如何迭代GLMS在H2O

Question

我的數據集是這樣的：

rownum a  b  y x 
1  | A | a |1 | a 
2  | B | a |1 | a 
3  | C | a |1 | a 
4  | D | a |0 | b 
5  | E | a |0 | a 
6  | F | a |0 | b 

我希望創建一個基於組織的身份很多h2o.frames。就像這樣：

一個：

rownum a  b  y x 
1  | A | a |1 | a 
2  | D | a |0 | a 
3  | F | a |0 | a 

B：

rownum a  b  y x 
1  | B | a |1 | b 
2  | C | a |1 | b 
3  | E | a |0 | b 

雖然我目前做手工，當我添加更多的組織到數據集變得困難。

我也想將這些h2o.frames推到h2o.glm並迭代保存模型。

"INSERT x NAME HERE" = h2o.glm(y = "y", x = 
c("a","b"), 
training_frame = ITERATE H2O FRAMES HERE, family = 'poisson') 

，然後保存模型

INSERT x NAME HERE <- h2o.saveModel(object= INSERT x NAME 
HERE, force=TRUE) 

我希望得到任何幫助或建議，您可能有。我確實知道GLM中的互動術語，但現在想要這樣做。

Answer 1

由於您沒有直接提供數據，我從上面複製了您的示例作爲R data.frame。

library(h2o) 
h2o.init() 

# Example data as an R data.frame 
df <- data.frame(genes = c("A","B","C","D","E","F"), 
       samples = c("a","a","a","a","a","a"), 
       y = c(1,1,1,0,0,0), 
       tissue = c("Muscle","Brain","Brain","Muscle","Brain","Muscle")) 

# Convert R data.frame to H2OFrame 
hf <- as.h2o(df) 

不過，我認爲你有一個CSV此計算機上的數據，因此在現實中，你會做的是：

# Load data from disk directly into H2O cluster 
hf <- h2o.importFile("tissue_samples.csv") 

現在你已經在H2OFrame數據，只有幾個步驟：

# List of unique tissue types 
tissue_types <- as.list(h2o.unique(hf$tissue)) 

# Create list of frames (one for each tissue type) 
frames <- sapply(tissue_types, function(t) hf[(hf[,"tissue"] == t),]) 

# Set up h2o.glm arguments 
x <- c("genes", "samples") 
y <- "y" 

# List of glms (one for each tissue type) 
glms <- sapply(frames, function(fr) h2o.glm(x = x, y = y, 
         family = "poisson", training_frame = fr)) 

# Save the models 
model_names <- sapply(glms, function(m) h2o.saveModel(m, path = "/Users/me/", force = TRUE)) 

# Look at model names 
print(model_names) 
# [1] "/Users/me/GLM_model_R_1497937770060_222" 
# [2] "/Users/me/GLM_model_R_1497937770060_223"