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iLearnPlusAutoML.py
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iLearnPlusAutoML.py
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#!/usr/bin/env python
# _*_ coding: utf-8 _*_
import sys, os
from PyQt5.QtWidgets import (QApplication, QWidget, QPushButton, QFileDialog, QLabel, QHBoxLayout, QGroupBox, QTextEdit,
QVBoxLayout, QTreeWidget, QTreeWidgetItem, QSplitter, QTableWidget, QTabWidget,
QTableWidgetItem, QInputDialog, QMessageBox, QFormLayout, QHeaderView, QAbstractItemView)
from PyQt5.QtGui import QIcon, QFont, QMovie
from PyQt5.QtCore import Qt, pyqtSignal
from util import (InputDialog, MachineLearning, ModelMetrics, PlotWidgets)
import qdarkstyle
import numpy as np
import pandas as pd
from itertools import combinations
import copy
import threading
import datetime
import joblib
import sip
import torch
class ILearnPlusAutoML(QWidget):
# signal
display_signal = pyqtSignal(list)
display_curves_signal = pyqtSignal()
append_msg_signal = pyqtSignal(str)
close_signal = pyqtSignal(str)
def __init__(self):
super(ILearnPlusAutoML, self).__init__()
# signal
self.display_signal.connect(self.display_metrics)
self.display_curves_signal.connect(self.display_curves)
self.append_msg_signal.connect(self.append_message)
self.MLData = None # Machine learning object
self.algorithms_selected = set([]) # selected algorithms
self.metrics = ModelMetrics.ModelMetrics() # data for display result
self.current_data_index = 0
self.boxplot_data = {} # dict
self.pen_color = [
(50, 116, 161),
(225, 129, 44),
(58, 146, 58),
(192, 61, 62),
(147, 114, 178),
(132, 91, 83),
(214, 132, 189),
(127, 127, 127)
]
self.lineStyle = {
0: Qt.SolidLine,
1: Qt.SolidLine,
2: Qt.SolidLine,
3: Qt.SolidLine,
4: Qt.SolidLine,
5: Qt.SolidLine,
6: Qt.SolidLine,
7: Qt.SolidLine,
8: Qt.DashLine,
9: Qt.DashLine,
10: Qt.DashLine,
11: Qt.DashLine,
12: Qt.DashLine,
13: Qt.DashLine,
14: Qt.DashLine,
15: Qt.DashLine,
}
self.ml_defatult_para = {
'FOLD': 5,
'cpu': 1,
'auto': False,
'n_trees': 100,
'tree_range': (100, 1000, 100),
'kernel': 'rbf',
'penality': 1.0,
'gamma': 'auto',
'penalityRange': (1.0, 15.0),
'gammaRange': (-10.0, 5.0),
'layer': '32;32',
'activation': 'relu',
'optimizer': 'adam',
'topKValue': 3,
'boosting_type': 'gbdt',
'num_leaves': 31,
'max_depth': -1,
'learning_rate': 0.01,
'leaves_range': (20, 100, 10),
'depth_range': (15, 55, 10),
'rate_range': (0.01, 0.15, 0.02),
'booster': 'gbtree',
'n_estimator': 100,
'colsample_bytree': 0.8,
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
} # default machine learning parameters
self.para_rf = {
'cpu': 1,
'auto': False,
}
self.para_svm = {
'auto': False,
}
self.para_lightgbm = {
'max_depth': -1,
'learning_rate': 0.01,
'cpu': 1,
'auto': False,
'depth_range': (15, 55, 10),
'rate_range': (0.01, 0.15, 0.02),
}
self.para_xgboost = {
'max_depth': -1,
'learning_rate': 0.01,
'n_estimator': 100,
'auto': False,
'depth_range': (15, 55, 10),
'rate_range': (0.01, 0.15, 0.02),
}
self.para_bagging = {
'n_estimator': 100,
'cpu': 1,
}
self.para_net_1 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_11 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_2 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_3 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_4 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_5 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.para_net_6 = {
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
self.ml_combination_para = {
'FOLD': 5,
'cpu': 1,
'auto': True,
'n_trees': 100,
'tree_range': (100, 1000, 100),
'kernel': 'rbf',
'penality': 1.0,
'gamma': 'auto',
'penalityRange': (1.0, 15.0),
'gammaRange': (-10.0, 5.0),
'layer': '32;32',
'activation': 'relu',
'optimizer': 'adam',
'topKValue': 3,
'boosting_type': 'gbdt',
'num_leaves': 31,
'max_depth': -1,
'learning_rate': 0.01,
'leaves_range': (20, 100, 10),
'depth_range': (15, 55, 10),
'rate_range': (0.01, 0.15, 0.02),
'booster': 'gbtree',
'n_estimator': 100,
'colsample_bytree': 0.8,
'input_channel': 1,
'input_length': 100,
'output_channel': 64,
'padding': 2,
'kernel_size': 5,
'dropout': 0.5,
'epochs': 1000,
'early_stopping': 100,
'batch_size': 64,
'fc_size': 64,
'rnn_hidden_size': 32,
'rnn_hidden_layers': 1,
'rnn_bidirection': False,
'mlp_input_dim': None,
}
# for combination of models
self.MLData_cb = None
self.bestPerformance = 0
self.bestCombinations = ()
self.bestMetrics = None
self.bestAUC = None
self.bestPRC = None
self.bestModels = None
self.bestTrainingScore = None
# status bar
self.gif = QMovie('images/progress_bar.gif')
self.initUI()
def initUI(self):
self.setWindowTitle('iLearnPlus AutoML')
self.resize(800, 600)
self.setWindowState(Qt.WindowMaximized)
self.setWindowIcon(QIcon('images/logo.ico'))
self.setup_UI()
def setup_UI(self):
# file
topGroupBox = QGroupBox('Load data', self)
topGroupBox.setFont(QFont('Arial', 10))
# topGroupBox.setMinimumHeight(100)
topGroupBoxLayout = QFormLayout()
trainFileButton = QPushButton('Open')
trainFileButton.clicked.connect(lambda:self.data_from_file_ml('Training'))
# testFileButton = QPushButton('Open')
# testFileButton.clicked.connect(lambda:self.data_from_file_ml('Testing'))
topGroupBoxLayout.addRow('Open training file:', trainFileButton)
# topGroupBoxLayout.addRow('Open testing file:', testFileButton)
topGroupBox.setLayout(topGroupBoxLayout)
# tree
treeGroupBox = QGroupBox('Machine learning algorithms', self)
treeGroupBox.setFont(QFont('Arial', 10))
treeLayout = QHBoxLayout()
self.ml_treeWidget = QTreeWidget()
self.ml_treeWidget.setColumnCount(2)
self.ml_treeWidget.setMinimumWidth(300)
self.ml_treeWidget.setColumnWidth(0, 150)
self.ml_treeWidget.setFont(QFont('Arial', 8))
self.ml_treeWidget.setHeaderLabels(['Methods', 'Definition'])
self.machineLearningAlgorighms = QTreeWidgetItem(self.ml_treeWidget)
self.machineLearningAlgorighms.setExpanded(True) # set node expanded
self.machineLearningAlgorighms.setText(0, 'Machine learning algorithms')
rf = QTreeWidgetItem(self.machineLearningAlgorighms)
rf.setText(0, 'RF')
rf.setText(1, 'Random Forest')
rf.setCheckState(0, Qt.Unchecked)
dtree = QTreeWidgetItem(self.machineLearningAlgorighms)
dtree.setText(0, 'DecisionTree')
dtree.setText(1, 'Decision Tree')
dtree.setCheckState(0, Qt.Unchecked)
lightgbm = QTreeWidgetItem(self.machineLearningAlgorighms)
lightgbm.setText(0, 'LightGBM')
lightgbm.setText(1, 'LightGBM')
lightgbm.setCheckState(0, Qt.Unchecked)
svm = QTreeWidgetItem(self.machineLearningAlgorighms)
svm.setText(0, 'SVM')
svm.setText(1, 'Support Verctor Machine')
svm.setCheckState(0, Qt.Unchecked)
mlp = QTreeWidgetItem(self.machineLearningAlgorighms)
mlp.setText(0, 'MLP')
mlp.setText(1, 'Multi-layer Perceptron')
mlp.setCheckState(0, Qt.Unchecked)
xgboost = QTreeWidgetItem(self.machineLearningAlgorighms)
xgboost.setText(0, 'XGBoost')
xgboost.setText(1, 'XGBoost')
xgboost.setCheckState(0, Qt.Unchecked)
knn = QTreeWidgetItem(self.machineLearningAlgorighms)
knn.setText(0, 'KNN')
knn.setText(1, 'K-Nearest Neighbour')
knn.setCheckState(0, Qt.Unchecked)
lr = QTreeWidgetItem(self.machineLearningAlgorighms)
lr.setText(0, 'LR')
lr.setText(1, 'Logistic Regression')
lr.setCheckState(0, Qt.Unchecked)
lda = QTreeWidgetItem(self.machineLearningAlgorighms)
lda.setText(0, 'LDA')
lda.setText(1, 'Linear Discriminant Analysis')
lda.setCheckState(0, Qt.Unchecked)
qda = QTreeWidgetItem(self.machineLearningAlgorighms)
qda.setText(0, 'QDA')
qda.setText(1, 'Quadratic Discriminant Analysis')
qda.setCheckState(0, Qt.Unchecked)
sgd = QTreeWidgetItem(self.machineLearningAlgorighms)
sgd.setText(0, 'SGD')
sgd.setText(1, 'Stochastic Gradient Descent')
sgd.setCheckState(0, Qt.Unchecked)
bayes = QTreeWidgetItem(self.machineLearningAlgorighms)
bayes.setText(0, 'NaiveBayes')
bayes.setText(1, 'NaiveBayes')
bayes.setCheckState(0, Qt.Unchecked)
bagging = QTreeWidgetItem(self.machineLearningAlgorighms)
bagging.setText(0, 'Bagging')
bagging.setText(1, 'Bagging')
bagging.setCheckState(0, Qt.Unchecked)
adaboost = QTreeWidgetItem(self.machineLearningAlgorighms)
adaboost.setText(0, 'AdaBoost')
adaboost.setText(1, 'AdaBoost')
adaboost.setCheckState(0, Qt.Unchecked)
gbdt = QTreeWidgetItem(self.machineLearningAlgorighms)
gbdt.setText(0, 'GBDT')
gbdt.setText(1, 'Gradient Tree Boosting')
gbdt.setCheckState(0, Qt.Unchecked)
# deep learning algorighms
net1 = QTreeWidgetItem(self.machineLearningAlgorighms)
net1.setText(0, 'Net_1_CNN')
net1.setText(1, 'Convolutional Neural Network')
net1.setCheckState(0, Qt.Unchecked)
net11 = QTreeWidgetItem(self.machineLearningAlgorighms)
net11.setText(0, 'Net_1_CNN_binary')
net11.setText(1, 'Convolutional Neural Network')
net11.setCheckState(0, Qt.Unchecked)
net2 = QTreeWidgetItem(self.machineLearningAlgorighms)
net2.setText(0, 'Net_2_RNN')
net2.setText(1, 'Recurrent Neural Network')
net2.setCheckState(0, Qt.Unchecked)
net3 = QTreeWidgetItem(self.machineLearningAlgorighms)
net3.setText(0, 'Net_3_BRNN')
net3.setText(1, 'Bidirectional Recurrent Neural Network')
net3.setCheckState(0, Qt.Unchecked)
net4 = QTreeWidgetItem(self.machineLearningAlgorighms)
net4.setText(0, 'Net_4_ABCNN')
net4.setText(1, 'Attention Based Convolutional Neural Network')
net4.setCheckState(0, Qt.Unchecked)
net5 = QTreeWidgetItem(self.machineLearningAlgorighms)
net5.setText(0, 'Net_5_ResNet')
net5.setText(1, 'Deep Residual Network')
net5.setCheckState(0, Qt.Unchecked)
net6 = QTreeWidgetItem(self.machineLearningAlgorighms)
net6.setText(0, 'Net_6_AE')
net6.setText(1, 'AutoEncoder')
net6.setCheckState(0, Qt.Unchecked)
treeLayout.addWidget(self.ml_treeWidget)
treeGroupBox.setLayout(treeLayout)
self.ml_treeWidget.clicked.connect(self.ml_tree_clicked)
self.ml_treeWidget.itemChanged.connect(self.ml_tree_checkState)
## parameter
paraGroupBox = QGroupBox('Set K-fold Cross-Validation', self)
paraGroupBox.setFont(QFont('Arial', 10))
paraLayout = QFormLayout(paraGroupBox)
self.fold_lineEdit = InputDialog.MyLineEdit('5')
self.fold_lineEdit.setFont(QFont('Arial', 8))
self.fold_lineEdit.clicked.connect(self.setFold)
paraLayout.addRow('Cross-Validation:', self.fold_lineEdit)
# operation
startGroupBox = QGroupBox('Operator', self)
startGroupBox.setFont(QFont('Arial', 10))
startLayout = QHBoxLayout(startGroupBox)
self.start_button = QPushButton('Start')
self.start_button.clicked.connect(self.run_ml)
self.start_button.setFont(QFont('Arial', 10))
startLayout.addWidget(self.start_button)
### layout
left_vertical_layout = QVBoxLayout()
left_vertical_layout.addWidget(topGroupBox)
left_vertical_layout.addWidget(treeGroupBox)
left_vertical_layout.addWidget(paraGroupBox)
left_vertical_layout.addWidget(startGroupBox)
#### widget
leftWidget = QWidget()
leftWidget.setLayout(left_vertical_layout)
resultTabWidget = QWidget()
resultTabLayout = QVBoxLayout(resultTabWidget)
resultTabControlLayout = QHBoxLayout()
self.resultSaveBtn = QPushButton('Save metrics')
self.resultSaveBtn.clicked.connect(self.save_result)
self.modelSaveBtn = QPushButton('Save model')
self.modelSaveBtn.clicked.connect(self.save_model)
self.displayCorrBtn = QPushButton(' Display Correlation ')
self.displayCorrBtn.clicked.connect(self.display_correlation_heatmap)
self.combineModelBtn = QPushButton(' Combine models ')
self.combineModelBtn.clicked.connect(self.combineModels)
resultTabControlLayout.addStretch(1)
resultTabControlLayout.addWidget(self.resultSaveBtn)
resultTabControlLayout.addWidget(self.modelSaveBtn)
resultTabControlLayout.addWidget(self.displayCorrBtn)
resultTabControlLayout.addWidget(self.combineModelBtn)
resultTabControlLayout.addStretch(1)
self.metricsTableWidget = QTableWidget()
self.metricsTableWidget.setFont(QFont('Arial', 8))
self.metricsTableWidget.setColumnCount(11)
self.metricsTableWidget.setHorizontalHeaderLabels(['Id', 'StartTime', 'EndTime', 'Sn (%)', 'Sp (%)', 'Pre (%)', 'Acc (%)', 'MCC', 'F1', 'AUROC', 'AUPRC'])
self.metricsTableWidget.verticalHeader().setHidden(True)
self.metricsTableWidget.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)
self.metricsTableWidget.setEditTriggers(QAbstractItemView.NoEditTriggers)
self.metricsTableWidget.horizontalHeader().setSectionResizeMode(0, QHeaderView.ResizeToContents)
self.metricsTableWidget.horizontalHeader().setSectionResizeMode(1, QHeaderView.ResizeToContents)
self.metricsTableWidget.horizontalHeader().setSectionResizeMode(2, QHeaderView.ResizeToContents)
self.metricsTableWidget.resizeRowsToContents()
resultTabLayout.addWidget(self.metricsTableWidget)
resultTabLayout.addLayout(resultTabControlLayout)
""" boxplot using matplotlib """
boxplotWidget = QWidget()
self.boxplotLayout = QVBoxLayout(boxplotWidget)
self.boxplotGraph = PlotWidgets.BoxplotWidget()
self.boxplotLayout.addWidget(self.boxplotGraph)
""" ROC curve using matplotlib """
rocWidget = QWidget()
self.rocLayout = QVBoxLayout(rocWidget)
self.rocCurveGraph = PlotWidgets.CurvesWidget()
self.rocLayout.addWidget(self.rocCurveGraph)
logWidget = QWidget()
logLayout = QHBoxLayout(logWidget)
self.logTextEdit = QTextEdit()
self.logTextEdit.setFont(QFont('Arial', 8))
logLayout.addWidget(self.logTextEdit)
plotTabWidget = QTabWidget()
plotTabWidget.addTab(resultTabWidget, ' Result ')
plotTabWidget.addTab(boxplotWidget, ' Box plot ')
plotTabWidget.addTab(rocWidget, ' ROC and PRC curve ')
plotTabWidget.addTab(logWidget, ' Log ')
##### splitter
splitter_1 = QSplitter(Qt.Horizontal)
splitter_1.addWidget(leftWidget)
splitter_1.addWidget(plotTabWidget)
splitter_1.setSizes([100, 1200])
###### vertical layout
vLayout = QVBoxLayout()
## status bar
statusGroupBox = QGroupBox('Status', self)
statusGroupBox.setFont(QFont('Arial', 10))
statusLayout = QHBoxLayout(statusGroupBox)
self.status_label = QLabel('Welcome to iLearnPlus Analysis')
self.progress_bar = QLabel()
self.progress_bar.setMaximumWidth(230)
statusLayout.addWidget(self.status_label)
statusLayout.addWidget(self.progress_bar)
splitter_2 = QSplitter(Qt.Vertical)
splitter_2.addWidget(splitter_1)
splitter_2.addWidget(statusGroupBox)
splitter_2.setSizes([1000, 100])
vLayout.addWidget(splitter_2)
self.setLayout(vLayout)
def data_from_file_ml(self, target='Training'):
file_name, ok = QFileDialog.getOpenFileName(self, 'Open', './data', 'CSV Files (*.csv);;TSV Files (*.tsv);;SVM Files(*.svm);;Weka Files (*.arff)')
if ok:
if self.MLData is None:
self.MLData = MachineLearning.ILearnMachineLearning(self.ml_defatult_para)
ok1 = self.MLData.load_data(file_name, target)
if ok1:
if target == 'Training':
shape = self.MLData.training_dataframe.values.shape
else:
shape = self.MLData.testing_dataframe.values.shape
self.logTextEdit.append('%s\tLoad %s file; Datashape: %s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), target, shape))
else:
self.logTextEdit.append('%s\tLoad %s file failed.' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), target))
def ml_tree_clicked(self, index):
item = self.ml_treeWidget.currentItem()
if item.text(0) in ['RF']:
num, range, cpu, auto, ok = InputDialog.QRandomForestInput.getValues()
if ok:
self.ml_defatult_para['n_trees'] = num
self.ml_defatult_para['tree_range'] = range
# self.ml_defatult_para['auto'] = auto
# self.ml_defatult_para['cpu'] = cpu
self.para_rf['auto'] = auto
self.para_rf['cpu'] = cpu
if auto:
self.logTextEdit.append('%s\tAlgorithm: %s\t[Auto-optimization\ttree_range=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), str(range)))
else:
self.logTextEdit.append('%s\tAlgorithm: %s\t[tree number=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), num))
elif item.text(0) in ['SVM']:
kernel, penality, gamma, auto, penalityRange, gammaRange, ok = InputDialog.QSupportVectorMachineInput.getValues()
if ok:
self.ml_defatult_para['kernel'] = kernel
self.ml_defatult_para['penality'] = penality
self.ml_defatult_para['gamma'] = gamma
# self.ml_defatult_para['auto'] = auto
self.para_svm['auto'] = auto
self.ml_defatult_para['penalityRange'] = penalityRange
self.ml_defatult_para['gammaRange'] = gammaRange
if auto:
self.logTextEdit.append('%s\tAlgorithm: %s\t[kernel=%s\tAuto-optimization\tpenality range=%s\tgamma range=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), kernel, str(penalityRange), str(gammaRange)))
else:
self.logTextEdit.append('%s\tAlgorithm: %s\t[kernel=%s\tpenality=%s\tgamma=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), kernel, penality, gamma))
elif item.text(0) in ['MLP']:
layer, epochs, activation, optimizer, ok = InputDialog.QMultiLayerPerceptronInput.getValues()
if ok:
self.ml_defatult_para['layer'] = layer
self.ml_defatult_para['epochs'] = epochs
self.ml_defatult_para['activation'] = activation
self.ml_defatult_para['optimizer'] = optimizer
self.logTextEdit.append('%s\tAlgorithm: %s\t[layer=%s\tepochs=%s\tactivation function=%s\toptimizer=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), layer, epochs, activation, optimizer))
elif item.text(0) in ['LR', 'SGD', 'DecisionTree', 'NaiveBayes', 'AdaBoost', 'GBDT', 'LDA', 'QDA']:
self.logTextEdit.append('%s\tAlgorithm: %s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0)))
elif item.text(0) in ['KNN']:
topKValue, ok = InputDialog.QKNeighborsInput.getValues()
if ok:
self.ml_defatult_para['topKValue'] = topKValue
self.logTextEdit.append('%s\tAlgorithm: %s\t[top K value=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), topKValue))
elif item.text(0) in ['LightGBM']:
type, leaves, depth, rate, leavesRange, depthRange, rateRange, threads, auto, ok = InputDialog.QLightGBMInput.getValues()
if ok:
self.ml_defatult_para['boosting_type'] = type
self.ml_defatult_para['num_leaves'] = leaves
# self.ml_defatult_para['max_depth'] = depth
# self.ml_defatult_para['learning_rate'] = rate
# self.ml_defatult_para['auto'] = auto
self.ml_defatult_para['leaves_range'] = leavesRange
# self.ml_defatult_para['depth_range'] = depthRange
# self.ml_defatult_para['rate_range'] = rateRange
# self.ml_defatult_para['cpu'] = threads
self.para_lightgbm['max_depth'] = depth
self.para_lightgbm['learning_rate'] = rate
self.para_lightgbm['auto'] = auto
self.para_lightgbm['depth_range'] = depthRange
self.para_lightgbm['rate_range'] = rateRange
self.para_lightgbm['cpu'] = threads
if auto:
self.logTextEdit.append('%s\tAlgorithm: %s\t[Auto optimization\tLeaves range=%s\tDepth range=%s\tLearning rate range=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), leavesRange, depthRange, rateRange))
else:
self.logTextEdit.append('%s\tAlgorithm: %s\t[boosting type=%s\tnumber of leaves=%s\tmax depth=%s\tlearning rate=%s\tnumber of threads=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), type, leaves, depth, rate, threads))
elif item.text(0) in ['XGBoost']:
booster, maxdepth, rate, estimator, colsample, depthRange, rateRange, threads, auto, ok = InputDialog.QXGBoostInput.getValues()
self.ml_defatult_para['booster'] = booster
# self.ml_defatult_para['max_depth'] = maxdepth
# self.ml_defatult_para['learning_rate'] = rate
# self.ml_defatult_para['n_estimator'] = estimator
self.ml_defatult_para['colsample_bytree'] = colsample
# self.ml_defatult_para['depth_range'] = depthRange
# self.ml_defatult_para['rate_range'] = rateRange
# self.ml_defatult_para['cpu'] = threads
# self.ml_defatult_para['auto'] = auto
self.para_xgboost['max_depth'] = maxdepth
self.para_xgboost['learning_rate'] = rate
self.para_xgboost['n_estimator'] = estimator
self.para_xgboost['depth_range'] = depthRange
self.para_xgboost['rate_range'] = rateRange
self.para_xgboost['cpu'] = threads
self.para_xgboost['auto'] = auto
if auto:
self.logTextEdit.append('%s\tAlgorithm: %s\t[Auto optimization\tDepth range=%s\tLearning rate range=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), depthRange, rateRange))
else:
self.logTextEdit.append('%s\tAlgorithm: %s\t[Booster=%s\tMax depth=%s\tLearning rate=%s\tn_estimator=%s\tcolsample_bytree=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), booster, maxdepth, rate, estimator, colsample))
elif item.text(0) in ['Bagging']:
n_estimators, threads, ok = InputDialog.QBaggingInput.getValues()
if ok:
self.para_bagging['n_estimator'] = n_estimators
self.para_bagging['cpu'] = threads
self.logTextEdit.append('%s\tAlgorithm: %s\t[n_estimators=%s\tThreads=%s]' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), n_estimators, threads))
elif item.text(0) in ['Net_1_CNN']:
if not self.MLData is None:
input_channel, input_length, output_channel, padding, kernel_size, dropout, learning_rate, epochs, early_stopping, batch_size, fc_size, ok = InputDialog.QNetInput_1.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_1['input_channel'] = input_channel
self.para_net_1['input_length'] = input_length
self.para_net_1['output_channel'] = output_channel
self.para_net_1['padding'] = padding
self.para_net_1['kernel_size'] = kernel_size
self.para_net_1['dropout'] = dropout
self.para_net_1['learning_rate'] = learning_rate
self.para_net_1['epochs'] = epochs
self.para_net_1['early_stopping'] = early_stopping
self.para_net_1['batch_size'] = batch_size
self.para_net_1['fc_size'] = fc_size
self.logTextEdit.append('%s\tAlgorithm: %s; Input channel=%s; Input_length=%s; Output_channel=%s; Padding=%s; Kernel_size=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, output_channel, padding, kernel_size, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_1_CNN_binary']:
if not self.MLData is None:
input_channel, input_length, output_channel, padding, kernel_size, dropout, learning_rate, epochs, early_stopping, batch_size, fc_size, ok = InputDialog.QNetInput_1.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_11['input_channel'] = input_channel
self.para_net_11['input_length'] = input_length
self.para_net_11['output_channel'] = output_channel
self.para_net_11['padding'] = padding
self.para_net_11['kernel_size'] = kernel_size
self.para_net_11['dropout'] = dropout
self.para_net_11['learning_rate'] = learning_rate
self.para_net_11['epochs'] = epochs
self.para_net_11['early_stopping'] = early_stopping
self.para_net_11['batch_size'] = batch_size
self.para_net_11['fc_size'] = fc_size
self.logTextEdit.append('%s\tAlgorithm: %s; Input channel=%s; Input_length=%s; Output_channel=%s; Padding=%s; Kernel_size=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, output_channel, padding, kernel_size, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_2_RNN']:
if not self.MLData is None:
input_channel, input_length, hidden_size, num_layers, dropout, learning_rate, epochs, early_stopping, batch_size, fc_size, ok = InputDialog.QNetInput_2.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_2['input_channel'] = input_channel
self.para_net_2['input_length'] = input_length
self.para_net_2['rnn_hidden_size'] = hidden_size
self.para_net_2['rnn_hidden_layers'] = num_layers
self.para_net_2['rnn_bidirection'] = False
self.para_net_2['dropout'] = dropout
self.para_net_2['learning_rate'] = learning_rate
self.para_net_2['epochs'] = epochs
self.para_net_2['early_stopping'] = early_stopping
self.para_net_2['batch_size'] = batch_size
self.para_net_2['rnn_bidirectional'] = False
self.logTextEdit.append('%s\tAlgorithm: %s; Input size=%s; Input_length=%s; Hidden_size=%s; Num_hidden_layers=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, hidden_size, num_layers, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_3_BRNN']:
if not self.MLData is None:
input_channel, input_length, hidden_size, num_layers, dropout, learning_rate, epochs, early_stopping, batch_size, fc_size, ok = InputDialog.QNetInput_2.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_3['input_channel'] = input_channel
self.para_net_3['input_length'] = input_length
self.para_net_3['rnn_hidden_size'] = hidden_size
self.para_net_3['rnn_hidden_layers'] = num_layers
self.para_net_3['rnn_bidirection'] = False
self.para_net_3['dropout'] = dropout
self.para_net_3['learning_rate'] = learning_rate
self.para_net_3['epochs'] = epochs
self.para_net_3['early_stopping'] = early_stopping
self.para_net_3['batch_size'] = batch_size
self.para_net_3['rnn_bidirectional'] = True
self.logTextEdit.append('%s\tAlgorithm: %s; Input size=%s; Input_length=%s; Hidden_size=%s; Num_hidden_layers=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, hidden_size, num_layers, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_4_ABCNN']:
if not self.MLData is None:
input_channel, input_length, dropout, learning_rate, epochs, early_stopping, batch_size, ok = InputDialog.QNetInput_4.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_4['input_channel'] = input_channel
self.para_net_4['input_length'] = input_length
self.para_net_4['dropout'] = dropout
self.para_net_4['learning_rate'] = learning_rate
self.para_net_4['epochs'] = epochs
self.para_net_4['early_stopping'] = early_stopping
self.para_net_4['batch_size'] = batch_size
self.logTextEdit.append('%s\tAlgorithm: %s; Input size=%s; Input_length=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_5_ResNet']:
if not self.MLData is None:
input_channel, input_length, learning_rate, epochs, early_stopping, batch_size, ok = InputDialog.QNetInput_5.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_5['input_channel'] = input_channel
self.para_net_5['input_length'] = input_length
self.para_net_5['learning_rate'] = learning_rate
self.para_net_5['epochs'] = epochs
self.para_net_5['early_stopping'] = early_stopping
self.para_net_5['batch_size'] = batch_size
self.logTextEdit.append('%s\tAlgorithm: %s; Input size=%s; Input_length=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_channel, input_length, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif item.text(0) in ['Net_6_AE']:
if not self.MLData is None:
input_dim, dropout, learning_rate, epochs, early_stopping, batch_size, ok = InputDialog.QNetInput_6.getValues(self.MLData.training_dataframe.values.shape[1])
if ok:
self.para_net_6['mlp_input_dim'] = input_dim
self.para_net_6['dropout'] = dropout
self.para_net_6['learning_rate'] = learning_rate
self.para_net_6['epochs'] = epochs
self.para_net_6['early_stopping'] = early_stopping
self.para_net_6['batch_size'] = batch_size
self.logTextEdit.append('%s\tAlgorithm: %s; Input dimension=%s; Dropout=%s; lr=%s; Epochs=%s; Early_stopping=%s; Batch_size=%s' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), item.text(0), input_dim, dropout, learning_rate, epochs, early_stopping, batch_size))
else:
QMessageBox.warning(self, 'Warning', 'Please input training data at first!', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
def ml_tree_checkState(self, item, column):
if item and item.text(0) not in ['Machine learning algorithms']:
self.ml_treeWidget.setCurrentItem(item)
if item.checkState(column) == Qt.Checked:
self.algorithms_selected.add(item.text(0))
if item.checkState(column) == Qt.Unchecked:
self.algorithms_selected.discard(item.text(0))
def setFold(self):
fold, ok = QInputDialog.getInt(self, 'Fold number', 'Setting K-fold cross-validation', 5, 2, 100, 1)
if ok:
self.fold_lineEdit.setText(str(fold))
self.ml_defatult_para['FOLD'] = fold
self.logTextEdit.append('%s\tSet %s-fold Cross-validation' %(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), fold))
def panel_clear(self):
# metricsTableWidget init
self.metricsTableWidget.clear()
self.metricsTableWidget.setColumnCount(11)
self.metricsTableWidget.setRowCount(0)
self.current_data_index = 0
self.metricsTableWidget.setHorizontalHeaderLabels(['Id', 'StartTime', 'EndTime', 'Sn (%)', 'Sp (%)', 'Pre (%)', 'Acc (%)', 'MCC', 'F1', 'AUROC', 'AUPRC'])
self.descriptor = None
self.fasta_file = None
#
self.metrics = ModelMetrics.ModelMetrics()
self.boxplot_data = {}
def restart_init(self):
# metricsTableWidget init
self.metricsTableWidget.clear()
self.metricsTableWidget.setColumnCount(11)
self.metricsTableWidget.setRowCount(0)
self.current_data_index = 0
self.metricsTableWidget.setHorizontalHeaderLabels(['Id', 'StartTime', 'EndTime', 'Sn (%)', 'Sp (%)', 'Pre (%)', 'Acc (%)', 'MCC', 'F1', 'AUROC', 'AUPRC'])
self.metrics = ModelMetrics.ModelMetrics()
self.boxplot_data = {}
self.combineModelBtn.setDisabled(False)
def run_ml(self):
self.setDisabled(True)
self.restart_init()
if self.MLData is None:
QMessageBox.critical(self, 'Error', 'Please load training file.', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif len(self.algorithms_selected) == 0:
QMessageBox.critical(self, 'Error', 'Please select at least one machine learning algorithm.', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
elif self.ml_defatult_para['FOLD'] <= 1:
QMessageBox.critical(self, 'Error', 'FOLD need > 1', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
else:
self.logTextEdit.append('%s\tJobs start ...' %datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
self.progress_bar.setMovie(self.gif)
self.gif.start()
t = threading.Thread(target=self.train_model)
t.start()
def train_model(self):
for algo in self.algorithms_selected:
self.status_label.setText('Start training %s model ...' % algo)
start_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if algo == 'RF':
for key in self.para_rf:
self.ml_defatult_para[key] = self.para_rf[key]
ok = self.MLData.RandomForest()
elif algo == 'SVM':
for key in self.para_svm:
self.ml_defatult_para[key] = self.para_svm[key]
ok = self.MLData.SupportVectorMachine()
elif algo == 'MLP':
ok = self.MLData.MultiLayerPerceptron()
elif algo == 'LR':
ok = self.MLData.LogisticRegressionClassifier()
elif algo == 'LDA':
ok = self.MLData.LDAClassifier()
elif algo == 'QDA':
ok = self.MLData.QDAClassifier()
elif algo == 'KNN':
ok = self.MLData.KNeighbors()
elif algo == 'LightGBM':
for key in self.para_lightgbm:
self.ml_defatult_para[key] = self.para_lightgbm[key]
ok = self.MLData.LightGBMClassifier()
elif algo == 'XGBoost':
for key in self.para_xgboost:
self.ml_defatult_para[key] = self.para_xgboost[key]
ok = self.MLData.XGBoostClassifier()
elif algo == 'SGD':
ok = self.MLData.StochasticGradientDescentClassifier()
elif algo == 'DecisionTree':
ok = self.MLData.DecisionTree()
elif algo == 'NaiveBayes':
ok = self.MLData.GaussianNBClassifier()
elif algo == 'AdaBoost':
ok = self.MLData.AdaBoost()
elif algo == 'Bagging':
for key in self.para_bagging:
self.ml_defatult_para[key] = self.para_bagging[key]
ok = self.MLData.Bagging()
elif algo == 'GBDT':
ok = self.MLData.GBDTClassifier()
elif algo == 'Net_1_CNN':
for key in self.para_net_1:
self.ml_defatult_para[key] = self.para_net_1[key]
ok = self.MLData.run_networks(1)
elif algo == 'Net_1_CNN_binary':
for key in self.para_net_11:
self.ml_defatult_para[key] = self.para_net_11[key]
ok = self.MLData.run_networks(11)
elif algo == 'Net_2_RNN':
for key in self.para_net_2:
self.ml_defatult_para[key] = self.para_net_2[key]
ok = self.MLData.run_networks(2)
elif algo == 'Net_3_BRNN':
for key in self.para_net_3:
self.ml_defatult_para[key] = self.para_net_3[key]
ok = self.MLData.run_networks(3)
elif algo == 'Net_4_ABCNN':
for key in self.para_net_4:
self.ml_defatult_para[key] = self.para_net_4[key]
ok = self.MLData.run_networks(4)
elif algo == 'Net_5_ResNet':
for key in self.para_net_5:
self.ml_defatult_para[key] = self.para_net_5[key]
ok = self.MLData.run_networks(5)
elif algo == 'Net_6_AE':
for key in self.para_net_6:
self.ml_defatult_para[key] = self.para_net_6[key]
ok = self.MLData.run_networks(6)
self.MLData.calculate_boxplot_data()
end_time = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
if ok:
self.status_label.setText('%s model training complete.' %algo)
data_list = ['%s_model' %algo, start_time, end_time] + list(self.MLData.metrics.values[-1])
item = pd.DataFrame([data_list], columns=['Id', 'StartTime', 'EndTime', 'Sn', 'Sp', 'Pre', 'Acc', 'MCC', 'F1', 'AUROC', 'AUPRC'])
self.metrics.insert_data(item, '%s_model' %algo, self.MLData.meanAucData, self.MLData.meanPrcData, self.MLData.training_score, self.MLData.best_model)
self.boxplot_data[algo] = self.MLData.metrics.iloc[0:self.ml_defatult_para['FOLD'], :]
self.display_signal.emit(data_list)
else:
self.status_label.setText('%s model training failed.' %algo)
self.display_curves_signal.emit()
def display_metrics(self, data_list):
index = self.current_data_index
self.metricsTableWidget.insertRow(index)
self.current_data_index += 1
for i in range(len(data_list)):
self.metricsTableWidget.setItem(index, i, QTableWidgetItem(str(data_list[i])))
self.metricsTableWidget.resizeRowsToContents()
def display_curves(self):
self.status_label.setText('Plotting ... ')
self.rocLayout.removeWidget(self.rocCurveGraph)
sip.delete(self.rocCurveGraph)
self.rocCurveGraph = PlotWidgets.CurvesWidget()
self.rocLayout.addWidget(self.rocCurveGraph)
try:
if self.MLData.task == 'binary':
self.rocCurveGraph.init_roc_data(0, 'ROC curve', self.metrics.aucData)
if self.MLData.task == 'binary':
self.rocCurveGraph.init_prc_data(1, 'PRC curve', self.metrics.prcData)
except Exception as e:
QMessageBox.warning(self, 'Warning', str(e), QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
""" boxplot using matplotlib """
self.boxplotLayout.removeWidget(self.boxplotGraph)
sip.delete(self.boxplotGraph)
self.boxplotGraph = PlotWidgets.BoxplotWidget()
self.boxplotLayout.addWidget(self.boxplotGraph)
try:
if not self.MLData is None and self.MLData.task == 'binary':
self.boxplotGraph.init_data(['Sn', 'Sp', 'Pre', 'Acc', 'MCC', 'F1', 'AUROC', 'AUPRC'], self.boxplot_data)
if not self.MLData is None and self.MLData.task == 'muti-task':
self.boxplotGraph.init_data(['Acc'], self.boxplot_data)
except Exception as e:
QMessageBox.warning(self, 'Warning', str(e), QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
# init data for p_value calculation (bootstrap test)
self.rocCurveGraph.init_prediction_scores(self.MLData.task, self.metrics.prediction_scores)
self.gif.stop()
self.progress_bar.clear()
self.start_button.setDisabled(False)
self.setDisabled(False)
self.status_label.setText('Operation complete.')
def save_result(self):
try:
saved_file, ok = QFileDialog.getSaveFileName(self, 'Save', './data', 'TSV Files (*.tsv)')
if ok:
self.metrics.metrics.to_csv(saved_file, sep='\t', header=True, index=False)
except Exception as e:
QMessageBox.critical(self, 'Error', str(e), QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
def save_model(self):
try:
model_list = self.metrics.metrics.loc[:, 'Id'].values.tolist()
if len(model_list) > 0:
model_id, ok = InputDialog.QSelectModel.getValues(model_list)
if ok:
if model_id in self.metrics.models:
save_directory = QFileDialog.getExistingDirectory(self, 'Save', './data')
if not os.path.exists(save_directory):
pass
else:
for i, model in enumerate(self.metrics.models[model_id]):
model_name = '%s/%s_%s.pkl' %(save_directory, model_id, i+1)
if 'predict_proba' in dir(model):
joblib.dump(model, model_name)
else:
torch.save(model, model_name)
QMessageBox.information(self, 'Model saved',
'The models have been saved to directory %s' % save_directory,
QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
else:
QMessageBox.critical(self, 'Error', 'An error has been encountered in saving the model.',
QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
else:
QMessageBox.critical(self, 'Error', 'No model can be saved.', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
except Exception as e:
QMessageBox.critical(self, 'Error', str(e), QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)
def combineModels(self):
model_list = self.metrics.metrics.loc[:, 'Id'].values.tolist()
if len(model_list) >= 2:
combineAlgorithm, ok = InputDialog.QSCombineModelDialog.getValues(model_list)
combinations_array = []
if ok:
for model_num in range(2, len(model_list) + 1):
combinations_array += list(combinations(model_list, model_num))
self.logTextEdit.append('%s\tStarting train combined models ...' %datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))
self.progress_bar.setMovie(self.gif)
self.gif.start()
t = threading.Thread(target=lambda : self.calculateCombinations(combineAlgorithm, combinations_array))
t.start()
else:
QMessageBox.critical(self, 'Error', 'At least two models needed.', QMessageBox.Ok | QMessageBox.No, QMessageBox.Ok)