我有一个数据框架形式的相当大的数据集,我想知道我如何能够将数据框架分成两个随机样本(80%和20%)进行训练和测试。
谢谢!
我有一个数据框架形式的相当大的数据集,我想知道我如何能够将数据框架分成两个随机样本(80%和20%)进行训练和测试。
谢谢!
当前回答
像这样从df中选择range row
row_count = df.shape[0]
split_point = int(row_count*1/5)
test_data, train_data = df[:split_point], df[split_point:]
其他回答
您可以使用df.as_matrix()函数并创建Numpy-array并传递它。
Y = df.pop()
X = df.as_matrix()
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2)
model.fit(x_train, y_train)
model.test(x_test)
如果你需要根据你的数据集中的lables列来分割你的数据,你可以使用这个:
def split_to_train_test(df, label_column, train_frac=0.8):
train_df, test_df = pd.DataFrame(), pd.DataFrame()
labels = df[label_column].unique()
for lbl in labels:
lbl_df = df[df[label_column] == lbl]
lbl_train_df = lbl_df.sample(frac=train_frac)
lbl_test_df = lbl_df.drop(lbl_train_df.index)
print '\n%s:\n---------\ntotal:%d\ntrain_df:%d\ntest_df:%d' % (lbl, len(lbl_df), len(lbl_train_df), len(lbl_test_df))
train_df = train_df.append(lbl_train_df)
test_df = test_df.append(lbl_test_df)
return train_df, test_df
并使用它:
train, test = split_to_train_test(data, 'class', 0.7)
如果你想控制分割随机性或使用一些全局随机种子,你也可以传递random_state。
您需要将pandas数据帧转换为numpy数组,然后将numpy数组转换回数据帧
import pandas as pd
df=pd.read_csv('/content/drive/My Drive/snippet.csv', sep='\t')
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.2)
train1=pd.DataFrame(train)
test1=pd.DataFrame(test)
train1.to_csv('/content/drive/My Drive/train.csv',sep="\t",header=None, encoding='utf-8', index = False)
test1.to_csv('/content/drive/My Drive/test.csv',sep="\t",header=None, encoding='utf-8', index = False)
我将使用scikit-learn自己的training_test_split,并从索引生成它
from sklearn.model_selection import train_test_split
y = df.pop('output')
X = df
X_train,X_test,y_train,y_test = train_test_split(X.index,y,test_size=0.2)
X.iloc[X_train] # return dataframe train
我会使用numpy的randn:
In [11]: df = pd.DataFrame(np.random.randn(100, 2))
In [12]: msk = np.random.rand(len(df)) < 0.8
In [13]: train = df[msk]
In [14]: test = df[~msk]
为了证明这是有效的:
In [15]: len(test)
Out[15]: 21
In [16]: len(train)
Out[16]: 79