From d1a050a48c60d2c862c17c6d2241e93ad396a98c Mon Sep 17 00:00:00 2001
From: Fadi <fadi.gattoussi@stud.th-deg.de>
Date: Sun, 24 Dec 2023 22:08:03 +0100
Subject: [PATCH] Added Disabling of input fields when checkboxes are unchecked
---
DisplayWindow.py | 49 +++++++++++++++++++--------
training3.ipynb | 86 ++++++++++++++++++++++--------------------------
2 files changed, 75 insertions(+), 60 deletions(-)
diff --git a/DisplayWindow.py b/DisplayWindow.py
index 8a6f34a..210cbe2 100644
--- a/DisplayWindow.py
+++ b/DisplayWindow.py
@@ -8,8 +8,6 @@ import modeling
from PyQt6.QtGui import QIcon
from sklearn.model_selection import train_test_split
import joblib
-from pyqtgraph import PlotWidget, plot
-import pyqtgraph as pg
from PyQt6.QtWidgets import (QApplication, QMainWindow, QWidget, QDialog,
QVBoxLayout, QGridLayout,
QLabel, QPushButton, QLineEdit, QComboBox, QTabWidget, QCheckBox)
@@ -107,24 +105,38 @@ class MainWindow(QMainWindow):
# Add the plot widget to the grid layout
self.grid_layout.addWidget(self.calculate_button, 2, 0, 1, 2)
self.grid_layout.addWidget(self.scatt, 3, 0, 1, 9)
- self.grid_layout.addWidget(QLabel("Predicted Price: ", self), 4, 0, 1, 1)
+ predicted_price_label = QLabel("Predicted Price: ", self)
+ predicted_price_label.setStyleSheet("font-size: 20px;")
+ self.grid_layout.addWidget(predicted_price_label, 4, 0, 1, 1)
self.display_price = QLabel(str(0))
+ self.display_price.setStyleSheet("font-size: 20px; font-weight: bold; color: purple; font-family: Ubuntu;")
self.grid_layout.addWidget(self.display_price, 4, 2, 1, 1)
self.setWindowTitle('Assistance Systems')
self.show()
+ def disable_input_field(self, field_name):
+ for key, value in self.menu_items.items():
+ if value == field_name:
+ self.input_fields[key].setDisabled(True)
+
+ def enable_input_field(self, field_name):
+ for key, value in self.menu_items.items():
+ if value == field_name:
+ self.input_fields[key].setEnabled(True)
+
def update_values(self):
self.GUI_selections = {}
- for key, value in self.menu_items.items():
- if Advanced.Advanced_selections[value]:
- if key < 3:
- self.GUI_selections[value] = self.input_fields[key].currentText()
- else:
- if self.validate_inputs(self.input_fields[key].text(), value):
- self.GUI_selections[value] = float(self.input_fields[key].text())
+ if Advanced.checkboxes_changed:
+ for key, value in self.menu_items.items():
+ if Advanced.Advanced_selections[value]:
+ if key < 3:
+ self.GUI_selections[value] = self.input_fields[key].currentText()
else:
- return
+ if self.validate_inputs(self.input_fields[key].text(), value):
+ self.GUI_selections[value] = float(self.input_fields[key].text())
+ else:
+ return
# convert GUI_selections to dataframe
X_test = pd.DataFrame(self.GUI_selections, index=[0])
@@ -159,6 +171,8 @@ class MainWindow(QMainWindow):
class Advanced(QMainWindow):
Advanced_selections = {'cut': True, 'color': True, 'clarity': True, 'carat': True, 'depth': True,
'table': True, 'x': True, 'y': True, 'z': True}
+
+ checkboxes_changed = False
def __init__(self):
super().__init__()
@@ -182,8 +196,10 @@ class Advanced(QMainWindow):
self.check_labels = ['cut', 'color', 'clarity', 'carat', 'depth', 'table', 'x', 'y', 'z']
self.graph_selector_X.addItems((self.check_labels + ['price']))
self.graph_selector_Y.addItems((self.check_labels + ['price']))
- self.regression_model.addItems(["Linear Regression", "XGBRegressor", "Neural Network",
- "Random Forest Regressor"])
+ self.regression_model.addItems(["Random Forest Regressor",
+ "Linear Regression",
+ "XGBRegressor",
+ "Neural Network"])
self.teach = QPushButton('Re-Train', self)
self.teach.clicked.connect(self.re_train)
self.plot_graph = QPushButton('PLOT', self)
@@ -214,12 +230,16 @@ class Advanced(QMainWindow):
self.checkboxes.append(checkbox) # Store checkboxes in the list
def handle_checkbox_state(self):
+ Advanced.checkboxes_changed = False
for i, checkbox in enumerate(self.checkboxes):
state = checkbox.checkState()
if state == Qt.CheckState.Unchecked:
Advanced.Advanced_selections[checkbox.text()] = False
+ window.calc.disable_input_field(checkbox.text())
else:
Advanced.Advanced_selections[checkbox.text()] = True
+ window.calc.enable_input_field(checkbox.text())
+
def re_train(self):
global diamonds
@@ -238,6 +258,7 @@ class Advanced(QMainWindow):
self.R2.setText(f"R2 = {str(model.r2)}")
self.MSE.setText(f"MSE = {str(model.mse)}")
+ Advanced.checkboxes_changed = True
def create_graph(self):
@@ -259,7 +280,7 @@ class Advanced(QMainWindow):
self.scatt.draw()
self.histogram.draw()
-
+
app = QApplication(sys.argv)
diff --git a/training3.ipynb b/training3.ipynb
index 4c5e3c5..6478f3a 100644
--- a/training3.ipynb
+++ b/training3.ipynb
@@ -1039,80 +1039,74 @@
},
{
"cell_type": "code",
- "execution_count": 28,
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Squared Error: 292726.7655844448\n",
+ "R^2 Score: 0.9817410390026241\n"
+ ]
+ }
+ ],
+ "source": [
+ "# random forest\n",
+ "from sklearn.ensemble import RandomForestRegressor\n",
+ "model3 = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+ "model3.fit(X_train, y_train)\n",
+ "\n",
+ "from sklearn.metrics import mean_squared_error, r2_score\n",
+ "y_pred3 = model3.predict(X_test)\n",
+ "mse3 = mean_squared_error(y_test, y_pred3)\n",
+ "r23 = r2_score(y_test, y_pred3)\n",
+ "print(\"Mean Squared Error:\", mse3)\n",
+ "print(\"R^2 Score:\", r23)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- "/home/fadi/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
+ "/home/fadi/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n",
" warnings.warn(\n"
]
},
{
"data": {
"text/plain": [
- "array([3672.24864574])"
+ "array([374.44])"
]
},
- "execution_count": 28,
+ "execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
- "test_diamond = [[\n",
- " 0.86,\n",
- " 3,\n",
- " 0,\n",
- " 61,\n",
- " 58,\n",
- " 6.15,\n",
- " 6.12,\n",
- " 3.74\n",
- "]]\n",
- "test_price = model.predict(test_diamond)\n",
+ "test_diamond = [[0.23, 0, 0, 61.5, 55.0, 3.95, 3.98, 2.43]]\n",
+ "test_price = model3.predict(test_diamond)\n",
"test_price\n"
]
},
{
"cell_type": "code",
- "execution_count": 29,
+ "execution_count": null,
"metadata": {},
- "outputs": [
- {
- "ename": "NameError",
- "evalue": "name 'y_pred3' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[29], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m model3\u001b[38;5;241m.\u001b[39mfit(X_train, y_train)\n\u001b[1;32m 6\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mean_squared_error, r2_score\n\u001b[0;32m----> 7\u001b[0m mse3 \u001b[38;5;241m=\u001b[39m mean_squared_error(y_test, \u001b[43my_pred3\u001b[49m)\n\u001b[1;32m 8\u001b[0m r23 \u001b[38;5;241m=\u001b[39m r2_score(y_test, y_pred3)\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMean Squared Error:\u001b[39m\u001b[38;5;124m\"\u001b[39m, mse3)\n",
- "\u001b[0;31mNameError\u001b[0m: name 'y_pred3' is not defined"
- ]
- }
- ],
+ "outputs": [],
"source": [
- "# random forest\n",
- "from sklearn.ensemble import RandomForestRegressor\n",
- "model3 = RandomForestRegressor(n_estimators=100, random_state=42)\n",
- "model3.fit(X_train, y_train)\n",
+ "import numpy as np\n",
"\n",
- "from sklearn.metrics import mean_squared_error, r2_score\n",
- "y_pred3 = model3.predict(X_test)\n",
- "mse3 = mean_squared_error(y_test, y_pred3)\n",
- "r23 = r2_score(y_test, y_pred3)\n",
- "print(\"Mean Squared Error:\", mse3)\n",
- "print(\"R^2 Score:\", r23)"
+ "data = np.array([[0.23, 0, 0, 61.5, 55.0, 3.95, 3.98, 2.43]])\n",
+ "print(data)\n",
+ "0.23\t0\t0\t61.5\t55.0\t3.95\t3.98\t2.43"
]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
}
],
"metadata": {
--
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