def main():
# Load the data set
df = data()
X = df[ [col for col in df if col not in ['label', 'class']]]
y = df['class'].values
# Binarize the categorical data using a DictVectorizer
# This requires the data be fed in the form of Python dicts
vectorizer = DV(sparse=False)
X_binarized = vectorizer.fit_transform(X.to_dict(orient='records'))
X_binarized = np.array(X_binarized)
# Split into train, cv and test sets
X_train, X_test, y_train, y_test = train_test_split(X_binarized, y, train_size=.8, random_state=42)
# My implementation
classifier = LogisticRegression()
classifier.fit(y_train, X_train, regularization='l1')
l1_error = 1 - classifier.accuracy(y_test, X_test)
classifier = LogisticRegression()
classifier.fit(y_train, X_train, regularization='l2')
l2_error = 1 - classifier.accuracy(y_test, X_test)
print('LogisticRegression with L1 regularization \nError: {}'.format(l1_error))
print('LogisticRegression with L2 regularization \nError: {}'.format(l2_error))
def train_model(self):
k = model.modeler()
ux = tf.placeholder(shape=(None, 4), dtype=float) # ux作为绝对正确值
loss = -tf.reduce_mean(ux * tf.log(tf.clip_by_value(k.model, 0, 1)))
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(loss)
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
data = gt.data()
for i in range(200000): # 此处可以使用配置文件中的参数
sess.run(train_step,
feed_dict={
k.x: data.train_future,
ux: data.train_label
})
print(
sess.run(loss,
feed_dict={
k.x: data.train_future,
ux: data.train_label
}))
# 训练结束后保存模型
saver = tf.train.Saver()
PATH = dnn.CONFIG['train']['save_path']
saver.save(sess, PATH)
def main():
df = data()
X = df[ [col for col in df if col not in ['class']]]
y = df['class'].values
# Binarize the categorical data using a DictVectorizer
# This requires the data be fed in the form of Python dicts
vectorizer = DV(sparse=False)
X_binarized = vectorizer.fit_transform( X.to_dict(orient='records') )
# Split into test and train sets
X_train, X_test, y_train, y_test = train_test_split(X_binarized, y, train_size=.8, random_state=42)
C = np.linspace(.1, 10, 100).tolist() + np.linspace(20,100, 5).tolist() + \
np.linspace(200, 1000, 9).tolist()
param_grid = list(ParameterGrid({'C': C, 'penalty': ['l1']}))
for params in param_grid:
classifier = LogisticRegression(**params)
# Fit the model to the training data
t0 = time()
classifier.fit(X_train, y_train)
t1 = time()
accuracy = classifier.score(X_test, y_test)
error = (1 - accuracy)
print '\nTest error: {} Time to train: {}'.format(error, (t1-t0))
print 'Params: {}'.format(params)
def main():
df = data()
# Test run
classifiers = [{'classifier': LogisticRegression,
'classifier_name': 'Logistic Regression L2',
'param_grid': {'C': [.1], 'max_iter': [10], 'penalty': ['l2']} }]
# To complete the full analysis, please uncomment the following block of code
# and run this script.
# NOTE This will take several hours to run
##classifiers = [{'classifier': LogisticRegression,
## 'classifier_name': 'Logistic Regression L2',
## 'param_grid': {'C': np.linspace(.1, 5, 50), 'max_iter': [10,50,100,200], 'penalty': ['l2']} }]
feature_selection = FeatureSelection(df, classifiers)
feature_selection.select_features()
def main():
df = data()
X = df[ [col for col in df if col not in ['class']]]
y = df['class'].values
# Binarize the categorical data using a DictVectorizer
# This requires the data be fed in the form of Python dicts
vectorizer = DV(sparse=False)
X_binarized = vectorizer.fit_transform( X.to_dict(orient='records') )
# Split into test and train sets
X_train, X_test, y_train, y_test = train_test_split(X_binarized, y, train_size=.8, random_state=42)
# Due to the amount of time it takes to train an SVM on the data, I've commented out the true
# parameters I used and only included a subset. If you'd like to run the full analysis, please
# comment out the next line and uncomment the line after that
# Note: This will still take several minutes to run
param_grid = list(ParameterGrid({'C': [1], 'kernel': ['rbf']}))
#param_grid = list(ParameterGrid({'C': [1, 10, 100, 1000], 'kernel': ['linear', 'rbf']}))
for params in param_grid:
classifier = SVC(**params)
# Fit the model to the training data
t0 = time()
classifier.fit(X_train, y_train)
t1 = time()
accuracy = classifier.score(X_test, y_test)
error = (1 - accuracy)
print '\nTest error: {} Time to train: {}'.format(error, (t1-t0))
print 'Params: {}'.format(params)
def main():
df = data()
for train_size in np.linspace(0.5, 0.9, 5):
train, test = train_test_split(df, train_size=train_size, random_state=42)
# Since there is only 1 sample with native-country == Holand-Netherlands,
# ensure that this sample is in the training set
if "Holand-Netherlands" in test["native-country"].unique():
train = train.append(test[test["native-country"] == "Holand-Netherlands"])
test = test[test["native-country"] != "Holand-Netherlands"]
for ignore_missing in [True, False]:
nb = NaiveBayes(ignore_missing=ignore_missing)
nb.learn_parameters(train)
acc = nb.score(test[test["native-country"] != "Holand-Netherlands"])
print(
"\nTrain size: {} Test error: {} Ignore features with missing values: {}".format(
train_size, (1 - acc), ignore_missing
)
)
# -*- coding: utf-8 -*-
import get_data as gt
import train
if __name__ == "__main__":
data = gt.data() # 得到数据
model = train.trainer(data.all_data) # 训练模型
result_model = model.model # 聚类结果
print(result_model.predict(data.all_data))
#translate each digit to array of length 8 (segs + decimal pt) for YSD-160AR4B-8
to7seg = {
0:[1,0,0,0,1,0,0,0],
1:[1,1,1,0,1,0,1,1],
2:[0,1,0,0,1,1,0,0],
3:[0,1,0,0,1,0,0,1],
4:[0,0,1,0,1,0,1,1],
5:[0,0,0,1,1,0,0,1],
6:[0,0,0,1,1,0,0,0],
7:[1,1,0,0,1,0,1,1],
8:[0,0,0,0,1,0,0,0],
9:[0,0,0,0,1,0,1,1]}
while(1):
#get info from get_data.py
data = data()
#choose what goes in each digit: 0-ones place, 1-tens place
swell_h0 = int(round(data['swell']['h']%10))
swell_h1 = int(data['swell']['h']/10)
swell_p0 = int(round(data['swell']['p']%10))
swell_p1 = int(data['swell']['p']/10)
wind_k0 = int(round(data['wind']['kts']%10))
wind_k1 = int(data['wind']['kts']/10)
wind_g0 = int(round(data['wind']['gust']%10))
wind_g1 = int(data['wind']['gust']/10)
#pair digits back up for sr
swell_h = to7seg[swell_h0] + to7seg[swell_h1]
swell_p = to7seg[swell_p0] + to7seg[swell_p1]
def main():
choice = input('which model u want to use?')
train_iterator, valid_iterator, test_iterator, input_dim, output_dim, pad_idx = data(
)
if choice == 'conv_seq2seq':
EMB_DIM = 256
HID_DIM = 512
ENC_LAYERS = 10
DEC_LAYERS = 10
ENC_KERNEL_SIZE = 3
DEC_KERNEL_SIZE = 3
ENC_DROPOUT = 0.25
DEC_DROPOUT = 0.25
N_EPOCHS = 6
CLIP = 1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
enc = Encoder2(input_dim, EMB_DIM, HID_DIM, ENC_LAYERS,
ENC_KERNEL_SIZE, ENC_DROPOUT, device)
dec = Decoder2(output_dim, EMB_DIM, HID_DIM, DEC_LAYERS,
DEC_KERNEL_SIZE, DEC_DROPOUT, pad_idx, device)
model = Seq2Seq2(enc, dec, device).to(device)
optimizer = optim.Adam(model.parameters())
criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss = train_conv(model, train_iterator, optimizer,
criterion, CLIP)
valid_loss = evaluate_conv(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'model_conv.pt')
print(f'Epoch: {epoch + 1:02} | Time: {epoch_mins}m {epoch_secs}s')
print(
f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}'
)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}'
)
choice = input('Do you want to evaluate the model?')
if choice == 'yes':
model.load_state_dict(torch.load('model_transformer.pt'))
test_loss = evaluate(model, test_iterator, criterion)
print(
f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |'
)
elif choice == 'transformer':
hid_dim = 512
n_layers = 6
n_heads = 8
pf_dim = 2048
dropout = 0.1
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
enc = Encoder(input_dim, hid_dim, n_layers, n_heads, pf_dim,
EncoderLayer, SelfAttention, PositionwiseFeedforward,
dropout, device)
dec = Decoder(output_dim, hid_dim, n_layers, n_heads, pf_dim,
DecoderLayer, SelfAttention, PositionwiseFeedforward,
dropout, device)
model = Seq2Seq(enc, dec, pad_idx, device).to(device)
for p in model.parameters():
if p.dim() > 1:
nn.init.xavier_uniform_(p)
optimizer = NoamOpt(
hid_dim, 1, 2000,
torch.optim.Adam(model.parameters(),
lr=0,
betas=(0.9, 0.98),
eps=1e-9))
criterion = nn.CrossEntropyLoss(ignore_index=pad_idx)
N_EPOCHS = 10
CLIP = 1
best_valid_loss = float('inf')
for epoch in range(N_EPOCHS):
start_time = time.time()
train_loss = train(model, train_iterator, optimizer, criterion,
CLIP)
valid_loss = evaluate(model, valid_iterator, criterion)
end_time = time.time()
epoch_mins, epoch_secs = epoch_time(start_time, end_time)
if valid_loss < best_valid_loss:
best_valid_loss = valid_loss
torch.save(model.state_dict(), 'model_transformer.pt')
print(f'Epoch: {epoch + 1:02} | Time: {epoch_mins}m {epoch_secs}s')
print(
f'\tTrain Loss: {train_loss:.3f} | Train PPL: {math.exp(train_loss):7.3f}'
)
print(
f'\t Val. Loss: {valid_loss:.3f} | Val. PPL: {math.exp(valid_loss):7.3f}'
)
choice = input('Do you want to evaluate the model?')
if choice == 'yes':
model.load_state_dict(torch.load('model_transformer.pt'))
test_loss = evaluate(model, test_iterator, criterion)
print(
f'| Test Loss: {test_loss:.3f} | Test PPL: {math.exp(test_loss):7.3f} |'
)
# -*- coding: utf-8 -*-
import model
import get_data as gt
import train
import sys
import presict as pre
sys.path.append('...')
from conf import dnn
if __name__ == "__main__":
q = train() # 训练并且将模型保存
u = gt.data()
test_data = u.birth_data(dnn.CONFIG['predict']['amount'])
result = pre.predict()
pre.predict_() # 加载模型并且预测预测
import tensorflow as tf
import numpy as np
from get_data import data
'''
seq2seq model
'''
#load data
data = data("data/small_vocab_en", "data/small_vocab_fr")
#encoder
def encoder(xs, source_lens, hidden_size, num_layers, embedding_size):
#eng_embed = tf.Variable([len(data.eng_word2id.keys()), embedding_size], dtype=tf.float32)
#seq_embedding = tf.nn.embedding_lookup(eng_embed, xs)
#print(seq_embedding.shape)
def get_lstmCell(hidden_size): #1.5之后不能再使用[lstm_cell]*num_layers这种方法
lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
return lstm_cell
#LSTM model
mlstm_cell = tf.contrib.rnn.MultiRNNCell(
[get_lstmCell(hidden_size) for _ in range(num_layers)])
#init_s = mlstm_cell.zero_state(batch_size, dtype=tf.float32)
seq_embedding = tf.contrib.layers.embed_sequence(
xs, len(data.eng_word2id.keys()), embedding_size)
en_outputs, en_final_states = tf.nn.dynamic_rnn(mlstm_cell,
seq_embedding,
source_lens,
dtype=tf.float32)
def __init__(self):
self.data = gt.data()
self.parameter = kn.CONFIG['train']['parameter']
self.model = self.get_model()
'''
Sequence to Sequence with Attention Model
'''
import tensorflow as tf
import numpy as np
import sys
sys.path.append('../seq2seq_machineTranslation')
from get_data import data
'''
seq2seq model
'''
#load data
data = data("../seq2seq_machineTranslation/data/small_vocab_en",
"../seq2seq_machineTranslation/data/small_vocab_fr")
#encoder
def encoder(xs, source_lens, hidden_size, num_layers, embedding_size):
#eng_embed = tf.Variable([len(data.eng_word2id.keys()), embedding_size], dtype=tf.float32)
#seq_embedding = tf.nn.embedding_lookup(eng_embed, xs)
#print(seq_embedding.shape)
def get_lstmCell(hidden_size): #1.5之后不能再使用[lstm_cell]*num_layers这种方法
lstm_cell = tf.contrib.rnn.BasicLSTMCell(hidden_size)
return lstm_cell
#LSTM model
mlstm_cell = tf.contrib.rnn.MultiRNNCell(
[get_lstmCell(hidden_size) for _ in range(num_layers)])