Update train_svm.py

train_svm.py

@@ -14,49 +14,45 @@
                         default=None)
     parser.add_argument('--cross_validate', action='store',
                         help="perform cross validation (test_path will be used only for final prediction)."
-                             "If group_k-fold is chosen, each source file will be considered a group "
+                             " If group_k-fold is chosen, each source file will be considered a group "
                              "(only relevant if sampling was performed and more than one file per class was provided)",
                         default=None, choices=['leave-one-out', 'k-fold', 'group-k-fold'], type=str)
     parser.add_argument('--k', action='store', help="k for k-fold", default=10, type=int)
-    parser.add_argument('--dim_reduc', action='store', choices=['pca'], help="optional dimensionality "
-                                                                             "reduction of input data", default=None)
+    parser.add_argument('--dim_reduc', action='store', choices=['pca'],
+                        help="optional dimensionality reduction of input data", default=None)
     parser.add_argument('--norms', action='store_true', help="perform normalisations? (default: True)",
                         default=True)
     parser.add_argument('--balance', action='store',
                         choices=["downsampling", "Tomek", "upsampling", "SMOTE", "SMOTETomek"],
-                        help="which "
-                             "strategy to use in case of imbalanced datasets: "
-                             "downsampling (random without replacement), "
-                             "Tomek (downs. by removing Tomek links), "
-                        # "ENN (EditedNearestNeighbours, downs. by removing samples close to the decision boundary), "                                                                                                                  
-                             "upsampling (random over sampling with replacement), "
-                             "SMOTE (upsampling with SMOTE - Synthetic Minority Over-sampling Technique), "
-                             "SMOTETomek (over+undersampling with SMOTE+Tomek)",
+                        help="which strategy to use in case of imbalanced datasets: "
+                             "downsampling, Tomek, upsampling, SMOTE, SMOTETomek",
                         default=None)
     parser.add_argument('--class_weights', action='store_true',
-                        help="whether to use class weights in imbalanced datasets "
-                             "(inversely proportional to total class samples)",
-                        default=False
-                        )
+                        help="use class weights in imbalanced datasets",
+                        default=False)
     parser.add_argument('--kernel', action='store',
-                        help="type of kernel to use (default and recommended choice is LinearSVC; "
-                             "possible alternatives are linear, sigmoid, rbf and poly, as per sklearn.svm.SVC)",
-                        default="LinearSVC", choices=['LinearSVC', 'linear', 'sigmoid', 'rbf', 'poly'], type=str)
+                        help="type of kernel to use (default LinearSVC)", default="LinearSVC",
+                        choices=['LinearSVC', 'linear', 'sigmoid', 'rbf', 'poly'], type=str)
     parser.add_argument('--final', action='store_true',
                         help="final analysis on unknown dataset (no evaluation)?",
                         default=False)
     parser.add_argument('--get_coefs', action='store_true',
-                        help="switch to write to disk and plot the most important coefficients"
-                             " for the training feats for each class",
+                        help="write to disk and plot most important coefficients",
                         default=False)
+    # My new arguments for rolling stylometry plotting
+    parser.add_argument('--plot_rolling', action='store_true',
+                        help="If final predictions are produced, also plot rolling stylometry.")
+    parser.add_argument('--plot_smoothing', action='store', type=int, default=3,
+                        help="Smoothing window size for rolling stylometry plot (default:3)."
+                             "Set to 0 or None to disable smoothing.")
+
     args = parser.parse_args()
 
     print(".......... loading data ........")
     train = pandas.read_csv(args.train_path, index_col=0)
 
     if args.test_path is not None:
         test = pandas.read_csv(args.test_path, index_col=0)
-
     else:
         test = None
 
@@ -69,7 +65,7 @@
     else:
         args.o = ''
 
-
+    # Save confusion matrix and misattributions if we have a cross-validate scenario or a test but not final
     if args.cross_validate is not None or (args.test_path is not None and not args.final):
         svm["confusion_matrix"].to_csv(args.o+"confusion_matrix.csv")
         svm["misattributions"].to_csv(args.o+"misattributions.csv")
@@ -80,6 +76,13 @@
         print(".......... Writing final predictions to " + args.o + "FINAL_PREDICTIONS.csv ........")
         svm["final_predictions"].to_csv(args.o+"FINAL_PREDICTIONS.csv")
 
+        # To get plot for rolling stylometry
+        if args.plot_rolling:
+            print(".......... Plotting rolling stylometry ........")
+            final_pred_path = args.o+"FINAL_PREDICTIONS.csv"
+            smoothing = args.plot_smoothing if args.plot_smoothing is not None else 0
+            superstyl.svm.plot_rolling_stylometry(final_pred_path, smoothing=smoothing)
+
     if args.get_coefs:
         print(".......... Writing coefficients to disk ........")
         svm["coefficients"].to_csv(args.o+"coefficients.csv")