Question

我正在尝试在MNIST示例数据集上实现KNearestNeighbors的使用。

当尝试使用cross_val_predict时，无论我将其保留多长时间，脚本都将继续运行。

有什么我想念的地方吗？

感谢任何反馈。

from sklearn.datasets import fetch_openml
import numpy as np
mnist = fetch_openml('mnist_784', version=1) #Imports the dataset into the notebook

X, y = mnist["data"], mnist["target"]

y=y.astype(np.uint8)
X=X.astype(np.uint8)#For machine learning models to understand the output must be casted to an interger not a string.

X.shape, y.shape

y=y.astype(np.uint8) #For machine learning models to understand the output must be casted to an interger not a string.
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:] #Separate the data into training and testing sets

from sklearn.neighbors import KNeighborsClassifier

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

from sklearn.model_selection import cross_val_predict
from sklearn.metrics import f1_score

y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3)

f1_score(y_train, y_train_knn_pred, average="macro")

Answer 1

使用n_jobs=-1

用于进行计算的CPU数量。除非没有，否则没有1 在joblib.parallel_backend上下文中。 -1表示使用所有处理器

from sklearn.datasets import fetch_openml
import numpy as np
mnist = fetch_openml('mnist_784', version=1) #Imports the dataset into the notebook

X, y = mnist["data"], mnist["target"]
y=y.astype(np.uint8)
X=X.astype(np.uint8)#For machine learning models to understand the output must be casted to an interger not a string.


y=y.astype(np.uint8) #For machine learning models to understand the output must be casted to an interger not a string.
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:] #Separate the data into training and testing sets

from sklearn.neighbors import KNeighborsClassifier

knn_clf = KNeighborsClassifier(n_jobs=-1) # HERE
knn_clf.fit(X_train, y_train) # this took seconds on my macbook pro

from sklearn.model_selection import cross_val_predict
from sklearn.metrics import f1_score

y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_train, cv=3, n_jobs=-1) # AND HERE

f1_score(y_train, y_train_knn_pred, average="macro")

Answer 2

我认为混淆的原因是，KNN算法的拟合调用比预测快得多。从另一个SO帖子：

Why is cross_val_predict so much slower than fit for KNeighborsClassifier?

KNN也称为惰性算法，因为在拟合过程中除了保存输入数据，什么也没有，特别是在全部。

在预测期间，实际发生的距离是每个测试数据点。因此，您可以了解使用 cross_val_predict，KNN必须在验证数据点上进行预测，这会增加计算时间！

因此，当您查看输入大小时，需要大量的计算能力。数据。使用多个cpus或最小化尺寸可能很有用。

如果要使用多个CPU内核，则可以将参数“ n_jobs”传递给cross_val_predict和KNeighborsClassifier，以设置要使用的内核数量。将其设置为-1以使用所有可用的内核

cross_val_predict未完成。没有错误消息

2 个答案: