|
|
"""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 |
|
|
|
|
|
|
|
|
simplefilter(action="ignore", category=FutureWarning) |
|
|
|
|
|
|
|
|
def summary_metrics(model=None, X=None, y=None, X_test=None, y_test=None): |
|
|
"""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, |
|
|
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}) |
|
|
|
