forked from kelmut/NeuroBook
354 lines
15 KiB
Python
354 lines
15 KiB
Python
# -------------------------------------------------------#
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# Script for the creation and comparative testing of #
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# multiple models using one dataset. #
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# The script creates three models: #
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# - 2-dimensional convolutional layer #
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# - recurrent network with LSTM block #
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# - Multi-Head Self-Attention #
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# When training models, from training dataset, script #
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# allocates 1% to validate the outputs. #
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# After training, the script tests the performance #
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# of the model on a test dataset (separate data file) #
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# -------------------------------------------------------#
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# Import Libraries
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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 as mp
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import matplotlib.pyplot as plt
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import matplotlib.font_manager as fm
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import MetaTrader5 as mt5
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# Add fonts
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font_list=fm.findSystemFonts()
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for f in font_list:
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if(f.__contains__('ClearSans')):
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fm.fontManager.addfont(f)
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# Set parameters for output graphs
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mp.rcParams.update({'font.family':'serif',
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'font.serif':'Clear Sans',
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'axes.titlesize': 'x-large',
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'axes.labelsize':'medium',
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'xtick.labelsize':'small',
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'ytick.labelsize':'small',
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'legend.fontsize':'small',
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'figure.figsize':[6.0,4.0],
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'axes.titlecolor': '#707070',
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'axes.labelcolor': '#707070',
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'axes.edgecolor': '#707070',
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'xtick.labelcolor': '#707070',
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'ytick.labelcolor': '#707070',
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'xtick.color': '#707070',
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'ytick.color': '#707070',
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'text.color': '#707070',
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'lines.linewidth': 0.8,
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'axes.linewidth': 0.5
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})
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# Connect to the MetaTrader 5 terminal
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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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# Request path to the Sandbox
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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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# Load training dataset
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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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# Split training dataset to input data and target
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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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# Model with 2-dimensional convolutional layer
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model3 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Reformat tensor into 4-dimensional. Specify 3 dimensions as the 4th dimension is determined by the batch size
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keras.layers.Reshape((-1,4,1)),
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# Convolutional later with 8 filters
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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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# Pooling layer
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keras.layers.MaxPooling2D((2,1),strides=1),
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# Reformat tensor to a 2-dimensional one for fully connected layers
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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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# Model LSTM block without fully connected layers
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model4 = keras.Sequential([keras.layers.InputLayer(input_shape=inputs),
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# Reformat tensor to a 3-dimensional one. Specify 2 dimensions as 3rd one is defined by batch size
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keras.layers.Reshape((-1,4)),
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# 2 consecutive LSTM blocks
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# 2st contains 40 elements
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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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# 2nd produces output instead of fully connected layer
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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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# Model 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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# Reformat tensor to a 3-dimensional one. Specify 2 dimensions
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# as the 3rd dimension is defined by batch size
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# 1st dimension - sequence elements
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# 2nd dimension - one element description vector
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keras.layers.Reshape((-1,4)),
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MHAttention(key_dimension,heads),
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# Reformat tensor to a 2-dimensional one for fully connected layers
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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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# Reformat tensor to a 3-dimensional one. Specify 2 dimensions
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# as the 3rd dimension is defined by batch size
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# 1st dimension - sequence elements
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# 2nd dimension - one element description vector
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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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# Reformat tensor to a 2-dimensional one for fully connected layers
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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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# Render model training results
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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 4 layers train')
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plt.plot(history6.history['val_loss'], label='MH Attention 4 layers validation')
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plt.ylabel('$MSE$ $loss$')
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plt.xlabel('$Epochs$')
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plt.title('Model training dynamics')
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plt.legend(loc='upper right', 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 4 layers train')
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plt.plot(history6.history['val_accuracy'], label='MH Attention 4 layers validation')
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plt.ylabel('$Accuracy$')
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plt.xlabel('$Epochs$')
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plt.title('Model training dynamics')
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plt.legend(loc='lower right', ncol=2)
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# Load testing dataset
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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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# Split test dataset to input data and target
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test_data=test[:,0:inputs]
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test_target=test[:,inputs:]
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# Check model results on a test dataset
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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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# Log testing results
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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 4 layers model')
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print('Test accuracy:', test_acc6)
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print('Test loss:', test_loss6)
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plt.figure()
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plt.bar(['Conv2D','LSTM', 'MH Attention','MH Attention\n4 layers'],[test_loss3,test_loss4,test_loss5,test_loss6])
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plt.ylabel('$MSE$ $loss$')
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plt.title('Test results')
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plt.figure()
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plt.bar(['Conv2D','LSTM', 'MH Attention','MH Attention\n4 layers'],[test_acc3,test_acc4,test_acc5,test_acc6])
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plt.ylabel('$Accuracy$')
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plt.title('Test results')
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plt.show()
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