add decision tree

Readme.md

@@ -34,7 +34,7 @@ Change sizes map in config.py
 VERTICAL_NUM_OF_FIELDS = 3
 HORIZONTAL_NUM_OF_FIELDS = 3
 ```
-\
+
 #### 4.1 Save generated map:
 ```bash
 python main.py --save-map

app/__init__.py

@@ -44,6 +44,7 @@ class App:
 
         if keys[pygame.K_w]:
             self.__tractor.move()
+            self.__tractor.choose_action()
             print(self.__tractor)
 
         if keys[pygame.K_n]:

app/board.py

@@ -66,6 +66,15 @@ class Board:
                 print(f"{j} - {type(self.__fields[i][j]).__name__}", end=" | ")
             print()
 
+    def convert_fields_to_vectors(self) -> list[list]:
+        list_of_vectors = []
+        for i in range(HORIZONTAL_NUM_OF_FIELDS):
+            list_of_vectors.append([])
+            for j in range(VERTICAL_NUM_OF_FIELDS):
+                list_of_vectors[i].append(self.__fields[i][j].transform())
+        print(list_of_vectors)
+        return list_of_vectors
+
     def convert_fields_to_list_of_types(self) -> list:
         data = []
         for i in range(HORIZONTAL_NUM_OF_FIELDS):
@@ -82,7 +91,7 @@ class Board:
 
     def load_map(self, filename: str):
         try:
-            with open(os.path.join(MAP_DIR,f"{filename}.{JSON}")) as f:
+            with open(os.path.join(MAP_DIR, f"{filename}.{JSON}")) as f:
                 data = json.load(f)
         except IOError:
             raise IOError(f"Cannot load file: {filename}.{JSON}!")
@@ -117,7 +126,7 @@ class Board:
         fields = self.convert_fields_to_list_of_types()
 
         try:
-            with open(os.path.join(MAP_DIR,f"{MAP_FILE_NAME}-{uuid.uuid4().hex}.{JSON}"), 'w') as f:
+            with open(os.path.join(MAP_DIR, f"{MAP_FILE_NAME}-{uuid.uuid4().hex}.{JSON}"), 'w') as f:
                 json.dump(fields, f)
         except IOError:
             raise IOError(f"Cannot save file:!")

app/decision_tree.py

@@ -0,0 +1,68 @@
+#!/usr/bin/python3
+import os
+from typing import Union
+import pydotplus
+import pandas as pd
+from joblib import dump, load
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import export_graphviz, export_text
+
+from app.weather import Weather
+from config import *
+
+
+class DecisionTree:
+    WEATHER = {W_SUNNY: 0, W_CLOUDY: 1, W_SNOW: 2, W_RAINY: 3}
+    SEASON = {S_AUTUMN: 0, S_WINTER: 1, S_SPRING: 2, S_SUMMER: 3}
+    FEATURES = ['Season', 'Weather', 'Fertilize', 'Hydrate', 'Sow', 'Harvest', 'Action']
+
+    def __int__(self):
+        self.tree = None
+
+    def learn_tree(self) -> None:
+        path = os.path.join(DATA_DIR, MODEL_TREE_FILENAME)
+        if os.path.exists(path):
+            self.tree = load(path)
+        else:
+            # read data
+            training_data = pd.read_csv(os.path.join(DATA_DIR, DATA_TRAINING_FOR_DECISION_TREE))
+            print(training_data.head())
+
+            training_data = self.map_data(training_data)
+            # print(training_data)
+
+            X = training_data[self.FEATURES[:-1]]
+            Y = training_data[self.FEATURES[-1]]
+
+            self.tree = DecisionTreeClassifier()
+            self.tree = self.tree.fit(X, Y)
+            dump(self.tree, path)
+
+        text = export_text(self.tree, feature_names=self.FEATURES[:-1])
+        print(text)
+
+        data = export_graphviz(self.tree, out_file=None, feature_names=self.FEATURES[:-1])
+        graph = pydotplus.graph_from_dot_data(data)
+        graph.write_png(os.path.join(DATA_DIR, IMG_DECISION_TREE))
+
+    def map_data(self, data: Union[pd.Series, pd.DataFrame]) -> Union[pd.Series, pd.DataFrame]:
+        # print(data)
+        data['Season'] = data['Season'].map(DecisionTree.SEASON)
+        data['Weather'] = data['Weather'].map(DecisionTree.WEATHER)
+        return data
+
+    def predict(self, vector: Union[pd.Series, pd.DataFrame]) -> str:
+        print(vector)
+        x = self.map_data(vector)
+        action = self.tree.predict(x)
+        return action
+
+    def make_decision(self, weather: Weather, v: list):
+        s, w = weather.randomize_weather()
+        tree = DecisionTree()
+        tree.learn_tree()
+        final_vector = [s, w] + v
+        print(final_vector)
+        df = pd.DataFrame([final_vector])
+        df.columns = DecisionTree.FEATURES[:-1]
+        return tree.predict(df)

app/fields.py

@@ -27,6 +27,9 @@ class Crops(Field):
         self.weight = 1.0
         self._value = VALUE_OF_CROPS
 
+    def transform(self) -> list:
+        return [0, 0, 0, 1]
+
 
 class Plant(Field):
     def __init__(self, img_path: str):
@@ -34,6 +37,9 @@ class Plant(Field):
         self.is_hydrated = False
         self._value = VALUE_OF_PLANT
 
+    def transform(self) -> list:
+        return [0, 1, 0, 0]
+
 
 class Clay(Soil):
     def __init__(self):
@@ -41,6 +47,9 @@ class Clay(Soil):
         self.is_fertilized = False
         self._value = VALUE_OF_CLAY
 
+    def transform(self) -> list:
+        return [1, 0, 0, 0]
+
 
 class Sand(Soil):
     def __init__(self):
@@ -49,6 +58,12 @@ class Sand(Soil):
         self.is_hydrated = False
         self._value = VALUE_OF_SAND
 
+    def transform(self) -> list:
+        if not self.is_hydrated :
+            return [0, 1, 0, 0]
+        else:
+            return [0, 0, 1, 0]
+
 
 class Grass(Plant):
     def __init__(self):

app/tractor.py

@@ -17,6 +17,8 @@ from app.fields import CROPS, PLANTS, Crops, Sand, Clay, Field
 from config import *
 
 from app.fields import Plant, Soil, Crops
+from app.decision_tree import DecisionTree
+from app.weather import Weather
 
 
 class Tractor(BaseField):
@@ -30,6 +32,8 @@ class Tractor(BaseField):
         self.__harvested_corps = []
         self.__fuel = 10
         self.__neural_network = None
+        self.__tree = DecisionTree()
+        self.__weather = Weather()
 
     def draw(self, screen: pygame.Surface) -> None:
         self.draw_field(screen, self.__pos_x + FIELD_SIZE / 2, self.__pos_y + FIELD_SIZE / 2,
@@ -304,3 +308,19 @@ class Tractor(BaseField):
             time.sleep(1)
 
         is_running.clear()
+
+    def choose_action(self) -> None:
+        vectors = self.__board.convert_fields_to_vectors()
+        print(vectors)
+        coords = None
+        action = None
+        for i in range(HORIZONTAL_NUM_OF_FIELDS):
+            for j in range(VERTICAL_NUM_OF_FIELDS):
+                action = self.__tree.make_decision(self.__weather, vectors[i][j])
+                if action != A_DO_NOTHING:
+                    coords = (i, j)
+                    break
+        print(coords, action)
+        if coords is not None:
+            # astar coords
+            pass

app/weather.py

@@ -0,0 +1,28 @@
+#!/usr/bin/python3
+import random
+from config import *
+
+
+class Weather:
+    def __init__(self):
+        self.months = (S_WINTER, S_WINTER, S_SPRING, S_SPRING, S_SPRING, S_SUMMER,
+                       S_SUMMER, S_SUMMER, S_AUTUMN, S_AUTUMN, S_AUTUMN, S_WINTER)
+        self.current_month = 0
+
+    def randomize_weather(self) -> tuple[str, str]:
+        season = self.months[self.current_month]
+
+        if season == S_WINTER:
+            weather = random.choices([W_SNOW, W_CLOUDY])
+        elif season == S_SUMMER:
+            weights = [0.5, 0.3, 0.2]
+            weather = random.choices([W_SUNNY, W_CLOUDY, W_RAINY], weights)
+        elif season == S_SPRING:
+            weights = [0.3, 0.5, 0.2]
+            weather = random.choices([W_SUNNY, W_CLOUDY, W_RAINY], weights)
+        else:
+            weights = [0.2, 0.3, 0.4]
+            weather = random.choices([W_SUNNY, W_CLOUDY, W_RAINY], weights)
+
+        self.current_month = (self.current_month + 1) % len(self.months)
+        return season, weather[0]

config.py

@@ -9,11 +9,14 @@ __all__ = (
     'SAND', 'CLAY', 'GRASS', 'CORN', 'SUNFLOWER',
     'FIELD_TYPES', 'TIME_OF_GROWING', 'AMOUNT_OF_CROPS',
     'M_GO_FORWARD', 'M_ROTATE_LEFT', 'M_ROTATE_RIGHT',
+    'S_AUTUMN', 'S_SPRING', 'S_SUMMER', 'S_WINTER', 'TYPES_OF_SEASON',
+    'W_SUNNY', 'W_CLOUDY', 'W_SNOW', 'W_RAINY', 'TYPES_OF_WEATHER',
     'A_SOW', 'A_HARVEST', 'A_HYDRATE', 'A_FERTILIZE', 'A_DO_NOTHING',
-    'D_NORTH', 'D_EAST', 'D_SOUTH', 'D_WEST',
+    'TYPES_OF_ACTION', 'D_NORTH', 'D_EAST', 'D_SOUTH', 'D_WEST',
     'VALUE_OF_CROPS', 'VALUE_OF_PLANT', 'VALUE_OF_SAND', 'VALUE_OF_CLAY',
     'MAP_FILE_NAME', 'JSON', 'SAVE_MAP', 'LOAD_MAP',
-    'TRAINING_SET_DIR', 'TEST_SET_DIR', 'ADAPTED_IMG_DIR', 'MODEL_DIR'
+    'TRAINING_SET_DIR', 'TEST_SET_DIR', 'ADAPTED_IMG_DIR', 'MODEL_DIR',
+    'DATA_DIR','IMG_DECISION_TREE','MODEL_TREE_FILENAME','DATA_TRAINING_FOR_DECISION_TREE'
 )
 
 # Board settings:
@@ -31,12 +34,17 @@ CAPTION = 'Tractor'
 BASE_DIR = os.path.dirname(__file__)
 RESOURCE_DIR = os.path.join(BASE_DIR, 'resources')
 MAP_DIR = os.path.join(BASE_DIR, 'maps')
+DATA_DIR = os.path.join(BASE_DIR, 'data')
 MAP_FILE_NAME = 'map'
 TRAINING_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_train')
 TEST_SET_DIR = os.path.join(RESOURCE_DIR, 'smaller_test')
 ADAPTED_IMG_DIR = os.path.join(RESOURCE_DIR, "adapted_images")
 MODEL_DIR = os.path.join(RESOURCE_DIR, 'saved_model')
 
+MODEL_TREE_FILENAME = 'tree_model.joblib'
+IMG_DECISION_TREE = 'decision_tree.png'
+DATA_TRAINING_FOR_DECISION_TREE = 'data_training.csv'
+
 # Picture format
 PNG = "png"
 
@@ -75,6 +83,7 @@ A_HARVEST = "harvest"
 A_HYDRATE = "hydrate"
 A_FERTILIZE = "fertilize"
 A_DO_NOTHING = "do nothing"
+TYPES_OF_ACTION = [A_SOW, A_HARVEST, A_HYDRATE, A_FERTILIZE, A_DO_NOTHING]
 
 # Costs fields:
 VALUE_OF_CROPS = 1
@@ -82,6 +91,21 @@ VALUE_OF_PLANT = 4
 VALUE_OF_SAND = 7
 VALUE_OF_CLAY = 10
 
+# Weather
+W_SUNNY = 'Sunny'
+W_CLOUDY = 'Cloudy'
+W_SNOW = 'Snow'
+W_RAINY = 'Rainy'
+TYPES_OF_WEATHER = [W_SUNNY, W_CLOUDY, W_SNOW, W_RAINY]
+
+# Seasons
+S_AUTUMN = 'Autumn'
+S_WINTER = 'Winter'
+S_SPRING = 'Spring'
+S_SUMMER = 'Summer'
+
+TYPES_OF_SEASON = [S_AUTUMN, S_WINTER, S_SPRING, S_SUMMER]
+
 # Times
 TIME_OF_GROWING = 2
 TIME_OF_MOVING = 2

data/data_training.csv

@@ -0,0 +1,49 @@
+Season,Weather,Fertilize,Hydrate,Sow,Harvest,Action
+Winter,Snow,0,0,0,1,do nothing
+Winter,Snow,0,0,1,0,do nothing
+Winter,Snow,0,1,0,0,do nothing
+Winter,Snow,1,0,0,0,do nothing
+Winter,Cloudy,0,0,0,1,do nothing
+Winter,Cloudy,0,0,1,0,do nothing
+Winter,Cloudy,0,1,0,0,do nothing
+Winter,Cloudy,1,0,0,0,do nothing
+Autumn,Cloudy,0,0,0,1,harvest
+Autumn,Cloudy,0,0,1,0,do nothing
+Autumn,Cloudy,0,1,0,0,do nothing
+Autumn,Cloudy,1,0,0,0,fertilize
+Autumn,Sunny,0,0,0,1,harvest
+Autumn,Sunny,0,0,1,0,Plant
+Autumn,Sunny,0,1,0,0,hydrate
+Autumn,Sunny,1,0,0,0,fertilize
+Autumn,Rainy,0,0,0,1,harvest
+Autumn,Rainy,0,0,1,0,do nothing
+Autumn,Rainy,0,1,0,0,do nothing
+Autumn,Rainy,1,0,0,0,do nothing
+Spring,Sunny,0,0,0,1,harvest
+Spring,Sunny,0,0,1,0,do nothing
+Spring,Sunny,0,1,0,0,hydrate
+Spring,Sunny,1,0,0,0,do nothing
+Spring,Cloudy,0,0,0,1,harvest
+Spring,Cloudy,0,0,1,0,do nothing
+Spring,Cloudy,0,1,0,0,hydrate
+Spring,Cloudy,1,0,0,0,do nothing
+Spring,Rainy,0,0,0,1,harvest
+Spring,Rainy,0,0,1,0,do nothing
+Spring,Rainy,0,1,0,0,do nothing
+Spring,Rainy,1,0,0,0,do nothing
+Spring,Rainy,0,0,0,1,harvest
+Spring,Rainy,0,0,1,0,do nothing
+Spring,Rainy,0,1,0,0,do nothing
+Spring,Rainy,1,0,0,0,do nothing
+Summer,Rainy,0,0,0,1,harvest
+Summer,Rainy,0,0,1,0,do nothing
+Summer,Rainy,0,1,0,0,do nothing
+Summer,Rainy,1,0,0,0,do nothing
+Summer,Sunny,0,0,0,1,harvest
+Summer,Sunny,0,0,1,0,do nothing
+Summer,Sunny,0,1,0,0,hydrate
+Summer,Sunny,1,0,0,0,fertilize
+Summer,Cloudy,0,0,0,1,harvest
+Summer,Cloudy,0,0,1,0,do nothing
+Summer,Cloudy,0,1,0,0,hydrate
+Summer,Cloudy,1,0,0,0,fertilize

requirements.txt

@@ -1,4 +1,8 @@
 pygame==2.0.1
 tensorflow~=2.5.0
 numpy~=1.19.5
-pillow~=8.2.0
+pillow~=8.2.0
+joblib~=1.0.1
+scikit-learn~=0.24.2
+pandas~=1.2.5
+pydotplus~=2.0.2