vectorize_dataset

Many machine learning and AI algorithms expect their input data to be in pure numerical form, i.e. not containing categorical variables, missing values etc. This step converts arbitrary datasets, potentially containing non-numerical variables and NaNs, into this expected form. It does this by defining for each possible type of input column a transformation from non-numeric to numeric values. As an example, ordered categorical variables (ordinals) such as the day of week, may be converted into a series of numbers (0..7). Non-ordered categorical variables of low-cardinality (containing few different categories) may be expanded into multiple new columns of 0s and 1s, indicating whether each row belongs to a specific category or not. Similar transformations are applied to dates, multivalued categoricals etc.

NaNs are imputed (replaced) with an appropriate value from the corresponding column (e.g. the median in a quantitative column). In addition, a new column of 0s and 1s is added, indicating whether the original column had a missing value or not.

The resulting dataset will almost certainly not contain the same number of columns as the original (as the example of categorical variables shows), and for simplicity, its columns will simply be numbered.

If desired, the n_components parameter may be used to select how many columns the new dataset should have, and if this is smaller than would result normally, a dimensionality reduction will be applied (UMAP by default).

The resulting numerical representation of the original data points aims to preserve the structure of similarities. I.e. if two original rows are similar to each other, than their (potentially reduced) numerical representations should also be similar. Equally, two very different rows should have representations that are also very different.

Note, if you need the output as a column of embedding vectors, rather than a dataset, use embed_dataset instead.

algorithm

string

default: "umap"

Algorithm. The name of a supported dimensionality reduction algorithm.

Values must be one of the following:

umap

encode_features

boolean

default: "true"

Toggle encoding of feature columns. When enabled, Graphext will auto-convert any column types to the numeric type before (optionally) reducing the data’s dimensionality. How this conversion is done can be configured using the feature_encoder option below.

If disabled, the dimensionality reduction algorithm applied in this step will assume that input data is already numerical and doesn’t contain 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.

number

object

Numeric encoder. Configures encoding of numeric features.

bool

object

Boolean encoder. Configures encoding of boolean features.

ordinal

object

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

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.

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.

datetime

object

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

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).

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.

Texts are excluded by default from the overall encoding of the dataset. See parameter include_text_features below to active it.

indicate_missing

boolean

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

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.

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.

Values must be in the following range:

2 ≤ n_components ≤ 1024

scaler

[null, string]

How to scale the encoded (numerical columns).

Values must be one of the following:

Euclidean
KNN
Norm
None

include_text_features

boolean

Whether to include or ignore text columns during the processing of input data. Enabling this will convert texts to their Tf-Idf 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.

weights

[object, null]

Weights used to multiply the normalized columns/features after vectorization. Should be a dictionary/object of {"column_name": weight, ...} items. Will be scaled using the parameters weights_max, and weights_exp before being applied. So only the relative weight of the columns is important here, not their absolute values.

type_weights

[object, null]

Weights used to multiply the normalized columns/features after vectorization. Should be a dictionary/object of "type": weight" items. Will be scaled using the parameters weights_max, and weights_exp before being applied. So only the relative weight of the columns is important here, not their absolute values.

weights_max

number

default: "32"

Maximum weight to scale the normalized columns with.

Values must be in the following range:

0 ≤ weights_max < inf

weights_exp

integer

default: "2"

Weight exponent. Weights will be raised to this power before(!) scaling to weights_max. This allows for a non-linear mapping from input weights to those used eventually to multiply the normalized columns.

n_neighbors

integer

default: "100"

Number of neighbours. Use smaller numbers to concentrate on the local structure in the data, and larger values to focus on the more global structure.

For further details see here.

Values must be in the following range:

1 ≤ n_neighbors < inf

min_dist

number

default: "0.1"

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.

Values must be in the following range:

0 ≤ min_dist < inf

n_components

integer

default: "2"

Dimensionality of the reduced data. Fixed at 2 for the purpose of a layout’s x and y coordinates.

Values must be in the following range:

1 ≤ n_components < inf

metric

string

default: "euclidean"

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

n_epochs

[integer, null]

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).

init

string

default: "auto"

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. “auto” selects between “spectral” and “random” automatically depending on the size of the dataset.

Values must be one of the following:

spectral
pca
tswspectral
random
auto

low_memory

[boolean, string, null]

default: "auto"

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. Setting it to “auto”, will enable this mode automatically depending on the size of the dataset.

Values must be one of the following:

True
False
auto
None

target

[string, null]

Target variable (labels) for supervised dimensionality reduction. Name of the column that contains your target values (labels).

target_weight

number

default: "0.5"

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.

densmap

boolean

Try to better preserve local densities in the data. Specifies whether the density-augmented objective of densMAP should be used for optimization. Turning on this option generates an embedding where the local densities are encouraged to be correlated with those in the original space.

dens_lambda

number

default: "2.0"

Strength of local density preservation. Controls the regularization weight of the density correlation term in densMAP. Higher values prioritize density preservation over the UMAP objective, and vice versa for values closer to zero. Setting this parameter to zero is equivalent to running the original UMAP algorithm.

random_state

[integer, null]

default: "42"

A random number to initialize the algorithm for reproducibility.

links_top_n

integer

default: "15"

Maintain links for n nearest neighbours only in graph.

Values must be in the following range:

1 ≤ links_top_n ≤ 15

scale

[null, string, number]

default: "null"

Scaling factor for the coordinates. The maximum (normalized) coordinates in positive and negative X and Y directions. Acts like a zoom, with a scale of 1 corresponding to zooming out to the maximum (maximal space between nodes), and 0 to the densest layout.

If set to "auto", will try to determine an appropriate scale taking into account the number of nodes.

If set to null, only changes calculated coordinates to ensure they’re within the allowed limits (16.000).

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