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0f698ae39c
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77379dcb1b
@ -91,8 +91,6 @@ Quite complicated, right? A ton of data packed into one small image, which could
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It can be overwhelming to look at at first, but once you understand what's going on, it's quite intuitive, anyway,
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the function that does all this is also saved in [analyze.py](analyze.py)
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All the graphs are saved in the archives, which you can download, [results.tar.gz](results.tar.gz) and [results.zip](results.zip)
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### Neural network
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Ah!
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18
analyze.py
18
analyze.py
@ -105,7 +105,7 @@ def plot_violin(data, labels, Fs, ps, title):
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grade_name_labels = ["Priemer známok", "Známka z matematiky", "Známka zo slovenčiny", "Známka z angličtiny"]
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fig, axs = plt.subplots(2, 2)
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fig.suptitle(title, fontsize=18)
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fig.suptitle(title)
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fig.set_size_inches(12, 9)
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for j in range(2):
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@ -114,9 +114,9 @@ def plot_violin(data, labels, Fs, ps, title):
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step = 1 if index > 0 else 0.5
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parts = axs[j, k].violinplot(data[index], showmedians=True, showmeans=True)
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axs[j, k].set_title(grade_names[index], fontsize=16)
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axs[j, k].set_xlabel(title, fontweight="bold", fontsize=14)
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axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
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axs[j, k].set_title(grade_names[index])
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axs[j, k].set_xlabel(title, fontweight="bold")
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axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold")
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# q1-q3 lines
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for ind, vec in enumerate(data[index]):
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@ -142,14 +142,12 @@ def plot_violin(data, labels, Fs, ps, title):
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p = ps[index]
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axs[j, k].text(0.01, 0.99, f"F-stat: {F:.4f}\np-val: {p:.4f}", ha="left", va="top",
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transform=axs[j, k].transAxes,
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fontweight="bold",
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fontsize=12)
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fontweight="bold")
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axs[j, k].text(0.99, 0.99,
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f"Na ľavo - priemer (červená)\nNa pravo - medián (zelená)\nSivá - medzi kvartilom 1 a 3",
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ha="right",
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va="top",
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transform=axs[j, k].transAxes,
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fontsize=12)
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transform=axs[j, k].transAxes)
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medians = list([np.median(a) for a in data[index]])
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means = list([a.mean() for a in data[index]])
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@ -157,8 +155,8 @@ def plot_violin(data, labels, Fs, ps, title):
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median = medians[l]
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mean = means[l]
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# left - mean, right - median
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axs[j, k].text(l + 1.13, median - 0.05, f"{median:.2f}", color="green", fontsize=12, fontweight="bold")
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axs[j, k].text(l + 0.87 - len(labels) * 0.065, mean - 0.05, f"{mean:.2f}", color="red", fontsize=12, fontweight="bold")
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axs[j, k].text(l + 1.13, median - 0.05, f"{median:.2f}", color="green")
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axs[j, k].text(l + 0.90 - len(labels) * 0.065, mean - 0.05, f"{mean:.2f}", color="red")
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fig.tight_layout()
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if save != "":
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@ -49,7 +49,7 @@ grade_names = ["Priemer", "Matematika", "Slovenčina", "Angličtina"]
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grade_name_labels = ["Priemer známok", "Známka z matematiky", "Známka zo slovenčiny", "Známka z angličtiny"]
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fig, axs = plt.subplots(2, 2)
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fig.suptitle("Absencia", fontsize=18)
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fig.suptitle("Absencia")
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fig.set_size_inches(12, 9)
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for j in range(2):
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@ -61,8 +61,8 @@ for j in range(2):
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x = data[index][0] # absence
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y = data[index][1] # grade
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axs[j, k].scatter(x, y)
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axs[j, k].set_xlabel("Počet vymeškaných hodín", fontweight="bold", fontsize=14)
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axs[j, k].set_ylabel(grade_name_labels[index], fontweight="bold", fontsize=14)
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axs[j, k].set_xlabel("Počet vymeškaných hodín")
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axs[j, k].set_ylabel(grade_name_labels[index])
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axs[j, k].set_yticks(np.arange(1, 6))
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# trendline
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@ -76,18 +76,15 @@ for j in range(2):
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# data[index][1] - grades
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# data[index][0][specific grade] - absences for that specific grande
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# loop 1 through 5 plug in ^^
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axs[j, k].set_xlabel(grade_name_labels[index], fontweight="bold", fontsize=14)
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axs[j, k].set_ylabel("Počet vymeškaných hodín", fontweight="bold", fontsize=14)
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axs[j, k].boxplot(by_grade, tick_labels=["1", "2", "3", "4", "5"])
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axs[j, k].set_title(grade_names[index], fontsize=16)
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axs[j, k].set_title(grade_names[index])
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tau = taus[index]
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p = ps[index]
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axs[j, k].text(0.01, 0.99, f"Tau τ: {tau:.4f}\np-val: {p:.4f}", ha="left", va="top",
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transform=axs[j, k].transAxes,
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fontweight="bold",
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fontsize=12)
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fontweight="bold")
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fig.tight_layout()
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if save != "":
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@ -35,8 +35,8 @@ def plot_pie(data, labels, title, explode=None):
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i += 1
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plt.figure(figsize=(8, 6))
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plt.pie(np.array(data), labels=labels, autopct=lambda pct: percent(pct / 100), explode=explode, textprops={"fontsize": 16})
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plt.title(title, fontsize=20)
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plt.pie(np.array(data), labels=labels, autopct=lambda pct: percent(pct / 100), explode=explode)
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plt.title(title)
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plt.tight_layout()
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if save:
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@ -53,9 +53,9 @@ def plot_hist(data, title, xlabel, ylabel):
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plt.figure(figsize=(8, 6))
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plt.hist(data, 25, edgecolor="black")
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plt.title(title, fontsize=20)
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plt.xlabel(xlabel, fontsize=16)
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plt.ylabel(ylabel, fontsize=16)
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plt.title(title)
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plt.xlabel(xlabel)
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plt.ylabel(ylabel)
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plt.tight_layout()
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if save:
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@ -103,7 +103,7 @@ print("--- GPA ---")
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print("n/a")
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print("")
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plot_hist(dataset[:, 2], "Distribúcia piemernu známok", "Piemerná známka", "Počet študent*iek")
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plot_hist(dataset[:, 2], "Distribúcia piemernu známok", "Piemerná známka", "Počet študentov/tiek")
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math = dataset[:, 3]
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math_dist = [
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@ -192,7 +192,7 @@ print(f"none : {percent(occupation_dist[5])}")
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print("")
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plot_pie(occupation_dist,
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["Práca 10 a viac\nhodín týždenne", "Práca menej ako\n10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
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["Práca 10 a viac hodín týždenne", "Práca menej ako 10 hodín týždenne", "Šport", "Hudba", "Niečo iné",
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"Žiadne"], "Distribúcia práce a aktivít")
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living = dataset[:, 8]
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@ -212,7 +212,7 @@ print(f"other : {percent(living_dist[4])}")
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print("")
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plot_pie(living_dist,
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["S rodinou", "\nS rodinnou príslušní*čkou", "Sám*a alebo so\nspolubývajúc*ou", "Intrák", "Iné"],
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["S rodinou", "S rodinným príslušníkom/ou", "Sám/a alebo so spolubývajúcim/ou", "Intrák", "Iné"],
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"Distribúcia životných situácií")
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commute = dataset[:, 9]
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@ -253,4 +253,4 @@ print("--- ABSENCE ---")
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print("n/a")
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print("")
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plot_hist(dataset[:, 11], "Distribúcia absencií", "Počet neprítomných hodín", "Počet študent*iek")
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plot_hist(dataset[:, 11], "Distribúcia absencií", "Počet neprítomných hodín", "Počet študentov/tiek")
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@ -1,9 +1,47 @@
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numpy
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matplotlib
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PyQt6
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pandas
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scipy
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scikit_posthocs
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tabulate
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torch
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scikit-learn
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contourpy==1.3.1
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cycler==0.12.1
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filelock==3.16.1
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fonttools==4.55.3
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fsspec==2024.12.0
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Jinja2==3.1.5
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joblib==1.4.2
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kiwisolver==1.4.7
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MarkupSafe==3.0.2
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matplotlib==3.10.0
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mpmath==1.3.0
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networkx==3.4.2
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numpy==2.2.1
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nvidia-cublas-cu12==12.4.5.8
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nvidia-cuda-cupti-cu12==12.4.127
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nvidia-cuda-nvrtc-cu12==12.4.127
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nvidia-cuda-runtime-cu12==12.4.127
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nvidia-cudnn-cu12==9.1.0.70
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nvidia-cufft-cu12==11.2.1.3
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nvidia-curand-cu12==10.3.5.147
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nvidia-cusolver-cu12==11.6.1.9
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nvidia-cusparse-cu12==12.3.1.170
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nvidia-nccl-cu12==2.21.5
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nvidia-nvjitlink-cu12==12.4.127
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nvidia-nvtx-cu12==12.4.127
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packaging==24.2
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pandas==2.2.3
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pandas-flavor==0.6.0
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patsy==1.0.1
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pillow==11.0.0
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pyparsing==3.2.0
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python-dateutil==2.9.0.post0
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pytz==2024.2
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scikit-learn==1.6.0
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scikit-posthocs==0.11.2
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scipy==1.14.1
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seaborn==0.13.2
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setuptools==75.6.0
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six==1.17.0
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statsmodels==0.14.4
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sympy==1.13.1
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tabulate==0.9.0
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threadpoolctl==3.5.0
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torch==2.5.1
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typing_extensions==4.12.2
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tzdata==2024.2
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xarray==2024.11.0
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BIN
results.tar.gz
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results.tar.gz
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results.zip
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