Merge pull request 'Decision_trees' (#3) from Decision_trees into master

Reviewed-on: #3

.idea/Traktor AI.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="jdk" jdkName="Python 3.9" jdkType="Python SDK" />
+    <orderEntry type="jdk" jdkName="Python 3.10 (Traktor)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>

.idea/misc.xml

@@ -1,4 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
-</project>

source/decision_tree.py

@@ -0,0 +1,45 @@
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import joblib
+from sklearn.tree import DecisionTreeClassifier, plot_tree
+import matplotlib.pyplot as plt
+dtype_dict = {
+    'season': str,
+    'anomalies': bool,
+    'weather': str,
+    'temp': int,
+    'water': int,
+    'nutri': int,
+    'pests': int,
+    'weeds': int,
+    'type': str,
+    'ripeness': int,
+    'target_column': str
+}
+# Wczytaj dane z pliku CSV
+data = pd.read_csv('resources/dataset.csv', header=0, dtype=dtype_dict)
+#print(data)
+X = data.drop('target_column', axis=1)
+y = data['target_column']
+X = pd.get_dummies(X)
+# X.to_csv('model_data1.csv', index=False)
+
+
+# Podział danych na zbiór treningowy i testowy
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Dopasowanie modelu drzewa decyzyjnego
+model = DecisionTreeClassifier()
+model.fit(X_train, y_train)
+
+
+fig = plt.figure(figsize=(25, 20))
+_ = plot_tree(model, feature_names=X.columns, class_names=model.classes_, filled=True)
+plt.savefig('drzewo_decyzyjne.png')
+plt.show()
+
+# 'model' to wcześniej wytrenowany model drzewa decyzyjnego
+joblib.dump(model, 'model.pkl')
+
+
+

source/ground.py

@@ -12,7 +12,7 @@ class Dirt:
 #   add a couple new properties
 
     def pests_and_weeds(self):
-        i = random.randint(1, 20)   # 5% szans na szkodniki, 10% na chwasty, 5 na obydwa 15 na kamien
+        i = random.randint(1, 20)   # 5% szans na szkodniki, 10% na chwasty, 5 na obydwa 5 na kamien
         if i == 1:
             self.pest = True
         elif i == 2 or i == 3:

source/main.py

@@ -1,6 +1,8 @@
 import pygame
 import time
 import random
+import pandas as pd
+import joblib
 
 from area.constants import WIDTH, HEIGHT, TILE_SIZE
 from area.field import drawWindow
@@ -68,15 +70,64 @@ def main():
         d1 = Dirt(random.randint(1, 100), random.randint(1,100))
         d1.pests_and_weeds()
         tile1.ground=d1
-        
-        #movement based on route-planning (test):
+        if d1.pest:
+            pe = 1
+        else:
+            pe = 0
+        if d1.weed:
+            we = 1
+        else:
+            we = 0
+        if p1.plant_type == 'cereal':
+            t1 = True
+            t2 = False
+            t3 = False
+            t4 = False
+        else:
+            t1 = False
+            if p1.plant_type == 'fruit':
+                t2 = True
+                t3 = False
+                t4 = False
+            else:
+                t2 = False
+                if p1.plant_type == 'vegetable':
+                    t4 = True
+                    t3 = False
+                else:
+                    t3 = True
+                    t4 = False
+
+        dane = {
+            'anomalies': [True],
+            'temp': [17],
+            'water': [d1.water_level],
+            'nutri': [d1.nutrients_level],
+            'pests': [pe],
+            'weeds': [we],
+            'ripeness': [p1.growth_level],
+            'season_autumn': [True], 'season_spring': [False], 'season_summer': [False], 'season_winter': [False],
+            'weather_heavyCloudy': [False], 'weather_partCloudy': [False], 'weather_precipitation': [False],
+            'weather_sunny': [True],
+            'type_cereal': [t1], 'type_fruit': [t2], 'type_none': [t3], 'type_vegetable': [t4]
+        }
+        df = pd.DataFrame(dane)
+        df.to_csv('model_data.csv', index=False)
+
+        model = joblib.load('model.pkl')
+        nowe_dane = pd.read_csv('model_data.csv')
+
+        predykcje = model.predict(nowe_dane)
+
+        # movement based on route-planning (test):
 
         tractor.draw_tractor(WIN)
         time.sleep(1)
         if moves != False:
             do_actions(tractor, WIN, moves)
-
-        tractor.work_on_field(tile1, d1, p1)
+        print(predykcje)
+        if predykcje == 'work':
+            tractor.work_on_field(tile1, d1, p1)
         time.sleep(30)
         print("\n")
 

source/model_data.csv

@@ -0,0 +1,2 @@
+anomalies,temp,water,nutri,pests,weeds,ripeness,season_autumn,season_spring,season_summer,season_winter,weather_heavyCloudy,weather_partCloudy,weather_precipitation,weather_sunny,type_cereal,type_fruit,type_none,type_vegetable
+True,17,32,2,0,0,54,True,False,False,False,False,False,False,True,True,False,False,False

source/resources/dataset.csv

@@ -0,0 +1,301 @@
+season,anomalies,weather,temp,water,nutri,pests,weeds,type,ripeness,target_column
+winter,False,precipitation,5,44,17,1,0,vegetable,76,doNothing
+spring,False,heavyCloudy,12,22,83,0,0,cereal,83,work
+summer,False,sunny,25,85,84,0,0,cereal,85,work
+summer,False,precipitation,24,24,67,0,0,none,0,work
+spring,False,sunny,4,44,35,1,0,vegetable,96,work
+summer,False,partCloudy,33,78,54,0,1,cereal,89,work
+winter,False,heavyCloudy,-1,27,87,1,1,cereal,1,doNothing
+autumn,True,sunny,15,45,99,1,1,cereal,49,doNothing
+autumn,False,partCloudy,20,54,81,0,1,none,0,work
+winter,False,heavyCloudy,-5,50,49,0,1,fruit,42,doNothing
+summer,False,heavyCloudy,35,95,31,1,1,none,0,work
+summer,False,partCloudy,36,14,42,0,1,vegetable,96,work
+summer,False,precipitation,31,62,45,1,0,vegetable,3,work
+summer,True,precipitation,35,35,41,1,0,none,0,doNothing
+summer,True,heavyCloudy,35,33,97,0,0,fruit,76,work
+autumn,False,sunny,3,24,62,1,1,vegetable,21,work
+winter,False,partCloudy,-18,80,37,0,0,cereal,24,doNothing
+spring,True,precipitation,21,15,58,1,0,fruit,49,doNothing
+autumn,False,precipitation,16,62,75,1,0,fruit,69,work
+winter,False,heavyCloudy,-11,69,9,0,1,none,0,doNothing
+autumn,False,sunny,21,6,24,0,0,vegetable,99,work
+summer,False,heavyCloudy,23,1,33,0,0,none,0,work
+autumn,False,sunny,18,80,42,0,0,vegetable,47,work
+summer,False,partCloudy,35,69,38,1,0,vegetable,40,work
+summer,False,heavyCloudy,27,98,34,0,1,vegetable,20,work
+winter,False,heavyCloudy,-3,9,3,0,1,none,0,doNothing
+summer,False,heavyCloudy,26,79,28,1,0,fruit,20,work
+spring,False,precipitation,3,9,53,0,1,vegetable,30,doNothing
+autumn,False,sunny,6,31,1,1,0,fruit,31,work
+autumn,False,sunny,8,2,70,1,0,vegetable,54,work
+spring,False,precipitation,8,21,1,1,0,fruit,92,doNothing
+autumn,False,heavyCloudy,11,4,32,1,1,fruit,63,work
+winter,True,precipitation,-10,57,67,1,1,cereal,43,doNothing
+winter,False,precipitation,-13,90,14,1,1,fruit,29,doNothing
+autumn,True,precipitation,10,68,46,0,0,fruit,18,doNothing
+spring,False,heavyCloudy,8,41,55,1,1,fruit,12,work
+winter,False,heavyCloudy,-10,24,25,0,0,vegetable,32,doNothing
+spring,False,heavyCloudy,0,40,6,0,0,none,0,work
+autumn,False,sunny,22,7,88,0,1,vegetable,71,work
+spring,True,precipitation,0,56,21,0,0,cereal,46,doNothing
+autumn,False,sunny,13,37,49,0,1,vegetable,94,work
+summer,False,precipitation,20,14,39,0,0,cereal,97,work
+autumn,False,heavyCloudy,18,74,38,1,0,vegetable,32,work
+autumn,False,heavyCloudy,2,100,70,0,0,none,0,work
+autumn,False,precipitation,19,30,16,0,1,cereal,17,work
+summer,False,sunny,26,94,83,1,1,fruit,25,work
+autumn,False,heavyCloudy,14,91,62,0,0,vegetable,25,work
+autumn,False,partCloudy,12,34,98,0,0,none,0,work
+spring,True,heavyCloudy,5,64,50,0,0,vegetable,31,doNothing
+summer,True,heavyCloudy,30,60,55,0,0,none,0,doNothing
+summer,False,sunny,22,35,2,0,1,vegetable,20,work
+spring,False,sunny,21,87,22,0,1,cereal,45,work
+winter,False,heavyCloudy,-2,48,83,0,1,vegetable,3,doNothing
+winter,False,heavyCloudy,-15,55,6,0,1,none,0,doNothing
+summer,False,heavyCloudy,35,5,68,1,0,vegetable,79,work
+summer,True,heavyCloudy,35,99,35,1,0,fruit,79,doNothing
+winter,False,sunny,-1,35,57,1,0,vegetable,80,doNothing
+summer,True,precipitation,31,55,73,0,1,vegetable,24,doNothing
+autumn,True,precipitation,21,26,84,0,0,vegetable,49,doNothing
+winter,False,precipitation,-8,13,30,1,0,none,0,doNothing
+autumn,False,heavyCloudy,20,58,15,1,0,cereal,56,work
+spring,False,partCloudy,12,25,61,1,1,vegetable,11,work
+winter,False,partCloudy,-14,58,54,0,1,none,0,doNothing
+autumn,False,sunny,6,4,96,1,0,cereal,42,work
+summer,False,sunny,29,74,98,0,1,vegetable,22,work
+summer,False,sunny,32,83,90,1,0,none,0,work
+summer,True,sunny,33,11,11,0,1,fruit,45,work
+spring,False,sunny,3,54,13,1,1,none,0,work
+autumn,False,heavyCloudy,20,67,97,0,0,cereal,68,doNothing
+spring,False,sunny,5,35,54,0,0,vegetable,37,work
+summer,False,precipitation,31,44,68,1,0,vegetable,9,work
+winter,False,heavyCloudy,-11,59,18,1,1,cereal,11,doNothing
+summer,False,precipitation,30,32,57,1,1,fruit,18,work
+summer,False,sunny,27,31,54,0,1,vegetable,67,work
+summer,False,precipitation,29,70,73,1,0,fruit,71,work
+spring,False,sunny,18,67,22,1,0,fruit,64,work
+spring,False,partCloudy,13,40,18,1,0,fruit,7,work
+summer,False,precipitation,23,24,6,1,0,vegetable,90,work
+autumn,False,heavyCloudy,1,59,84,0,0,none,0,work
+spring,False,precipitation,2,47,99,0,0,vegetable,83,doNothing
+summer,False,precipitation,32,9,94,1,0,cereal,18,work
+winter,False,partCloudy,-14,56,74,1,1,fruit,24,doNothing
+winter,False,sunny,-5,100,55,0,0,cereal,57,doNothing
+summer,False,sunny,30,99,99,1,1,cereal,31,work
+winter,True,precipitation,-13,56,58,1,0,none,0,doNothing
+spring,False,precipitation,1,24,21,1,1,none,0,work
+spring,False,heavyCloudy,2,38,56,0,0,fruit,9,work
+winter,False,partCloudy,-7,60,71,0,0,cereal,70,doNothing
+autumn,False,precipitation,2,71,2,0,1,cereal,26,doNothing
+autumn,False,sunny,19,99,7,1,1,none,0,work
+spring,False,sunny,8,41,35,0,1,vegetable,89,work
+winter,False,sunny,-14,58,94,0,1,vegetable,29,doNothing
+summer,True,precipitation,30,3,58,1,0,none,0,doNothing
+winter,False,precipitation,8,17,76,1,0,vegetable,47,doNothing
+spring,True,precipitation,20,38,65,0,1,cereal,49,doNothing
+winter,False,partCloudy,-4,8,10,0,1,cereal,74,doNothing
+autumn,False,sunny,2,100,7,1,1,none,0,work
+autumn,False,partCloudy,17,88,89,0,1,vegetable,17,work
+autumn,False,partCloudy,16,76,54,1,0,vegetable,98,work
+spring,False,sunny,2,14,25,0,1,vegetable,8,work
+spring,False,precipitation,5,34,1,1,0,none,0,work
+autumn,False,heavyCloudy,9,20,28,0,0,cereal,88,work
+autumn,False,heavyCloudy,20,17,84,0,0,cereal,15,work
+winter,False,heavyCloudy,-18,43,33,0,1,cereal,31,doNothing
+autumn,False,partCloudy,2,1,3,1,1,cereal,86,work
+summer,False,heavyCloudy,34,58,23,0,0,cereal,27,work
+summer,False,precipitation,31,22,14,0,1,fruit,33,work
+winter,False,sunny,-5,98,51,1,0,none,0,doNothing
+winter,False,precipitation,-17,74,78,1,0,none,0,doNothing
+summer,False,precipitation,29,3,54,0,0,none,0,work
+autumn,False,precipitation,7,45,29,1,1,cereal,52,doNothing
+spring,False,sunny,7,21,30,0,1,fruit,38,work
+summer,False,heavyCloudy,28,73,75,1,1,none,0,work
+summer,False,heavyCloudy,32,49,43,0,0,vegetable,74,work
+autumn,True,precipitation,0,99,66,0,0,none,0,doNothing
+summer,True,partCloudy,32,46,99,0,1,none,0,work
+autumn,False,heavyCloudy,21,26,44,1,0,none,0,work
+summer,True,precipitation,30,28,89,1,1,cereal,65,doNothing
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+winter,False,partCloudy,-3,78,63,1,0,vegetable,18,doNothing
+spring,False,partCloudy,22,8,86,1,1,none,0,work
+winter,False,partCloudy,2,34,30,1,0,fruit,95,doNothing
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+winter,False,partCloudy,9,80,24,1,1,vegetable,67,doNothing
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+spring,False,sunny,14,14,29,1,1,none,0,work
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+spring,True,partCloudy,9,62,24,0,1,fruit,67,doNothing
+spring,False,precipitation,2,87,100,1,0,vegetable,39,doNothing
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+winter,False,partCloudy,-17,24,42,1,1,fruit,71,doNothing
+spring,False,heavyCloudy,20,45,71,1,1,vegetable,58,work
+winter,False,precipitation,-8,78,81,1,1,fruit,8,doNothing
+spring,False,partCloudy,5,31,82,0,1,fruit,18,work
+autumn,False,sunny,6,75,85,0,0,fruit,18,doNothing
+spring,False,precipitation,14,9,1,1,0,none,0,work
+winter,False,heavyCloudy,2,37,5,1,1,cereal,1,doNothing
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+winter,False,partCloudy,-4,97,90,1,1,vegetable,94,doNothing
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+spring,False,sunny,15,97,67,1,1,none,0,work
+summer,False,partCloudy,33,40,14,1,0,none,0,work
+spring,False,partCloudy,4,23,51,0,0,vegetable,8,work
+summer,False,sunny,20,66,76,1,1,vegetable,71,work
+winter,False,sunny,-11,2,98,0,1,vegetable,29,doNothing
+spring,False,sunny,9,67,74,0,0,none,0,work
+autumn,False,precipitation,17,23,94,1,1,none,0,work
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+winter,False,heavyCloudy,-2,35,82,1,1,cereal,20,doNothing
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+summer,False,sunny,32,44,20,1,1,vegetable,48,work
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+winter,False,heavyCloudy,-1,69,34,0,0,none,0,doNothing
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+summer,False,partCloudy,26,86,12,0,1,none,0,work
+winter,True,precipitation,5,62,62,1,1,cereal,85,doNothing
+winter,False,partCloudy,9,29,83,1,0,vegetable,14,doNothing
+spring,False,sunny,8,22,33,0,1,cereal,23,work
+summer,False,partCloudy,34,38,2,1,1,fruit,29,work
+autumn,False,heavyCloudy,2,65,91,1,1,vegetable,49,work
+spring,False,sunny,3,73,32,0,0,fruit,61,work
+spring,False,precipitation,7,68,70,0,0,none,0,work
+winter,False,partCloudy,10,68,2,0,0,cereal,80,doNothing
+winter,False,precipitation,-16,89,7,0,0,fruit,92,doNothing
+winter,False,precipitation,0,31,49,0,1,cereal,18,doNothing
+spring,False,heavyCloudy,7,44,86,1,1,none,0,work
+summer,False,sunny,24,1,75,0,1,none,0,work
+summer,True,heavyCloudy,39,3,100,1,0,cereal,30,work
+winter,False,heavyCloudy,2,88,69,1,1,cereal,79,doNothing
+spring,False,heavyCloudy,16,100,21,0,1,cereal,57,work
+autumn,True,heavyCloudy,1,17,62,0,1,cereal,92,doNothing
+autumn,False,sunny,1,69,54,0,0,cereal,62,work
+autumn,False,sunny,17,62,84,0,1,vegetable,18,work
+summer,False,heavyCloudy,24,57,84,0,1,vegetable,1,work
+spring,False,partCloudy,8,80,61,0,0,none,0,work
+autumn,False,heavyCloudy,1,93,79,0,0,fruit,62,work
+winter,False,sunny,-4,51,33,1,0,none,0,doNothing
+autumn,False,precipitation,4,51,73,1,1,fruit,57,doNothing
+autumn,True,heavyCloudy,5,88,81,0,1,fruit,25,doNothing
+summer,False,precipitation,30,26,45,0,0,none,0,work
+winter,False,partCloudy,3,88,46,1,0,none,0,doNothing
+autumn,False,heavyCloudy,8,20,34,0,1,fruit,62,work
+spring,False,partCloudy,15,34,33,0,0,vegetable,16,work
+winter,False,heavyCloudy,-20,80,25,1,0,vegetable,45,doNothing
+spring,False,precipitation,6,34,78,1,0,cereal,6,doNothing
+spring,True,heavyCloudy,3,97,65,1,0,fruit,21,doNothing
+summer,False,precipitation,34,49,65,1,0,vegetable,10,work
+summer,True,precipitation,26,29,2,0,0,cereal,56,doNothing
+autumn,False,partCloudy,10,3,19,0,0,vegetable,67,work
+spring,False,sunny,19,60,71,0,1,cereal,5,work
+winter,False,partCloudy,5,11,66,0,0,none,0,doNothing
+winter,False,heavyCloudy,-13,86,29,1,1,cereal,98,doNothing
+summer,False,precipitation,33,19,2,1,1,cereal,78,work
+spring,False,precipitation,13,6,40,0,0,cereal,49,work
+summer,False,partCloudy,31,15,70,1,0,cereal,100,work
+spring,False,heavyCloudy,8,40,1,0,0,cereal,83,work
+spring,False,partCloudy,10,77,95,1,1,fruit,85,work
+spring,False,partCloudy,3,96,32,1,1,fruit,16,work
+winter,False,sunny,-11,24,5,1,0,none,0,doNothing
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