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docs: add code samples for metrics.{accuracy_score, confusion_matrix} #478

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Mar 21, 2024
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Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,24 @@
def accuracy_score(y_true, y_pred, normalize=True) -> float:
"""Accuracy classification score.

**Examples:**

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

>>> y_true = bpd.DataFrame([0, 2, 1, 3])
>>> y_pred = bpd.DataFrame([0, 1, 2, 3])
>>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred)
>>> accuracy_score
0.5

If False, return the number of correctly classified samples:

>>> accuracy_score = bigframes.ml.metrics.accuracy_score(y_true, y_pred, normalize=False)
>>> accuracy_score
2

Args:
y_true (Series or DataFrame of shape (n_samples,)):
Ground truth (correct) labels.
Expand Down Expand Up @@ -58,6 +76,30 @@ def confusion_matrix(
:math:`C_{0,0}`, false negatives is :math:`C_{1,0}`, true positives is
:math:`C_{1,1}` and false positives is :math:`C_{0,1}`.

**Examples:**

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

>>> y_true = bpd.DataFrame([2, 0, 2, 2, 0, 1])
>>> y_pred = bpd.DataFrame([0, 0, 2, 2, 0, 2])
>>> confusion_matrix = bigframes.ml.metrics.confusion_matrix(y_true, y_pred)
>>> confusion_matrix
0 1 2
0 2 0 0
1 0 0 1
2 1 0 2

>>> y_true = bpd.DataFrame(["cat", "ant", "cat", "cat", "ant", "bird"])
>>> y_pred = bpd.DataFrame(["ant", "ant", "cat", "cat", "ant", "cat"])
>>> confusion_matrix = bigframes.ml.metrics.confusion_matrix(y_true, y_pred)
>>> confusion_matrix
ant bird cat
ant 2 0 0
bird 0 0 1
cat 1 0 2

Args:
y_true (Series or DataFrame of shape (n_samples,)):
Ground truth (correct) target values.
Expand Down
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