standardized URI get combined, with earlier rows given more weight.
// Re-label the DataFrame with standardized, non-unique row labels
frame.index = pd.Series(
[standardized_uri(language, label) for label in frame.index],
name="term"
)
// Assign row n a weight of 1/(n+1) for weighted averaging
nrows = frame.shape[0]
weights = 1.0 / np.arange(1, nrows + 1)
label_weights = pd.Series(weights, index=frame.index)
// groupby(level=0).sum() means to add rows that have the same label
relabeled = frame.mul(weights, axis="rows").sort_index().groupby(level=0).sum()
combined_weights = label_weights.sort_index().groupby(level=0).sum()
return relabeled.div(combined_weights, axis="rows")
def l1_normalize_columns(frame):
After Change
// groupby(level=0).sum() means to add rows that have the same label
relabeled = frame.mul(weights, axis="rows").sort_index().groupby(level=0).sum()
combined_weights = label_weights.sort_index().groupby(level=0).sum()
scaled = relabeled.div(combined_weights, axis="rows")
// Rearrange the items in descending order of weight, similar to the order
// we get them in from word2vec and GloVe
combined_weights.sort(ascending=False)
result = scaled.loc[combined_weights.index.drop_duplicates()]
assert not result.index.has_duplicates
return result
def l1_normalize_columns(frame):
