import warnings
import numpy as np
import plotly.graph_objs as go
from sklearn.metrics import roc_curve, auc
from immuneML.reports.ReportOutput import ReportOutput
from immuneML.reports.ReportResult import ReportResult
from immuneML.reports.ml_reports.MLReport import MLReport
from immuneML.util.PathBuilder import PathBuilder
[docs]class ROCCurve(MLReport):
"""
A report that plots the ROC curve for a binary classifier.
YAML specification:
.. indent with spaces
.. code-block:: yaml
reports:
my_roc_report: ROCCurve
"""
[docs] @classmethod
def build_object(cls, **kwargs):
name = kwargs["name"] if "name" in kwargs else "ROC_curve"
return ROCCurve(name=name)
def _generate(self) -> ReportResult:
x = self.test_dataset.encoded_data
y_score = self.method.predict_proba(x, self.label)[self.label.name]
fpr, tpr, _ = roc_curve(x.labels[self.label.name], y_score[:, 0])
roc_auc = auc(fpr, tpr)
trace1 = go.Scatter(x=fpr, y=tpr,
mode='lines',
line=dict(color='darkorange', width=2),
name=f"ROC curve (area = {roc_auc})")
trace2 = go.Scatter(x=[0, 1], y=[0, 1],
mode='lines',
line=dict(color='navy', width=2, dash='dash'),
showlegend=False)
layout = go.Layout(title='Receiver operating characteristic example',
xaxis=dict(title='False Positive Rate'),
yaxis=dict(title='True Positive Rate'))
fig = go.Figure(data=[trace1, trace2], layout=layout)
PathBuilder.build(self.result_path)
path_htm = self.result_path / f"{self.name}.html"
path_csv = self.result_path / f"{self.name}.csv"
csv_result = np.concatenate((fpr.reshape(1, -1), tpr.reshape(1, -1)))
fig.write_html(str(path_htm))
np.savetxt(str(path_csv), csv_result, header="fpr,tpr")
return ReportResult(self.name,
info="A report that plots the ROC curve for a binary classifier.",
output_figures=[ReportOutput(path_htm)],
output_tables=[ReportOutput(path_csv)])
[docs] def check_prerequisites(self):
if not hasattr(self, "result_path") or self.result_path is None:
warnings.warn(f"{self.__class__.__name__} requires an output"
f" 'path' to be set. {self.__class__.__name__}"
f" report will not be created.")
return False
if self.test_dataset.encoded_data is None:
warnings.warn(
f"{self.__class__.__name__}: test dataset is"
f" not encoded and can not be run."
f"{self.__class__.__name__} report will not be created.")
return False
if self.method is None:
warnings.warn(
f"{self.__class__.__name__}: method is"
f" not defined and can not be run."
f"{self.__class__.__name__} report will not be created.")
return True