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Fix documentation rendering (flyteorg#906)
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* Fix formatting

Signed-off-by: 3t8 <[email protected]>

* Update whylogs_example.py

Signed-off-by: 3t8 <[email protected]>

Signed-off-by: 3t8 <[email protected]>
Co-authored-by: Samhita Alla <[email protected]>
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3t8 and samhita-alla authored Oct 6, 2022
1 parent cfcf017 commit 093a9b8
Showing 1 changed file with 7 additions and 7 deletions.
Original file line number Diff line number Diff line change
Expand Up @@ -41,9 +41,9 @@ def get_reference_data() -> pd.DataFrame:


# %%
# To some extent, we wanted to show kinds of drift in our example
# So in order to reproduce some of what real-life data behaves
# We will take an arbitrary subset of the reference dataset
# To some extent, we wanted to show kinds of drift in our example,
# so in order to reproduce some of what real-life data behaves
# we will take an arbitrary subset of the reference dataset
@task
def get_target_data() -> pd.DataFrame:
diabetes = load_diabetes()
Expand All @@ -54,9 +54,9 @@ def get_target_data() -> pd.DataFrame:

# %%
# Now we will define a task that can take in any pandas DataFrame
# and return a `:class:DatasetProfileView, which is our data profile.
# and return a ``DatasetProfileView``, which is our data profile.
# With it, users can either visualize and check overall statistics
# Or even run a constraint suite on top of it.
# or even run a constraint suite on top of it.
@task
def create_profile_view(df: pd.DataFrame) -> DatasetProfileView:
result = why.log(df)
Expand All @@ -65,7 +65,7 @@ def create_profile_view(df: pd.DataFrame) -> DatasetProfileView:

# %%
# And we will also define a constraints report task
# That will run some checks in our existing profile.
# that will run some checks in our existing profile.
@task
def constraints_report(profile_view: DatasetProfileView) -> bool:
builder = ConstraintsBuilder(dataset_profile_view=profile_view)
Expand All @@ -84,7 +84,7 @@ def constraints_report(profile_view: DatasetProfileView) -> bool:

# %%
# This is a representation of a prediction task. Since we are looking
# To take some of the complexity away from our demonstrations,
# to take some of the complexity away from our demonstrations,
# our model prediction here will be represented by generating a bunch of
# random numbers with numpy. This task will take place if we pass our
# constraints suite.
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