Data Transformations Reference - Amazon Machine Learning

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Data Transformations Reference

N-gram Transformation

The n-gram transformation takes a text variable as input and produces strings corresponding to sliding a window of (user-configurable) n words, generating outputs in the process. For example, consider the text string "I really enjoyed reading this book".

Specifying the n-gram transformation with window size=1 simply gives you all the individual words in that string:

{"I", "really", "enjoyed", "reading", "this", "book"}

Specifying the n-gram transformation with window size =2 gives you all the two-word combinations as well as the one-word combinations:

{"I really", "really enjoyed", "enjoyed reading", "reading this", "this book", "I", "really", "enjoyed", "reading", "this", "book"}

Specifying the n-gram transformation with window size = 3 will add the three-word combinations to this list, yielding the following:

{"I really enjoyed", "really enjoyed reading", "enjoyed reading this", "reading this book", "I really", "really enjoyed", "enjoyed reading", "reading this", "this book", "I", "really", "enjoyed", "reading", "this", "book"}

You can request n-grams with a size ranging from 2-10 words. N-grams with size 1 are generated implicitly for all inputs whose type is marked as text in the data schema, so you do not have to ask for them. Finally, keep in mind that n-grams are generated by breaking the input data on whitespace characters. That means that, for example, punctuation characters will be considered a part of the word tokens: generating n-grams with a window of 2 for string "red, green, blue" will yield {"red,", "green,", "blue,", "red, green", "green, blue"}. You can use the punctuation remover processor (described later in this document) to remove the punctuation symbols if this is not what you want.

To compute n-grams of window size 3 for variable var1:

"ngram(var1, 3)"

Orthogonal Sparse Bigram (OSB) Transformation

The OSB transformation is intended to aid in text string analysis and is an alternative to the bi-gram transformation (n-gram with window size 2). OSBs are generated by sliding the window of size n over the text, and outputting every pair of words that includes the first word in the window.

To build each OSB, its constituent words are joined by the "_" (underscore) character, and every skipped token is indicated by adding another underscore into the OSB. Thus, the OSB encodes not just the tokens seen within a window, but also an indication of number of tokens skipped within that same window.

To illustrate, consider the string "The quick brown fox jumps over the lazy dog", and OSBs of size 4. The six four-word windows, and the last two shorter windows from the end of the string are shown in the following example, as well OSBs generated from each:

Window, {OSBs generated}

"The quick brown fox", {The_quick, The__brown, The___fox} "quick brown fox jumps", {quick_brown, quick__fox, quick___jumps} "brown fox jumps over", {brown_fox, brown__jumps, brown___over} "fox jumps over the", {fox_jumps, fox__over, fox___the} "jumps over the lazy", {jumps_over, jumps__the, jumps___lazy} "over the lazy dog", {over_the, over__lazy, over___dog} "the lazy dog", {the_lazy, the__dog} "lazy dog", {lazy_dog}

Orthogonal sparse bigrams are an alternative for n-grams that might work better in some situations. If your data has large text fields (10 or more words), experiment to see which works better. Note that what constitutes a large text field may vary depending on the situation. However, with larger text fields, OSBs have been empirically shown to uniquely represent the text due to the special skip symbol (the underscore).

You can request a window size of 2 to 10 for OSB transformations on input text variables.

To compute OSBs with window size 5 for variable var1:

"osb(var1, 5)"

Lowercase Transformation

The lowercase transformation processor converts text inputs to lowercase. For example, given the input "The Quick Brown Fox Jumps Over the Lazy Dog", the processor will output "the quick brown fox jumps over the lazy dog".

To apply lowercase transformation to the variable var1:


Remove Punctuation Transformation

Amazon ML implicitly splits inputs marked as text in the data schema on whitespace. Punctuation in the string ends up either adjoining word tokens, or as separate tokens entirely, depending on the whitespace surrounding it. If this is undesirable, the punctuation remover transformation may be used to remove punctuation symbols from generated features. For example, given the string "Welcome to AML - please fasten your seat-belts!", the following set of tokens is implicitly generated:

{"Welcome", "to", "Amazon", "ML", "-", "please", "fasten", "your", "seat-belts!"}

Applying the punctuation remover processor to this string results in this set:

{"Welcome", "to", "Amazon", "ML", "please", "fasten", "your", "seat-belts"}

Note that only the prefix and suffix punctuation marks are removed. Punctuations that appear in the middle of a token, e.g. the hyphen in "seat-belts", are not removed.

To apply punctuation removal to the variable var1:


Quantile Binning Transformation

The quantile binning processor takes two inputs, a numerical variable and a parameter called bin number, and outputs a categorical variable. The purpose is to discover non-linearity in the variable's distribution by grouping observed values together.

In many cases, the relationship between a numeric variable and the target is not linear (the numeric variable value does not increase or decrease monotonically with the target). In such cases, it might be useful to bin the numeric feature into a categorical feature representing different ranges of the numeric feature. Each categorical feature value (bin) can then be modeled as having its own linear relationship with the target. For example, let's say you know that the continuous numeric feature account_age is not linearly correlated with likelihood to purchase a book. You can bin age into categorical features that might be able to capture the relationship with the target more accurately.

The quantile binning processor can be used to instruct Amazon ML to establish n bins of equal size based on the distribution of all input values of the age variable, and then to substitute each number with a text token containing the bin. The optimum number of bins for a numeric variable is dependent on characteristics of the variable and its relationship to the target, and this is best determined through experimentation. Amazon ML suggests the optimal bin number for a numeric feature based on data statistics in the Suggested Recipe.

You can request between 5 and 1000 quantile bins to be computed for any numeric input variable.

To following example shows how to compute and use 50 bins in place of numeric variable var1:

"quantile_bin(var1, 50)"

Normalization Transformation

The normalization transformer normalizes numeric variables to have a mean of zero and variance of one. Normalization of numeric variables can help the learning process if there are very large range differences between numeric variables because variables with the highest magnitude could dominate the ML model, no matter if the feature is informative with respect to the target or not.

To apply this transformation to numeric variable var1, add this to the recipe:


This transformer can also take a user defined group of numeric variables or the pre-defined group for all numeric variables (ALL_NUMERIC) as input:



It is not mandatory to use the normalization processor for numeric variables.

Cartesian Product Transformation

The Cartesian transformation generates permutations of two or more text or categorical input variables. This transformation is used when an interaction between variables is suspected. For example, consider the bank marketing dataset that is used in Tutorial: Using Amazon ML to Predict Responses to a Marketing Offer. Using this dataset, we would like to predict whether a person would respond positively to a bank promotion, based on the economic and demographic information. We might suspect that the person's job type is somewhat important (perhaps there is a correlation between being employed in certain fields and having the money available), and the highest level of education attained is also important. We might also have a deeper intuition that there is a strong signal in the interaction of these two variables—for example, that the promotion is particularly well-suited to customers who are entrepreneurs who earned a university degree.

The Cartesian product transformation takes categorical variables or text as input, and produces new features that capture the interaction between these input variables. Specifically, for each training example, it will create a combination of features, and add them as a standalone feature. For example, let's say our simplified input rows look like this:

target, education, job

0,, technician

0,, services

1,, admin

If we specify that the Cartesian transformation is to be applied to the categorical variables education and job fields, the resultant feature education_job_interaction will look like this:

target, education_job_interaction

0, university.degree_technician

0, high.school_services

1, university.degree_admin

The Cartesian transformation is even more powerful when it comes to working on sequences of tokens, as is the case when one of its arguments is a text variable that is implicitly or explicitly split into tokens. For example, consider the task of classifying a book as being a textbook or not. Intuitively, we might think that there is something about the book's title that can tell us it is a textbook (certain words might occur more frequently in textbooks' titles), and we might also think that there is something about the book's binding that is predictive (textbooks are more likely to be hardcover), but it's really the combination of some words in the title and binding that is most predictive. For a real-world example, the following table shows the results of applying the Cartesian processor to the input variables binding and title:

Textbook Title Binding Cartesian product of no_punct(Title) and Binding
1 Economics: Principles, Problems, Policies Hardcover {"Economics_Hardcover", "Principles_Hardcover", "Problems_Hardcover", "Policies_Hardcover"}
0 The Invisible Heart: An Economics Romance Softcover {"The_Softcover", "Invisible_Softcover", "Heart_Softcover", "An_Softcover", "Economics_Softcover", "Romance_Softcover"}
0 Fun With Problems Softcover {"Fun_Softcover", "With_Softcover", "Problems_Softcover"}

The following example shows how to apply the Cartesian transformer to var1 and var2:

cartesian(var1, var2)