Data Science

Describe the bug

The binary classifier error message in _get_response:

    classification_error = "{} should be a bin

Screenshot

Description

chart 3 dot menu is behind the chart title panel in chart maximize mode

Apache Arrow has a first-class tabular file format, Feather, that the Ray Datasets IO layer should support. Combined with Ray Datasets' existing .from_arrow() and .to_arrow() APIs, this would round out our "all-Arrow" experience, which should be as nice as possible given our "distributed Arrow dataset" positioning.

Implementation Note

We currently print a warning as shown below when a user sets both a widget default value in the function defining the widget as well as a widget value via the widget's key in st.session_state

While we certainly want to do this by default since doing both is not recommended, we should provide a

The docs for IPython.core.interactiveshell.InteractiveShell.set_custom_exc have horribly mangled a warning message into a list of arguments. I can't work out at a glance why this is happening; it might be a sphinx.ext.napoleon bug, or a sphi

In recent versions (can't say from exactly when), there seems to be an off-by-one error in dcc.DatePickerRange. I set max_date_allowed = datetime.today().date(), but in the calendar, yesterday is the maximum date allowed. I see it in my apps, and it is also present in the first example on the DatePickerRange documentation page.

E

🚀 Feature

Support the following:

@dataclass
class MyDataModule(LightningDataModule):
    pass

Motivation

To reduce boilerplate code is at the core of philosophy in Lightning. It should be compatible with dataclasses.

Code sample

Here is an example. It currently does not work as we have some internal attributes that don't play well with the dataclass.

Bug summary

The new matplotlib.colors.make_norm_from_scale helper dynamically generates a norm class from a scale class. Currently, in the codebase, it is only used as a decorator to create "toplevel" classes (e.g., it is used to generate LogNorm from LogScale, etc.), but it can also be used within other functions to dynamically generate a norm class based on a user-given arbitrary scale (

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

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.