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Aggregator

Introduction​

The H2O Aggregator method is a clustering-based method for reducing a numerical/categorical dataset into a dataset with fewer rows. If the dataset has categorical columns, then for each categorical column, Aggregator will:

  1. Accumulate the category frequencies.
  2. For the top 1,000 or fewer categories (by frequency), generate dummy variables (called one-hot encoding by ML people, called dummy coding by statisticians).
  3. Calculate the first eigenvector of the covariance matrix of these dummy variables.
  4. Replace the row values on the categorical column with the value from the eigenvector corresponding to the dummy values.

Aggregator maintains outliers as outliers, but lumps together dense clusters into exemplars with an attached count column showing the member points.

The Aggregator method behaves just any other unsupervised model. You can ignore columns, which will then be dropped for distance computation. Training itself creates the aggregated H2O Frame, which also includes the count of members for every row/exemplar. The aggregated frame always includes the full original content of the training frame, even if some columns were ignored for the distance computation. Scoring/prediction is overloaded with a function that returns the members of a given exemplar row index from 0…Nexemplars (this time without a count).

MOJO support​

Aggregator currently does not support MOJOs.

Defining an Aggregator model​

Parameters are optional unless specified as required.

Algorithm-specific parameters​

  • num_iteration_without_new_exemplar: The number of iterations to run before aggregator exits if the number of exemplars collected doesn't change. This option defaults to 500.
  • rel_tol_num_exemplars: Specify the relative tolerance for the number of exemplars (for example, 0.5 is +/- 50 percent). This option defaults to 0.5.
  • save_mapping_frame: When this option is enabled, the mapping of rows in an aggregated frame to the one in the original/raw frame will be created and exported. This option defaults to False (disabled).
  • target_num_exemplars: Specify a value for the targeted number of exemplars. This option defaults to 5000.

Common parameters​

  • categorical_encoding: Specify one of the following encoding schemes for handling categorical features:

    • auto or AUTO (default): Allow the algorithm to decide. In Aggregator, the algorithm will automatically perform enum encoding.
    • one_hot_internal or OneHotInternal: On the fly N+1 new cols for categorical features with N levels.
    • binary: No more than 32 columns per categorical feature.
    • eigen or Eigen: k columns per categorical feature, keeping projections of one-hot-encoded matrix onto k-dim eigen space only.
    • label_encoder or LabelEncoder: Convert every enum into the integer of its index (for example, level 0 -> 0, level 1 -> 1, etc.).
    • enum_limited or EnumLimited: Automatically reduce categorical levels to the most prevalent ones during Aggregator training and only keep the T (10) most frequent levels.

  • export_checkpoints_dir: Specify a directory to which generated models will be automatically exported.

  • ignore_const_cols: Enable this option to ignore constant training columns since no information can be gained from them. This option defaults to True (enabled).

  • model_id: Specify a custom name for the model to use as a reference. By default, H2O automatically generates a destination key.

  • training_frame: Required Specify the dataset used to build the model.

  • transform: Specify the transformation method for numeric columns in the training data. One of

    • "none"
    • "standardize"
    • "normalize" (default)
    • "demean"
    • "descale"

  • x: Specify a vector containing the character names of the predictors in the model.

Aggregator output​

The output of the aggregation is a new aggregated frame that can be accessed in R and Python.

Examples​

This code illustrates how to build an Aggregator model using H2O-3 Secure in both R and Python, including data frame creation, aggregation with eigen categorical encoding, and viewing the resulting aggregated data.

# Create a random frame with 5 columns and 100 rows
df <- h2o.createFrame(
  rows = 100,
  cols = 5,
  categorical_fraction = 0.6,
  integer_fraction = 0,
  binary_fraction = 0,
  real_range = 100,
  integer_range = 100,
  missing_fraction = 0,
  seed = 123
)

# View the dataframe
df
      C1        C2     C3        C4     C5
1 c0.l53  10.94351 c2.l88 -93.64087 c4.l56
2 c0.l21 -93.70999 c2.l37  39.10130 c4.l97
3 c0.l96  55.43136  c2.l7 -43.47587 c4.l23
4 c0.l78  27.41477 c2.l63  83.09211 c4.l81
5 c0.l95 -77.98143 c2.l17 -93.95397  c4.l8
6 c0.l90  12.54660 c2.l36  60.54920 c4.l56

[100 rows x 5 columns]

# Build an aggregated frame using eigen categorical encoding
target_num_exemplars <- 1000
rel_tol_num_exemplars <- 0.5
encoding <- "Eigen"
agg <- h2o.aggregator(training_frame = df, 
                      target_num_exemplars = target_num_exemplars, 
                      rel_tol_num_exemplars = rel_tol_num_exemplars, 
                      categorical_encoding = encoding)

# Use the aggregated frame to create a new dataframe 
new_df <- h2o.aggregated_frame(agg)

#View the new dataframe
new_df
      C1        C2     C3        C4     C5 counts
1 c0.l53  10.94351 c2.l88 -93.64087 c4.l56      1
2 c0.l21 -93.70999 c2.l37  39.10130 c4.l97      1
3 c0.l96  55.43136  c2.l7 -43.47587 c4.l23      1
4 c0.l78  27.41477 c2.l63  83.09211 c4.l81      1
5 c0.l95 -77.98143 c2.l17 -93.95397  c4.l8      1
6 c0.l90  12.54660 c2.l36  60.54920 c4.l56      1

[100 rows x 6 columns]

References​

Wilkinson, Leland. “Visualizing Outliers.” (2016).

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