Fix simple impute #788
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Fix simple impute #788
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CI failing for linting issues |
| @@ -70,12 +70,18 @@ def _fit_frame(self, X): | |||
| if self.strategy == "mean": | |||
| avg = X.mean(axis=0).values | |||
| elif self.strategy == "median": | |||
| avg = X.quantile().values | |||
| avg = [np.median(X[col].dropna()) for col in X.columns] | |||
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I believe this will eagerly compute the values, thanks to np.median. Since that's done in a list comprehension, we'd end up executing the graph for X once per column. We want to delay computation till the end.
I also think this will end up pulling all the data for a column into a single ndarray, to do the median, which we also want to avoid.
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How about using delayed here?
avg = [dask.delayed(np.median(X[col].dropna())) for col in X.columns]| for col in X.columns: | ||
| val_counts = X[col].value_counts().reset_index() | ||
| if isinstance(X, dd.DataFrame): | ||
| x = val_counts.to_dask_array(lengths=True) |
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Do we need lengths here? This also triggers a computation.
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This is needed to compute chunk sizes ... any suggestion on how to avoid it? Thanks,
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Fix #787.
Also related to #779