min_cluster_size.
Can be used to predict the clusters of new data without changing the existing clustering.
Usage
The following example shows how the step can be used in a recipe.Examples
Examples
Train an HDBSCAN model with default parameters.
Inputs & Outputs
The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced
by name e.g. "churn-clf").
Inputs
Inputs
Should contain the target column and the feature columns you wish to use in the model.
Outputs
Outputs
Configuration
The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e.step(..., {"param": "value", ...}) -> (output).
Parameters
Parameters
Toggle encoding of feature columns.
When enabled, Graphext will auto-convert any column types to the numeric type before
fitting the model. How this conversion is done can be configured using the
feature_encoder
option below.If disabled, any model trained in this step will assume that input data
is already in an appropriate format (e.g. numerical and not containing any missing values).
Configures encoding of feature columns.
By default (
null), Graphext chooses automatically how to convert any column types the model
may not understand natively to a numeric type.A configuration object can be passed instead to overwrite specific parameter values with respect
to their default values.Properties
Properties
Numeric encoder.
Configures encoding of numeric features.
Properties
Properties
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
Further parameters passed to the
scaler function.
Details depend no the particular scaler used.Boolean encoder.
Configures encoding of boolean features.
Ordinal encoder.
Configures encoding of categorical features that have a natural order.
Category encoder.
May contain either a single configuration for all categorical variables, or two different configurations
for low- and high-cardinality variables. For further details pick one of the two options below.
Options
Options
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
MostFrequentConstNone
Maximum number of unique categories to encode.
Only the N-1 most common categories will be encoded, and the rest will be grouped into a single
“Others” category.Values must be in the following range:
How to encode categories.Values must be one of the following:
OneHot Label Ordinal Binary Frequency NoneWhether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
Multilabel encoder.
Configures encoding of multivalued categorical features (variable length lists of categories,
or the semantic type
list[category] for short). May contain either a single configuration for
all multilabel variables, or two different configurations for low- and high-cardinality variables.
For further details pick one of the two options below.Options
Options
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
How to encode categories/labels in multilabel (list[category]) columns.Values must be one of the following:
BinarizerTfIdfNone
Maximum number of categories/labels to encode.
If a number is provided, the result of the encoding will be reduced to these many dimensions (columns)
using scikit-learn’s truncated SVD.
When applied together with (after a) Tf-Idf encoding, this performs a kind of
latent semantic analysis.Values must be in the following range:
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
Datetime encoder.
Configures encoding of datetime (timestamp) features.
Properties
Properties
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
A list of numerical components to extract.
Will create one numeric column for each component.
Array items
Array items
Each item in array.Values must be one of the following:
day dayofweek dayofyear hour minute month quarter season second week weekday weekofyear yearA list of cyclical time features to extract.
“Cycles” are numerical transformations of features that should be represented on a circle. E.g. months,
ranging from 1 to 12, should be arranged such that 12 and 1 are next to each other, rather than on
opposite ends of a linear scale. We represent such cyclical time features on a circle by creating two
columns for each original feature: the sin and cos of the numerical feature after appropriate scaling.
Array items
Array items
Each item in array.Values must be one of the following:
daydayofweekdayofyearhourmonth
Whether to include the epoch as new feature (seconds since 01/01/1970).
Whether and how to impute (replace/fill) missing values.Values must be one of the following:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
Embedding/vector encoder.
Configures encoding of multivalued numerical features (variable length lists of numbers, i.e. vectors, or the semantic type
list[number] for short).Text encoder.
Configures encoding of text (natural language) features. Currently only allows
Tf-Idf embeddings to represent texts. If you wish
to use other embeddings, e.g. semantic, Word2Vec etc., transform your text column first using
another step, and then use that result instead of the original texts.
Texts are excluded by default from the overall encoding of the dataset. See parameter
include_text_features below to active it.Properties
Properties
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
Parameters to be passed to the text encoder (Tf-Idf parameters only for now).
See scikit-learn’s documentation
for detailed parameters and their explanation.
How many output features to generate.
The resulting Tf-Idf vectors will be reduced to these many dimensions (columns) using scikit-learn’s
truncated SVD.
This performs a kind of latent semantic analysis.
By default we will reduce to 200 components.Values must be in the following range:
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
Whether to include or ignore text columns during the processing of input data.
Enabling this will convert texts to their TfIdf representation. Each text will be
converted to an N-dimensional vector in which each component measures the relative
“over-representation” of a specific word (or n-gram) relative to its overall
frequency in the whole dataset. This is disabled by default because it will
often be better to convert texts explicitly using a previous step, such as
embed_text or embed_text_with_model.Model parameters.
Also see official HDBSCAN documentation for details.
Properties
Properties
The minimum size of clusters.
Intuitively, the smallest size grouping you wish to consider a cluster. When selecting a flat clustering from the cluster
hierarchy, splits that contain fewer points than this will be considered points “falling out” of a cluster rather than a
cluster splitting into two new clusters.Values must be in the following range:
Determines how conservative the clustering is.
The larger the value, the more points will be declared as noise, and clusters will be restricted to progressively
more dense areas.Values must be in the following range:
Distance threshold.
Clusters below this value will be merged. If default parameters result in areas with a large number of micro-clusters,
this parameter can help merging these clusters together. For example, set the value to 0.5 if you don’t want to separate
clusters that are less than 0.5 units apart (the distance distribution depends on your specific data).Values must be in the following range:
Method used to select clusters from the cluster hierarchy.
The default, “excess of mass” (
eom), can sometimes pick one or two large clusters and then a number
of small extra clusters. If you’re interested in a more fine-grained clustering with a larger number of more homogeously
sized clusters, you may prefer selecting leaf clustering (leaf).Values must be one of the following:eofleaf
Seed for random number generator ensuring reproducibility.Values must be in the following range: