5 examples of 'roc curve sklearn' in Python

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499def test_roc(self):
500 self.lr_roc_curve(self.max_lr_f1(True))
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109def plot_roc(y_test, y_pred, label=''):
110 """Compute ROC curve and ROC area"""
111
112 fpr, tpr, _ = roc_curve(y_test, y_pred)
113 roc_auc = auc(fpr, tpr)
114
115 # Plot of a ROC curve for a specific class
116 plt.figure()
117 plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
118 plt.plot([0, 1], [0, 1], 'k--')
119 plt.xlim([0.0, 1.0])
120 plt.ylim([0.0, 1.05])
121 plt.xlabel('False Positive Rate')
122 plt.ylabel('True Positive Rate')
123 plt.title('Receiver operating characteristic' + label)
124 plt.legend(loc="lower right")
125 plt.show()
302def get_roc_curve(model, data, thread_count=-1, plot=False):
303 """
304 Build points of ROC curve.
305
306 Parameters
307 ----------
308 model : catboost.CatBoost
309 The trained model.
310
311 data : catboost.Pool or list of catboost.Pool
312 A set of samples to build ROC curve with.
313
314 thread_count : int (default=-1)
315 Number of threads to work with.
316 If -1, then the number of threads is set to the number of CPU cores.
317
318 plot : bool, optional (default=False)
319 If True, draw curve.
320
321 Returns
322 -------
323 curve points : tuple of three arrays (fpr, tpr, thresholds)
324 """
325 if type(data) == Pool:
326 data = [data]
327 if not isinstance(data, list):
328 raise CatBoostError('data must be a catboost.Pool or list of pools.')
329 for pool in data:
330 if not isinstance(pool, Pool):
331 raise CatBoostError('one of data pools is not catboost.Pool')
332
333 roc_curve = _get_roc_curve(model._object, data, thread_count)
334
335 if plot:
336 with _import_matplotlib() as plt:
337 _draw(plt, roc_curve[0], roc_curve[1], 'False Positive Rate', 'True Positive Rate', 'ROC Curve')
338
339 return roc_curve
21def ROC_plot(features,X_,y_, pred_,title):
22 fpr_, tpr_, thresholds = roc_curve(y_, pred_)
23 optimal_idx = np.argmax(tpr_ - fpr_)
24#https://stackoverflow.com/questions/28719067/roc-curve-and-cut-off-point-python
25 optimal_threshold = thresholds[optimal_idx]
26 auc_ = auc(fpr_, tpr_)
27 title = "{} auc=".format(title)
28 print("{} auc={} OT={:.4g}".format(title, auc_,optimal_threshold))
29 plt.plot(fpr_, tpr_, label="{}:{:.4g}".format(title, auc_))
30 plt.xlabel('False positive rate')
31 plt.ylabel('True positive rate')
32 plt.title('SMPLEs={} Features={} OT={:.4g}'.format(X_.shape[0],len(features),optimal_threshold))
33 plt.legend(loc='best')
34 plt.savefig("./_auc_[{}].jpg".format(features))
35 plt.show()
36 return auc_,optimal_threshold
220def precision_recall_curve(clf, x_test, y_test):
221 from sklearn.metrics import precision_recall_curve
222
223 for i in range(2):
224 y_probabilities = [x[i] for x in clf.predict_proba(x_test)]
225 precision, recall, thresholds = precision_recall_curve(y_test, y_probabilities)
226
227 plt.title('Precision Recall Curve')
228 plt.plot(recall, precision, 'b')
229
230 plt.show()

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