def classify_comment(self, comment, classifier_type='SVM', no_classes=5): sentiment = None preprocessor = Preprocessing() vectorizer = VectorQuantization() if classifier_type == 'SVM': preprocessed_comment = preprocessor.preprocessing(comment) comment_vector = vectorizer.morphosyntactic_vector(preprocessed_comment) sentiment = classifier_svm.classify(comment_vector) if no_classes == 5: return sentiment elif no_classes == 3: if sentiment == u'positivo' or sentiment == u'muy_positivo': return u'positivo' elif sentiment == u'negativo' or sentiment == u'muy_negativo': return u'negativo' else: return sentiment elif classifier_type == 'MNB': preprocessed_comment = preprocessor.preprocessing(comment) comment_vector = vectorizer.bigram_vector(preprocessed_comment) sentiment = classifier_mnb.classify(comment_vector) if no_classes == 5: return sentiment elif no_classes == 3: if sentiment == u'positivo' or sentiment == u'muy_positivo': return u'positivo' elif sentiment == u'negativo' or sentiment == u'muy_negativo': return u'negativo' else: return sentiment
def preprocess_tweets(self): """ Process tweets according to mode and set arrays """ processObject = Preprocessing(self.mode, self.tweets) processObject.preprocess_tweets() if ( "stem" in self.mode): self.stemmed_tweets_array = processObject.stemmed_tweets_array if ( "token" in self.mode): self.tokenized_tweets_array = processObject.tokenized_tweets_array if ( "pos" in self.mode): self.pos_tweets_array = processObject.pos_tweets_array if ( "lemma" in self.mode): self.lemmatized_tweets_array = processObject.lemmatized_tweets_array
def preprocess_tweets(self, mode, tweets_dict, filename):
""" Process tweets according to mode and set arrays """
processObject = Preprocessing(mode, tweets_dict, filename)
processObject.preprocess_tweets()
if "stem" in mode:
self.stemmed_tweets_array = processObject.stemmed_tweets_array
if "token" in mode:
self.tokenized_tweets_array = processObject.tokenized_tweets_array
if "pos" in mode:
self.pos_tweets_array = processObject.pos_tweets_array
if "lemma" in mode:
self.lemmatized_tweets_array = processObject.lemmatized_tweets_array
def main(args, config):
wDir = os.getcwd()
#Instance Preprocessing class
window = Preprocessing(args.fasta_file, config['win_length'], config['win_step'])
window.output_window()
print >> sys.stderr, "Creating windows_sequence.fasta"
#Instance Similarity and Composition class
sim = Similarity(args.fasta_file, config['score_adj'],wDir)
sim_matrix = sim.mcl_perform()
comp_results = Composition(config['kmer_len'])
comp_matrix = comp_results.joined()
#Join similarity and composition matrix for PCA
join = pd.concat([comp_matrix, sim_matrix], axis= 1, join='inner')
print >> sys.stderr, "Calculating similarity and composition matrix"
#Instance Reduction class
pca = Reduction(join, config['pca_comp'])
pca_data = pca.perform_pca()
print >> sys.stderr, "Performing PCA"
#Instance Clustering class
cluster = Clustering(pca_data)
clust_obj = cluster.plot()
print >> sys.stderr, "Performing clustering plot"
#Instance ClusterReport class
report = ClusterReport(clust_obj)
file_name, querySeq = report.output_queryseq()
print >> sys.stderr, "Doing report of clusters"
#Instance Validate class
valid = Validate(file_name, args.fasta_file,wDir)
jfileComp, jfileMinus = valid.roundTwo()
print >> sys.stderr, "Validation of results"
#Instance ParseJplace Class
parsing = ParseJplace(jfileComp, jfileMinus)
corrMat = parsing.correlation()
print >> sys.stderr, "Doing profiles"
#Instance Profile Class
ttest = Profiles(corrMat, querySeq)
bestWin = ttest.windowsAssigment()
print >>sys.stderr, "Doing permutations"
#Instance StatsBinom
finalResult = StatsBinom(args.fasta_file, config['win_length'],bestWin)
finalResult.binomial()
cleaning(file_name)
def classify(self, nl_query):
#pos_tree = tagger.to_tree(nl_query)
tagged_yield = tagger.tagged_labeled_yield(nl_query)
pos_tree = []
for i in tagged_yield:
pos_tree.append(i['ValueAnnotation'])
pos_tree = " ".join(pos_tree)
_, labels, trees = Preprocessing.data()
text_clf = Pipeline([ ('vect', CountVectorizer(min_n=1, max_n=1)), ('tfidf', TfidfTransformer(use_idf=False)), ('clf', LinearSVC()) ])
_ = text_clf.fit(trees, labels)
predicted = text_clf.predict([pos_tree])[0]
predicted = Preprocessing.query(predicted)
return predicted
class Frequencies():
"""
Compute term frequencies (Top 100).
"""
def __init__(self, path, limit):
self.path = path
self.limit = limit
self.preprocessing = Preprocessing()
def countFreq(self):
t0 = time()
data = self.preprocessing.read_with_numpy(self.path, self.limit)
print("Number of loaded Tweets: " + str(len(data)) + " - loaded and preprocessed in %0.3fs" % (time()-t0))
print()
print("Count frequencies", end=": ")
t0 = time()
# Use a dictionary to keep track of the frequency of tokens.
token_counter = {}
# Get a line/tweet from the corpus.
for line in data:
# Split the text on whitespace to get a list of words.
for word in line.split():
# Counting with a dictionary the EAFP way.
# Try to add one to value of a key in the dictionary.
try:
token_counter[word] += 1
# If the dictionary raises a KeyError,
# add the key to the dictionary and set its value to 1.
except KeyError:
token_counter[word] = 1
# Sort the dictionary by frequency.
frequency_list = sorted(token_counter.iteritems(), key=lambda x: x[1],
reverse=True)
# Print the 100 most frequent tokens.
# print("Most frequent tokens (top 100):")
# index = 1
# for pair in frequency_list[:100]:
# print(str(index).ljust(2), pair[0].ljust(20), str(pair[1]))
# index += 1
# Print the 100 least frequent tokens.
print("\nLeast common tokens (top 100):")
index = 1
for pair in frequency_list[-10000:]:
if pair[1] < 11:
print(str(index).ljust(2), pair[0].ljust(20), str(pair[1]))
index += 1
print("done in %0.3fs" % (time()-t0))
def generalize_sql(query_list):
skeletons = []
for query in query_list:
skeleton = Preprocessing.to_skeleton(query)
skeletons.append(skeleton)
return skeletons
def getPreprocessed(self):
preprocessing = Preprocessing()
# postprocessing = Postprocessing()
frame = self.cam.get_frame()
pre_options = preprocessing.options
# Apply preprocessing methods toggled in the UI
preprocessed = preprocessing.run(frame, pre_options)
height, width, channels = frame.shape
# model_positions, regular_positions = self.vision.locate(frame)
# model_positions = postprocessing.analyze(model_positions)
# print model_positions
# frame_h = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
# print frame[(464-128), 111]
return preprocessed
def test(self):
_, labels, trees = Preprocessing.data()
test_labels = labels[2::3]
test_trees = trees[2::3]
labels = labels[0::3] + labels[1::3]
trees = trees[0::3] + trees[1::3]
text_clf = Pipeline([ ('vect', CountVectorizer(min_n=2, max_n=3)), ('tfidf', TfidfTransformer(use_idf=False)), ('clf', MultinomialNB()) ])
# text_clf = Pipeline([ ('vect', CountVectorizer(min_n=1, max_n=1)), ('tfidf', TfidfTransformer(use_idf=False)), ('clf', LinearSVC()) ])
_ = text_clf.fit(trees, labels)
predicted = text_clf.predict(test_trees)
accuracy = np.mean(predicted == test_labels)
return accuracy
def create(self, corpus_path, model_type="morphosyntactic"):
preprocessor = Preprocessing()
vectorizer = VectorQuantization()
document_list = []
with codecs.open(corpus_path, 'r', 'utf-8') as corpus:
line = corpus.readline()
while line:
comment = preprocessor.preprocessing(line.split('/|/')[1])
category = line.split('/|/')[2].split('\n')[0]
if model_type == "morphosyntactic":
comment_vector = vectorizer.morphosyntactic_vector(comment)
elif model_type == "bigram":
comment_vector = vectorizer.bigram_vector(comment)
else:
print "No model defined using default: morphosyntactic"
comment_vector = vectorizer.morphosyntactic_vector(comment)
if comment_vector:
document_list.append(pattern_Document(comment_vector,
type=category))
line = corpus.readline()
model = pattern_Model(documents=document_list, weight=None)
return model
def preprocess(self):
""" Preprocess the suspicious and source document. """
susp_fp = codecs.open(self.susp, 'r', 'utf-8')
self.susp_text = susp_fp.read()
self.susp_bow = Preprocessing.tokenize(self.susp_text, self.susp_offsets, self.susp_sents)
Preprocessing.ss_treat(self.susp_bow, self.susp_offsets, self.min_sentlen, self.rssent)
susp_fp.close()
src_fp = codecs.open(self.src, 'r', 'utf-8')
self.src_text = src_fp.read()
self.src_bow = Preprocessing.tokenize(self.src_text, self.src_offsets, self.src_sents)
Preprocessing.ss_treat(self.src_bow, self.src_offsets, self.min_sentlen, self.rssent)
src_fp.close()
def dumps_scale_test(self): instance=Preprocessing(self.config) instance.output_dumps_scale() pass
def one_map(df, y_col):
cols = df.columns
# # ['bkg_volumes_original', 'bkg_volumes_after', 'bkg_surfaces',
# 'bkg_size_z', 'bkg_size_x', 'bkg_size_y', 'bkg_max_value',
# 'bkg_sum_value', 'bkg_var_value', 'a_volumes', 'a_max_value',
# 'a_sum_value', 'a_var_value', 'b_volumes', 'b_max_value', 'b_sum_value',
# 'b_var_value', 'c_volumes', 'c_max_value', 'c_sum_value',
# 'c_var_value']
bkg_props = [[
'bkg_volumes_after', 'bkg_surfaces', 'bkg_size_z', 'bkg_size_x',
'bkg_size_y', 'bkg_max_value', 'bkg_sum_value', 'bkg_var_value'
]]
bkg_prop_combs = list(combinations(bkg_props, 2))
print(bkg_prop_combs)
all_X_cols = [
['bkg_volumes_after', 'bkg_surfaces'],
['bkg_size_z', 'bkg_size_x', 'bkg_size_y'],
['bkg_max_value', 'bkg_sum_value', 'bkg_var_value'],
[
'bkg_volumes_after', 'bkg_surfaces', 'bkg_size_z', 'bkg_size_x',
'bkg_size_y'
],
[
'bkg_size_z', 'bkg_size_x', 'bkg_size_y', 'bkg_max_value',
'bkg_sum_value', 'bkg_var_value'
],
[
'bkg_volumes_after', 'bkg_surfaces', 'bkg_size_z', 'bkg_size_x',
'bkg_size_y', 'bkg_max_value', 'bkg_sum_value', 'bkg_var_value'
],
]
all_X_cols += bkg_props + bkg_prop_combs
df = df[df[y_col] != "All nan"] # copy.copy()
print(df[df[y_col] == "All nan"])
filter_particles = [
k for k in df.index
if "particle_-1" not in k and "particle_1617" not in k
]
# pd.to_numeric(df[y_col], errors='coerce').isnull().index
# #
# abc_props = ["bkg_var_value", "a_volumes", "a_max_value", "a_sum_value"
# "a_var_value", "b_volumes", "b_max_value", "b_sum_value",
# "b_var_value", "c_volumes", "c_max_value", "c_sum_value",
# "c_var_value"]
# filter_particles = df.eq(df.loc[:, 0], axis=0).all(1)
for X_cols in all_X_cols:
X = np.array(df.loc[filter_particles, X_cols].values)
y = np.array(df.loc[filter_particles, y_col].values.ravel())
print("X.shape before: ", X.shape)
X, y = filter_array(X, y)
print("X.shape after: ", X.shape)
# # normalize
scaler, X_norm = normalize(X)
n_particles = X_norm.shape[0]
scale = 300
mkl_methods = [] # "MLKR", "LFDA"
dimreduc_methods = ["tsne", "mds", "isomap"]
methods = mkl_methods + dimreduc_methods
for method in methods:
if method in mkl_methods:
model = EmbeddingSpace(embedding_method=method)
model.fit(X_train=X_norm, y_train=y)
X_trans = model.transform(X_val=X_norm, get_min_dist=False)
if method in dimreduc_methods:
print("X shape", X.shape)
print("y shape", y.shape)
scaler, Xy_norm = normalize(np.c_[X, y]) #
model = Preprocessing(similarity_matrix=Xy_norm)
if method == "tsne":
X_trans, _ = model.tsne(n_components=2,
perplexity=20,
early_exaggeration=200,
learning_rate=200.0,
n_iter=1000,
n_iter_without_progress=300,
min_grad_norm=1e-07,
metric='euclidean',
init='random',
verbose=0,
random_state=None,
method='barnes_hut',
angle=0.5,
n_jobs=None)
if method == "isomap":
X_trans, _ = model.iso_map(n_neighbors=int(n_particles /
scale),
n_components=2,
eigen_solver='auto',
tol=0,
max_iter=None,
path_method='auto',
neighbors_algorithm='auto',
n_jobs=None)
if method == "mds":
X_trans, _ = model.mds(n_components=2,
metric=True,
n_init=4,
max_iter=300,
verbose=0,
eps=0.001,
n_jobs=None,
random_state=None,
dissimilarity='euclidean')
save_at = summary_folder + "/{0}/{1}/comb_{2}/{3}/dens.pdf".format(
y_col, method, len(X_cols), "|".join(X_cols))
print("Save at:", save_at)
title = save_at.replace(ResultDir, "").replace("/", "\n")
x = X_trans[:, 0]
y = X_trans[:, 1]
xlabel = "{0} dim 1".format(method)
ylabel = "{0} dim 2".format(method)
joint_plot_2(x=x,
y=y,
xlabel=xlabel,
ylabel=ylabel,
xlim=(min(x) - 0.1, max(x) + 0.1),
ylim=(min(y) - 0.1, max(y) + 0.1),
title=title,
save_at=save_at)
scatter_plot_4(x=x,
y=y,
color_array=None,
xvlines=None,
yhlines=None,
sigma=None,
mode='scatter',
lbl=None,
name=None,
s=30,
alphas=0.6,
title=title,
x_label='x',
y_label='y',
save_file=save_at.replace("dens.pdf",
"scatter.pdf"),
interpolate=False,
color='blue',
preset_ax=None,
linestyle='-.',
marker='o')
class Dictionary:
def __init__(self):
self.redis_handler = redis.Redis(db=1, decode_responses=True)
self.preprocessing = Preprocessing()
self.prepared_dic = dict()
self.prepared_bigram = dict()
self.prepared_lencat = dict()
self.bigram_prefix = 'bigram_'
self.lencat_prefix = 'lencat_'
self.DB_DICTIONARY = 'dic_exists'
self.DB_BIGRAM = 'bigram_exists'
self.DB_LENCAT = 'lencat_exists'
self.DB_COMMON = 'common_exists'
self.CASE_UPPER = '1'
self.CASE_LOWER = '2'
self.CASE_BOTH = '0'
def words_really_different(self, main_word, lemma_word):
pattern = "^{}(es|s)?$".format(lemma_word.lower())
try:
if (not re.match(r"^[a-zA-Z]+$", main_word)):
return False
if (re.match(pattern, main_word.lower())):
return False
except re.error as e:
print("{}: {}".format(pattern, main_word.lower()))
raise Exception(str(e))
return True
def database_exists(self, keyword):
return True if self.get_single_word_from_dic(keyword) == '1' else False
def prepare_word2dic(self, main_word, root_word):
word2store = root_word.lower()
prev_case = self.prepared_dic[
word2store] if word2store in self.prepared_dic else None
current_case = self.get_word_case(main_word, prev_case=prev_case)
self.prepared_dic[word2store] = current_case
if (self.words_really_different(main_word, root_word)):
self.prepared_dic[main_word.lower()] = current_case
self.prepare_lencat2dic(main_word, root_word)
def prepare_lencat2dic(self, main_word, root_word):
word1 = root_word.lower()
word2 = main_word.lower()
lencat_index = "{}{}".format(self.lencat_prefix, len(word1))
if (lencat_index in self.prepared_lencat):
self.prepared_lencat[lencat_index].add(word1)
else:
self.prepared_lencat[lencat_index] = {word1}
if (word1 != word2):
lencat_index = "{}{}".format(self.lencat_prefix, len(word2))
if (lencat_index in self.prepared_lencat):
self.prepared_lencat[lencat_index].add(word2)
else:
self.prepared_lencat[lencat_index] = {word2}
def prepare_bigram2dic(self, word, prev_word):
word2look = "{}{}".format(self.bigram_prefix, word[0].lower())
word_pos = word[0] if self.preprocessing.is_customized_word(
word[0]) else word[1]
prev_w = None if prev_word[0] == None else prev_word[0]
if (prev_word[0] == None):
prev_p = None
elif (self.preprocessing.is_customized_word(prev_word[0])):
prev_p = prev_word[0]
else:
prev_p = prev_word[1]
if (word2look in self.prepared_bigram):
self.prepared_bigram[word2look]['pos'].add(word_pos.lower())
self.prepared_bigram[word2look]['frequency'] += 1
if (prev_w != None):
self.prepared_bigram[word2look]['prev_words'].add(
prev_w.lower())
if (prev_p != None):
self.prepared_bigram[word2look]['prev_pos'].add(prev_p.lower())
else:
self.prepared_bigram[word2look] = {
'pos': {word_pos.lower()},
'frequency': 1,
'prev_words': set() if prev_w == None else {prev_w.lower()},
'prev_pos': set() if prev_p == None else {prev_p.lower()}
}
def store_prepared_data(self):
result = False
set_dbs = set()
if (len(self.prepared_dic) > 0):
if (self.redis_handler.mset(self.prepared_dic)):
result = True
set_dbs.add(self.DB_DICTIONARY)
if (len(self.prepared_bigram) > 0):
with self.redis_handler.pipeline() as pipe:
for word, data in self.prepared_bigram.items():
try:
pipe.set("{}_frequency".format(word),
data['frequency'])
if (len(data['pos']) > 0):
pipe.sadd("{}_pos".format(word), *data['pos'])
if (len(data['prev_words']) > 0):
pipe.sadd("{}_prev_words".format(word),
*data['prev_words'])
if (len(data['prev_pos']) > 0):
pipe.sadd("{}_prev_pos".format(word),
*data['prev_pos'])
except TypeError as e:
print(str(e))
print("{}: {}".format(word, data))
return
pipe_result = pipe.execute()
if (not False in pipe_result):
result = True
set_dbs.add(self.DB_BIGRAM)
if (len(self.prepared_lencat) > 0):
with self.redis_handler.pipeline() as pipe:
for index, words in self.prepared_lencat.items():
pipe.sadd(index, *words)
pipe_result = pipe.execute()
if (not False in pipe_result):
result = True
set_dbs.add(self.DB_LENCAT)
else:
print("{} => {}".format(self.DB_BIGRAM, pipe_result))
if (result):
with self.redis_handler.pipeline() as pipe:
for db in set_dbs:
self.redis_handler.set(db, '1')
pipe.execute()
self.prepared_dic = dict()
self.prepared_bigram = dict()
self.prepared_lencat = dict()
return result
def add_single_word2dic(self, main_word, root_word):
word2store = root_word.lower()
value = self.get_single_word_from_dic(root_word)
word_type = self.get_word_case(main_word, prev_case=value)
with self.redis_handler.pipeline() as pipe:
pipe.set(word2store, word_type)
pipe.sadd("{}{}".format(self.lencat_prefix, len(word2store)),
word2store)
if (self.words_really_different(main_word, root_word)):
pipe.set(main_word.lower(), word_type)
pipe.sadd("{}{}".format(self.lencat_prefix, len(main_word)),
main_word.lower())
pipe.execute()
return word_type
def get_single_word_from_dic(self,
word2look,
bigram=False,
postfix=None,
type_set=False):
word = word2look.lower() if not bigram else "{}{}_{}".format(
self.bigram_prefix, word2look.lower(), postfix)
word = self.redis_handler.get(
word) if not type_set else self.redis_handler.smembers(word)
if (word != None and type(word) is not set):
return word
elif (word != None and type(word) is set and len(word) > 0):
return set([term for term in word if term != None])
return None
def add_single_word2bigram(self, word, prev_word):
word2look = "{}{}".format(self.bigram_prefix, word[0].lower())
word_pos = word[0] if self.preprocessing.is_customized_word(
word[0]) else word[1]
prev_w = None if prev_word[0] == None else prev_word[0]
if (prev_w == None):
prev_p = None
elif (self.preprocessing.is_customized_word(prev_w)):
prev_p = prev_w
else:
prev_p = prev_word[1]
with self.redis_handler.pipeline() as pipe:
if (self.get_single_word_from_dic("{}_frequency".format(word2look))
!= None):
pipe.incr("{}_frequency".format(word2look))
else:
pipe.set("{}_frequency".format(word2look), 1)
pipe.sadd("{}_pos".format(word2look), *{word_pos.lower()})
if (prev_w != None):
pipe.sadd("{}_prev_words".format(word2look), *{prev_w.lower()})
if (prev_p != None):
pipe.sadd("{}_prev_pos".format(word2look), *{prev_p.lower()})
print(word)
pipe.execute()
def get_single_word_from_bigram(self, word):
frequency = self.get_single_word_from_dic(word,
bigram=True,
postfix='frequency')
if (frequency != None):
return {
'pos':
self.get_single_word_from_dic(word,
bigram=True,
postfix='pos',
type_set=True),
'frequency':
frequency,
'prev_words':
self.get_single_word_from_dic(word,
bigram=True,
postfix='prev_words',
type_set=True),
'prev_pos':
self.get_single_word_from_dic(word,
bigram=True,
postfix='prev_pos',
type_set=True)
}
return None
def get_word_case(self, word, prev_case=None):
if (prev_case == None):
return self.CASE_UPPER if word.isupper() else self.CASE_LOWER
elif (prev_case == self.CASE_BOTH):
return self.CASE_BOTH
else:
current_case = self.get_word_case(word)
return self.CASE_BOTH if current_case != prev_case else current_case
def get_words_by_length(self, length_list):
pipe_result = []
with self.redis_handler.pipeline() as pipe:
for index in length_list:
pipe.smembers("{}{}".format(self.lencat_prefix, index))
pipe_result = pipe.execute()
return pipe_result
def store_common_words(self, words):
if (not self.database_exists(self.DB_COMMON)):
common_words = set()
for word in words:
for cw in word:
break
common_words.add(cw)
if (len(common_words) > 0 and self.redis_handler.sadd(
'common_words', *common_words)):
self.redis_handler.set(self.DB_COMMON, '1')
for i in range(9):
x_array.append(" ")
y_array.append(" ")
l_array.append(" ")
accuracy_total_accumulation = 0
precision_total_accumulation = 0
recall_total_accumulation = 0
fmeasure_total_accumulation = 0
for i in range(len(data_train)):
kfold_per_combination.append(i+1)
y_test = []
y_pred = []
prepro = Preprocessing()
cleaned_data, terms = prepro.preprocessing(data_train[i]["tweet"])
tbrs = TermBasedRandomSampling(X=x, Y=y, L=l)
stopwords = tbrs.create_stopwords(cleaned_data,terms)
prepro2 = Preprocessing()
new_cleaned_data, new_terms = prepro2.remove_stopword(cleaned_data, stopwords)
weight = Weighting(new_cleaned_data, new_terms)
tfidf = weight.get_tf_idf_weighting()
idf = weight.get_idf()
nb = NBMultinomial()
nb.fit(new_cleaned_data,new_terms,data_train[i]["target"],stopwords,idf,tfidf)
def lstm_model_headline_body_combin(body_length, numb_epoch):
fexc = Preprocessing()
data = load_data()
# Loading train data from files
data.set_path(path='fnc-1-master')
train_stance_data = data.get_headline_body_stance()
train_bodies_data = data.get_body_id_text()
train_headlines, train_bodies, train_stances = data.get_mapped_id_body(
train_stance_data, train_bodies_data)
# Removing punctuation and stop words from the headline and body of train data
train_headlines_cl = fexc.get_clean_data(train_headlines)
train_bodies_cl = fexc.get_clean_data(train_bodies)
train_stances_cl = fexc.get_clean_data(train_stances)
# Convert labels to integer
train_stances_in = fexc.convert_lable_int(train_stances_cl)
# Load the test data
data.set_name("test")
test_stance_data = data.get_headline_body_stance()
test_bodies_data = data.get_body_id_text()
test_headlines, test_bodies = data.get_mapped_id_body(test_stance_data,
test_bodies_data,
data_type="test")
# Removing punctuation and stop words from the headline and body of test data
test_headlines_cl = fexc.get_clean_data(test_headlines)
test_bodies_cl = fexc.get_clean_data(test_bodies)
# Remove Stop words #
test_headlines_cl = fexc.remove_stop_words_list(test_headlines_cl)
test_bodies_cl = fexc.remove_stop_words_list(test_bodies_cl)
# Set the tokenizer
alltext = train_headlines_cl + train_bodies_cl + test_headlines_cl + test_bodies_cl
token = Tokenizer(num_words=30000)
token.fit_on_texts(alltext)
print('Number of Unique words: ' + str(len(token.word_index.keys())))
# Combine the headline and bodies of training data
train_data = fexc.combine_heading_body(train_headlines_cl, train_bodies_cl)
word_index = token.word_index
# Converting train data to sequence
train_data = token.texts_to_sequences(train_data)
# Padding train data
train_data = pad_sequences(train_data,
maxlen=(MAX_HEADLINE_LENGTH + int(body_length)))
# Converting the labels to one hot encoder
onehotencoder = OneHotEncoder()
train_stances_in = onehotencoder.fit_transform(train_stances_in).toarray()
# Splitting the data in train and validation
train_data, val_data, train_stances_final, stances_val = \
train_test_split(train_data, train_stances_in, test_size=0.2, random_state=42)
# Combining test data
test_data = fexc.combine_heading_body(test_headlines_cl, test_bodies_cl)
# Converting test data to sequence
test_data = token.texts_to_sequences(test_data)
# Padding test data
test_data = pad_sequences(test_data,
maxlen=MAX_HEADLINE_LENGTH + int(body_length))
# Getting embedding index
embeddings_index = models.get_embeddings_index(GLOVE_DIR)
print('Found %s word vectors.' % len(embeddings_index))
# Getting embedding matrix
embedding_matrix = models.get_embedding_matrix(
embedding_dim=EMBEDDING_DIM,
embeddings_index=embeddings_index,
word_index=word_index)
# Building the Model
fake_nn = models.lstm_with_combine_headline_body(
headline_length=MAX_HEADLINE_LENGTH,
body_length=int(body_length),
embedding_dim=EMBEDDING_DIM,
word_index=word_index,
embedding_matrix=embedding_matrix,
activation='relu',
drop_out=0.5,
numb_layers=300,
cells=200)
# Early stopping and model checkpoint
early_stopping = EarlyStopping(monitor='val_loss', patience=10)
bst_model_path = 'Fake_news_nlp.h5'
model_checkpoint = ModelCheckpoint(bst_model_path,
save_best_only=True,
save_weights_only=True)
# Fitting the model
fake_hist = fake_nn.fit(train_data,
train_stances_final,
batch_size=128,
epochs=int(numb_epoch),
shuffle=True,
validation_data=(val_data, stances_val),
callbacks=[early_stopping, model_checkpoint])
# Storing the training and validation accuracy and loss in file for plot
lstm_data = []
with open(
os.path.join(
OBJECT_DUMP,
"lstm_headline_body_combine" + str(body_length) + ".txt"),
'wb') as bow_hist:
lstm_data.append(fake_hist.history['acc'])
lstm_data.append(fake_hist.history['val_acc'])
lstm_data.append(fake_hist.history['loss'])
lstm_data.append(fake_hist.history['val_loss'])
pickle.dump(lstm_data, bow_hist)
# Predict the labels for test data
result = fake_nn.predict([test_data], batch_size=128)
# Store the results in the result file
result_str = fexc.convert_lable_string(result)
with io.open(TEST_FILE, mode='r', encoding='utf8') as read_file:
test_stance = csv.DictReader(read_file)
with io.open(RESULT_FILE + "_" + str(body_length) + ".csv",
mode='w',
encoding='utf8') as write_file:
writer = csv.DictWriter(
write_file, fieldnames=['Headline', 'Body ID', 'Stance'])
writer.writeheader()
for sample, prediction in zip(test_stance, result_str):
writer.writerow({
'Body ID': sample['Body ID'],
'Headline': sample['Headline'],
'Stance': prediction
})
# Print the Accuracy, competition score and confusion matrix
print_result("fnc-1-master/competition_test_stances.csv",
RESULT_FILE + "_" + str(body_length) + ".csv")
def preprocessing(self,
doPreprocessing,
doFeatureSelection,
take_feature,
threshold,
progress,
qc,
label=None):
features = None
if self.con != None:
if self.training_table:
if label != None:
label.setText("Getting data training ...")
self.dataTraining = self.con.getDataAsDF(self.training_table)
progress.setValue(10)
if self.dataTraining is not None:
p = Preprocessing(con=self.con)
oritext = None
uniqFeature = []
features = {}
originalFeatureCount = 0
progressP = 10
progressS = (70 - progressP) / len(self.dataTraining.index)
if label != None:
label.setText("Preprocessing data training ...")
for index, row in self.dataTraining.iterrows():
text = row[self.text_col]
if doPreprocessing:
pretext = p.process(text)
oritext = pretext['oritext']
pretext = pretext['stemmed_text']
else:
pretext = p.processNoPre(text)
t = p.processNoPre(pretext).split(
" ") # bad performance
uniqFeature.extend(t) # bad performance
# print("Ori : ",text)
# print("Preprocessed : ",pretext," -> ",row[self.class_col])
self.dataTraining.at[index, self.text_col] = pretext
progressP += progressS
progress.setValue(progressP)
# time.sleep(0.5)
qc.processEvents()
progress.setValue(70)
qc.processEvents()
uniqFeature = set(uniqFeature) # bad performance
qc.processEvents()
features['featurebefore'] = len(
uniqFeature) # bad performance
qc.processEvents()
progress.setValue(80)
features['vsm'] = self.builtVSM(doFeatureSelection,
take_feature,
threshold,
qc=qc,
label=label)
features['oritext'] = oritext
progress.setValue(90)
else:
print("No training table!")
progress.setValue(100)
return features
proba(term | topic) = beta[topic][term]
We shall for each topic find the top 20 words that contribute
to a document being classified as said topic
"""
top_20_per_topic = np.argsort(self.beta * (-1), axis=1)
for i in range(self.nb_topics):
for j in range(self.nb_terms):
if top_20_per_topic[i][j] < 20:
print(self.index[j], end=" ")
print()
if __name__ == "__main__":
"""
Example of application using newsgroups
"""
from sklearn.datasets import fetch_20newsgroups
train = fetch_20newsgroups(subset='train',
remove=('headers', 'footers', 'quotes'))
pp = Preprocessing()
index, bow = pp.build_bow(pp.corpus_preproc(train["data"]))
lda = LDA(5, bow, index, alpha=0.1, set_alpha=True)
lda.estimation(max_iter_em=100, max_iter_var=10)
lda.display_word_topic_association()
def load_location_json_test(self):
instance=Preprocessing(self.config)
#instance.load_location_json('UserInfo_2')
instance.output_coor_scale()
#If '-specificity' flag is not set, default is in str format, if flag is set, it's in list format
specificity = None
if type(args.specificity) == str:
specificity = args.specificity
else:
specificity = args.specificity[0]
#'-result' flag cannot be more specific than '-specificity' flag
#ie: if you are searching by paragraph, how can you print sentences?
if resultDir[args.results[0]] < specificityDir[specificity]:
sys.exit('Search results specificity cannot be broader than search algorithm specificity, program terminating')
#For each file in folder, reading and tokenizing text according to '-result' flag
fileTokenTouplesResults = []
for fileName in os.listdir(searchDocPath):
fileObj = Preprocessing(resultDir[args.results[0]], fileName)
fileObj.readFile()
fileObj.tokenizeText()
#Appending text and file info as tuple into list
fileTokenTouplesResults.append((fileObj.tokenizedList, fileObj.fileName))
equalBool = 0
fileTokenTouplesSpecificity = []
#If '-specificity' flag is the same as '-result' flag, reuse computed tuple list from '-result'
if resultDir[args.results[0]] == specificityDir[specificity]:
fileTokenTouplesSpecificity = fileTokenTouplesResults
equalBool = 1
#If they are different, read and tokenize text according to '-specificity' flag
else:
for fileName in os.listdir(searchDocPath):
fileObj = Preprocessing(specificityDir[specificity], fileName)
import networkx as nx
import matplotlib.pyplot as plt
from community import community_louvain
from preprocessing import Preprocessing
preprocessingClass = Preprocessing("lastfm_similars.db")
nodes = preprocessingClass.get_all_nodes()
indexes = preprocessingClass.giving_indexes_to_tIds(nodes)
allData = preprocessingClass.getting_all_data(indexes)
trackIds = []
inFile = open('track_list.txt')
for line in inFile:
fields = line.strip().split('<S>')
trackIds.append(fields[0])
inFile.close()
G = nx.Graph()
print("getting the graph start")
for node in allData:
if (allData[node]["tid"] in trackIds):
for i in range(len(allData[node]["similars"])):
G.add_edge(node,
allData[node]["similars"][i],
weight=allData[node]["weights"][i])
# nx.draw(G, pos=nx.circular_layout(G), node_color='r', edge_color='b')
# plt.show()
from preprocessing import Preprocessing
from segmentaion import Segmentation
from matplotlib import pyplot as plt
from skimage import io
import numpy as np
from skimage.feature import greycomatrix, greycoprops
from skimage import data
from skimage.color import rgb2gray
# you can make a loop to handling all images at once
preprocessing = Preprocessing()
preprocessing.preproces('ImageFile')
#preprocessing.preproces('C:/Users/Teja/Desktop/internship/project/braintumor/Cl/defect.jpg')
preprocessing.binarization()
preprocessing.removingSkul()
preprocessing.enhanceImage()
preprocessing.segmentation()
image = preprocessing.getInfectedRegion()
# ### Extract GLCM Texture Features
# In[ ]:
im = io.imread(
'C:/Users/Teja/Desktop/internship/project/braintumor/tmp/tumourImage.jpg')
# GLCM Texture Features
ds = []
cr = []
cn = []
am = []
def main():
# create a logger
logging = log.Logging(user_messages=False, timer_messages=True)
# filter configurations
filter_conf = fc.FilterConfiguration(logging)
filter_conf.import_filters()
# preprocessing configurations
pp_config = pc.PreprocessingConfiguration(logging)
pp_config.config()
# save configurations
save_config = sc.SaveConfiguration(logging)
save_config.config()
# tweepy
if (filter_conf.tweepy):
# Twitter connection
twitter_conn = tt.TwitterAuthenticator(logging)
twitter_conn.connect()
# Twitter query
tt_query_tweepy = qt.QueryTweets(twitter_conn,
filter_conf.list_filters, True,
logging)
tt_query_tweepy.query_manager()
# Twitter preprocessing
preprocessing = pp.Preprocessing(pp_config,
tt_query_tweepy.dict_df_posts,
logging)
preprocessing.preprocessing()
# Twitter save
save = sv.Save(save_config, tt_query_tweepy.dict_df_posts, logging)
save.save()
# twint
if (filter_conf.twint):
# Twitter query
tt_query_twint = qt2.QueryTweetsV2(filter_conf.list_filters, True,
logging)
tt_query_twint.query_manager()
# Twitter preprocessing
preprocessing = pp.Preprocessing(pp_config,
tt_query_twint.dict_df_posts,
logging)
preprocessing.preprocessing()
# Twitter save
save = sv.Save(save_config, tt_query_twint.dict_df_posts, logging)
save.save()
# praw
if (filter_conf.praw):
# Reddit connection
reddit_conn = rd.RedditAuthenticator(logging)
reddit_conn.connect()
# Reddit query
rt_query_praw = qr.QueryRedditPosts(reddit_conn,
filter_conf.list_filters, True,
logging)
rt_query_praw.query_manager()
# Reddit preprocessing
preprocessing = pp.Preprocessing(pp_config,
rt_query_praw.dict_df_posts,
logging)
preprocessing.preprocessing()
# Reddit save
save = sv.Save(save_config, rt_query_praw.dict_df_posts, logging)
save.save()
# pmaw
if (filter_conf.pmaw):
# Reddit query
rt_query_pmaw = qr2.QueryRedditPostsV2(filter_conf.list_filters,
True, logging)
rt_query_pmaw.query_manager()
# Reddit preprocessing
preprocessing = pp.Preprocessing(pp_config,
rt_query_pmaw.dict_df_posts,
logging)
preprocessing.preprocessing()
# Reddit save
save = sv.Save(save_config, rt_query_pmaw.dict_df_posts, logging)
save.save()
import pandas as pd
import numpy as np
from knn import KNN
from classifier import Classifier
import matplotlib.pyplot as plt
import visualization
from sklearn.model_selection import train_test_split
from preprocessing import Dataset, Preprocessing
from decisiontree import DecisionTree
# -----------------------------------------------------------------------------
# 0. Preprocessing
# get peprocessed and cleaned dataframes
cancer_df, hepatitis_df = Preprocessing.get_preprocessed_datasets()
cancer_features, cancer_labels = Preprocessing.get_labels_features(cancer_df)
hepatitis_features, hepatitis_labels = Preprocessing.get_labels_features(
hepatitis_df)
# Dataset 1 (Breast Cancer)
X_train_c, X_test_c, y_train_c, y_test_c = train_test_split(cancer_features,
cancer_labels,
test_size=0.33)
# Dataset 2 (Hepatitis)
X_train_h, X_test_h, y_train_h, y_test_h = train_test_split(hepatitis_features,
hepatitis_labels,
test_size=0.33)
# -----------------------------------------------------------------------------
# 1. Compare the accuracy of KNN and Decision Tree algorithm on the two datasets.
class Geo():
"""
Loads data, loads preprocessing and does country mapping.
"""
def __init__(self):
self.pp = Preprocessing()
def getGeoTweets(self, path, limit):
data = self.loadGeoData(path, limit)
country_map = self.country_mapping(data)
return data, country_map
def loadGeoData(self, path, limit=None):
counter = 0
print("Loading data from " + path, end=" - ")
t0 = time()
data = numpy.loadtxt(path, dtype='str', delimiter="\t", usecols = [2,5,3,3,0], comments=None)
print("done in %0.3fs" % (time() - t0))
print("Preprocessing tweet texts", end=" - ")
t0 = time()
empty_row = []
for i in range(len(data)):
counter+=1
tweet = self.pp.preprocess_tweet(data[i][2])
data[i][2] = tweet
if tweet == "":
empty_row.append(i)
if limit and counter >= limit:
if len(empty_row) > 1:
empty_row.sort(reverse=True)
for id in empty_row:
data = numpy.delete(data, (id), axis=0)
print("done in %0.3fs" % (time() - t0))
print()
return data[:limit]
print("done in %0.3fs" % (time() - t0))
print()
if len(empty_row) > 1:
empty_row.sort(reverse=True)
for id in empty_row:
data = numpy.delete(data, (id), axis=0)
return data
def country_mapping(self, data):
"""
Mapping: country (key) - corresponding tweet ids (value list).
"""
country_map = {}
for i in range(len(data)):
country = data[i][1]
if country in country_map.keys():
tmp = country_map[country]
tmp.append(i)
country_map[country] = tmp
else:
country_map[country] = [i]
return country_map
def __init__(self):
self.pre_processing = Preprocessing()
self.LOWER_GRAY_2 = np.array([0, 0, 100])
self.UPPER_GRAY_2 = np.array([255, 80, 175])
def preprocessing(file,originalSamplingRate=48000,originalChannels=2, sampleRate=48000, channels=2, alpha = 0.97,frameLength=20,windowType ="hanning"):
prep = Preprocessing()
prep.readSoundFile(file,sr=originalSamplingRate,n_channels=originalChannels)
prep.channelConversion(channels)
prep.resampling(sampleRate)
prep.pre_emphasis(alpha)
prep.framing(frameLength)
prep.addWindow(windowType)
prep.vad()
return prep.frames
def get_location_test(self): instance=Preprocessing(self.config) instance.get_location()
dataset = VideoLoader(
args.csv,
framerate=1 if args.type == '2d' else 24,
size=224 if args.type == '2d' else 112,
centercrop=(args.type == '3d'),
)
n_dataset = len(dataset)
sampler = RandomSequenceSampler(n_dataset, 10)
loader = DataLoader(
dataset,
batch_size=1,
shuffle=False,
num_workers=args.num_decoding_thread,
sampler=sampler if n_dataset > 10 else None,
)
preprocess = Preprocessing(args.type)
model = get_model(args)
feat_root = args.feat_root
with th.no_grad():
for k, data in enumerate(loader):
input_file = data['input'][0]
output_file = data['output'][0]
output_path = os.path.join(feat_root, output_file)
if len(data['video'].shape) > 3:
print('Computing features of video {}/{}: {}'.format(
k + 1, n_dataset, input_file))
video = data['video'].squeeze()
if len(video.shape) == 4:
video = preprocess(video)
n_chunk = len(video)
features = th.cuda.FloatTensor(n_chunk, 2048).fill_(0)
from preprocessing import Preprocessing
from language import Language
from image import Image_modality
import util
from sklearn.metrics import classification_report, confusion_matrix
import numpy as np
from keras.models import Model, Input
from sklearn.metrics import accuracy_score
if __name__ == '__main__':
pre = Preprocessing()
df_input = pre.data
train_data = pre.train_data
test_data = pre.test_data
Y_train = pre.Y_train
Y_test = pre.Y_test
lng = Language(df_input)
print("Preparing the language data")
train_tokens = train_data['title'].apply(util.get_tokens)
lng_data_train = lng.get_encoded_data(train_tokens)
test_tokens = test_data['title'].apply(util.get_tokens)
lng_data_test = lng.get_encoded_data(test_tokens)
language_model = lng.lng_model
print("training the language model (bi-lstm), this might take some time")
language_model.fit(lng_data_train, Y_train, verbose=1, validation_split=0.2, nb_epoch=5)
## printing precision_recall- language modality
def output_missing_scale_test(self): instance=Preprocessing(self.config) instance.output_missing_scale()
def processFrameOf(self, camera):
if not camera.isUp():
#self.log("WARN", "Video stream for Camera: " + camera._id + " not available")
return False
maxt = 10
frame = None
for i in range(1, maxt + 1):
#self.log("INFO", "Trying to accesss frame {}/{}".format(i, maxt))
try:
ret, f = camera.read()
if ret:
frame = f
except:
yyyyy = 1
if frame is None:
#self.log("WARN", "Couldn't access a valid frame")
return False
if camera._id in self.preprocessings:
self.log("INFO", "Pre-Processing frame of camera: " + camera._id)
st = time.time()
lineCoords = [(5, frame.shape[0] - 30 * (i + 1)) for i in range(3)]
pp = self.preprocessings[camera._id]
if 'brightness' in pp:
bv = pp['brightness']
frame = Preprocessing.adjustBrightness(frame, bv)
Preprocessing.putText(frame, "Brightness: " + str(bv),
lineCoords[0])
if 'sharpness' in pp:
sv = pp['sharpness']
frame = Preprocessing.sharpenImage(frame, k=sv)
Preprocessing.putText(frame, "Sharpness: " + str(sv),
lineCoords[1])
if 'denoise' in pp:
dv = pp['denoise']
if dv > 0:
frame = Preprocessing.denoiseImage(frame, strength=dv)
Preprocessing.putText(frame, "denoise: " + str(dv),
lineCoords[2])
et = time.time()
self.log("TIME", "Action took {:2.6f}s".format((et - st)))
#person detection
#plt.imshow(frame)
self.log("INFO", "Detecting People in the frame")
bboxes, conf = self.pd.detect(frame, drawOnFrame=False)
#overlapping bounding boxes
self.log("INFO", "Applying nms")
bboxes = non_max_suppression(np.array(bboxes),
probs=None,
overlapThresh=0.65)
#tracking
if len(bboxes) > 0:
tbboxes, tids = camera.tk.track(frame,
bboxes,
conf,
drawOnFrame=False)
if len(tbboxes) > 0:
self.log("INFO", "Tracking people {}".format(len(tids)))
for i in range(len(tbboxes)):
tbbox = np.array(tbboxes[i], np.int32)
tid = tids[i]
#increasing fps by selective recognition
if camera.track.hasPerson(tid):
if camera.track.people[tid].isSuspect():
if time.time() - camera.track.people[
tid].whenRecognized < self.recognizeThresh:
continue
person = frame[tbbox[1]:tbbox[3], tbbox[0]:tbbox[2]]
#cv2.imshow("person: ", person)
faces = fdr.extractFaces(person, drawOnFrame=False)
if len(faces) <= 0:
continue
face = faces[0]
fe = fdr.getEmbedding(face[0])
#check if he/she is a suspect
suspectDetected = False
for k, suspect in self.suspects.items():
#{"face":face, "em":em, "path":path}
for pic in suspect.pictures:
em = pic['em']
if fdr.is_match(em, fe):
camera.track.suspectDetected(
tid, suspect, time.time(), frame,
self.SERVER_ID, camera._id)
suspectDetected = True
break
if suspectDetected:
break
#update track
camera.track.updatePositions(tbboxes, tids)
camera.track.clearForgotten()
#display bboxes and everything
camera.track.draw(frame)
#udpate the processedFrame
#cv2.imshow("Frame", frame)
t = time.localtime()
text = "Server: " + time.strftime("%H:%M:%S", t)
cv2.putText(frame, text, (10, 60), cv2.FONT_HERSHEY_COMPLEX, 0.5,
(0, 255, 255), 1)
with self.lock:
camera.processedFrame = frame
camera.processedFrameTime = time.time()
self.xo = 1
return True
class Summarization(object):
def __init__(self,
lang_code,
method="LSA",
n_words=200,
k=1,
sv_threshold=0.5,
min_df=0,
max_df=.1,
use_idf=True):
self.lang_code = lang_code
self.method = method
self.n_words = n_words
self.k = k # num topics
self.sv_threshold = sv_threshold
self.min_df = min_df
self.max_df = max_df
self.use_idf = use_idf
self.valid_langs = ["en"]
if self.lang_code in self.valid_langs:
self.p = Preprocessing(lang_code=lang_code)
self.tfidf = TfidfVectorizer(min_df=min_df,
max_df=max_df,
use_idf=use_idf)
def generate_doc_term_matrix(self, norm_sents):
""" Generate document term matrix from normalized sentences """
dt_matrix = self.tfidf.fit_transform(norm_sents)
dt_matrix = dt_matrix.toarray()
return dt_matrix
def generate_term_doc_matrix(self, dt_matrix):
""" Generate term document matrix from document term matrix """
td_matrix = dt_matrix.T
return td_matrix
def generate_summary(self, sents, top_sentence_indices):
""" Generate summary from original sentences using top sentence indices """
sents = np.array(sents)
summary = "\n".join(sents[top_sentence_indices])
return summary
def summarize(self, text, n_sents=3):
""" Summarize a given text and get top sentences """
try:
prediction = dict()
if text:
if self.lang_code in self.valid_langs:
if Utility.get_doc_length(text) > self.n_words:
# generate sentences, normalized sentences from text
sents, norm_sents = self.p.text_preprocessing(text)
# generate doc-term-matrix, term-doc-matrix
dt_matrix = self.generate_doc_term_matrix(norm_sents)
td_matrix = self.generate_term_doc_matrix(dt_matrix)
if self.method == "LSA":
lsa = LSA(self.k, td_matrix)
term_topic_matrix, singular_values, topic_doc_matrix = lsa.u, lsa.s, lsa.vt
# remove singular values below given treshold
singular_values = lsa.filter_singular_values(
singular_values, self.sv_threshold)
# get salience scores from top singular values & topic document matrix
salience_scores = lsa.get_salience_scores(
singular_values, topic_doc_matrix)
# get the top sentence indices for summarization
top_sentence_indices = lsa.get_top_sent_indices(
salience_scores, n_sents)
summary = self.generate_summary(
sents, top_sentence_indices)
elif self.method == "TEXT_RANK":
tr = TextRank(dt_matrix, td_matrix)
# build similarity graph
similarity_matrix = tr.similiarity_matrix
similarity_graph = tr.get_similarity_graph(
similarity_matrix)
# compute pagerank scores for all sentences
ranked_sents = tr.rank_sentences(similarity_graph)
# get the top sentence indices for summarization
top_sentence_indices = tr.get_top_sentence_indices(
ranked_sents, n_sents)
summary = self.generate_summary(
sents, top_sentence_indices)
else:
return "no method found"
# apply cleaning for readability
summary = Utility.remove_multiple_whitespaces(summary)
summary = Utility.remove_trailing_whitespaces(summary)
prediction["summary"] = summary
prediction["message"] = "successful"
else:
return "required at least {} words".format(
self.n_words)
else:
return "language not supported".format()
else:
return "required textual content"
return prediction
except Exception:
logging.error("exception occured", exc_info=True)
class Model():
"""
Model Class
"""
def __init__(self):
self.__utils = Utils()
self.__preprocessing = Preprocessing()
self.model_fit = self.fit()
self.__preprocessing.execute()
@property
def dataset_preprocessed(self):
"""
Returns
-------
DataFrame Houses Preprocessed
"""
df = pd.read_csv("data/houses_clean.csv")
df.drop('Unnamed: 0', axis=1, inplace=True)
return df
def get_prepared_df(self):
"""
Prepare dataframe for modelling
Parameters
----------
df : Dataframe Data
Returns
-------
Array to Model
"""
df = self.dataset_preprocessed
df['size'] = df['size'].apply(lambda x: math.ceil(x / 5.0) * 5.0)
#Property Type Union
df['propertyType'] = np.where((df['propertyType'] == 'studio') |
(df['propertyType'] == 'duplex'), 'flat',
df['propertyType'])
#Select Features
df = df[[
'price', 'size', 'propertyType', 'district', 'status', 'roomsCat',
'bathroomsCat'
]] #,'box_posto_auto','hasTerrace',
#'hasGarden','hasSwimmingPool']]
return df
@property
def labels_dataset(self):
"""
Returns
-------
Labels Dataset Numpy Array
"""
df = self.get_prepared_df()
labels = np.array(df['price'])
return labels
@property
def features_dataset(self):
"""
Returns
-------
Features Dataset Numpy Array
"""
df = self.get_prepared_df()
features = df.drop('price', axis=1)
features = np.array(features)
return features
@property
def feat_tsf_dataset(self):
"""
Returns
-------
Features Dataset Numpy Array with Category Encoders
"""
features = self.features_dataset
labels = self.labels_dataset
#Encoder
encoder = ce.GLMMEncoder(cols=self.cat_index)
#Encoder Cv
cv_encoder = NestedCVWrapper(feature_encoder=encoder,
cv=5,
shuffle=True,
random_state=7)
#Apply Transform to all datasets
feat_tsf = cv_encoder.fit_transform(features, labels)
return feat_tsf
@property
def features_list(self):
"""
Returns
-------
Features List
"""
df = self.get_prepared_df()
features = df.drop('price', axis=1)
# Saving feature names for later use
feature_list = list(features.columns)
return feature_list
@property
def n_features(self):
"""
Returns
-------
Number of features
"""
return len(self.features_list)
@property
def cat_index(self):
"""
Returns
-------
Index position categorical columns
"""
df = self.get_prepared_df()
df.drop('price', axis=1, inplace=True)
categorical_features_indices = np.where((df.dtypes != np.int)
& (df.dtypes != np.float))[0]
index = categorical_features_indices.reshape(1, -1).tolist()[0]
return index
def search_best_rf(self, n_trees=2500, saveStats=True):
"""
Seach Best Random Forest Model
Parameters
----------
df : DataFrame prepared (method prepared_data)
Returns
-------
JSON File (model_params_rf.json).
"""
#Process Time
start = time.time()
#Datasets
feat_tsf = self.feat_tsf_dataset
labels = self.labels_dataset
#Generate random state
#min_samples_split_values to test
max_features_list = np.arange(0.20, 0.66, 0.01).tolist()
max_features_list = [round(elem, 2) for elem in max_features_list]
max_features_list.append('sqrt')
max_features_list.append('auto')
#Get max n_trees
max_n_trees = self.depth_of_trees.max()[0]
max_depth_list = np.arange(int(max_n_trees / 4), max_n_trees,
1).tolist()
max_depth_list.append(None)
#min_impurity_decrease
min_impurity_decrease_list = np.arange(0.01, 0.26, 0.01).tolist()
min_impurity_decrease_list = [
round(elem, 2) for elem in min_impurity_decrease_list
]
#min_samples_leaf_list.append(None)
param_grid = {
"max_features": max_features_list,
"max_depth": max_depth_list,
"min_impurity_decrease": min_impurity_decrease_list
}
#RF Model to test
rf = RandomForestRegressor(bootstrap=True,
oob_score=True,
n_estimators=n_trees,
random_state=7)
#Define and execute pipe
grid_cv = HalvingRandomSearchCV(estimator=rf,
param_distributions=param_grid,
random_state=7,
max_resources='auto',
verbose=3).fit(feat_tsf, labels)
df_results = pd.DataFrame(grid_cv.cv_results_)
#Save CV Results
if saveStats:
df_results.to_csv('data/cv_hyperparams_model.csv')
print("Best Params:")
print(grid_cv.best_params_)
print("Saving model in 'model_params.joblib'")
# Writing joblibfile with best model
dump(grid_cv.best_estimator_, 'model_params.joblib')
#Save json file with params best model
json_txt = json.dumps(grid_cv.best_params_, indent=4)
with open('model_params', 'w') as file:
file.write(json_txt)
#End Time
end = time.time()
time_elapsed = round((end - start) / 60, 1)
return ('Time elapsed minutes: %1.f' % (time_elapsed))
def fit(self):
"""
Returns
-------
Fit Best Params Model
"""
#Datasets
feat_tsf = self.feat_tsf_dataset
labels = self.labels_dataset
#Open params
with open('model_params', 'r') as file:
params_model = json.load(file)
#Model
rf = RandomForestRegressor(**params_model)
#Fit & Metrics
rf.fit(feat_tsf, labels)
oob_score = (rf.oob_score_) * 100
print("OOB Score: %.2f" % oob_score)
return rf
@property
def oob_score(self):
"""
Returns
-------
Best Model OOB Score
"""
return self.model_fit.oob_score_
@property
def params(self):
"""
Returns
-------
Best Model Params
"""
return self.model_fit.get_params()
def predict(
self,
size,
propertyType,
district,
status,
rooms,
bathrooms,
#box_posto_auto,
#hasGarden,
#hasTerrace,
#hasSwimmingPool
):
"""
Parameters
----------
district : str (category)
status : str (category)
rooms : int
bathrooms : int
box_posto_auto : Bool(1,0)
garden : Bool(1,0)
terrace : Bool(1,0)
hasSwimmingPool : Bool(1,0)
Returns
-------
Prediction : Best Model Prediction
"""
"""
#Avg Price Zone
avg_price_zone_df = self.dataset_preprocessed[['district','avgPriceZone']]
avg_price_zone_df = avg_price_zone_df.drop_duplicates()
avgPriceZone = avg_price_zone_df.loc[
avg_price_zone_df['district']==district]['avgPriceZone'].values[0]
"""
#Rooms Category
roomsCat = self.roomsCategory(rooms)
#Bathrooms Logic
bathroomsCat = self.bathroomsCategory(bathrooms)
#Array for prediction
array = np.array([
size,
propertyType,
district,
status,
roomsCat,
bathroomsCat,
#box_posto_auto,
#hasGarden,
#hasTerrace,
#hasSwimmingPool
]).reshape(1, -1)
#Encoder
encoder = ce.GLMMEncoder(cols=self.cat_index)
#Encoder CV KFold
cv_encoder = NestedCVWrapper(encoder,
cv=5,
shuffle=True,
random_state=7)
#Datasets
features = self.features_dataset
labels = self.labels_dataset
#Apply Transform to all datasets
feat_tsf = cv_encoder.fit_transform(features, labels, array)
#Prediction
prediction = self.model_fit.predict(feat_tsf[1])[0]
return prediction
@property
def permutation_importance(self):
"""
Permutation Features Importance
Returns
-------
Graph Permutation Importance
"""
#Datasets
feat_tsf = self.feat_tsf_dataset
labels = self.labels_dataset
rf = load('model_params.joblib')
#Fit
rf.fit(feat_tsf, labels)
#Permutation importance
result = permutation_importance(rf,
feat_tsf,
labels,
n_repeats=10,
random_state=7,
n_jobs=2)
df = (pd.DataFrame({
"ft": self.features_list,
'imp_mean': result.importances_mean,
'imp_dsvt': result.importances_std
}))
df.sort_values(by='imp_mean', ascending=False, inplace=True)
sorted_idx = result.importances_mean.argsort()
fig, ax = plt.subplots()
ax.boxplot(result.importances[sorted_idx].T,
vert=False,
labels=self.get_prepared_df().iloc[:,
1:].columns[sorted_idx])
ax.set_title("Permutation Importances")
fig.tight_layout()
return plt.show()
def plot_tree(self, tree_number=0):
"""
Parameters
----------
number : Int. Tree to plot. The default is 0.
Returns
-------
Tree Image
"""
model_rf = self.model_fit
fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(20, 30), dpi=800)
tree.plot_tree(model_rf.estimators_[tree_number],
feature_names=self.features_list,
class_names='price',
filled=True)
fig.savefig('data/rf_individualtree.png')
return fig
def feature_imp(self):
"""
Feature Importance Model Method
Returns
-------
Dataframe with features Importance
"""
df = (pd.DataFrame({
"ft": self.features_list,
'imp': self.model_fit.feature_importances_
}))
df.sort_values(by='imp', ascending=False, inplace=True)
return df
@property
def depth_of_trees(self):
"""
Returns
-------
Dataframe with Trees depth
"""
#Get depth of trees
max_depth_list = []
rf = RandomForestRegressor(n_estimators=2500, max_features=0.35)
feat_tsf = self.feat_tsf_dataset
labels = self.labels_dataset
rf.fit(feat_tsf, labels)
for i in rf.estimators_:
max_depth_list.append(i.get_depth())
print("Max depht: %i trees" % max(max_depth_list))
return pd.DataFrame(max_depth_list, columns=['trees'])
def train_test_samples(self,
features,
labels,
test_size=0.20,
random_state=None):
feat_tsf = self.feat_tsf_dataset
labels = self.labels_dataset
X_train, X_test, y_train, y_test = train_test_split(
feat_tsf, labels, test_size=test_size, random_state=random_state)
return X_train, X_test, y_train, y_test
def avg_price_district(self, district):
df = self.dataset_preprocessed
df = df.groupby('district').mean()['priceByArea']
return int(df.loc[df.index == district].values[0])
@property
def propertyTypeList(self):
propertyTypelist = ['Flat', 'Attic', 'Villa', 'Country House']
return propertyTypelist
def propertyTypeConverter(self, propertyType):
"""
Parameters
----------
propertyType : Str Selected Property Type.
Returns
-------
Property Type str
"""
#Options list
propertyTypelist = self.propertyTypeList
#Lower elements list
propertyTypelist = [i.lower() for i in propertyTypelist]
#Assertion
assert propertyType.lower() in propertyTypelist
#Default Value
propertyTypeOutput = 'flat'
if propertyType.lower() == 'Flat':
propertyTypeOutput = 'flat'
elif propertyType.lower() == 'Attic':
propertyTypeOutput = 'penthouse'
elif propertyType.lower() == 'Villa':
propertyTypeOutput = 'villa'
elif propertyType.lower() == 'CountryHouse':
propertyTypeOutput = 'countryHouse'
return propertyTypeOutput
@property
def statusList(self):
status_it = ['To be restructured', 'Good', 'New Construction ']
return status_it
def roomsCategory(self, rooms):
"""
Parameters
----------
rooms : Int
Rooms
Returns
-------
Rooms Category
"""
roomsCat = 1
if rooms >= 4:
roomsCat = 4
else:
roomsCat = rooms
return roomsCat
def bathroomsCategory(self, bathrooms):
"""
Parameters
----------
bathrooms : Int
bathRooms
Returns
-------
Barthooms Category
"""
bathroomsCat = 1
if bathrooms >= 2:
bathroomsCat = 2
else:
bathroomsCat = bathrooms
return bathroomsCat
def evalSentence(self, model, sentence):
return self.classificator.classifyWithModel(
model, sentence, Preprocessing(con=self.con))
def __init__(self):
self.__utils = Utils()
self.__preprocessing = Preprocessing()
self.model_fit = self.fit()
self.__preprocessing.execute()
description=('Estimate the Gibbs energy of a reaction. For example,'
'the following calculates dGr0 for ATP hydrolysis '
'at pH 6: calc_dGr0.py --ph 6 "C00002 + C00001 = '
'C00008 + C00009"'))
parser.add_argument('--ph', type=float, help='pH level', default=7.0)
parser.add_argument('--i', type=float,
help='ionic strength in M',
default=0.1)
parser.add_argument('reaction', type=str, help='reaction in KEGG notation')
return parser
###############################################################################
parser = MakeParser()
args = parser.parse_args()
logging.getLogger().setLevel(logging.WARNING)
print 'pH: %.1f' % args.ph
print 'I: %.1f M' % args.i
print 'Reaction: ' + args.reaction
# parse the reaction
reaction = Reaction.parse_formula(args.reaction)
p = Preprocessing()
# use the preprocessing class to calculate the estimated dG0 and uncertainty
dG0_prime, U = p.dG0_prime(reaction, pH=args.ph, I=args.i)
print u'dGr0 = %.2f \u00B1 %.2f kJ/mol' % (dG0_prime[0, 0], U[0, 0])
output_2 = Dropout(0.1)(output_2)
output_2 = Dense(32, activation='relu')(output_2)
output_2 = Dropout(0.1)(output_2)
output = Dense(outputdim, activation='softmax')(output_2)
model = Model(inputs=[
input_scene, input_before_sents, input_sents, input_before_char
],
outputs=output)
return model
if __name__ == '__main__':
embedding_dim = 62
preprocessing = Preprocessing()
characters, paraid2scene, paraid2chars, paraid2sents, episodeid2paraid, para_number, word_dict = preprocessing.load_dataset(
)
_, id2char, _, id2word, char_number, word_number, embedding_matrix = preprocessing.encoding_reduction(
characters, word_dict)
X, Y, X_test_2, Y_test_2 = preprocessing.generate_X_Y_split_beforechar(
characters, paraid2scene, paraid2chars, paraid2sents, episodeid2paraid,
para_number, word_dict)
print('data loaded')
X_test_1 = [
np.array(X[0][10000:20000]),
np.array(X[1][10000:20000]),
np.array(X[2][10000:20000]),
np.array(X[3][10000:20000])
]
tuples = sorted(tuples, key=lambda x: x[0])
plt.figure(figsize=(10, 10))
key_color = map(lambda x: 1 if x.startswith('PRI') else 0, zip(*tuples)[1])
colors = np.asarray(['c', 'g'])
plt.barh(bottom=pos,
width=zip(*tuples)[0],
color=colors[key_color],
edgecolor=None,
alpha=0.7)
plt.yticks(np.arange(1, max(pos) + 0.5), zip(*tuples)[1], fontsize='small')
plt.xlabel('Correlation', fontsize='small')
plt.title('Ranking by point biserial correlation - Features v. Class',
fontsize='small')
plt.savefig('../Graphs/pbc.png')
plt.tight_layout()
if __name__ == "__main__":
hd = HiggsData(path=settings.get('paths', 'path_data'), imputation=True)
df = Preprocessing.remove_missing_values(hd.processed_input, np.NaN)
b_processed = Preprocessing.get_features(df[df.Label == -1])
s_processed = Preprocessing.get_features(df[df.Label == 1])
labels = hd.raw_input.ix[df.index]['Label']
df_features = Preprocessing.get_features(df)
cols = df_features.columns
fishers = plot_fishers_ratio(b_processed, s_processed)
pbc = plot_feature_class_corr_matrix(df_features, labels, cols)
def train(ink_dir, lg_dir):
"""
This function is used for training model
:param ink_dir:
:param lg_dir:
:return:
"""
lg_files = os.listdir(lg_dir)
pre = Preprocessing()
feature_matrix = []
targets = []
c = 0
total = len(lg_files)
for file in lg_files:
print(file, total - c, c)
symbols = {}
with open(lg_dir + "/" + file) as f:
for line in f:
if line.startswith("O"):
filt_line = line.strip().split(",")
symbols[filt_line[1].strip()] = [
filt_line[2], filt_line[4:]
]
inkml_file = file.replace(".lg", ".inkml")
with open(ink_dir + "/" + inkml_file) as f:
soup = bs.BeautifulSoup(f, 'html.parser')
for key in symbols:
label = symbols[key][0]
strokes = symbols[key][1]
id_list = []
X = []
Y = []
for id in strokes:
st_id = id.strip()
trace = soup.findAll("trace", {'id': st_id})
coords = trace[0].text.strip().split(",")
x = []
y = []
for coord in coords:
trace_parts = coord.strip().split(' ')
x.append(float(trace_parts[0]))
y.append(float(trace_parts[1]))
X.append(x)
Y.append(y)
id_list.append(st_id)
X, Y = pre.dopreprocess(x=X, y=Y, parser=True)
symbols[key] = Symbol(label=label, x=X, y=Y, stroke_id=id_list)
# relations section
with open(lg_dir + "/" + file) as f:
for line in f:
if line.startswith("EO"):
filt_line = line.strip().split(",")
sym1 = symbols[filt_line[1].strip()]
sym2 = symbols[filt_line[2].strip()]
relation = filt_line[3].strip()
writing_slope = sym1.writing_slope(sym2)
writing_curve = sym1.writing_curvature(sym2)
bb_dist = sym1.distance_between_box(sym2)
distance, horizontal_ofsset, vertical_distance = sym1.distance_between_average_centres(
sym2)
max_point_pair = sym1.maximal_point_distance(sym2)
feature_matrix.append([
writing_slope, writing_curve, bb_dist, distance,
horizontal_ofsset, vertical_distance, max_point_pair
])
targets.append(relation)
c += 1
print("Shape of Training matrix")
print(len(feature_matrix), "x", len(feature_matrix[0]))
print("Unique labels : ", np.unique(targets))
rf = RandomForestClassifier(n_estimators=100, n_jobs=-1)
rf.fit(X=feature_matrix, y=targets)
joblib.dump(rf,
"relation_classifier_bonus.pkl",
protocol=pickle.HIGHEST_PROTOCOL)
rf = joblib.load("relation_classifier_bonus.pkl")
score = accuracy_score(y_true=targets,
y_pred=rf.predict(feature_matrix),
normalize=True)
print("accuracy of model is :", (score * 100))
def apply_preprocessing(self):
data = self.get_data()
data = Preprocessing().categorical_column_to_numerical(data)
data = Preprocessing().normalize_numerical_columns(data)
return data
fmeasure_pos = []
acc_per_fold = []
f = open("stopword_tala.txt", "r")
stopwords = f.read().split()
fold = list(range(1, 11))
for i in range(len(data_train)):
print("Fold ke " + str(i + 1))
# print(len(data_train[i]["tweet"]))
# print(len(data_test[i]["tweet"]))
y_test = []
y_pred = []
# TAHAP PEMBUATAN STOPWORD
prepro = Preprocessing()
new_cleaned_data, new_terms = prepro.preprocessing(data_train[i]["tweet"],
stopwords=stopwords)
# TAHAP PELATIHAN
weight = Weighting(new_cleaned_data, new_terms)
tfidf = weight.get_tf_idf_weighting()
idf = weight.get_idf()
nb = NBMultinomial()
nb.fit(new_cleaned_data, new_terms, data_train[i]["target"], stopwords,
idf, tfidf)
for j in range(len(data_test[i]["tweet"])):
print("Test ke " + str(j))
def __init__(self):
self.pp = Preprocessing()
from preprocessing import Preprocessing
from model import UnetModel
data_path = '..'
"""
To train different classes
for buildings: class_dict = {1: 0}
for road: class_dict = {3: 0}
for tracks: class_dict = {4, 0}
"""
class_dict = {1:0}
Patch_size = 224
N_split = 15
n_classes = len(class_dict)
inp_shape = (Patch_size, Patch_size, 20)
preprocessor = Preprocessing(data_path, class_dict)
unet_model = UnetModel(inp_shape, n_classes)
unet_model.getModel(0.2)
print ("Model generated")
unet_model.compileModel()
print ("Model compiled")
epochs = 100
batch_size = 16
train_image_data_gen = preprocessor.imagePatchGenerator(batch_size)
val_image_data_gen = preprocessor.imagePatchGenerator(batch_size, val_data = True)
print("batch generators generated")
print("Training...")
trained_model = unet_model.train_generator(batch_size,
from keras import optimizers, losses, metrics
from preprocessing import Preprocessing
from simulation import Simulation
from spp import SpatialPyramidPooling, R2
import numpy as np
import matplotlib.pyplot as plt
# Parameters setting
num_of_cells = 2
num_of_CUEs = 2
num_of_D2Ds = (2, 3)
batch_size = 64
epochs = 10
# Get the image data format which Keras follows
image_data_format = Preprocessing.GetImageDataFormat()
# Get the input data and target data
input_data_list = [
Preprocessing.GetInputData(num_of_cells, num_of_CUEs, i,
(2000, 8000, 10000), image_data_format)
for i in num_of_D2Ds
]
target_data_list = [
Preprocessing.GetTargetData(num_of_cells, num_of_CUEs, i,
(2000, 8000, 10000)) for i in num_of_D2Ds
]
# Reshape the input data
for index, input_data in enumerate(input_data_list):
rows, cols, channels = Preprocessing.GetInputShape(input_data)
def output_category_num_test(self): instance=Preprocessing(self.config) instance.output_category_num_scale()
def __init__(self, path, limit):
self.path = path
self.limit = limit
self.preprocessing = Preprocessing()
from gensim.models import CoherenceModel, LdaModel, LsiModel, HdpModel
from gensim.models.wrappers import LdaMallet
from gensim.corpora import Dictionary
from gensim.matutils import argsort
from gensim import corpora, models, similarities
from gensim.topic_coherence import segmentation, probability_estimation, direct_confirmation_measure, aggregation,indirect_confirmation_measure
from pprint import pprint
from collections import namedtuple
from sklearn.decomposition import LatentDirichletAllocation
from preprocessing import Preprocessing
from sklearn.model_selection import GridSearchCV
from custom_vectorizer import OwnCountVectorizer
# nltk.download('words')
preprocessing = Preprocessing()
def prepare_data(filename):
''' Load and prepare the data for topic modeling. '''
print 'Loading dataset...'
data = pd.read_csv(filename, encoding = 'utf-8')
data = preprocessing.remove_null(data)
print 'Dataset loaded'
print 'Preparing text inputs...'
texts = preprocessing.preprocess_text(texts)
titles = preprocessing.preprocess_text(titles)
titles = titles[0:len(texts)]
text_input = concat_text_input(titles, texts)
text_data = data['scraped_title'] + ' ' + data['scraped_content']
return text_input, text_data
import pandas as pd
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')
from preprocessing import Preprocessing
Preprocessing = Preprocessing()
from models import models
models = models()
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, auc, roc_curve, normalized_mutual_info_score
data = pd.read_csv('./data.csv',index_col=0)
data.drop(['patkey', 'index_date', 'MATCHID'], axis=1)
data['age_at_index'] = data['age_at_index']-5
data = Preprocessing.FeatureEncoding(data)
data = Preprocessing.MissingData(data)
data.to_csv('data_complete.csv')
data = pd.read_csv('./data_complete.csv',index_col=0)
#==========================================================================================
#After using the KNN to deal with missing data, count and plot the histogram of features
'''
print(data.loc[:,'Smoking_status'].value_counts())
print(data.loc[:,'BMI_group'].value_counts())
print(data.loc[:,'Alcohol_status'].value_counts())
def autolabel(rects):
for rect in rects:
height = rect.get_height()
plt.text(rect.get_x()+rect.get_width()/2.-0.2, 1.03*height, '%s' % int(height))
def save_trails(encoder, alg, scr, data, path):
"""
pre_parameters= pre_encoder(encoder)[0]
preprocessor =pre_encoder(encoder)[1]
alg: Algorithm, a dict(), each one is a name of that function {'knn':[KNN(),knn_params]}
scr: scorings, a dict(), each one is a function such as 'acc':ACC
data: a dict() of dataset {'dataname':[trainset, testset]}
path = ['~/all_models','~/best_models'] , to decide first chart or second chart.
"""
# parameters
params = Preprocessing.pre_encoder(encoder)[0]
preprocessor_ = Preprocessing.pre_encoder(encoder)[1]
alg_name = list(alg)[0]
data_name = list(data)[0]
clsf = alg[alg_name][0]
param = alg[alg_name][1]
params.append(param)
dataset = data[data_name][0]
test_set = data[data_name][1]
pipeline = Pipeline([('preprocessing', preprocessor_),
('classifier', clsf)])
X = dataset.drop(columns=['target'])
y = dataset.target
print(y.unique())
if len(y.unique()) > 2:
# this will transform y into 1,0 ndarray
lb = LabelBinarizer().fit(y)
y = lb.transform(y)
record_scores = {}
for i in range(len(list(scr))):
score_name = list(scr)[i]
score = scr[score_name]
print("Dataset is : ", data_name.upper())
record_scores[score_name] = {}
score_details = {}
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=0.2)
clf = GridSearchCV(pipeline,
params,
scoring=score,
cv=5,
n_jobs=-1,
refit=True,
return_train_score=True,
verbose=True)
clf.fit(X_train, y_train)
# test dataset
best_model = clf.best_estimator_
X_test = test_set.drop(columns=['target'])
y_test = test_set.target
y_pred = best_model.predict(X_test)
testset_score = (y_test, y_pred)
score_details['testset_score'] = testset_score
score_details['best_score'] = clf.best_score_
score_details['results'] = clf.cv_results_
# save models to path
save_models = os.path.join(path[0], alg_name, data_name,
score_name)
save_best_model = os.path.join(path[1], alg_name, data_name,
score_name)
self.check_directory([save_best_model, save_models])
joblib.dump(clf, os.path.join(save_models, 'all_models.pkl'))
joblib.dump(best_model,
os.path.join(save_best_model, 'best_model.pkl'))
print('All models scored in ' + score_name + ' saved in ',
save_models)
print('Best model scored in ' + score_name + ' saved in ',
save_best_model)
record_scores.update(score_details)
print(record_scores)
return record_scores
class Segmentation:
def __init__(self):
self.pre_processing = Preprocessing()
self.LOWER_GRAY_2 = np.array([0, 0, 100])
self.UPPER_GRAY_2 = np.array([255, 80, 175])
def segment(self, image):
mask, image_no_background = self.pre_processing.cut_out_backgound(image)
image_hue = self.pre_processing.get_mask_brightness(image_no_background)
#cv2.imshow("img_hue", image_no_background)
mat_points = self.map_out(mask, image_no_background)
image = self.pre_processing.equalize_clahe(image)
return mat_points, image#image_no_background
def map_out(self, img_bin, image):
mat_points = []
contours, inheriters = cv2.findContours(img_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for c in contours:
moments = cv2.moments(c)
if moments['m00'] > 100:
x = []
y = []
for i in c:
for j in i:
x.append(j[0])
y.append(j[1])
max_x, min_x, max_y, min_y = np.argmax(x), np.argmin(x), np.argmax(y), np.argmin(y)
mat_points.append((x[min_x], y[min_y], x[max_x], y[max_y]))
return mat_points
def get_points_min_max(self, array):
x, y = cv2.split(array)
min_x = min(x)[0]
x = list(x)
pos_min_x = x.index(min_x)
min_y = y[pos_min_x][0]
max_x = max(x)[0]
pos_max_x = x.index(max_x)
max_y = y[pos_max_x][0]
return (min_x, min_y),(max_x, max_y)
def highlight_smoke_contours(self, image):
mask = self.pre_processing.enhance_color(self.LOWER_GRAY_2, self.UPPER_GRAY_2, image)
contours, cany = self.pre_processing.border_image(mask, image)
i = 0
sub_mats = []
for c in contours:
extension = cv2.contourArea(c)
if extension > 600:
contour = contours[i]
(x_min, y_min), (x_max, y_max) = self.get_points_min_max(contour)
sub_mats.append((x_min, y_min, x_max, y_max))
self.pre_processing.draw_image(image, 1, [(x_min, y_min), (x_max, y_max)], (255,0,0))
self.pre_processing.draw_image(image, 2, contour, (0,255,0))
self.pre_processing.draw_image(cany, 2, contour,(0,255,0))
i = i + 1
return image, cany, sub_mats
def perfectly_segmented_parser(ink_dir, bonus=False):
"""
This is a parser for perfectly segmented symbols
:param ink_dir: inkml directory
:param bonus: boolean for bonus
:return:
"""
start = time.time()
lg_dir = dir.strip().split("/")[0] + "_output_lg"
if not os.path.exists(lg_dir):
os.mkdir(lg_dir)
ink_files = os.listdir(ink_dir)
if bonus:
print("Loaded Bonus classifier")
clf = joblib.load("relation_classifier_bonus.pkl")
else:
print("Loaded relationship classifier")
clf = joblib.load('relation_classifier4.pkl')
pre = Preprocessing()
total = len(ink_files)
c = 0
gt_c = 0
for file in ink_files:
print("Processing file : ", file, " Files remaining : ", total - c,
" Files completed : ", c)
f = open(os.path.join(ink_dir, file))
soup = bs.BeautifulSoup(f, 'html.parser')
trace_groups = soup.find_all('tracegroup')
symbol_list = []
#loop to isolate symbols
for tracegroup in trace_groups[1:]:
traceview = tracegroup.find_all('traceview')
trace_id = []
#loop to get strokes in a single symbol
for t in traceview:
trace_id.append(t['tracedataref'])
gt = tracegroup.annotation.text
gt_c += 1
X = []
Y = []
#extract stroke coordinates
for id in trace_id:
traces = soup.findAll("trace", {'id': id})
for trace in traces:
coords = trace.text.strip().split(",")
x = []
y = []
for coord in coords:
trace_parts = coord.strip().split(' ')
x.append(float(trace_parts[0]))
y.append(float(trace_parts[1]))
X.append(x)
Y.append(y)
X, Y = pre.dopreprocess(x=X, y=Y, parser=True)
if gt == ",":
gt = "COMMA"
sym_obj = Symbol(x=X, y=Y, label=gt, stroke_id=trace_id)
symbol_list.append(sym_obj)
symbol_count = {}
#Run through list of symbols to get their count
for sym in symbol_list:
if sym.symbol not in symbol_count:
symbol_count[sym.symbol] = 1
sym.sym_ct = symbol_count[sym.symbol]
else:
symbol_count[sym.symbol] += 1
sym.sym_ct = symbol_count[sym.symbol]
#perform line of sight
graph, labels = line_of_sight(symbol_list, clf)
#run edmonds on los graph
relations = edmonds(graph)
#write result to lg
write_to_lg(file=file,
symbol_list=symbol_list,
relations=relations,
labels=labels,
lg_dir=lg_dir)
c += 1
print("System executed in ", (time.time() - start) / 60, " minutes.")
import metrics_reduced
from preprocessing import Preprocessing
import codecs
from collections import Counter
from constants import *
s = metrics_reduced.SpanishTools()
p = Preprocessing()
with codecs.open('corpus/Version_2_classes/corpus_2_classes.txt', 'r', 'utf-8') as file:
features = {}
line = file.readline()
while line:
line_bigram_list = s.n_grams(p.preprocessing(line.split('/|/')[1]))
for e in line_bigram_list:
if e in features:
features[e] += 1
else:
features[e] = 1
line = file.readline()
with codecs.open('bigram_features_filtered.txt', 'w', 'utf-8') as file:
counter = Counter(features)
common = counter.most_common(10000)
temp_list=[]
for k in common:
for e in k[0].split():
if e in positive_words or e in negative_words:
temp_list.append(k[0])
def get_city_rank_test(self): instance=Preprocessing(self.config) instance.output_city_rank()
