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:
Accumulate the category frequencies.
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).
Calculate the first eigenvector of the covariance matrix of these dummy variables.
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).
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 (e.g.
0.5is +/- 50 percent). This option defaults to0.5.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:
autoorAUTO(default): Allow the algorithm to decide. In Aggregator, the algorithm will automatically performenumencoding.one_hot_internalorOneHotInternal: On the fly N+1 new cols for categorical features with N levels.binary: No more than 32 columns per categorical feature.eigenorEigen: k columns per categorical feature, keeping projections of one-hot-encoded matrix onto k-dim eigen space only.label_encoderorLabelEncoder: Convert every enum into the integer of its index (for example, level 0 -> 0, level 1 -> 1, etc.).enum_limitedorEnumLimited: 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.
NOTE: In Flow, if you click the Build a model button from the
Parsecell, the training frame is entered automatically.transform: Specify the transformation method for numeric columns in the training data. One of
"none""standardize""normalize"(default)"demean""descale"
x: Specify a vector contaitning 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¶
Below is a simple example showing how to build a Aggregator model.
# 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 eigan 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]
import h2o
h2o.init()
from h2o.estimators.aggregator import H2OAggregatorEstimator
# Create a random data frame with 5 columns and 100 rows
df = h2o.create_frame(
rows=100,
cols=5,
categorical_fraction=0.6,
integer_fraction=0,
binary_fraction=0,
real_range=100,
integer_range=100,
missing_fraction=0,
seed=1234
)
# View the dataframe
>>> df
C1 C2 C3 C4 C5
-------- ------ ------ -------- ------
56.3978 c1.l74 c2.l58 36.4711 c4.l66
-41.3355 c1.l31 c2.l43 -54.4267 c4.l4
79.9964 c1.l4 c2.l68 -13.5409 c4.l49
73.4546 c1.l5 c2.l25 -23.6456 c4.l12
12.2449 c1.l7 c2.l49 -71.3769 c4.l61
-20.2171 c1.l41 c2.l92 -70.2103 c4.l50
80.6089 c1.l28 c2.l18 -34.7444 c4.l19
-99.6821 c1.l21 c2.l74 93.7822 c4.l31
-56.1135 c1.l35 c2.l8 -79.3114 c4.l75
-71.4061 c1.l77 c2.l83 -32.2047 c4.l65
[100 rows x 5 columns]
# Build an aggregated frame using eigan categorical encoding
params = {
"target_num_exemplars": 1000,
"rel_tol_num_exemplars": 0.5,
"categorical_encoding": "eigen"
}
agg = H2OAggregatorEstimator(**params)
agg.train(training_frame=df)
# Use the aggregated model to create a new dataframe using aggregated_frame
new_df = agg.aggregated_frame
# View the new dataframe
new_df
C1 C2 C3 C4 C5 counts
-------- ------ ------ -------- ------ --------
56.3978 c1.l74 c2.l58 36.4711 c4.l66 1
-41.3355 c1.l31 c2.l43 -54.4267 c4.l4 1
79.9964 c1.l4 c2.l68 -13.5409 c4.l49 1
73.4546 c1.l5 c2.l25 -23.6456 c4.l12 1
12.2449 c1.l7 c2.l49 -71.3769 c4.l61 1
-20.2171 c1.l41 c2.l92 -70.2103 c4.l50 1
80.6089 c1.l28 c2.l18 -34.7444 c4.l19 1
-99.6821 c1.l21 c2.l74 93.7822 c4.l31 1
-56.1135 c1.l35 c2.l8 -79.3114 c4.l75 1
-71.4061 c1.l77 c2.l83 -32.2047 c4.l65 1
[100 rows x 6 columns]