cleanlab

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Detected by automated pattern matching (rule DO-BAS) with medium confidence. May be a false positive.

Decoded base64 content: ��?��bn����r�)�

Detected by automated pattern matching (rule DO-BAS) with medium confidence. May be a false positive.

Decoded base64 content: �+a�����,���y�

Detected by automated pattern matching (rule DO-BAS) with medium confidence. May be a false positive.

Dynamic code evaluation via eval()

Detected by automated pattern matching (rule SC-004) with medium confidence. May be a false positive.

    71:         X = self.data[property_of_interest].values.reshape(-1, 1)
    72:         y = self.labels
>>> 73:         mean_accuracy = _train_and_eval(X, y)
    74:         return relative_room_for_improvement(baseline_accuracy, float(mean_accuracy))
    75: 

Dynamic code evaluation via eval()

Detected by automated pattern matching (rule SC-004) with medium confidence. May be a false positive.

    75: 
    76: 
>>> 77: def _train_and_eval(X, y, cv=5) -> float:
    78:     classifier = GaussianNB()  # TODO: Make this a parameter
    79:     cv_accuracies = cross_val_score(classifier, X, y, cv=cv, scoring="accuracy")

Decoded base64 content: J����� ��zV����

Detected by automated pattern matching (rule DO-BAS) with medium confidence. May be a false positive.

Dynamic code evaluation via eval()

Detected by automated pattern matching (rule SC-004) with medium confidence. May be a false positive.

    203: def evaluate(test_loader, model1, model2):
    204:     print("Evaluating Co-Teaching Model")
>>> 205:     model1.eval()  # Change model to 'eval' mode.
    206:     correct1 = 0
    207:     total1 = 0

Dynamic code evaluation via eval()

Detected by automated pattern matching (rule SC-004) with medium confidence. May be a false positive.

    214:         correct1 += (pred1.cpu() == labels).sum()
    215: 
>>> 216:     model2.eval()  # Change model to 'eval' mode
    217:     correct2 = 0
    218:     total2 = 0

Dynamic code evaluation via eval()

Detected by automated pattern matching (rule SC-004) with medium confidence. May be a false positive.

    350: 
    351:         # sets model.train(False) inactivating dropout and batch-norm layers
>>> 352:         self.model.eval()
    353: 
    354:         # Run forward pass on model to compute outputs

Dynamic code execution via exec()

Detected by automated pattern matching (rule SC-003) with medium confidence. May be a false positive.

    20: 
    21: # Get version number and store it in __version__
>>> 22: exec(open("cleanlab/version.py").read())
    23: 
    24: DATALAB_REQUIRE = [

High-entropy string (4.7 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

Possible Base64-encoded payload (long encoded string)

Detected by automated pattern matching (rule OB-001) with medium confidence. May be a false positive.

    251: Do not add your new issue type to the set of issues that Datalab detects by default, our team can add it to this default set later on once it's utility has been thoroughly validated.
    252: 
>>> 253: Don't forget to update the [issue type descriptions guide](https://github.com/cleanlab/cleanlab/blob/master/docs/source/cleanlab/datalab/guide/issue_type_description.rst) with a brief description of your new issue type.
    254: It is ideal to stick to a format that maintains consistency and readability.
    255: Generally, the format includes a title, explanation of the issue, required arguments, then any additional information.

Possible Base64-encoded payload (long encoded string)

Detected by automated pattern matching (rule OB-001) with medium confidence. May be a false positive.

    256: It would be helpful to include a tip for users on how to detect the issue using Datalab.
    257: 
>>> 258: Try to add tests for this new issue type. It's a good idea to start with some tests in a separate module in the [issue manager test directory](https://github.com/cleanlab/cleanlab/tree/master/tests/datalab/issue_manager). 
    259: 
    260: 

High-entropy string (4.9 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.6 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.8 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.7 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.5 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.6 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.

High-entropy string (4.6 bits/char) — possible encoded payload

Detected by automated pattern matching (rule EN-001) with medium confidence. May be a false positive.