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SOL4Py Sample: RandomForestClassifier
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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
# RandomForestClassifier.py
# This is base on the following sample program:
# http://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html
#
# Author: Tim Head <betatim@gmail.com>
#
# License: BSD 3 clause
# See also:
# http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html
# https://github.com/scikit-learn/scikit-learn/blob/f0ab589f/sklearn/ensemble/forest.py#L1019
#
# 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
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import confusion_matrix, classification_report
from sklearn.model_selection import train_test_split
from sklearn import datasets
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 *
Iris = 0
Digits = 1
Wine = 2
BreastCancer = 3
############################################################
# Classifier Model clas
class RandomForestClassifierModel(ZMLModel):
##
# Constructor
def __init__(self, dataset_id, mainv):
super(RandomForestClassifierModel, self).__init__(dataset_id, mainv)
self.roc_label_position = 1
def run(self):
self.write("====================================")
self._start(self.run.__name__)
try:
self.load_dataset()
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)
self.X_train, self.X_train_lr, self.y_train, self.y_train_lr = train_test_split(self.X_train, self.y_train, test_size=0.3)
self._end(self.load_dataset.__name__)
def build(self):
self._start(self.build.__name__)
self.model = RandomForestClassifier(max_depth=10, n_estimators= 10)
self.enc = OneHotEncoder()
self.lr = LogisticRegression()
self._end(self.run.__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)
self.enc.fit(self.model.apply(self.X_train))
self.lr.fit(self.enc.transform(self.model.apply(self.X_train_lr)), self.y_train_lr)
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__)
# RandomForestClassifer.predict
self.pred_test = self.model.predict(self.X_test)
# Call LogisticRegression.predic_prob
self.y_pred = self.lr.predict_proba(self.enc.transform(self.model.apply(self.X_test)))[:, 1]
self._end(self.predict.__name__)
def visualize(self):
cmatrix = confusion_matrix(self.y_test, self.pred_test)
false_positive_rate, true_positive_rate, _ = roc_curve(self.y_test, self.y_pred, pos_label=self.roc_label_position)
self.view.visualize(cmatrix, false_positive_rate, true_positive_rate, self.roc_label_position)
############################################################
# 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 Create 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 Create a description to display dataset.DESCR.
self.description = QTextEdit()
self.description.setLineWrapColumnOrWidth(600)
self.description.setLineWrapMode(QTextEdit.FixedPixelWidth)
# 4 Create a tabbed_window
self.tabbed_window = ZTabbedWindow(self, 0, 0, width/2, height)
# 5 Create a figure_view to the right pane of the center area.
self.figure_view = ZScalableScrolledFigureView(self, 0, 0, width/2, height)
# 6 Create a roc_curve to the right pane of the center area.
self.roc_curve = 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.tabbed_window.add("ROC Curve", self.roc_curve)
self.figure_view.hide()
self.roc_curve.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 RandomForestClassifier.
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 = RandomForestClassifierModel(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()
self.roc_curve.hide()
plt.close()
def visualize(self, cmatrix, false_positive_rate, true_positive_rate, label_position):
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)
plt.close()
self.roc_curve.show()
self.write("AUC :" + str( auc(false_positive_rate, true_positive_rate) ))
plt.plot([0, 1], [0, 1], 'k--')
plt.plot(false_positive_rate, true_positive_rate, label='RT + LR')
plt.xlabel('False positive rate')
plt.ylabel('True positive rate')
plt.title('ROC curve: label_position=' + str(label_position))
plt.legend(loc='best')
self.roc_curve.set_figure(plt)
plt.close()
############################################################
#
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