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这是一个创建于 2377 天前的主题，其中的信息可能已经有所发展或是发生改变。
本人准备学习 python 和机器学习，刚刚搭建好环境，从书上抄了一段程序试验一下，无奈运行了两天还没出结果。CPU 占用率一直接近 100%。请各位帮忙看一下是我的程序有问题呢还是真的没运行完？大概需要多少时间？我的配置是 E5-2650,8 核 16 线程，主频好像是 2.0G ，8G 内存。
程序如下：
# Load libraries
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import validation_curve
# Load data
digits = load_digits()
# Create feature matrix and target vector
features, target = digits.data, digits.target
# Create range of values for parameter
#param_range = np.arange(1, 250, 2)
param_range = np.arange(1, 250, 25)
# Calculate accuracy on training and test set using range of parameter values
train_scores, test_scores = validation_curve(
# Classifier
RandomForestClassifier(),
# Feature matrix
features,
# Target vector
target,
# Hyperparameter to examine
param_name="n_estimators",
# Range of hyperparameter's values
param_range=param_range,
# Number of folds
cv=3,
# Performance metric
scoring="accuracy",
# Use all computer cores
n_jobs=-1)
# Calculate mean and standard deviation for training set scores
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
# Calculate mean and standard deviation for test set scores
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
# Plot mean accuracy scores for training and test sets
plt.plot(param_range, train_mean, label="Training score", color="black")
plt.plot(param_range, test_mean, label="Cross-validation score",
color="dimgrey")
# Plot accurancy bands for training and test sets
plt.fill_between(param_range, train_mean - train_std,
train_mean + train_std, color="gray")
plt.fill_between(param_range, test_mean - test_std,
test_mean + test_std, color="gainsboro")
# Create plot
plt.title("Validation Curve With Random Forest")
plt.xlabel("Number Of Trees")
plt.ylabel("Accuracy Score")
plt.tight_layout()
plt.legend(loc="best")
plt.show()
程序实际上就一个函数 validation_curve，就是看一下不同的超参数对随机森林决策精度的影响。
程序如下：
# Load libraries
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_digits
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import validation_curve
# Load data
digits = load_digits()
# Create feature matrix and target vector
features, target = digits.data, digits.target
# Create range of values for parameter
#param_range = np.arange(1, 250, 2)
param_range = np.arange(1, 250, 25)
# Calculate accuracy on training and test set using range of parameter values
train_scores, test_scores = validation_curve(
# Classifier
RandomForestClassifier(),
# Feature matrix
features,
# Target vector
target,
# Hyperparameter to examine
param_name="n_estimators",
# Range of hyperparameter's values
param_range=param_range,
# Number of folds
cv=3,
# Performance metric
scoring="accuracy",
# Use all computer cores
n_jobs=-1)
# Calculate mean and standard deviation for training set scores
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
# Calculate mean and standard deviation for test set scores
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
# Plot mean accuracy scores for training and test sets
plt.plot(param_range, train_mean, label="Training score", color="black")
plt.plot(param_range, test_mean, label="Cross-validation score",
color="dimgrey")
# Plot accurancy bands for training and test sets
plt.fill_between(param_range, train_mean - train_std,
train_mean + train_std, color="gray")
plt.fill_between(param_range, test_mean - test_std,
test_mean + test_std, color="gainsboro")
# Create plot
plt.title("Validation Curve With Random Forest")
plt.xlabel("Number Of Trees")
plt.ylabel("Accuracy Score")
plt.tight_layout()
plt.legend(loc="best")
plt.show()
程序实际上就一个函数 validation_curve，就是看一下不同的超参数对随机森林决策精度的影响。
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1
princelai 2018-05-23 10:46:06 +08:00
正好我有环境，帮你试了下，，源代码一点没动，2 秒出结果。。。。。。你是不是 python 环境有问题？
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2
princelai 2018-05-23 10:50:18 +08:00
贴出我的环境，供你参考
pd.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.6.5.final.0 python-bits: 64 OS: Linux OS-release: 4.14.42-1-MANJARO machine: x86_64 processor: byteorder: little LC_ALL: None LANG: zh_CN.UTF-8 LOCALE: zh_CN.UTF-8 pandas: 0.23.0 pytest: 3.5.1 pip: 10.0.1 setuptools: 39.2.0 Cython: 0.28.2 numpy: 1.14.3 scipy: 1.1.0 pyarrow: None xarray: None IPython: 6.4.0 sphinx: 1.7.4 patsy: 0.5.0 dateutil: 2.7.3 pytz: 2018.4 blosc: None bottleneck: 1.2.1 tables: 3.4.3 numexpr: 2.6.5 feather: None matplotlib: 2.2.2 openpyxl: 2.5.3 xlrd: 1.1.0 xlwt: 1.3.0 xlsxwriter: 1.0.4 lxml: 4.2.1 bs4: 4.6.0 html5lib: 1.0.1 sqlalchemy: 1.2.7 pymysql: None psycopg2: None jinja2: 2.10 s3fs: None fastparquet: None pandas_gbq: None pandas_datareader: None |
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3
acone2003 OP 多谢 princelai，我找找原因。
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4
John60676 2018-05-23 18:21:05 +08:00
macbook pro 2015 8G，也是秒出结果
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5
neosfung 2018-05-23 19:03:32 +08:00 via iPhone
load_digits 是否会下载数据集呢
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