我正在进行具有两个数据帧的机器学习计算 - 一个用于因子,另一个用于目标值。我必须将它们分成训练和测试部分。在我看来,我找到了方法,但我正在寻找更优雅的解决方案。这是我的代码:
import pandas as pd
import numpy as np
import random
df_source = pd.DataFrame(np.random.randn(5,2),index = range(0,10,2), columns=list('AB'))
df_target = pd.DataFrame(np.random.randn(5,2),index = range(0,10,2), columns=list('CD'))
rows = np.asarray(random.sample(range(0, len(df_source)), 2))
df_source_train = df_source.iloc[rows]
df_source_test = df_source[~df_source.index.isin(df_source_train.index)]
df_target_train = df_target.iloc[rows]
df_target_test = df_target[~df_target.index.isin(df_target_train.index)]
print('rows')
print(rows)
print('source')
print(df_source)
print('source train')
print(df_source_train)
print('source_test')
print(df_source_test)
----编辑 - ubuntu解决方案(修改)---
np.random.seed(2013)
percentile = .6
rows = np.random.binomial(1, percentile, size=len(df_source)).astype(bool)
df_source_train = df_source[rows]
df_source_test = df_source[~rows]
df_target_train = df_target[rows]
df_target_test = df_target[~rows]
如果你使rows
成为长度为len(df)
的布尔数组,那么你可以使用True
得到df[rows]
行并使用False
获取df[~rows]
行:
import pandas as pd
import numpy as np
import random
np.random.seed(2013)
df_source = pd.DataFrame(
np.random.randn(5, 2), index=range(0, 10, 2), columns=list('AB'))
rows = np.random.randint(2, size=len(df_source)).astype('bool')
df_source_train = df_source[rows]
df_source_test = df_source[~rows]
print(rows)
# [ True True False True False]
# if for some reason you need the index values of where `rows` is True
print(np.where(rows))
# (array([0, 1, 3]),)
print(df_source)
# A B
# 0 0.279545 0.107474
# 2 0.651458 -1.516999
# 4 -1.320541 0.679631
# 6 0.833612 0.492572
# 8 1.555721 1.741279
print(df_source_train)
# A B
# 0 0.279545 0.107474
# 2 0.651458 -1.516999
# 6 0.833612 0.492572
print(df_source_test)
# A B
# 4 -1.320541 0.679631
# 8 1.555721 1.741279
下面你可以找到我的解决方案,它不涉及任何额外的变量。
.sample
方法获取数据样本.index
方法来获取索引slice()
应用dataframe
ing例如。假设您有X和Y,并且您希望每个都获得10个样本。当然,它应该是相同的样本
X_sample = X.sample(10)
y_sample = y[X_sample.index]