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har_en.py
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#!/usr/bin/env python
# -*- coding:utf-8 _*-
from sklearn.metrics import *
from tensorflow.keras import Model
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, CSVLogger, ReduceLROnPlateau
from tensorflow.keras.layers import *
from sklearn.model_selection import StratifiedKFold
from load_data import *
from load_inv_data import *
from utils import *
os.environ['CUDA_VISIBLE_DEVICES'] = '1'
# 创建路径
if not os.path.exists('./result'):
os.mkdir('./result')
if not os.path.exists('./models'):
os.mkdir('./models')
if not os.path.exists('./logs'):
os.mkdir('./logs')
# gpus = tf.config.experimental.list_physical_devices(device_type='GPU')
# print(gpus)
# if gpus:
# gpu0 = gpus[0] # 如果有多个GPU，仅使用第0个GPU
# tf.config.experimental.set_memory_growth(gpu0, True) # 设置GPU显存用量按需使用
# # 或者也可以设置GPU显存为固定使用量(例如：4G)
# # tf.config.experimental.set_virtual_device_configuration(gpu0,
# # [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=4096)])
# tf.config.set_visible_devices([gpu0], "GPU")
train_features, _, test_features = load_features_data(feature_id=2)
train_lstm, y1, test_lstm, seq_len, _ = load_lstm_data()
train_lstm_inv, _, test_lstm_inv, _, _ = load_lstm_inv_data()
y = load_y()
def multi_conv2d(input_forward):
input = Reshape((60, train_lstm.shape[2], 1), input_shape=(60, train_lstm.shape[2]))(input_forward)
X = Conv2D(filters=64,
kernel_size=(3, 3),
activation='relu',
padding='same')(input)
X = BatchNormalization()(X)
X = Conv2D(filters=128,
kernel_size=(3, 3),
activation='relu',
padding='same')(X)
X = BatchNormalization()(X)
X = MaxPooling2D()(X)
X = Dropout(0.2)(X)
X = Conv2D(filters=256,
kernel_size=(3, 3),
activation='relu',
padding='same')(X)
# X = BatchNormalization()(X)
X = Dropout(0.3)(X)
X = Conv2D(filters=512,
kernel_size=(3, 3),
activation='relu',
padding='same')(X)
# X = BatchNormalization()(X)
X = GlobalAveragePooling2D()(X)
X = Dropout(0.5)(X)
# X = BatchNormalization()(Dropout(0.2)(Dense(128, activation='relu')(Flatten()(X))))
return X
def Net():
input_forward = Input(shape=(60, train_lstm.shape[2]))
input_backward = Input(shape=(60, train_lstm.shape[2]))
X_forward = multi_conv2d(input_forward)
X_backward = multi_conv2d(input_backward)
feainput = Input(shape=(train_features.shape[1],))
dense = Dense(32, activation='relu')(feainput)
dense = BatchNormalization()(dense)
dense = Dropout(0.2)(dense)
dense = Dense(64, activation='relu')(dense)
dense = Dropout(0.2)(dense)
dense = Dense(128, activation='relu')(dense)
dense = Dropout(0.2)(dense)
dense = Dense(256, activation='relu')(dense)
dense = BatchNormalization()(dense)
output = Concatenate(axis=-1)([X_forward, X_backward, dense])
output = BatchNormalization()(Dropout(0.2)(Dense(640, activation='relu')(Flatten()(output))))
output = Dense(19, activation='softmax')(output)
return Model([input_forward, input_backward, feainput], output)
acc_scores = []
combo_scores = []
proba_t = np.zeros((7500, 19))
kfold = StratifiedKFold(5, shuffle=True, random_state=42)
# 类别权重设置
class_weight = np.array([0.03304992, 0.09270433, 0.05608886, 0.04552935, 0.05965442,
0.04703785, 0.10175535, 0.03236423, 0.0449808, 0.0393582,
0.03236423, 0.06157433, 0.10065826, 0.03990675, 0.01727921,
0.06555129, 0.04731212, 0.03551838, 0.04731212])
for fold, (train_index, valid_index) in enumerate(kfold.split(train_lstm, y)):
print("{}train {}th fold{}".format('==' * 20, fold + 1, '==' * 20))
y_ = to_categorical(y, num_classes=19)
model = Net()
model.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['acc'])
model.summary()
plateau = ReduceLROnPlateau(monitor="val_acc",
verbose=1,
mode='max',
factor=0.5,
patience=20)
early_stopping = EarlyStopping(monitor='val_acc',
verbose=1,
mode='max',
patience=30)
checkpoint = ModelCheckpoint(f'models/fold{fold}.h5',
monitor='val_acc',
verbose=0,
mode='max',
save_best_only=True)
csv_logger = CSVLogger('logs/log.csv', separator=',', append=True)
x_train_lstm = train_lstm[train_index]
y_train_lstm = y_[train_index]
x_train_copy = np.copy(x_train_lstm)
y_train_copy = np.copy(y_train_lstm)
x_en, y_en = data_enhance('noise', x_train_copy, y_train_copy)
x_train_lstm = np.r_[x_train_lstm, x_en]
y_train_lstm = np.r_[y_train_lstm, y_en]
x_train_lstminv = train_lstm_inv[train_index]
y_train_lstminv = y_[train_index]
x_train_copy = np.copy(x_train_lstminv)
y_train_copy = np.copy(y_train_lstminv)
x_en, y_en = data_enhance('noise', x_train_copy, y_train_copy)
x_train_lstminv = np.r_[x_train_lstminv, x_en]
y_train_lstminv = np.r_[y_train_lstminv, y_en]
x_train_features = train_features[train_index]
y_train_features = y_[train_index]
x_train_copy = np.copy(x_train_features)
y_train_copy = np.copy(y_train_features)
x_en, y_en = data_enhance('noise', x_train_copy, y_train_copy)
x_train_features = np.r_[x_train_features, x_en]
y_train_features = np.r_[y_train_features, y_en]
from sklearn.utils import shuffle
x_train_lstm, x_train_lstminv, x_train_features, y_train_lstminv = shuffle(x_train_lstm, x_train_lstminv,
x_train_features, y_train_lstminv)
print('Data enhanced (%s) => %d' % (' '.join('noise'), len(x_train_lstminv)))
history = model.fit([x_train_lstm,
x_train_lstminv,
x_train_features],
y_train_lstminv,
epochs=500,
batch_size=256,
verbose=1,
shuffle=True,
class_weight=dict(enumerate((1 - class_weight) ** 3)),
validation_data=([train_lstm[valid_index],
train_lstm_inv[valid_index],
train_features[valid_index]],
y_[valid_index]),
callbacks=[plateau, early_stopping, checkpoint, csv_logger])
model.load_weights(f'models/fold{fold}.h5')
proba_x = model.predict([train_lstm[valid_index], train_lstm_inv[valid_index], train_features[valid_index]],
verbose=0, batch_size=1024)
proba_t += model.predict([test_lstm, test_lstm_inv, test_features], verbose=0, batch_size=1024) / 5.
oof_y = np.argmax(proba_x, axis=1)
score1 = accuracy_score(y[valid_index], oof_y)
# print('accuracy_score',score1)
score = sum(acc_combo(y_true, y_pred) for y_true, y_pred in zip(y[valid_index], oof_y)) / oof_y.shape[0]
print('accuracy_score', score1, 'acc_combo', score)
acc_scores.append(score1)
combo_scores.append(score)
print("5kflod mean acc score:{}".format(np.mean(acc_scores)))
print("5kflod mean combo score:{}".format(np.mean(combo_scores)))
sub = pd.read_csv('data/提交结果示例.csv')
sub.behavior_id = np.argmax(proba_t, axis=1)
sub.to_csv('result/har_acc{}_combo{}.csv'.format(np.mean(acc_scores), np.mean(combo_scores)), index=False)
pd.DataFrame(proba_t, columns=['pred_{}'.format(i) for i in range(19)]).to_csv(
'result/har_proba_t_{}.csv'.format(np.mean(acc_scores)), index=False)