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Enable optimizations in the 'mixed' mode of Jackknife/Bootstrap.compute_on_sql. #199

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10 changes: 6 additions & 4 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,8 @@ Currently built-in metrics include:
Sum(denominator)`.
+ `Quantile(variable, quantile(s))`: calculates the `quantile(s)` quantile for
`variable`.
+ `Nth(variable, sort_by, n, ascending=True, dropna=False)` computes the `n`th
value after sorting by `sort_by`.
+ `Variance(variable, unbiased=True)`: calculates the variance of `variable`;
`unbiased` determines whether the unbiased (sample) or population estimate
is used.
Expand Down Expand Up @@ -300,10 +302,10 @@ It can help you to sanity check complex Metrics.

## SQL

You can get the SQL query for all built-in Metrics and Operations (except
weighted Quantile) by calling `to_sql(sql_data_source,
split_by)` on the Metric. `sql_data_source` could be a table or a subquery. The
dialect it uses is the [standard SQL](https://cloud.google.com/bigquery/docs/reference/standard-sql)
You can get the SQL query for all built-in Metrics and Operations by calling
`to_sql(sql_data_source, split_by)` on the Metric. `sql_data_source` could be a
table or a subquery. The dialect it uses is the
[standard SQL](https://cloud.google.com/bigquery/docs/reference/standard-sql)
in Google Cloud's BigQuery. For example,

```python
Expand Down
237 changes: 232 additions & 5 deletions meterstick_demo.ipynb
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Expand Up @@ -2299,6 +2299,236 @@
"Mean('clicks', 'impressions').compute_on(df)"
]
},
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"## Nth\n",
"\n",
"`Nth(var, sort_by, n, ascending=True, dropna=False)` computes the `n`th value of `var` after sorting by `sort_by`."
]
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" 1st(clicks) sort by impressions asc\n",
"0 1.163701"
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Expand Down Expand Up @@ -18261,12 +18491,9 @@
"source": [
"#SQL\n",
"\n",
"You can easily get SQL query for all built-in Metrics and Operations, except for weighted Quantile/CV/Correlation/Cov, by calling\n",
"\n",
"\u003e to_sql(sql_table, split_by).\n",
"You can easily get SQL query for all built-in Metrics and Operations by calling `to_sql(sql_table, split_by)`.\n",
"\n",
"You can also directly execute the query by calling\n",
"\u003e compute_on_sql(sql_table, split_by, execute, melted),\n",
"You can also directly execute the query by calling `compute_on_sql(sql_table, split_by, execute, melted)`,\n",
"\n",
"where `execute` is a function that can execute SQL queries. The return is very similar to compute_on().\n",
"\n",
Expand Down
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