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Commit d1a050a4 authored by Fadi Gattoussi's avatar Fadi Gattoussi
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Added Disabling of input fields when checkboxes are unchecked

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DisplayWindow.py
+ 35
− 14
View file @ d1a050a4
......@@ -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)
......
training3.ipynb
+ 40
− 46
View file @ d1a050a4
%% Cell type:code id: tags:
``` python
import pandas as pd
import sklearn as sk
import matplotlib.pyplot as plt
import json
import math
from sklearn.model_selection import train_test_split
```
%% Cell type:markdown id: tags:
### 1. Read Dataset and remove NAN values
%% Cell type:code id: tags:
``` python
diamonds = pd.read_csv('diamonds.csv')
diamonds.dropna(inplace=True)
diamonds.head()
```
%% Output
carat cut color clarity depth table price x y z
0 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43
1 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31
2 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31
3 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63
4 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75
%% Cell type:code id: tags:
``` python
test_diamond = [[0.21, 0, 0, 1, 59.8, 61, 3.89, 3.84, 2.31]]
test_price = model2.predict(test_diamond)
test_price
```
%% Output
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[4], line 2
1 test_diamond = [[0.21, 0, 0, 1, 59.8, 61, 3.89, 3.84, 2.31]]
----> 2 test_price = model2.predict(test_diamond)
3 test_price
NameError: name 'model2' is not defined
%% Cell type:markdown id: tags:
### 2. Mapping non-numerical values to numerical ones
%% Cell type:markdown id: tags:
**Cut Quality**
| Cut | Mapping |
|------------|------|
| Ideal | 0 |
| Premium | 1 |
| Good | 2 |
| Very Good | 3 |
| Fair | 4 |
**Color Grade**
| Color | Mapping |
|-------|------|
| E | 0 |
| I | 1 |
| J | 2 |
| H | 3 |
| F | 4 |
| G | 5 |
| D | 6 |
**Clarity Grade**
| Clarity | Mapping |
|---------|------|
| SI2 | 0 |
| SI1 | 1 |
| VS1 | 2 |
| VS2 | 3 |
| VVS2 | 4 |
| VVS1 | 5 |
| I1 | 6 |
| IF | 7 |
%% Cell type:code id: tags:
``` python
cut_mapping = {cut: i for i, cut in enumerate(diamonds['cut'].unique())}
color_mapping = {color: i for i, color in enumerate(diamonds['color'].unique())}
clarity_mapping = {clarity: i for i, clarity in enumerate(diamonds['clarity'].unique())}
```
%% Cell type:markdown id: tags:
### 3. Replace the non-numerical columns with their numerical mappings
%% Cell type:code id: tags:
``` python
diamonds_numerical = diamonds.copy()
diamonds_numerical['cut'] = diamonds['cut'].map(cut_mapping)
diamonds_numerical['color'] = diamonds['color'].map(color_mapping)
diamonds_numerical['clarity'] = diamonds['clarity'].map(clarity_mapping)
```
%% Cell type:code id: tags:
``` python
# store rows where x or y or z is 0
zero_rows = diamonds[(diamonds['x'] == 0) | (diamonds['y'] == 0) | (diamonds['z'] == 0)]
zero_rows
```
%% Output
carat cut color clarity depth table price x y z
2207 1.00 Premium G SI2 59.1 59.0 3142 6.55 6.48 0.0
2314 1.01 Premium H I1 58.1 59.0 3167 6.66 6.60 0.0
4791 1.10 Premium G SI2 63.0 59.0 3696 6.50 6.47 0.0
5471 1.01 Premium F SI2 59.2 58.0 3837 6.50 6.47 0.0
10167 1.50 Good G I1 64.0 61.0 4731 7.15 7.04 0.0
11182 1.07 Ideal F SI2 61.6 56.0 4954 0.00 6.62 0.0
11963 1.00 Very Good H VS2 63.3 53.0 5139 0.00 0.00 0.0
13601 1.15 Ideal G VS2 59.2 56.0 5564 6.88 6.83 0.0
15951 1.14 Fair G VS1 57.5 67.0 6381 0.00 0.00 0.0
24394 2.18 Premium H SI2 59.4 61.0 12631 8.49 8.45 0.0
24520 1.56 Ideal G VS2 62.2 54.0 12800 0.00 0.00 0.0
26123 2.25 Premium I SI1 61.3 58.0 15397 8.52 8.42 0.0
26243 1.20 Premium D VVS1 62.1 59.0 15686 0.00 0.00 0.0
27112 2.20 Premium H SI1 61.2 59.0 17265 8.42 8.37 0.0
27429 2.25 Premium H SI2 62.8 59.0 18034 0.00 0.00 0.0
27503 2.02 Premium H VS2 62.7 53.0 18207 8.02 7.95 0.0
27739 2.80 Good G SI2 63.8 58.0 18788 8.90 8.85 0.0
49556 0.71 Good F SI2 64.1 60.0 2130 0.00 0.00 0.0
49557 0.71 Good F SI2 64.1 60.0 2130 0.00 0.00 0.0
51506 1.12 Premium G I1 60.4 59.0 2383 6.71 6.67 0.0
%% Cell type:code id: tags:
``` python
# drop rows with outliers (x = 0, y = 0 or z = 0)
mask = (diamonds_numerical['x'] == 0) | (diamonds_numerical['y'] == 0) | (diamonds_numerical['z'] == 0)
diamonds_numerical = diamonds_numerical.drop(diamonds_numerical[mask].index)
# write diamonds_numerical to csv
diamonds_numerical.to_csv('diamonds_numerical.csv', index=True)
```
%% Cell type:code id: tags:
``` python
# Creating separate plots for each feature against price
fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(15, 15))
fig.subplots_adjust(hspace=0.5, wspace=0.3)
# List of all the features except price
features = diamonds_numerical.columns.drop('price')
# Plot each feature against price
for ax, feature in zip(axes.flatten(), features):
ax.scatter(diamonds_numerical[feature], diamonds_numerical['price'], alpha=0.5)
ax.set_title(f'{feature} vs. Price')
ax.set_xlabel(feature)
ax.set_ylabel('Price')
plt.show()
```
%% Output
%% Cell type:code id: tags:
``` python
X = diamonds_numerical.drop('price', axis = 1)
X = X.drop('cut', axis = 1)
y = diamonds_numerical['price']
X
```
%% Output
carat color clarity depth table x y z
0 0.23 0 0 61.5 55.0 3.95 3.98 2.43
1 0.21 0 1 59.8 61.0 3.89 3.84 2.31
2 0.23 0 2 56.9 65.0 4.05 4.07 2.31
3 0.29 1 3 62.4 58.0 4.20 4.23 2.63
4 0.31 2 0 63.3 58.0 4.34 4.35 2.75
... ... ... ... ... ... ... ... ...
53935 0.72 6 1 60.8 57.0 5.75 5.76 3.50
53936 0.72 6 1 63.1 55.0 5.69 5.75 3.61
53937 0.70 6 1 62.8 60.0 5.66 5.68 3.56
53938 0.86 3 0 61.0 58.0 6.15 6.12 3.74
53939 0.75 6 0 62.2 55.0 5.83 5.87 3.64
[53920 rows x 8 columns]
%% Cell type:code id: tags:
``` python
y
```
%% Output
0 326
1 326
2 327
3 334
4 335
...
53935 2757
53936 2757
53937 2757
53938 2757
53939 2757
Name: price, Length: 53920, dtype: int64
%% Cell type:code id: tags:
``` python
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
```
%% Cell type:code id: tags:
``` python
from sklearn.linear_model import LinearRegression
model = LinearRegression()
# Train the models
model.fit(X_train, y_train)
```
%% Output
LinearRegression()
%% Cell type:code id: tags:
``` python
from sklearn.metrics import mean_squared_error, r2_score
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print("Mean Squared Error:", mse)
print("R^2 Score:", r2)
```
%% Output
Mean Squared Error: 1920307.5427267884
R^2 Score: 0.8802199708126777
%% Cell type:code id: tags:
``` python
from xgboost import XGBRegressor
model2 = XGBRegressor(n_estimators=100,random_state=42)
model2.fit(X_train, y_train)
```
%% Output
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
Cell In[25], line 3
1 from xgboost import XGBRegressor
2 model2 = XGBRegressor(n_estimators=100,random_state=42)
----> 3 model2.fit(X_train, y_train)
File ~/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/core.py:730, in require_keyword_args.<locals>.throw_if.<locals>.inner_f(*args, **kwargs)
728 for k, arg in zip(sig.parameters, args):
729 kwargs[k] = arg
--> 730 return func(**kwargs)
File ~/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/sklearn.py:1090, in XGBModel.fit(self, X, y, sample_weight, base_margin, eval_set, eval_metric, early_stopping_rounds, verbose, xgb_model, sample_weight_eval_set, base_margin_eval_set, feature_weights, callbacks)
1079 obj = None
1081 (
1082 model,
1083 metric,
(...)
1088 xgb_model, eval_metric, params, early_stopping_rounds, callbacks
1089 )
-> 1090 self._Booster = train(
1091 params,
1092 train_dmatrix,
1093 self.get_num_boosting_rounds(),
1094 evals=evals,
1095 early_stopping_rounds=early_stopping_rounds,
1096 evals_result=evals_result,
1097 obj=obj,
1098 custom_metric=metric,
1099 verbose_eval=verbose,
1100 xgb_model=model,
1101 callbacks=callbacks,
1102 )
1104 self._set_evaluation_result(evals_result)
1105 return self
File ~/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/core.py:730, in require_keyword_args.<locals>.throw_if.<locals>.inner_f(*args, **kwargs)
728 for k, arg in zip(sig.parameters, args):
729 kwargs[k] = arg
--> 730 return func(**kwargs)
File ~/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/training.py:181, in train(params, dtrain, num_boost_round, evals, obj, feval, maximize, early_stopping_rounds, evals_result, verbose_eval, xgb_model, callbacks, custom_metric)
179 if cb_container.before_iteration(bst, i, dtrain, evals):
180 break
--> 181 bst.update(dtrain, i, obj)
182 if cb_container.after_iteration(bst, i, dtrain, evals):
183 break
File ~/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/xgboost/core.py:2051, in Booster.update(self, dtrain, iteration, fobj)
2047 self._assign_dmatrix_features(dtrain)
2049 if fobj is None:
2050 _check_call(
-> 2051 _LIB.XGBoosterUpdateOneIter(
2052 self.handle, ctypes.c_int(iteration), dtrain.handle
2053 )
2054 )
2055 else:
2056 pred = self.predict(dtrain, output_margin=True, training=True)
KeyboardInterrupt:
%% Cell type:code id: tags:
``` python
y_pred2 = model2.predict(X_test)
mse2 = mean_squared_error(y_test, y_pred2)
r22 = r2_score(y_test, y_pred2)
print("Mean Squared Error:", mse2)
print("R^2 Score:", r22)
```
%% Output
Mean Squared Error: 303969.39467222965
R^2 Score: 0.9810397750590555
%% Cell type:code id: tags:
``` python
from sklearn.neural_network import MLPRegressor
model4 = MLPRegressor(random_state=42, max_iter=500)
# model4.fit(X_train, y_train)
```
%% Output
/home/fadi/Desktop/dev/sas2/clone2/ws-23-sas-02/sas2/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.
warnings.warn(
MLPRegressor(max_iter=500, random_state=42)
%% Cell type:code id: tags:
``` python
from sklearn.metrics import mean_squared_error, r2_score
# Predict on the test set
y_pred = model.predict(X_test)
y_pred2 = model2.predict(X_test)
y_pred4 = model4.predict(X_test)
# Evaluate the model
mse = mean_squared_error(y_test, y_pred)
mse2 = mean_squared_error(y_test, y_pred2)
mse4 = mean_squared_error(y_test, y_pred4)
r2 = r2_score(y_test, y_pred)
r22 = r2_score(y_test, y_pred2)
r24 = r2_score(y_test, y_pred4)
print("Mean Squared Error:", mse)
print("MSE 2 ", mse2)
print("MSE 4 ", mse4)
print("R^2 Score:", r2)
print("R^2 Score:", r22)
print("R^2 Score:", r24)
```
%% Output
Mean Squared Error: 1896296.209209171
MSE 2 303969.39467222965
MSE 4 1366191.3819000595
R^2 Score: 0.881717688321761
R^2 Score: 0.9810397750590555
R^2 Score: 0.9147832105230972
%% Cell type:code id: tags:
``` python
test_diamond = [[
0.86,
3,
0,
61,
58,
6.15,
6.12,
3.74
]]
test_price = model.predict(test_diamond)
test_price
```
%% Output
/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
warnings.warn(
array([3672.24864574])
%% Cell type:code id: tags:
``` python
# random forest
from sklearn.ensemble import RandomForestRegressor
model3 = RandomForestRegressor(n_estimators=100, random_state=42)
model3.fit(X_train, y_train)
from sklearn.metrics import mean_squared_error, r2_score
y_pred3 = model3.predict(X_test)
mse3 = mean_squared_error(y_test, y_pred3)
r23 = r2_score(y_test, y_pred3)
print("Mean Squared Error:", mse3)
print("R^2 Score:", r23)
```
%% Output
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
Cell In[29], line 7
4 model3.fit(X_train, y_train)
6 from sklearn.metrics import mean_squared_error, r2_score
----> 7 mse3 = mean_squared_error(y_test, y_pred3)
8 r23 = r2_score(y_test, y_pred3)
9 print("Mean Squared Error:", mse3)
NameError: name 'y_pred3' is not defined
Mean Squared Error: 292726.7655844448
R^2 Score: 0.9817410390026241
%% Cell type:code id: tags:
``` python
test_diamond = [[0.23, 0, 0, 61.5, 55.0, 3.95, 3.98, 2.43]]
test_price = model3.predict(test_diamond)
test_price
```
%% Output
/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
warnings.warn(
array([374.44])
%% Cell type:code id: tags:
``` python
import numpy as np
data = np.array([[0.23, 0, 0, 61.5, 55.0, 3.95, 3.98, 2.43]])
print(data)
0.23 0 0 61.5 55.0 3.95 3.98 2.43
```
......
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