機械学習におけるpopの役割

Question

/mnist/train.csvというファイルを学習させて、画像内の数字を判別する機械学習したいのですが、
以下の部分の処理がわかりません。なぜ「２」という数字が出てきたのでしょうか？

また、cols.pop(0)の部分は０番目の要素を削除するということだと思うのですが、なぜそうする必要があるのでしょうか？

 if len(cols) < 2: continue
            labels.append(int(cols.pop(0)))

元のコード

from sklearn import cross_validation, svm, metrics

# CSVファイルを読んで学習用データに整形 --- (※1)
def load_csv(fname):
    labels = []
    images = []
    with open(fname, "r") as f:
        for line in f:
            cols = line.split(",")
            if len(cols) < 2: continue
            labels.append(int(cols.pop(0)))
            vals = list(map(lambda n: int(n) / 256, cols))
            images.append(vals)
    return {"labels":labels, "images":images}

data = load_csv("./mnist/train.csv")
test = load_csv("./mnist/t10k.csv")

# 学習 --- (※2)
clf = svm.SVC()
clf.fit(data["images"], data["labels"])

# 予測 --- (※3)
predict = clf.predict(test["images"])

# 結果がどの程度合っていたか確認 --- (※4)
ac_score = metrics.accuracy_score(test["labels"], predict)
cl_report = metrics.classification_report(test["labels"], predict)
print("正解率=", ac_score)
print("レポート=")
print(cl_report)

ちなみにcsvは以下のように
なっており、一つの要素は０〜２５６の数字で表されています。
一つの行が一つの画像を表しています。

5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,18,18,18,126,136,175,26,166,255,247,127,0,0,0,0,0,0,0,0,0,0,0,0,30,36,94,154,170,253,253,253,253,253,225,172,253,242,195,64,0,0,0,0,0,0,0,0,0,0,0,49,238,253,253,253,253,253,253,253,253,251,93,82,82,56,39,0,0,0,0,0,0,0,0,0,0,0,0,18,219,253,253,253,253,253,198,182,247,241,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,80,156,107,253,253,205,11,0,43,154,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,14,1,154,253,90,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,139,253,190,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,11,190,253,70,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,35,241,225,160,108,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,81,240,253,253,119,25,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,45,186,253,253,150,27,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,16,93,252,253,187,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,249,253,249,64,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,46,130,183,253,253,207,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,39,148,229,253,253,253,250,182,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,114,221,253,253,253,253,201,78,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,23,66,213,253,253,253,253,198,81,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,18,171,219,253,253,253,253,195,80,9,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,55,172,226,253,253,253,253,244,133,11,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,136,253,253,253,212,135,132,16,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,51,159,253,159,50,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,48,238,252,252,252,237,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,54,227,253,252,239,233,252,57,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,10,60,224,252,253,252,202,84,252,253,122,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,163,252,252,252,253,252,252,96,189,253,167,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,51,238,253,253,190,114,253,228,47,79,255,168,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,48,238,252,252,179,12,75,121,21,0,0,253,243,50,0,0,0,0,0,0,0,0,0,0,0,0,0,38,165,253,233,208,84,0,0,0,0,0,0,253,252,165,0,0,0,0,0,0,0,0,0,0,0,0,7,178,252,240,71,19,28,0,0,0,0,0,0,253,252,195,0,0,0,0,0,0,0,0,0,0,0,0,57,252,252,63,0,0,0,0,0,0,0,0,0,253,252,195,0,0,0,0,0,0,0,0,0,0,0,0,198,253,190,0,0,0,0,0,0,0,0,0,0,255,253,196,0,0,0,0,0,0,0,0,0,0,0,76,246,252,112,0,0,0,0,0,0,0,0,0,0,253,252,148,0,0,0,0,0,0,0,0,0,0,0,85,252,230,25,0,0,0,0,0,0,0,0,7,135,253,186,12,0,0,0,0,0,0,0,0,0,0,0,85,252,223,0,0,0,0,0,0,0,0,7,131,252,225,71,0,0,0,0,0,0,0,0,0,0,0,0,85,252,145,0,0,0,0,0,0,0,48,165,252,173,0,0,0,0,0,0,0,0,0,0,0,0,0,0,86,253,225,0,0,0,0,0,0,114,238,253,162,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,85,252,249,146,48,29,85,178,225,253,223,167,56,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,85,252,252,252,229,215,252,252,252,196,130,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,199,252,252,253,252,252,233,145,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,25,128,252,253,252,141,37,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
4,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,・・・・・・・・・・・

Answer 1

各行の１番目(cols[0])はラベル、それ以降は画像データです。
ラベル部分を取り除いて画像データだけを抽出するためにcols.pop(0)が使われています。
labels.append(int(cols.pop(0)))#<-----cols[0]を取り出してlabelsに追加し、cols[0]を削除している。

可読性を考えると、ラベル・画像の位置を明示的に与え、colsそのものに変更を加えないほうがよいでしょう。
例：
labels.append(int(cols[0]))
vals = list(map(lambda n: int(n) / 256, cols[1::]))
images.append(vals)

Comments

[renren643]

回答ありがとうございます。
len(cols) < 2: continueの２はどういう意味でしょうか？

[WathMorks]

csvの最後の行を見ないとはっきりしたことは言えませんが、おそらく意味はないと思います。
試しにその部分を取り除いて実行してみてください。