Add PyTorch-based name generation model with dataset and training script

[leonvanbokhorst]

Summary by Sourcery

New Features:

• Introduce a PyTorch-based name generation system using LSTM architecture for character-level language modeling.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR introduces a character-level name generation system using PyTorch. The implementation consists of a custom dataset class for processing name data and an LSTM-based neural network model for generating new names. The system includes comprehensive training and generation utilities with configurable parameters.

Sequence diagram for name generation process

sequenceDiagram
    participant User
    participant NameDataset
    participant NameGenerator
    participant Model
    User->>NameDataset: Initialize with names
    User->>NameGenerator: Initialize with vocab size
    User->>Model: Train model
    loop Generate names
        User->>Model: Generate name
        Model->>NameDataset: Get context
        Model->>NameGenerator: Predict next character
        NameGenerator-->>Model: Return prediction
        alt End token or max length
            Model-->>User: Return generated name
        end
    end

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Class diagram for NameDataset and NameGenerator

classDiagram
    class NameDataset {
        -List~str~ names
        -int context_size
        -List~str~ chars
        -List~str~ special_tokens
        -Dict~str, int~ stoi
        -Dict~int, str~ itos
        -torch.Tensor X
        -torch.Tensor y
        +__init__(List~str~ names, int context_size=3)
        +_build_dataset() Tuple~torch.Tensor, torch.Tensor~
        +__len__() int
        +__getitem__(int idx) Tuple~torch.Tensor, torch.Tensor~
    }
    class NameGenerator {
        -nn.Embedding embedding
        -nn.LSTM lstm
        -nn.Linear fc
        +__init__(int vocab_size, int embedding_dim=24, int hidden_dim=128)
        +forward(torch.Tensor x) torch.Tensor
    }

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File-Level Changes

Change	Details	Files
Implementation of a custom PyTorch Dataset for name processing	Created a dataset class that handles data cleaning and normalization Implemented character-to-index and index-to-character mapping Added special tokens handling for sequence start and end Built sliding context window generation for training data	pocs/makemore_names.py
Development of an LSTM-based name generation model	Implemented character embedding layer Added LSTM layer for sequence processing Created final linear layer for character prediction Implemented forward pass logic	pocs/makemore_names.py
Implementation of training and generation utilities	Created training loop with loss tracking and optimization Implemented single name generation with sampling Added batch generation of unique names Included statistics and validation against training data	pocs/makemore_names.py

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[sourcery-ai]

Hey @leonvanbokhorst - I've reviewed your changes - here's some feedback:

Overall Comments:

• The NameDataset class is missing a call to _build_dataset() in init to initialize self.X and self.y. This will cause runtime errors when len or getitem are called.

Here's what I looked at during the review

• 🟡 General issues: 2 issues found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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