Deduplicating misspelled categories#

Real world datasets often come with misspellings, for instance in manually inputted categorical variables. Such misspelling break data analysis steps that require exact matching, such as a GROUP BY operation.

Merging multiple variants of the same category is known as deduplication. It is implemented in skrub with the deduplicate() function.

Deduplication relies on unsupervised learning. It finds structures in the data without providing a-priori known and explicit labels/categories. Specifically, measuring the distance between strings can be used to find clusters of strings that are similar to each other (e.g. differ only by a misspelling) and hence, flag and regroup potentially misspelled category names in an unsupervised manner.

A typical use case#

Let’s take an example: as a data scientist, your job is to analyze the data from a hospital ward. In the data, we notice that in most cases, the doctor prescribes one of three following medications: “Contrivan”, “Genericon” or “Zipholan”.

However, data entry is manual and - either because the doctor’s handwriting was hard to decipher, or due to mistakes during input - there are multiple spelling mistakes in the dataset.

Let’s generate this example dataset:

import pandas as pd
import numpy as np
from skrub.datasets import make_deduplication_data

duplicated_names = make_deduplication_data(
    examples=["Contrivan", "Genericon", "Zipholan"],  # our three medication names
    entries_per_example=[500, 100, 1500],  # their respective number of occurrences
    prob_mistake_per_letter=0.05,  # 5% probability of typo per letter
    random_state=42,  # set seed for reproducibility
)

duplicated_names[:5]
['Contrivan', 'Cvntrivan', 'Contrivan', 'Coqtrivan', 'Contriian']

We then extract the unique medication names in the data and visualize how often they appear:

import matplotlib.pyplot as plt

unique_examples, counts = np.unique(duplicated_names, return_counts=True)

plt.figure(figsize=(10, 15))
plt.barh(unique_examples, counts)
plt.ylabel("Medication name")
plt.xlabel("Count")
plt.show()
05 deduplication

We clearly see the structure of the data: the three original medications (“Contrivan”, “Genericon” and “Zipholan”) are the most common ones, but there are many spelling mistakes or slight variations of the original names.

The idea behind deduplicate() is to use the fact that the string distance of misspelled medications will be closest to their original (most frequent) medication name - and therefore form clusters.

Deduplication: suggest corrections of misspelled names#

The deduplicate() function uses clustering based on string similarities to group duplicated names.

Let’s deduplicate our data:

Contrivan    Contrivan
Cvntrivan    Contrivan
Contrivan    Contrivan
Coqtrivan    Contrivan
Contriian    Contrivan
dtype: object

And that’s it! We now have the deduplicated data.

We can visualize the distribution of categories in the deduplicated data:

05 deduplication

Here, the silhouette score finds the ideal number of clusters (3) and groups the spelling mistakes.

In practice, the translation/deduplication will often be imperfect and require some tweaks. In this case, we can construct and update a translation table based on the data returned by deduplicate().

# create a table that maps original to corrected categories
translation_table = pd.Series(deduplicated_data, index=duplicated_names)

# remove duplicates in the original data
translation_table = translation_table[~translation_table.index.duplicated(keep="first")]

translation_table.head()
Contrivan    Contrivan
Cvntrivan    Contrivan
Coqtrivan    Contrivan
Contriian    Contrivan
Contaivan    Contrivan
dtype: object

In this table, we have the category name on the left, and the cluster it was translated to on the right. If we want to adapt the translation table, we can modify it manually.

Visualizing string pair-wise distance between names#

Below, we use a heatmap to visualize the pairwise-distance between medication names. A darker color means that two medication names are closer together (i.e. more similar), a lighter color means a larger distance.

from skrub import compute_ngram_distance
from scipy.spatial.distance import squareform

ngram_distances = compute_ngram_distance(unique_examples)
square_distances = squareform(ngram_distances)

import seaborn as sns

fig, ax = plt.subplots(figsize=(14, 12))
sns.heatmap(
    square_distances, yticklabels=unique_examples, xticklabels=unique_examples, ax=ax
)
plt.show()
05 deduplication

We have three clusters appearing - the original medication names and their misspellings that form a cluster around them.

Conclusion#

In this example, we have seen how to use the deduplicate() function to automatically detect and correct misspelled category names.

Note that deduplication is especially useful when we either know our ground truth (e.g. the original medication names), or when the similarity across strings does not carry useful information for our machine learning task. Otherwise, we prefer using encoding methods such as GapEncoder or MinHashEncoder.

Total running time of the script: (0 minutes 4.728 seconds)

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