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docs: add the code samples for metrics{auc, roc_auc_score, roc_curve} #520

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Mar 26, 2024
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docs: add the code samples for metrics{auc, roc_auc_score, roc_curve} #520

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docs: add the code samples for metrics{auc, roc_auc_score, roc_curve}
ashleyxuu Mar 26, 2024
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Merge branch 'main' into ashleyxu-add-metrics-docs
ashleyxuu Mar 26, 2024
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78 changes: 78 additions & 0 deletions third_party/bigframes_vendored/sklearn/metrics/_ranking.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,29 @@ def auc(x, y) -> float:
way to summarize a precision-recall curve, see
:func:`average_precision_score`.

**Examples:**

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics
>>> bpd.options.display.progress_bar = None

>>> x = bpd.DataFrame([1, 1, 2, 2])
>>> y = bpd.DataFrame([2, 3, 4, 5])
>>> auc = bigframes.ml.metrics.auc(x, y)
>>> auc
3.5

The input can be Series:

>>> df = bpd.DataFrame(
... {"x": [1, 1, 2, 2],
... "y": [2, 3, 4, 5],}
... )
>>> auc = bigframes.ml.metrics.auc(df["x"], df["y"])
>>> auc
3.5


Args:
x (Series or DataFrame of shape (n_samples,)):
X coordinates. These must be either monotonic increasing or monotonic
Expand All @@ -44,6 +67,28 @@ def roc_auc_score(y_true, y_score) -> float:
"""Compute Area Under the Receiver Operating Characteristic Curve (ROC AUC) \
from prediction scores.

**Examples:**

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics
>>> bpd.options.display.progress_bar = None

>>> y_true = bpd.DataFrame([0, 0, 1, 1, 0, 1, 0, 1, 1, 1])
>>> y_score = bpd.DataFrame([0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.5, 0.3, 0.6, 0.45])
>>> roc_auc_score = bigframes.ml.metrics.roc_auc_score(y_true, y_score)
>>> roc_auc_score
0.625

The input can be Series:

>>> df = bpd.DataFrame(
... {"y_true": [0, 0, 1, 1, 0, 1, 0, 1, 1, 1],
... "y_score": [0.1, 0.4, 0.35, 0.8, 0.65, 0.9, 0.5, 0.3, 0.6, 0.45],}
... )
>>> roc_auc_score = bigframes.ml.metrics.roc_auc_score(df["y_true"], df["y_score"])
>>> roc_auc_score
0.625

Args:
y_true (Series or DataFrame of shape (n_samples,)):
True labels or binary label indicators. The binary and multiclass cases
Expand Down Expand Up @@ -72,6 +117,39 @@ def roc_curve(
):
"""Compute Receiver operating characteristic (ROC).

**Examples:**

>>> import bigframes.pandas as bpd
>>> import bigframes.ml.metrics
>>> bpd.options.display.progress_bar = None

>>> y_true = bpd.DataFrame([1, 1, 2, 2])
>>> y_score = bpd.DataFrame([0.1, 0.4, 0.35, 0.8])
>>> fpr, tpr, thresholds = bigframes.ml.metrics.roc_curve(y_true, y_score, drop_intermediate=False)
>>> fpr
0 0.0
1 0.0
2 0.0
3 0.0
4 0.0
Name: fpr, dtype: Float64

>>> tpr
0 0.0
1 0.333333
2 0.5
3 0.833333
4 1.0
Name: tpr, dtype: Float64

>>> thresholds
0 inf
1 0.8
2 0.4
3 0.35
4 0.1
Name: thresholds, dtype: Float64

Args:
y_true: Series or DataFrame of shape (n_samples,)
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
Expand Down