> ## 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.
# infer_missing
> Train and use a machine learning model to predict (impute) the missing values in a column.
Non-missing values in the target column will be used to train a prediction model (a [Catboost](https://catboost.ai/) regressor or classifier), which then predicts (imputes) the missing values. Only simple numerical or categorical input data can be imputed.
## Usage
The following example shows how the step can be used in a recipe.
To automatically select the a model (classifier vs regressor) based on the kind of target
variable (numeric or categorical), simply use:
```stan theme={null}
infer_missing(ds, {"target": "incomplete_col"}) -> (ds.complete_col)
```
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}
infer_missing(ds: dataset, {
"param": value,
...
}) -> (predicted: column)
```
## 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"`).
A dataset containing the column to be imputed as well as any other column to use as predictors in the model.
A column containing the predicted values for all rows.
## 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)`.
Name of the column to impute.
The step will predict the missing values for this column, using rows in the dataset where the values are not missing to train the prediction model.
Predict non-missing values.
When set to true, all values are predicted. Set this param to false to maintain original values when they are not missing.
Confidence threshold.
Every prediction with probability strictly below this threshold will be set to NaN (missing).
Values must be in the following range:
```javascript theme={null}
0 ≤ threshold < 1
```
CatBoost configuration parameters.
You can check the official documentation for more details about Catboost's parameters [here](https://catboost.ai/en/docs/references/training-parameters/).
The maximum depth of the tree.
Values must be in the following range:
```javascript theme={null}
2 ≤ depth ≤ 16
```
Number of iterations.
The maximum number of trees that can be built when solving machine learning problems. When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.
Values must be in the following range:
```javascript theme={null}
1 ≤ iterations < inf
```
Maximum cardinality of variables to be one-hot encoded.
Use one-hot encoding for all categorical features with a number of different values less than or equal to this value. Other variables will be target-encoded. Note that one-hot encoding is faster than the alternatives, so decreasing this value makes it more likely slower methods will be used. See [CatBoost details](https://catboost.ai/docs/concepts/algorithm-main-stages_cat-to-numberic.html) for further information.
Values must be in the following range:
```javascript theme={null}
2 ≤ one_hot_max_size < inf
```
The maximum number of features that can be combined when transforming categorical variables.
Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.
Values must be in the following range:
```javascript theme={null}
1 ≤ max_ctr_complexity ≤ 4
```
Coefficient at the L2 regularization term of the cost function.
Values must be in the following range:
```javascript theme={null}
0.0 < l2_leaf_reg < inf
```
The number of splits for numerical features.
Values must be in the following range:
```javascript theme={null}
1 ≤ border_count ≤ 65535
```
The amount of randomness to use for scoring splits.
Use this parameter to avoid overfitting the model. The value multiplies the variance of a random variable (with
zero mean) that is added to the score used to select splits when a tree is grown.
Values must be in the following range:
```javascript theme={null}
0 < random_strength < inf
```
The method for processing missing values in the input dataset.
Possible values:
* “Forbidden”:
Missing values are not supported, their presence is interpreted as an error.
* “Min”:
Missing values are processed as the minimum value (less than all other values) for the feature.
It is guaranteed that a split that separates missing values from all other values is considered
when selecting trees.
* “Max”:
Missing values are processed as the maximum value (greater than all other values) for the feature.
It is guaranteed that a split that separates missing values from all other values is considered when
selecting trees.
Using the Min or Max value of this parameter guarantees that a split between missing values and other
values is considered when selecting a new split in the tree.
Values must be one of the following:
* `Forbidden`
* `Min`
* `Max`
Boosting type.
Boosting scheme. Possible values are
* Ordered: Usually provides better quality on small datasets, but it may be slower than the Plain scheme.
* Plain: The classic gradient boosting scheme.
Values must be one of the following:
* `Ordered`
* `Plain`
Random subspace method.
The percentage of features to use at each split selection, when features are selected over again at random. The value `null` is equivalent to 1.0 (all features). You can set this to values \< 1.0 when the dataset has many features (e.g. > 20) to speed up training.
Values must be in the following range:
```javascript theme={null}
0 < rsm ≤ 1.0
```
The random seed used for training.
Whether and how to limit memory usage.
Select the maximum Ram used using strings like "2GB" or "100mb" (non case\_sensitive).
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`.
Configure model validation.
Allows evaluation of model performance via cross-validation with custom metrics. If not
specified, will by default perform 5-fold cross-validation with automatically selected
metrics.
Number of train-test splits to evaluate the model on.
Will split the dataset into training and test set `n_splits` times, train on the former
and evaluate on the latter using specified or automatically selected `metrics`.
What proportion of the data to use for testing in each split.
If `null` or not provided, will use [cross-validation]() to split the dataset. E.g. if `n_splits`
is 5, the dataset will be split into 5 equal-sized parts. For five iterations four parts will then
be used for training and the remaining part for testing. If `test_size` is a number between 0 and 1,
in contrast, validation is done using a [shuffle-split]() approach. Here, instead of splitting the data into
`n_splits` equal parts up front, in each iteration we randomize the data and sample a proportion equal
to `test_size` to use for evaluation and the remaining rows for training.
Values must be in the following range:
```javascript theme={null}
0 < test_size < 1
```
One or more metrics/scoring functions to evaluate the model with.
When none is provided, will measure default metrics appropriate for the prediction task
(classification vs. regression determined from model or type of target column). See
[sklearn model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values)
for further details.
null.
array.
Each item in array.
Values must be one of the following:
`accuracy` `balanced_accuracy` `explained_variance` `f1_micro` `f1_macro` `f1_samples` `f1_weighted` `neg_mean_squared_error` `neg_median_absolute_error` `neg_root_mean_squared_error` `precision_micro` `precision_macro` `precision_samples` `precision_weighted` `recall_micro` `recall_macro` `recall_samples` `recall_weighted` `r2`
Configure hypertuning.
Configures the optimization of model hyper-parameters via cross-validated grid- or randomized search.
The parameter values to explore.
Allows tuning of any and all of the parameters that can be set also as constants in the
"params" attribute.
Keys in this object should be strings identifying parameter names, and values should be
*lists* of values to explore for that parameter. E.g. `"depth": [3, 5, 7]`.
List of depths values to explore.
The maximum depth of the tree.
Values must be in the following range:
```javascript theme={null}
2 ≤ Item ≤ 16
```
List of iterations values to explore.
Number of iterations.
The maximum number of trees that can be built when solving machine learning problems. When using other parameters that limit the number of iterations, the final number of trees may be less than the number specified in this parameter.
Values must be in the following range:
```javascript theme={null}
1 ≤ Item < inf
```
List of values configuring max cardinality for one-hot encoding.
Maximum cardinality of variables to be one-hot encoded.
Use one-hot encoding for all categorical features with a number of different values less than or equal to this value. Other variables will be target-encoded. Note that one-hot encoding is faster than the alternatives, so decreasing this value makes it more likely slower methods will be used. See [CatBoost details](https://catboost.ai/docs/concepts/algorithm-main-stages_cat-to-numberic.html) for further information.
Values must be in the following range:
```javascript theme={null}
2 ≤ Item < inf
```
List of values configuring variable combination complexity.
The maximum number of features that can be combined when transforming categorical variables.
Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.
Values must be in the following range:
```javascript theme={null}
1 ≤ Item ≤ 4
```
List of leaf regularization strengths.
Coefficient at the L2 regularization term of the cost function.
Values must be in the following range:
```javascript theme={null}
0.0 < Item < inf
```
List of border counts.
The number of splits for numerical features.
Values must be in the following range:
```javascript theme={null}
1 ≤ Item ≤ 65535
```
List of random strengths.
The amount of randomness to use for scoring splits.
Use this parameter to avoid overfitting the model. The value multiplies the variance of a random variable (with
zero mean) that is added to the score used to select splits when a tree is grown.
Values must be in the following range:
```javascript theme={null}
0 < Item < inf
```
Which search strategy to use for optimization.
Grid search explores all possible combinations of parameters specified in `params`.
Randomized search, on the other hand, randomly samples `iterations` parameter combinations
from the distributions specified in `params`.
Values must be one of the following:
* `grid`
* `random`
How many randomly sampled parameter combinations to test in randomized search.
Values must be in the following range:
```javascript theme={null}
1 < iterations < inf
```
Configure model validation.
Allows evaluation of model performance via cross-validation with custom metrics. If not
specified, will by default perform 5-fold cross-validation with automatically selected
metrics.
Number of train-test splits to evaluate the model on.
Will split the dataset into training and test set `n_splits` times, train on the former
and evaluate on the latter using specified or automatically selected `metrics`.
What proportion of the data to use for testing in each split.
If `null` or not provided, will use [cross-validation]() to split the dataset. E.g. if `n_splits`
is 5, the dataset will be split into 5 equal-sized parts. For five iterations four parts will then
be used for training and the remaining part for testing. If `test_size` is a number between 0 and 1,
in contrast, validation is done using a [shuffle-split]() approach. Here, instead of splitting the data into
`n_splits` equal parts up front, in each iteration we randomize the data and sample a proportion equal
to `test_size` to use for evaluation and the remaining rows for training.
Values must be in the following range:
```javascript theme={null}
0 < test_size < 1
```
One or more metrics/scoring functions to evaluate the model with.
When none is provided, will measure default metrics appropriate for the prediction task
(classification vs. regression determined from model or type of target column). See
[sklearn model evaluation](https://scikit-learn.org/stable/modules/model_evaluation.html#common-cases-predefined-values)
for further details.
null.
array.
Each item in array.
Each item in array.
Values must be one of the following:
`accuracy` `balanced_accuracy` `explained_variance` `f1_micro` `f1_macro` `f1_samples` `f1_weighted` `neg_mean_squared_error` `neg_median_absolute_error` `neg_root_mean_squared_error` `precision_micro` `precision_macro` `precision_samples` `precision_weighted` `recall_micro` `recall_macro` `recall_samples` `recall_weighted` `r2`
Seed for random number generator ensuring reproducibility.
Values must be in the following range:
```javascript theme={null}
0 ≤ seed < inf
```