link_similar_columns
Calculates all pair-wise column dependencies (by default mutual information).
Create a new network dataset where nodes (rows) represent the original dataset’s variables (its columns),
and links represent dependencies between variables (i.e. associations/correlations). By default the step
measures “mutual information” between variables.
In effect, all pair-wise “correlations” between the original dataset’s columns are calculated. A threshold
is then applied to extract only the largest (most interesting) “correlations”. These are then translated into
network links between nodes representing the original variables. Each node/row in the new dataset will also
contain information about its correlation with all other nodes (variables).
Note: in this first version, only quantitative and categorical variables will be analyzed (but not tags, lists,
embeddings etc.). You can pass a dataset with arbitrary column types, but those not supported by the selected
correlation method will be ignored in the result.
Usage
The following examples show how the step can be used in a recipe.Examples
Examples
The following example calculates all correlations without filtering, leading to a fully connected correlation network unless some correlations are exactly 0.
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
Input dataset containing arbitrary columns to calculate “correlations” for.
Outputs
Outputs
A dataset containing M rows and M+2 columns (where M is the number of columns in the input dataset).
Each row represents a variable in the original dataset, and the columns contain the “correlations” with
the remaining variables. An additional 2 columns (“targets” and “weights”) contain links connecting original
variables to other variables they’re correlated with.
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
Correlation method/statistic.
Which statistic to use to measure variable association/correlation.The default is
"mutual_information", which applies scikit-learn`s k-nearest neighbors implementations (see
here and
here).
It supports both categorical and quantitative variables, is relatively fast, but doesn’t have a natural upper bound
(i.e. values are not in the range [0, 1]).The "distance_correlation" method also
supports both categorical and quantitative variables, and has a natural upper bound of 1. It’s relatively
slow though, so make sure to select a reasonable value for n_samples."distance_correlation_fast" uses an optimized implementation
of distance correlation, but only supports quantitative variables."pearson" is the standard Pearson correlation coefficient,
which also only supports quantitative variables.Lastly, "predictive_power" calculates a version of the predictive power score (PPS).
This essentially fits a decision tree to predict variable y using only variable x as a predictor, and measures
it performance relative to a dummy/baseline prediction. It supports both categorical and quantitative variables.Values must be one of the following:mutual_informationdistance_correlationdistance_correlation_fastpearsonpredictive_power
Absolute similarity threshold.
The minimum “correlation” for the creation of a link between two variables.
Similarity threshold expressed as a quantile.
E.g. a value of 0.6 means the bottom 60% of “correlations” will be discarded. Both minima (absolute and
quantile) must be exceeded for a link to be created.Values must be in the following range:
Replacement for discarded correlations.
The weight of links whose correlations don’t pass the minimum threshold. Links with weights of
null will be
discarded (the default behavior). Can be set e.g. to 0, to generate all possible links.Number of Samples.
It represents the maximum number of samples to use when measuring “correlations”.
Maximum number of links per node.
Ranks the links by weight and keeps only the most similar targets.
The random seed used if applicable for selected
method.