Stock-Forecast/LSTMPredictStock/core/model.py

import os
import math
import numpy as np
import datetime as dt
from numpy import newaxis
from LSTMPredictStock.core.utils import Timer
from keras.layers import Dense, Activation, Dropout, LSTM
from keras.models import Sequential, load_model
from keras.callbacks import EarlyStopping, ModelCheckpoint

class Model():
	"""A class for an building and inferencing an lstm model"""

	def __init__(self):
		self.model = Sequential()

	def load_model(self, filepath):
		'''
		从本地保存的模型参数来加载模型
		filepath: .h5 格式文件
		'''
		print('[Model] Loading model from file %s' % filepath)
		self.model = load_model(filepath)

	def build_model(self, configs):
		"""
		新建一个模型
		configs:配置文件
		"""
		timer = Timer()
		timer.start()

		for layer in configs['model']['layers']:
			neurons = layer['neurons'] if 'neurons' in layer else None
			dropout_rate = layer['rate'] if 'rate' in layer else None
			activation = layer['activation'] if 'activation' in layer else None
			return_seq = layer['return_seq'] if 'return_seq' in layer else None
			input_timesteps = layer['input_timesteps'] if 'input_timesteps' in layer else None
			input_dim = layer['input_dim'] if 'input_dim' in layer else None

			if layer['type'] == 'dense':
				self.model.add(Dense(neurons, activation=activation))
			if layer['type'] == 'lstm':
				self.model.add(LSTM(neurons, input_shape=(input_timesteps, input_dim), return_sequences=return_seq))
			if layer['type'] == 'dropout':
				self.model.add(Dropout(dropout_rate))

		self.model.compile(loss=configs['model']['loss'], optimizer=configs['model']['optimizer'])

		print('[Model] Model Compiled')
		timer.stop()	#输出构建一个模型耗时

	def train(self, x, y, epochs, batch_size, save_dir):
		timer = Timer()
		timer.start()
		print('[Model] Training Started')
		print('[Model] %s epochs, %s batch size' % (epochs, batch_size))
		
		save_fname = os.path.join(save_dir, '%s-e%s.h5' % (dt.datetime.now().strftime('%d%m%Y-%H%M%S'), str(epochs)))
		callbacks = [
			EarlyStopping(monitor='val_loss', patience=2),
			ModelCheckpoint(filepath=save_fname, monitor='val_loss', save_best_only=True)
		]
		self.model.fit(
			x,
			y,
			epochs=epochs,
			batch_size=batch_size,
			callbacks=callbacks
		)
		self.model.save(save_fname)	#保存训练好的模型

		print('[Model] Training Completed. Model saved as %s' % save_fname)
		timer.stop()	#输出训练耗时

	def train_generator(self, data_gen, epochs, batch_size, steps_per_epoch, save_dir,save_name):
		'''
		由data_gen数据产生器来，逐步产生训练数据，而不是一次性将数据读入到内存
		'''
		timer = Timer()
		timer.start()
		print('[Model] Training Started')
		print('[Model] %s epochs, %s batch size, %s batches per epoch' % (epochs, batch_size, steps_per_epoch))
		
		# save_fname = os.path.join(save_dir, '%s-e%s.h5' % (dt.datetime.now().strftime('%d%m%Y-%H%M%S'), str(epochs)))
		save_fname = os.path.join(save_dir, save_name+'.h5')
		callbacks = [
			ModelCheckpoint(filepath=save_fname, monitor='loss', save_best_only=True)
		]
		self.model.fit_generator(
			data_gen,
			steps_per_epoch=steps_per_epoch,
			epochs=epochs,
			callbacks=callbacks,
			workers=1
		)
		
		print('[Model] Training Completed. Model saved as %s' % save_fname)
		timer.stop()

    #　data必须是三维数据，即shape:(a,b,c)
	def predict_point_by_point(self, data):
		#Predict each timestep given the last sequence of true data, in effect only predicting 1 step ahead each time
		predicted = self.model.predict(data)    # data有多少行就输出多少个预测值,返回的预测值是一个2维数组:(a,1)	a:为data的行数
		predicted = np.reshape(predicted, (predicted.size,)) # 这里将二维数组，变成一维数组
		return predicted    # 返回一维数组，元素个数与data的a相同

    # 对data进行多段预测，每段预测基于一个窗口大小（window_size）的数据，然后输出prediction_len长的预测值（一维数组）
    # 再从上一个窗口移动prediction_len的长度，得到下一个窗口的数据，并基于该数据再预测prediction_len长的预测值
    # 所以prediction_len决定了窗口的移位步数，每次的窗口大小是一样的，所以最后预测的段数 = 窗口个数/预测长度
    # 相当于多次调用predict_1_win_sequence方法
	def predict_sequences_multiple(self, data, window_size, prediction_len):
		#Predict sequence of 50 steps before shifting prediction run forward by 50 steps
		prediction_seqs = []
		for i in range(int(len(data)/prediction_len)):
			curr_frame = data[i*prediction_len]
			predicted = []
			for j in range(prediction_len):
				predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0])  # newaxis：增加新轴，使得curr_frame变成(1,x,x)三维数据
				curr_frame = curr_frame[1:]
				curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0)
			prediction_seqs.append(predicted)
		return prediction_seqs

    # 输入一个窗口的数据，指定预测的长度，data:依旧是三维数组(1,win_len,fea_len)
    # 返回预测数组
	def predict_sequence_full(self, data, window_size): # window_size：为输入数据的长度
		#Shift the window by 1 new prediction each time, re-run predictions on new window
		curr_frame = data[0]    # 基于data[0]一个窗口的数据，来预测len(data)个输出
		predicted = []
		for i in range(len(data)):
			predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0])  # append了一个预测值（标量）
			curr_frame = curr_frame[1:]
			curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0)  # 插入位置[window_size-2]:curr_frame的末尾，predicted[-1]：插入值
		return predicted

    # 输入一个窗口的数据，指定预测的长度，data:依旧是三维数组(1,win_len,fea_len)
    # 返回预测数组
	def predict_1_win_sequence(self, data, window_size,predict_length): # window_size：data的窗口大小
		#Shift the window by 1 new prediction each time, re-run predictions on new window
		curr_frame = data[0]
		predicted = []
		for i in range(predict_length): # range(len(data))
			predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0])  # append了一个预测值（标量）
			curr_frame = curr_frame[1:]
			curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0)  # 插入位置[window_size-2]:curr_frame的末尾，predicted[-1]：插入值
		return predicted