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%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

# ¼ÓÔØÊý¾Ý
data = pd.read_csv('datasets/pima-indians-diabetes/diabetes.csv')
print('dataset shape {}'.format(data.shape))
data.head()

dataset shape (768, 9)

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	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1

data.groupby("Outcome").size()

Outcome
0    500
1    268
dtype: int64

X = data.iloc[:, 0:8]
Y = data.iloc[:, 8]
print('shape of X {}; shape of Y {}'.format(X.shape, Y.shape))

shape of X (768, 8); shape of Y (768,)

from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2);

from sklearn.neighbors import KNeighborsClassifier, RadiusNeighborsClassifier

models = []
models.append(("KNN", KNeighborsClassifier(n_neighbors=2)))
models.append(("KNN with weights", KNeighborsClassifier(
    n_neighbors=2, weights="distance")))
models.append(("Radius Neighbors", RadiusNeighborsClassifier(
    n_neighbors=2, radius=500.0)))

results = []
for name, model in models:
    model.fit(X_train, Y_train)
    results.append((name, model.score(X_test, Y_test)))
for i in range(len(results)):
    print("name: {}; score: {}".format(results[i][0],results[i][1]))

name: KNN; score: 0.7077922077922078
name: KNN with weights; score: 0.6428571428571429
name: Radius Neighbors; score: 0.6753246753246753

from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score

results = []
for name, model in models:
    kfold = KFold(n_splits=10)
    cv_result = cross_val_score(model, X, Y, cv=kfold)
    results.append((name, cv_result))
for i in range(len(results)):
    print("name: {}; cross val score: {}".format(
        results[i][0],results[i][1].mean()))

name: KNN; cross val score: 0.7147641831852358
name: KNN with weights; cross val score: 0.6770505809979495
name: Radius Neighbors; cross val score: 0.6497265892002735

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knn = KNeighborsClassifier(n_neighbors=2)
knn.fit(X_train, Y_train)
train_score = knn.score(X_train, Y_train)
test_score = knn.score(X_test, Y_test)
print("train score: {}; test score: {}".format(train_score, test_score))

train score: 0.8501628664495114; test score: 0.7077922077922078

from sklearn.model_selection import ShuffleSplit
from common.utils import plot_learning_curve

knn = KNeighborsClassifier(n_neighbors=2)
cv = ShuffleSplit(n_splits=10, test_size=0.2, random_state=0)
plt.figure(figsize=(10, 6), dpi=200)
plot_learning_curve(plt, knn, "Learn Curve for KNN Diabetes", 
                    X, Y, ylim=(0.0, 1.01), cv=cv);

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from sklearn.feature_selection import SelectKBest

selector = SelectKBest(k=2)
X_new = selector.fit_transform(X, Y)
X_new[0:5]

array([[148. ,  33.6],
       [ 85. ,  26.6],
       [183. ,  23.3],
       [ 89. ,  28.1],
       [137. ,  43.1]])

results = []
for name, model in models:
    kfold = KFold(n_splits=10)
    cv_result = cross_val_score(model, X_new, Y, cv=kfold)
    results.append((name, cv_result))
for i in range(len(results)):
    print("name: {}; cross val score: {}".format(
        results[i][0],results[i][1].mean()))

name: KNN; cross val score: 0.725205058099795
name: KNN with weights; cross val score: 0.6900375939849623
name: Radius Neighbors; cross val score: 0.6510252904989747

# »­³öÊý¾Ý
plt.figure(figsize=(10, 6), dpi=200)
plt.ylabel("BMI")
plt.xlabel("Glucose")
plt.scatter(X_new[Y==0][:, 0], X_new[Y==0][:, 1], c='r', s=20, marker='o');         # »­³öÑù±¾
plt.scatter(X_new[Y==1][:, 0], X_new[Y==1][:, 1], c='g', s=20, marker='^');         # »­³öÑù±¾