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"""Define plots used by multiple sklearn model classes."""
from warnings import simplefilter
import numpy as np
import wandb
from wandb.integration.sklearn import calculate, utils
# ignore all future warnings
simplefilter(action="ignore", category=FutureWarning)
def summary_metrics(model=None, X=None, y=None, X_test=None, y_test=None): # noqa: N803
"""Logs a chart depicting summary metrics for a model.
Should only be called with a fitted model (otherwise an error is thrown).
Args:
model: (clf or reg) Takes in a fitted regressor or classifier.
X: (arr) Training set features.
y: (arr) Training set labels.
X_test: (arr) Test set features.
y_test: (arr) Test set labels.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_summary_metrics(model, X_train, y_train, X_test, y_test)
```
"""
not_missing = utils.test_missing(
model=model, X=X, y=y, X_test=X_test, y_test=y_test
)
correct_types = utils.test_types(
model=model, X=X, y=y, X_test=X_test, y_test=y_test
)
model_fitted = utils.test_fitted(model)
if not_missing and correct_types and model_fitted:
metrics_chart = calculate.summary_metrics(model, X, y, X_test, y_test)
wandb.log({"summary_metrics": metrics_chart})
def learning_curve(
model=None,
X=None, # noqa: N803
y=None,
cv=None,
shuffle=False,
random_state=None,
train_sizes=None,
n_jobs=1,
scoring=None,
):
"""Logs a plot depicting model performance against dataset size.
Please note this function fits the model to datasets of varying sizes when called.
Args:
model: (clf or reg) Takes in a fitted regressor or classifier.
X: (arr) Dataset features.
y: (arr) Dataset labels.
For details on the other keyword arguments, see the documentation for
`sklearn.model_selection.learning_curve`.
Returns:
None: To see plots, go to your W&B run page then expand the 'media' tab
under 'auto visualizations'.
Example:
```python
wandb.sklearn.plot_learning_curve(model, X, y)
```
"""
not_missing = utils.test_missing(model=model, X=X, y=y)
correct_types = utils.test_types(model=model, X=X, y=y)
if not_missing and correct_types:
if train_sizes is None:
train_sizes = np.linspace(0.1, 1.0, 5)
y = np.asarray(y)
learning_curve_chart = calculate.learning_curve(
model, X, y, cv, shuffle, random_state, train_sizes, n_jobs, scoring
)
wandb.log({"learning_curve": learning_curve_chart})
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