C2W2 Failed test case: preprocessing_fn has incorrect type. Expected: typing.Callable, but got: <class 'NoneType'>

Course Q&A Machine Learning Engineering for Production(MLOps) Machine Learning Data Lifecycle in Production
1

Hi I am getting an error on the preprocessing_fn test case even if the output of transformed_data matches the expected output.
Any hints where I am doing a mistake?

%%writefile {_traffic_transform_module_file}

import tensorflow as tf
import tensorflow_transform as tft

import traffic_constants

Unpack the contents of the constants module

_DENSE_FLOAT_FEATURE_KEYS = traffic_constants.DENSE_FLOAT_FEATURE_KEYS
_RANGE_FEATURE_KEYS = traffic_constants.RANGE_FEATURE_KEYS
_VOCAB_FEATURE_KEYS = traffic_constants.VOCAB_FEATURE_KEYS
_VOCAB_SIZE = traffic_constants.VOCAB_SIZE
_OOV_SIZE = traffic_constants.OOV_SIZE
_CATEGORICAL_FEATURE_KEYS = traffic_constants.CATEGORICAL_FEATURE_KEYS
_BUCKET_FEATURE_KEYS = traffic_constants.BUCKET_FEATURE_KEYS
_FEATURE_BUCKET_COUNT = traffic_constants.FEATURE_BUCKET_COUNT
_VOLUME_KEY = traffic_constants.VOLUME_KEY
_transformed_name = traffic_constants.transformed_name

def preprocessing_fn(inputs):
“”“tf.transform’s callback function for preprocessing inputs.
Args:
inputs: map from feature keys to raw not-yet-transformed features.
Returns:
Map from string feature key to transformed feature operations.
“””
outputs = {}

### START CODE HERE

# Scale these features to the z-score.
for key in _DENSE_FLOAT_FEATURE_KEYS:
    # Scale these features to the z-score.
    outputs[_transformed_name(key)] = tft.scale_to_z_score(inputs[key])
        

# Scale these feature/s from 0 to 1
for key in _RANGE_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.scale_to_0_1(inputs[key])
        

# Transform the strings into indices 
# hint: use the VOCAB_SIZE and OOV_SIZE to define the top_k and num_oov parameters
for key in _VOCAB_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.compute_and_apply_vocabulary(
        inputs[key], 
        top_k=_VOCAB_SIZE, 
        num_oov_buckets=_OOV_SIZE)

# Bucketize the feature
for key in _BUCKET_FEATURE_KEYS:
    outputs[_transformed_name(key)] = tft.bucketize(
        inputs[key], 
        _FEATURE_BUCKET_COUNT[key])
        

# Keep as is. No tft function needed.
for key in _CATEGORICAL_FEATURE_KEYS:
    outputs[_transformed_name(key)] = inputs[key]

    
# Use `tf.cast` to cast the label key to float32 and fill in the missing values.
traffic_volume = tf.cast(inputs[_VOLUME_KEY], tf.float32)


# Create a feature that shows if the traffic volume is greater than the mean and cast to an int
outputs[_transformed_name(_VOLUME_KEY)] = tf.cast(  
    
    # Use `tf.greater` to check if the traffic volume in a row is greater than the mean of the entire traffic volumn column
    tf.greater(traffic_volume, tft.mean(tf.cast(traffic_volume, tf.float32))),
tf.int64)                                                                 

### END CODE HERE
return outputs
2

Hello @Philipp_Schutzbach which error are you getting after running the above cell?

3

Getting no error but the grader tells me 0/10 points

Output of the cell: Overwriting traffic_transform.py

Grader on preprocessing_fn:
0/10 points earned
Failed test case: preprocessing_fn has incorrect type.
Expected:
typing.Callable,
but got:
<class ‘NoneType’>.

4

Try adding this code at the end of that cell: # grade up to here Then try to grade the assignment again

5

Found the issue… forgot a # grade until here tag in a cell above

6

Happy that you’re now sorted. All the best & and if you have any other question feel free to ask again.