Source code for immuneML.reports.ml_reports.ROCCurve

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 """ def __init__(self, name: str = None): super().__init__(name=name)
[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] fpr, tpr, _ = roc_curve(x.labels[self.label], 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) path_htm = self.result_path / f"{}.html" path_csv = self.result_path / f"{}.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(, 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