⚡ Adds graph for absence

analyze.py

@@ -52,10 +52,10 @@ def plot_violin(data, labels, Fs, ps, title):
 
             F = round(Fs[index], 2)
             p = round(ps[index], 4)
-            axs[j, k].text(0.01, 0.99, f"F-stat: {F}\np-val: {p}", ha="left", va="top", transform=axs[j, k].transAxes,
+            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")
 
-            means = list([a.mean() for a in data[j * 2 + k]])
+            means = list([a.mean() for a in data[index]])
             for l in range(len(means)):
                 mean = round(means[l], 2)
                 axs[j, k].text(l + 1.05, mean + 0.05, f"{mean}")

analyze_absence.py

@@ -1,5 +1,13 @@
+import argparse
+
 import numpy as np
-from scipy.stats import kendalltau
+import scipy.stats as stats
+import matplotlib.pyplot as plt
+
+parser = argparse.ArgumentParser()
+parser.add_argument("-g", "--graph", action="store_true", default=False, help="Plot graph")
+args = parser.parse_args()
+graph = args.graph
 
 dataset = np.load("clean.npy")
 print(f"dataset shape: {dataset.shape}; analyzing column 11 (absence)")
@@ -9,7 +17,7 @@ print("")
 
 def analyze_absence(name: str, col: np.ndarray):
     absence_col = dataset[:, 11]
-    tau, p = kendalltau(absence_col, col)
+    tau, p = stats.kendalltau(absence_col, col)
     print(f"ken-tau for {name}: {tau}")
     print(f"p-value for {name}: {p}")
 
@@ -18,8 +26,57 @@ def analyze_absence(name: str, col: np.ndarray):
     else:
         print("statistically significant\n")
 
+    return (absence_col, col), tau, p
 
-analyze_absence("gpa", dataset[:, 2])
-analyze_absence("math", dataset[:, 3])
-analyze_absence("slovak", dataset[:, 4])
-analyze_absence("english", dataset[:, 5])
+
+data_gpa, tau_gpa, p_gpa = analyze_absence("gpa", dataset[:, 2])
+data_math, tau_math, p_math = analyze_absence("math", dataset[:, 3])
+data_slovak, tau_slovak, p_slovak = analyze_absence("slovak", dataset[:, 4])
+data_english, tau_english, p_english = analyze_absence("english", dataset[:, 5])
+data = [data_gpa, data_math, data_slovak, data_english]
+taus = [tau_gpa, tau_math, tau_slovak, tau_english]
+ps = [p_gpa, p_math, p_slovak, p_english]
+
+if not graph:
+    exit(0)
+
+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.set_size_inches(12, 9)
+
+for j in range(2):
+    for k 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])
+        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")
+
+        axs[j, k].set_title(grade_names[index])
+
+        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")
+
+        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}")
+
+fig.tight_layout()
+fig.show()
+plt.show()