Coverage metrics #38
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Coverage metrics #38
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Codecov Report
@@ Coverage Diff @@
## main #38 +/- ##
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Coverage 100.00% 100.00%
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Files 44 45 +1
Lines 2209 2230 +21
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+ Hits 2209 2230 +21
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feldlime
requested changes
May 22, 2023
| float | ||
| Value of metric. | ||
| """ | ||
| reco_k_first_ranks = reco[reco[Columns.Rank] <= self.k] |
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As we need only the items column, let's take only it.
This will be more memory efficient and faster as well
items = reco.loc[reco[Columns.Rank] <= self.k, Columns.Item]
| Value of metric. | ||
| """ | ||
| reco_k_first_ranks = reco[reco[Columns.Rank] <= self.k] | ||
| return len(reco_k_first_ranks[Columns.Item].unique()) / len(catalog) |
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There is nunique method, no need to use len
| reco_k_first_ranks = reco[reco[Columns.Rank] <= self.k] | ||
| return len(reco_k_first_ranks[Columns.Item].unique()) / len(catalog) | ||
|
|
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| def calc_per_user(self, reco: pd.DataFrame, catalog: Catalog) -> pd.Series: |
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Maybe for this metric calc_per_user method is meaningless since recommendations for user are unique and coverage depends only on k that we set up here
| pd.Series | ||
| Values of metric (index - user id, values - metric value for every user). | ||
| """ | ||
| reco_k_first_ranks = reco[reco[Columns.Rank] <= self.k] |
| Values of metric (index - user id, values - metric value for every user). | ||
| """ | ||
| reco_k_first_ranks = reco[reco[Columns.Rank] <= self.k] | ||
| return reco_k_first_ranks.groupby(Columns.User)[Columns.Item].count().rename(None) |
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It's better to store results of complex expressions to the separate variables
feldlime
reviewed
Sep 25, 2023
| class NumRetrieved(MetricAtK): | ||
| """ | ||
| Number of recommendations retrieved is a metric that shows | ||
| how much items were recommended to users by first k recommendations (less or equal k) |
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Item coverage and num retrieved