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SOL4Py Sample: LightGBMClassifier
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#******************************************************************************
#
# Copyright (c) 2018 Antillia.com TOSHIYUKI ARAI. ALL RIGHTS RESERVED.
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
#******************************************************************************
# 2018/09/01
# LightGBMClassifier.py
# encodig: utf-8
import sys
import os
import cv2
import time
import traceback
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pickle
import lightgbm as lgb
from sklearn.model_selection import train_test_split
#from sklearn.model_selection import GridSearchCV, RandomizedSearchCV
from sklearn import datasets
from sklearn.utils import shuffle
from sklearn.metrics import mean_squared_error
from sklearn.metrics import confusion_matrix, classification_report
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
sys.path.append('../')
from SOL4Py.ZMLModel import *
from SOL4Py.ZApplicationView import *
from SOL4Py.ZLabeledComboBox import *
from SOL4Py.ZPushButton import *
from SOL4Py.ZVerticalPane import *
from SOL4Py.ZTabbedWindow import *
from SOL4Py.ZScalableScrolledFigureView import *
from SOL4Py.ZScalableScrolledFigureView import *
# sklearn datasets
Iris = 0
Digits = 1
Wine = 2
BreastCancer = 3
############################################################
# Classifier Model clas
class LightGBMClassifierModel(ZMLModel):
##
# Constructor
def __init__(self, dataset_id, mainv):
super(LightGBMClassifierModel, self).__init__(dataset_id, mainv)
def run(self):
self.write("====================================")
self._start(self.run.__name__)
try:
self.load_dataset()
self.build()
if self.trained():
self.load()
else:
self.build()
self.train()
self.save()
self.predict()
self.visualize()
except:
traceback.print_exc()
self._end(self.run.__name__)
def load_dataset(self):
self._start(self.load_dataset.__name__)
if self.dataset_id == Iris:
self.dataset= datasets.load_iris()
self.write("loaded iris dataset")
if self.dataset_id == Digits:
self.dataset= datasets.load_digits()
self.write("loaded Digits dataset")
if self.dataset_id == Wine:
self.dataset= datasets.load_wine()
self.write("loaded Wine dataset")
if self.dataset_id == BreastCancer:
self.dataset= datasets.load_breast_cancer()
self.write("loaded BreastCancer dataset")
attr = dir(self.dataset)
self.write("dir:" + str(attr))
if "feature_names" in attr:
self.write("feature_names:" + str(self.dataset.feature_names))
if "target_names" in attr:
self.write("target_names:" + str(self.dataset.target_names))
self.set_model_filename()
self.view.description.setText(self.dataset.DESCR)
X, y = self.dataset.data, self.dataset.target
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, test_size=0.3, random_state=42)
self._end(self.load_dataset.__name__)
def build(self):
self._start(self.build.__name__)
self.model = lgb.LGBMClassifier(
objective='multiclass',
num_class = 2,
num_leaves = 31,
learning_rate=0.1,
min_child_samples=10,
n_estimators=100)
self._end(self.build.__name__)
def train(self):
self._start(self.train.__name__)
start = time.time()
# Class fit method of the classifier
self.model.fit(self.X_train, self.y_train,
eval_set=[(self.X_test, self.y_test)],
eval_metric='multi_logloss',
early_stopping_rounds=10)
elapsed_time = time.time() - start
elapsed = str("Train elapsed_time:{0}".format(elapsed_time) + "[sec]")
self.write(elapsed)
self._end(self.train.__name__)
def predict(self):
self._start(self.predict.__name__)
self.pred_test = self.model.predict(self.X_test, num_iteration=self.model.best_iteration_)
report = str (classification_report(self.y_test, self.pred_test) )
self.write(report)
self._end(self.predict.__name__)
def visualize(self):
cmatrix = confusion_matrix(self.y_test, self.pred_test)
self.view.visualize(cmatrix)
############################################################
# Classifier View
class MainView(ZApplicationView):
# Class variables
# ClassifierView Constructor
def __init__(self, title, x, y, width, height):
super(MainView, self).__init__(title, x, y, width, height)
self.font = QFont("Arial", 10)
self.setFont(self.font)
# 1 Add a labeled combobox to top dock area
self.add_datasets_combobox()
# 2 Add a textedit to the left pane of the center area.
self.text_editor = QTextEdit()
self.text_editor.setLineWrapColumnOrWidth(600)
self.text_editor.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 3 Add a description to display dataset.DESCR.
self.description = QTextEdit()
self.description.setLineWrapColumnOrWidth(600)
self.description.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 4 Add a tabbed_window to the tabbed_window.
self.tabbed_window = ZTabbedWindow(self, 0, 0, width/2, height)
# 5 Add a figure_view to the tabbed_window.
self.figure_view = ZScalableScrolledFigureView(self, 0, 0, width/2, height)
self.add(self.text_editor)
self.add(self.tabbed_window)
self.tabbed_window.add("Description", self.description)
self.tabbed_window.add("ConfusionMatrix", self.figure_view)
self.figure_view.hide()
self.show()
def add_datasets_combobox(self):
self.dataset_id = Iris
self.datasets_combobox = ZLabeledComboBox(self, "Datasets", Qt.Horizontal)
# We use the following datasets of sklearn to test XGBClassifier.
self.datasets = {"Iris": Iris, "Digits": Digits, "Wine": Wine, "BreastCancer": BreastCancer}
title = self.get_title()
self.setWindowTitle( "Iris" + " - " + title)
self.datasets_combobox.add_items(self.datasets.keys())
self.datasets_combobox.add_activated_callback(self.datasets_activated)
self.datasets_combobox.set_current_text(self.dataset_id)
self.start_button = ZPushButton("Start", self)
self.clear_button = ZPushButton("Clear", self)
self.start_button.add_activated_callback(self.start_button_activated)
self.clear_button.add_activated_callback(self.clear_button_activated)
self.datasets_combobox.add(self.start_button)
self.datasets_combobox.add(self.clear_button)
self.set_top_dock(self.datasets_combobox)
def write(self, text):
self.text_editor.append(text)
self.text_editor.repaint()
def datasets_activated(self, text):
self.dataset_id = self.datasets[text]
title = self.get_title()
self.setWindowTitle(text + " - " + title)
def start_button_activated(self, text):
self.model = LightGBMClassifierModel(self.dataset_id, self)
self.start_button.setEnabled(False)
self.clear_button.setEnabled(False)
try:
self.model.run()
except:
pass
self.start_button.setEnabled(True)
self.clear_button.setEnabled(True)
def clear_button_activated(self, text):
self.text_editor.setText("")
self.description.setText("")
self.figure_view.hide()
plt.close()
def visualize(self, cmatrix):
self.figure_view.show()
plt.close()
sns.set()
df = pd.DataFrame(cmatrix)
sns.heatmap(df, annot=True, fmt="d")
# Set a new figure to the figure_view.
self.figure_view.set_figure(plt)
############################################################
#
if main(__name__):
try:
app_name = os.path.basename(sys.argv[0])
applet = QApplication(sys.argv)
main_view = MainView(app_name, 40, 40, 800, 500)
main_view.show ()
applet.exec_()
except:
traceback.print_exc()
Last modified: 6 May 2018