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Zaktualizuj 'src/decisionTree.py'

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Dominik Zawadzki 2020-06-05 12:16:19 +00:00
parent d89e37e6eb
commit 2abfdf6239
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src/decisionTree.py
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import numpy as np import numpy as np
import pandas as pd import pandas as pd
import pprint import pprint
from src.graphics import * from .matrix import Matrix
from .waiter import Waiter from src.graphics import *
from .waiter import Waiter
eps = np.finfo(float).eps
tasksList = [] eps = np.finfo(float).eps
tasksQueue = [] tasksList = []
tasksQueue = []
class DecisionTree:
def __init__(self):
graphics = Graphics() class DecisionTree:
self.waiter = Waiter(graphics) def __init__(self):
graphics = Graphics()
def BuildDf(self): self.waiter = Waiter(graphics)
actionName = 'order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check'.split(',') self.matrix = Matrix(graphics=graphics)
distance = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27'.split(',')
priority = '1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,2,2,2,2,2,3,3,3,3,3,3,3,4,4,4,4,4,4,4'.split(',') def BuildDf(self):
actionName = 'order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,order,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,goToBar,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,eat,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check,check'.split(',')
dataset ={'actionName':actionName,'distance':distance,'priority':priority} distance = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27'.split(',')
df = pd.DataFrame(dataset,columns=['actionName','distance','priority']) priority = '1,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,1,1,1,1,2,2,2,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4'.split(',')
return df
dataset ={'actionName':actionName,'distance':distance,'priority':priority}
#Obliczanie entropii dla calego zestawu df = pd.DataFrame(dataset,columns=['actionName','distance','priority'])
def FindPriorityEntropy(self,df): return df
entropyNode = 0
values = df.priority.unique() #Obliczanie entropii dla calego zestawu
for value in values: def FindPriorityEntropy(self,df):
propability = df.priority.value_counts()[value]/len(df.priority) entropyNode = 0
entropyNode += -propability*np.log2(propability) values = df.priority.unique()
return entropyNode for value in values:
propability = df.priority.value_counts()[value]/len(df.priority)
#Obliczanie entropii dla wszystkich atrybut<75>w entropyNode += -propability*np.log2(propability)
def FindAttributesEntropy(self, df, attribute): return entropyNode
targetVariables = df.priority.unique()
variables = df[attribute].unique() #Obliczanie entropii dla wszystkich atrybut<75>w
def FindAttributesEntropy(self, df, attribute):
entropy2 = 0 targetVariables = df.priority.unique()
for variable in variables: variables = df[attribute].unique()
entropy = 0
for targetVariable in targetVariables: entropy2 = 0
num = len(df[attribute][df[attribute]==variable][df.priority == targetVariable]) for variable in variables:
den = len(df[attribute][df[attribute]==variable]) entropy = 0
propability = num/(den + eps) for targetVariable in targetVariables:
entropy += propability*np.log2(propability+eps) num = len(df[attribute][df[attribute]==variable][df.priority == targetVariable])
propability2 = den/len(df) den = len(df[attribute][df[attribute]==variable])
entropy2 += -propability2*entropy propability = num/(den + eps)
return abs(entropy2) entropy += propability*np.log2(propability+eps)
propability2 = den/len(df)
#Znajdowanie wierzcholka o najwyzszym info Gain entropy2 += -propability2*entropy
def FindWinner(self, df): return abs(entropy2)
infoGain = []
for key in df.keys()[:-1]: #Znajdowanie wierzcholka o najwyzszym info Gain
infoGain.append(self.FindPriorityEntropy(df) - self.FindAttributesEntropy(df, key)) def FindWinner(self, df):
return df.keys()[:-1][np.argmax(infoGain)] infoGain = []
for key in df.keys()[:-1]:
def GetSubtable(self, df, node, value): infoGain.append(self.FindPriorityEntropy(df) - self.FindAttributesEntropy(df, key))
return df[df[node] == value].reset_index(drop=True) return df.keys()[:-1][np.argmax(infoGain)]
#Budowanie drzewa def GetSubtable(self, df, node, value):
def BuildTree(self, df, tree=None): return df[df[node] == value].reset_index(drop=True)
node = self.FindWinner(df)
#Budowanie drzewa
attValues = np.unique(df[node]) def BuildTree(self, df, tree=None):
node = self.FindWinner(df)
if tree is None:
tree = {} attValues = np.unique(df[node])
tree[node] = {}
if tree is None:
for value in attValues: tree = {}
subtable = self.GetSubtable(df, node, value) tree[node] = {}
clValue,counts = np.unique(subtable['priority'],return_counts=True)
for value in attValues:
if len(counts) == 1: subtable = self.GetSubtable(df, node, value)
tree[node][value] = clValue[0] clValue,counts = np.unique(subtable['priority'],return_counts=True)
else:
tree[node][value] = self.BuildTree(subtable) if len(counts) == 1:
tree[node][value] = clValue[0]
return tree else:
tree[node][value] = self.BuildTree(subtable)
#Dodawanie zadan do listy zadan return tree
def TasksList(self, name, coordinate):
waiterNode = self.waiter.Node()
distance = abs(waiterNode[0] - coordinate[0]) + abs(waiterNode[1] - coordinate[1])
#Dodawanie zadan do listy zadan
tasksList.append([name, distance]) def TasksList(self, name, coordinate):
distance = []
#Kolejkowanie zadan waiterNode = [self.waiter.X, self.waiter.Y]
def Queue(self, tasksList): if name != "goToBar":
df = self.BuildDf() if self.matrix.matrix[coordinate[0] - 1][coordinate[1] - 1].walk_through == 1:
tree = self.BuildTree(df) distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] - 1, coordinate[1] - 1]])
if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
winnerNode = self.FindWinner(df) distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
if self.matrix.matrix[coordinate[0] + 1][coordinate[1]].walk_through == 1:
for i in tasksList: distance.append([abs(coordinate[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - coordinate[1]), [coordinate[0] + 1, coordinate[1] ]])
if winnerNode is "actionName": if self.matrix.matrix[coordinate[0] + 1][coordinate[1] - 1].walk_through == 1:
distance.append([abs(waiterNode[0] - (coordinate[0] + 1)) + abs(waiterNode[1] - (coordinate[1] - 1)), [coordinate[0] + 1, coordinate[1] - 1]])
subtable = tree[winnerNode][i[0]] if self.matrix.matrix[coordinate[0] - 1][coordinate[1] + 1].walk_through == 1:
if subtable in ['0','1','2','3']: distance.append([abs(waiterNode[0] - (coordinate[0] - 1)) + abs(waiterNode[1] - (coordinate[1] + 1)), [coordinate[0] - 1, coordinate[1] + 1]])
tasksQueue.append([i[0], i[1], subtable])
else: else:
tasksQueue.append([i[0], i[1], tree[winnerNode][i[0]]['distance'][str(i[1])]]) distance.append([abs(waiterNode[0] - 0) + abs(waiterNode[1] - 12), [0, 12]])
elif winnerNode is "distance": distance.append([abs(waiterNode[0] - 1) + abs(waiterNode[1] - 12), [1, 12]])
distance.append([abs(waiterNode[0] - 2) + abs(waiterNode[1] - 12), [2, 12]])
subtable = tree[winnerNode][i[1]] distance.append([abs(waiterNode[0] - 3) + abs(waiterNode[1] - 12), [3, 12]])
if subtable in ['0','1','2','3']: distance.append([abs(waiterNode[0] - 4) + abs(waiterNode[1] - 12), [4, 12]])
tasksQueue.append([i[0], i[1], subtable]) distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 13), [5, 13]])
else: distance.append([abs(waiterNode[0] - 5) + abs(waiterNode[1] - 14), [5, 14]])
tasksQueue.append([i[0], i[1], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
distance.sort(key=lambda x: x[0])
tasksList.append([name, distance[0][0], distance[0][1]])
tasksQueue.sort(key=lambda x: x[2])
print(tasksQueue)
#Kolejkowanie zadan
def print(self): def Queue(self, tasksList):
df = self.BuildDf() df = self.BuildDf()
#a_entropy = {k:self.FindAttributesEntropy(df,k) for k in df.keys()[:-1]} tree = self.BuildTree(df)
#print(a_entropy) winnerNode = self.FindWinner(df)
#print('\n Info Gain: ', self.FindWinner(df))
print(tasksList) for i in tasksList:
self.Queue(tasksList) if winnerNode is "actionName":
subtable = tree[winnerNode][i[0]]
if subtable in ['0','1','2','3']:
tasksQueue.append([i[0], i[1], i[2], subtable])
else:
tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[0]]['distance'][str(i[1])]])
elif winnerNode is "distance":
subtable = tree[winnerNode][i[1]]
if subtable in ['0','1','2','3']:
tasksQueue.append([i[0], i[1], i[2], subtable])
else:
tasksQueue.append([i[0], i[1], i[2], tree[winnerNode][i[1]]['actionName'][str(i[0])]])
tasksQueue.sort(key=lambda x: x[3])
return tasksQueue
def ReturnQueueList(self):
if tasksQueue == []:
queue = self.Queue(tasksList)
else:
queue = tasksQueue
while queue[0][3] == '0':
queue.pop(0)
print(queue)
return queue
def print(self):
print(tasksList)