325 lines
16 KiB
Python
325 lines
16 KiB
Python
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# -------------------------------------------------------#
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# Скрипт для созданиия и сравнительного тестирования #
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# нескольких моделей на одном наборе данных. #
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# В скрипте создается три модели: #
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# - 2-х мерный свёрточный слой #
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# - реккурентная сеть с LSTM блоком #
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# - Multi-Head Self-Attention #
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# При обучении моделей из обучающей выборки выделяется #
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# 1% выборки для валидации результатов. #
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# После обучения проводится проверка работоспособности #
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# модели на тестовой выборке (отдельный файл данных) #
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# -------------------------------------------------------#
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# Импорт библиотек
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import os
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import pandas as pd
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import numpy as np
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import tensorflow as tf
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from tensorflow import keras
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import matplotlib.pyplot as plt
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import MetaTrader5 as mt5
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# Подключаемся к терминалу MetaTrader 5
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if not mt5.initialize():
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print("initialize() failed, error code =",mt5.last_error())
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quit()
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# Запрашиваем путь в "песочницу"
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path=os.path.join(mt5.terminal_info().data_path,r'MQL5\Files')
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mt5.shutdown()
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# Загрузка обучающей выборки
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filename = os.path.join(path,'study_data.csv')
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data = np.asarray( pd.read_table(filename,
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sep=',',
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header=None,
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skipinitialspace=True,
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encoding='utf-8',
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float_precision='high',
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dtype=np.float64,
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low_memory=False))
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# Разделение обучающей выборки на исходные данные и цели
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inputs=data.shape[1]-2
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targerts=2
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train_data=data[:,0:inputs]
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train_target=data[:,inputs:]
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# Модель с 2-х мерным свёрточным слоем
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model3 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Переформатируем тензор в 4-х мерный. Указываем 3 измерения, т.к. 4-е измерение определяется размером пакета
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keras.layers.Reshape((-1,4,1)),
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# Свёрточный слой с 8-ю фильтрами
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keras.layers.Conv2D(8,(3,1),1,activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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# Подвыборочный слой
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keras.layers.MaxPooling2D((2,1),strides=1),
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# Переформатируем тензор в 2-х мерный для полносвязных слоёв
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keras.layers.Flatten(),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model3.summary()
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#keras.utils.plot_model(model3, show_shapes=True, to_file=os.path.join(path,'model3.png'),dpi=72,show_layer_names=False,rankdir='LR')
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# Модель LSTM блок без полносвязных слоёв
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model4 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Переформатируем тензор в 3-х мерный. Указываем 2 измерения, т.к. 3-е измерение определяется размером пакета
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keras.layers.Reshape((-1,4)),
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# 2 последовательных LSTM блока
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# 1-й содержит 40 элементами
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keras.layers.LSTM(40,
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kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5),
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return_sequences=False),
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# 2-й выдаёт результат вместо полносвязного слоя
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keras.layers.Reshape((-1,2)),
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keras.layers.LSTM(targerts)
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])
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model4.summary()
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#keras.utils.plot_model(model4, show_shapes=True, to_file=os.path.join(path,'model4.png'),dpi=72,show_layer_names=False,rankdir='LR')
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# Модель Multi-Head Self-Attention
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@tf.keras.utils.register_keras_serializable(package="Custom", name='MHAttention')
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class MHAttention(tf.keras.layers.Layer):
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def __init__(self,key_size, heads, **kwargs):
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super(MHAttention, self).__init__(**kwargs)
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self.m_iHeads = heads
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self.m_iKeysSize = key_size
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self.m_iDimension=self.m_iHeads*self.m_iKeysSize;
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self.m_cQuerys = tf.keras.layers.Dense(self.m_iDimension)
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self.m_cKeys = tf.keras.layers.Dense(self.m_iDimension)
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self.m_cValues = tf.keras.layers.Dense(self.m_iDimension)
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self.m_cNormAttention=tf.keras.layers.LayerNormalization(epsilon=1e-6)
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self.m_cNormOutput=tf.keras.layers.LayerNormalization(epsilon=1e-6)
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def build(self, input_shape):
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self.m_iWindow=input_shape[-1]
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self.m_cW0 = tf.keras.layers.Dense(self.m_iWindow)
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self.m_cFF1=tf.keras.layers.Dense(4*self.m_iWindow, activation=tf.nn.swish)
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self.m_cFF2=tf.keras.layers.Dense(self.m_iWindow)
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def split_heads(self, x, batch_size):
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x = tf.reshape(x, (batch_size, -1, self.m_iHeads, self.m_iKeysSize))
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return tf.transpose(x, perm=[0, 2, 1, 3])
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def call(self, data):
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batch_size = tf.shape(data)[0]
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query = self.m_cQuerys(data)
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key = self.m_cKeys(data)
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value = self.m_cValues(data)
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query = self.split_heads(query, batch_size)
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key = self.split_heads(key, batch_size)
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value = self.split_heads(value, batch_size)
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score = tf.matmul(query, key, transpose_b=True)
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score = score / tf.math.sqrt(tf.cast(self.m_iKeysSize, tf.float32))
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score = tf.nn.softmax(score, axis=-1)
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attention = tf.matmul(score, value)
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attention = tf.transpose(attention, perm=[0, 2, 1, 3])
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attention = tf.reshape(attention,(batch_size, -1, self.m_iDimension))
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attention = self.m_cW0(attention)
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attention=self.m_cNormAttention(data + attention)
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output=self.m_cFF1(attention)
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output=self.m_cFF2(output)
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output=self.m_cNormOutput(attention+output)
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return output
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def get_config(self):
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config={'key_size': self.m_iKeysSize,
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'heads': self.m_iHeads,
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'dimension': self.m_iDimension,
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'window': self.m_iWindow
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}
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base_config = super(MHAttention, self).get_config()
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return dict(list(base_config.items()) + list(config.items()))
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@classmethod
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def from_config(cls, config):
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dimension=config.pop('dimension')
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window=config.pop('window')
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layer = cls(**config)
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layer._build_from_signature(dimension, window)
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return layer
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def _build_from_signature(self, dimension, window):
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self.m_iDimension=dimension
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self.m_iWindow=window
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heads=8
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key_dimension=4
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model5 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Переформатируем тензор в 3-х мерный. Указываем 2 измерения,
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# т.к. 3-е измерение определяется размером пакета
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# первое измерени - элементы последовательности
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# второе измерение - ветор описаниие одного элемента
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keras.layers.Reshape((-1,4)),
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MHAttention(key_dimension,heads),
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# Переформатируем тензор в 2-х мерный для полносвязных слоёв
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keras.layers.Flatten(),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish, kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model5.summary()
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#keras.utils.plot_model(model5, show_shapes=True, to_file=os.path.join(path,'model5.png'),dpi=72,show_layer_names=False,rankdir='LR')
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model6 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Переформатируем тензор в 3-х мерный. Указываем 2 измерения,
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# т.к. 3-е измерение определяется размером пакета
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# первое измерени - элементы последовательности
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# второе измерение - ветор описаниие одного элемента
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keras.layers.Reshape((-1,4)),
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MHAttention(key_dimension,heads),
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MHAttention(key_dimension,heads),
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MHAttention(key_dimension,heads),
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MHAttention(key_dimension,heads),
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# Переформатируем тензор в 2-х мерный для полносвязных слоёв
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keras.layers.Flatten(),
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keras.layers.Dense(40, activation=tf.nn.swish,
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kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish,
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kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(40, activation=tf.nn.swish,
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kernel_regularizer=keras.regularizers.l1_l2(l1=1e-7, l2=1e-5)),
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keras.layers.Dense(targerts, activation=tf.nn.tanh)
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])
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model6.summary()
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model3.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model4.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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model5.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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#model5=keras.models.load_model(os.path.join(path,'attention.h5'))
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model6.compile(optimizer='Adam',
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loss='mean_squared_error',
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metrics=['accuracy'])
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callback = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=5)
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history3 = model3.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.01,
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shuffle=True)
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model3.save(os.path.join(path,'conv2d'))
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history4 = model4.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.01,
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shuffle=False)
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model4.save(os.path.join(path,'rnn'))
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history5 = model5.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.01,
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shuffle=True)
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model5.save(os.path.join(path,'attention'))
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history6 = model6.fit(train_data, train_target,
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epochs=500, batch_size=1000,
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callbacks=[callback],
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verbose=2,
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validation_split=0.01,
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shuffle=True)
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model6.save(os.path.join(path,'attention2'))
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# Отрисовка результатов обучения моделей
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plt.figure()
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plt.plot(history3.history['loss'], label='Conv2D Train')
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plt.plot(history3.history['val_loss'], label='Conv2D Validation')
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plt.plot(history4.history['loss'], label='LSTM only Train')
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plt.plot(history4.history['val_loss'], label='LSTM only Validation')
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plt.plot(history5.history['loss'], label='MH Attention Train')
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plt.plot(history5.history['val_loss'], label='MH Attention Validation')
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plt.plot(history6.history['loss'], label='MH Attention 4l Train')
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plt.plot(history6.history['val_loss'], label='MH Attention 4l Validation')
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plt.ylabel('$MSE$ $Loss$')
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plt.xlabel('$Epochs$')
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plt.title('Dinamic of Models train')
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plt.legend(loc='upper right',fontsize='x-small',ncol=2)
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plt.figure()
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plt.plot(history3.history['accuracy'], label='Conv2D Train')
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plt.plot(history3.history['val_accuracy'], label='Conv2D Validation')
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plt.plot(history4.history['accuracy'], label='LSTM only Train')
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plt.plot(history4.history['val_accuracy'], label='LSTM only Validation')
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plt.plot(history5.history['accuracy'], label='MH Attention Train')
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plt.plot(history5.history['val_accuracy'], label='MH Attention Validation')
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plt.plot(history6.history['accuracy'], label='MH Attention 4l Train')
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plt.plot(history6.history['val_accuracy'], label='MH Attention 4l Validation')
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plt.ylabel('$Accuracy$')
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plt.xlabel('$Epochs$')
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plt.title('Dinamic of Models train')
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plt.legend(loc='lower right',fontsize='x-small',ncol=2)
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# Загрузка тестовой выборки
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test_filename = os.path.join(path,'test_data.csv')
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test = np.asarray( pd.read_table(test_filename,
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sep=',',
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header=None,
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skipinitialspace=True,
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encoding='utf-8',
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float_precision='high',
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dtype=np.float64,
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low_memory=False))
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# Разделение тестовой выборки на исходные данные и цели
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test_data=test[:,0:inputs]
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test_target=test[:,inputs:]
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# Проверка результатов моделей на тестовой выборке
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test_loss3, test_acc3 = model3.evaluate(test_data, test_target, verbose=2)
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test_loss4, test_acc4 = model4.evaluate(test_data, test_target, verbose=2)
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test_loss5, test_acc5 = model5.evaluate(test_data, test_target, verbose=2)
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test_loss6, test_acc6 = model6.evaluate(test_data, test_target, verbose=2)
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# Вывод результатов тестирования в журнал
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print('Conv2D Model')
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print('Test accuracy:', test_acc3)
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print('Test loss:', test_loss3)
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print('LSTM only Model')
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print('Test accuracy:', test_acc4)
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print('Test loss:', test_loss4)
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print('MH Attention Model')
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print('Test accuracy:', test_acc5)
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print('Test loss:', test_loss5)
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print('MH Attention 4l Model')
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|
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print('Test accuracy:', test_acc6)
|
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|
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print('Test loss:', test_loss6)
|
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plt.figure()
|
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|
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plt.bar(['Conv2D','LSTM', 'MH Attention','MH Attention 4l'],[test_loss3,test_loss4,test_loss5,test_loss6])
|
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|
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plt.ylabel('$MSE$ $Loss$')
|
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|
|
plt.title('Result of test')
|
||
|
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|
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|
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plt.figure()
|
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|
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plt.bar(['Conv2D','LSTM', 'MH Attention','MH Attention 4l'],[ttest_acc3,test_acc4,test_acc5,test_acc6])
|
||
|
|
plt.ylabel('$Accuracy$')
|
||
|
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plt.title('Result of test')
|
||
|
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|
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|
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plt.show()
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