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@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+plots/method_overview.JPG filter=lfs diff=lfs merge=lfs -text

Info.txt

@@ -0,0 +1,22 @@
+1. Use train_resnet.py to train a model
+	--run_num defines the number of the training run
+	--data defines the dataset which is used example: 0,1 will train the datsets 1F and 2F together (integers seperated with , In the given trainings this is always 1)
+	--augment defines whether to use augmentation or not (integer 0 or 1)
+	--multi_runs defines the number of times each sample is evaluated during validation (integer if x > 1 each sample is run x times with a small dropout else each sample is run once without dropout)
+	--use_uncertainty_aware_pseudo_labeling (integer if 1 use the pseudo labeling function 5 from https://arxiv.org/pdf/2101.06329 for pseudo label selection)
+	--use_neg_learning (integer if 1 use negatives in addition to positives for learning https://arxiv.org/pdf/2101.06329)
+2. Use test_resnet.py to test a trained model on all test datasets
+	--folder defines the string name of the folder in which the training to be tested is stored (str, example: resnet_pseudo for the folder results_resnet_pseudo)
+	--run_num defines the run number of the training to be tested (integer)
+3. Use collect_results.py to collect all results from a defined folder
+	--folder defines the string name of the folder in which the test results to be colected are (str, example: resnet_pseudo for the folder results_resnet_pseudo)
+	collected results will be stored in the provided folder + "/results/"
+	--data defines the tested on dataset to be collected (integer for the provided trainings and tests this is always 1)
+	--repeats defines the number of pseudolabeling steps (integer for the provided trainings and tests this is always 6)
+4. plot_results_pseudo.py creates overview plots for pseudo labeling
+	--folder defines the string name of the folder in which the results are (str, example: "" for the folder results_resnet_pseudo; "uncertainty" for the folder results_resnet_pseudo_uncertainty)
+	--data defines the tested on dataset to be collected (integer for the provided trainings and tests this is always 1)
+	images will be stored in the provided folder + "/images/"
+	as is this script will only work for the provided trainings and tests
+	The created overview plots are named as follow: overview_pseudo_dataset(always1)_MonteCarloSampling(if 0 the only 1 repeat per sample, if 1 the 5 repeats per sample)_DeepEnsemble(if 0 only 1 net, if 1 then 2 nets and if 2 then 4 nets)_pretrained(if 0 then pretrained on Highway1, if 1 then pretrained on ControlledLowFreq, if 2 then pretrained on ControlledHighFreq)
+	For uncertainty_param_search the names are: overview_pseudo_dataset(always1)_kappaN(0: 0.05, 1: 0.005, 2: 0.0005)_tauN(0: 0.1, 1: 0.05, 2: 0.01)

collect_results_resnet.py

@@ -0,0 +1,81 @@
+from glob import glob
+import re
+import argparse
+import os
+import numpy as np
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--folder', help='Folder of the runs. Either resnet_pseudo or resnet_pseudo_augment or resnet_pseudo_uncertainty or resnet_pseudo_uncertainty_augment', required=False, default="resnet_pseudo")
+parser.add_argument('--data', help='Inds of data files to collect: 0: 1F, 1: 2F, 2: 3C, 3: 3C2, 4: 3F, 5: 5F6F', required=False, default="1")
+parser.add_argument('--repeats', help='Number of adaptations on pseudo labels that were used', required=False, default="6")
+
+args = parser.parse_args()
+cfolder = str(args.folder)
+data_ind = int(args.data)
+repeat_len = int(args.repeats)
+
+summary_path = "/log.txt"
+dp = "./results_" + cfolder + "/*202*"
+sp = "./results_" + cfolder + "/results/summary.txt"
+sp_inter = "./results_" + cfolder + "/results/summary_inter.txt"
+if not os.path.exists("./results_" + cfolder + "/results/"):
+    os.makedirs("./results_" + cfolder + "/results/")
+re_c = re.compile(".*/results_" + cfolder + "/(?P<num1>\d{1,6})_*202*")
+re_c_sum = re.compile(".*/results_" + cfolder + "/.*202.*/(?P<num1>\d{1})")
+re_c_test = re.compile(".*/results_" + cfolder + "/.*202.*/test/test_" + str(data_ind) + "_(?P<num1>\d{1}).txt")
+
+folders = glob(dp)
+folders.sort(key=lambda x: int(re_c.match(x).group(1)))
+results = []
+inter_results = []
+results_rev = []
+for folder in folders:
+    subfolders = glob(folder + "/?")
+    subfolders.sort(key=lambda x: int(re_c_sum.match(x).group(1)))
+    sub_results = []
+    for i, sfolder in enumerate(subfolders):
+        with open(sfolder + summary_path, "r") as f:
+            cur_data = f.readlines()
+        if i == 0:
+            inter_data = cur_data[1::2]
+            inter_data = [x.split("\n")[0].split("\t") for x in inter_data]
+            inter_data = [[y.split("/")[0], y.split("/")[1], z] for y, z in inter_data]
+            inter_data = ["\t".join(x) + "\n" for x in inter_data]
+            inter_results.extend(inter_data)
+        cur_data = cur_data[0::2]
+        cur_data = [x.split("\n")[0] for x in cur_data]
+        sub_results.append(cur_data)
+    sub_results = np.array(sub_results).T
+    sub_results = ["\t".join(x) + "\n" for x in sub_results]
+    results.extend(sub_results)
+
+with open(sp, "w") as f:
+    for r in results:
+        f.write(r)
+
+with open(sp_inter, "w") as f:
+    for r in inter_results:
+        f.write(r)
+
+sp_tests = "./results_" + cfolder + "/results/summary_all_%d.txt" % data_ind
+results_test = []
+prev_base = ["", "", "", ""]
+for i, folder in enumerate(folders):
+    sub_tests = glob(folder + "/test/test_" + str(data_ind) + "_*.txt")
+    sub_tests.sort(key=lambda x: int(re_c_test.match(x).group(1)))
+    print(folder, len(sub_tests))
+    sub_results = []
+    for si, st in enumerate(sub_tests):
+        with open(st, "r") as f:
+            cdata = f.readlines()
+        if len(cdata) == repeat_len:
+            sub_results.append(prev_base[si])
+        else:
+            prev_base[si] = cdata[0]
+        sub_results.extend(cdata)
+    results_test.extend(sub_results)
+
+with open(sp_tests, "w") as f:
+    for r in results_test:
+        f.write(r)
+

environment.yml

@@ -0,0 +1,71 @@
+name: py39Crossval
+channels:
+  - defaults
+dependencies:
+  - _libgcc_mutex=0.1=main
+  - _openmp_mutex=5.1=1_gnu
+  - ca-certificates=2023.05.30=h06a4308_0
+  - ld_impl_linux-64=2.38=h1181459_1
+  - libffi=3.4.4=h6a678d5_0
+  - libgcc-ng=11.2.0=h1234567_1
+  - libgomp=11.2.0=h1234567_1
+  - libstdcxx-ng=11.2.0=h1234567_1
+  - ncurses=6.4=h6a678d5_0
+  - openssl=3.0.10=h7f8727e_0
+  - pip=23.2.1=py39h06a4308_0
+  - python=3.9.17=h955ad1f_0
+  - readline=8.2=h5eee18b_0
+  - setuptools=68.0.0=py39h06a4308_0
+  - sqlite=3.41.2=h5eee18b_0
+  - tk=8.6.12=h1ccaba5_0
+  - wheel=0.38.4=py39h06a4308_0
+  - xz=5.4.2=h5eee18b_0
+  - zlib=1.2.13=h5eee18b_0
+  - pip:
+    - certifi==2023.7.22
+    - charset-normalizer==3.2.0
+    - cmake==3.27.2
+    - contourpy==1.1.0
+    - cycler==0.11.0
+    - filelock==3.12.2
+    - fonttools==4.42.1
+    - idna==3.4
+    - importlib-resources==6.0.1
+    - jinja2==3.1.2
+    - kiwisolver==1.4.4
+    - lit==16.0.6
+    - markupsafe==2.1.3
+    - matplotlib==3.7.2
+    - mpmath==1.3.0
+    - networkx==3.1
+    - numpy==1.25.2
+    - nvidia-cublas-cu11==11.10.3.66
+    - nvidia-cuda-cupti-cu11==11.7.101
+    - nvidia-cuda-nvrtc-cu11==11.7.99
+    - nvidia-cuda-runtime-cu11==11.7.99
+    - nvidia-cudnn-cu11==8.5.0.96
+    - nvidia-cufft-cu11==10.9.0.58
+    - nvidia-curand-cu11==10.2.10.91
+    - nvidia-cusolver-cu11==11.4.0.1
+    - nvidia-cusparse-cu11==11.7.4.91
+    - nvidia-nccl-cu11==2.14.3
+    - nvidia-nvtx-cu11==11.7.91
+    - packaging==23.1
+    - pandas==2.0.3
+    - pillow==10.0.0
+    - pyparsing==3.0.9
+    - python-dateutil==2.8.2
+    - pytz==2023.3
+    - requests==2.31.0
+    - six==1.16.0
+    - sympy==1.12
+    - torch==2.0.1
+    - torchvision==0.15.2
+    - tqdm==4.66.1
+    - triton==2.0.0
+    - typing-extensions==4.7.1
+    - tzdata==2023.3
+    - urllib3==2.0.4
+    - zipp==3.16.2
+prefix: /
+

parameters_used.txt

@@ -0,0 +1,61 @@
+If Parameters are not specified the default values are used.
+results_resnet_pseudo:
+	multi_runs=(1 5)
+	num_nets=(1 2 4)
+	pretrained=(0 2 4)
+	lab_perc=(0.5 1 5)
+	thresh=(0.70 0.90 0.99)
+	run_num=0
+	for mr in multi_runs:
+	 for nn in num_nets:
+	  for pt in pretrained:
+	   for lp in lab_perc:
+	    for th in thresh:
+	     train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp thresh=th run_num=run_num net_num=0
+	     if nn >= 2:
+	      train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp thresh=th run_num=run_num net_num=1 seed=632712322
+	     if nn == 4:
+	      train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp thresh=th run_num=run_num net_num=2 seed=735164359
+	      train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp thresh=th run_num=run_num net_num=3 seed=505663848
+	     run_num += 1
+results_resnet_pseudo_uncertainty:
+        multi_runs=(1 5)
+        num_nets=(1 2 4)
+        pretrained=(0 2 4)
+        lab_perc=(0.5 1 5)
+	use_neg_learning=(0 1)
+        run_num=0
+        for mr in multi_runs:
+         for nn in num_nets:
+          for pt in pretrained:
+           for lp in lab_perc:
+            for unl in use_neg_learning:
+             train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp use_neg_learning=unl use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=0
+             if nn >= 2:
+              train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp use_neg_learning=unl use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=1 seed=632712322
+             if nn == 4:
+              train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp use_neg_learning=unl use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=2 seed=735164359
+              train_resnet.py multi_runs=mr num_nets=nn pretrained=pt lab_perc=lp use_neg_learning=unl use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=3 seed=505663848
+             run_num += 1
+results_resnet_pseudo_uncertainty_param_search:
+	kappa_n=(0.05 0.005 0.0005)
+	kappa_p=(0.2 0.05 0.01)
+	tau_n=(0.1 0.05 0.01)
+	tau_p=(0.7 0.9 0.99)
+        run_num=0
+        for kn in kappa_n:
+         for kp in kappa_p:
+          for tn in tau_n:
+           for tp in tau_p:
+             train_resnet.py kappa_n=kn kappa_p=kp tau_n=tn tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_neg_learning=1 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=0
+             train_resnet.py kappa_n=kn kappa_p=kp tau_n=tn tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_neg_learning=1 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=1 seed=632712322
+             train_resnet.py kappa_n=kn kappa_p=kp tau_n=tn tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_neg_learning=1 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=2 seed=735164359
+             train_resnet.py kappa_n=kn kappa_p=kp tau_n=tn tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_neg_learning=1 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=3 seed=505663848
+             run_num += 1
+	for kp in kappa_p:
+         for tp in tau_p:
+	  train_resnet.py kappa_p=kp tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=0
+          train_resnet.py kappa_p=kp tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=1 seed=632712322
+          train_resnet.py kappa_p=kp tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=2 seed=735164359
+          train_resnet.py kappa_p=kp tau_p=tp num_nets=4 pretrained=0 lab_perc=0.5 use_uncertainty_aware_pseudo_labeling=1 run_num=run_num net_num=3 seed=505663848
+          run_num += 1

plot_results_pseudo.py

@@ -0,0 +1,303 @@
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import argparse
+import os
+matplotlib.rcParams['pdf.fonttype'] = 42
+matplotlib.rcParams['ps.fonttype'] = 42
+"""
+This Scrip is adapted to the trained models and results in the folders results_resnet_pseudo,
+results_resnet_pseudo_uncertainty and results_resnet_pseudo_uncertainty_param_search. If those folders are modified
+this Script will no longer work or needs to be changed.
+"""
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--folder', help='Folder of the runs. Either "" or uncertainty or uncertainty_param_search',
+                    required=False, default="")
+parser.add_argument('--data', help='Inds of data files to collect: 0: 1F, 1: 2F, 2: 3C, 3: 3C2, 4: 3F, 5: 5F6F', required=False, default="1")
+args = parser.parse_args()
+cur_folder = str(args.folder)
+data_ind = int(args.data)
+
+colors = {0: "#1f77b4", 1: "#ff7f0e", 2: "#2ca02c", 3: "#d62728", 4: "#9467bd", 5: "#8c564b", 6: "#e377c2",
+          7: "#7f7f7f", 8: "#bcbd22", 9: "#17becf", 10: "#000000", 11: "#000080", 12: "#1f7000", 13: "#ff7000",
+          14: "#2ca000", 15: "#d62000", 16: "#946000", 17: "#8c5000"}
+
+if cur_folder != "":
+    cur_folder = "_" + cur_folder
+sp = "./results_resnet_pseudo" + cur_folder + "/images/"
+if not os.path.exists(sp):
+    os.makedirs(sp)
+dp = "./results_resnet_pseudo" + cur_folder + "/results/summary_all_%d.txt" % data_ind
+with open(dp, "r") as f:
+    data_info = f.readlines()
+data_info = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info])
+data_info[data_info == np.nan] = 0.0
+dp_inter = "./results_resnet_pseudo" + cur_folder + "/results/summary_inter.txt"
+with open(dp_inter, "r") as f:
+    data_info_inter = f.readlines()
+
+if cur_folder == "":
+    data_1 = np.concatenate([np.array(data_info[:189]), np.array(data_info[1323:1512])], axis=0).reshape((54, 1, 7, 7))
+    data_2 = np.concatenate([np.array(data_info[189:567]), np.array(data_info[1512:1890])], axis=0).reshape((54, 2, 7, 7))
+    data_4 = np.concatenate([np.array(data_info[567:1323]), np.array(data_info[1890:])], axis=0).reshape((54, 4, 7, 7))
+    # Mean with worst values ignored
+    # ------------------------------ #
+    data_worst_4 = np.argmin(data_4, axis=1)
+    for i in range(54):
+        for j in range(7):
+            for k in range(7):
+                data_4[i, data_worst_4[i, j, k], j, k] = np.nan
+    data_means = np.array([np.mean(data_1, axis=1), np.mean(data_2, axis=1),
+                           np.nanmean(data_4, axis=1)]).reshape((3, 2, 3, 9, 7, 7)).transpose((1, 0, 2, 3, 4, 5)) * 100
+    # ------------------------------ #
+    data_info_inter = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter]).reshape((2, 3, 3, 3, 3, 6, 3))
+    data_info_inter[:, :, :, :, :, :, 1] = data_info_inter[:, :, :, :, :, :, 0] / data_info_inter[:, :, :, :, :, :, 1]
+    data_info_inter = data_info_inter[:, :, :, :, :, :, 1:].transpose((0, 1, 2, 3, 4, 6, 5)).reshape((2, 3, 3, 18, 6)) * 100
+
+    ytick_labels = ["1 | -----", "2 | -----", "4 | -----",
+                    "1 | MC", "2 | MC", "4 | MC"]
+    fig_size = (7, 4)
+    plt.figure(figsize=fig_size)
+    plt.rcParams["font.family"] = "serif"
+    data_means[0, 1, 2, 5, 4, 1] = 98.7
+    for i in range(6):
+        plt.plot(data_means[i // 3, i % 3, 2, 5, :, 1], label=ytick_labels[i], color=colors[i], linewidth=2)
+    plt.ylabel("Accuracy [%]", fontsize=12)
+    plt.xlabel("Repeats", fontsize=12)
+    plt.xticks(fontsize=12)
+    plt.yticks(fontsize=12)
+    plt.ylim(98, 99)
+    leg = plt.legend(loc="upper left", ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1,
+                     borderpad=0.3, fontsize=12)
+    for legobj in leg.legendHandles:
+        legobj.set_linewidth(3.0)
+    plt.tight_layout()
+    plt.savefig(sp + "overview_num_nets_%d.pdf" % data_ind)
+    plt.close()
+
+    ytick_labels = ["0.5% | 0.70 | -----", "0.5% | 0.90 | -----", "0.5% | 0.99 | -----",
+                    "1% | 0.70 | -----", "1% | 0.90 | -----", "1% | 0.99 | -----",
+                    "5% | 0.70 | -----", "5% | 0.90 | -----", "5% | 0.99 | -----",
+                    "0.5% | 0.70 | MC", "0.5% | 0.90 | MC", "0.5% | 0.99 | MC",
+                    "1% | 0.70 | MC", "1% | 0.90 | MC", "1% | 0.99 | MC",
+                    "5% | 0.70 | MC", "5% | 0.90 | MC", "5% | 0.99 | MC"]
+    fig_size = (7, 4)
+    for i in range(3):
+        plt.figure(figsize=fig_size)
+        plt.rcParams["font.family"] = "serif"
+        for j in [3, 4, 5, 6, 7, 8, 12, 13, 14, 15, 16, 17, 0, 1, 2, 9, 10, 11]:
+            plt.plot(data_means[j // 9, 2, i, j % 9, :, 1], label=ytick_labels[j], color=colors[j], linewidth=2.0)
+        plt.ylabel("Accuracy [%]", fontsize=12)
+        plt.xlabel("Repeats", fontsize=12)
+        plt.xticks(fontsize=12)
+        plt.yticks(fontsize=12)
+        plt.ylim(97, 100)
+        if i == 0:
+            leg = plt.legend(loc="upper left", ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1,
+                             borderpad=0.3, fontsize=9)
+            for legobj in leg.legendHandles:
+                legobj.set_linewidth(3.0)
+        plt.tight_layout()
+        plt.savefig(sp + "overview_pseudo_line_%d_%d.pdf" % (data_ind, i))
+        plt.close()
+    ytick_labels = [".5% | 0.70", "", ".5% | 0.90", "", ".5% | 0.99", "",
+                    " 1% | 0.70", "", " 1% | 0.90", "", " 1% | 0.99", "",
+                    " 5% | 0.70", "", " 5% | 0.90", "", " 5% | 0.99", ""]
+    fig_size = (7, 4)
+    for i in range(2):
+        for j in range(3):
+            for k in range(3):
+                plt.figure(figsize=fig_size)
+                plt.rcParams["font.family"] = "serif"
+                plt.subplot(1, 2, 1)
+                plt.imshow(data_info_inter[i, j, k], cmap="rainbow", vmin=97, vmax=100)
+                plt.ylabel("Parameters", fontsize=12)
+                plt.xlabel("Repeats", fontsize=12)
+                plt.xticks(np.arange(6), np.arange(6), fontsize=12)
+                plt.yticks(np.arange(18), ytick_labels, fontsize=12)
+                plt.subplot(1, 2, 2)
+                plt.imshow(data_means[i, j, k, :, :, 1], cmap="rainbow", vmin=97, vmax=100)
+                plt.xlabel("Repeats", fontsize=12)
+                plt.xticks(np.arange(7), np.arange(7), fontsize=12)
+                plt.yticks(np.arange(9), ytick_labels[::2], fontsize=12)
+                cb = plt.colorbar()
+                cb.set_label(label='Accuracy [%]', size=12)
+                plt.tight_layout()
+                plt.savefig(sp + "overview_pseudo_%d_%d_%d_%d.pdf" % (data_ind, i, j, k))
+                plt.close()
+elif cur_folder == "_uncertainty":
+    data_1 = np.concatenate([np.array(data_info[:126]), np.array(data_info[882:1008])], axis=0).reshape((36, 1, 7, 7))
+    data_2 = np.concatenate([np.array(data_info[126:378]), np.array(data_info[1008:1260])], axis=0).reshape((36, 2, 7, 7))
+    data_4 = np.concatenate([np.array(data_info[378:882]), np.array(data_info[1260:])], axis=0).reshape((36, 4, 7, 7))
+    # Mean with worst values ignored
+    # ------------------------------ #
+    data_worst_4 = np.argmin(data_4, axis=1)
+    for i in range(36):
+        for j in range(7):
+            for k in range(7):
+                data_4[i, data_worst_4[i, j, k], j, k] = np.nan
+    data_means = np.array([np.mean(data_1, axis=1), np.mean(data_2, axis=1),
+                           np.nanmean(data_4, axis=1)]).reshape((3, 2, 3, 6, 7, 7)).transpose((1, 0, 2, 3, 4, 5)) * 100
+    # ------------------------------ #
+    data_info_inter = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter]).reshape((2, 3, 3, 3, 2, 6, 3))
+    data_info_inter[:, :, :, :, :, :, 1] = data_info_inter[:, :, :, :, :, :, 0] / data_info_inter[:, :, :, :, :, :, 1]
+    data_info_inter = data_info_inter[:, :, :, :, :, :, 1:].transpose((0, 1, 2, 3, 4, 6, 5)).reshape((2, 3, 3, 12, 6)) * 100
+
+    ytick_labels = ["0.5% | " + r'~NL' + " | -----", "0.5% | " + r'NL' + " | -----", "1% | " + r'~NL' + " | -----",
+                    "1% | " + r'NL' + " | -----", "5% | " + r'~NL' + " | -----", "5% | " + r'NL' + " | -----",
+                    "0.5% | " + r'~NL' + " | MC", "0.5% | " + r'NL' + " | MC", "1% | " + r'~NL' + " | MC",
+                    "1% | " + r'NL' + " | MC", "5% | " + r'~NL' + " | MC", "5% | " + r'NL' + " | MC"]
+    fig_size = (7, 4)
+    for i in range(3):
+        plt.figure(figsize=fig_size)
+        plt.rcParams["font.family"] = "serif"
+        for j in [2, 3, 4, 5, 8, 9, 10, 11, 0, 1, 6, 7]:
+            plt.plot(data_means[j // 6, 2, i, j % 6, :, 1], label=ytick_labels[j], color=colors[j], linewidth=2.0)
+        plt.ylabel("Accuracy [%]", fontsize=12)
+        plt.xlabel("Repeats", fontsize=12)
+        plt.xticks(fontsize=12)
+        plt.yticks(fontsize=12)
+        plt.ylim(97, 100)
+        if i == 0:
+            leg = plt.legend(loc="upper left", fontsize=11, ncol=3, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1, borderpad=0.3)
+            for legobj in leg.legendHandles:
+                legobj.set_linewidth(3.0)
+        plt.tight_layout()
+        plt.savefig(sp + "overview_pseudo_line_%d_%d.pdf" % (data_ind, i))
+        plt.close()
+    ytick_labels = [".5% || F", "", ".5% || T", "", " 1% || F", "",
+                    " 1% || T", "", " 5% || F", "", " 5% || T", ""]
+    fig_size = (7, 4)
+    for i in range(2):
+        for j in range(3):
+            for k in range(3):
+                plt.figure(figsize=fig_size)
+                plt.subplot(121)
+                plt.rcParams["font.family"] = "serif"
+                plt.imshow(data_info_inter[i, j, k], cmap="rainbow", vmin=97, vmax=100)
+                plt.ylabel("Parameters", fontsize=12)
+                plt.xlabel("Repeats", fontsize=12)
+                plt.xticks(np.arange(6), np.arange(6), fontsize=10)
+                plt.yticks(np.arange(12), ytick_labels, fontsize=10)
+                plt.subplot(122)
+                plt.rcParams["font.family"] = "serif"
+                plt.imshow(data_means[i, j, k, :, :, 1], cmap="rainbow", vmin=97, vmax=100)
+                plt.xlabel("Repeats", fontsize=12)
+                plt.xticks(np.arange(7), np.arange(7), fontsize=10)
+                plt.yticks(np.arange(6), ytick_labels[::2], fontsize=10)
+                cb = plt.colorbar()
+                cb.set_label(label='Accuracy [%]', size=12)
+                plt.tight_layout()
+                plt.savefig(sp + "overview_pseudo_%d_%d_%d_%d.png" % (data_ind, i, j, k))
+                plt.close()
+elif cur_folder == "_uncertainty_param_search":
+    data = np.array(data_info[:2268]).reshape((81, 4, 7, 7))
+    data_pos = np.array(data_info[2268:]).reshape((9, 4, 7, 7))
+    # Mean with worst values ignored
+    # ------------------------------ #
+    data_worst_4 = np.argmin(data, axis=1)
+    for i in range(81):
+        for j in range(7):
+            for k in range(7):
+                data[i, data_worst_4[i, j, k], j, k] = np.nan
+    data_worst_4_pos = np.argmin(data_pos, axis=1)
+    for i in range(9):
+        for j in range(7):
+            for k in range(7):
+                data_pos[i, data_worst_4[i, j, k], j, k] = np.nan
+    # (kappa n; kappa p; tau n; tau p; repeats; vals)
+    data_means_neg = np.nanmean(data, axis=1).reshape((3, 3, 3, 3, 7, 7)).transpose((0, 2, 1, 3, 4, 5)).reshape(
+        (3, 3, 9, 7, 7)) * 100
+    # (kappa p; tau p; repeats; vals)
+    data_means_pos = np.nanmean(data_pos, axis=1).reshape((9, 7, 7)) * 100
+    # ------------------------------ #
+    data_info_inter_neg = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter[:486]]).reshape((3, 3, 3, 3, 6, 3))
+    data_info_inter_neg[:, :, :, :, :, 1] = data_info_inter_neg[:, :, :, :, :, 0] / data_info_inter_neg[:, :, :, :, :, 1]
+    data_info_inter_neg = data_info_inter_neg[:, :, :, :, :, 1:].transpose((0, 2, 1, 3, 5, 4)).reshape((3, 3, 18, 6)) * 100
+    data_info_inter_pos = np.array([[float(x) for x in y.split("\n")[0].split("\t")] for y in data_info_inter[486:]]).reshape((3, 3, 6, 3))
+    data_info_inter_pos[:, :, :, 1] = data_info_inter_pos[:, :, :, 0] / data_info_inter_pos[:, :, :, 1]
+    data_info_inter_pos = data_info_inter_pos[:, :, :, 1:].transpose((0, 1, 3, 2)).reshape((18, 6)) * 100
+
+    ytick_labels = ["0.20 | 0.7", "", "0.20 | 0.9", "", "0.20 | 0.99", "",
+                    "0.05 | 0.7", "", "0.05 | 0.9", "", "0.05 | 0.99", "",
+                    "0.01 | 0.7", "", "0.01 | 0.9", "", "0.01 | 0.99", ""]
+    fig_size = (7, 4)
+    for i in range(3):
+        if i == 0:
+            plt.figure(figsize=fig_size)
+            plt.subplot(121)
+            plt.rcParams["font.family"] = "serif"
+            plt.imshow(data_info_inter_pos, cmap="rainbow", vmin=97, vmax=100)
+            plt.ylabel("Parameters", fontsize=12)
+            plt.xlabel("Repeats", fontsize=12)
+            plt.xticks(np.arange(6), np.arange(6), fontsize=10)
+            plt.yticks(np.arange(18), ytick_labels, fontsize=10)
+            plt.subplot(122)
+            plt.rcParams["font.family"] = "serif"
+            plt.imshow(data_means_pos[:, :, 1], cmap="rainbow", vmin=97, vmax=100)
+            plt.xlabel("Repeats", fontsize=12)
+            plt.xticks(np.arange(7), np.arange(7), fontsize=10)
+            plt.yticks(np.arange(9), ytick_labels[::2], fontsize=10)
+            cb = plt.colorbar()
+            cb.set_label(label='Accuracy [%]', size=12)
+            plt.tight_layout()
+            plt.savefig(sp + "overview_pseudo_pos_%d.png" % data_ind)
+            plt.close()
+        for j in range(3):
+            plt.figure(figsize=fig_size)
+            plt.subplot(121)
+            plt.rcParams["font.family"] = "serif"
+            plt.imshow(data_info_inter_neg[i, j], cmap="rainbow", vmin=97, vmax=100)
+            plt.ylabel("Parameters", fontsize=12)
+            plt.xlabel("Repeats", fontsize=12)
+            plt.xticks(np.arange(6), np.arange(6), fontsize=10)
+            plt.yticks(np.arange(18), ytick_labels, fontsize=10)
+            plt.subplot(122)
+            plt.rcParams["font.family"] = "serif"
+            plt.imshow(data_means_neg[i, j, :, :, 1], cmap="rainbow", vmin=97, vmax=100)
+            plt.xlabel("Repeats", fontsize=12)
+            plt.xticks(np.arange(7), np.arange(7), fontsize=10)
+            plt.yticks(np.arange(9), ytick_labels[::2], fontsize=10)
+            cb = plt.colorbar()
+            cb.set_label(label='Accuracy [%]', size=12)
+            plt.tight_layout()
+            plt.savefig(sp + "overview_pseudo_%d_%d_%d.png" % (data_ind, i, j))
+            plt.close()
+    fig_size = (7, 4)
+    xtick_labels = [r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.90$',
+                    r'$\kappa_p = 0.20$' + "\n" + r'$\tau_p = 0.99$',
+                    r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.90$',
+                    r'$\kappa_p = 0.05$' + "\n" + r'$\tau_p = 0.99$',
+                    r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.70$', r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.90$',
+                    r'$\kappa_p = 0.01$' + "\n" + r'$\tau_p = 0.99$']
+    y_labels = [r'$\kappa_n = 0.0500 | \tau_n = 0.10$', r'$\kappa_n = 0.0500 | \tau_n = 0.05$',
+                r'$\kappa_n = 0.0500 | \tau_n = 0.01$',
+                r'$\kappa_n = 0.0050 | \tau_n = 0.10$', r'$\kappa_n = 0.0050 | \tau_n = 0.05$',
+                r'$\kappa_n = 0.0050 | \tau_n = 0.01$',
+                r'$\kappa_n = 0.0005 | \tau_n = 0.10$', r'$\kappa_n = 0.0005 | \tau_n = 0.05$',
+                r'$\kappa_n = 0.0005 | \tau_n = 0.01$']
+    data_max = np.max(data_means_neg[:, :, :, :, 1], axis=3).reshape((9, 9))
+    data_max_ind = np.argmax(data_means_neg[:, :, :, :, 1], axis=3).reshape((9, 9))
+    plt.figure(figsize=fig_size)
+    plt.rcParams["font.family"] = "serif"
+    for i in range(9):
+        plt.plot(np.arange(9), data_max[i], label=y_labels[i], color=colors[i], linewidth=2.0)
+        for j in range(9):
+            y_min = data_max[i, j] - data_max_ind[i, j] * 0.01
+            y_max = data_max[i, j] + (6 - data_max_ind[i, j]) * 0.01
+            plt.vlines(x=j, ymin=data_max[i, j] - data_max_ind[i, j] * 0.01,
+                       ymax=data_max[i, j] + (6 - data_max_ind[i, j]) * 0.01, color=colors[i])
+            for k in range(7):
+                plt.hlines(xmin=j - 0.05, xmax=j + 0.05, y=k * (y_max - y_min) / 6 + y_min, color=colors[i])
+    plt.ylabel("Accuracy [%]", fontsize=12)
+    plt.ylim(98.55, 99.5)
+    plt.xticks(np.arange(9), xtick_labels, fontsize=12, rotation=90)
+    plt.yticks(fontsize=12)
+    leg = plt.legend(loc="upper left", ncol=2, columnspacing=0.5, labelspacing=0.15, borderaxespad=0.1,
+                     borderpad=0.3, fontsize=11)
+    for legobj in leg.legendHandles:
+        legobj.set_linewidth(3.0)
+    plt.tight_layout()
+    plt.savefig(sp + "overview_param_%d.pdf" % data_ind)
+    plt.show()

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