NimbusML
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microsoft
Python machine learning package providing simple interoperability between ML.NET and scikit-learn components.
**Describe the bug** When using the mlnet auto-train tool to create a model, and then load that model using NimbusML, an exception is being thrown. **To Reproduce** Steps to reproduce...
When specifying `split_start='after_transforms'` in `CV.fit()`, the user defined transforms are not split up correctly. See the graph created by the `fit()` call in the code below. It seems like if...
NimbusML only has support for string based categorical columns. Numerical categorical columns (`KeyDataViewType`) which are returned from ML.Net are not converted back to their original representation even though Pandas does...
Using a transform, which only acts on a subset of the input columns, before a predictor and not explicitly specifying the features to the predictor will only pass the output...
Repro ` from nimbusml.datasets import get_dataset from nimbusml import FileDataStream from nimbusml.preprocessing import OnnxRunner from nimbusml.feature_extraction.text import NGramFeaturizer from nimbusml.feature_extraction.text.extractor import Ngram path = get_dataset("wiki_detox_train").as_filepath() data = FileDataStream.read_csv(path, sep='\t') transformer...
Currently, each return value is transferred from managed code to native code one at a time. See `NativeDataInterop.cs`. ```csharp for (int i = 0; i < fillers.Length; i++) { fillers[i].Set();...
Repro: r0 = Pipeline([MinMaxScaler()]) r0.fit(train_df) r1 = Pipeline([DatasetTransformer(r0.model)]) r1.fit_transform(train_df)
**Describe the bug** in the LightLda module, I wanna get the topic-words summary outputs, setting: output_topic_word_summary=True, num_summary_term_per_topic=20 but i can not get the topic words summary in outputs. the only...
Fixes #269 . `pathlib`'s Path objects can be converted to strings just by casting, and vice versa. I added a check in `FileDataStream`'s init function to convert a Path object...
The inputs and outputs which are produced by `Pipeline.get_fit_info` are not valid. See `inputs`, `outputs` and `schema_after` in the `RangeFilter` section of the output: ```python train_data = {'c1': [2, 3,...