tfx-moe 0.1.0

Multi-output evaluator TFX component for deep learning models that utilize TensorFlow and TensorFlow Extended.

MultiOutputEvaluator (TFX Component)

A lightweight and flexible TFX component for evaluating multi-output TensorFlow models. It computes per-output and global metrics and writes a TFMA-compatible JSON artifact for downstream analysis and reporting.

---

📦 Installation

Install from PyPI:

pip install tfx-moe

---

⚙️ Features

• ✅ Evaluate multi-output models (e.g., shape (batch, num_outputs))
• ✅ Computes per-output and global metrics
• ✅ Currently supports MSE and MAE (easy to extend)
• ✅ TFMA-compatible JSON output (ModelEvaluation artifact)
• ✅ Works with TFX Transform artifacts (TransformGraph)
• ✅ Pluggable dataset input function (your data, your logic)

---

📚 API Reference

MultiOutputEvaluator Component

MultiOutputEvaluator(
            model,                      # Channel[Model]
            examples,                   # Channel[Examples]
            transform_graph,            # Channel[TransformGraph]
            output_names,               # list[str]: names of model outputs in order
            metrics,                    # list[str]: e.g., ['mse', 'mae']
            input_fn_path,              # str: "pkg.mod:func" or "pkg.mod.func"
            input_fn_kwargs=None,       # dict: extra kwargs for input function
            example_split='test'        # str: which split to evaluate
        )

Parameters

1. model: standard_artifacts.Model – Trained SavedModel (expects predictions of shape (batch, num_outputs)).
2. examples: standard_artifacts.Examples – TFRecords location for the chosen split.
3. transform_graph: standard_artifacts.TransformGraph – Loaded via tft.TFTransformOutput.
4. output_names (list[str]): Logical names per output index; order must correspond to your model’s output dimension.
5. metrics (list[str]): Currently ['mse', 'mae'] supported.
6. input_fn_path (str): Dotted path to your dataset builder function.
7. input_fn_kwargs (dict, optional): Keyword args passed to the dataset function.
8. example_split (str, default 'test'): Split folder name under Examples artifact (e.g., 'Split-test' if that’s how your pipeline writes splits).

---

🚀 Quickstart (TFX Usage)

[1] Add Component to your Pipeline and Run

from tfx_moe import MultiOutputEvaluator

# InteractiveContext Pipeline Runner
multi_output_evaluator = MultiOutputEvaluator(
    model=trainer.outputs['model'],
    examples=transform.outputs["transformed_examples"],
    transform_graph=transform.outputs['transform_graph'],
    
    output_names=OUTPUT_KEYS,                     # e.g., ["y_loc1", "y_loc2", ...]
    metrics=['mse', 'mae'],

    input_fn_path="my_pkg.mdata:input_fn",        # Your dataset builder function
    example_split='Split-test'                    # Match your artifact split naming
)

# Run (InteractiveContext example)
context.run(multi_output_evaluator)

# Access TFMA-like JSON results
evaluation_uri = multi_output_evaluator.outputs['evaluation'].get()[0].uri
print(evaluation_uri)

[2] Parse Results (per-output + global)

import json
import pandas as pd
import tensorflow as tf

with tf.io.gfile.GFile(evaluation_uri, 'r') as f:
    data = json.load(f)                     # TFMA-style dict

output_names = []
mse_values = []
mae_values = []

for key, value in data['metrics'].items():
    if key.endswith('>>mse'):
        output_name = key.split('>>')[0]         # 'per_output>>{name}>>mse' → '{name}'
        output_names.append(output_name)
        mse_values.append(value)
    elif key.endswith('>>mae'):
        mae_values.append(value)

df = pd.DataFrame({
    'Output Name': output_names,
    'MSE': mse_values,
    'MAE': mae_values
})


last_row_index = df.index[-1]
global_mse = df["MSE"].iloc[last_row_index]
global_mae = df["MAE"].iloc[last_row_index]
df = df.drop(index=[last_row_index])              # keep per-output rows only

[3] Global Metrics

print(f'Global MSE {global_mse}')
print(f'Global MAE {global_mae}')

[4] Aggregate Across Outputs

global_mse_all_outputs = df["MSE"].mean()
global_mae_all_outputs = df["MAE"].mean()

print(f'Global MSE per All Locations {global_mse_all_outputs}')
print(f'Global MAE per All Locations {global_mae_all_outputs}')

---

🧠 How It Works

The Executor

1. Loads TransformGraph (tft.TFTransformOutput) and the SavedModel.
2. Builds an evaluation dataset via your input function (input_fn_path).
3. Computes per-output and global metrics across the dataset.
4. Writes a TFMA-like JSON to the ModelEvaluation artifact.

Metrics Supported

• mse, mae (easy to extend in code).

Output Format (metrics dictionary keys)

1. Per-output: per_output>>{output_name}>>{metric}
2. Global: global>>{metric}

---

📥 Dataset Function Contract

You supply the dataset function via input_fn_path, e.g. "my_pkg.mdata:input_fn".

Expected Signature

def input_fn(file_pattern: str, tf_transform_output, **kwargs) -> tf.data.Dataset:
    # returns a dataset of (features, labels)

Expected Yields:

1. features: a structure compatible with your model’s SavedModel signature.
2. labels: a Tensor shaped (batch, 1, num_outputs) so that:
   • labels[:, 0, i] is the true label vector for output_names[i].
3. Your model’s predictions should have shape (batch, num_outputs) so that:
   • predictions[:, i] aligns with output_names[i].

If your labels are shaped differently, update either your input function or the executor’s slicing logic to align shapes.

---

📤 Artifact Output

• Writes a single JSON file under the ModelEvaluation artifact directory:

  • evaluation.json

• Example keys:

  • metrics["per_output>>y_loc3>>mse"] = 0.0123
  • metrics["global>>mae"] = 0.1357

---

📜 License

This project is licensed under the MIT License.
© 2025 Dr. Ahmed Moussa

---

🤝 Contributing

Pull requests are welcome.
For major changes, please open an issue first to discuss what you would like to change.

---

📫 Contact

For feedback, bugs, or collaboration ideas:

• GitHub: @real-ahmed-moussa

---

⭐️ Show Your Support

If you find this project useful, consider giving it a ⭐️ on GitHub!