Implement log_loss Function and Add Comprehensive Test Coverage#28800
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muzakkirhussain011 wants to merge 6 commits intoivy-llc:mainfrom
Draft
Implement log_loss Function and Add Comprehensive Test Coverage#28800muzakkirhussain011 wants to merge 6 commits intoivy-llc:mainfrom
log_loss Function and Add Comprehensive Test Coverage#28800muzakkirhussain011 wants to merge 6 commits intoivy-llc:mainfrom
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func_wrapper.py is a Python module designed to streamline the integration of Hugging Face Transformers into your natural language processing (NLP) projects. It provides a set of input and output conversion wrappers to simplify the process of passing data between your custom functions and Transformers' data structures. Input Conversion Wrappers: inputs_to_transformers_tensors: This wrapper converts input data (text, tensors, etc.) into Transformers-compatible data structures. It is particularly useful when your custom functions expect diverse input types. Output Conversion Wrappers: outputs_to_pytorch_tensors: After your custom function returns data, this wrapper ensures that the output data is converted into PyTorch tensors or other appropriate formats. Usage: Import func_wrapper.py into your project. Initialize a Hugging Face Transformers model and tokenizer. Wrap your custom function with to_transformers_tensors_and_back. This wrapped function can now accept and return Transformers-compatible data. Here's a simple example of how to use func_wrapper.py: import torch from transformers import BertForSequenceClassification, BertTokenizer from ivy.functional.frontends.transformers.func_wrapper import to_transformers_tensors_and_back # Initialize the model and tokenizer model_name = "bert-base-uncased" model = BertForSequenceClassification.from_pretrained(model_name) tokenizer = BertTokenizer.from_pretrained(model_name) # Wrap your custom function using the conversion wrappers wrapped_function = to_transformers_tensors_and_back(your_function, model, tokenizer) # Prepare sample input data sample_input_text = "This is a sample input text." sample_input_tensor = torch.rand((3, 3)) # Call your wrapped function with the sample input data output = wrapped_function(sample_input_text, sample_input_tensor) # The output is automatically converted to PyTorch tensors print(output) Please note that func_wrapper.py is still in development, and further enhancements and refinements are expected. Your feedback and contributions to improve its functionality are welcome.
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PR Description
This PR introduces the following changes:
Feature Implementation:
log_lossFunction:log_lossfunction to the metrics module.sample_weightinput to handle weighted samples.y_trueandy_predinputs have the same shape and includes validation for edge cases such as improper input shapes or invalid values.Test Implementation:
test_sklearn_log_loss:log_lossfunction across various scenarios.y_true) and corresponding predicted probabilities (y_pred).sample_weightand validates the function's performance across different backends (e.g., TensorFlow, PyTorch).log_lossimplementation with the reference implementation fromsklearn.metrics.log_lossto ensure correctness.Additional Notes:
ivy.clip,ivy.log,ivy.mean, etc.) have been cross-checked and confirmed to be available within the Ivy framework.Testing:
log_lossfunction performs as expected under various conditions.Checklist