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从Sparklyr中提取和可视化模型树

[英]Extract and Visualize Model Trees from Sparklyr

 原文  2018-11-02 18:14:14  8   1    r/ apache-spark/ random-forest/ decision-tree/ sparklyr

问题描述

Does anyone have any advice about how to convert the tree information from sparklyr's ml_decision_tree_classifier, ml_gbt_classifier, or ml_random_forest_classifier models into a.) a format that can be understood by other R tree-related libraries and (ultimately) b.) a visualization of the trees for non-technical consumption? 有没有人有任何关于如何将树信息从sparklyr的ml_decision_tree_classifier，ml_gbt_classifier或ml_random_forest_classifier模型转换为。）格式的建议，这种格式可以被其他R树相关的库理解，并且（最终）b。）树的可视化用于非技术消费？ This would include the ability to convert back to the actual feature names from the substituted string indexing values that are produced during the vector assembler. 这将包括从向量汇编器期间生成的替换字符串索引值转换回实际要素名称的能力。

The following code is copied liberally from a sparklyr blog post for the purposes of providing an example: 为了提供一个例子，下面的代码从sparklyr博客文章中大量复制： 

library(sparklyr)
library(dplyr)

# If needed, install Spark locally via `spark_install()`
sc <- spark_connect(master = "local")
iris_tbl <- copy_to(sc, iris)

# split the data into train and validation sets
iris_data <- iris_tbl %>%
  sdf_partition(train = 2/3, validation = 1/3, seed = 123)


iris_pipeline <- ml_pipeline(sc) %>%
  ft_dplyr_transformer(
    iris_data$train %>%
      mutate(Sepal_Length = log(Sepal_Length),
             Sepal_Width = Sepal_Width ^ 2)
  ) %>%
  ft_string_indexer("Species", "label")

iris_pipeline_model <- iris_pipeline %>%
  ml_fit(iris_data$train)

iris_vector_assembler <- ft_vector_assembler(
  sc, 
  input_cols = setdiff(colnames(iris_data$train), "Species"), 
  output_col = "features"
)
random_forest <- ml_random_forest_classifier(sc,features_col = "features")

# obtain the labels from the fitted StringIndexerModel
iris_labels <- iris_pipeline_model %>%
  ml_stage("string_indexer") %>%
  ml_labels()

# IndexToString will convert the predicted numeric values back to class labels
iris_index_to_string <- ft_index_to_string(sc, "prediction", "predicted_label", 
                                      labels = iris_labels)

# construct a pipeline with these stages
iris_prediction_pipeline <- ml_pipeline(
  iris_pipeline, # pipeline from previous section
  iris_vector_assembler, 
  random_forest,
  iris_index_to_string
)

# fit to data and make some predictions
iris_prediction_model <- iris_prediction_pipeline %>%
  ml_fit(iris_data$train)
iris_predictions <- iris_prediction_model %>%
  ml_transform(iris_data$validation)
iris_predictions %>%
  select(Species, label:predicted_label) %>%
  glimpse()

After trial and error based on advice from here I was able to print out a formulation of the underlying decision tree in an "if/else" format cast as a string: 在根据此处的建议进行反复试验之后，我能够以“if / else”格式打印出基础决策树的表达式，并将其转换为字符串： 

model_stage <- iris_prediction_model$stages[[3]]

spark_jobj(model_stage) %>% invoke(., "toDebugString") %>% cat()
##print out below##
RandomForestClassificationModel (uid=random_forest_classifier_5c6a1934c8e) with 20 trees
  Tree 0 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 2 <= 4.95)
      If (feature 3 <= 1.65)
       Predict: 0.0
      Else (feature 3 > 1.65)
       If (feature 0 <= 1.7833559100698644)
        Predict: 0.0
       Else (feature 0 > 1.7833559100698644)
        Predict: 2.0
     Else (feature 2 > 4.95)
      If (feature 2 <= 5.05)
       If (feature 1 <= 6.505000000000001)
        Predict: 2.0
       Else (feature 1 > 6.505000000000001)
        Predict: 0.0
      Else (feature 2 > 5.05)
       Predict: 2.0
  Tree 1 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 3 <= 1.75)
      If (feature 1 <= 5.0649999999999995)
       If (feature 3 <= 1.05)
        Predict: 0.0
       Else (feature 3 > 1.05)
        If (feature 0 <= 1.8000241202036602)
         Predict: 2.0
        Else (feature 0 > 1.8000241202036602)
         Predict: 0.0
      Else (feature 1 > 5.0649999999999995)
       If (feature 0 <= 1.8000241202036602)
        Predict: 0.0
       Else (feature 0 > 1.8000241202036602)
        If (feature 2 <= 5.05)
         Predict: 0.0
        Else (feature 2 > 5.05)
         Predict: 2.0
     Else (feature 3 > 1.75)
      Predict: 2.0
  Tree 2 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 0 <= 1.7664051342320237)
      Predict: 0.0
     Else (feature 0 > 1.7664051342320237)
      If (feature 3 <= 1.45)
       If (feature 2 <= 4.85)
        Predict: 0.0
       Else (feature 2 > 4.85)
        Predict: 2.0
      Else (feature 3 > 1.45)
       If (feature 3 <= 1.65)
        If (feature 1 <= 8.125)
         Predict: 2.0
        Else (feature 1 > 8.125)
         Predict: 0.0
       Else (feature 3 > 1.65)
        Predict: 2.0
  Tree 3 (weight 1.0):
    If (feature 0 <= 1.6675287895788053)
     If (feature 2 <= 2.5)
      Predict: 1.0
     Else (feature 2 > 2.5)
      Predict: 0.0
    Else (feature 0 > 1.6675287895788053)
     If (feature 3 <= 1.75)
      If (feature 3 <= 1.55)
       If (feature 1 <= 7.025)
        If (feature 2 <= 4.55)
         Predict: 0.0
        Else (feature 2 > 4.55)
         Predict: 2.0
       Else (feature 1 > 7.025)
        Predict: 0.0
      Else (feature 3 > 1.55)
       If (feature 2 <= 5.05)
        Predict: 0.0
       Else (feature 2 > 5.05)
        Predict: 2.0
     Else (feature 3 > 1.75)
      Predict: 2.0
  Tree 4 (weight 1.0):
    If (feature 2 <= 4.85)
     If (feature 2 <= 2.5)
      Predict: 1.0
     Else (feature 2 > 2.5)
      Predict: 0.0
    Else (feature 2 > 4.85)
     If (feature 2 <= 5.05)
      If (feature 0 <= 1.8484238118815566)
       Predict: 2.0
      Else (feature 0 > 1.8484238118815566)
       Predict: 0.0
     Else (feature 2 > 5.05)
      Predict: 2.0
  Tree 5 (weight 1.0):
    If (feature 2 <= 1.65)
     Predict: 1.0
    Else (feature 2 > 1.65)
     If (feature 3 <= 1.65)
      If (feature 0 <= 1.8325494627242664)
       Predict: 0.0
      Else (feature 0 > 1.8325494627242664)
       If (feature 2 <= 4.95)
        Predict: 0.0
       Else (feature 2 > 4.95)
        Predict: 2.0
     Else (feature 3 > 1.65)
      Predict: 2.0
  Tree 6 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 2 <= 5.05)
      If (feature 3 <= 1.75)
       Predict: 0.0
      Else (feature 3 > 1.75)
       Predict: 2.0
     Else (feature 2 > 5.05)
      Predict: 2.0
  Tree 7 (weight 1.0):
    If (feature 3 <= 0.55)
     Predict: 1.0
    Else (feature 3 > 0.55)
     If (feature 3 <= 1.65)
      If (feature 2 <= 4.75)
       Predict: 0.0
      Else (feature 2 > 4.75)
       Predict: 2.0
     Else (feature 3 > 1.65)
      If (feature 2 <= 4.85)
       If (feature 0 <= 1.7833559100698644)
        Predict: 0.0
       Else (feature 0 > 1.7833559100698644)
        Predict: 2.0
      Else (feature 2 > 4.85)
       Predict: 2.0
  Tree 8 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 3 <= 1.85)
      If (feature 2 <= 4.85)
       Predict: 0.0
      Else (feature 2 > 4.85)
       If (feature 0 <= 1.8794359129669855)
        Predict: 2.0
       Else (feature 0 > 1.8794359129669855)
        If (feature 3 <= 1.55)
         Predict: 0.0
        Else (feature 3 > 1.55)
         Predict: 0.0
     Else (feature 3 > 1.85)
      Predict: 2.0
  Tree 9 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 2 <= 4.95)
      Predict: 0.0
     Else (feature 2 > 4.95)
      Predict: 2.0
  Tree 10 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 2 <= 4.95)
      Predict: 0.0
     Else (feature 2 > 4.95)
      If (feature 2 <= 5.05)
       If (feature 3 <= 1.55)
        Predict: 2.0
       Else (feature 3 > 1.55)
        If (feature 3 <= 1.75)
         Predict: 0.0
        Else (feature 3 > 1.75)
         Predict: 2.0
      Else (feature 2 > 5.05)
       Predict: 2.0
  Tree 11 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 2 <= 5.05)
      If (feature 2 <= 4.75)
       Predict: 0.0
      Else (feature 2 > 4.75)
       If (feature 3 <= 1.75)
        Predict: 0.0
       Else (feature 3 > 1.75)
        Predict: 2.0
     Else (feature 2 > 5.05)
      Predict: 2.0
  Tree 12 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 3 <= 1.75)
      If (feature 3 <= 1.35)
       Predict: 0.0
      Else (feature 3 > 1.35)
       If (feature 0 <= 1.695573522904327)
        Predict: 0.0
       Else (feature 0 > 1.695573522904327)
        If (feature 1 <= 8.125)
         Predict: 2.0
        Else (feature 1 > 8.125)
         Predict: 0.0
     Else (feature 3 > 1.75)
      If (feature 0 <= 1.7833559100698644)
       Predict: 0.0
      Else (feature 0 > 1.7833559100698644)
       Predict: 2.0
  Tree 13 (weight 1.0):
    If (feature 3 <= 0.55)
     Predict: 1.0
    Else (feature 3 > 0.55)
     If (feature 2 <= 4.95)
      If (feature 2 <= 4.75)
       Predict: 0.0
      Else (feature 2 > 4.75)
       If (feature 0 <= 1.8000241202036602)
        If (feature 1 <= 9.305)
         Predict: 2.0
        Else (feature 1 > 9.305)
         Predict: 0.0
       Else (feature 0 > 1.8000241202036602)
        Predict: 0.0
     Else (feature 2 > 4.95)
      Predict: 2.0
  Tree 14 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 3 <= 1.65)
      If (feature 3 <= 1.45)
       Predict: 0.0
      Else (feature 3 > 1.45)
       If (feature 2 <= 4.95)
        Predict: 0.0
       Else (feature 2 > 4.95)
        Predict: 2.0
     Else (feature 3 > 1.65)
      If (feature 0 <= 1.7833559100698644)
       If (feature 0 <= 1.7664051342320237)
        Predict: 2.0
       Else (feature 0 > 1.7664051342320237)
        Predict: 0.0
      Else (feature 0 > 1.7833559100698644)
       Predict: 2.0
  Tree 15 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 3 <= 1.75)
      If (feature 2 <= 4.95)
       Predict: 0.0
      Else (feature 2 > 4.95)
       If (feature 1 <= 8.125)
        Predict: 2.0
       Else (feature 1 > 8.125)
        If (feature 0 <= 1.9095150692894909)
         Predict: 0.0
        Else (feature 0 > 1.9095150692894909)
         Predict: 2.0
     Else (feature 3 > 1.75)
      Predict: 2.0
  Tree 16 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 0 <= 1.7491620461964392)
      Predict: 0.0
     Else (feature 0 > 1.7491620461964392)
      If (feature 3 <= 1.75)
       If (feature 2 <= 4.75)
        Predict: 0.0
       Else (feature 2 > 4.75)
        If (feature 0 <= 1.8164190316151556)
         Predict: 2.0
        Else (feature 0 > 1.8164190316151556)
         Predict: 0.0
      Else (feature 3 > 1.75)
       Predict: 2.0
  Tree 17 (weight 1.0):
    If (feature 0 <= 1.695573522904327)
     If (feature 2 <= 1.65)
      Predict: 1.0
     Else (feature 2 > 1.65)
      Predict: 0.0
    Else (feature 0 > 1.695573522904327)
     If (feature 2 <= 4.75)
      If (feature 2 <= 2.5)
       Predict: 1.0
      Else (feature 2 > 2.5)
       Predict: 0.0
     Else (feature 2 > 4.75)
      If (feature 3 <= 1.75)
       If (feature 1 <= 5.0649999999999995)
        Predict: 2.0
       Else (feature 1 > 5.0649999999999995)
        If (feature 3 <= 1.65)
         Predict: 0.0
        Else (feature 3 > 1.65)
         Predict: 0.0
      Else (feature 3 > 1.75)
       Predict: 2.0
  Tree 18 (weight 1.0):
    If (feature 3 <= 0.8)
     Predict: 1.0
    Else (feature 3 > 0.8)
     If (feature 3 <= 1.65)
      Predict: 0.0
     Else (feature 3 > 1.65)
      If (feature 0 <= 1.7833559100698644)
       Predict: 0.0
      Else (feature 0 > 1.7833559100698644)
       Predict: 2.0
  Tree 19 (weight 1.0):
    If (feature 2 <= 2.5)
     Predict: 1.0
    Else (feature 2 > 2.5)
     If (feature 2 <= 4.95)
      If (feature 1 <= 8.705)
       Predict: 0.0
      Else (feature 1 > 8.705)
       If (feature 2 <= 4.85)
        Predict: 0.0
       Else (feature 2 > 4.85)
        If (feature 0 <= 1.8164190316151556)
         Predict: 2.0
        Else (feature 0 > 1.8164190316151556)
         Predict: 0.0
     Else (feature 2 > 4.95)
      Predict: 2.0

As you can see, this format is less than optimal for passing into one of the many beautiful methods of visualizing decision tree graphics that I have seen (eg revolution analytics or statmethods ) 正如您所看到的，这种格式不是最佳的，可以用于我所见过的许多可视化决策树图形的漂亮方法之一（例如革命分析或statmethods ）

1 个解决方案

解决方案1
 4 已采纳  2018-11-02 22:54:38

As of today (Spark 2.4.0 release already approved and waiting for the official announcement) your best bet*, without involving complex 3rd party tools (you can take a look MLeap for example), is probably to save the model and read back the specification : 截至今天（火花2.4.0版本已经批准，并等待官方公布）你最好的选择*，不涉及复杂的第三方工具（你可以看看MLeap为例），可能是保存模型和回读的规格 ： 

ml_stage(iris_prediction_model, "random_forest") %>% 
  ml_save("/tmp/model")

rf_spec <- spark_read_parquet(sc, "rf", "/tmp/model/data/")

The result will be a Spark DataFrame with following schema: 结果将是具有以下模式的Spark DataFrame ： 

rf_spec %>% 
  spark_dataframe() %>% 
  invoke("schema") %>% invoke("treeString") %>% 
  cat(sep = "\n")

root
 |-- treeID: integer (nullable = true)
 |-- nodeData: struct (nullable = true)
 |    |-- id: integer (nullable = true)
 |    |-- prediction: double (nullable = true)
 |    |-- impurity: double (nullable = true)
 |    |-- impurityStats: array (nullable = true)
 |    |    |-- element: double (containsNull = true)
 |    |-- gain: double (nullable = true)
 |    |-- leftChild: integer (nullable = true)
 |    |-- rightChild: integer (nullable = true)
 |    |-- split: struct (nullable = true)
 |    |    |-- featureIndex: integer (nullable = true)
 |    |    |-- leftCategoriesOrThreshold: array (nullable = true)
 |    |    |    |-- element: double (containsNull = true)
 |    |    |-- numCategories: integer (nullable = true)

providing information about all nodes and splits. 提供有关所有节点和拆分的信息。

Feature mapping can be retrieved using column metadata: 可以使用列元数据检索要素映射： 

meta <- iris_predictions %>% 
    select(features) %>% 
    spark_dataframe() %>% 
    invoke("schema") %>% invoke("apply", 0L) %>% 
    invoke("metadata") %>% 
    invoke("getMetadata", "ml_attr") %>% 
    invoke("getMetadata", "attrs") %>% 
    invoke("json") %>%
    jsonlite::fromJSON() %>% 
    dplyr::bind_rows() %>% 
    copy_to(sc, .) %>%
    rename(featureIndex = idx)

meta

# Source: spark<?> [?? x 2]
  featureIndex name        
*        <int> <chr>       
1            0 Sepal_Length
2            1 Sepal_Width 
3            2 Petal_Length
4            3 Petal_Width

And labels mapping you've already retrieved: 您已经检索过的标签映射： 

labels <- tibble(prediction = seq_along(iris_labels) - 1, label = iris_labels) %>%
  copy_to(sc, .)

Finally you can combine all of these: 最后，您可以将所有这些结合起来： 

full_rf_spec <- rf_spec %>% 
  spark_dataframe() %>% 
  invoke("selectExpr", list("treeID", "nodeData.*", "nodeData.split.*")) %>% 
  sdf_register() %>% 
  select(-split, -impurityStats) %>% 
  left_join(meta, by = "featureIndex") %>% 
  left_join(labels, by = "prediction")

full_rf_spec

# Source: spark<?> [?? x 12]
   treeID    id prediction impurity    gain leftChild rightChild featureIndex
 *  <int> <int>      <dbl>    <dbl>   <dbl>     <int>      <int>        <int>
 1      0     0          1   0.636   0.379          1          2            2
 2      0     1          1   0      -1             -1         -1           -1
 3      0     2          0   0.440   0.367          3          8            2
 4      0     3          0   0.0555  0.0269         4          5            3
 5      0     4          0   0      -1             -1         -1           -1
 6      0     5          0   0.5     0.5            6          7            0
 7      0     6          0   0      -1             -1         -1           -1
 8      0     7          2   0      -1             -1         -1           -1
 9      0     8          2   0.111   0.0225         9         12            2
10      0     9          2   0.375   0.375         10         11            1
# ... with more rows, and 4 more variables: leftCategoriesOrThreshold <list>,
#   numCategories <int>, name <chr>, label <chr>

which, collected and separated by treeID , should give enough information** to mimic tree-like object (you can get a good understanding of the required structure by checking rpart::rpart.object documentation and/or unclass ing an rpart model. tree::tree would require less work, but its plotting utilities are far from impressive), and build a decent plot. 其中，收集和分离treeID ，应给予足够的信息**模拟树状对象（您可以通过检查得到所需要的结构有很好的理解rpart::rpart.object文档和/或unclass荷兰国际集团的rpart模型。 tree::tree需要较少的工作，但它的绘图实用程序远非令人印象深刻），并建立一个体面的情节。

An alternative path is to export your data to PMML using Sparklyr2PMML and use this representation. 另一种方法是使用Sparklyr2PMML将数据导出到PMML并使用此表示。

You can also check How do I visualise / plot a decision tree in Apache Spark (PySpark 1.4.1)? 您还可以检查如何在Apache Spark（PySpark 1.4.1）中可视化/绘制决策树？ which suggests third party Python package to solve the same problem. 这表明第三方Python包解决了同样的问题。

If you don't need anything fancy you can create a crude plot with igraph : 如果您不需要任何花哨的东西，您可以使用igraph创建原始图： 

library(igraph)

gframe <- full_rf_spec %>% 
  filter(treeID == 0) %>%   # Take the first tree
  mutate(
    leftCategoriesOrThreshold = ifelse(
      size(leftCategoriesOrThreshold) == 1,
      # Continuous variable case
      concat("<= ", round(concat_ws("", leftCategoriesOrThreshold), 3)),
      # Categorical variable case. Decoding variables might be involved
      # but can be achieved if needed, using column metadata or indexer labels
      concat("in {", concat_ws(",", leftCategoriesOrThreshold), "}")
    ),
    name = coalesce(name, label)) %>% 
 select(
   id, label, impurity, gain, 
   leftChild, rightChild, leftCategoriesOrThreshold, name) %>%
 collect()

vertices <- gframe %>% rename(label = name, name = id)

edges <- gframe %>%
  transmute(from = id, to = leftChild, label = leftCategoriesOrThreshold) %>% 
  union_all(gframe %>% select(from = id, to = rightChild)) %>% 
  filter(to != -1)

g <- igraph::graph_from_data_frame(edges, vertices = vertices)

plot(
  g, layout = layout_as_tree(g, root = c(1)),
  vertex.shape = "rectangle",  vertex.size = 45)

树情节

* It should improve in the nearest future, with newly introduced format agnostic ML writer API (which already supports PMML writer for selected models. Hopefully new models and formats will follow). *它应该在不久的将来改进，新引入的格式不可知的ML编写器API（已经支持所选模型的PMML编写器。希望新的模型和格式将遵循）。

** If you work with categorical features you might want to map leftCategoriesOrThreshold to respective indexed levels. **如果您使用分类功能，则可能需要将leftCategoriesOrThreshold映射到相应的索引级别。

If feature vector contains catagorical variables the output of jsonlite::fromJSON() will contain nominal group. 如果特征向量包含catagorical变量， jsonlite::fromJSON()的输出将包含nominal组。 For example if you had indexed column foo with three levels, assembled at the first position it will be something like this: 例如，如果您已将三个级别的列foo编入索引，则在第一个位置汇编它将是这样的： 

$nominal
     vals idx      name
1 a, b, c   1       foo

where vals column is a list of variable length vectors. 其中vals列是可变长度向量的列表。 

length(meta$nominal$vals[[1]])

[1] 3

The labels correspond to indices of this structure so in the example: 标签对应于此结构的索引，因此在示例中：

  • a has label 0.0 (not that labels are double precision floating point numbers, and numbering starts from 0.0) a具有标签0.0（不是标签是双精度浮点数，编号从0.0开始）
  • b has label 1.0 b标签为1.0

and so on, and if you have split with leftCategoriesOrThreshold equal to let's say c(0.0, 2.0) it means that split is on labels {"a", "c"} . 等等，如果你用leftCategoriesOrThreshold拆分等于让我们说c(0.0, 2.0) leftCategoriesOrThreshold c(0.0, 2.0)则意味着拆分在标签{"a", "c"} 。

Please also note that if categorical data is present you might have to process it before calling copy_to - it doesn't look like it supports complex fields as of now. 另请注意，如果存在分类数据，您可能必须在调用copy_to之前copy_to进行处理 - 它看起来不像现在那样支持复杂字段。

In Spark <= 2.3 you will have to use R code for mapping (on local structure some purrr should do just fine). 在Spark <= 2.3中，你将不得不使用R代码进行映射（在本地结构上，一些purrr应该做得很好）。 In Spark 2.4 (not supported yet in sparklyr AFAIK) it might be easier to read metadata directly with Spark's JSON reader and map with its higher order functions. 在Spark 2.4（在sparklyr AFAIK中尚不支持）中，使用Spark的JSON阅读器直接读取元数据并使用其更高阶函数进行映射可能更容易。

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