Plot results classifiers.

riemannian_utils/plot_results_classifiers.py

@@ -220,23 +220,65 @@ def plot_bayes(path):
     plt.title('Valence and Arousal Average Accuracy for 32 Test Subjects \n GB Classifier')
     plt.savefig('figures_plot_results/GG-GB-OFICIAL-15-36HZ.eps', format='eps', dpi=1000)
 
-path1 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/FGMDM-OFICIAL-15-36HZ'
-plot_fgmdm(path1)
-
-path2 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/MDM-OFICIAL-15-36HZ'
-plot_mdm(path2)
-
-path3 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/LR-TS-OFICIAL-15-36HZ'
-plot_lr(path3)
-
-path4 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/SVM-TS-OFICIAL-15-36HZ'
-plot_svm(path4)
+def plot_lr_2(path):
+    file = pd.read_csv(path)
+    print("---------------------------------------------------------------------")
+    print("\nLR NTS: Perfomance Valence: ", file.valence_Score_Acc_3.mean(), "% - STD: ", file.valence_Score_Acc_3.std())
+    print("\nLR NTS Perfomance Arousal: ", file.arousal_Score_Acc_3.mean(), "% - STD: ", file.arousal_Score_Acc_3.std())
 
-path5 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/SLDA-TS-OFICIAL-15-36HZ'
-plot_slda(path5)
+    plt.plot(file.subject, file.valence_Score_Acc_3, color='blue', linewidth=1.0, linestyle="-", marker='o',
+             label='Valence')
+    plt.plot(file.subject, file.arousal_Score_Acc_3, color='red', linewidth=1.0, linestyle="-.", marker='o',
+             label='Arousal')
+    plt.ylabel('Accuracy (in %)')
+    plt.xlabel('Subjects')
+    plt.legend()
+    plt.grid(True)
+    plt.xlim(0, 35)
+    plt.title('Valence and Arousal Average Accuracy for 32 Test Subjects \n LR Classifier')
+    plt.savefig('figures_plot_results/GG-LR-OFICIAL-15-36HZ.eps', format='eps', dpi=1000)
 
-path6 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/KNN-TS-OFICIAL-15-36HZ'
-plot_knn(path6)
+def plot_slda_2(path):
+    file = pd.read_csv(path)
+    print("---------------------------------------------------------------------")
+    print("\nsLDA NTS Perfomance Valence: ", file.valence_Score_Acc_3.mean(), "% - STD: ", file.valence_Score_Acc_3.std())
+    print("\nsLDA NTS Perfomance Arousal: ", file.arousal_Score_Acc_3.mean(), "% - STD: ", file.arousal_Score_Acc_3.std())
+    plt.figure()
+    plt.plot(file.subject, file.valence_Score_Acc_3, color='blue', linewidth=1.0, linestyle="-", marker='o',
+             label='Valence')
+    plt.plot(file.subject, file.arousal_Score_Acc_3, color='red', linewidth=1.0, linestyle="-.", marker='o',
+             label='Arousal')
+    plt.ylabel('Accuracy (in %)')
+    plt.xlabel('Subjects')
+    plt.legend()
+    plt.grid(True)
+    plt.xlim(0, 35)
+    plt.title('Valence and Arousal Average Accuracy for 32 Test Subjects \n sLDA Classifier')
+    plt.savefig('figures_plot_results/GG-sLDA-OFICIAL-15-36HZ.eps', format='eps', dpi=1000)
 
-path7 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/BAYES-TS-OFICIAL-15-36HZ'
-plot_bayes(path7)
+# path1 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/FGMDM-OFICIAL-15-36HZ'
+# plot_fgmdm(path1)
+#
+# path2 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/MDM-OFICIAL-15-36HZ'
+# plot_mdm(path2)
+#
+# path3 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/LR-TS-OFICIAL-15-36HZ'
+# plot_lr(path3)
+#
+# path4 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/SVM-TS-OFICIAL-15-36HZ'
+# plot_svm(path4)
+#
+# path5 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/SLDA-TS-OFICIAL-15-36HZ'
+# plot_slda(path5)
+#
+# path6 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/KNN-TS-OFICIAL-15-36HZ'
+# plot_knn(path6)
+#
+# path7 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/BAYES-TS-OFICIAL-15-36HZ'
+# plot_bayes(path7)
+
+path8 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/LR-NO-TS-OFICIAL-15-36HZ'
+plot_lr_2(path8)
+
+path9 = '/Users/ricklicona/PycharmProjects/deap_pBCI_Riemannian/DEAP/RIEMANNIAN_MDM/csv_results_classifiers/SLDA-NO-TS-OFICIAL-15-36HZ'
+plot_slda_2(path9)
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