Set encoding types

When you add your original table data to a generator, MOSTLY AI assigns an encoding type to each column. The assignment depends on the data inside the columns (for file uploads and cloud storage files) or on the data types defined in the database schema (for database data sources).

About encoding types

The encoding type defines how your original data is encoded before AI model training. This is a requirement for the training phase, when the data needs to be in a format that can be processed for the purposes of machine learning.

As a best practice, check the auto-detected encoding types and, if needed, select an encoding type that is a better match for your data.

Set the encoding type of a column

When you add a table to a generator in the web application, MOSTLY AI analyzes the data in each column and auto-assigns the best possible encoding type. You can always select a different encoding type.

With the Python client, you do not need to specify an encoding type when you submit a new generator configuration for training. MOSTLY AI will again analyze each column and assign the best possible encoding type. You can still use the model_encoding_type key to enforce an encoding type.

config = {
   "name": "us-census-income",
   "tables": [
      {
         "name": "us-census-income",
         "data": "https://mostly.ai/docs/datasets/us-census-income.csv",
         "columns": [
            {
               "name": "age",
               "model_encoding_type": "TABULAR_NUMERIC_DISCRETE"
            },
            {
               "name": "income",
               "model_encoding_type": "TABULAR_CATEGORICAL"
            }
         ]
      }
   ]
}
 
g = mostly.train(config = config, start=True, wait=True)

Encoding types

Tabular/Categorical

A categorical variable has a fixed set of possible values that are already present in the input data. An example of such a variable is T-shirt size, which could consist of the following categories: 'XS, S, M, L, XL'. The synthetic data will only contain categories that were present in the original data. Categorical variables thus prevent random values (for instance, 'XM, A, B') from appearing in your synthetic dataset.

If the automatic encoding type detection does not recognize a Numeric or Datetime column as such, it is encoded as Categorical.

You can control how the rare category protection mechanism works with Value protection settings and the Rare category replacement method which appear on the Model configuration page of a generator. These settings help with protecting rare categories. Rare categories may cause re-identification of outliers among your data subjects if they’re present in the resulting synthetic data.

There are two rare category protection methods available with which you can mask these categories:

Constant
Replaces rare categories with the value _RARE_.

Sample
Replaces rare categories with categories that will appear in the synthetic version of this column. The categories are sampled from the original data based on their frequency. The more frequent a category is, the more likely it will be selected.

Tabular/Numeric: Auto

With Numeric: Auto, MOSTLY AI uses heuristics to decide the most appropriate Numeric encoding type based on the data in a column. For most cases, select Numeric:Auto or leave it selected by default.

Tabular/Numeric: Discrete

Discrete treats the numeric data in the column as categorical values. You can use this option for columns that have categorical numeric codes, such as:

• ZIP codes, postal codes, country phone codes
• binary True or False that are represented as the numeric values 0 and 1
• any categorical data which are represented with numeric values

Rare category protection also applies to Numeric: Discrete columns. When you generate a synthetic dataset, MOSTLY AI replaces rare numeric categories with a non-rare numeric category in the column (applying the Sample method as explained above for Categorical columns). _RARE_ values are not generated for Numeric: Discrete columns.

Tabular/Numeric: Binned

You can use Binned for columns containing large integers or long decimals. MOSTLY AI bins the numerical values into 100 bins and considers each a category during training. During generation, MOSTLY AI samples values from the corresponding bin to generate the synthetic values in the column.

Tabular/Numeric: Digit

Numeric: Digit recognizes the data in the column as numeric values.

Tabular/Character

Use the Character encoding type to synthesize short strings with a consistent pattern, such as phone numbers, license plate numbers, company ID’s, transaction ID, and social security ID’s.

Tabular/Datetime

Datetime refers to values that contain a date part and a time part. This encoding type enables MOSTLY AI to synthesize them and generate valid and statistically representative dates and times.

The following formats are supported:

Tabular/Datetime: Relative

Datetime: Relative models the time interval between two subsequent events in the synthetic dataset. This encoding type causes the time between events to become very accurate, but the dates become less accurate.

Tabular/Latitude, Longitude

Use the Latitude, Longitude encoding type to synthesize geolocation coordinates.

MOSTLY AI requires a geolocation coordinate to be encoded in a single field with the latitude and longitude as comma-separated values. The latitude must be on the comma’s left side and the longitude on the right.

The values must be in decimal degrees format and range from -90 to 90 for latitude and -180 to 180 for longitude. Their precision cannot be larger than five digits after the decimal dot. This translates to an accuracy of approx. 1 meter. Any additional digits will be ignored.

Language/Text

For synthetic text, MOSTLY AI provides a non-pre-trained LSTM model as well as the option to use a text generation model from HuggingFace (opens in a new tab) that is configured for use in MOSTLY AI.

HuggingFace text models

When you deploy MOSTLY AI in your organization's private cloud, you can integrate with as many HuggingFace text generation models as needed. For configuration details, see the Models configuration page.

On the MOSTLY AI Platform at https://app.mostly.ai (opens in a new tab), you can find the following models available.

MOSTLY AI LSTM

Use the LSTM model type to synthesize unstructured natural language texts up to 1,000 characters long. This model is not pre-trained.

You can use this encoding type to generate realistic, representative, and anonymous financial transaction texts, short user feedback, medical assessments, PII fields, etc. As the resulting synthetic texts are representative of the terms, tokens, and their co-occurrence in the original data, they can be confidently used in analytics and machine learning use cases, such as sentiment analysis and named-entity recognition. Even though they might look noisy and not very human-readable, they will work perfectly for these use cases.

Our text synthesis model is language-agnostic and doesn’t contain the biases of some pre-trained models—any content is solely learned from the original training data. This means that it can process any language, vernacular, and slang present in the original data.

The amount of data required to produce usable results depends on the diversity of the original texts' vocabulary, categories, names, etc. As a rule of thumb, the more structure there is, the fewer samples are needed.

The synthetic texts are generated in a context-aware manner—the messages from a teenager are different from those of an 85-year old grandmother, for instance. By considering the other attributes of a synthetic subject’s profile, MOSTLY AI is capable of synthesizing appropriate natural language texts for each of them.

Below, you can find two examples. The first example demonstrates MOSTLY AI’s ability to synthesize entirely new names from a multilingual dataset. And the second example shows the result of synthesizing Tripadvisor reviews. Here you can see that the resulting texts accurately retain the context of the establishment they discuss (Restaurant or Hotel) and the synthesized rating.

Multilingual names dataset

   Nationality     Name
1: Czech           Svoboda
2: Greek           Chrysanthopoulos
3: Spanish         Ventura
4: Russian         Gagarin
5: Japanese        Yokoyama
6: English         Parsons
7: Spanish         Ruiz
8: Russian         Chekhov
9: English         Blake
10: English         Wigley

    Nationality     Name
 1: English         Olsewood
 2: German          Kort
 3: Japanese        Misaghi
 4: English         Roger
 5: Russian         Lusov
 6: Russian         Zhuszenko
 7: Japanese        Noraghi
 8: English         Dalman
 9: Russian         Michov
10: Polish          Poskan
11: Arabic          Shaif

Tripadvisor reviews

   Establishment    Rating  Review
1: Restaurant       6       Not bad, great interior but let down by
                            unimaginative food. Perfectly good for a
                            quick lunch or drink though, good ales
2: Hotel            2       Awful!!!! stunk of smoke guttering outside
                            window ledge filled with cigarette ends and
                            bottles. NOISY air con unit in room, husband
                            had no sleep.
3: Restaurant       8       Helpful staff, pleasant enough with quick
                            service. Sat at bar by revolving food server.
                            Everything seemed nice and fresh. Good value.
4: Hotel            8       We stayed in a standard room at the hotel.
                            The room was adequate, though a bit short on
                            cupboard drawer space.
5: Hotel            4       Expected much more from here and they just
                            didn't deliver, for the price of the room it
                            was no different than any of the other cheaper
                            aparthotels.

   Establishment    Rating  Review
1: Restaurant       8       My only complaint are the portion sizes. Lovely
                            restaurant with good food, though.
2: Restaurant       8       I'm a year-round regular. Service is really
                            friendly. The starters are OK and the seafood
                            buffet is amazing and tasty. Overall a nice menu
                            throughout and our children love it.
3: Hotel            8       This is a fantastic hotel. Great food but few
                            options, a brilliant room and spent an excellent
                            time. Very clean environment and a high level of
                            service.
4: Hotel            2       I booked an offer for a spa day. The food was
                            below-average, the room was dated, smelled of
                            fried fish, and the staff has an attitude.
5: Hotel            2       We've been here before. But for £70, the rooms
                            are still poor and glamourless. We spent a few
                            days with 5 people and 4 were not impressed.