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
将df分成训练,验证,测试。给定增广数据的df,只选择相关列和独立列。将最近的10%的行(使用'dates'列)分配给test_df。随机将剩余行的10%分配给validate_df,其余的分配给train_df。不要重新索引。检查所有行是否都是唯一分配的。只使用本地蟒和熊猫库。
方法1:将行分割为训练、验证、测试数据框架。
train_df = augmented_df[dependent_and_independent_columns]
test_df = train_df.sort_values('dates').tail(int(len(augmented_df)*0.1)) # select latest 10% of dates for test data
train_df = train_df.drop(test_df.index) # drop rows assigned to test_df
validate_df = train_df.sample(frac=0.1) # randomly assign 10%
train_df = train_df.drop(validate_df.index) # drop rows assigned to validate_df
assert len(augmented_df) == len(set(train_df.index).union(validate_df.index).union(test_df.index)) # every row must be uniquely assigned to a df
方法2:当validate必须是train的子集时拆分行(fastai)
train_validate_test_df = augmented_df[dependent_and_independent_columns]
test_df = train_validate_test_df.loc[augmented_df.sort_values('dates').tail(int(len(augmented_df)*0.1)).index] # select latest 10% of dates for test data
train_validate_df = train_validate_test_df.drop(test_df.index) # drop rows assigned to test_df
validate_df = train_validate_df.sample(frac=validate_ratio) # assign 10% to validate_df
train_df = train_validate_df.drop(validate_df.index) # drop rows assigned to validate_df
assert len(augmented_df) == len(set(train_df.index).union(validate_df.index).union(test_df.index)) # every row must be uniquely assigned to a df
# fastai example usage
dls = fastai.tabular.all.TabularDataLoaders.from_df(
train_validate_df, valid_idx=train_validate_df.index.get_indexer_for(validate_df.index))
其他回答
我会用K-fold交叉验证。 它已被证明比train_test_split提供更好的结果。下面是一篇关于如何在sklearn中应用它的文章,来自文档本身:https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html
在我的例子中,我想用特定的数字分割训练、测试和开发中的数据帧。我在这里分享我的解决方案
首先,为数据帧分配一个唯一的id(如果已经不存在的话)
import uuid
df['id'] = [uuid.uuid4() for i in range(len(df))]
以下是我的分割数字:
train = 120765
test = 4134
dev = 2816
分裂函数
def df_split(df, n):
first = df.sample(n)
second = df[~df.id.isin(list(first['id']))]
first.reset_index(drop=True, inplace = True)
second.reset_index(drop=True, inplace = True)
return first, second
现在分成培训,测试,开发
train, test = df_split(df, 120765)
test, dev = df_split(test, 4134)
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]
如果你想把它分成训练集、测试集和验证集,你可以使用这个函数:
from sklearn.model_selection import train_test_split
import pandas as pd
def train_test_val_split(df, test_size=0.15, val_size=0.45):
temp, test = train_test_split(df, test_size=test_size)
total_items_count = len(df.index)
val_length = total_items_count * val_size
new_val_propotion = val_length / len(temp.index)
train, val = train_test_split(temp, test_size=new_val_propotion)
return train, test, val
这是我在需要分割数据帧时所写的。我考虑过使用上面安迪的方法,但不喜欢我不能精确地控制数据集的大小(例如,有时是79,有时是81,等等)。
def make_sets(data_df, test_portion):
import random as rnd
tot_ix = range(len(data_df))
test_ix = sort(rnd.sample(tot_ix, int(test_portion * len(data_df))))
train_ix = list(set(tot_ix) ^ set(test_ix))
test_df = data_df.ix[test_ix]
train_df = data_df.ix[train_ix]
return train_df, test_df
train_df, test_df = make_sets(data_df, 0.2)
test_df.head()