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
如果你希望有一个数据帧和两个数据帧(不是numpy数组),这应该可以做到:
def split_data(df, train_perc = 0.8):
df['train'] = np.random.rand(len(df)) < train_perc
train = df[df.train == 1]
test = df[df.train == 0]
split_data ={'train': train, 'test': test}
return split_data
其他回答
示例方法选择数据的一部分,您可以先通过传递种子值来打乱数据。
train = df.sample(frac=0.8, random_state=42)
对于测试集,您可以删除通过train DF索引的行,然后重置新DF的索引。
test = df.drop(train_data.index).reset_index(drop=True)
import pandas as pd
from sklearn.model_selection import train_test_split
datafile_name = 'path_to_data_file'
data = pd.read_csv(datafile_name)
target_attribute = data['column_name']
X_train, X_test, y_train, y_test = train_test_split(data, target_attribute, test_size=0.8)
上面有很多很好的答案,所以我只想再加一个例子,在这种情况下,你想通过使用numpy库来指定火车和测试集的确切样本数量。
# set the random seed for the reproducibility
np.random.seed(17)
# e.g. number of samples for the training set is 1000
n_train = 1000
# shuffle the indexes
shuffled_indexes = np.arange(len(data_df))
np.random.shuffle(shuffled_indexes)
# use 'n_train' samples for training and the rest for testing
train_ids = shuffled_indexes[:n_train]
test_ids = shuffled_indexes[n_train:]
train_data = data_df.iloc[train_ids]
train_labels = labels_df.iloc[train_ids]
test_data = data_df.iloc[test_ids]
test_labels = data_df.iloc[test_ids]
有许多方法可以创建训练/测试甚至验证样本。
案例1:没有任何选项的经典方法train_test_split:
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.3)
案例2:非常小的数据集(<500行):为了通过这种交叉验证获得所有行的结果。最后,您将对可用训练集的每一行都有一个预测。
from sklearn.model_selection import KFold
kf = KFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
reg = RandomForestRegressor(n_estimators=50, random_state=0)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
clf = reg.fit(X_train, y_train)
y_hat = clf.predict(X_test)
y_hat_all.append(y_hat)
案例3a:用于分类的不平衡数据集。下面是情形1的等价解:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, test_size=0.3)
案例3b:用于分类的不平衡数据集。在情形2之后,等价解如下:
from sklearn.model_selection import StratifiedKFold
kf = StratifiedKFold(n_splits=10, random_state=0)
y_hat_all = []
for train_index, test_index in kf.split(X, y):
reg = RandomForestRegressor(n_estimators=50, random_state=0)
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
clf = reg.fit(X_train, y_train)
y_hat = clf.predict(X_test)
y_hat_all.append(y_hat)
案例4:你需要在大数据上创建一个训练/测试/验证集来调优超参数(60%训练,20%测试和20% val)。
from sklearn.model_selection import train_test_split
X_train, X_test_val, y_train, y_test_val = train_test_split(X, y, test_size=0.6)
X_test, X_val, y_test, y_val = train_test_split(X_test_val, y_test_val, stratify=y, test_size=0.5)
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:]