Comments (6)
Thanks for looking into this. Yes, the API docs are out of date, and while we work on updating them, you can instead refer to the docstrings on the functions, either by looking at the code or by using Python's help
builtin.
from model-analysis.
Besides, I think I found a bug on
I'm getting:
...
/usr/local/lib/python2.7/dist-packages/tensorflow_model_analysis/api/model_eval_lib.pyc in ExtractEvaluateAndWriteResults(examples, eval_shared_model, output_path, display_only_data_location, slice_spec, desired_batch_size, extractors, evaluators, writers, write_config, num_bootstrap_samples)
566
567 example_weight_metric_key = metric_keys.EXAMPLE_COUNT
--> 568 if eval_shared_model.example_weight_key:
569 example_weight_metric_key = metric_keys.EXAMPLE_WEIGHT
570
AttributeError: 'str' object has no attribute 'example_weight_key'
This happened when I called
tfma.run_model_analysis(eval_shared_model='WORKING_DIR/model_analysis',
data_location='WORKING_DIR')
I would like to create a mockup with all the emulation of this bug but I find it too difficult to reproduce (sorry). I think that from the last version of tfma to this one you did some changes around example_weight_key
and forgot to create some tests for it.
from model-analysis.
Thanks for bringing this to our attention! It looks like we neglected to rebuild the docs before releasing. We will do so shortly.
from model-analysis.
After exploring a bit more, we found that the cause seems to be just deprecated documentation. Here is the contradiction:
- The documentation says that run_model_analysis
- has the argument
example_weight_key
- The first argument is
model_location
- has the argument
- The actual implementation of the function
- does not have the argument
example_weight_key
- The first argument is
eval_shared_model
, which in turn has anexample_weight_key
- does not have the argument
from model-analysis.
Can you please fix this issue, it's been there for three months and it's in the Getting started guide, the very first thing that users read.
from model-analysis.
Fixed.
from model-analysis.
Related Issues (20)
- Unable to use custom Keras metrics with TFX evaluator component HOT 4
- AttributeError: partially initialized module error, test.py HOT 2
- Incorrect Documentation for MetricConfig
- Custom multilabel Keras metrics: dynamically initialize weight shape HOT 1
- Fairness indicator metrics do not show up HOT 5
- Validation bug when using feature values as slicing specs HOT 2
- No longer compatible with Dataflow runner v1 after v0.29
- Renaming Custom Layer breaks TFMA Evaluator HOT 6
- Error in merge_accumulators when using keras metrics on dataflow HOT 3
- TFMA analyze_raw_data function support with MultiClassConfusionMatrixPlot HOT 3
- Analytics HOT 1
- Breaking changes: tfma.metrics.MetricComputation `preprocessor` argument changed from accepting beam DoFn to `preprocessors` accepting a list of `Preprocessor` HOT 1
- Move to numpy >=1.20 because <1.20 is difficult to build on Apple Silicon HOT 4
- TFMA on Flink does not seems to be parallelizing work. HOT 5
- only integer values should be passed to num_instances metric. HOT 3
- Render_plot is failing HOT 2
- Feat: Have ability to return the HTML rather than creating the plots HOT 1
- CVE in Pyarrow dependency HOT 1
- CNN HOT 2
- pip install tensorflow-model-analysis fails on WSL2 on Windows 11 HOT 2
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