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1D-CNN.py
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1D-CNN.py
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# coding: utf-8
# In[1]:
import pickle
with open('undersampled_data.pkl', 'rb') as lf:
labels = pickle.load(lf)
with open('undersampled_posts.pkl', 'rb') as pf:
posts = pickle.load(pf)
# In[2]:
# Tokenizing data
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
import numpy as np
max_len = 50
max_features = 10000
test_samples = 1500
max_words = 10000
# In[3]:
tokenizer = Tokenizer(num_words=max_words)
tokenizer.fit_on_texts(posts)
sequences = tokenizer.texts_to_sequences(posts)
word_index = tokenizer.word_index
print('Found %s unique tokens' % len(word_index))
# In[4]:
data = pad_sequences(sequences, maxlen=max_len)
labels = np.asarray(labels)
# In[5]:
print('Shape of data tensor:', data.shape)
print('Shape of label tensor:', labels.shape)
# In[6]:
indices = np.arange(data.shape[0])
np.random.shuffle(indices)
data = data[indices]
labels = labels[indices]
# In[7]:
type_dictionary = {
'0000':'INTJ',
'0001':'INTP',
'0010':'INFJ',
'0011':'INFP',
'0100':'ISTJ',
'0101':'ISTP',
'0110':'ISFJ',
'0111':'ISFP',
'1000':'ENTJ',
'1001':'ENTP',
'1010':'ENFJ',
'1011':'ENFP',
'1100':'ESTJ',
'1101':'ESTP',
'1110':'ESFJ',
'1111':'ESFP',
}
type_labels=['INTJ','INTP','INFJ','INFP','ISTJ','ISTP','ISFJ','ISFP', 'ENTJ','ENTP','ENFJ','ENFP','ESTJ','ESTP','ESFJ','ESFP',]
one_hot_types = []
for label in labels:
bin_type = []
if (label[0] == 'I'):
bin_type.append(0)
else:
bin_type.append(1)
if (label[1] == 'N'):
bin_type.append(0)
else:
bin_type.append(1)
if (label[2] == 'T'):
bin_type.append(0)
else:
bin_type.append(1)
if (label[3] == 'J'):
bin_type.append(0)
else:
bin_type.append(1)
one_hot_types.append(bin_type)
# In[8]:
y = np.asarray(one_hot_types).astype('float32')
x_test = data[:test_samples]
y_test = y[:test_samples]
x_train = data[test_samples:]
y_train = y[test_samples:]
# In[9]:
from keras.models import Sequential
from keras import layers, regularizers
from keras.optimizers import RMSprop
model = Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(4))
model.compile(optimizer=RMSprop(lr=1e-4),
loss='binary_crossentropy',
metrics=['acc'])
history = model.fit(x_train, y_train,
epochs=30,
batch_size=128,
validation_split=0.2)
# In[11]:
import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.figure()
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.show()
# In[12]:
model = Sequential()
model.add(layers.Embedding(max_features, 128, input_length=max_len))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.MaxPooling1D(5))
model.add(layers.Conv1D(32, 7, activation='relu'))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Dense(4))
model.compile(optimizer=RMSprop(lr=1e-4),
loss='binary_crossentropy',
metrics=['acc'])
model.fit(x_train, y_train,
epochs=17,
batch_size=128)
y_pred = model.predict(x_test)
results = model.evaluate(x_test, y_test)
results
# In[19]:
for i in range(0, len(y_pred)):
if 2.0 == y_pred[i]:
print(i)
# In[14]:
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt
import itertools
import datetime
now = datetime.datetime.now()
pictures = []
folder = "1DCNN"
def byte_to_mbti(byte):
binary = ''
for letter in byte:
binary +=(str(int(letter)))
return type_dictionary[binary]
decoded_y_true = [byte_to_mbti(label) for label in y_test.round()]
decoded_y_pred = [byte_to_mbti(label) for label in y_pred.round()]
matrix = confusion_matrix(decoded_y_true, decoded_y_pred, labels=type_labels)
matrix_I_E = confusion_matrix(y_test[:,0], y_pred[:,0].round(), labels=[0, 1])
matrix_N_S = confusion_matrix(y_test[:,1], y_pred[:,1].round(), labels=[0, 1])
matrix_T_F = confusion_matrix(y_test[:,2], y_pred[:,2].round(), labels=[0, 1])
matrix_J_P = confusion_matrix(y_test[:,3], y_pred[:,3].round(), labels=[0, 1])
def plot_confusion_matrix(cm, classes,
normalize=False,
title='Confusion matrix',
cmap=plt.cm.Blues):
"""
This function prints and plots the confusion matrix.
Normalization can be applied by setting `normalize=True`.
"""
if normalize:
cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
print("Normalized confusion matrix")
else:
print('Confusion matrix, without normalization')
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title)
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
plt.text(j, i, format(cm[i, j], fmt),
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.tight_layout()
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.figure(figsize=(15,10))
plot_confusion_matrix(matrix, classes=type_labels, normalize=True,
title='Confusion matrix')
name = str(folder) + "/" + str(now.month) + "-" + str(now.day) + "-" + str(now.hour) + "-" + str(now.minute) + "_" + "CM0.png"
plt.savefig(name)
plt.show()
pictures.append(name)
plt.clf()
plt.figure(figsize=(5,5))
plot_confusion_matrix(matrix_I_E, classes=["I", "E"], normalize=True,
title='I-E Confusion matrix')
name = str(folder) + "/" + str(now.month) + "-" + str(now.day) + "-" + str(now.hour) + "-" + str(now.minute) + "_" + "CM1.png"
plt.savefig(name)
plt.show()
pictures.append(name)
plt.clf()
plt.figure(figsize=(5,5))
plot_confusion_matrix(matrix_N_S, classes=["N", "S"], normalize=True,
title='N-S Confusion matrix')
name = str(folder) + "/" + str(now.month) + "-" + str(now.day) + "-" + str(now.hour) + "-" + str(now.minute) + "_" + "CM2.png"
plt.savefig(name)
plt.show()
pictures.append(name)
plt.clf()
plt.figure(figsize=(5,5))
plot_confusion_matrix(matrix_T_F, classes=["T", "F"], normalize=True,
title='T-F Confusion matrix')
name = str(folder) + "/" + str(now.month) + "-" + str(now.day) + "-" + str(now.hour) + "-" + str(now.minute) + "_" + "CM3.png"
plt.savefig(name)
plt.show()
pictures.append(name)
plt.clf()
plt.figure(figsize=(5,5))
plot_confusion_matrix(matrix_J_P, classes=["J", "P"], normalize=True,
title='J-P Confusion matrix')
name = str(folder) + "/" + str(now.month) + "-" + str(now.day) + "-" + str(now.hour) + "-" + str(now.minute) + "_" + "CM4.png"
plt.savefig(name)
plt.show()
pictures.append(name)