Link similar rows

network • similarity

Create network links calculating similarity between multidimensional and multitype documents.

Creates a link between each row and the N rows most similar to it. Broadly, the similarity between two rows is a weighted similarity of the individual columns. The step accepts all data types, i.e. texts, quantitative, categorical columns etc.

Usage

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

Step signature

link_similar_rows(ds: dataset, {
    "param": value
}) -> (targets: column, weights: 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

Example call (in recipe editor)

link_similar_rows(ds[["bio", "salary", "age", "department"]]) -> (links)

Inputs

---

ds: dataset

A dataset containing the columns to be included in the calculation of pair-wise similarities. Note: a subset of columns can always be selected in a recipe using the ds[["column1", "column2", ...]] syntax. Or to exclude: ds[!["column1", "column2", ...]].

Outputs

---

targets: column

A column containing for each row a list of row numbers identfying all other rows it is similar to.

---

weights: column

A column containing for each row a list of weights identfying the "importance" of each link to other rows identified in the targets column (identifying how similar the rows are).

Parameters

---

n_similar_docs: integer = 10

Number of similar docs.

Range: 1 ≤ n_similar_docs < inf

---

minhash: boolean = False

Whether to use minhash as a similarity measure.

---

n_terms: integer = 15

Number of terms to use.

Range: 1 ≤ n_terms < inf

---

min_term_freq: integer = 2

Minimum term frequency. (For TFIDF).

Range: 0 ≤ min_term_freq < inf

---

min_doc_freq: integer = 2

Minimum doc frequency. (For TFIDF).

Range: 0 ≤ min_doc_freq < inf

---

max_doc_perc: number = 0.9

Maximum doc percentage. (For TFIDF).

Range: 0 ≤ max_doc_perc ≤ 1

---

min_should_match: integer = 2

Minimum of terms that should match.

Range: 0 ≤ min_should_match < inf

---

separator: string = "[\W0-9]{1,100}"

Regex to recognize as string separator.

---

stopwords_langs: string = "ES,EN"

Languages to use for stopwords. supports ES, EN and both using commas "ES,EN".

Must be one of: "ES", "EN", "ES,EN", "EN,ES"