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Unified_RNN embeds each sequence_v2 field with SequenceEmbeddingModel and
encodes it bottom-up, level by level: a set level mean-pools each group,
a sequence or series level runs an RNN over each group and keeps the last
output. A group without records is skipped and summarized as zeros.
Static tensor fields are used directly or through a projection; the
summaries and statics are concatenated and a linear layer predicts the
label.
- Per-field settings (hidden_dim, rnn_type, num_layers, dropout,
bidirectional, time_feature) take one value or a {field: value} dict.
- time_feature adds the time since the previous record of the group on
timed levels.
- Per-step labels: one prediction per label record, from each field's
top-level state after its last record at or before the label's time,
so a prediction never sees later records. Fields need a series top
level and the RNNs must be causal.
- RNN and MultimodalRNN now point to Unified_RNN for per-step labels.
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Fourth PR in the stack toward a unified sequence representation, on top of #1352. It adds
Unified_RNN, the first model that consumesSequenceProcessorV2fields and per-step labels.Changes
sequence_v2field is embedded bySequenceEmbeddingModeland encoded bottom-up, level by level. Asetlevel mean-pools each group; asequenceorserieslevel runs an RNN over each group and keeps the last output. A group without records is skipped and summarized as zeros.tensorfields are used directly or through a projection. The field summaries and statics are concatenated, and a linear layer predicts the label. Other processors are rejected; sequences, sets and tokens are expressed withsequence_v2.hidden_dim,rnn_type,num_layers,dropout,bidirectionalandtime_featuretake one value, or a{field: value}dict.time_featureadds the time since the previous record of the group on timed levels.seriestop level, and bidirectional RNNs are rejected.RNNandMultimodalRNNnow point toUnified_RNNfor per-step labels.Unified_RNN, and the YAIB tasks are tested end to end with it.