torch_frame.nn
torch_frame.nn.encoder
Base class for feature encoder that transforms input |
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Feature encoder that transforms each stype tensor into embeddings and performs the final concatenation. |
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Base class for stype encoder. |
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An embedding look-up based encoder for categorical features. |
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An embedding look-up based encoder for multi_categorical features. |
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A linear function based encoder for numerical features. |
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A numerical converter that transforms a tensor into a piecewise linear representation, followed by a linear transformation. |
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A periodic encoder that utilizes sinusoidal functions to transform the input tensor into a 3-dimensional tensor. |
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An attention based encoder that transforms input numerical features to a 3-dimensional tensor. |
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Linear function based encoder for pre-computed embedding features. |
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Linear function based encoder with a specified model outputs embedding features. |
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Simply stack input numerical features of shape |
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TimestampEncoder for timestamp stype. |
torch_frame.nn.encoding
Cyclic encoding for input data containing values between 0 and 1. |
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Positional encoding introduced in "Attention Is All You Need" paper. |
torch_frame.nn.conv
Base class for table convolution that transforms the input column-wise pytorch tensor. |
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The FT-Transformer backbone in the "Revisiting Deep Learning Models for Tabular Data" paper. |
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The Trompt cell introduced in the "Trompt: Towards a Better Deep Neural Network for Tabular Data" paper. |
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The ExcelFormer Layer introduced in the "ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data" paper. |
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The TabTransformer Layer introduced in the "TabTransformer: Tabular Data Modeling Using Contextual Embeddings" paper. |
torch_frame.nn.decoder
Base class for decoder that transforms the input column-wise PyTorch tensor into output tensor on which prediction head is applied. |
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The Trompt downstream introduced in "Trompt: Towards a Better Deep Neural Network for Tabular Data" paper. |
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The ExcelFormer decoder introduced in the "ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data" paper. |
torch_frame.nn.models
The Trompt model introduced in the "Trompt: Towards a Better Deep Neural Network for Tabular Data" paper. |
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The FT-Transformer model introduced in the "Revisiting Deep Learning Models for Tabular Data" paper. |
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The ExcelFormer model introduced in the "ExcelFormer: A Neural Network Surpassing GBDTs on Tabular Data" paper. |
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The TabNet model introduced in the "TabNet: Attentive Interpretable Tabular Learning" paper. |
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The ResNet model introduced in the "Revisiting Deep Learning Models for Tabular Data" paper. |
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The Tab-Transformer model introduced in the "TabTransformer: Tabular Data Modeling Using Contextual Embeddings" paper. |
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The light-weight MLP model that mean-pools column embeddings and applies MLP over it. |