words = []
for topic in nmf.components_:
word_idx = np.argsort(topic)[::-1][0:num_top_words]
topic_words.append([vocab[i] for i in word_idx])
print(topic_words)
print(word_idx)
#
#