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docs: add code samples for metrics.{recall_score, precision_score, f11_score} #502

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gcf-merge-on-green merged 1 commit into from
Mar 23, 2024
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docs: add code samples for metrics.{recall_score, precision_score, f11_score} #502

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48 changes: 48 additions & 0 deletions third_party/bigframes_vendored/sklearn/metrics/_classification.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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.
Expand All @@ -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
Expand All @@ -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.
Expand All @@ -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 \
Expand All @@ -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.
Expand Down