def max_credit_model():
'''
Computes the accuracy of a "max credit" model where each statement is
labeled with the most frequent label of the previous statements by the
speaker.
'''
print("NUM CORRECT", "\t", "ACCURACY")
train_labels, _, _, _, train_credit = get_data(var.TRAINING_DATA_PATH)
val_labels, _, _, _, val_credit = get_data(var.VALIDATION_DATA_PATH)
test_labels, _, _, _, test_credit = get_data(var.TEST_DATA_PATH)
# 9: barely true counts.
# 10: false counts.
# 11: half true counts.
# 12: mostly true counts.
# 13: pants on fire counts.
credit_mapping = [
"barely-true", "false", "half-true", "mostly-true", "pants-fire"
]
train_correct = 0.0
train_credit = clean_credit(train_labels, train_credit)
for i in range(len(train_labels)):
max_credit_index = train_credit[i].index(max(train_credit[i]))
if credit_mapping[max_credit_index] == train_labels[i]:
train_correct += 1.0
print(train_correct, "\t\t", train_correct / len(train_labels),
"\tTraining Data")
val_correct = 0.0
val_credit = clean_credit(val_labels, val_credit)
for i in range(len(val_labels)):
max_credit_index = val_credit[i].index(max(val_credit[i]))
if credit_mapping[max_credit_index] == val_labels[i]:
val_correct += 1.0
print(val_correct, "\t\t", val_correct / len(val_labels),
"\tValidation Data")
test_correct = 0.0
test_credit = clean_credit(test_labels, test_credit)
for i in range(len(test_labels)):
max_credit_index = test_credit[i].index(max(test_credit[i]))
if credit_mapping[max_credit_index] == test_labels[i]:
test_correct += 1.0
print(test_correct, "\t\t", test_correct / len(test_labels), "\tTest Data")
def main():
initialize()
data = parse_data.get_data()
all_predictions = []
all_gold = []
for target in data:
if config.verbose:
print target
train_X, train_Y, feat_dict = create_feature_matrix(data[target]["train"],
feat_dict=None)
tune_X, tune_Y, _ = create_feature_matrix(data[target]["tune"],
feat_dict=feat_dict)
test_X, test_Y, _ = create_feature_matrix(data[target]["test"],
feat_dict=feat_dict)
model = train_model(train_X, train_Y, tune_X, tune_Y)
predictions = model.predict(test_X)
_, _, f1 = compute_f1(predictions, test_Y)
if config.verbose:
print "\t", "F1:", round(f1 * 100, 2)
all_predictions += list(predictions)
all_gold += list(test_Y)
f1_against, f1_favor, f1_overall = compute_f1(all_predictions, all_gold)
print "F1 Against:", round(f1_against * 100, 2)
print "F1 Favor:", round(f1_favor * 100, 2)
print "---> Overall F1:", round(f1_overall * 100, 2)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from parse_data import get_data
from draw_confusion_matrix import plot_confusion_matrix
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score
import time
from sklearn.neighbors import NearestNeighbors, KNeighborsClassifier, DistanceMetric
from sklearn.model_selection import KFold
if __name__ == '__main__':
data = get_data()
X = data['normalized_feature_matrix']
Y = data['target_vector']
KNN_rs = KNeighborsClassifier(n_neighbors=5,
algorithm='kd_tree',
metric='euclidean')
start_time = time.time()
KNN_rs.fit(X, Y)
used_time = time.time() - start_time
print(
"KD Tree using cosine distance with resubstitution method training time is %s seconds"
% used_time)
start_time = time.time()
predicted_rs = KNN_rs.predict(X)
used_time = time.time() - start_time
print(
"KD Tree using cosine distance with resubstitution method querying time is %s seconds"
% used_time)
cfm_rs = confusion_matrix(Y, predicted_rs, labels=range(1, 11))
def train_model():
'''
Trains a model using the parameters specified in var.py. The
model training checkpoints are saved to var.FOLDER_NAME. A
copy of the var.py used is also saved in that folder. Only
the lowest val loss models for each trained model is saved.
'''
copyfile('var.py', os.path.join(var.FOLDER_NAME, 'var.py'))
print("Reading word vectors... ")
embeddings = None
if var.USE_WORD2VEC:
embeddings = KeyedVectors.load_word2vec_format(var.WORD2VEC_BIN_PATH,
binary=True)
else:
embeddings = get_glove_vectors(var.GLOVE_VECTOR_PATH)
print("--- DONE ---")
print("Getting input data... ")
train_labels, train_sentences, train_subjects, train_party, train_credit = get_data(
var.TRAINING_DATA_PATH)
val_labels, val_sentences, val_subjects, val_party, val_credit = get_data(
var.VALIDATION_DATA_PATH)
print("--- DONE ---")
if var.NO_STOP_WORDS:
train_sentences = remove_stop_words(train_sentences)
val_sentences = remove_stop_words(val_sentences)
print("Preparing data for model... ")
# Convert the input sentences into sequences of integers
# length MAX_SEQUENCE_LENGTH where each integer maps to a
# word and the sequence only considers the first
# MAX_SEQUENCE_LENGTH words in the statement being evaluated
tokenizer = Tokenizer(num_words=var.MAX_NUM_WORDS)
tokenizer.fit_on_texts(train_sentences)
train_sequences = tokenizer.texts_to_sequences(train_sentences)
val_sequences = tokenizer.texts_to_sequences(val_sentences)
word_index = tokenizer.word_index
x_train = pad_sequences(train_sequences, maxlen=var.MAX_SEQUENCE_LENGTH)
x_val = pad_sequences(val_sequences, maxlen=var.MAX_SEQUENCE_LENGTH)
y_train = to_categorical(
np.asarray([var.LABEL_MAPPING[label] for label in train_labels]))
y_val = to_categorical(
np.asarray([var.LABEL_MAPPING[label] for label in val_labels]))
# Get the Part of Speech frequencies
x_train_pos = np.asarray(get_pos_freqs(train_sentences))
x_val_pos = np.asarray(get_pos_freqs(val_sentences))
# Populate SUBJECT_MAPPING with freq information from training data
var.SUBJECT_MAPPING = get_mapping(train_subjects)
# Create one hot vectors for the subject
x_train_subject = np.asarray(
get_one_hot_vectors(train_subjects, var.NUM_SUBJECTS,
var.SUBJECT_MAPPING))
x_val_subject = np.asarray(
get_one_hot_vectors(val_subjects, var.NUM_SUBJECTS,
var.SUBJECT_MAPPING))
# Convert party to list of list format
train_party = [[party] for party in train_party]
val_party = [[party] for party in val_party]
# Populate PARTY_MAPPING with training data
var.PARTY_MAPPING = get_mapping(train_party)
# Get One Hot Vectors representing Party
x_train_party = np.asarray(
get_one_hot_vectors(train_party, var.NUM_PARTIES, var.PARTY_MAPPING))
x_val_party = np.asarray(
get_one_hot_vectors(val_party, var.NUM_PARTIES, var.PARTY_MAPPING))
# Remove current label from credit vector
train_credit = clean_credit(train_labels, train_credit)
val_credit = clean_credit(val_labels, val_credit)
# Normalize Credit Vector
x_train_credit = np.asarray(normalize_vectors(train_credit))
x_val_credit = np.asarray(normalize_vectors(val_credit))
# Create embedding matrix for embedding layer. Matrix will be
# (num_words + 1) x EMBEDDING_DIM since word_index starts at 1
num_words = min(var.MAX_NUM_WORDS, len(word_index))
embedding_matrix = np.zeros((num_words + 1, var.EMBEDDING_DIM))
for word, rank in word_index.items():
if rank <= var.MAX_NUM_WORDS:
embedding = None
if var.USE_WORD2VEC:
if word in embeddings.vocab:
embedding = embeddings[word]
else:
embedding = embeddings.get(word, None)
if embedding is not None:
embedding_matrix[rank] = embedding
print("--- DONE ---")
for i in range(var.NUM_MODELS):
print("Creating model " + str(i + 1) + " out of " +
str(var.NUM_MODELS) + " ...")
if var.MODEL_TYPE == "CNN":
model = cnn_model(embedding_matrix,
num_words,
pooling=var.POOLING,
subject=var.USE_SUBJECTS,
party=var.USE_PARTY,
credit=var.USE_CREDIT,
pos=var.USE_POS)
elif var.MODEL_TYPE == "BI_LSTM":
model = bi_lstm_model(embedding_matrix,
num_words,
subject=var.USE_SUBJECTS,
party=var.USE_PARTY,
credit=var.USE_CREDIT,
pos=var.USE_POS)
elif var.MODEL_TYPE == "BI_LSTM_CNN":
model = bi_lstm_cnn_model(embedding_matrix,
num_words,
pooling=var.POOLING,
subject=var.USE_SUBJECTS,
party=var.USE_PARTY,
credit=var.USE_CREDIT,
pos=var.USE_POS)
elif var.MODEL_TYPE == "CNN_BI_LSTM":
model = cnn_bi_lstm_model(embedding_matrix,
num_words,
subject=var.USE_SUBJECTS,
party=var.USE_PARTY,
credit=var.USE_CREDIT,
pos=var.USE_POS)
elif var.MODEL_TYPE == "PARALLEL":
model = parallel_cnn_bi_lstm_model(embedding_matrix,
num_words,
pooling=var.POOLING,
subject=var.USE_SUBJECTS,
party=var.USE_PARTY,
credit=var.USE_CREDIT,
pos=var.USE_POS)
else:
raise Exception("Invalid MODEL_TYPE")
print("--- DONE ---")
print("Training model... ")
# Save trained model after each epoch
checkpoint_file = os.path.join(var.FOLDER_NAME,
str(i).zfill(2) + '_' + var.FILE_NAME)
checkpoint = ModelCheckpoint(checkpoint_file,
monitor='val_loss',
verbose=1,
save_best_only=True)
callbacks = [checkpoint]
train_input = [x_train]
val_input = [x_val]
if var.USE_SUBJECTS:
train_input.append(x_train_subject)
val_input.append(x_val_subject)
if var.USE_PARTY:
train_input.append(x_train_party)
val_input.append(x_val_party)
if var.USE_CREDIT:
train_input.append(x_train_credit)
val_input.append(x_val_credit)
if var.USE_POS:
train_input.append(x_train_pos)
val_input.append(x_val_pos)
print("train_input_size", len(train_input))
model.fit(train_input,
y_train,
validation_data=(val_input, y_val),
epochs=var.NUM_EPOCHS,
batch_size=var.BATCH_SIZE,
callbacks=callbacks)
print("--- DONE ---")
model.summary()
del model
"""
@copyright: 2013 by Pauli Rikula <*****@*****.**>
@license: MIT <http://www.opensource.org/licenses/mit-license.php>
"""
#for parsing
from parse_data import get_data
#for audio
from signal_tools import generate_wavs
if __name__ == "__main__":
print "generating wavs"
generate_wavs(get_data(),"temperature")
def main(args_parser):
#Dataset
parser = args_parser
args = parser.parse_args()
train_image_data, train_label_data, train_filename, valid_image_data, valid_label_data, valid_filename, unique_classes = get_data(
)
#tf.reset_default_graph()
DATASET_PATH = args.datasetPath
LEARNING_RATE_1 = args.learningRate
EPOCHS = args.epochs
BATCH_SIZE = args.batchSize
NUM_CLASSES = len(unique_classes)
Z_SCORE = args.zScore
WEIGHT_DECAY_1 = args.weightDecay
print("Current Setup:-")
print(
"Starting Learning Rate: {}, Epochs: {}, Batch Size: {}, Confidence Interval Z-Score {}, Number of classes: {}, Starting Weight Decay: {}"
.format(LEARNING_RATE_1, EPOCHS, BATCH_SIZE, Z_SCORE, NUM_CLASSES,
WEIGHT_DECAY_1))
#Placeholders
learning_rate = tf.placeholder(tf.float32, shape=[], name='learning_rate')
weight_decay = tf.placeholder(tf.float32, shape=[], name="weight_decay")
#Dataset
training_dataset = tf.data.Dataset.from_generator(
lambda: itertools.zip_longest(train_image_data, train_label_data,
train_filename),
output_types=(tf.float32, tf.float32),
output_shapes=(tf.TensorShape([None, None, 3]), tf.TensorShape([None]),
tf.TensorShape([None])))
training_dataset = training_dataset.repeat(EPOCHS).batch(
BATCH_SIZE).prefetch(1)
train_iterator = training_dataset.make_initializable_iterator()
train_features, train_labels, train_filename = train_iterator.get_next()
valid_dataset = tf.data.Dataset.from_generator(
lambda: itertools.zip_longest(valid_image_data, valid_label_data,
valid_filename),
output_types=(tf.float32, tf.float32),
output_shapes=(tf.TensorShape([None, None, 3]), tf.TensorShape([None]),
tf.TensorShape([None])))
valid_dataset = valid_dataset.repeat(EPOCHS).batch(BATCH_SIZE).prefetch(1)
valid_iterator = valid_dataset.make_initializable_iterator()
valid_features, valid_labels, valid_filename = valid_iterator.get_next()
#Model
_, train_op, train_cross_entropy, train_conf_matrix_op, train_accuracy = initiate_vgg_model(
train_features,
train_labels,
train_filename,
NUM_CLASSES,
weight_decay,
learning_rate,
handle="training",
reuse_model=None)
_, _, valid_cross_entropy, valid_conf_matrix_op, valid_accuracy = initiate_vgg_model(
valid_features,
valid_labels,
valid_filename,
NUM_CLASSES,
weight_decay,
learning_rate,
handle="validation",
reuse_model=True)
saver = tf.train.Saver()
if not os.path.exists(os.path.join("./short_dl_research_train/")):
os.mkdir(os.path.join("./short_dl_research_train/"))
with tf.Session() as sess:
with np.printoptions(threshold=np.inf):
train_writer = tf.summary.FileWriter(
"./short_tensorboard_training_logs/")
valid_writer = tf.summary.FileWriter(
"./short_tensorboard_validation_logs/")
train_writer.add_graph(sess.graph)
valid_writer.add_graph(sess.graph)
train_highest_acc = 0
valid_highest_acc = 0
sess.run([
tf.global_variables_initializer(),
tf.local_variables_initializer()
])
for epoch in range(EPOCHS):
print("Current Epoch: {}/{}".format(epoch, EPOCHS))
i = 0
try:
sess.run(train_iterator.initializer)
while True:
print("Current Training Iteration : {}/{}".format(
i, floor(int(157252) / BATCH_SIZE)))
train_acc, _, _, train_ce = util.training(
BATCH_SIZE, NUM_CLASSES, learning_rate,
weight_decay, sess, train_op, train_conf_matrix_op,
LEARNING_RATE_1, WEIGHT_DECAY_1,
train_cross_entropy, train_accuracy)
train_value1, train_value2 = util.confidence_interval(
train_acc, Z_SCORE, BATCH_SIZE)
print("Training Accuracy : {}".format(train_acc))
print("Training Loss (Cross Entropy) : {}".format(
train_ce))
print("Training Confidence Interval: [{} , {}]".format(
train_value2, train_value1))
if train_highest_acc <= train_acc:
train_highest_acc = train_acc
print(
"Highest Training Accuracy Reached: {}".format(
train_highest_acc))
#For every epoch, we will save the model
saver.save(
sess,
os.path.join("./short_dl_research_train/",
"model.ckpt"))
print(
"Latest Model is saving and Tensorboard Logs are updated"
)
train_writer.add_summary(
tf.summary.merge_all().eval(),
epoch * (floor(int(157252) / BATCH_SIZE)) + i)
i = i + 1
except tf.errors.OutOfRangeError:
print("End of the training dataset, proceed to validation")
pass
j = 0
try:
sess.run(valid_iterator.initializer)
while True:
print("Current Validation Iteration : {}/{}".format(
j, floor(int(19657) / BATCH_SIZE)))
valid_acc, _, valid_ce = util.validation(
BATCH_SIZE, NUM_CLASSES, learning_rate,
weight_decay, sess, valid_conf_matrix_op,
LEARNING_RATE_1, WEIGHT_DECAY_1,
valid_cross_entropy, valid_accuracy)
valid_value1, valid_value2 = util.confidence_interval(
valid_acc, Z_SCORE, BATCH_SIZE)
print("Validation Accuracy : {}".format(valid_acc))
print("validation Loss (Cross Entropy) : {}".format(
valid_ce))
print(
"Validation Confidence Interval: [{} , {}]".format(
valid_value2, valid_value1))
if valid_highest_acc <= valid_acc:
valid_highest_acc = valid_acc
print("Highest Validation Accuracy Reached: {}".
format(valid_highest_acc))
valid_writer.add_summary(
tf.summary.merge_all().eval(),
epoch * (floor(int(19657) / BATCH_SIZE)) + j)
j = j + 1
except tf.errors.OutOfRangeError:
print("End of validation dataset, go to the next epoch")
pass
import iris.coord_categorisation
from cf_units import Unit
import iris.quickplot as qplt
import iris.plot as iplt
import iris.analysis as ia
import numpy as np
from scipy import interpolate
import parse_data
data = parse_data.get_data()
data = {k: data[k] for k in ["altitude", "windspeed"]}
percentiles = [1, 5, 10, 25, 50, 75, 90, 95, 99]
import matplotlib.pyplot as plt
from matplotlib import style, ticker
import seaborn as sns
from labellines import labelLines
sns.set(font_scale=1)
style.use("default")
fig, ax = plt.subplots(1, 1, figsize=(8, 6), dpi=200)
colors = plt.cm.rainbow(np.linspace(0, 1, len(percentiles)))
altitude_mean = np.array(data["altitude"].collapsed(
["latitude", "longitude", "time"], ia.MEAN).data)
np.save("analysis_wind_speed_percentiles_at_altitude/altitude.npy",
altitude_mean)
for i, percentile in enumerate(percentiles):
windspeed_pct = np.array(
from pulp import *
from parse_data import get_data
NUTRITION_FACTS = get_data()
FOODS = ["avocado", "beans", "cheese", "rice", "spinach"]
pound_per_gram = 1 / 453.5924
FOOD_COSTS = { ### cost per pound * pound per gram * number of grams the nutrition data is based on
"avocado": (1.25 * 2) * pound_per_gram *
136, # 1.25 avos * (2 avos per pound ) * lb per gram * number of grams
"beans": 1.0 * pound_per_gram * 130,
"cheese": 7.71 * pound_per_gram * 28,
"rice": 1.0 * pound_per_gram * 42,
"spinach": 7.0 * pound_per_gram * 340
}
DAILY_NUTRITION = {
'protein': ['gte', 56],
'calories': ['eq', 2000],
'sodium': ['lte', 2400],
'vitaminC': ['gte', 90],
'vitaminA': ['gte', 700],
'saturatedFat': ['lte', 20]
}
my_lp_problem = LpProblem("My LP Problem", LpMinimize)
# setup variables (the individual food items are the variables)
food_vars = LpVariable.dicts("Foods", FOODS, 0)
""" from parse_data import get_data import numpy as np import pylab as pl from sklearn import cluster #, datasets #from sklearn.metrics import euclidean_distances #from sklearn.neighbors import kneighbors_graph from sklearn.preprocessing import Scaler from sklearn.decomposition import PCA data = get_data()[:5000] temperature_0 = [ r[1] for r in data[:-50]] temperature_1 = [ r[1] for r in data[50:]] X = np.c_[temperature_0,temperature_1] #X = Scaler().fit_transform(X) pca = PCA(n_components=3) X = pca.fit(X).transform(X) #spectral = cluster.SpectralClustering(n_clusters=2, mode='arpack', # affinity="nearest_neighbors")
"""
@copyright: 2013 by Pauli Rikula <*****@*****.**>
@license: MIT <http://www.opensource.org/licenses/mit-license.php>
"""
from parse_data import get_data
import numpy as np
import matplotlib.pyplot as plt
data = get_data()
temperature_0 = [ r[1] for r in data[:-1]]
temperature_1 = [ r[1] for r in data[1:]]
H, xedges, yedges = np.histogram2d(temperature_0,temperature_1, bins=(128,128))
H_log = np.log(H)
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
plt.imshow(H_log, extent=extent, interpolation='nearest')
plt.colorbar()
plt.xlabel("T_n")
# TenserFlow and tf.keras
import tensorflow as tf
from tensorflow import keras
# detection of face + use of webcam
import cv2
# Helper Librairies
import numpy as np
import keyboard
# parsing of profils
from parse_data import get_data
from train_data import get_train_data
train_images, train_labels, names = get_data()
# normalization
train_images = np.asarray(train_images) / 255.0
# create a neural network
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(len(names), activation=tf.nn.softmax)
])
# giving the network rules
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
dataset = {'k': [[1, 2], [2, 3], [3, 1]], 'r': [[6, 5], [7, 7], [8, 6]]}
new_features = [2, 5]
def kNN(data, predict, k=3):
if len(data) >= k:
print('k is less than total voting groups')
distances = []
for group in data:
for features in data[group]:
euclidean_distance = np.linalg.norm(
np.array(features) - np.array(predict))
distances.append([euclidean_distance, group])
votes = [i[1] for i in sorted(distances)[:k]]
vote_result = Counter(votes).most_common(1)[0][0]
confidence = Counter(votes).most_common(1)[0][1]
return vote_result, confidence
train_set, test_set = parse_data.get_data()
total = correct = 0
for group in test_set:
for data in test_set[group]:
vote, confidence = kNN(train_set, data, k=5)
if group == vote:
correct += 1
total += 1
print("Accuracy: ", correct * 1.0 / total)
import numpy as np import tflearn import csv from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.estimator import regression os.environ['TF_CPP_MIN_LOG_LEVEL']='2' # not required coz not jupyter import tensorflow as tf tf.reset_default_graph() image_dim = parse_data.image_dim training_data, testing_data = parse_data.get_data() model_path = '11.cogs-and-dats.model' # Make training set and testing set from the entire labeled training data test_set = int(len(training_data)*0.8) X = np.array([t[0] for t in training_data[:test_set]]) Y = np.array([t[1] for t in training_data[:test_set]]) X = X.reshape([-1, image_dim, image_dim, 1]) test_x = np.array([t[0] for t in training_data[test_set:]]) test_y = np.array([t[1] for t in training_data[test_set:]]) test_x = test_x.reshape([-1, image_dim, image_dim, 1]) # Make array from testing data that can be passed for prediction predict_this_X = np.array([t[0] for t in testing_data]) predict_this_X = predict_this_X.reshape([-1, image_dim, image_dim, 1]) predict_this_X_id = np.array([t[1] for t in testing_data])
#!/usr/bin/env python3
import numpy as np
import parse_data as ps
from sklearn.naive_bayes import GaussianNB
if __name__ == '__main__':
train_data = ps.get_data('train_clean.csv')
test_data = ps.get_test_matrix('test_clean.csv')
X = train_data['feature_matrix']
Y = train_data['target_vector']
label = test_data['label']
TestX = test_data['feature_matrix']
GNB = GaussianNB()
GNB.fit(X, Y)
Predict_Y = GNB.predict(X)
TestY = GNB.predict(TestX)
for i in range(len(label)):
print("%s is %s" % (label[i][0], TestY[i]))
def test_model():
'''
Evaluates a saved model against eval and test sets. The trained model
folder path should be the first argument. All saved models inside
that folder will be evaluated.
'''
paths = None
if len(sys.argv) < 2:
raise Exception(
"Must specify the path to the folder of the saved model to test")
model_path = sys.argv[1]
folder = ''
if os.path.isdir(model_path):
paths = [
os.path.join(model_path, f) for f in os.listdir(model_path) if
os.path.isfile(os.path.join(model_path, f)) and f.endswith(".hdf5")
]
folder = model_path
elif os.path.isfile(model_path):
paths = [model_path]
print()
print("List of files to test")
print(paths)
print()
model_results = []
for i, path in enumerate(paths):
print()
print("Testing file " + str(i + 1) + " out of " + str(len(paths)))
model = load_model(path)
# Recreate the input tokenizer
train_labels, train_sentences, train_subjects, train_party, train_credit = get_data(
var.TRAINING_DATA_PATH)
if var.NO_STOP_WORDS:
train_sentences = remove_stop_words(train_sentences)
train_party = [[party] for party in train_party]
tokenizer = Tokenizer(num_words=var.MAX_NUM_WORDS)
tokenizer.fit_on_texts(train_sentences)
# Get the val input via tokenizer and val labels
val_labels, val_sentences, val_subjects, val_party, val_credit = get_data(
var.VALIDATION_DATA_PATH)
if var.NO_STOP_WORDS:
val_sentences = remove_stop_words(val_sentences)
val_party = [[party] for party in val_party]
val_sequences = tokenizer.texts_to_sequences(val_sentences)
x_val = pad_sequences(val_sequences, maxlen=var.MAX_SEQUENCE_LENGTH)
y_val = to_categorical(
np.asarray([var.LABEL_MAPPING[label] for label in val_labels]))
# Get the test input via tokenizer and test labels
test_labels, test_sentences, test_subjects, test_party, test_credit = get_data(
var.TEST_DATA_PATH)
if var.NO_STOP_WORDS:
test_sentences = remove_stop_words(test_sentences)
test_party = [[party] for party in test_party]
test_sequences = tokenizer.texts_to_sequences(test_sentences)
x_test = pad_sequences(test_sequences, maxlen=var.MAX_SEQUENCE_LENGTH)
y_test = to_categorical(
np.asarray([var.LABEL_MAPPING[label] for label in test_labels]))
x_test_pos = np.asarray(get_pos_freqs(test_sentences))
x_val_pos = np.asarray(get_pos_freqs(val_sentences))
var.SUBJECT_MAPPING = get_mapping(train_subjects)
x_test_subjects = np.asarray(
get_one_hot_vectors(test_subjects, var.NUM_SUBJECTS,
var.SUBJECT_MAPPING))
x_val_subjects = np.asarray(
get_one_hot_vectors(val_subjects, var.NUM_SUBJECTS,
var.SUBJECT_MAPPING))
var.PARTY_MAPPING = get_mapping(train_party)
x_test_party = np.asarray(
get_one_hot_vectors(test_party, var.NUM_PARTIES,
var.PARTY_MAPPING))
x_val_party = np.asarray(
get_one_hot_vectors(val_party, var.NUM_PARTIES, var.PARTY_MAPPING))
test_credit = clean_credit(test_labels, test_credit)
val_credit = clean_credit(val_labels, val_credit)
x_test_credit = np.asarray(normalize_vectors(test_credit))
x_val_credit = np.asarray(normalize_vectors(val_credit))
test_input = [x_test]
val_input = [x_val]
if var.USE_SUBJECTS:
test_input.append(x_test_subjects)
val_input.append(x_val_subjects)
if var.USE_PARTY:
test_input.append(x_test_party)
val_input.append(x_val_party)
if var.USE_CREDIT:
test_input.append(x_test_credit)
val_input.append(x_val_credit)
if var.USE_POS:
test_input.append(x_test_pos)
val_input.append(x_val_pos)
test_score = model.evaluate(test_input,
y_test,
batch_size=var.BATCH_SIZE)
val_score = model.evaluate(val_input, y_val, batch_size=var.BATCH_SIZE)
print()
print("model = " + str(path))
print("val loss = %0.4f, val acc = %0.4f" %
(val_score[0], val_score[1]))
print("test loss = %0.4f, test acc = %0.4f" %
(test_score[0], test_score[1]))
model_results.append((os.path.basename(path), round(val_score[0], 4),
round(val_score[1],
4), round(test_score[0],
4), round(test_score[1], 4)))
average_val_loss = round(
sum([info[1] for info in model_results]) / len(model_results), 4)
average_val_acc = round(
sum([info[2] for info in model_results]) / len(model_results), 4)
average_test_loss = round(
sum([info[3] for info in model_results]) / len(model_results), 4)
average_test_acc = round(
sum([info[4] for info in model_results]) / len(model_results), 4)
best_val = sorted(model_results, key=lambda x: x[2], reverse=True)[:5]
best_test = sorted(model_results, key=lambda x: x[4], reverse=True)[:5]
with open(os.path.join(folder, "stats.txt"), "w") as f:
f.write("Num models = " + str(len(model_results)) + "\n")
f.write("Average val_loss = " + str(average_val_loss) + "\n")
f.write("Average val_acc = " + str(average_val_acc) + "\n")
f.write("Average test_loss = " + str(average_test_loss) + "\n")
f.write("Average test_acc = " + str(average_test_acc) + "\n")
f.write("\n")
f.write("Top 5 validation accuracies:\n")
for model in best_val:
f.write("\t" + str(model[2]) + "\t" + str(model[0]) + "\n")
f.write("\n")
f.write("Top 5 test accuracies:\n")
for model in best_test:
f.write("\t" + str(model[4]) + "\t" + str(model[0]) + "\n")
f.write("\n")
f.write("Results for all models:\n")
for model in sorted(model_results, key=lambda x: x[0]):
f.write("\t" + str(model) + "\n")
print()
print("---------------------- OVERALL STATISTICS -----------------------")
print()
with open(os.path.join(folder, "stats.txt"), "r") as f:
print(f.read())
"""
@copyright: 2013 by Pauli Rikula <*****@*****.**>
@license: MIT <http://www.opensource.org/licenses/mit-license.php>
"""
#for parsing
from parse_data import get_data
from plot_tools import makeplot, daily, monthly
if __name__ == "__main__":
signal = get_data()
makeplot(signal, daily)
makeplot(signal, monthly)