> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphext.com/llms.txt
> Use this file to discover all available pages before exploring further.
# train_dimensionality_reduction
> Train and store a machine learning model to be loaded at a later point for prediction.
Dimensionality reduction with ["Uniform Manifold Approximation and Projection" (UMAP)](https://umap-learn.readthedocs.io/en/latest/)
Generates numeric embeddings (vectors) of the input data with reduced dimensionality, preserving
local and global similarities between data points. Can be used for visualisation, for example,
to arrange data in 2 dimensions according to their similarity, or to create nearest neighbour graphs/networks
(also see step `link_embeddings` in the latter case).
Can be used in supervised mode (providing a `target` column as parameter) or unsupervised (without target).
The output will always be a new column with the trained model's predictions on the training data,
as well as a saved and named model file that can be used in other projects for prediction of new data.
## Usage
The following examples show how the step can be used in a recipe.
Train an unsupervised UMAP model.
```stan theme={null}
train_embeddings(ds) -> (ds.predicted, model)
```
Train a supervised UMAP model.
```stan theme={null}
train_embeddings(ds, {"target": "reference"}) -> (ds.predicted, model)
```
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
```stan theme={null}
train_dimensionality_reduction(ds: dataset, {
"param": value,
...
}) -> (predicted: list[number], model: model_dimensionality_reduction[ds])
```
## 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"`).
Should contain the target column and the feature columns you wish to use in the model.
Column containing results of the model.
Zip file containing the trained model and associated information.
## 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)`.
Target variable.
Name of the column that contains your target values.
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.
Numeric encoder.
Configures encoding of numeric features.
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:
* `Mean`
* `Median`
* `MostFrequent`
* `Const`
* `None`
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
* `Standard`
* `Robust`
* `KNN`
* `None`
Further parameters passed to the `scaler` function.
Details depend no the particular scaler used.
Boolean encoder.
Configures encoding of boolean features.
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:
* `MostFrequent`
* `Const`
* `None`
Ordinal encoder.
Configures encoding of categorical features that have a natural order.
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:
* `MostFrequent`
* `Const`
* `None`
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.
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:
* `MostFrequent`
* `Const`
* `None`
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:
```javascript theme={null}
1 ≤ max_categories < inf
```
How to encode categories.
Values must be one of the following:
`OneHot` `Label` `Ordinal` `Binary` `Frequency` `None`
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
* `Standard`
* `Robust`
* `KNN`
* `None`
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.
Values must be in the following range:
```javascript theme={null}
3 ≤ cardinality_treshold < inf
```
Low cardinality configuration.
Used for categories with fewer than `cardinality_threshold` unique categories.
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:
* `MostFrequent`
* `Const`
* `None`
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:
```javascript theme={null}
1 ≤ max_categories < inf
```
How to encode categories.
Values must be one of the following:
`OneHot` `Label` `Ordinal` `Binary` `Frequency` `None`
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
* `Standard`
* `Robust`
* `KNN`
* `None`
High cardinality configuration.
Used for categories with more than `cardinality_threshold` unique categories.
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:
* `MostFrequent`
* `Const`
* `None`
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:
```javascript theme={null}
1 ≤ max_categories < inf
```
How to encode categories.
Values must be one of the following:
`OneHot` `Label` `Ordinal` `Binary` `Frequency` `None`
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
* `Standard`
* `Robust`
* `KNN`
* `None`
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.
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:
* `Binarizer`
* `TfIdf`
* `None`
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](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html).
When applied together with (after a) Tf-Idf encoding, this performs a kind of
[latent semantic analysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis).
Values must be in the following range:
```javascript theme={null}
2 ≤ max_categories < inf
```
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
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.
Values must be in the following range:
```javascript theme={null}
3 ≤ cardinality_treshold < inf
```
Low cardinality configuration.
Used for mulitabel columns with fewer than `cardinality_threshold` unique categories/labels.
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:
* `Binarizer`
* `TfIdf`
* `None`
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](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html).
When applied together with (after a) Tf-Idf encoding, this performs a kind of
[latent semantic analysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis).
Values must be in the following range:
```javascript theme={null}
2 ≤ max_categories < inf
```
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
High cardinality configuration.
Used for categories with more than `cardinality_threshold` unique categories.
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:
* `Binarizer`
* `TfIdf`
* `None`
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](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html).
When applied together with (after a) Tf-Idf encoding, this performs a kind of
[latent semantic analysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis).
Values must be in the following range:
```javascript theme={null}
2 ≤ max_categories < inf
```
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
Datetime encoder.
Configures encoding of datetime (timestamp) features.
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.
Each item in array.
Values must be one of the following:
`day` `dayofweek` `dayofyear` `hour` `minute` `month` `quarter` `season` `second` `week` `weekday` `weekofyear` `year`
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.
Each item in array.
Values must be one of the following:
* `day`
* `dayofweek`
* `dayofyear`
* `hour`
* `month`
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:
* `Mean`
* `Median`
* `MostFrequent`
* `Const`
* `None`
Whether and how to scale the final numerical values (across a single column).
Values must be one of the following:
* `Standard`
* `Robust`
* `KNN`
* `None`
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
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).
Toggle the addition of a column using 0s and 1s to indicate where an input column contained missing values.
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
Text encoder.
Configures encoding of text (natural language) features. Currently only allows
[Tf-Idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf) 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.
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](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)
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](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html).
This performs a kind of [latent semantic analysis](https://en.wikipedia.org/wiki/Latent_semantic_analysis).
By default we will reduce to 200 components.
Values must be in the following range:
```javascript theme={null}
2 ≤ n_components ≤ 1024
```
How to scale the encoded (numerical columns).
Values must be one of the following:
* `Euclidean`
* `KNN`
* `Norm`
* `None`
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.
See [official UMAP documentation](https://umap-learn.readthedocs.io/en/latest/parameters.html) for details.
Number of neighbors.
This determines the number of neighboring points used in local approximations of manifold structure.
Larger values will result in more global structure being preserved at the loss of detailed local
structure. In general this parameter should often be in the range 5 to 50, with a choice of 10 to 15
being a sensible default.
Values must be in the following range:
```javascript theme={null}
1 ≤ n_neighbors < inf
```
Minimum distance between reduced data points.
Controls how tightly UMAP is allowed to pack points together in the reduced space. Smaller values will lead to points more tightly
packed together (potentially useful if result is used to cluster the points). Larger values will distribute points with more space
between them (which may be desirable for visualization, or to focus more on the global structure of the date).
For further details see [here](https://umap-learn.readthedocs.io/en/latest/parameters.html#min-dist).
Values must be in the following range:
```javascript theme={null}
0 ≤ min_dist < inf
```
Number of n\_components.
Allows the user to determine the dimensionality of the reduced dimension space we will be embedding the
data into.
Values must be in the following range:
```javascript theme={null}
1 ≤ n_components < inf
```
Metric to use for measuring similarity between data points.
Values must be one of the following:
`euclidean` `manhattan` `chebyshev` `minkowski` `canberra` `braycurtis` `haversine` `mahalanobis` `wminkowski` `seuclidean` `cosine` `correlation` `hamming` `jaccard` `dice` `russellrao` `kulsinski` `rogerstanimoto` `sokalmichener` `sokalsneath` `yule`
Number of training iterations used in optimizing the embedding.
Larger values result in more accurate embeddings. If `null` is specified a value will be selected based on the size of the input dataset
(200 for large datasets, 500 for small).
How to initialize the low dimensional embedding.
When "spectral", uses a (relatively expensive) spectral embedding. "pca" uses the first `n_components`
from a principal component analysis. "tswspectral" is a cheaper alternative to "spectral". When "random",
assigns initial embedding positions at random. This uses the least amount of memory and time but may make UMAP
slower to converge on the optimal embedding.
Values must be one of the following:
* `spectral`
* `pca`
* `tswspectral`
* `random`
Avoid excessive memory use.
For some datasets nearest neighbor computations can consume a lot of memory. If you find the step is failing due to memory constraints,
consider setting this option to `true`. This approach is more computationally expensive, but avoids excessive memory use.
Weighting factor between features and target.
A value of 0.0 weights entirely on data, and a value of 1.0 weights entirely on target. The default of 0.5 balances
the weighting equally between data and target.
Seed for random number generator ensuring reproducibility.
Values must be in the following range:
```javascript theme={null}
0 ≤ seed < inf
```