Unclear Error Message

[wschin]

Describe the bug
I tried calling the summary of a trained pipeline but got a strange error message. That message didn't tell which module is not supported.

To Reproduce
Execute

# coding: utf-8

###############################################################################

# FactorizationMachineBinaryClassifier

import numpy
from nimbusml import Pipeline, FileDataStream
from nimbusml.datasets import get_dataset
from nimbusml.decomposition import FactorizationMachineBinaryClassifier
from nimbusml.feature_extraction.categorical import OneHotVectorizer

# data input (as a FileDataStream)
path = get_dataset('infert').as_filepath()
data = FileDataStream.read_csv(path, sep=',',
                               numeric_dtype=numpy.float32,
                               names={0: 'row_num', 5: 'case'})
print(data.head())
#    age  case education  induced  parity  pooled.stratum  row_num  ...
# 0  26.0   1.0    0-5yrs      1.0     6.0             3.0      1.0  ...
# 1  42.0   1.0    0-5yrs      1.0     1.0             1.0      2.0  ...
# 2  39.0   1.0    0-5yrs      2.0     6.0             4.0      3.0  ...
# 3  34.0   1.0    0-5yrs      2.0     4.0             2.0      4.0  ...
# 4  35.0   1.0   6-11yrs      1.0     3.0            32.0      5.0  ...

# define the training pipeline
pipeline = Pipeline([
    OneHotVectorizer(columns={'edu': 'education'}),
    FactorizationMachineBinaryClassifier(feature=['induced', 'edu', 'parity'],
                                         label='case')
])

# train, predict, and evaluate
# TODO: Replace with CV
metrics, predictions = pipeline.fit(data).test(data, output_scores=True)

# print predictions
print(predictions.head())

#   PredictedLabel  Probability     Score
# 0             0.0     0.370519 -0.529990
# 1             0.0     0.420984 -0.318737
# 2             0.0     0.364432 -0.556180
# 3             0.0     0.380421 -0.487761
# 4             0.0     0.365351 -0.552214

# print evaluation metrics
print(metrics)

#        AUC  Accuracy  Positive precision  Positive recall  ...
# 0  0.609639  0.665323                   0                0  ...


# In[13]:


import nimbusml; print(nimbusml.__version__)
print(pipeline.steps[1])
pipeline.summary()

Expected behavior
It should say FieldAwareFactorizationMachine doesn't have summary function.

[mstfbl]

Currently, calling pipeline.summary() produces the following output:

                                       PredictorName
0  Microsoft.ML.Trainers.FieldAwareFactorizationM...

The full output of the above row is:
Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters

The commit that is referenced above by @Stephen0620 catches the usage of summary() with the FactorizationMachineBinaryClassifier.