SVM.py

from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn import svm
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from sklearn import metrics
from sklearn.preprocessing import OrdinalEncoder
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import MinMaxScaler
from matplotlib import style
from sklearn.decomposition import PCA

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

# Load dữ liệu
rawData = pd.read_csv('dataset4classfication.csv', header=0)
# Thay thế các giá trị NaN bằng Mean
rawData.fillna(rawData.mean())
# Lấy giá trị các features
tranningData = rawData.iloc[:, 0:13]
# Lấy giá trị của 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 dữ liệu train và test, 80 train, 20 test
X_train, X_test, y_train, y_test = train_test_split(tranningData, labels, test_size=0.2,random_state=109) 
# Tạo svc Classifier
clf = svm.SVC(C=1, kernel='linear') # Linear Kernel

# Train dữ liệu
clf.fit(X_train, y_train)

# Dự đoán
y_pred = clf.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))

print("Coef: ", clf.coef_)