Question

我有11784条记录的数据分为测试（2946）和火车（8838）以运行h20算法，但得到了与我试图创建的数据框相关的错误，作为链接预测的最终输出和预测的ID。

此行错误：
df_y_test＆lt; - data.frame（ID = df_labels，Status = df_y_test $ predict）

data.frame出错（ID = df_labels，Status = df_y_test $ predict）：参数意味着行数不同：2946,2950

通过论坛查看并了解df_y_test中的行数是2950导致了这一点，但无法弄清楚为什么因为df_y_test也来自同一个'test'变量整体只有2946行 - 会请为任何指导感到高兴，请在下面发布完整的脚本以供参考

数据：117个变量的11784个障碍测试：4546个变量2946个火车：8838障碍46个变量
df_labels：2946个1变量的blind df_y_test：4个变量2950个障碍

# Load Data
data <- read.csv('Data.csv')

# Partition Data 
library(caTools)
set.seed(75)
split <- sample.split(data$Status, SplitRatio = 0.75)
train <- subset(data, split == TRUE)
test <- subset(data, split == FALSE)

# Dropping the column to be predicted from Test 
test <- subset(test[,-c(2)])

library(readr)
library(h2o)

# Init h2o
localh2o <- h2o.init(max_mem_size = '2g', nthreads = -1)

# convert status values (to be predicted) in second column to factors in h2o
train[,2] <- as.factor(train[,2])
train_h2o <- as.h2o(train)
test_h2o <- as.h2o(test)

# Running H2O
model <- h2o.deeplearning(x=c(1, 3:46), 
                       y=2,
                       training_frame = train_h2o,
                       activation = "RectifierWithDropout", 
                       input_dropout_ratio = 0.2,
                       hidden_dropout_ratios = c(0.5, 0.5),
                       balance_classes = TRUE,
                       hidden = c(100,100),
                       nesterov_accelerated_gradient = T,
                       epochs = 15 ) 

h2o_y_test <- h2o.predict(model, test_h2o)

# Converting to data frames from h2o
df_y_test <- as.data.frame(h2o_y_test)
df_labels <- as.data.frame(test[,1])
df_y_test <- data.frame(ID = df_labels, Status = df_y_test$predict)

write.csv(df_y_test, file="predictionsH2o.csv", row.names = FALSE)

h2o.shutdown(prompt = FALSE)

R Dataframes：参数意味着行数不同

0 个答案: