From bdea00c5b2397e8ce6ffd59ebb29603640481e85 Mon Sep 17 00:00:00 2001
From: Mateusz Kantorski <matkan1@st.amu.edu.pl>
Date: Tue, 30 May 2023 19:09:24 +0200
Subject: [PATCH] Read tree from file

---
 DecisionTree/wyuczone_drzewo.pkl | Bin 0 -> 8185 bytes
 drzewo_decyzyjne.py              |  37 ++++++++++++-------------------
 main.py                          |  10 +++++----
 3 files changed, 20 insertions(+), 27 deletions(-)
 create mode 100644 DecisionTree/wyuczone_drzewo.pkl

diff --git a/DecisionTree/wyuczone_drzewo.pkl b/DecisionTree/wyuczone_drzewo.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..751d4545d499e2fb5db3d0b38926e750ab38eb25
GIT binary patch
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diff --git a/drzewo_decyzyjne.py b/drzewo_decyzyjne.py
index f8efe20..a563a7e 100644
--- a/drzewo_decyzyjne.py
+++ b/drzewo_decyzyjne.py
@@ -1,4 +1,5 @@
 import graphviz
+import joblib
 import pandas as pd
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.tree import export_graphviz
@@ -23,36 +24,26 @@ def make_tree():
     x = pd.read_csv('DecisionTree/training_data.txt', delimiter=';',
                     names=['wielkosc', 'waga,', 'priorytet', 'ksztalt', 'kruchosc', 'dolna', 'gorna', 'g > d'])
     y = pd.read_csv('DecisionTree/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)
+
+    # Zapis drzewa do pliku
+    joblib.dump(clf, 'DecisionTree/wyuczone_drzewo.pkl')
+
     return clf
 
-# # 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)
 
+def stworz_plik_drzewa_w_pdf(clf, feature_names, class_names):
+    # Wygenerowanie pliku .dot reprezentującego drzewo
+    dot_data = export_graphviz(clf, out_file=None, feature_names=feature_names, class_names=class_names, filled=True,
+                               rounded=True)
+    # Tworzenie obiektu graphviz z pliku .dot
+    graph = graphviz.Source(dot_data)
 
-# 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)
+    # Wyświetlanie drzewa
+    graph.view()
diff --git a/main.py b/main.py
index 868f509..2282dc5 100644
--- a/main.py
+++ b/main.py
@@ -1,13 +1,13 @@
 import sys
+
+import joblib
 import pygame
 from paczka import Paczka
 from wozek import Wozek
 import wyszukiwanie
 import ekran
 from grid import GridCellType, SearchGrid
-from sklearn.tree import DecisionTreeClassifier
-import pandas as pd
-import drzewo_decyzyjne
+
 
 from plansza import a_pix, b_pix
 pygame.init()
@@ -19,7 +19,9 @@ def main():
     p2 = Paczka('maly', 1, 'ogród', False, True, False, any, any, any, any, any)
     ekran.dodaj_paczki_na_rampe(p1, p2)
     grid_points = SearchGrid()
-    drzewo = drzewo_decyzyjne.make_tree()
+
+    # Odczyt drzewa z pliku
+    drzewo = joblib.load('DecisionTree/wyuczone_drzewo.pkl')
 
     while True:
         for event in pygame.event.get():