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# 言語処理100本ノック 2020 (Rev 2) 第6章: 機械学習 54. 正解率の計測

2023/01/14に公開約3,000字

### 問題

#### 54. 正解率の計測

52で学習したロジスティック回帰モデルの正解率を，学習データおよび評価データ上で計測せよ．

solution54.py
``````import pandas as pd
import numpy as np
import re
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

def preprocessing(data):
x = []
y = []
label = {"b":0, "e":1, "t":2, "m":3}

for title, category in data:
title = re.sub("[0-9]+", "0", title)
x.append(title.lower())
y.append(label[category])

return x,y

def score(model, X):
pred = model.predict([X])
pred_proba = model.predict_proba([X])[0, pred]
return pred[0], pred_proba[0]

np.random.seed(123)
df = pd.read_csv("chapter06/NewsAggregatorDataset/newsCorpora.csv", header=None, sep="\t", names=["ID", "TITLE", "URL", "PUBLISHER", "CATEGORY", "STORY", "HOSTNAME", "TIMESTAMP"])
df = df.loc[df["PUBLISHER"].isin(["Reuters", "Huffington Post", "Businessweek", "Contactmusic.com", "Daily Mail"]), ["TITLE", "CATEGORY"]]

train, valid_test = train_test_split(df, test_size=0.2, shuffle=True)
valid, test = train_test_split(valid_test, test_size=0.5, shuffle=True)

train = np.array(train)
valid = np.array(valid)
test = np.array(test)

X_train, Y_train = preprocessing(train)
X_valid, Y_valid = preprocessing(valid)
X_test, Y_test = preprocessing(test)

tfidfvectorizer = TfidfVectorizer(min_df=0.001)

X_train = tfidfvectorizer.fit_transform(X_train).toarray()
X_valid = tfidfvectorizer.transform(X_valid).toarray()
X_test = tfidfvectorizer.transform(X_test).toarray()

model = LogisticRegression()
model.fit(X_train, Y_train)

train_pred = []
test_pred = []

for X in X_train:
train_pred.append(score(model, X)[0])

for X in X_test:
test_pred.append(score(model, X)[0])

train_acc = accuracy_score(Y_train, train_pred)
test_acc = accuracy_score(Y_test, test_pred)

print("train accuracy", train_acc)
print("test accuracy",test_acc)
``````
output
``````train accuracy 0.9162293853073463
test accuracy 0.8673163418290855
``````

この問題では、`train_pred`リストと`test_pred`リストは、それぞれ`X_train``X_test`に対して`score`関数を実行して、予測クラスを格納します。`score`関数は、入力データ`X`に対して、モデルが予測したクラスとその確率を返します。`accuracy_score`関数を使用して、`Y_train``train_pred``Y_test``test_pred`の正解率を計算します。最終的に、学習データとテストデータの正解率を表示します。

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