ch6-F1-score.py

from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline

import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)

from sklearn.preprocessing import LabelEncoder
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEncoder()
y = le.fit_transform(y)

from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=1)

from sklearn.svm import SVC
pipe_svc = Pipeline([('scl', StandardScaler()),  ('clf', SVC(random_state=1))])


pipe_svc.fit(X_train, y_train)
y_pred = pipe_svc.predict(X_test)

from sklearn.metrics import precision_score
from sklearn.metrics  import recall_score, f1_score

print('Precision: %.3f' % precision_score(y_true=y_test, y_pred=y_pred))
print('Recall: %.3f' % recall_score(y_true=y_test, y_pred=y_pred))
print('F1: %.3f' % f1_score(y_true=y_test, y_pred=y_pred))