📝 Adds paper link to readme

.gitignore

@@ -6,6 +6,7 @@ venv/
 __pycache__/
 
 results/
+paper/
 
 *.zip
 *.csv
@@ -13,6 +14,10 @@ results/
 
 *.jasp
 *.pth
+*.png
+!structure.png
+!example-graph.png
+*.drawio
 
 *.tar.gz
 *.zip

CREDITS.md

@@ -0,0 +1,19 @@
+## Credits
+
+These are the people who made this project possible:
+
+- *Mgr. Martina Šandor*
+  - primary consultant
+  - psychology consultant
+- *Ing. Martin Berki*
+  - neural network
+  - statistics consultant
+- *Ing. Mária Dvorská*
+  - economics consultant
+- *Mgr. Marcel Sokolovič*
+  - sociology consultant
+- *Georgie Polymenakou*
+  - statistics consultant
+- and everyone else who offered a helping hand
+
+I thank you all again, I couldn't have done it without you

LICENSE

@@ -0,0 +1,675 @@
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+permissions.  However, no additional obligations are imposed on any
+author or copyright holder as a result of your choosing to follow a
+later version.
+
+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
+APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
+HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
+OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
+THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+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>.
+

Makefile

@@ -0,0 +1,2 @@
+make analyze:
+	./analyze.sh

README.md

@@ -1,22 +1,139 @@
 # Hello!
 
-Welcome, you either don't know what the hell this is or know exactly what the hell this is. Either way, here's a quick
-explanation:
+Welcome to the technical repository for my 2024/2025 SOC paper,
+this is where I keep all my scripts, scientific tests, algorithms, and graphing programs,
+let me walk you through how it works, I've split it into multiple sections:
 
-I decided to write a special paper at my highschool - why? It's kind of a competition, a bunch of students submit their
-SOC paper and the best one wins. They're actually graded by a whole ahh comittee and it's a big deal and whatever.
-Anyway, I'm here cuz it's fun, not cuz I wanna win (obviously I wanna win but that's not why I decided to do the SOC)
+1. [Tools and libraries](#tools-and-libraries)
+2. [Dataset](#dataset)
+3. [Distribution](#distribution)
+4. [Analysis and scientific tests](#analysis-and-scientific-tests)
+5. [Graphing](#graphing)
+6. [Neural network](#neural-network)
 
-I will eventually (probably Feb 2025) publish the paper, which I will also send to everyone who participated in the
-survey (fr thanks to everyone who did). Here I wanna keep all the scripts I used while writing the paper, it's mostly
-stuff for cleaning, analyzing, and graphing the dataset
+Don't forget to check out the [conclusion](#conclusion) and the [credits](CREDITS.md)
 
-I will also probably send out a link to this repo along with the paper, so if you got here from that link, welcome!
-Thanks so much for participating in the survey, you can check out the scripts and the other markdowns (to be added)
-where I explain what I did in a slightly more friendly way
+The actual paper can be found [here](Daniel%20Svitan%20SOC%202025.pdf)
 
-And if you're interested in the dataset, no I'm not publishing it, sorry (mom said no)
+### Tools and libraries
+
+Basically all scripts are written in [python](https://www.python.org/), except for one shell script, and these are the
+libraries that were used: 
+
+- [numpy](https://numpy.org/) - to load and manipulate the data
+- [pandas](https://pandas.pydata.org/) - to construct tables
+- [scipy](https://scipy.org/) - to perform statistical tests
+- [matplotlib](https://matplotlib.org/) - to create and render graphs
+- [pytorch](https://pytorch.org/) - to model and train the neural network
+
+Google Forms provides the data as a `.csv` file, which is converted into a `.npy` (numpy) file
 
 ### Dataset
 
-The documentation for the dataset structure can be found [here](https://github.com/Streamer272/soc-2024/blob/main/DATASET.md)
+The documentation for the dataset structure can be found in [DATASET.md](DATASET.md),
+this is only interesting for the nerds
+
+### Distribution
+
+This is probably the easiest part of this whole thing, it's basically just making charts and computing percentages,
+say you have 12 male and 15 female respondents, what is the distribution? It's quite simple, here:
+
+`(number of elements in a group) / (number of elements in the dataset)`
+
+So in this case, the distribution of male respondents would be `(12) / (12 + 15) = ~44%`, and female `(15) / (12 + 15) = ~56%`,
+now that we know this, we can make a pretty pie graph! The script that does all of this is [distribution.py](distribution.py)
+
+### Analysis and scientific tests
+
+This is where stuff gets interesting, the script that does all the heavy lifting is [analyze.py](analyze.py),
+then, you have the specified analysis scripts, like [analyze_sex.py](analyze_sex.py)
+(which, surprisingly, only analyzes sex)
+
+[analyze_sex.py](analyze_sex.py) only picks out its data from the dataset and passes it down to [analyze.py](analyze.py) to do all the analyses, where the following things happen:
+
+1. the received data is put into groups, each receiving an assigned letter (A, B, C, etc), groups of insufficient size are removed
+2. if there are less than 2 groups, analysis aborts
+3. [Kruskal-Wallis test](https://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_test) is performed and `F` and `p` values are received
+4. if `p` is greater than 0.05, the difference between those groups is not statistically significant and analysis aborts
+5. post-hoc [Dunn test](https://www.statology.org/dunns-test/) is performed and `p` values are saved
+6. a result table is created, a comparison between each group is added as well as their [rank-biserial correlation](https://www.statisticshowto.com/rank-biserial-correlation/), difference in medians, difference in means, and post-hoc `p` value
+
+Problem solved!
+If the difference is statistically insignificant,
+or we don't have sufficient data to perform a statistical test, the analysis aborts,
+otherwise, we get our `F` value, `p` value, and the result table, which could look something like this:
+
+| Skupina 1 | Skupina 2 | Veľkosť účinku | Rozdiel priemerov | Rozdiel mediánov | Post-Hoc p-hodnota |
+|-----------|-----------|----------------|-------------------|------------------|--------------------|
+| A         | B         | 0.0440         | 0.4198            | 0.0000           | 0.0497             |
+| A         | C         | 0.0399         | 0.2723            | 0.0000           | 0.5239             |
+| B         | C         | -0.0084        | -0.1475           | 0.0000           | 0.3706             |
+
+### Graphing
+
+Once the analysis is complete, successful or not, we can graph the data, we mostly use violin plots,
+which are quite easy to understand and interpret, it goes like this:
+
+1. the window is split into four subplots, top left for average grade, top right for math grade, bottom left for slovak grade, and bottom right for english grade
+2. all groups get added to each subplot as a violin plot, so for sex, each subplot would contain a violin plot for males and a violin plot for females
+3. the `F` and `p` values get added to the top left corner of each subplot
+4. the legend gets added to the top right corner of each subplot and axes are marked
+5. each violin plot contains five pieces of valuable information:
+   1. the shaded background that shows the distribution of the data
+   2. the gray line that represents the data between the first and third quartile
+   3. the red mean line with the mean value on the left
+   4. the green median line with the median value on the right
+   5. the minimum and maximum bounds
+6. labels get added to each violin plot
+
+Quite complicated, right? A ton of data packed into one small image, which could look something like this:
+
+![example graph](example-graph.png)
+
+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!
+AI stuff!
+Well, it didn't work in the end because of the abysmal amount of data, but the structure and
+the training process is still here and can be looked at
+
+The script that trains the neural network is [train_nn.py](train_nn.py) (yes, I am very creative when it comes to
+naming stuff, I am aware), it uses the [pytorch](https://pytorch.org/) library to do all the math stuff that goes on
+behind the scenes, but the important part is the structure of the neural network, right here:
+
+![structure of a neural network](structure.png)
+
+Of course, we have to use the `.npy` file format to load the data into our program, so how do we convert the `.csv`
+data provided by the Google Forms into a `.npy`?
+The answer lies in [clean.py](clean.py), but I'm not going to go
+into how it all works, since the script just cleans the data
+
+The whole training thing is pretty complicated, so if you don't know anything about neural networks, just forget about
+it and attribute it to magic, but if you do, read through [train_nn.py](train_nn.py),
+it's a pretty clean and readable code
+
+## Conclusion
+
+Hopefully you learned something when you read through this README or the various scripts, because that's the main
+reason why I decided to make this repository public, so folks can look at this and learn new stuff
+
+I had a lot of fun on this project, gathering data, writing scripts, conducting scientific tests, and writing the paper,
+it was an unforgettable experience, and even though it was really hard, it was definitely worth it and I would
+definitely do it again, and I recommend you try this sort of thing as well
+
+If you have read this whole README till the end, I thank you, because it took a Saturday afternoon to write that I
+could've spent playing video games, but it was worth it as long as at least one person took a quick glance at it
+
+If you have any questions about the paper, this repository, the technical details and the specific techniques, or even
+if you're thinking about writing a paper yourself, feel free to reach out to me at
+[daniel@svitan.dev](mailto:daniel@svitan.dev) or send me a message on discord (Streamer272), I will gladly answer
+any questions and talk about this project for hours
+
+### License
+
+This project is licensed under the [GNU GPLv3](LICENSE) license

analyze.py

@@ -1,8 +1,11 @@
 from typing import List
+import itertools
 import argparse
 
 import numpy as np
+import pandas as pd
 import scipy.stats as stats
+import scikit_posthocs as sp
 import matplotlib.pyplot as plt
 
 parser = argparse.ArgumentParser()
@@ -12,34 +15,84 @@ args = parser.parse_args()
 graph = args.graph
 save = args.save
 
+colors = ["lightblue", "lightgreen", "lightcoral"]
+edge_colors = ["blue", "green", "red"]
 
+
+# source: mostly ChatGPT (ain't no way i'm writing this shit myself)
 def analyze(name: str, data: List[np.ndarray]):
-    #print(f"Checking if normally distributed for {name}")
-    #for i in range(len(data)):
-    #    _, normal_p = stats.shapiro(data[i])
-    #    if normal_p > 0.05:
-    #        print(f"\tGroup {i}: normally distributed")
-    #    else:
-    #        print(f"\tGroup {i}: NOT normally distributed")
-
     filtered_data = []
+    group_names = []
+    all_values = []
     for index, item in enumerate(data):
-        if len(item) > 5:
-            filtered_data.append(item)
+        numeric_data = [x for x in item if isinstance(x, (int, float))]
+        if len(numeric_data) > 5:
+            filtered_data.append(numeric_data)
+            group_names.append(chr(65 + index))
+            all_values.extend(numeric_data)
         else:
-            print(f"Data group at index {index} removed due to insufficient size ({len(item)})")
+            print(f"Data group at index {index} removed due to insufficient size ({len(numeric_data)})")
 
+    if len(filtered_data) < 2:
+        print(f"Insufficient number of groups for Kruskal-Wallis test in {name}")
+        return None, None
+
+    # Kruskal-Wallis Test
     F, p = stats.kruskal(*filtered_data)
-    print(f"F-stats for {name}: {F}")
-    print(f"p-value for {name}: {p}")
+    print(f"\nF-stats for {name}: {F:.8f}")
+    print(f"p-value for {name}: {p:.8f}")
 
-    if round(p, 4) > 0.05:
+    if p > 0.05:
         print("statistically insignificant\n")
         return F, p
 
     print("statistically significant")
-    tukey_results = stats.tukey_hsd(*filtered_data)
-    print(tukey_results)
+
+    # Post-Hoc Dunn Test (Bonferroni-adjusted p-values)
+    all_ranks = stats.rankdata(all_values)  # Rank all values together
+    group_ranks = [all_ranks[start:start + len(group)] for start, group in
+                   zip(np.cumsum([0] + [len(g) for g in filtered_data[:-1]]), filtered_data)]
+    posthoc_results = sp.posthoc_conover(filtered_data, p_adjust='bonferroni')
+
+    results = []
+    total_sample_size = len(all_values)
+    for group1, group2 in itertools.combinations(group_names, 2):
+        idx1 = group_names.index(group1)
+        idx2 = group_names.index(group2)
+
+        mean_rank_1 = np.mean(group_ranks[idx1])
+        mean_rank_2 = np.mean(group_ranks[idx2])
+        rank_diff = mean_rank_1 - mean_rank_2
+
+        n1 = len(filtered_data[idx1])
+        n2 = len(filtered_data[idx2])
+
+        # Effect size (Rank-Biserial Correlation)
+        z_stat = rank_diff / np.sqrt((n1 + n2) * (n1 * n2) / total_sample_size)
+        effect_size = z_stat / np.sqrt(total_sample_size)
+
+        # Mean difference
+        mean_diff = np.mean(filtered_data[idx1]) - np.mean(filtered_data[idx2])
+
+        # Median difference
+        median_diff = np.median(filtered_data[idx1]) - np.median(filtered_data[idx2])
+
+        # Post-Hoc Dunn p-value
+        p_value = posthoc_results.loc[idx1 + 1, idx2 + 1]
+
+        results.append({
+            "Skupina 1": group1,
+            "Skupina 2": group2,
+            "Veľkosť účinku": f"{effect_size:.4f}",
+            "Rozdiel priemerov": f"{mean_diff:.4f}",
+            "Rozdiel mediánov": f"{median_diff:.4f}",
+            "Post-Hoc p-hodnota": f"{p_value:.4f}"
+        })
+
+    results_df = pd.DataFrame(results, dtype="object")
+    print("\nSummary Table of Effect Size, Mean, and Median Differences:")
+    print(results_df.to_markdown(index=False, tablefmt="github", disable_numparse=True))
+    print("")
 
     return F, p
 
@@ -52,7 +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):
@@ -60,25 +113,57 @@ def plot_violin(data, labels, Fs, ps, title):
             index = j * 2 + k
             step = 1 if index > 0 else 0.5
 
-            axs[j, k].violinplot(data[index], showmedians=True)
-            axs[j, k].set_title(grade_names[index])
-            axs[j, k].set_xlabel(title, fontweight="bold")
-            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold")
+            parts = axs[j, k].violinplot(data[index], showmedians=True, showmeans=True)
+            axs[j, k].set_title(grade_names[index], fontsize=16)
+            axs[j, k].set_xlabel(title, fontweight="bold", fontsize=14)
+            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
+
+            # q1-q3 lines
+            for ind, vec in enumerate(data[index]):
+                quartile1, median, quartile3 = np.percentile(vec, [25, 50, 75])
+                if quartile1 == quartile3:
+                    if quartile1 >= 0.1:
+                        quartile1 -= 0.1
+                    if quartile3 <= max(vec) - 0.1:
+                        quartile3 += 0.1
+                axs[j, k].vlines(ind + 1, quartile1, quartile3, color="gray", linewidths=3)
+
             axs[j, k].set_xticks(np.arange(1, len(labels) + 1), labels=labels)
             axs[j, k].set_yticks(np.arange(1, 5.01, step))
 
-            F = round(Fs[index], 2)
-            p = round(ps[index], 4)
-            axs[j, k].text(0.01, 0.99, f"F-stat: {F:.2f}\np-val: {p:.4f}", ha="left", va="top", transform=axs[j, k].transAxes,
-                           fontweight="bold")
+            parts["cmeans"].set_color("red")
+            parts["cmedians"].set_color("green")
+
+            for i, part in enumerate(parts["bodies"]):
+                part.set_facecolor(colors[i % len(colors)])
+                part.set_edgecolor(edge_colors[i % len(edge_colors)])
+
+            F = Fs[index]
+            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",
+                           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,
+                           fontsize=12)
 
             medians = list([np.median(a) for a in data[index]])
-            for l in range(len(medians)):
-                median = round(medians[l], 2)
-                axs[j, k].text(l + 1.05, median + 0.05, f"{median}")
+            means = list([a.mean() for a in data[index]])
+            for l in range(len(data[index])):
+                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", fontsize=12, fontweight="bold")
+                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()
-    fig.show()
     if save != "":
         plt.savefig(save)
     else:

analyze.sh

@@ -0,0 +1,25 @@
+#!/usr/bin/bash
+
+rm results/*
+
+./venv/bin/python3 distribution.py --graph --save | tee results/distribution.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 analyze_sex.py --graph --save "results/Figure_13.png" | tee results/sex.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 analyze_ses.py --graph --save "results/Figure_14.png" | tee results/ses.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 analyze_occupation.py --graph --save "results/Figure_15.png" | tee results/occupation.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 analyze_living.py --graph --save "results/Figure_16.png" | tee results/living.txt
+echo -e "\n\n\n\n"
+./venv/bin/python3 analyze_commute.py --graph --save "results/Figure_17.png" | tee results/commute.txt
+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

@@ -6,8 +6,13 @@ import matplotlib.pyplot as plt
 
 parser = argparse.ArgumentParser()
 parser.add_argument("-g", "--graph", action="store_true", default=False, help="Plot graph")
+parser.add_argument("-s", "--save", default="", help="Graph save location")
 args = parser.parse_args()
 graph = args.graph
+save = args.save
+
+colors = ["lightblue", "lightgreen", "lightcoral"]
+edge_colors = ["blue", "green", "red"]
 
 dataset = np.load("clean.npy")
 print(f"dataset shape: {dataset.shape}; analyzing column 11 (absence)")
@@ -44,7 +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):
@@ -52,31 +57,43 @@ for j in range(2):
         index = j * 2 + k
         step = 1 if index > 0 else 0.5
 
-        if index == 0:
-            axs[j, k].scatter(dataset[:, 11], dataset[:, 2])
-            axs[j, k].set_xlabel("Počet vymeškaných hodín")
-            axs[j, k].set_ylabel(grade_name_labels[index])
+        if not index:
+            x = data[index][0]  # absence
+            y = data[index][1]  # grade
+            axs[j, k].scatter(x, y)
+            axs[j, k].set_xlabel("Počet vymeškaných hodín", fontweight="bold", fontsize=14)
+            axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
+            axs[j, k].set_yticks(np.arange(1, 6))
+
+            # trendline
+            z = np.polyfit(x, y, 1)
+            p = np.poly1d(z)
+
+            axs[j, k].plot(x, p(x), color="gray")
         else:
-            current = list([data[index][0][data[index][1] == i + 1] for i in range(5)])  # i wanna kms
-            axs[j, k].violinplot(list(filter(lambda x: len(x), current)), showmeans=True)
-            axs[j, k].set_xticks(np.arange(1, 6, 1), labels=["1", "2", "3", "4", "5"])
-            axs[j, k].set_xlabel(grade_name_labels[index])
-            axs[j, k].set_ylabel("Počet vymeškaných hodín")
+            by_grade = list([data[index][0][data[index][1] == i + 1] for i in range(5)])
+            # data[index][0] - absences
+            # 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 = round(taus[index], 2)
-        p = round(ps[index], 4)
-        axs[j, k].text(0.01, 0.99, f"Tau τ: {tau:.2f}\np-val: {p:.4f}", ha="left", va="top", transform=axs[j, k].transAxes,
-                       fontweight="bold")
+        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",
+                       fontsize=12)
 
-        if index:
-            by_grade = [data[index][0][data[index][1] == i + 1] for i in range(5)]
-            means = list([a.mean() for a in filter(lambda b: len(b), by_grade)])
-            for l in range(len(means)):
-                mean = round(means[l], 2)
-                axs[j, k].text(l + 1.02, mean + 5, f"{mean}")
+        if p < 0.05:
+            axs[j, k].set_facecolor("#ffff99")
 
 fig.tight_layout()
-fig.show()
-plt.show()
+if save != "":
+    plt.savefig(save)
+else:
+    plt.show()

distribution.py

@@ -7,8 +7,11 @@ parser = argparse.ArgumentParser(
     prog="distribution"
 )
 parser.add_argument("-g", "--graph", action="store_true", default=False, help="Display graphs")
+parser.add_argument("-s", "--save", action="store_true", default=False, help="Save graphs")
 args = parser.parse_args()
 graph = args.graph
+save = args.save
+graph_index = 1
 
 dataset = np.load("clean.npy")
 print(f"dataset shape: {dataset.shape}; analyzing distribution\n")
@@ -19,6 +22,10 @@ def percent(fraction: float) -> str:
 
 
 def plot_pie(data, labels, title, explode=None):
+    global graph_index
+    if not graph:
+        return
+
     i = 0
     while i < len(data):
         if data[i] == 0:
@@ -28,22 +35,34 @@ 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.title(title)
+    plt.pie(np.array(data), labels=labels, autopct=lambda pct: percent(pct / 100), explode=explode, textprops={"fontsize": 16})
+    plt.title(title, fontsize=20)
 
     plt.tight_layout()
-    plt.show()
+    if save:
+        plt.savefig(f"results/Figure_{graph_index}.png")
+        graph_index += 1
+    else:
+        plt.show()
 
 
 def plot_hist(data, title, xlabel, ylabel):
+    global graph_index
+    if not graph:
+        return
+
     plt.figure(figsize=(8, 6))
     plt.hist(data, 25, edgecolor="black")
-    plt.title(title)
-    plt.xlabel(xlabel)
-    plt.ylabel(ylabel)
+    plt.title(title, fontsize=20)
+    plt.xlabel(xlabel, fontsize=16)
+    plt.ylabel(ylabel, fontsize=16)
 
     plt.tight_layout()
-    plt.show()
+    if save:
+        plt.savefig(f"results/Figure_{graph_index}.png")
+        graph_index += 1
+    else:
+        plt.show()
 
 
 grade = dataset[:, 0]
@@ -62,12 +81,11 @@ print(f"4st year: {percent(grade_dist[3])}")
 print(f"5st year: {percent(grade_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(
-        grade_dist,
-        ["Prvý ročník", "Druhý ročník", "Tretí ročník", "Štvrtý ročník", "Piaty ročník"],
-        "Distribúcia ročníkov",
-    )
+plot_pie(
+    grade_dist,
+    ["Prvý ročník", "Druhý ročník", "Tretí ročník", "Štvrtý ročník", "Piaty ročník"],
+    "Distribúcia ročníkov",
+)
 
 sex = dataset[:, 1]
 sex_dist = [
@@ -79,15 +97,13 @@ print(f"Female: {percent(sex_dist[0])}")
 print(f"Male: {percent(sex_dist[1])}")
 print("")
 
-if graph:
-    plot_pie(sex_dist, ["Ženy", "Muži"], "Distribúcia pohlavia")
+plot_pie(sex_dist, ["Ženy", "Muži"], "Distribúcia pohlavia")
 
 print("--- GPA ---")
 print("n/a")
 print("")
 
-if graph:
-    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 = [
@@ -105,8 +121,7 @@ print(f"4: {percent(math_dist[3])}")
 print(f"5: {percent(math_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(math_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok z matematiky")
+plot_pie(math_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok z matematiky")
 
 slovak = dataset[:, 4]
 slovak_dist = [
@@ -124,8 +139,7 @@ print(f"4: {percent(slovak_dist[3])}")
 print(f"5: {percent(slovak_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(slovak_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok zo slovenčiny", (0, 0, 0, 0.25, 0.5))
+plot_pie(slovak_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok zo slovenčiny", (0, 0, 0, 0.25, 0.5))
 
 english = dataset[:, 5]
 english_dist = [
@@ -143,8 +157,7 @@ print(f"4: {percent(english_dist[3])}")
 print(f"5: {percent(english_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(english_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok z angličtiny")
+plot_pie(english_dist, ["1", "2", "3", "4", "5"], "Distribúcia známok z angličtiny")
 
 ses = dataset[:, 6]
 ses_dist = [
@@ -158,8 +171,7 @@ print(f"Middle: {percent(ses_dist[1])}")
 print(f"Upper: {percent(ses_dist[2])}")
 print("")
 
-if graph:
-    plot_pie(ses_dist, ["Nižšia trieda", "Stredná trieda", "Vyššia trieda"], "Distribúcia socio-ekonomických tried")
+plot_pie(ses_dist, ["Nižšia trieda", "Stredná trieda", "Vyššia trieda"], "Distribúcia socio-ekonomických tried")
 
 occupation = dataset[:, 7]
 occupation_dist = [
@@ -179,10 +191,9 @@ print(f"other                  : {percent(occupation_dist[4])}")
 print(f"none                   : {percent(occupation_dist[5])}")
 print("")
 
-if graph:
-    plot_pie(occupation_dist,
-             ["Práca 10 a viac hodín týždenne", "Práca menej ako 10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
-              "Žiadne"], "Distribúcia práce a aktivít")
+plot_pie(occupation_dist,
+         ["Práca 10 a viac\nhodín týždenne", "Práca menej ako\n10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
+          "Žiadne"], "Distribúcia práce a aktivít")
 
 living = dataset[:, 8]
 living_dist = [
@@ -200,10 +211,9 @@ print(f"dorms             : {percent(living_dist[3])}")
 print(f"other             : {percent(living_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(living_dist,
-             ["S rodinou", "S rodinným príslušníkom/ou", "Sám/a alebo so spolubývajúcim/ou", "Intrák", "Iné"],
-             "Distribúcia životných situácií")
+plot_pie(living_dist,
+         ["S rodinou", "\nS rodinnou príslušní*čkou", "Sám*a alebo so\nspolubývajúc*ou", "Intrák", "Iné"],
+         "Distribúcia životných situácií")
 
 commute = dataset[:, 9]
 commute_dist = [
@@ -221,10 +231,9 @@ print(f"<= 1h : {percent(commute_dist[3])}")
 print(f"> 1h  : {percent(commute_dist[4])}")
 print("")
 
-if graph:
-    plot_pie(commute_dist,
-             ["Intrák", "Menej ako 15 minút", "Menej ako 30 minút", "Menej ako hodinu", "Viac ako hodinu"],
-             "Distribúcia dochádzania")
+plot_pie(commute_dist,
+         ["Intrák", "Menej ako 15 minút", "Menej ako 30 minút", "Menej ako hodinu", "Viac ako hodinu"],
+         "Distribúcia dochádzania")
 
 sleep = dataset[:, 10]
 sleep_dist = [
@@ -238,12 +247,10 @@ print(f"medium sleepers: {percent(sleep_dist[1])}")
 print(f"long sleepers  : {percent(sleep_dist[2])}")
 print("")
 
-if graph:
-    plot_pie(sleep_dist, ["6 hodín a menej", "7 až 8 hodín", "9 a viac hodín"], "Distribúcia spánku")
+plot_pie(sleep_dist, ["6 hodín a menej", "7 až 8 hodín", "9 a viac hodín"], "Distribúcia spánku")
 
 print("--- ABSENCE ---")
 print("n/a")
 print("")
 
-if graph:
-    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

@@ -1,40 +1,9 @@
-contourpy==1.3.1
-cycler==0.12.1
-filelock==3.16.1
-fonttools==4.55.3
-fsspec==2024.12.0
-Jinja2==3.1.5
-joblib==1.4.2
-kiwisolver==1.4.7
-MarkupSafe==3.0.2
-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
-pillow==11.0.0
-pyparsing==3.2.0
-python-dateutil==2.9.0.post0
-pytz==2024.2
-scikit-learn==1.6.0
-scipy==1.14.1
-setuptools==75.6.0
-six==1.17.0
-sympy==1.13.1
-threadpoolctl==3.5.0
-torch==2.5.1
-typing_extensions==4.12.2
-tzdata==2024.2
+numpy
+matplotlib
+PyQt6
+pandas
+scipy
+scikit_posthocs
+tabulate
+torch
+scikit-learn

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):
@@ -119,7 +121,7 @@ for epoch in range(epochs):
             pred = model(X)
             loss = loss_fn(pred, y)
 
-            test_loss = loss.item() * X.size(0)
+            test_loss += loss.item() * X.size(0)
 
     test_loss /= len(test_dataset)
     test_losses.append(test_loss)
@@ -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.ylabel("Strata")
-    plt.title("Priebeh trénovania")
+    plt.xlabel("Epocha", fontweight="bold", fontsize=14)
+    plt.ylabel("Strata", fontweight="bold", fontsize=14)
+    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()