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Infer missing

inference · model · missing data · NaN · imputation

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 regressor or classifier), which then predicts (imputes) the missing values. Only simple numerical or categorical input data can be imputed.

Usage


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

Step signature
infer_missing(ds: dataset, {"param": value}) -> (predicted: column)

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

To automatically select the a model (classifier vs regressor) based on the kind of target variable (numeric or categorical), simply use:

Example call (in recipe editor)
infer_missing(ds, {"target": "incomplete_col"}) -> (ds.complete_col)

Inputs


ds: dataset

A dataset containing the column to be imputed as well as any other column to use as predictors in the model.

Outputs


predicted: column

A column containing the predicted values for all rows.

Parameters


target: string

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.


infer_all: boolean = True

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.


params: object

CatBoost configuration parameters. You can check the official documentation for more details about Catboost's parameters here.

Items in params

depth: integer = 6

Depth of the tree.

Range: 2 ≤ depth ≤ 16


nan_mode: string = "Min"

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.

Must be one of: "Forbidden", "Min", "Max"


iterations: integer | null = 1000

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.

Range: 1 ≤ iterations < inf


allow_writing_files: boolean = False

Allow Writing Files. Allow to write analytical and snapshot files during training. If set to “false”, the snapshot and data visualization tools are unavailable.


random_seed: integer = 0

The random seed used for training.


one_hot_max_size: integer = 10

One Hot Max Size. Use one-hot encoding for all categorical features with a number of different values less than or equal to the given parameter value. Ctrs are not calculated for such features.

Range: 2 ≤ one_hot_max_size < inf


max_ctr_complexity: number = 2

The maximum number of features that can be combined. Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.

Range: 1 ≤ max_ctr_complexity < inf


boosting_type: string = "Plain"

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.

Must be one of: "Ordered", "Plain"


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"


scaler: null | string

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

Must be one of: "Standard", "Robust", "KNN"


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"


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"


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

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"


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"


scaler: null | string

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

Must be one of: "Standard", "Robust", "KNN"

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

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"


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"


scaler: null | string

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

Must be one of: "Standard", "Robust", "KNN"


high_cardinality: object

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

Items in high_cardinality

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"


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"


scaler: null | string

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

Must be one of: "Standard", "Robust", "KNN"


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

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


encoder: null | string

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

Must be one of: "Binarizer", "TfIdf"


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"

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

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


encoder: null | string

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

Must be one of: "Binarizer", "TfIdf"


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"


high_cardinality: object

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

Items in high_cardinality

indicate_missing: boolean

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


encoder: null | string

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

Must be one of: "Binarizer", "TfIdf"


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"


datetime: object

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

Items in datetime

indicate_missing: boolean

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


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"


component_scaler: null | string

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

Must be one of: "Standard", "Robust", "KNN"


vector_scaler: null | string

How to scale the encoded (numerical columns).

Must be one of: "Euclidean", "KNN", "Norm"


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

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


scaler: null | string

How to scale the encoded (numerical columns).

Must be one of: "Euclidean", "KNN", "Norm"


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

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.

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"


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


validate: object | null

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.

Items in validate

n_splits: integer | null = 5

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


test_size: number | null

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.

Range: 0 < test_size < 1


metrics: null | array[string]

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 for further details.

Must be one of: "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"


tune: object

Configure hypertuning. Configures the optimization of model hyper-parameters via cross-validated grid- or randomized search.

Items in tune

params: object

CatBoost configuration parameters. You can check the official documentation for more details about Catboost's parameters here.

Items in params

depth: integer = 6

Depth of the tree.

Range: 2 ≤ depth ≤ 16


nan_mode: string = "Min"

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.

Must be one of: "Forbidden", "Min", "Max"


iterations: integer | null = 1000

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.

Range: 1 ≤ iterations < inf


allow_writing_files: boolean = False

Allow Writing Files. Allow to write analytical and snapshot files during training. If set to “false”, the snapshot and data visualization tools are unavailable.


random_seed: integer = 0

The random seed used for training.


one_hot_max_size: integer = 10

One Hot Max Size. Use one-hot encoding for all categorical features with a number of different values less than or equal to the given parameter value. Ctrs are not calculated for such features.

Range: 2 ≤ one_hot_max_size < inf


max_ctr_complexity: number = 2

The maximum number of features that can be combined. Each resulting combination consists of one or more categorical features and can optionally contain binary features in the following form: “numeric feature > value”.

Range: 1 ≤ max_ctr_complexity < inf


boosting_type: string = "Plain"

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.

Must be one of: "Ordered", "Plain"


strategy: string = "grid"

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

Must be one of: "grid", "random"


iterations: integer = 10

How many randomly sampled parameter combinations to test in randomized search.

Range: 1 < iterations < inf


validate: object | null

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.

Items in validate

n_splits: integer | null = 5

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


test_size: number | null

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.

Range: 0 < test_size < 1


metrics: null | array[string]

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 for further details.


scorer: string

Metric used to select best model.

Must be one of: "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: integer

Seed for random number generator ensuring reproducibility.

Range: 0 ≤ seed < inf

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