# Train clustering¶

inference • models • clusters • hdbscan

Train and store a machine learning model to be loaded at a later point for prediction.

Density-based clustering with "HDBSCAN"

Generates a hierarchy of clusters, but then automatically selects the best *flat* clustering based on the stability
of clusters across a range of density thresholds. Roughly speaking, if a cluster's subclusters persists over a larger
range of the density parameter then the parent cluster itself, the subclusters will be selected, otherwise the parent.
The main parameter influencing cluster selection is `min_cluster_size`

.

Can be used to predict the clusters of new data without changing the existing clustering.

## Usage¶

The following are the step's expected inputs and outputs and their specific types.

```
train_clustering(ds: dataset, {
"param": value
}) -> (predicted: category, model: model_clustering[ds])
```

where the object `{"param": value}`

is optional in most cases and if present may contain any of the parameters described in the
corresponding section below.

#### Example¶

Train an HDBSCAN model with default parameters.

```
train_clustering(ds) -> (ds.predicted, model)
```

## Inputs¶

ds: dataset

Should contain the target column and the feature columns you wish to use in the model.

## Outputs¶

predicted: column:category

Column containing results of the model.

model: file:model_clustering[ds]

Zip file containing the trained model and associated information.

## Parameters¶

encode_features: boolean = True

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.

**Warning!** 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).

feature_encoder: null | object

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.

## Items in `feature_encoder`

number: object

Numeric encoder. Configures encoding of numeric features.

## Items in `number`

indicate_missing: boolean

Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"Mean"`

,
`"Median"`

,
`"MostFrequent"`

,
`"Const"`

,
`None`

scaler: null | string

Whether and how to scale the final numerical values (across a single column).

Must be one of:
`"Standard"`

,
`"Robust"`

,
`"KNN"`

,
`None`

scaler_params: object

Further parameters passed to the `scaler`

function. Details depend no the particular scaler used.

## Items in `scaler_params`

bool: object

Boolean encoder. Configures encoding of boolean features.

## Items in `bool`

indicate_missing: boolean

Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"MostFrequent"`

,
`"Const"`

,
`None`

ordinal: object

Ordinal encoder. Configures encoding of categorical features that have a natural order.

## Items in `ordinal`

indicate_missing: boolean

Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"MostFrequent"`

,
`"Const"`

,
`None`

category: object | object

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.

## Items in `category`

indicate_missing: boolean

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"MostFrequent"`

,
`"Const"`

,
`None`

max_categories: null | integer

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.

Range: `1 ≤ max_categories < inf`

encoder: null | string

How to encode categories.

Must be one of:
`"OneHot"`

,
`"Label"`

,
`"Ordinal"`

,
`"Binary"`

,
`"Frequency"`

,
`None`

scaler: null | string

Whether and how to scale the final numerical values (across a single column).

Must be one of:
`"Standard"`

,
`"Robust"`

,
`"KNN"`

,
`None`

cardinality_treshold: integer

Condition for application of low- or high-cardinality configuration. Number of unique categories below which the `low_cardinality`

configuration is used,
and above which the `high_cardinality`

configuration is used.

Range: `3 ≤ cardinality_treshold < inf`

low_cardinality: object

Low cardinality configuration. Used for categories with fewer than `cardinality_threshold`

unique categories.

## Items in `low_cardinality`

indicate_missing: boolean

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"MostFrequent"`

,
`"Const"`

,
`None`

max_categories: null | integer

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.

Range: `1 ≤ max_categories < inf`

encoder: null | string

How to encode categories.

Must be one of:
`"OneHot"`

,
`"Label"`

,
`"Ordinal"`

,
`"Binary"`

,
`"Frequency"`

,
`None`

scaler: null | string

Whether and how to scale the final numerical values (across a single column).

Must be one of:
`"Standard"`

,
`"Robust"`

,
`"KNN"`

,
`None`

high_cardinality: object

High cardinality configuration. Used for categories with more than `cardinality_threshold`

unique categories.

## Items in `high_cardinality`

indicate_missing: boolean

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"MostFrequent"`

,
`"Const"`

,
`None`

max_categories: null | integer

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.

Range: `1 ≤ max_categories < inf`

encoder: null | string

How to encode categories.

Must be one of:
`"OneHot"`

,
`"Label"`

,
`"Ordinal"`

,
`"Binary"`

,
`"Frequency"`

,
`None`

scaler: null | string

Whether and how to scale the final numerical values (across a single column).

Must be one of:
`"Standard"`

,
`"Robust"`

,
`"KNN"`

,
`None`

multilabel: object | object

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.

## Items in `multilabel`

indicate_missing: boolean

encoder: null | string

How to encode categories/labels in multilabel (list[category]) columns.

Must be one of:
`"Binarizer"`

,
`"TfIdf"`

,
`None`

max_categories: null | integer

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.

Range: `2 ≤ max_categories < inf`

scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

cardinality_treshold: integer

Condition for application of low- or high-cardinality configuration. Number of unique categories below which the `low_cardinality`

configuration is used,
and above which the `high_cardinality`

configuration is used.

Range: `3 ≤ cardinality_treshold < inf`

low_cardinality: object

Low cardinality configuration. Used for mulitabel columns with fewer than `cardinality_threshold`

unique categories/labels.

## Items in `low_cardinality`

indicate_missing: boolean

encoder: null | string

How to encode categories/labels in multilabel (list[category]) columns.

Must be one of:
`"Binarizer"`

,
`"TfIdf"`

,
`None`

max_categories: null | integer

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.

Range: `2 ≤ max_categories < inf`

scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

high_cardinality: object

High cardinality configuration. Used for categories with more than `cardinality_threshold`

unique categories.

## Items in `high_cardinality`

indicate_missing: boolean

encoder: null | string

How to encode categories/labels in multilabel (list[category]) columns.

Must be one of:
`"Binarizer"`

,
`"TfIdf"`

,
`None`

max_categories: null | integer

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.

Range: `2 ≤ max_categories < inf`

scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

datetime: object

Datetime encoder. Configures encoding of datetime (timestamp) features.

## Items in `datetime`

indicate_missing: boolean

components: array[string]

A list of numerical components to extract. Will create one numeric column for each component.

## Items in `components`

item: string

Must be one of:
`"day"`

,
`"dayofweek"`

,
`"dayofyear"`

,
`"hour"`

,
`"minute"`

,
`"month"`

,
`"quarter"`

,
`"season"`

,
`"second"`

,
`"week"`

,
`"weekday"`

,
`"weekofyear"`

,
`"year"`

cycles: array[string]

A 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.

## Items in `cycles`

item: string

Must be one of:
`"day"`

,
`"dayofweek"`

,
`"dayofyear"`

,
`"hour"`

,
`"month"`

epoch: null | boolean

Whether to include the epoch as new feature (seconds since 01/01/1970).

imputer: null | string

Whether and how to impute (replace/fill) missing values.

Must be one of:
`"Mean"`

,
`"Median"`

,
`"MostFrequent"`

,
`"Const"`

,
`None`

component_scaler: null | string

Whether and how to scale the final numerical values (across a single column).

Must be one of:
`"Standard"`

,
`"Robust"`

,
`"KNN"`

,
`None`

vector_scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

embedding: object

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).

## Items in `embedding`

indicate_missing: boolean

scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

text: object

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.

**Warning!** Texts are *excluded* by default from the overall encoding of the dataset. See parameter
`include_text_features`

below to active it.

## Items in `text`

indicate_missing: boolean

encoder_params: object

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.

## Items in `encoder_params`

n_components: integer

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.

Range: `2 ≤ n_components ≤ 1024`

scaler: null | string

How to scale the encoded (numerical columns).

Must be one of:
`"Euclidean"`

,
`"KNN"`

,
`"Norm"`

,
`None`

include_text_features: boolean = False

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`

params: object

Model parameters. Also see official HDBSCAN documentation for details.

## Items in `params`

min_cluster_size: integer = 50

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.

Range: `1 ≤ min_cluster_size < inf`

min_samples: integer = 5

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.

Range: `1 ≤ min_samples < inf`

cluster_selection_epsilon: number = 0.0

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).

Range: `0.0 ≤ cluster_selection_epsilon < inf`

cluster_selection_method: string = "eof"

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`

).

Must be one of:
`"eof"`

,
`"leaf"`

seed: integer

Seed for random number generator ensuring reproducibility.

Range: `0 ≤ seed < inf`