SVM_kernel_RBF.py

# Import packages to do the classifying
import numpy as np
from sklearn import svm
import pandas as pd
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC
from sklearn import metrics
from sklearn.model_selection import train_test_split

# Object để encode features data
enc = OrdinalEncoder()
# Object để encode label data
lb_enc = LabelEncoder()

# Lấy dữ liệu từ file csv
rawData = pd.read_csv('dataset4classfication.csv', header = 0)
# Thay thế các giá trị NaN bằng Mean
rawData.fillna(rawData.mean())
# Lấy dữ liệu từ các cột features
tranningData = rawData[rawData.columns[0:13]]

# Lấy dữ liệu cột label
labels = rawData['Label']

# Encode trainning data to number
enc.fit(tranningData)
tranningData = enc.transform(tranningData)
scaler = MinMaxScaler()  
tranningData = scaler.fit_transform(tranningData)
# Encode labels to number
lb_enc.fit(labels)
labels = lb_enc.transform(labels)

# Tạo tập dữ liệu training và tập dữ liệu test
X_train, X_test, y_train, y_test = train_test_split(tranningData,labels,test_size = 0.20)


svclassifier = SVC(kernel='rbf', gamma=0.5)
# Training dữ liệu
svclassifier.fit(X_train, y_train)

# Dự đoán
y_pred = svclassifier.predict(X_test)

# Model Accuracy: how often is the classifier correct?
print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
# Compute the precision
# The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives.
# average="micro" : Calculate metrics globally by counting the total true positives, false negatives and false positives.
print("Precision score: ", metrics.precision_score(y_test, y_pred, average="micro"))
# Compute the recall
# The recall is the ratio tp / (tp + fn) where tp is the number of true positives and fn the number of false negatives
# average="macro": Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
print("Recall score: ", metrics.recall_score(y_test, y_pred, average="macro"))
# Compute confusion matrix
print("Confusion Matrix: \n", metrics.confusion_matrix(y_test, y_pred))