svm.py

#  #  Attribute                     Domain
#    -- -----------------------------------------
#    1. Sample code number            id number
#    2. Clump Thickness               1 - 10
#    3. Uniformity of Cell Size       1 - 10
#    4. Uniformity of Cell Shape      1 - 10
#    5. Marginal Adhesion             1 - 10
#    6. Single Epithelial Cell Size   1 - 10
#    7. Bare Nuclei                   1 - 10
#    8. Bland Chromatin               1 - 10
#    9. Normal Nucleoli               1 - 10
#   10. Mitoses                       1 - 10
#   11. Class:                        (2 for benign, 4 for malignant)

import io
import numpy as np
from sklearn import preprocessing, model_selection, svm
import pandas as pd
import requests

res = requests.get('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data')
dataset = res.text

df = pd.read_csv(io.StringIO(dataset), names=['id','clum_thickness','unif_cell_size','unif_cell_shape','marg_adhesion','single_epith_cell_size','bare_nuclei','bland_chrom','norm_nucleoi','mitoses','class'
])
df.replace('?', -99999, inplace=True)
df.drop(['id'], 1, inplace=True)

X = np.array(df.drop(['class'], 1))
y = np.array(df['class'])

X_train, X_test, y_train, y_test = model_selection.train_test_split(X,y,test_size=0.2)

clf = svm.SVC(kernel="linear")
clf.fit(X_train, y_train)

accuracy = clf.score(X_test, y_test)
print(accuracy)

example_measures = np.array([[4,2,1,1,1,2,3,2,1], [4,2,1,2,2,2,3,2,1]])
example_measures = example_measures.reshape(len(example_measures), -1)

prediction = clf.predict(example_measures)
print(prediction)