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.
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:
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 = False
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",
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
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
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
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
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
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
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
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",
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
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",
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
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",
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
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",
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
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",
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
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",
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
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