From c2079d881154f0f34c30dc0e8b0fa8db2a1a78e7 Mon Sep 17 00:00:00 2001
From: Mateusz Kantorski <matkan1@st.amu.edu.pl>
Date: Sun, 28 May 2023 13:41:17 +0200
Subject: [PATCH] Add decison tree and its visualization

---
 DecisionTree/Source.gv           | 197 ++++++++++++++++++++++++++++++
 DecisionTree/Source.gv.pdf       | Bin 0 -> 39357 bytes
 DecisionTree/dane.csv            | 201 +++++++++++++++++++++++++++++++
 DecisionTree/drzewo_decyzyjne.py |  57 +++++++++
 DecisionTree/training_data.txt   | 200 ++++++++++++++++++++++++++++++
 5 files changed, 655 insertions(+)
 create mode 100644 DecisionTree/Source.gv
 create mode 100644 DecisionTree/Source.gv.pdf
 create mode 100644 DecisionTree/dane.csv
 create mode 100644 DecisionTree/drzewo_decyzyjne.py
 create mode 100644 DecisionTree/training_data.txt

diff --git a/DecisionTree/Source.gv b/DecisionTree/Source.gv
new file mode 100644
index 0000000..488deb9
--- /dev/null
+++ b/DecisionTree/Source.gv
@@ -0,0 +1,197 @@
+digraph Tree {
+node [shape=box, style="filled, rounded", color="black", fontname="helvetica"] ;
+edge [fontname="helvetica"] ;
+0 [label="g > d <= 0.5\nentropy = 0.997\nsamples = 200\nvalue = [94, 106]\nclass = 1", fillcolor="#e9f4fc"] ;
+1 [label="waga, <= 0.5\nentropy = 0.803\nsamples = 98\nvalue = [74, 24]\nclass = 0", fillcolor="#edaa79"] ;
+0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
+2 [label="wielkosc <= 1.5\nentropy = 0.998\nsamples = 34\nvalue = [16, 18]\nclass = 1", fillcolor="#e9f4fc"] ;
+1 -> 2 ;
+3 [label="priorytet <= 0.5\nentropy = 0.887\nsamples = 23\nvalue = [7, 16]\nclass = 1", fillcolor="#90c8f0"] ;
+2 -> 3 ;
+4 [label="kruchosc <= 0.5\nentropy = 0.439\nsamples = 11\nvalue = [1, 10]\nclass = 1", fillcolor="#4da7e8"] ;
+3 -> 4 ;
+5 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+4 -> 5 ;
+6 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [1, 3]\nclass = 1", fillcolor="#7bbeee"] ;
+4 -> 6 ;
+7 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+6 -> 7 ;
+8 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+7 -> 8 ;
+9 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+7 -> 9 ;
+10 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+9 -> 10 ;
+11 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+9 -> 11 ;
+12 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+6 -> 12 ;
+13 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 12\nvalue = [6, 6]\nclass = 0", fillcolor="#ffffff"] ;
+3 -> 13 ;
+14 [label="entropy = 0.0\nsamples = 5\nvalue = [5, 0]\nclass = 0", fillcolor="#e58139"] ;
+13 -> 14 ;
+15 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 7\nvalue = [1, 6]\nclass = 1", fillcolor="#5aade9"] ;
+13 -> 15 ;
+16 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
+15 -> 16 ;
+17 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+15 -> 17 ;
+18 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+17 -> 18 ;
+19 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+17 -> 19 ;
+20 [label="ksztalt <= 0.5\nentropy = 0.684\nsamples = 11\nvalue = [9, 2]\nclass = 0", fillcolor="#eb9d65"] ;
+2 -> 20 ;
+21 [label="dolna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+20 -> 21 ;
+22 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+21 -> 22 ;
+23 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+22 -> 23 ;
+24 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+22 -> 24 ;
+25 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+21 -> 25 ;
+26 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
+20 -> 26 ;
+27 [label="gorna <= 0.5\nentropy = 0.449\nsamples = 64\nvalue = [58, 6]\nclass = 0", fillcolor="#e88e4d"] ;
+1 -> 27 ;
+28 [label="entropy = 0.0\nsamples = 33\nvalue = [33, 0]\nclass = 0", fillcolor="#e58139"] ;
+27 -> 28 ;
+29 [label="wielkosc <= 1.5\nentropy = 0.709\nsamples = 31\nvalue = [25, 6]\nclass = 0", fillcolor="#eb9f69"] ;
+27 -> 29 ;
+30 [label="ksztalt <= 0.5\nentropy = 0.918\nsamples = 18\nvalue = [12, 6]\nclass = 0", fillcolor="#f2c09c"] ;
+29 -> 30 ;
+31 [label="kruchosc <= 0.5\nentropy = 1.0\nsamples = 10\nvalue = [5, 5]\nclass = 0", fillcolor="#ffffff"] ;
+30 -> 31 ;
+32 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [4, 1]\nclass = 0", fillcolor="#eca06a"] ;
+31 -> 32 ;
+33 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+32 -> 33 ;
+34 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+33 -> 34 ;
+35 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+33 -> 35 ;
+36 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+32 -> 36 ;
+37 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
+31 -> 37 ;
+38 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
+37 -> 38 ;
+39 [label="waga, <= 1.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+37 -> 39 ;
+40 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+39 -> 40 ;
+41 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+39 -> 41 ;
+42 [label="waga, <= 1.5\nentropy = 0.544\nsamples = 8\nvalue = [7, 1]\nclass = 0", fillcolor="#e99355"] ;
+30 -> 42 ;
+43 [label="entropy = 0.0\nsamples = 4\nvalue = [4, 0]\nclass = 0", fillcolor="#e58139"] ;
+42 -> 43 ;
+44 [label="wielkosc <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
+42 -> 44 ;
+45 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+44 -> 45 ;
+46 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [2, 1]\nclass = 0", fillcolor="#f2c09c"] ;
+44 -> 46 ;
+47 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+46 -> 47 ;
+48 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+46 -> 48 ;
+49 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+48 -> 49 ;
+50 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+48 -> 50 ;
+51 [label="entropy = 0.0\nsamples = 13\nvalue = [13, 0]\nclass = 0", fillcolor="#e58139"] ;
+29 -> 51 ;
+52 [label="wielkosc <= 1.5\nentropy = 0.714\nsamples = 102\nvalue = [20, 82]\nclass = 1", fillcolor="#69b5eb"] ;
+0 -> 52 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
+53 [label="waga, <= 0.5\nentropy = 0.469\nsamples = 70\nvalue = [7, 63]\nclass = 1", fillcolor="#4fa8e8"] ;
+52 -> 53 ;
+54 [label="entropy = 0.0\nsamples = 21\nvalue = [0, 21]\nclass = 1", fillcolor="#399de5"] ;
+53 -> 54 ;
+55 [label="ksztalt <= 0.5\nentropy = 0.592\nsamples = 49\nvalue = [7, 42]\nclass = 1", fillcolor="#5aade9"] ;
+53 -> 55 ;
+56 [label="wielkosc <= 0.5\nentropy = 0.25\nsamples = 24\nvalue = [1, 23]\nclass = 1", fillcolor="#42a1e6"] ;
+55 -> 56 ;
+57 [label="entropy = 0.0\nsamples = 15\nvalue = [0, 15]\nclass = 1", fillcolor="#399de5"] ;
+56 -> 57 ;
+58 [label="kruchosc <= 0.5\nentropy = 0.503\nsamples = 9\nvalue = [1, 8]\nclass = 1", fillcolor="#52a9e8"] ;
+56 -> 58 ;
+59 [label="dolna <= 0.5\nentropy = 0.722\nsamples = 5\nvalue = [1, 4]\nclass = 1", fillcolor="#6ab6ec"] ;
+58 -> 59 ;
+60 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+59 -> 60 ;
+61 [label="gorna <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+59 -> 61 ;
+62 [label="priorytet <= 0.5\nentropy = 1.0\nsamples = 2\nvalue = [1, 1]\nclass = 0", fillcolor="#ffffff"] ;
+61 -> 62 ;
+63 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+62 -> 63 ;
+64 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+62 -> 64 ;
+65 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+61 -> 65 ;
+66 [label="entropy = 0.0\nsamples = 4\nvalue = [0, 4]\nclass = 1", fillcolor="#399de5"] ;
+58 -> 66 ;
+67 [label="kruchosc <= 0.5\nentropy = 0.795\nsamples = 25\nvalue = [6, 19]\nclass = 1", fillcolor="#78bced"] ;
+55 -> 67 ;
+68 [label="priorytet <= 0.5\nentropy = 0.98\nsamples = 12\nvalue = [5, 7]\nclass = 1", fillcolor="#c6e3f8"] ;
+67 -> 68 ;
+69 [label="dolna <= 0.5\nentropy = 0.764\nsamples = 9\nvalue = [2, 7]\nclass = 1", fillcolor="#72b9ec"] ;
+68 -> 69 ;
+70 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
+69 -> 70 ;
+71 [label="gorna <= 0.5\nentropy = 1.0\nsamples = 4\nvalue = [2, 2]\nclass = 0", fillcolor="#ffffff"] ;
+69 -> 71 ;
+72 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
+71 -> 72 ;
+73 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+71 -> 73 ;
+74 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+68 -> 74 ;
+75 [label="dolna <= 0.5\nentropy = 0.391\nsamples = 13\nvalue = [1, 12]\nclass = 1", fillcolor="#49a5e7"] ;
+67 -> 75 ;
+76 [label="entropy = 0.0\nsamples = 7\nvalue = [0, 7]\nclass = 1", fillcolor="#399de5"] ;
+75 -> 76 ;
+77 [label="gorna <= 0.5\nentropy = 0.65\nsamples = 6\nvalue = [1, 5]\nclass = 1", fillcolor="#61b1ea"] ;
+75 -> 77 ;
+78 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+77 -> 78 ;
+79 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+78 -> 79 ;
+80 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+78 -> 80 ;
+81 [label="entropy = 0.0\nsamples = 3\nvalue = [0, 3]\nclass = 1", fillcolor="#399de5"] ;
+77 -> 81 ;
+82 [label="gorna <= 0.5\nentropy = 0.974\nsamples = 32\nvalue = [13, 19]\nclass = 1", fillcolor="#c0e0f7"] ;
+52 -> 82 ;
+83 [label="kruchosc <= 0.5\nentropy = 0.65\nsamples = 12\nvalue = [10, 2]\nclass = 0", fillcolor="#ea9a61"] ;
+82 -> 83 ;
+84 [label="entropy = 0.0\nsamples = 7\nvalue = [7, 0]\nclass = 0", fillcolor="#e58139"] ;
+83 -> 84 ;
+85 [label="waga, <= 1.5\nentropy = 0.971\nsamples = 5\nvalue = [3, 2]\nclass = 0", fillcolor="#f6d5bd"] ;
+83 -> 85 ;
+86 [label="priorytet <= 0.5\nentropy = 0.918\nsamples = 3\nvalue = [1, 2]\nclass = 1", fillcolor="#9ccef2"] ;
+85 -> 86 ;
+87 [label="entropy = 0.0\nsamples = 2\nvalue = [0, 2]\nclass = 1", fillcolor="#399de5"] ;
+86 -> 87 ;
+88 [label="entropy = 0.0\nsamples = 1\nvalue = [1, 0]\nclass = 0", fillcolor="#e58139"] ;
+86 -> 88 ;
+89 [label="entropy = 0.0\nsamples = 2\nvalue = [2, 0]\nclass = 0", fillcolor="#e58139"] ;
+85 -> 89 ;
+90 [label="dolna <= 0.5\nentropy = 0.61\nsamples = 20\nvalue = [3, 17]\nclass = 1", fillcolor="#5caeea"] ;
+82 -> 90 ;
+91 [label="entropy = 0.0\nsamples = 11\nvalue = [0, 11]\nclass = 1", fillcolor="#399de5"] ;
+90 -> 91 ;
+92 [label="kruchosc <= 0.5\nentropy = 0.918\nsamples = 9\nvalue = [3, 6]\nclass = 1", fillcolor="#9ccef2"] ;
+90 -> 92 ;
+93 [label="waga, <= 0.5\nentropy = 0.811\nsamples = 4\nvalue = [3, 1]\nclass = 0", fillcolor="#eeab7b"] ;
+92 -> 93 ;
+94 [label="entropy = 0.0\nsamples = 1\nvalue = [0, 1]\nclass = 1", fillcolor="#399de5"] ;
+93 -> 94 ;
+95 [label="entropy = 0.0\nsamples = 3\nvalue = [3, 0]\nclass = 0", fillcolor="#e58139"] ;
+93 -> 95 ;
+96 [label="entropy = 0.0\nsamples = 5\nvalue = [0, 5]\nclass = 1", fillcolor="#399de5"] ;
+92 -> 96 ;
+}
diff --git a/DecisionTree/Source.gv.pdf b/DecisionTree/Source.gv.pdf
new file mode 100644
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diff --git a/DecisionTree/dane.csv b/DecisionTree/dane.csv
new file mode 100644
index 0000000..b138639
--- /dev/null
+++ b/DecisionTree/dane.csv
@@ -0,0 +1,201 @@
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diff --git a/DecisionTree/drzewo_decyzyjne.py b/DecisionTree/drzewo_decyzyjne.py
new file mode 100644
index 0000000..6d4842d
--- /dev/null
+++ b/DecisionTree/drzewo_decyzyjne.py
@@ -0,0 +1,57 @@
+import graphviz
+import pandas as pd
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_graphviz
+
+plikZPrzecinkami = open("training_data.txt", 'w')
+
+with open('200permutations_table.txt', 'r') as plik:
+    for linia in plik:
+        liczby = linia.strip()
+        wiersz = ""
+        licznik = 0
+        for liczba in liczby:
+            wiersz += liczba
+            wiersz += ";"
+        wiersz = wiersz[:-1]
+        wiersz += '\n'
+        plikZPrzecinkami.write(wiersz)
+
+plikZPrzecinkami.close()
+
+x = pd.read_csv('training_data.txt', delimiter=';',
+                names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
+y = pd.read_csv('decisions.txt', names=['polka'])
+# X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=1)  # 70% treningowe and 30% testowe
+
+# Tworzenie instancji klasyfikatora ID3
+clf = DecisionTreeClassifier(criterion='entropy')
+
+# Trenowanie klasyfikatora
+clf.fit(x.values, y.values)
+# clf.fit(X_train, y_train)
+
+
+# Predykcja na nowych danych
+new_data = [[2, 2, 1, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
+predictions = clf.predict(new_data)
+# y_pred = clf.predict(X_test)
+
+
+print(predictions)
+# print("Accuracy:", clf.score(new_data, predictions))
+# print("Accuracy:", metrics.accuracy_score(y_test, y_pred))
+
+
+# Wygenerowanie pliku .dot reprezentującego drzewo
+dot_data = export_graphviz(clf, out_file=None, feature_names=list(x.columns), class_names=['0', '1'], filled=True,
+                           rounded=True)
+
+# Tworzenie obiektu graphviz z pliku .dot
+graph = graphviz.Source(dot_data)
+
+# Wyświetlanie drzewa
+graph.view()
+
+z = pd.concat([x, y], axis=1)
+z.to_csv('dane.csv', index=False)
diff --git a/DecisionTree/training_data.txt b/DecisionTree/training_data.txt
new file mode 100644
index 0000000..06a4ff8
--- /dev/null
+++ b/DecisionTree/training_data.txt
@@ -0,0 +1,200 @@
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