Usage
The following examples show how the step can be used in a recipe.Examples
Examples
Train a regression model with default parameters. By default, a Catboost model will be trained, but this can be changed to any of the supported models by specifying the
model parameter (see below for details):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").
Inputs
Inputs
Should contain the target column and the feature columns you wish to use in the model.
Outputs
Outputs
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).
Parameters
Parameters
Train a Catboost regressor.
I.e. gradient boosted decision trees with support for categorical variables and missing values.
Target variable (labels).
Name of the column that contains your target values (labels).
Importance of each feature in the model.
Whether and how to measure each feature’s contribution to the model’s predictions. The higher the value,
the more important the feature was in the model. Only relative values are meaningful, i.e. the importance
of a feature relative to other features in the model.Also note that feature importance is usually meaningful only for models that fit the data well.The default (
null, true or "native") uses the classifier’s native feature importance measure, e.g.
prediction-value-change in the case
of Catboost, Gini importance
in the case of scikit-learn’s DecisionTreeClassifier, and the mean of absolute coefficients in the case of
logistic regression.When set to "permutation", uses permutation importance,
i.e. measures the decrease in model score when a single feature’s values are randomly shuffled. This is
considerably slower than native feature importance (the model needs to be evaluated an additional k*n times,
where k is the number of features and n the number of repetitions to average over). On the positive side it is
model-agnostic and doesn’t suffer from bias towards high cardinality features (like some tree-based feature
importances). On the negative side, it can be sensitive to strongly correlated features, as the unshuffled
correlated variable is still available to the model when shuffling the original variable.When set to false, no feature importance will be calculated.Values must be one of the following:TrueFalsenativepermutationnull
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.Properties
Properties
Numeric encoder.
Configures encoding of numeric features.
Properties
Properties
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:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
Further parameters passed to the
scaler function.
Details depend no the particular scaler used.Boolean encoder.
Configures encoding of boolean features.
Ordinal encoder.
Configures encoding of categorical features that have a natural order.
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.
Options
Options
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:
MostFrequentConstNone
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:
How to encode categories.Values must be one of the following:
OneHot Label Ordinal Binary Frequency NoneWhether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
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.Options
Options
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:
BinarizerTfIdfNone
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.Values must be in the following range:
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
Datetime encoder.
Configures encoding of datetime (timestamp) features.
Properties
Properties
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.
Array items
Array items
Each item in array.Values must be one of the following:
day dayofweek dayofyear hour minute month quarter season second week weekday weekofyear yearA 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.
Array items
Array items
Each item in array.Values must be one of the following:
daydayofweekdayofyearhourmonth
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:
MeanMedianMostFrequentConstNone
Whether and how to scale the final numerical values (across a single column).Values must be one of the following:
StandardRobustKNNNone
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
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 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.Properties
Properties
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
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.
This performs a kind of latent semantic analysis.
By default we will reduce to 200 components.Values must be in the following range:
How to scale the encoded (numerical columns).Values must be one of the following:
EuclideanKNNNormNone
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.CatBoost configuration parameters.
You can check the official documentation for more details about Catboost’s parameters here.
Properties
Properties
The maximum depth of the tree.Values must be in the following range:
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:
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 for further information.Values must be in the following range:
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:
Coefficient at the L2 regularization term of the cost function.Values must be in the following range:
The number of splits for numerical features.Values must be in the following range:
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:
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.
ForbiddenMinMax
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.
OrderedPlain
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: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).
Whether and how to assign weights to different predicted classes.
The options are:
- null: No class weighting
- Balanced: Inversely proportional to the number of samples/rows in each class
- SqrtBalanced: Using the square root of the “Balanced” option.
BalancedSqrtBalancedNoneNone
Configure model validation.
Allows evaluation of model performance via cross-validation
using custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected
metrics.
Properties
Properties
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.Values must be in the following range:What proportion of the data to use for testing in each split.
If
null or not provided, will use k-fold 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:Whether to split the data by time.
Most recent data will be used for testing and previous data for training. Assumes data is passed
already sorted ascending by time.
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.
Array items
Array items
Each item in array.Values must be one of the following:
explained_variance neg_mean_absolute_error neg_median_absolute_error neg_mean_squared_error neg_root_mean_squared_error r2Configure hypertuning.
Configures the optimization of model hyper-parameters via cross-validated grid- or randomized search.
Properties
Properties
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:gridrandom
How many randomly sampled parameter combinations to test in randomized search.Values must be in the following range:
Configure model validation.
Allows evaluation of model performance via cross-validation
using custom metrics. If not specified, will by default perform 5-fold cross-validation with automatically selected
metrics.
Properties
Properties
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.Values must be in the following range:What proportion of the data to use for testing in each split.
If
null or not provided, will use k-fold 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:Whether to split the data by time.
Most recent data will be used for testing and previous data for training. Assumes data is passed
already sorted ascending by time.
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.
Array items
Array items
Each item in array.Values must be one of the following:
explained_variance neg_mean_absolute_error neg_median_absolute_error neg_mean_squared_error neg_root_mean_squared_error r2Metric used to select best model.Values must be one of the following:
explained_variance neg_mean_absolute_error neg_median_absolute_error neg_mean_squared_error neg_root_mean_squared_error r2The 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].Properties
Properties
List of depths values to explore.
Array items
Array items
The maximum depth of the tree.Values must be in the following range:
List of iterations values to explore.
Array items
Array items
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:
List of values configuring max cardinality for one-hot encoding.
Array items
Array items
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 for further information.Values must be in the following range:
List of values configuring variable combination complexity.
Array items
Array items
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:
List of leaf regularization strengths.
Array items
Array items
Coefficient at the L2 regularization term of the cost function.Values must be in the following range:
List of border counts.
Array items
Array items
The number of splits for numerical features.Values must be in the following range:
List of random strengths.
Array items
Array items
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:
List of criterion values to explore.
Array items
Array items
Function to measure the quality of a split.
Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.Values must be one of the following:
ginientropy
List of splitter values to explore.
Array items
Array items
Strategy used to choose the split at each node.
Supported strategies are “best” to choose the best split and “random” to choose the best random split.Values must be one of the following:
bestrandom
List of max_depth values to explore.
Array items
Array items
Maximum depth of the tree.
The maximum depth of the tree. If null, then nodes are expanded until all leaves are pure
or until all leaves contain less than min_samples_split samples.Values must be in the following range:
List of min_samples_split values to explore.
Array items
Array items
Minimum number of samples required to split an internal node.
The minimum number of samples required to split an internal node. If int, then consider min_samples_split
as the minimum count. If float, then min_samples_split is a fraction and
ceil(min_samples_split * n_samples)
are the minimum number of samples for each split.List of min_samples_leaf values to explore.
Array items
Array items
Minimum number of samples required to be at a leaf node.
The minimum number of samples required to be at a leaf node. A split point at any depth will only be
considered if it leaves at least
min_samples_leaf training samples in each of the left and right branches.
This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum count. If float, then min_samples_leaf is
a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.List of max_leaf_nodes values to explore.
Array items
Array items
Grow a tree with
max_leaf_nodes in best-first fashion.
Best nodes are defined as relative reduction in impurity. If null then unlimited number of leaf nodes.List of max_features values to explore.
Array items
Array items
Number of features to consider when looking for the best split.
The number of features to consider when looking for the best split:
- If int, then consider
max_featuresfeatures at each split. - If float, then
max_featuresis a fraction andint(max_features * n_features)features are considered at each split. - If “auto”, then
max_features=sqrt(n_features). - If “sqrt”, then
max_features=sqrt(n_features). - If “log2”, then
max_features=log2(n_features). - If null, then
max_features=n_features.
max_features features.List of ccp_alpha values to explore.
Array items
Array items
Complexity parameter used for Minimal Cost-Complexity Pruning.
Minimal Cost-Complexity Pruning recursively finds the node with the “weakest link”. The weakest link is
characterized by an effective alpha, where the nodes with the smallest effective alpha are pruned first.
As alpha increases, more of the tree is pruned, which increases the total impurity of its leaves.Values must be in the following range:
Seed for random number generator ensuring reproducibility.Values must be in the following range: