def test_cmap(self): np.random.seed(0) clf = LogisticRegression() clf.fit(self.X, self.y) probas = clf.predict_proba(self.X) plot_roc_curve(self.y, probas, cmap='nipy_spectral') plot_roc_curve(self.y, probas, cmap=plt.cm.nipy_spectral)
def test_curve_diffs(self): np.random.seed(0) clf = LogisticRegression() clf.fit(self.X, self.y) probas = clf.predict_proba(self.X) ax_macro = plot_roc_curve(self.y, probas, curves='macro') ax_micro = plot_roc_curve(self.y, probas, curves='micro') ax_class = plot_roc_curve(self.y, probas, curves='each_class') self.assertNotEqual(ax_macro, ax_micro, ax_class)
def test_ax(self): np.random.seed(0) clf = LogisticRegression() clf.fit(self.X, self.y) probas = clf.predict_proba(self.X) fig, ax = plt.subplots(1, 1) out_ax = plot_roc_curve(self.y, probas) assert ax is not out_ax out_ax = plot_roc_curve(self.y, probas, ax=ax) assert ax is out_ax
def plot_analysis(combine, test_name, y_true, y_pred, y_proba, labels, verbose, library, save=True, show=True, sessionid="testing", prefix=""): met_index = 0 plt.rcParams.update({'font.size': 14}) # TODO: Find a way to do this better pltmetrics.plot_confusion_matrix(y_true, y_pred) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "confusion_matrix", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 plt.rcParams.update({'font.size': 12}) pltmetrics.plot_roc_curve(y_true, y_proba) for text in plt.gca().legend_.get_texts(): text.set_text(text.get_text().replace("ROC curve of class", "class")) text.set_text(text.get_text().replace("area =", "AUC: ")) text.set_text(text.get_text().replace("micro-average ROC curve", "micro-avg")) text.set_text(text.get_text().replace("macro-average ROC curve", "macro-avg")) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "roc_curves", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 if len(labels) < 3: pltmetrics.plot_ks_statistic(y_true, y_proba) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "ks_statistics", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 pltmetrics.plot_precision_recall_curve(y_true, y_proba) for text in plt.gca().legend_.get_texts(): text.set_text(text.get_text().replace( "Precision-recall curve of class", "class")) text.set_text(text.get_text().replace("area =", "AUC: ")) text.set_text(text.get_text().replace( "micro-average Precision-recall curve", "micro-avg")) text.set_text(text.get_text().replace("macro-average Precision-recall", "macro-avg")) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "precision_recall_curve", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 if len(labels) < 3: pltmetrics.plot_cumulative_gain(y_true, y_proba) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "cumulative_gain", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 if len(labels) < 3: pltmetrics.plot_lift_curve(y_true, y_proba) if not combine: #plt.gcf().set_size_inches(3.65,3.65) save_show(plt, library + "/" + prefix, sessionid, "lift_curve", show, save, False, True, True, False) else: plt.subplot(2, 4, met_index + 1) met_index += 1 if combine: plt.suptitle(test_name) plt.tight_layout(rect=[0, 0.03, 1, 0.95]) save_show(plt, library, sessionid, figname, show, save, True, analysis=True)
def test_array_like(self): plot_roc_curve([0, 'a'], [[0.8, 0.2], [0.2, 0.8]]) plot_roc_curve([0, 1], [[0.8, 0.2], [0.2, 0.8]]) plot_roc_curve(['b', 'a'], [[0.8, 0.2], [0.2, 0.8]])
def test_string_classes(self): np.random.seed(0) clf = LogisticRegression() clf.fit(self.X, convert_labels_into_string(self.y)) probas = clf.predict_proba(self.X) plot_roc_curve(convert_labels_into_string(self.y), probas)
print('Confusion matrix, without normalization') print(cm) plt.imshow(cm, interpolation='nearest', cmap=cmap) plt.title(title) plt.colorbar() tick_marks = np.arange(len(classes)) plt.xticks(tick_marks, classes, rotation=45) plt.yticks(tick_marks, classes) fmt = '.2f' if normalize else 'd' thresh = cm.max() / 2. for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black") plt.tight_layout() plt.ylabel('True label') plt.xlabel('Predicted label') plt.figure() plot_confusion_matrix(conf_matrix, classes, title='Matrice de confusion') plt.show() skplt.plot_roc_curve(trueTarget, predicted_proba) plt.show()