def main(**kwargs):
kwargs = parse_cl(sys.argv[1:])
initialize_logger(kwargs['args'].out, kwargs['args'].debug, kwargs['args'].print_debug)
logger = mylog.getLogger(__name__)
start = time.time()
if "prepare" in kwargs:
logger.info("Run prepare")
prepare(kwargs["args"])
elif "cluster" in kwargs:
logger.info("Run cluster")
cluster(kwargs["args"])
elif "report" in kwargs:
logger.info("Run report")
report(kwargs["args"])
elif "predict" in kwargs:
logger.info("Run predictions")
predictions(kwargs["args"])
elif "explore" in kwargs:
logger.info("Run explore")
explore(kwargs["args"])
elif "stats" in kwargs:
logger.info("Run stats")
stats(kwargs["args"])
elif "collapse" in kwargs:
logger.info("Run collapse")
collapse_fastq(kwargs["args"])
elif "simulator" in kwargs:
logger.info("Run simulator")
# function to simulator
logger.info('It took %.3f minutes' % ((time.time()-start)/60))
def get_test_data(max_size, n_cats):
with open('word_to_index_top_30000.json', 'r') as f:
d = json.load(f)
data = []
labels = []
print("collecting test data and labels..")
with open('test_data.csv', 'r', encoding="utf8") as csvfile:
reader = csv.reader(csvfile)
for r in reader:
words = preprocess_string(r[0], CUSTOM_FILTERS)
nums = [0] * len(words)
for i, word in enumerate(words):
if word in d:
nums[i] = d[word]
data.append(nums)
labels.append(r[-1])
print("collected test data and labels succesfully.")
print("preparing test data and labels..")
x_test, y_test = prepare(X=data[1:], y=labels[1:], max_size=max_size, n_cats=n_cats, shuffle_data=True)
print("prepared test data and labels succesfully.")
return x_test, y_test
def get_train_data(max_size, n_cats):
data = []
labels = []
print('collecting training data..')
with open('data.csv', 'r') as f:
for row in csv.reader(f):
nums = [0] * len(row)
for i, d in enumerate(row):
nums[i] = int(d)
data.append(nums)
f.close()
print('collected training data successfully.')
print('collecting training labels..')
with open('labels.csv', 'r') as f:
for row in csv.reader(f):
labels.append(int(row[0]))
f.close()
print('collected training labels successfully.')
print("preparing training data and labels..")
x_train, y_train = prepare(X=data, y=labels, max_size=max_size, n_cats=n_cats, shuffle_data=True)
print("prepared training data and labels successfully.")
return x_train, y_train
def main(**kwargs):
kwargs = parse_cl(sys.argv[1:])
initialize_logger(kwargs['args'].out, kwargs['args'].debug,
kwargs['args'].print_debug)
logger = mylog.getLogger(__name__)
start = time.time()
if "prepare" in kwargs:
logger.info("Run prepare")
prepare(kwargs["args"])
elif "cluster" in kwargs:
logger.info("Run cluster")
cluster(kwargs["args"])
elif "report" in kwargs:
logger.info("Run report")
report(kwargs["args"])
elif "predict" in kwargs:
logger.info("Run predictions")
predictions(kwargs["args"])
elif "target" in kwargs:
logger.info("Run target annotation")
targets_enrichment(kwargs["args"])
elif "seqbuster" in kwargs:
logger.info("Run seqbuster")
miraligner(kwargs["args"])
elif "explore" in kwargs:
logger.info("Run explore")
explore(kwargs["args"])
elif "stats" in kwargs:
logger.info("Run stats")
stats(kwargs["args"])
elif "collapse" in kwargs:
logger.info("Run collapse")
collapse_fastq(kwargs["args"])
elif "simulator" in kwargs:
logger.info("Run simulator")
simulate(kwargs["args"])
logger.info('It took %.3f minutes' % ((time.time() - start) / 60))
def train(data_dir, review_dir, embedding_dir, model_dir):
"""
This module uses natural language toolkit (nltk) to divide reviews into single words. Based on that gensim model is
trained in order to provide embedding vectors.
"""
embedding_dim = 100
x_train, x_test, _, _ = prepare(data_dir)
all_reviews = x_test + x_train
review_lines = []
counter = 0
for line in all_reviews:
tokens = word_tokenize(line)
tokens = [w.lower() for w in tokens]
table = str.maketrans('', '', string.punctuation)
stripped = [w.translate(table) for w in tokens]
words = [word for word in stripped if word.isalpha()]
stop_words = set(stopwords.words('english'))
words = [w for w in words if w not in stop_words]
review_lines.append(words)
counter += 1
if counter % 10000 == 0:
print(counter, '/', len(all_reviews))
with open(review_dir, 'w') as f:
wr = csv.writer(f)
wr.writerows(review_lines)
print(review_lines[0])
print(review_lines[3])
print(len(review_lines))
model = gensim.models.Word2Vec(sentences=review_lines, size=embedding_dim, window=5, workers=4, min_count=10)
words = list(model.wv.vocab)
print('Vocabulary size: %d' % len(words))
model_dir = model_dir
model.save(model_dir)
filename = embedding_dir
model.wv.save_word2vec_format(filename, binary=False)
import prepare_data
import pandas
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
titanic = prepare_data.prepare("train.csv")
titanic_test = prepare_data.prepare("test.csv")
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
algorithms = [
[RandomForestClassifier(random_state=1, n_estimators=10000, min_samples_split=5, min_samples_leaf=2), ["Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title", "FamilyId"]],
[LogisticRegression(random_state=1), ["Pclass", "Sex", "Fare", "FamilySize", "Title", "Age", "Embarked"]]
]
predictions = []
for train, test in kf:
train_target = titanic["Survived"].iloc[train]
full_test_predictions = []
for alg, predictors in algorithms:
alg.fit(titanic[predictors].iloc[train,:], train_target)
test_predictions = alg.predict_proba(titanic[predictors].iloc[test,:].astype(float))[:,1]
full_test_predictions.append(test_predictions)
if j == inp.num_of_relay_positions:
print("|", end='|')
print()
if i == inp.num_of_relay_positions:
print("---" * 20)
print("========")
print('optimal value = ', solver.Objective().Value())
print()
print("Time = ", solver.WallTime(), " milliseconds")
return dict_constant["l"] * solver.Objective().Value(
), connect_matrix_result, solver.WallTime()
if __name__ == '__main__':
_dict_constant, _data_path = parse_config()
logger = init_log()
paths = glob.glob(_data_path)
print(paths)
# paths.reverse()
for path in paths:
logger.info("input path %s: ", path)
_inp, _is_adj_matrix, _distance_matrix = prepare(path)
result, connect_matrix, t = solve_by_or_tools(_inp, _is_adj_matrix,
_distance_matrix,
_dict_constant)
# logger.info("Connected Matrix: \n%s", connect_matrix)
logger.info("Result: %s", result)
logger.info("Time: %s", t)
def train_network(data_dir, review_dir, embedding_dir, models_dir, logs_dir, batch,
epochs, transfer):
"""
In this function structure of neural network is defined. All the training takes place here based on provided data.
"""
embedding_dim = 100
_, _, y_train, y_test = prepare(data_dir)
embedding_matrix, tokenizer_obj, num_words = use(embedding_dir)
with open(review_dir, 'r') as f:
reader = csv.reader(f)
review_lines = list(list(rec) for rec in csv.reader(f, delimiter=','))
avg_lenght = 0
for i in review_lines:
avg_lenght += len(i)
avg_lenght = int(avg_lenght / len(review_lines) + 100)
print('Data prepared')
print('')
model = Sequential()
if transfer:
embedding_layer = Embedding(num_words, embedding_dim,
input_length=avg_lenght,
trainable=False)
embedding_layer.build((None,))
embedding_layer.set_weights([embedding_matrix])
else:
embedding_layer = Embedding(num_words, embedding_dim,
input_length=avg_lenght,
trainable=True)
model.add(embedding_layer)
model.add(Flatten())
model.add(Dense(256, activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='sigmoid'))
adam = optimizers.adam(lr=0.001)
model.compile(loss="binary_crossentropy",
optimizer=adam,
metrics=['accuracy'])
checkpointer = ModelCheckpoint(filepath=models_dir,
verbose=1, save_best_only=False, save_weights_only=False, period=1)
tensorboard = TensorBoard(log_dir=logs_dir.format(time()))
x_train = review_lines[200000:]
x_test = review_lines[:200000]
x_shuffle_train = list(zip(x_train, y_train))
random.shuffle(x_shuffle_train)
x_train, y_train = zip(*x_shuffle_train)
x_train = list(x_train)
y_train = list(y_train)
x_shuffle_test = list(zip(x_test, y_test))
random.shuffle(x_shuffle_test)
x_test, y_test = zip(*x_shuffle_test)
x_test = list(x_test)
y_test = list(y_test)
y_train = to_categorical(y_train, num_classes=2)
y_test = to_categorical(y_test, num_classes=2)
training_batch_generator = Generator(batch, x_train, y_train, tokenizer_obj, avg_lenght)
validation_batch_generator = Generator(batch, x_test, y_test, tokenizer_obj, avg_lenght)
print('Model prepared, start training...')
model.fit_generator(generator=training_batch_generator,
steps_per_epoch=(300000 // batch),
epochs=epochs,
verbose=1,
validation_data=validation_batch_generator,
validation_steps=(200000 // batch),
use_multiprocessing=False,
max_queue_size=1,
callbacks=[checkpointer, tensorboard])
if transfer:
model.layers[0].trainable = True
sgd = optimizers.sgd(lr=0.00001)
model.compile(loss="binary_crossentropy",
optimizer=sgd,
metrics=['accuracy'])
model.fit_generator(generator=training_batch_generator,
steps_per_epoch=(300000 // batch),
epochs=2,
verbose=1,
validation_data=validation_batch_generator,
validation_steps=(200000 // batch),
use_multiprocessing=False,
max_queue_size=1,
callbacks=[checkpointer, tensorboard])
import prepare_data
import pandas
from sklearn.linear_model import LogisticRegression
from sklearn.cross_validation import KFold
from sklearn import cross_validation
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import GradientBoostingClassifier
import numpy as np
titanic = prepare_data.prepare("train.csv")
titanic_test = prepare_data.prepare("test.csv")
kf = KFold(titanic.shape[0], n_folds=3, random_state=1)
algorithms = [[
RandomForestClassifier(random_state=1,
n_estimators=10000,
min_samples_split=5,
min_samples_leaf=2),
[
"Pclass", "Sex", "Age", "Fare", "Embarked", "FamilySize", "Title",
"FamilyId"
]
],
[
LogisticRegression(random_state=1),
[
"Pclass", "Sex", "Fare", "FamilySize", "Title", "Age",
"Embarked"
]
]]
for j in range(T):
this_dset[j] = views[i][j]
dsets.append(this_dset)
"""
colors = []
for article in p[:10]:
colors.append(colorsys.hsv_to_rgb(0.588, 0.2, random.uniform(0.4, 0.7)))
# stacked_graph(dsets, baseline_fn = min_weighted_wiggles, color_seq='random')
sg = pystreamgraph.StreamGraph(views, colors=colors, labels=escaped_p)
sg.draw("generated_figure.svg", "MH370 related articles", show_labels=True, width=1800, height=8400)
# pl.savefig('generated_figure.png')
# pl.show()
if __name__ == '__main__':
nicknames = ['olympics', 'mh370', 'ebola']
for nickname in nicknames:
p = pickle.load(open('../page/' + nickname + '_cluster.pickle'))
views = prepare_data.prepare(p)
v = np.array(views)
v2 = np.fliplr(np.rot90(v.copy(),-1))
template = open('streamgraph.js/template.tpl')
t = template.read()
t = t.replace("<DATA>", str(v2.tolist()))
t = t.replace("<TITLES>", str(p))
h = open('streamgraph.js/data/' + nickname + '.js', "w")
h.write(t)
h.close()
import sys, getopt, random
import os
import pandas as pd
from pyspark.sql import SparkSession
from pyspark.sql.types import IntegerType
import pyspark.sql.functions as f
from pyspark.ml.stat import Correlation
from pyspark.ml.feature import VectorAssembler
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from pyspark.sql.functions import percent_rank
from pyspark.sql import Window
from prepare_data import prepare
from prepare_data import train_val_test_split
from pyspark.mllib.tree import RandomForest
filepath = "hdfs:/user/ct2522"
data =prepare(filepath)
train,val,test=train_val_test_split(data)
train_col=train.columns
train_col.remove("Popularity")
rf = RandomForest.trainClassifier(train)
rf.predict(val.drop("Popularity"))