for dataset in data_cleaner: #用中位数填充 dataset['Age'].fillna(dataset['Age'].median(), inplace = True) dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace = True) dataset['Fare'].fillna(dataset['Fare'].median(), inplace = True) #删除部分数据 drop_column = ['...
Pro Tip:For programmatic access, use the Kaggle API integration: from kaggle.api.kaggle_api_extended import KaggleApi api = KaggleApi() api.authenticate() api.dataset_download_files(‘dataset_owner/dataset_name’) Step 5. Understand the Data ...
label = torch.tensor([x[-1] for x in data]) return input_ids, attention_mask, token_type_ids, label 最后定义Dataset,需要将多选项转为单个选择和问题的匹配过程。 import torch from torch.utils.data import Dataset, DataLoader, TensorDataset class TextDataset(Dataset): def __init__(self, data...
from.read_data importDataSourceimport randomclassTrain_Dataset(Dataset)
# Readinthe datasetasa dataframetrain=pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')test=pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')train.shape,test.shape Output[3]: ((1460, 81), (1459, 80)) ...
# Moving the continent column's position in the dataset to the second column Happiness <- Happiness %>% select(Country,Continent, everything()) # Changing Continent column to factor Happiness$Continent<- as.factor(Happiness$Continent) str(Happiness) ...
for dataset in full_data: dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1 基于特征FamilySize创建新的特征IsAlone,因为一个人的话,顾虑没有那么多,只需要管好自己,生存的几率会大点,其中又分‘male’和‘female’,因为我记得电影中是有这样的一句台词“让女人和小孩先走”,所以,我们...
for dataset in data_cleaner: #用中位数填充 dataset['Age'].fillna(dataset['Age'].median(), inplace = True) dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace = True) dataset['Fare'].fillna(dataset['Fare'].median(), inplace = True) ...
print(data_val.isnull().sum()) 创建家庭成员人数,独身,头衔,费用bin和年级bin等特征 min_num = 10 for dataset in data_cleaner: # 添加家庭人口,是否单身,将价格和年龄划分区间特征 dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1 ...
anonymized all essays and extracted tf-idf features with the same feature extractor with the training dataset. Then, I trained light GBM models to predict external scores. the predicted external scores (x-axis) are well correlated with the scores of this competition (y-axis). ...