assert masks TypeError

[sakird]

On line 101 of custom.py there is an assert line:

assert handler is not None

and below it, there is a TypeError raised if assert is true:

if handler is None: raise TypeError("Your input type to custom model is not currently supported")

this TypeError is not returning to developer and makes it hard to understand the cause of the error.

It seems asserting line is not necessary. Can we remove it?

[sakird]

Here is an image of relevant code and its link:

snowflake-ml-python/snowflake/ml/model/_packager/model_handlers/custom.py

Line 101 in 7bc5f40

assert handler is not None

[sfc-gh-xjiang]

Hey @sakird , thanks for reporting this issue and we removed this assertion in the next release.

Apart from the the assertion, is it possible for you to provide a minimal reproducible example so that we can help investigate the issue?

Thanks,
Xinyi

[sakird]

Would it be OK to provide code here as a comment? Or is there any other way for that? I did not do anything like this before.

[sfc-gh-xjiang]

Hey @sakird, you can delete your sensitive information, create dummy data and share codes in the comment.
Otherwise, if you have any point of contact sales engineer, you can also share with them and i'm happy to help.

Thanks
Xinyi

[sfc-gh-xjiang]

Hey @sakird , I wrote an example that may work in your use case, can you try?

import pickle

import os

from snowflake.ml.model import custom_model
from sklearn.svm import SVC
import pandas as pd


# Step 1: Train the model
model = SVC() <<please_replace_with_your_model>> # Example model, please replace with your own model
data = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [4, 5, 6]})
target = [1, 2, 3]
model.fit(data, target)

# Step 2: Save the model to a file
os.makedirs('path_to_save', exist_ok=True)

with open('path_to_save/model.pkl', 'wb') as f:
    pickle.dump(model, f)

# Step 3: Define a ModelContext with the path to the saved model
mc = custom_model.ModelContext(
    model_file='path_to_save/model.pkl',
)

# Step 4: Define a custom model class that loads the model and makes prediction
class ExampleBringYourOwnModel(custom_model.CustomModel):
    def __init__(self, context: custom_model.ModelContext) -> None:
        super().__init__(context)
        with open(context['model_file'], 'rb') as f:
            self.model = pickle.load(f)

    @custom_model.inference_api
    def predict(self, input: pd.DataFrame) -> pd.DataFrame:
        # Return predictions and probabilities as a DataFrame
        return pd.DataFrame({'output': self.model.predict(input)})

# Example usage:
# Initialize the custom model and run predictions
bring_your_own_model = ExampleBringYourOwnModel(context=mc)
result = bring_your_own_model.predict(data)
print(result)


# Snowpark for Python
from snowflake.ml.registry import Registry
session = <<create_your_session>>

# Step 5: Log the custom model to the registry
reg = Registry(session=session, database_name=session.get_current_database(), schema_name=session.get_current_schema())

mv = reg.log_model(bring_your_own_model,
            model_name="my_model",
            version_name="version1",
            conda_dependencies=["scikit-learn"],  #<<please_include_dependency_here>>
            comment="My Custom ML Model Pipeline",
            sample_input_data=data)

# Step 6: Run the model in the registry
output_df = mv.run(data)
print(output_df)

Please let me know if you've met any issues
Xinyi