时间序列数据预测（广告打点、正弦波、sp500）

import os import math import numpy as np import datetime as dt from numpy import newaxis from 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(): """LSTM 模型""" def __init__(self): self.model = Sequential() def load_model(self, filepath): print('[Model] Loading model from file %s' % filepath) self.model = load_model(filepath) def build_model(self, configs): 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() return self.model 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) print('[Model] Training Completed. Model saved as %s' % save_fname) timer.stop() def train_generator(self, data_gen, epochs, batch_size, steps_per_epoch, save_dir): 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))) 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() def predict_point_by_point(self, data,debug=False): if debug == False: print('[Model] Predicting Point-by-Point...') predicted = self.model.predict(data) predicted = np.reshape(predicted, (predicted.size,)) else: print('[Model] Predicting Point-by-Point...') print (np.array(data).shape) predicted = self.model.predict(data) print (np.array(predicted).shape) predicted = np.reshape(predicted, (predicted.size,)) print (np.array(predicted).shape) return predicted def predict_sequences_multiple(self, data, window_size, prediction_len,debug=False): if debug == False: print('[Model] Predicting Sequences Multiple...') 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]) 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 else : print('[Model] Predicting Sequences Multiple...') prediction_seqs = [] for i in range(int(len(data)/prediction_len)): print (data.shape) curr_frame = data[i*prediction_len] print (curr_frame) predicted = [] for j in range(prediction_len): predict_result = self.model.predict(curr_frame[newaxis,:,:]) print (predict_result) final_result = predict_result[0,0] predicted.append(final_result) curr_frame = curr_frame[1:] print (curr_frame) curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0) print (curr_frame) prediction_seqs.append(predicted) def predict_sequence_full(self, data, window_size): print('[Model] Predicting Sequences Full...') curr_frame = data[0] predicted = [] for i in range(len(data)): predicted.append(self.model.predict(curr_frame[newaxis,:,:])[0,0]) curr_frame = curr_frame[1:] curr_frame = np.insert(curr_frame, [window_size-2], predicted[-1], axis=0) return predicted