def test_get_callable(self):
fn = get_callable('sklearn.metrics.accuracy_score')
from sklearn.metrics import accuracy_score
y_true = [1, 1, 0, 1, 0]
y_pred = [1, 1, 1, 0, 0]
assert accuracy_score(y_true, y_pred) == fn(y_true, y_pred)
def display_model_performance(self):
"""
Displays the performance of the model. The metrics are computed using the config dict.
Metrics should be given as a list of dict. Each dict contains they following keys :
'path' (path to the metric function, ex: 'sklearn.metrics.mean_absolute_error'),
'name' (optional, name of the metric as displayed in the report),
and 'use_proba_values' (optional, possible values are False (default) or True
if the metric uses proba values instead of predicted values).
For example :
config['metrics'] = [
{
'path': 'sklearn.metrics.mean_squared_error',
'name': 'Mean absolute error', # Optional : name that will be displayed next to the metric
'y_pred': 'predicted_values' # Optional
},
{
'path': 'Scoring_AP.utils.lift10', # Custom function path
'name': 'Lift10',
'y_pred': 'proba_values' # Use proba values instead of predicted values
}
]
"""
if self.y_test is None:
logging.info(
"No labels given for test set. Skipping model performance part"
)
return
print_md("### Univariate analysis of target variable")
df = pd.concat([
pd.DataFrame({
self.target_name: self.y_pred
}).assign(_dataset="pred"),
pd.DataFrame({
self.target_name: self.y_test
}).assign(_dataset="true") if self.y_test is not None else None
])
self._perform_and_display_analysis_univariate(
df=df,
col_splitter="_dataset",
split_values=["pred", "true"],
names=["Prediction values", "True values"],
group_id='target-distribution')
if 'metrics' not in self.config.keys():
logging.info(
"No 'metrics' key found in report config dict. Skipping model performance part."
)
return
print_md("### Metrics")
for metric in self.config['metrics']:
if 'name' not in metric.keys():
metric['name'] = metric['path']
if metric['path'] in ['confusion_matrix', 'sklearn.metrics.confusion_matrix'] or \
metric['name'] == 'confusion_matrix':
print_md(f"**{metric['name']} :**")
print_html(
convert_fig_to_html(
generate_confusion_matrix_plot(y_true=self.y_test,
y_pred=self.y_pred)))
else:
try:
metric_fn = get_callable(path=metric['path'])
# Look if we should use proba values instead of predicted values
if 'use_proba_values' in metric.keys(
) and metric['use_proba_values'] is True:
y_pred = self.explainer.proba_values
else:
y_pred = self.y_pred
res = metric_fn(self.y_test, y_pred)
except Exception as e:
logging.info(
f"Could not compute following metric : {metric['path']}. \n{e}"
)
continue
if isinstance(res, Number):
res = display_value(round_to_k(res, 3))
print_md(f"**{metric['name']} :** {res}")
elif isinstance(res, (list, tuple, np.ndarray)):
print_md(f"**{metric['name']} :**")
print_html(
pd.DataFrame(res).to_html(classes="greyGridTable"))
elif isinstance(res, str):
print_md(f"**{metric['name']} :**")
print_html(f"<pre>{res}</pre>")
else:
logging.info(
f"Could not compute following metric : {metric['path']}. \n"
f"Result of type {res} cannot be displayed")
print_md('---')
