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A link (or association) A->B is created between items A and B if the presence of A makes the presence of B in the same session N times more likely. For further details about the algorithm see e.g. association rule learning.

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Usage

The following example shows how the step can be used in a recipe.

Examples

  • Example 1
  • Signature
The following call creates links between pairs of items A and B, if:
  • A occurs in at least 7 sessions
  • B occurs in at least 25% of sessions containing A
  • The presence of A in a session makes the presence of B in the same session at least twice as likely.
Note that the last condition is equivalent to saying that the overall frequency of B in all sessions must be less than 12.5% (half of 25%). In other words, a minimum lift of 2 means that the frequency of B, in sessions already containing A, must be twice the background frequency of B in general.As an example, the percentage of shopping baskets containing milk (item B) may be 10%. However, amongst those baskets already containing cereals, the percentage containing milk is likely to be higher. If milk occured e.g. in 30% of baskets also having cereals, than the lift of the rule cereal->milk would be 3. The buying of cereal make the buying of milk 3 times more likely.
link_session_items(items.id, sessions.item_ids, {
  "min_support": 7
  "min_confidence": 25
  "min_lift": 2
}) -> (items.targets, items.weights)

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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").
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items
column[category|number]
required
A column containing the IDs of items to analyze.
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sessions
column[list[category]|list[number]]
required
A column containing lists of IDs corresponding to items in the same sessions, basket etc.
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targets
column
required
A column containing for each item a list of IDs (row numbers) identfying other items it will be linked to.
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weights
column
required
A column containing for each item a list of weights identfying the “importance” of each link to other items identified in the targets column.

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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

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min_support
[number, integer]
default:"10"
Minimum Support. Minimum support of a rule antecedent. If it is < 1 it will be taken as a proportion. In any other case it will be expected as a positive integer representing the count. Create link A->B only if A occurred in at least this many sessions.
  • number
  • integer
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{_}
number
number.Values must be in the following range:
0 < {_} < 1
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min_confidence
number
default:"20"
Minimum Confidence. Expressed as a rule as a percentage. Include link A->B only if B occurred in at least this percentage of sessions also containing A.Values must be in the following range:
0 ≤ min_confidence ≤ 100
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min_lift
[number, null]
Minimum Lift. Expressed as multipler/ratio. Include link A->B only if A makes the presence of B in the same sessions at least this many times more likely.
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weight_metric
string
default:"rule_lift_pct"
Metric for link weight.Values must be one of the following:itemset_support_abs itemset_support_pct filter_metric_abs filter_metric_pct antecedent_support_abs antecedent_support_pct consequent_support_abs consequent_support_pct rule_confidence_pct rule_lift_abs rule_lift_pct
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