python plotエラー|: Jupyter notebook上でPythonを実行しています。

python plotエラー|: Jupyter notebook上でPythonを実行しています。

交差検証を行い、True Positive rateを求め、その値をプロットしたいです。 #レポート課題#データの読み込みimport numpy as npimport pandas.

Jupyter notebook上でPythonを実行しています。 

交差検証を行い、True Positive rateを求め、その値をプロットしたいです。

#レポート課題
#データの読み込み
import numpy as np
import pandas as pd
#読み込みと削除
pima_tr = pd.read_csv('data2/pima_tr.csv' , encoding='UTF-8' , index_col=0)
pima_te = pd.read_csv('data2/pima_te.csv' , encoding='UTF-8' , index_col=0)
#結合
group_data = pd.concat([pima_tr, pima_te], ignore_index = True)

#モジュール読み込み
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix

## データのスケーリングとtrainデータとtestデータに分ける
X = preprocessing.scale(group_data[["npreg","glu","bp","skin","bmi","ped","age"]])
y = group_data.type
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.7)

knn = KNeighborsClassifier()
knn.fit(X_train, y_train)

from sklearn import linear_model
clf = linear_model.LogisticRegression()    

neighbors = list(range(2, 50))
mean_score= list()
for k in neighbors:
    scores = cross_val_score(clf, X, y, cv=k)
    mean_score.append(np.mean(scores))
    y_pred = knn.predict(X_train)
    cmat = confusion_matrix(y_train, y_pred)
    #True Positive rateの計算
    tpr=cmat[1,0]/(cmat[1,0]+cmat[1,1])
    tpr_scores.append(tpr)

#最適なk(True Positive rateの計算が最大)の表示
import matplotlib.pyplot as plt
%matplotlib inline
optimal_k = neighbors[tpr_scores.index(max(filter(lambda v: v <1 , tpr_scores)))]
print("The best number of k is %d." % optimal_k)

## 結果の可視化
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(neighbors, tpr_scores)
ax.set_xlabel('Number of Neighbors K')
ax.set_ylabel('True Positive rate')

python

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