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Data Science
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The Mixed Time-Series chart type allows for configuring the title of the primary and the secondary y-axis.
However, while only the title of the primary axis is shown next to the axis, the title of the secondary one is placed at the upper end of the axis where it gets hidden by bar values and zoom controls.
How to reproduce the bug
- Create a mixed time-series chart
- Configure axi
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Feb 7, 2022 - Jupyter Notebook
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Feb 11, 2022 - Jupyter Notebook
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Jan 6, 2022 - Jupyter Notebook
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Nov 4, 2021 - Python
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Feb 11, 2022 - Python
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Jun 28, 2021 - Python
According to FastAPI's docs, response_model can accept type annotations that are not pydantic models. However, the code referenced below is checking for the __fields__ attribute, which won't be on type annotations such as list[float], for example.
https://github.com/ray-project/ray/blob/e60a5f52eb93c851b186cb78fa1f70d
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Feb 9, 2022
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Feb 3, 2022
Summary
Aesthetically trivial, yet I've spotted a discrepancy with font sizes in our tooltip (front-end + back-end screenshots below).
I believe sections #1 and #2 should have the same font size?

 because of its awkward dependence on the axis scales. ...
As an alternative, we want to introduce a similar replacement vector(x, y, dx, dy, ...) where the parameters are still in data space, but the arrow shape is not tied to the data. This should be implemented based on FancyArrowPatch which is also the basis for `an
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Feb 10, 2022 - Jupyter Notebook
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May 20, 2020
In gensim/models/fasttext.py:
model = FastText(
vector_size=m.dim,
vector_size=m.dim,
window=m.ws,
window=m.ws,
epochs=m.epoch,
epochs=m.epoch,
negative=m.neg,
negative=m.neg,
# FIXME: these next 2 lines read in unsupported FB FT modes (loss=3 softmax or loss=4 onevsall,
# or model=3 supervi-
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Dec 30, 2021
Although the results look nice and ideal in all TensorFlow plots and are consistent across all frameworks, there is a small difference (more of a consistency issue). The result training loss/accuracy plots look like they are sampling on a lesser number of points. It looks more straight and smooth and less wiggly as compared to PyTorch or MXNet.
It can be clearly seen in chapter 6([CNN Lenet](ht
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Feb 12, 2022 - Python
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Jul 30, 2021 - Jupyter Notebook
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Feb 11, 2022 - Python
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Feb 2, 2022
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Feb 7, 2022 - Go
Is your feature request related to a problem? Please describe.
I typically used compressed datasets (e.g. gzipped) to save disk space. This works fine with AllenNLP during training because I can write my dataset reader to load the compressed data. However, the predict command opens the file and reads lines for the Predictor. This fails when it tries to load data from my compressed files.
- Wikipedia
- Wikipedia
Describe the issue linked to the documentation
Many legitimate notebook style examples have been broken, and specifically by the following PR
scikit-learn/scikit-learn#9061
Here are all the examples that use patterns like
# #######(found byag -l '# #####*\s#' examples | sort, note there may be false positives ... for example examples/impute/plot_missing_val