ExternalRiskEstimate seems to be hard coded into HELOC data processing, but I cannot find it.

[BrianBlackman]

If I change the name:

---

ValueError Traceback (most recent call last)
in
2 from aix360.algorithms.rbm import FeatureBinarizer
3 fb = FeatureBinarizer(negations=True, returnOrd=True)
----> 4 dfTrain, dfTrainStd = fb.fit_transform(dfTrain)
5 dfTest, dfTestStd = fb.transform(dfTest)
6 dfTrain['MostRecentBillAmountRaw'].head()

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
697 if y is None:
698 # fit method of arity 1 (unsupervised transformation)
--> 699 return self.fit(X, **fit_params).transform(X)
700 else:
701 # fit method of arity 2 (supervised transformation)

~/PycharmProjects/AIX360/aix360/algorithms/rbm/features.py in fit(self, X)
111 self.ordinal = ordinal
112 # Fit StandardScaler to ordinal features
--> 113 self.scaler = StandardScaler().fit(data[ordinal])
114 return self
115

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/preprocessing/_data.py in fit(self, X, y, sample_weight)
728 # Reset internal state before fitting
729 self._reset()
--> 730 return self.partial_fit(X, y, sample_weight)
731
732 def partial_fit(self, X, y=None, sample_weight=None):

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/preprocessing/_data.py in partial_fit(self, X, y, sample_weight)
766 X = self._validate_data(X, accept_sparse=('csr', 'csc'),
767 estimator=self, dtype=FLOAT_DTYPES,
--> 768 force_all_finite='allow-nan', reset=first_call)
769 n_features = X.shape[1]
770

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/base.py in _validate_data(self, X, y, reset, validate_separately, **check_params)
419 out = X
420 elif isinstance(y, str) and y == 'no_validation':
--> 421 X = check_array(X, **check_params)
422 out = X
423 else:

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/utils/validation.py in inner_f(*args, **kwargs)
61 extra_args = len(args) - len(all_args)
62 if extra_args <= 0:
---> 63 return f(*args, **kwargs)
64
65 # extra_args > 0

~/opt/anaconda3/envs/aix360/lib/python3.6/site-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
538
539 if all(isinstance(dtype, np.dtype) for dtype in dtypes_orig):
--> 540 dtype_orig = np.result_type(*dtypes_orig)
541
542 if dtype_numeric:

<array_function internals> in result_type(*args, **kwargs)

ValueError: at least one array or dtype is required

[dennislwei]

Hi @BrianBlackman, you'll have to elaborate. 'ExternalRiskEstimate' is a column in the HELOC dataset. What do you mean by not being able to find it? And what do you mean by changing the name? 'MostRecentBillAmountRaw' is not a column in the dataset.

[BrianBlackman]

I am trying to run another data set through the notebook example. I can use most of the original headers of my new data set, but some seem to be fixed. 'ExternalRiskEstimate' is one that I seemingly MUST use, even though it is not the target column, 'RiskPerformance'. I understand the need to specify the target column explicitly. My new data doesn't include the external risk estimate or anything like it. 'MostRecentBillAmountRaw' is a column in the new data.

[dennislwei]

I'm still not sure I understand where your problem is. Regarding the line dfTrain['ExternalRiskEstimate'].head(), you can omit it as it doesn't do anything. It simply prints out the 'ExternalRiskEstimate' columns of the DataFrame to show how it was transformed by FeatureBinarizer.

[BrianBlackman]

I really appreciate your patience with me. I think this solves the issue I raised. I am sorry I raised it.

[dennislwei]

No need to be sorry. Please let us know if you do find a problem with FeatureBinarizer.