222 lines
6.9 KiB
Python
222 lines
6.9 KiB
Python
from itertools import product
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import numpy as np
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import pandas as pd
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import csv
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import id3test
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import pprint
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import sys
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import pandas
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from sklearn import tree
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import pydotplus
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from sklearn.tree import DecisionTreeClassifier
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import matplotlib.pyplot as plt
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import matplotlib.image as pltimg
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field_states = ['toPlow', 'toWater', 'toSeed', 'toFertilize', 'toCut']
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header = ['Equipped', 'Not Equipped']
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hitch = ['Tillage Unit', 'Crop Trailer']
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tillage_unit = ['Seeds', 'Water', 'Fertilizer', 'Nothing']
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engine_working = ['Yes', 'No']
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in_base = ['Yes', 'No']
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fuel = ['Enough', 'Not_Enough']
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# output = list(product(field_states, header, hitch, tillage_unit, engine_working, in_base, fuel))
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output = list(product(field_states, header, hitch, tillage_unit, in_base))
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dict = []
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# for x in range(len(output)):
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# # dict.append({'field': output[x][0], 'header' : output[x][1], 'hitch' : output[x][2],
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# # 'tillage_unit' : output[x][3], 'engine_working' : output[x][4],
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# # 'in_base' : output[x][5], 'fuel': output[x][6]})
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# print(x+1, output[x])
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decisions = []
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curr_decision = "Make Action"
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for x in range(len(output)):
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aField = output[x][0]
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aHeader = output[x][1]
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aHitch = output[x][2]
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aTillage_unit = output[x][3]
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aIn_base = output[x][4]
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while True:
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curr_decision = "Make Action"
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if aField == 'toCut':
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if not aHeader == 'Equipped':
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if aIn_base == 'Yes':
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curr_decision = 'Change Header'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if not aHitch == 'Crop Trailer':
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if aIn_base == 'Yes':
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curr_decision = 'Change Hitch'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if aField == 'toPlow':
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if aHeader == 'Equipped':
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if aIn_base == "Yes":
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curr_decision = 'Change Header'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if not aHitch == "Tillage Unit":
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if aIn_base == "Yes":
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curr_decision = "Change Hitch"
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break
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else:
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curr_decision = "Go To Base"
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break
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if not aTillage_unit == "Nothing":
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if aIn_base == "Yes":
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curr_decision = "Change Load"
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break
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else:
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curr_decision = "Go To Base"
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break
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if aField == 'toSeed':
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if aHeader == 'Equipped':
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if aIn_base == "Yes":
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curr_decision = 'Change Header'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if not aHitch == "Tillage Unit":
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if aIn_base == "Yes":
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curr_decision = "Change Hitch"
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break
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else:
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curr_decision = "Go To Base"
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break
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if not aTillage_unit == "Seeds":
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if aIn_base == "Yes":
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curr_decision = "Change Load"
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break
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else:
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curr_decision = "Go To Base"
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break
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if aField == 'toWater':
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if aHeader == 'Equipped':
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if aIn_base == "Yes":
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curr_decision = 'Change Header'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if not aHitch == "Tillage Unit":
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if aIn_base == "Yes":
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curr_decision = "Change Hitch"
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break
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else:
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curr_decision = "Go To Base"
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break
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if not aTillage_unit == "Water":
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if aIn_base == "Yes":
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curr_decision = "Change Load"
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break
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else:
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curr_decision = "Go To Base"
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break
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if aField == 'toFertilize':
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if aHeader == 'Equipped':
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if aIn_base == "Yes":
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curr_decision = 'Change Header'
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break
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else:
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curr_decision = 'Go To Base'
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break
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if not aHitch == "Tillage Unit":
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if aIn_base == "Yes":
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curr_decision = "Change Hitch"
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break
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else:
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curr_decision = "Go To Base"
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break
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if not aTillage_unit == "Fertilizer":
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if aIn_base == "Yes":
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curr_decision = "Change Load"
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break
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else:
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curr_decision = "Go To Base"
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break
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if aIn_base == 'Yes':
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curr_decision = 'Go To Field'
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break
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dict.append({'Field': aField, 'Header': aHeader, 'Hitch': aHitch, 'Tillage_Unit': aTillage_unit,
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'In_Base': aIn_base, 'Decision': curr_decision})
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print(dict)
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fields = ['Field', 'Header', 'Hitch', 'Tillage_Unit', 'In_Base', 'Decision']
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filename = "treedata\\data2.csv"
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with open(filename, 'w') as csvfile:
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writer = csv.DictWriter(csvfile, fieldnames=fields)
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writer.writeheader()
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writer.writerows(dict)
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df = pandas.read_csv("treedata\\data2.csv")
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print(df)
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# Map text values to number values
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d = {'toPlow': 0, 'toWater': 1, 'toSeed': 2, 'toFertilize': 3, 'toCut': 4}
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df['Field'] = df['Field'].map(d)
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d = {'Not Equipped': 0, 'Equipped': 1}
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df['Header'] = df['Header'].map(d)
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d = {'Tillage Unit': 0, 'Crop Trailer': 1}
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df['Hitch'] = df['Hitch'].map(d)
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d = {'Nothing': 0, 'Seeds': 1, 'Water': 2, 'Fertilizer': 3}
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df['Tillage_Unit'] = df['Tillage_Unit'].map(d)
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d = {'No': 0, 'Yes': 1}
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df['In_Base'] = df['In_Base'].map(d)
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d = {'Make Action': 0, 'Change Header': 1, 'Go To Base': 2, 'Change Hitch': 3, 'Change Load': 4, 'Go To Field': 5}
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df['Decision'] = df['Decision'].map(d)
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# Separate the feature columns from targert columns
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features = ['Field', 'Header', 'Hitch', 'Tillage_Unit', 'In_Base']
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X = df[features]
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y = df['Decision']
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# FIELD 'toPlow' : 0, 'toWater' : 1, 'toSeed' : 2, 'toFertilize' : 3, 'toCut' : 4
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# HEADER 'Not Equipped' : 0, 'Equipped' : 1
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# HITCH 'Tillage Unit' : 0, 'Crop Trailer' : 1
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# TILLAGE 'Nothing' : 0, 'Seeds' : 1, 'Water': 2, 'Fertilizer': 3
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# IN BASE 'No' : 0, 'Yes' : 1
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dtree = DecisionTreeClassifier()
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dtree = dtree.fit(X, y)
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data = tree.export_graphviz(dtree, out_file=None, feature_names=features)
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graph = pydotplus.graph_from_dot_data(data)
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graph.write_png('mydecisiontree.png')
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img = pltimg.imread('mydecisiontree.png')
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imgplot = plt.imshow(img)
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plt.show()
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print(dtree.predict([[0, 1, 1, 3, 0]]))
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