# Examples of using parameters within additionalParams for tuning model-training configuration
Examples

 The following examples demonstrate how to utilize the "additionalParams" feature in property-graph and RDF data models to configure various aspects of the model training process for a Neptune ML application. The examples cover a wide range of functionality, including specifying default split rates for training/validation/test data, defining node classification, regression, and link prediction tasks, as well as configuring different feature types such as numerical buckets, text embeddings, datetime, and categorical data. These detailed configurations allow you to tailor the machine learning pipeline to your specific data and modeling requirements, unlocking the full potential of the Neptune ML capabilities. 

**Contents**
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## Property-graph examples using additionalParams
](#machine-learning-property-graph-additionalParams-examples)
  + [

### Specifying a default split rate for model-training configuration
](#machine-learning-property-graph-additionalParams-default-split-rate-example)
  + [

### Specifying a node-classification task for model-training configuration
](#machine-learning-property-graph-additionalParams-node-classification-example)
  + [

### Specifying a multi-class node classification task for model-training configuration
](#machine-learning-property-graph-additionalParams-multi-class-example)
  + [

### Specifying a node regression task for model-training configuration
](#machine-learning-property-graph-additionalParams-node-regression-example)
  + [

### Specifying an edge-classification task for model-training configuration
](#machine-learning-property-graph-additionalParams-edge-classification-example)
  + [

### Specifying a multi-class edge classification task for model-training configuration
](#machine-learning-property-graph-additionalParams-multi-edge-classification-example)
  + [

### Specifying an edge regression for model-training configuration
](#machine-learning-property-graph-additionalParams-edge-regression-example)
  + [

### Specifying a link prediction task for model-training configuration
](#machine-learning-property-graph-additionalParams-link-prediction-example)
  + [

### Specifying a numerical bucket feature
](#machine-learning-property-graph-additionalParams-numeric-bucket-example)
  + [

### Specifying a `Word2Vec` feature
](#machine-learning-property-graph-additionalParams-word2vec-example)
  + [

### Specifying a `FastText` feature
](#machine-learning-property-graph-additionalParams-fasttext-example)
  + [

### Specifying a `Sentence BERT` feature
](#machine-learning-property-graph-additionalParams-sbert-example)
  + [

### Specifying a `TF-IDF` feature
](#machine-learning-property-graph-additionalParams-tf-idf-example)
  + [

### Specifying a `datetime` feature
](#machine-learning-property-graph-additionalParams-datetime-example)
  + [

### Specifying a `category` feature
](#machine-learning-property-graph-additionalParams-category-example)
  + [

### Specifying a `numerical` feature
](#machine-learning-property-graph-additionalParams-numerical-example)
  + [

### Specifying an `auto` feature
](#machine-learning-property-graph-additionalParams-auto-example)
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## RDF examples using `additionalParams`
](#machine-learning-RDF-additionalParams-examples)
  + [

### Specifying a default split rate for model-training configuration
](#machine-learning-RDF-additionalParams-default-split-rate-example)
  + [

### Specifying a node-classification task for model-training configuration
](#machine-learning-RDF-additionalParams-node-classification-example)
  + [

### Specifying a node regression task for model-training configuration
](#machine-learning-RDF-additionalParams-node-regression-example)
  + [

### Specifying a link prediction task for particular edges
](#machine-learning-RDF-additionalParams-link-prediction-example)
  + [

### Specifying a link prediction task for all edges
](#machine-learning-RDF-additionalParams-link-prediction-example)

## Property-graph examples using additionalParams
Property-graph examples

### Specifying a default split rate for model-training configuration
Setting the default split rate

In the following example, the `split_rate` parameter sets the default split rate for model training. If no default split rate is specified, the training uses a value of [0.9, 0.1, 0.0]. You can override the default value on a per-target basis by specifying a `split_rate` for each target.

In the following example, the `default split_rate` field indicates that a split rate of `[0.7,0.1,0.2]` should be used unless overridden on a per-target basis:"

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "split_rate": [0.7,0.1,0.2],
    "targets": [
      (...)
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a node-classification task for model-training configuration
Node classification

To indicate which node property contains labeled examples for training purposes, add a node classification element to the `targets` array, using `"type" : "classification"`. Add a `split_rate` field if you want to override the default split rate.

In the following example, the `node` target indicates that the `genre` property of each `Movie` node should be treated as a node class label. The `split_rate` value overrides the default split rate:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "node": "Movie",
        "property": "genre",
        "type": "classification",
        "split_rate": [0.7,0.1,0.2]
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a multi-class node classification task for model-training configuration
Multi-class node classification

To indicate which node property contains multiple labeled examples for training purposes, add a node classification element to the targets array, using `"type" : "classification"`, and `separator` to specify a character that can be used to split a target property value into multiple categorical values. Add a `split_rate` field if you want to override the default split rate.

In the following example, the `node` target indicates that the `genre` property of each `Movie` node should be treated as a node class label. The `separator` field indicates that each genre property contains multiple semicolon-separated values:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "node": "Movie",
        "property": "genre",
        "type": "classification",
        "separator": ";"
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a node regression task for model-training configuration
Node regression

To indicate which node property contains labeled regressions for training purposes, add a node regression element to the targets array, using `"type" : "regression"`. Add a split\$1rate field if you want to override the default split rate.

The following `node` target indicates that the `rating` property of each `Movie` node should be treated as a node regression label:

```
    "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "node": "Movie",
        "property": "rating",
        "type" : "regression",
        "split_rate": [0.7,0.1,0.2]
      }
    ],
    "features": [
      ...
    ]
  }
}
```

### Specifying an edge-classification task for model-training configuration
Edge-classification

To indicate which edge property contains labeled examples for training purposes, add an edge element to the `targets` array, using `"type" : "regression"`. Add a split\$1rate field if you want to override the default split rate.

The following `edge` target indicates that the `metAtLocation` property of each `knows` edge should be treated as an edge class label:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "edge": ["Person", "knows", "Person"],
        "property": "metAtLocation",
        "type": "classification"
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a multi-class edge classification task for model-training configuration
Multi-class edge classification

To indicate which edge property contains multiple labeled examples for training purposes, add an edge element to the `targets` array, using `"type" : "classification"`, and a `separator` field to specify a character used to split a target property value into multiple categorical values. Add a `split_rate` field if you want to override the default split rate.

The following `edge` target indicates that the `sentiment` property of each `repliedTo` edge should be treated as an edge class label. The separator field indicates that each sentiment property contains multile comma-separated values:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "edge": ["Person", "repliedTo", "Message"],
        "property": "sentiment",
        "type": "classification",
        "separator": ","
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying an edge regression for model-training configuration
Edge regression

To indicate which edge property contains labeled regression examples for training purposes, add an `edge` element to the `targets` array, using `"type" : "regression"`. Add a `split_rate` field if you want to override the default split rate.

The following `edge` target indicates that the `rating` property of each `reviewed` edge should be treated as an edge regression:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "edge": ["Person", "reviewed", "Movie"],
        "property": "rating",
        "type" : "regression"
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a link prediction task for model-training configuration
Link prediction

To indicate which edges should be used for link prediction training purposes, add an edge element to the targets array using `"type" : "link_prediction"`. Add a `split_rate` field if you want to override the default split rate.

The following `edge` target indicates that `cites` edges should be used for link prediction:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "edge": ["Article", "cites", "Article"],
        "type" : "link_prediction"
      }
    ],
    "features": [
      (...)
    ]
  }
}
```

### Specifying a numerical bucket feature
Numerical buckets

You can specify a numerical data feature for a node property by adding `"type": "bucket_numerical"` to the `features` array.

The following `node` feature indicates that the `age` property of each `Person` node should be treated as a numerical bucket feature:

```
  "additionalParams": {
  "neptune_ml": {
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Person",
        "property": "age",
        "type": "bucket_numerical",
        "range": [1, 100],
        "bucket_cnt": 5,
        "slide_window_size": 3,
        "imputer": "median"
      }
    ]
  }
}
```

### Specifying a `Word2Vec` feature
`text_word2vec` features

You can specify a `Word2Vec` feature for a node property by adding `"type": "text_word2vec"` to the `features` array.

The following `node` feature indicates that the `description` property of each `Movie` node should be treated as a `Word2Vec` feature:

```
"additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Movie",
        "property": "description",
        "type": "text_word2vec",
        "language": "en_core_web_lg"
      }
    ]
  }
}
```

### Specifying a `FastText` feature
`text_fasttext` features

You can specify a `FastText` feature for a node property by adding `"type": "text_fasttext"` to the `features` array. The `language` field is required, and must specify one of the following languages codes:
+ `en`   (English)
+ `zh`   (Chinese)
+ `hi`   (Hindi)
+ `es`   (Spanish)
+ `fr`   (French)

Note that the `text_fasttext` encoding cannot handle more than one language at a time in a feature.

The following `node` feature indicates that the French `description` property of each `Movie` node should be treated as a `FastText` feature:

```
"additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Movie",
        "property": "description",
        "type": "text_fasttext",
        "language": "fr",
        "max_length": 1024
      }
    ]
  }
}
```

### Specifying a `Sentence BERT` feature
`text_sbert` features

You can specify a `Sentence BERT` feature for a node property by adding `"type": "text_sbert"` to the `features` array. You don't need to specify the language, since the method automatically encodes text features using a multilingual language model.

The following `node` feature indicates that the `description` property of each `Movie` node should be treated as a `Sentence BERT` feature:

```
"additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Movie",
        "property": "description",
        "type": "text_sbert128",
      }
    ]
  }
}
```

### Specifying a `TF-IDF` feature
TF-IDF features

You can specify a `TF-IDF` feature for a node property by adding `"type": "text_tfidf"` to the `features` array.

The following `node` feature indicates that the `bio` property of each `Person` node should be treated as a `TF-IDF` feature:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Movie",
        "property": "bio",
        "type": "text_tfidf",
        "ngram_range": [1, 2],
        "min_df": 5,
        "max_features": 1000
      }
    ]
  }
}
```

### Specifying a `datetime` feature
Datetime features

The export process automatically infers `datetime` features for date properties. However, if you want to limit the `datetime_parts` used for a `datetime` feature, or override a feature specification so that a property that would normally be treated as an `auto` feature is explicitly treated as a `datetime` feature, you can do so by adding a `"type": "datetime"` to the features array.

The following `node` feature indicates that the `createdAt` property of each `Post` node should be treated as a `datetime` feature:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Post",
        "property": "createdAt",
        "type": "datetime",
        "datetime_parts": ["month", "weekday", "hour"]
      }
    ]
  }
}
```

### Specifying a `category` feature
Category features

The export process automatically infers `auto` features for string properties and numeric properties containing multiples values. For numeric properties containing single values, it infers `numerical` features. For date properties it infers `datetime` features.

If you want to override a feature specification so that a property is treated as a categorical feature, add a `"type": "category"` to the features array. If the property contains multiple values, include a `separator` field. For example:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Post",
        "property": "tag",
        "type": "category",
        "separator": "|"
      }
    ]
  }
}
```

### Specifying a `numerical` feature
Numerical features

The export process automatically infers `auto` features for string properties and numeric properties containing multiples values. For numeric properties containing single values, it infers `numerical` features. For date properties it infers `datetime` features.

If you want to override a feature specification so that a property is treated as a `numerical` feature, add `"type": "numerical"` to the features array. If the property contains multiple values, include a `separator` field. For example:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "Recording",
        "property": "duration",
        "type": "numerical",
        "separator": ","
      }
    ]
  }
}
```

### Specifying an `auto` feature
Auto features

The export process automatically infers `auto` features for string properties and numeric properties containing multiples values. For numeric properties containing single values, it infers `numerical` features. For date properties it infers `datetime` features.

If you want to override a feature specification so that a property is treated as an `auto` feature, add `"type": "auto"` to the features array. If the property contains multiple values, include a `separator` field. For example:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      ...
    ],
    "features": [
      {
        "node": "User",
        "property": "role",
        "type": "auto",
        "separator": ","
      }
    ]
  }
}
```

## RDF examples using `additionalParams`
RDF examples

### Specifying a default split rate for model-training configuration
Setting the default split rate

In the following example, the `split_rate` parameter sets the default split rate for model training. If no default split rate is specified, the training uses a value of [0.9, 0.1, 0.0]. You can override the default value on a per-target basis by specifying a `split_rate` for each target.

In the following example, the `default split_rate` field indicates that a split rate of `[0.7,0.1,0.2]` should be used unless overridden on a per-target basis:"

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "split_rate": [0.7,0.1,0.2],
    "targets": [
      (...)
    ]
  }
}
```

### Specifying a node-classification task for model-training configuration
Node classification

To indicate which node property contains labeled examples for training purposes, add a node classification element to the `targets` array, using `"type" : "classification"`. Add a node field to indicate the node type of target nodes. Add a `predicate` field to define which literal data is used as the target node feature of the target node. Add a `split_rate` field if you want to override the default split rate.

In the following example, the `node` target indicates that the `genre` property of each `Movie` node should be treated as a node class label. The `split_rate` value overrides the default split rate:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "node": "http://aws.amazon.com/neptune/csv2rdf/class/Movie",
        "predicate": "http://aws.amazon.com/neptune/csv2rdf/datatypeProperty/genre",
        "type": "classification",
        "split_rate": [0.7,0.1,0.2]
      }
    ]
  }
}
```

### Specifying a node regression task for model-training configuration
Node regression

To indicate which node property contains labeled regressions for training purposes, add a node regression element to the targets array, using `"type" : "regression"`. Add a `node` field to indicate the node type of target nodes. Add a `predicate` field to define which literal data is used as the target node feature of the target node. Add a `split_rate` field if you want to override the default split rate.

The following `node` target indicates that the `rating` property of each `Movie` node should be treated as a node regression label:

```
    "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "node": "http://aws.amazon.com/neptune/csv2rdf/class/Movie",
        "predicate": "http://aws.amazon.com/neptune/csv2rdf/datatypeProperty/rating",
        "type": "regression",
        "split_rate": [0.7,0.1,0.2]
      }
    ]
  }
}
```

### Specifying a link prediction task for particular edges
Focused link prediction

To indicate which edges should be used for link prediction training purposes, add an edge element to the targets array using `"type" : "link_prediction"`. Add `subject`, `predicate` and `object` fields to specify the edge type. Add a `split_rate` field if you want to override the default split rate.

The following `edge` target indicates that `directed` edges that connect `Directors` to `Movies` should be used for link prediction:

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "subject": "http://aws.amazon.com/neptune/csv2rdf/class/Director",
        "predicate": "http://aws.amazon.com/neptune/csv2rdf/datatypeProperty/directed",
        "object": "http://aws.amazon.com/neptune/csv2rdf/class/Movie",
        "type" : "link_prediction"
      }
    ]
  }
}
```

### Specifying a link prediction task for all edges
General link prediction

To indicate that all edges should be used for link prediction training purposes, add an `edge` element to the targets array using `"type" : "link_prediction"`. Do not add `subject`, `predicate`, or `object` fields. Add a `split_rate` field if you want to override the default split rate.

```
  "additionalParams": {
  "neptune_ml": {
    "version": "v2.0",
    "targets": [
      {
        "type" : "link_prediction"
      }
    ]
  }
}
```