def test_tokenizing(self):
tweet = "One Two Three"
self.assertEqual(preprocess(tweet), ['One', 'Two', 'Three'])
tweet = ":) ;) :op ;OP"
self.assertEqual(preprocess(tweet), [':)', ';)', ':op', ';OP'])
tweet = "RT @marcobonzanini: just an example! :D http://example.com #NLP"
self.assertEqual(preprocess(tweet), ['RT', '@marcobonzanini', ':', 'just', 'an', 'example', '!', ':D', 'http://example.com', '#NLP'])
def train_classifier(self):
trial = y = data = None
test = False
while not test:
trial, y, data = self.trial, self.y, self.data
test = trial.shape[0] == y.shape[0] and y.shape[0] == data.shape[0]
# last 6 trials (3 trials per class)
n_back = 12
min_trial = max(0, self.current_trial - (n_back - 1))
good = np.logical_and(y != 2, trial >= min_trial)
sigs_train = data[good]
y_train = y[good] #.astype('float32')
if classifier.should_preprocess:
classifier.train_pre_flow(sigs_train)
sigs_train, y_train = classifier.preprocess(sigs_train, y_train)
# print(sigs_train.shape, y_train.shape)
# print(list(y_train))
y_train = y_train.astype('float32')
# print(self.data.shape, self.y.shape, self.trial.shape)
# inp = classifier.get_inp_xy(sigs_train, y_train)
f = self.flow
try:
print('training classifier...')
self.flow = classifier.get_flow(sigs_train, y_train)
self.should_classify = True
print('updated classifier!')
except FlowException as e:
self.flow = f
print "FlowException error:\n{0}".format(e)
def classify(self):
X = self.data[-500:]
if classifier.should_preprocess:
X = classifier.preprocess(X)
# print(X.shape)
out = self.flow(X)
s = out[-1]
if abs(s) > self.threshold:
s = np.sign(s)
else:
s = 0
if time.time() > self.start_trial + 0.5 and (not self.pause_now) and (not self.running_arm):
#if (not self.pause_now) and (not self.running_arm):
if s == self.current_class:
self.good_times += 1
self.total_times += 1
output = out[-1][0]
if self.running_arm and self.arm:
if s == 1:
self.arm.write('a')
elif s == -1:
self.arm.write('A')
# else: # bias
# if self.current_class != 2:
# output += self.current_class * 0.3
# output = np.clip(output, -1, 1)
if not self.pause_now:
self.send_it('state', output)
print('classify', output)
X = X_res
Y = resDataset.iloc[:, 5]
else:
resDataset = collection(dataset, parameter='res')
data = open('ResData', 'ab')
pickle.dump(resDataset, data)
data.close()
X_res = resDataset.iloc[:, resDataset.columns != 'Class']
X = X_res
Y = resDataset.iloc[:, 5]
# Preparing dataset for all parameter.
if parameter == 'all':
allDataset = [X_dia, X_sys, X_eda, X_res]
X = pd.concat(allDataset, axis=1)
# box_plot(X) # For plotting the box plot.
print('Dataset for ' + str(parameter) + ' is processed.')
accuracy, precision, recall, matrix = preprocess(
X, Y) # Classifying the processed data.
# Printing the results.
print('Results')
print('The Average accuracy of the Classifier for ' + str(parameter) +
' is: ' + str(accuracy))
print('The Average precision is: ' + str(precision))
print('The Average recall is: ' + str(recall))
print('The Average confusion matrix is: ' + str(matrix))
import tensorflow as tf from neural_net import LeNet from neural_net import evaluate from classifier import import_dataset from classifier import preprocess # Load and normalize dataset X_train, y_train, X_valid, y_valid, X_test, y_test = import_dataset() X_train_norm, X_valid_norm, X_test_norm = preprocess(X_train, X_valid, X_test) #Prepare to load trained network save_file = './saved_models/lenet' keep_prob = 1 tf.reset_default_graph() x = tf.placeholder(tf.float32, (None, 32, 32, 3)) y = tf.placeholder(tf.int32, (None)) one_hot_y = tf.one_hot(y, 43) logits = LeNet(x, keep_prob) cross_entropy = tf.nn.softmax_cross_entropy_with_logits(labels=one_hot_y, logits=logits) loss_operation = tf.reduce_mean(cross_entropy) optimizer = tf.train.AdamOptimizer(learning_rate=0.0001) training_operation = optimizer.minimize(loss_operation) correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(one_hot_y, 1)) accuracy_operation = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) saver = tf.train.Saver() BATCH_SIZE = 128 with tf.Session() as sess:
import pandas as pd
from classifier import preprocess
from classifier import get_pos_tags
from sklearn.feature_extraction.text import TfidfVectorizer
#load data
df = load_dataset('dataset/dataset_dummy_classes.csv')
tweets = df['text']
#Get POS tags for tweets and save as a string
tweet_tags = []
for t in tweets:
clean = preprocess(t)
tags =get_pos_tags(clean)
tag_str = " ".join(tags)
tweet_tags.append(tag_str)
#We can use the TFIDF vectorizer to get a token matrix for the POS tags
pos_vectorizer = TfidfVectorizer(
tokenizer=None,
lowercase=False,
preprocessor=None,
ngram_range=(1, 3),
stop_words=None,
use_idf=False,
smooth_idf=False,
norm=None,
