sklearn.metrics¶

sklearn.metrics.accuracy_score¶

sklearn.metrics.accuracy_score(y_true, y_pred, normalize=True, sample_weight=None)

Supported Arguments

- y_true: 1d array-like. - y_pred: 1d array-like. - normalize: bool. - sample_weight: 1d numeric array-like or None.

Note

y_true, y_pred, and sample_weight (if provided) must be of same length.

Example Usage

>>> import bodo
>>> import numpy as np
>>> from sklearn.metrics import accuracy_score
>>> y_pred = np.array([0, 2, 1, 3])
>>> y_true = np.array([0, 1, 2, 3])
>>> @bodo.jit
>>> def test_accuracy_score(y_true, y_pred):
...   print(accuracy_score(y_true, y_pred))
>>> test_accuracy_score(y_true, y_pred)
0.5

sklearn.metrics.confusion_matrix¶

sklearn.metrics.confusion_matrix(y_true, y_pred, labels=None, sample_weight=None, normalize=None)

Supported Arguments

- y_true: 1d array-like. - y_pred: 1d array-like. - labels: 1d array-like. - sample_weight: 1d numeric array-like or None. - normalize: Must be one of 'true', 'pred', 'all', or None

Note

y_true, y_pred, and sample_weight (if provided) must be of same length.

Example Usage
```
>>> import bodo
>>> from sklearn.metrics import confusion_matrix
>>> y_true = [2, 0, 2, 2, 0, 1]
>>> y_pred = [0, 0, 2, 2, 0, 2]
>>> @bodo.jit
>>> def test_confusion_matirx(y_true, y_pred):
...   print(confusion_matrix(y_true, y_pred))
>>> test_confusion_matrix(y_true, y_pred)
[[2 0 0]
[0 0 1]
[1 0 2]]
```

sklearn.metrics.f1_score¶

sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')

Supported Arguments

- y_true: 1d array-like. - y_pred: 1d array-like. - average: Must be one of 'micro', 'macro', 'samples', 'weighted', 'binary', or None.

Note

y_true and y_pred must be of same length.

Example Usage
```
>>> import bodo
>>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> @bodo.jit
>>> def test_f1_score(y_true, y_pred):
...   print(f1_score(y_true, y_pred, average='macro'))
>>> test_f1_score(y_true, y_pred)
0.26666666666666666
```

sklearn.metrics.mean_absolute_error¶

sklearn.metrics.mean_absolute_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

Supported Arguments

- y_true: NumPy array. - y_pred: NumPy array. - sample_weight: Numeric NumPy array or None. - multioutput: Must be one of 'raw_values', 'uniform_average', or array-like.

Note

y_true, y_pred, and sample_weight (if provided) must be of same length.

Example Usage

>>> import bodo
>>> import numpy as np
>>> from sklearn.metrics import mean_absolute_error
>>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
>>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
>>> @bodo.jit
>>> def test_mean_absolute_error(y_true, y_pred):
...   print(mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7]))
>>> test_mean_absolute_error(y_true, y_pred)
0.85

sklearn.metrics.mean_squared_error¶

sklearn.metrics.mean_squared_error(y_true, y_pred, sample_weight=None, multioutput='uniform_average', squared=True)

Supported Arguments

- y_true: NumPy array. - y_pred: NumPy array. - sample_weight: Numeric NumPy array or None. - multioutput: Must be one of 'raw_values', 'uniform_average', or array-like.

Note

y_true, y_pred, and sample_weight (if provided) must be of same length.

Example Usage

>>> import bodo
>>> import numpy as np
>>> from sklearn.metrics import mean_squared_error
>>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
>>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
>>> @bodo.jit
>>> def test_mean_squared_error(y_true, y_pred):
...   print(mean_squared_error(y_true, y_pred, multioutput=[0.3, 0.7]))
>>> test_mean_squared_error(y_true, y_pred)
0.825

sklearn.metrics.precision_score¶

sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')

Supported Arguments

- y_true: 1d array-like. - y_pred: 1d array-like. - average: Must be one of 'micro', 'macro', 'samples', 'weighted', 'binary', or None.

Note

y_true and y_pred must be of same length.

Example Usage

>>> import bodo
>>> from sklearn.metrics import precision_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> @bodo.jit
>>> def test_precision_score(y_true, y_pred):
...   print(precision_score(y_true, y_pred, average='macro'))
>>> test_precision_score(y_true, y_pred)
0.2222222222222222

sklearn.metrics.r2_score¶

sklearn.metrics.r2_score(y_true, y_pred, sample_weight=None, multioutput='uniform_average')

Supported Arguments

- y_true: NumPy array. - y_pred: NumPy array. - sample_weight: Numeric NumPy array or None. - multioutput: Must be one of 'raw_values', 'uniform_average', 'variance_weighted', None, or array-like.

Note

y_true, y_pred, and sample_weight (if provided) must be of same length.

Example Usage

>>> import bodo
>>> import numpy as np
>>> from sklearn.metrics import r2_score
>>> y_true = np.array([[0.5, 1], [-1, 1], [7, -6]])
>>> y_pred = np.array([[0, 2], [-1, 2], [8, -5]])
>>> @bodo.jit
>>> def test_r2_score(y_true, y_pred):
...   print(r2_score(y_true, y_pred, multioutput=[0.3, 0.7]))
>>> test_r2_score(y_true, y_pred)
0.9253456221198156

sklearn.metrics.recall_score¶

sklearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')

Supported Arguments

- y_true: 1d array-like. - y_pred: 1d array-like. - average: Must be one of 'micro', 'macro', 'samples', 'weighted', 'binary', or None.

Note

y_true and y_pred must be of same length.

Example Usage

>>> import bodo
>>> from sklearn.metrics import recall_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> @bodo.jit
>>> def test_recall_score(y_true, y_pred):
...   print(recall_score(y_true, y_pred, average='macro'))
>>> test_recall_score(y_true, y_pred)
0.3333333333333333