# Author : Amir Shokri
# github link : https://github.com/amirshnll/Lung-Cancer
# dataset link : http://archive.ics.uci.edu/ml/datasets/Lung+Cancer
# email : amirsh.nll@gmail.com
import numpy as np, matplotlib.pyplot as plt
import pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import accuracy_score
from sklearn import tree
from sklearn.neural_network import MLPClassifier
from sklearn.linear_model import LogisticRegression
import seaborn as sns
sns.set()
def Read_Data(address, Name='*.csv', Sperator=';'):
Data = pd.read_csv(address+Name, sep=Sperator, header=None)
# Data = Data.dropna()
X = Data.drop([0], axis=1)
Y = Data.iloc[:,0]
return X, Ydef KNN_Plot(X, Y, n1, n2, knn_title):
'''
n1 and n2 are Neighbours
'''
neighbors = np.arange(n1, n2)
train_acc = np.empty(len(neighbors))
test_acc = np.empty(len(neighbors))
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=.2,
random_state=42,
stratify=Y)
for i, k in enumerate(neighbors):
knn_model = KNeighborsClassifier(n_neighbors=k, weights='distance',
algorithm='auto', p=2)
knn_model.fit(x_train, y_train)
TAcc = knn_model.score(x_train, y_train)
TAcc *= 100
TAcc = float(format(TAcc,'.2f'))
train_acc[i] = TAcc
pred = knn_model.predict(x_test)
Test_acc = accuracy_score(y_test, pred)
Test_acc *= 100
Test_acc = float(format(Test_acc,'.2f'))
test_acc[i] = Test_acc
plt.plot(neighbors, train_acc, label='Train Accuracy')
plt.plot(neighbors, test_acc, label='Test Accuracy')
plt.legend(loc='best')
plt.title(knn_title)
plt.xlabel('Neighbors')
plt.ylabel('Accuracy (%)')
plt.xticks(neighbors)
plt.show()
return knn_modeldef NB(x, y):
x_train, x_test, y_train, y_test = train_test_split(x, y,
test_size=.2,
random_state=42,
stratify=y)
nb_clf = GaussianNB()
nb_clf.fit(x_train, y_train)
Predict_nb_clf = nb_clf.predict(x_test)
Accuracy = accuracy_score(y_test, Predict_nb_clf)
Accuracy *= 100
print('GaussianNB Accuracy: ')
print(float(format(Accuracy,'.2f')))
print('---------------------------------\n')
return Accuracydef Tree(X, Y):
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=.2,
random_state=42,
stratify=Y)
clf = tree.DecisionTreeClassifier(random_state=80)
clf.fit(x_train, y_train)
Predict = clf.predict(x_test)
Accuracy = accuracy_score(y_test, Predict)
Accuracy *= 100
print('DecisionTree Accuracy: ')
print(float(format(Accuracy,'.2f')))
print('---------------------------------\n')
return Accuracydef MLP(X, Y):
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=.2,
random_state=42,
stratify=Y)
mlp = MLPClassifier(hidden_layer_sizes=(800,), max_iter=1000, random_state=50)
mlp.fit(x_train, y_train)
Predict = mlp.predict(x_test)
Accuracy = accuracy_score(y_test, Predict)
Accuracy *= 100
print('MLP Accuracy: ')
print(float(format(Accuracy,'.2f')))
print('---------------------------------\n')
return Accuracydef LogisticRegressionClf(X, Y):
x_train, x_test, y_train, y_test = train_test_split(X,
Y,
test_size=.2,
random_state=50,
stratify=Y)
clf = LogisticRegression(random_state=50, solver='lbfgs', max_iter=200)
clf.fit(x_train, y_train)
Predict = clf.predict(x_test)
Accuracy = accuracy_score(y_test, Predict)
Accuracy *= 100
print('LogisticRegression Accuracy: ')
print(float(format(Accuracy,'.2f')))
print('---------------------------------\n')
return Accuracyaddress = 'C:/'
X, Y = Read_Data(address, Name='lc.csv', Sperator=';')
print(X,Y)
1 2 3 4 5 6 7 8 9 10 ... 47 48 49 50 51 52 53 \
0 0 3 3 1 0 3 1 3 1 1 ... 2 2 2 2 2 2 2
1 0 3 3 2 0 3 3 3 1 1 ... 2 2 2 2 2 2 2
2 0 2 3 2 1 3 3 3 1 2 ... 2 2 2 2 2 2 2
3 0 3 2 1 1 3 3 3 2 2 ... 2 2 2 2 2 2 2
4 0 3 3 2 0 3 3 3 1 2 ... 2 2 2 2 2 2 2
5 0 3 2 1 0 3 3 3 1 2 ... 2 2 2 2 1 2 2
6 0 2 2 1 0 3 1 3 3 3 ... 2 2 1 2 2 2 2
7 0 3 1 1 0 3 1 3 1 1 ... 2 2 2 2 2 2 2
8 0 2 3 2 0 2 2 2 1 2 ... 2 2 2 1 3 2 1
9 0 2 2 0 0 3 2 3 1 1 ... 2 2 2 2 2 2 2
10 0 2 3 2 0 1 2 1 1 2 ... 2 2 2 2 2 1 1
11 0 2 1 1 0 1 2 2 1 2 ... 2 2 2 2 2 2 2
12 0 2 2 1 1 2 3 3 1 1 ... 2 2 2 2 2 1 1
13 0 3 2 2 1 2 2 2 1 1 ... 2 2 2 2 2 2 2
14 0 3 2 2 0 1 1 3 1 1 ... 2 2 2 2 2 2 2
15 0 2 1 1 0 2 1 3 1 1 ... 2 2 2 2 2 1 1
16 0 1 2 1 0 3 3 3 1 2 ... 2 2 2 2 2 1 1
17 0 3 3 2 0 2 1 3 1 1 ... 2 2 1 2 2 2 2
18 0 2 3 1 1 2 2 1 1 1 ... 3 3 3 3 1 3 3
19 0 2 3 1 1 1 2 1 1 1 ... 2 2 2 2 2 2 2
20 0 3 3 1 0 3 3 1 1 1 ... 2 2 2 2 3 2 2
21 0 2 2 2 0 2 1 2 1 1 ... 2 2 2 2 2 2 2
22 0 2 2 1 0 2 2 2 1 1 ... 3 3 2 2 3 2 2
23 0 3 2 2 0 2 2 2 1 1 ... 2 2 2 3 1 2 2
24 0 2 1 1 0 2 2 1 1 1 ... 2 2 3 2 2 2 2
25 0 2 3 2 1 2 2 3 1 1 ... 2 2 2 2 2 2 2
26 0 2 3 1 0 2 3 3 1 1 ... 2 2 2 2 2 2 2
54 55 56
0 1 2 2
1 2 1 2
2 2 2 2
3 1 2 2
4 2 1 2
5 2 1 2
6 1 2 2
7 1 2 2
8 1 2 2
9 2 2 2
10 2 2 1
11 1 2 2
12 1 2 2
13 2 2 2
14 1 2 2
15 1 2 2
16 2 2 1
17 2 1 2
18 2 2 1
19 2 2 1
20 2 2 1
21 1 2 1
22 2 2 1
23 2 2 2
24 2 2 1
25 1 2 2
26 2 2 2
[27 rows x 56 columns] 0 1
1 1
2 1
3 1
4 1
5 1
6 1
7 1
8 2
9 2
10 2
11 2
12 2
13 2
14 2
15 2
16 2
17 2
18 3
19 3
20 3
21 3
22 3
23 3
24 3
25 3
26 3
Name: 0, dtype: int64
n1 = 1
n2 = 12
knn_title = 'lung cancer knn Classifier'
KNN_Plot(X, Y, n1, n2, knn_title)
KNeighborsClassifier(n_neighbors=11, weights='distance')
GaussianNB Accuracy:
66.67
---------------------------------
DecisionTree Accuracy:
83.33
---------------------------------
MLP Accuracy:
16.67
---------------------------------
LGR_Accuraccy = LogisticRegressionClf(X, Y)
LogisticRegression Accuracy:
50.0
---------------------------------
