📝 Adds paper link to readme

LICENSE

@@ -1,21 +1,675 @@
-MIT License
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
 
-Copyright (c) 2025 Daniel Svitan
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    The 2024/2025 SOC Paper and the related scripts for aggregating, analyzing, and graphing the dataset.
+    Copyright (C) 2025  Daniel Svitaň
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    soc-2024  Copyright (C) 2025  Daniel Svitaň
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
 
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.

README.md

@@ -13,6 +13,8 @@ let me walk you through how it works, I've split it into multiple sections:
 
 Don't forget to check out the [conclusion](#conclusion) and the [credits](CREDITS.md)
 
+The actual paper can be found [here](Daniel%20Svitan%20SOC%202025.pdf)
+
 ### Tools and libraries
 
 Basically all scripts are written in [python](https://www.python.org/), except for one shell script, and these are the
@@ -91,6 +93,8 @@ Quite complicated, right? A ton of data packed into one small image, which could
 It can be overwhelming to look at at first, but once you understand what's going on, it's quite intuitive, anyway,
 the function that does all this is also saved in [analyze.py](analyze.py)
 
+All the graphs are saved in the archives, which you can download, [results.tar.gz](results.tar.gz) and [results.zip](results.zip)
+
 ### Neural network
 
 Ah!
@@ -132,4 +136,4 @@ any questions and talk about this project for hours
 
 ### License
 
-This project is licensed under the [MIT License](LICENSE)
+This project is licensed under the [GNU GPLv3](LICENSE) license

analyze.py

@@ -105,7 +105,7 @@ def plot_violin(data, labels, Fs, ps, title):
     grade_name_labels = ["Priemer známok", "Známka z matematiky", "Známka zo slovenčiny", "Známka z angličtiny"]
 
     fig, axs = plt.subplots(2, 2)
-    fig.suptitle(title)
+    fig.suptitle(title, fontsize=18)
     fig.set_size_inches(12, 9)
 
     for j in range(2):
@@ -114,9 +114,9 @@ def plot_violin(data, labels, Fs, ps, title):
             step = 1 if index > 0 else 0.5
 
             parts = axs[j, k].violinplot(data[index], showmedians=True, showmeans=True)
-            axs[j, k].set_title(grade_names[index])
+            axs[j, k].set_title(grade_names[index], fontsize=16)
-            axs[j, k].set_xlabel(title, fontweight="bold")
+            axs[j, k].set_xlabel(title, fontweight="bold", fontsize=14)
-            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold")
+            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
 
             # q1-q3 lines
             for ind, vec in enumerate(data[index]):
@@ -142,12 +142,14 @@ def plot_violin(data, labels, Fs, ps, title):
             p = ps[index]
             axs[j, k].text(0.01, 0.99, f"F-stat: {F:.4f}\np-val: {p:.4f}", ha="left", va="top",
                            transform=axs[j, k].transAxes,
-                           fontweight="bold")
+                           fontweight="bold",
+                           fontsize=12)
             axs[j, k].text(0.99, 0.99,
                            f"Na ľavo - priemer (červená)\nNa pravo - medián (zelená)\nSivá - medzi kvartilom 1 a 3",
                            ha="right",
                            va="top",
-                           transform=axs[j, k].transAxes)
+                           transform=axs[j, k].transAxes,
+                           fontsize=12)
 
             medians = list([np.median(a) for a in data[index]])
             means = list([a.mean() for a in data[index]])
@@ -155,8 +157,11 @@ def plot_violin(data, labels, Fs, ps, title):
                 median = medians[l]
                 mean = means[l]
                 # left - mean, right - median
-                axs[j, k].text(l + 1.13, median - 0.05, f"{median:.2f}", color="green")
+                axs[j, k].text(l + 1.13, median - 0.05, f"{median:.2f}", color="green", fontsize=12, fontweight="bold")
-                axs[j, k].text(l + 0.90 - len(labels) * 0.065, mean - 0.05, f"{mean:.2f}", color="red")
+                axs[j, k].text(l + 0.87 - len(labels) * 0.065, mean - 0.05, f"{mean:.2f}", color="red", fontsize=12, fontweight="bold")
+
+            if p < 0.05:
+                axs[j, k].set_facecolor("#ffff99")
 
     fig.tight_layout()
     if save != "":

analyze.sh

@@ -1,6 +1,6 @@
 #!/usr/bin/bash
 
-find results ! -name 'train.txt' -type f -exec rm -f {} +
+rm results/*
 
 ./venv/bin/python3 distribution.py --graph --save | tee results/distribution.txt
 echo -e "\n\n\n\n"
@@ -17,3 +17,9 @@ echo -e "\n\n\n\n"
 ./venv/bin/python3 analyze_sleep.py --graph --save "results/Figure_18.png" | tee results/sleep.txt
 echo -e "\n\n\n\n"
 ./venv/bin/python3 analyze_absence.py --graph --save "results/Figure_19.png" | tee results/absence.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 train_nn.py --graph --save "results/Figure_20.png" | tee results/train.txt
+echo -e "\n\n\n\n"
+
+tar cvzf results.tar.gz results/
+zip results.zip results/*

analyze_absence.py

@@ -49,7 +49,7 @@ grade_names = ["Priemer", "Matematika", "Slovenčina", "Angličtina"]
 grade_name_labels = ["Priemer známok", "Známka z matematiky", "Známka zo slovenčiny", "Známka z angličtiny"]
 
 fig, axs = plt.subplots(2, 2)
-fig.suptitle("Absencia")
+fig.suptitle("Absencia", fontsize=18)
 fig.set_size_inches(12, 9)
 
 for j in range(2):
@@ -61,8 +61,8 @@ for j in range(2):
             x = data[index][0]  # absence
             y = data[index][1]  # grade
             axs[j, k].scatter(x, y)
-            axs[j, k].set_xlabel("Počet vymeškaných hodín")
+            axs[j, k].set_xlabel("Počet vymeškaných hodín", fontweight="bold", fontsize=14)
-            axs[j, k].set_ylabel(grade_name_labels[index])
+            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
             axs[j, k].set_yticks(np.arange(1, 6))
 
             # trendline
@@ -76,15 +76,21 @@ for j in range(2):
             # data[index][1] - grades
             # data[index][0][specific grade] - absences for that specific grande
             # loop 1 through 5 plug in ^^
+            axs[j, k].set_xlabel(grade_name_labels[index], fontweight="bold", fontsize=14)
+            axs[j, k].set_ylabel("Počet vymeškaných hodín", fontweight="bold", fontsize=14)
             axs[j, k].boxplot(by_grade, tick_labels=["1", "2", "3", "4", "5"])
 
-        axs[j, k].set_title(grade_names[index])
+        axs[j, k].set_title(grade_names[index], fontsize=16)
 
         tau = taus[index]
         p = ps[index]
         axs[j, k].text(0.01, 0.99, f"Tau τ: {tau:.4f}\np-val: {p:.4f}", ha="left", va="top",
                        transform=axs[j, k].transAxes,
-                       fontweight="bold")
+                       fontweight="bold",
+                       fontsize=12)
+
+        if p < 0.05:
+            axs[j, k].set_facecolor("#ffff99")
 
 fig.tight_layout()
 if save != "":

distribution.py

@@ -35,8 +35,8 @@ def plot_pie(data, labels, title, explode=None):
             i += 1
 
     plt.figure(figsize=(8, 6))
-    plt.pie(np.array(data), labels=labels, autopct=lambda pct: percent(pct / 100), explode=explode)
+    plt.pie(np.array(data), labels=labels, autopct=lambda pct: percent(pct / 100), explode=explode, textprops={"fontsize": 16})
-    plt.title(title)
+    plt.title(title, fontsize=20)
 
     plt.tight_layout()
     if save:
@@ -53,9 +53,9 @@ def plot_hist(data, title, xlabel, ylabel):
 
     plt.figure(figsize=(8, 6))
     plt.hist(data, 25, edgecolor="black")
-    plt.title(title)
+    plt.title(title, fontsize=20)
-    plt.xlabel(xlabel)
+    plt.xlabel(xlabel, fontsize=16)
-    plt.ylabel(ylabel)
+    plt.ylabel(ylabel, fontsize=16)
 
     plt.tight_layout()
     if save:
@@ -103,7 +103,7 @@ print("--- GPA ---")
 print("n/a")
 print("")
 
-plot_hist(dataset[:, 2], "Distribúcia piemernu známok", "Piemerná známka", "Počet študentov/tiek")
+plot_hist(dataset[:, 2], "Distribúcia piemernu známok", "Piemerná známka", "Počet študent*iek")
 
 math = dataset[:, 3]
 math_dist = [
@@ -192,7 +192,7 @@ print(f"none                   : {percent(occupation_dist[5])}")
 print("")
 
 plot_pie(occupation_dist,
-         ["Práca 10 a viac hodín týždenne", "Práca menej ako 10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
+         ["Práca 10 a viac\nhodín týždenne", "Práca menej ako\n10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
           "Žiadne"], "Distribúcia práce a aktivít")
 
 living = dataset[:, 8]
@@ -212,7 +212,7 @@ print(f"other             : {percent(living_dist[4])}")
 print("")
 
 plot_pie(living_dist,
-         ["S rodinou", "S rodinným príslušníkom/ou", "Sám/a alebo so spolubývajúcim/ou", "Intrák", "Iné"],
+         ["S rodinou", "\nS rodinnou príslušní*čkou", "Sám*a alebo so\nspolubývajúc*ou", "Intrák", "Iné"],
          "Distribúcia životných situácií")
 
 commute = dataset[:, 9]
@@ -253,4 +253,4 @@ print("--- ABSENCE ---")
 print("n/a")
 print("")
 
-plot_hist(dataset[:, 11], "Distribúcia absencií", "Počet neprítomných hodín", "Počet študentov/tiek")
+plot_hist(dataset[:, 11], "Distribúcia absencií", "Počet neprítomných hodín", "Počet študent*iek")

requirements.txt

@@ -1,47 +1,9 @@
-contourpy==1.3.1
+numpy
-cycler==0.12.1
+matplotlib
-filelock==3.16.1
+PyQt6
-fonttools==4.55.3
+pandas
-fsspec==2024.12.0
+scipy
-Jinja2==3.1.5
+scikit_posthocs
-joblib==1.4.2
+tabulate
-kiwisolver==1.4.7
+torch
-MarkupSafe==3.0.2
+scikit-learn
-matplotlib==3.10.0
-mpmath==1.3.0
-networkx==3.4.2
-numpy==2.2.1
-nvidia-cublas-cu12==12.4.5.8
-nvidia-cuda-cupti-cu12==12.4.127
-nvidia-cuda-nvrtc-cu12==12.4.127
-nvidia-cuda-runtime-cu12==12.4.127
-nvidia-cudnn-cu12==9.1.0.70
-nvidia-cufft-cu12==11.2.1.3
-nvidia-curand-cu12==10.3.5.147
-nvidia-cusolver-cu12==11.6.1.9
-nvidia-cusparse-cu12==12.3.1.170
-nvidia-nccl-cu12==2.21.5
-nvidia-nvjitlink-cu12==12.4.127
-nvidia-nvtx-cu12==12.4.127
-packaging==24.2
-pandas==2.2.3
-pandas-flavor==0.6.0
-patsy==1.0.1
-pillow==11.0.0
-pyparsing==3.2.0
-python-dateutil==2.9.0.post0
-pytz==2024.2
-scikit-learn==1.6.0
-scikit-posthocs==0.11.2
-scipy==1.14.1
-seaborn==0.13.2
-setuptools==75.6.0
-six==1.17.0
-statsmodels==0.14.4
-sympy==1.13.1
-tabulate==0.9.0
-threadpoolctl==3.5.0
-torch==2.5.1
-typing_extensions==4.12.2
-tzdata==2024.2
-xarray==2024.11.0

train_nn.py

@@ -11,8 +11,10 @@ parser = argparse.ArgumentParser(
     prog="train_nn"
 )
 parser.add_argument("-g", "--graph", action="store_true", default=False, help="Graph losses")
+parser.add_argument("-s", "--save", default="", help="Graph save location")
 args = parser.parse_args()
 graph = args.graph
+save = args.save
 
 
 class NeuralNetwork(nn.Module):
@@ -169,14 +171,17 @@ if graph:
     plt.plot(x, train_losses, color="red", label="Strata trénovania")
     plt.plot(x, test_losses, color="blue", label="Strata testovania")
 
-    plt.xlabel("Epocha")
+    plt.xlabel("Epocha", fontweight="bold", fontsize=14)
-    plt.ylabel("Strata")
+    plt.ylabel("Strata", fontweight="bold", fontsize=14)
-    plt.title("Priebeh trénovania")
+    plt.title("Priebeh trénovania", fontsize=20)
 
     plt.text(0.99, 0.99,
              f"Presnosť: {accuracy:.4f}\nPrecíznosť: {precision:.4f}\nOdvolanie: {recall:.4f}\nF1 skóre: {f1:.4f}",
-             ha="right", va="top", transform=plt.gca().transAxes, fontweight="bold")
+             ha="right", va="top", transform=plt.gca().transAxes, fontweight="bold", fontsize=14)
 
     plt.legend()
     plt.tight_layout()
-    plt.show()
+    if save != "":
+        plt.savefig(save)
+    else:
+        plt.show()