From 370fe9087848862d02f0e5a333fcb4cd37cf5ca0 Mon Sep 17 00:00:00 2001
From: Ashley Xu <139821907+ashleyxuu@users.noreply.github.com>
Date: Fri, 22 Mar 2024 18:06:09 -0700
Subject: [PATCH] docs: add code samples for metrics.{recall_score,
 precision_score, f11_score} (#502)
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…_score}

Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly:
- [ ] Make sure to open an issue as a [bug/issue](https://togithub.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code!  That way we can discuss the change, evaluate designs, and agree on the general idea
- [ ] Ensure the tests and linter pass
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- [ ] Appropriate docs were updated (if necessary)

Fixes #<issue_number_goes_here> 🦕
---
 .../sklearn/metrics/_classification.py        | 48 +++++++++++++++++++
 1 file changed, 48 insertions(+)

diff --git a/third_party/bigframes_vendored/sklearn/metrics/_classification.py b/third_party/bigframes_vendored/sklearn/metrics/_classification.py
index 35c22f4cd0..00bbf8cd60 100644
--- a/third_party/bigframes_vendored/sklearn/metrics/_classification.py
+++ b/third_party/bigframes_vendored/sklearn/metrics/_classification.py
@@ -128,6 +128,22 @@ def recall_score(
 
     The best value is 1 and the worst value is 0.
 
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.ml.metrics
+        >>> bpd.options.display.progress_bar = None
+
+        >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2])
+        >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1])
+        >>> recall_score = bigframes.ml.metrics.recall_score(y_true, y_pred, average=None)
+        >>> recall_score
+        0    1
+        1    0
+        2    0
+        dtype: int64
+
+
     Args:
         y_true (Series or DataFrame of shape (n_samples,)):
             Ground truth (correct) target values.
@@ -137,6 +153,7 @@ def recall_score(
                 default='binary'):
             This parameter is required for multiclass/multilabel targets.
             Possible values are 'None', 'micro', 'macro', 'samples', 'weighted', 'binary'.
+            Only average=None is supported.
 
     Returns:
         float (if average is not None) or Series of float of shape n_unique_labels,): Recall
@@ -160,6 +177,21 @@ def precision_score(
 
     The best value is 1 and the worst value is 0.
 
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.ml.metrics
+        >>> bpd.options.display.progress_bar = None
+
+        >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2])
+        >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1])
+        >>> precision_score = bigframes.ml.metrics.precision_score(y_true, y_pred, average=None)
+        >>> precision_score
+        0    0.666667
+        1    0.000000
+        2    0.000000
+        dtype: float64
+
     Args:
         y_true: Series or DataFrame of shape (n_samples,)
             Ground truth (correct) target values.
@@ -169,6 +201,7 @@ def precision_score(
                 default='binary'
             This parameter is required for multiclass/multilabel targets.
             Possible values are 'None', 'micro', 'macro', 'samples', 'weighted', 'binary'.
+            Only average=None is supported.
 
     Returns:
         precision: float (if average is not None) or Series of float of shape \
@@ -195,6 +228,21 @@ def f1_score(
     the F1 score of each class with weighting depending on the ``average``
     parameter.
 
+    **Examples:**
+
+        >>> import bigframes.pandas as bpd
+        >>> import bigframes.ml.metrics
+        >>> bpd.options.display.progress_bar = None
+
+        >>> y_true = bpd.DataFrame([0, 1, 2, 0, 1, 2])
+        >>> y_pred = bpd.DataFrame([0, 2, 1, 0, 0, 1])
+        >>> f1_score = bigframes.ml.metrics.f1_score(y_true, y_pred, average=None)
+        >>> f1_score
+        0    0.8
+        1    0.0
+        2    0.0
+        dtype: float64
+
     Args:
         y_true: Series or DataFrame of shape (n_samples,)
             Ground truth (correct) target values.