The features field in neptune_ml - Amazon Neptune
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The features field in neptune_ml

Property values and RDF literals come in different formats and data types. To achieve good performance in machine learning, it is essential to convert those values to numerical encodings known as features.

Neptune ML performs feature extraction and encoding as part of the data-export and data-processing steps, as described in Feature encoding in Neptune ML.

For property-graph datasets, the export process automatically infers auto features for string properties and for numeric properties that contain multiples values. For numeric properties containing single values, it infers numerical features. For date properties it infers datetime features.

If you want to override an auto-inferred feature specification, or add a bucket numerical, TF-IDF, FastText, or SBERT specification for a property, you can control the feature encoding using the features field.

Note

You can only use the features field to control the feature specifications for property-graph data, not for RDF data.

For free-form text, Neptune ML can use several different models to convert the sequence of tokens in a string property value into a fixed-size real-value vector:

  • text_fasttext   –   Uses fastText encoding. This is the recommended encoding for features that use one and only one of the five languages that fastText supports.

  • text_sbert   –   Uses the Sentence BERT (SBERT) encoding models. This is the recommended encoding for text that text_fasttext does not support.

  • text_word2vec   –   Uses Word2Vec algorithms originally published by Google to encode text. Word2Vec only supports English.

  • text_tfidf   –   Uses a term frequency–inverse document frequency (TF-IDF) vectorizer for encoding text. TF-IDF encoding supports statistical features that the other encodings do not.

The features field contains a JSON array of node property features. Objects in the array can contain the following fields:

The node field in features

The node field specifies a property-graph label of a feature vertex. For example:

  "node": "Person"

If a vertex has multiple labels, use an array to contain them. For example:

  "node": ["Admin", "Person"]

The edge field in features

The edge field specifies the edge type of a feature edge. An edge type consists of an array containing the property-graph label(s) of the source vertex, the property-graph label of the edge, and the property-graph label(s) of the destination vertex. You must supply all three values when specifying an edge feature. For example:

  "edge": ["User", "reviewed", "Movie"]

If a source or destination vertex of an edge type has multiple labels, use another array to contain them. For example:

  "edge": [["Admin", "Person"]. "edited", "Post"]

The property field in features

Use the property parameter to specify a property of the vertex identified by the node parameter. For example:

  "property" : "age"

Possible values of the type field for features

The type parameter specifies the type of feature being defined. For example:

  "type": "bucket_numerical"
Possible values of the type parameter

  • "auto"   –   Specifies that Neptune ML should automatically detect the property type and apply a proper feature encoding. An auto feature can also have an optional separator field.

    See Auto feature encoding in Neptune ML.

  • "category"   –   This feature encoding represents a property value as one of a number of categories. In other words, the feature can take one or more discrete values. A category feature can also have an optional separator field.

    See Categorical features in Neptune ML.

  • "numerical"   –   This feature encoding represents numerical property values as numbers in a continuous interval where "greater than" and "less than" have meaning.

    A numerical feature can also have optional norm, imputer, and separator fields.

    See Numerical features in Neptune ML.

  • "bucket_numerical"   –   This feature encoding divides numerical property values into a set of buckets or categories.

    For example, you could encode people's ages in 4 buckets: kids (0-20), young-adults (20-40), middle-aged (40-60), and elders (60 and up).

    A bucket_numerical feature requires a range and a bucket_cnt field, and can optionally also include an imputer and/or slide_window_size field.

    See Bucket-numerical features in Neptune ML.

  • "datetime"   –   This feature encoding represents a datetime property value as an array of these categorical features: year, month, weekday, and hour.

    One or more of these four categories can be eliminated using the datetime_parts parameter.

    See Datetime features in Neptune ML.

  • "text_fasttext"   –   This feature encoding converts property values that consist of sentences or free-form text into numeric vectors using fastText models. It supports five languages, namely English (en), Chinese (zh), Hindi (hi), Spanish (es), and French (fr). For text property values in any one those five languages, text_fasttext is the recommended encoding. However, it cannot handle cases where the same sentence contains words in more than one language.

    For other languages than the ones that fastText supports, use text_sbert encoding.

    If you have many property value text strings longer than, say, 120 tokens, use the max_length field to limit the number of tokens in each string that "text_fasttext" encodes.

    See fastText encoding of text property values in Neptune ML.

  • "text_sbert"   –   This encoding converts text property values into numeric vectors using Sentence BERT (SBERT) models. Neptune supports two SBERT methods, namely text_sbert128, which is the default if you just specify text_sbert, and text_sbert512. The difference between them is the maximum number of tokens in a text property that gets encoded. The text_sbert128 encoding only encodes the first 128 tokens, while text_sbert512 encodes up to 512 tokens. As a result, using text_sbert512 can require more processing time than text_sbert128. Both methods are slower than text_fasttext.

    The text_sbert* methods support many languages, and can encode a sentence that contains more than one language.

    See Sentence BERT (SBERT) encoding of text features in Neptune ML.

  • "text_word2vec"   –   This encoding converts text property values into numeric vectors using Word2Vec algorithms. It only supports English.

    See Word2Vec encoding of text features in Neptune ML.

  • "text_tfidf"   –   This encoding converts text property values into numeric vectors using a term frequency–inverse document frequency (TF-IDF) vectorizer.

    You define the parameters of a text_tfidf feature encoding using the ngram_range field, the min_df field, and the max_features field.

    See TF-IDF encoding of text features in Neptune ML.

  • "none"   –   Using the none type causes no feature encoding to occur. The raw property values are parsed and saved instead.

    Use none only if you plan to perform your own custom feature encoding as part of custom model training.

The norm field

This field is required for numerical features. It specifies a normalization method to use on numeric values:

"norm": "min-max"

The following normalization methods are supported:

  • "min-max"   –   Normalize each value by subtracting the minimum value from it and then dividing it by the difference between the maximum value and the minimum.

  • "standard"   –   Normalize each value by dividing it by the sum of all the values.

  • "none"   –   Don't normalize the numerical values during encoding.

See Numerical features in Neptune ML.

The language field

The language field specifies the language used in text property values. Its usage depends on the text encoding method:

  • For text_fasttext encoding, this field is required, and must specify one of the following languages:

    • en   (English)

    • zh   (Chinese)

    • hi   (Hindi)

    • es   (Spanish)

    • fr   (French)

  • For text_sbert encoding, this field is not used, since SBERT encoding is multilingual.

  • For text_word2vec encoding, this field is optional, since text_word2vec only supports English. If present, it must specify the name of the English language model:

    "language" : "en_core_web_lg"

  • For text_tfidf encoding, this field is not used.

The max_length field

The max_length field is optional for text_fasttext features, where it specifies the maximum number of tokens in an input text feature that will be encoded. Input text that is longer than max_length is truncated. For example, setting max_length to 128 indicates that any tokens after the 128th in a text sequence will be ignored:

"max_length": 128

The separator field

This field is used optionally with category, numerical and auto features. It specifies a character that can be used to split a property value into multiple categorical values or numerical values:

"separator": ";"

Only use the separator field when the property stores multiple delimited values in a single string, such as "Actor;Director" or "0.1;0.2".

See Categorical features, Numerical features, and Auto encoding.

The range field

This field is required for bucket_numerical features. It specifies the range of numerical values that are to be divided into buckets, in the format [lower-bound, upper-bound]:

"range" : [20, 100]

If a property value is smaller than the lower bound then it is assigned to the first bucket, or if it's larger than the upper bound, it's assigned to the last bucket.

See Bucket-numerical features in Neptune ML.

The bucket_cnt field

This field is required for bucket_numerical features. It specifies the number of buckets that the numerical range defined by the range parameter should be divided into:

"bucket_cnt": 10

See Bucket-numerical features in Neptune ML.

The slide_window_size field

This field is used optionally with bucket_numerical features to assign values to more than one bucket:

"slide_window_size": 5

The way a slide window works is that Neptune ML takes the window size s and transforms each numeric value v of a property into a range from v - s/2 through v + s/2 . The value is then assigned to every bucket that the range overlaps.

See Bucket-numerical features in Neptune ML.

The imputer field

This field is used optionally with numerical and bucket_numerical features to provide an imputation technique for filling in missing values:

"imputer": "mean"

The supported imputation techniques are:

  • "mean"

  • "median"

  • "most-frequent"

If you don't include the imputer parameter, data preprocessing halts and exits when a missing value is encountered.

See Numerical features in Neptune ML and Bucket-numerical features in Neptune ML.

The max_features field

This field is used optionally by text_tfidf features to specify the maximum number of terms to encode:

"max_features": 100

A setting of 100 causes the TF-IDF vectorizer to encode only the 100 most common terms. The default value if you don't include max_features is 5,000.

See TF-IDF encoding of text features in Neptune ML.

The min_df field

This field is used optionally by text_tfidf features to specify the minimum document frequency of terms to encode:

"min_df": 5

A setting of 5 indicates that a term must appear in at least 5 different property values in order to be encoded.

The default value if you don't include the min_df parameter is 2.

See TF-IDF encoding of text features in Neptune ML.

The ngram_range field

This field is used optionally by text_tfidf features to specify what size sequences of words or tokens should be considered as potential individual terms to encode:

"ngram_range": [2, 4]

The value [2, 4] specifies that sequences of 2, 3 and 4 words should be considered as potential individual terms.

The default if you don't explicitly set ngram_range is [1, 1], meaning that only single words or tokens are considered as terms to encode.

See TF-IDF encoding of text features in Neptune ML.

The datetime_parts field

This field is used optionally by datetime features to specify which parts of the datetime value to encode categorically: 

"datetime_parts": ["weekday", "hour"]

If you don't include datetime_parts, by default Neptune ML encodes the year, month, weekday and hour parts of the datetime value. The value ["weekday", "hour"] indicates that only the weekday and hour of datetime values should be encoded categorically in the feature.

If one of the parts does not have more than one unique value in the training set, it is not encoded.

See Datetime features in Neptune ML.