record.proto

syntax = "proto2";

package aialgs.data;

option java_package = "com.amazonaws.aialgorithms.proto";
option java_outer_classname = "RecordProtos";

// A sparse or dense rank-R tensor that stores data as doubles (float64).
message Float32Tensor   {
    // Each value in the vector. If keys is empty this is treated as a
    // dense vector.
    repeated float values = 1 [packed = true];

    // If not empty then the vector is treated as sparse with
    // each key specifying the location of the value in the sparse vector.
    repeated uint64 keys = 2 [packed = true];

    // Optional shape which will allow the vector to represent a matrix.
    // e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
    // and keys[i] % 20 will give the column.
    // This also supports n-dimensonal tensors.
    // NB. this must be specified if the tensor is sparse.
    repeated uint64 shape = 3 [packed = true];
}

// A sparse or dense rank-R tensor that stores data as doubles (float64).
message Float64Tensor {
    // Each value in the vector. If keys is empty this is treated as a
    // dense vector.
    repeated double values = 1 [packed = true];

    // If not empty then the vector is treated as sparse with
    // each key specifying the location of the value in the sparse vector.
    repeated uint64 keys = 2 [packed = true];

    // Optional shape which will allow the vector to represent a matrix.
    // e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
    // and keys[i] % 20 will give the column.
    // This also supports n-dimensonal tensors.
    // NB. this must be specified if the tensor is sparse.
    repeated uint64 shape = 3 [packed = true];
}

// A sparse or dense rank-R tensor that stores data as 32-bit ints (int32).
message Int32Tensor {
    // Each value in the vector. If keys is empty this is treated as a
    // dense vector.
    repeated int32 values = 1 [packed = true];

    // If not empty then the vector is treated as sparse with
    // each key specifying the location of the value in the sparse vector.
    repeated uint64 keys = 2 [packed = true];

    // Optional shape which will allow the vector to represent a matrix.
    // e.g. if shape = [ 10, 20 ] then floor(keys[i] / 10) will give the row
    // and keys[i] % 20 will give the column.
    // This also supports n-dimensonal tensors.
    // NB. this must be specified if the tensor is sparse.
    repeated uint64 shape = 3 [packed = true];
}

// Support for storing binary data for parsing in other ways (such as JPEG/etc).
// This is an example of another type of value and may not immediately be supported.
message Bytes {
    repeated bytes value = 1;

    // Stores the content type of the data if known.
    // This will allow the possibility of using decoders for common formats
    // in the future.
    optional string content_type = 2;
}

message Value {
    oneof value {
        // The numbering assumes the possible use of:
        // - float16, float128
        // - int8, int16, int32
        Float32Tensor float32_tensor = 2;
        Float64Tensor float64_tensor = 3;
        Int32Tensor int32_tensor = 7;
        Bytes bytes = 9;
    }
}

message Record {
    // Map from the name of the feature to the value.
    //
    // For vectors and libsvm-like datasets,
    // a single feature with the name `values`
    // should be specified.
    map<string, Value> features = 1;

    // Optional set of labels for this record.
    // Similar to features field above, the key used for
    // generic scalar / vector labels should ve 'values'
    map<string, Value> label = 2;

    // Unique identifier for this record in the dataset.
    //
    // Whilst not necessary, this allows better
    // debugging where there are data issues.
    //
    // This is not used by the algorithm directly.
    optional string uid = 3;

    // Textual metadata describing the record.
    //
    // This may include JSON-serialized information
    // about the source of the record.
    //
    // This is not used by the algorithm directly.
    optional string metadata = 4;

    // Optional serialized JSON object that allows per-record
    // hyper-parameters/configuration/other information to be set.
    //
    // The meaning/interpretation of this field is defined by
    // the algorithm author and may not be supported.
    //
    // This is used to pass additional inference configuration
    // when batch inference is used (e.g. types of scores to return).
    optional string configuration = 5;
}