def CreaMatrice():
filename = askopenfilename(title="Ouvrir votre document",
filetypes=[('txt files', '.txt'),
('all files', '.*')])
fichier = open(filename, "r")
content = fichier.read()
fichier = open(filename, "r")
first_ligne = fichier.readline()
L = first_ligne.split()
nbAttributs = len(L)
fichierX = []
ligne = fichier.readline()
compte = 0
while (ligne):
fichierX.append(ligne.split())
compte = compte + 1
ligne = fichier.readline()
attributCible = []
for i in range(len(fichierX)):
attributCible.append(fichierX[i][-1])
fichierX[i].pop()
feature_names = L
X = np.array(fichierX)
y = np.array(attributCible)
clf = Id3Estimator()
clf.fit(X, y, check_input=False)
print(export_text(clf.tree_, feature_names))
save = open("matrice.txt", "w")
save.write(export_text(clf.tree_, feature_names))
save.close()
fichier.close()
def draw_graph(self, x, y):
# Decision Tree Graph
clf = Id3Estimator()
clf.fit(x, y, check_input=True)
#clf.predict_proba(x)
print(export_text(clf.tree_, self.feature_names))
# export tree.dot as pdf file to write Decision Tree as a graph
dot_data = StringIO()
#tree.export_graphviz(clf, out_file = dot_data)
export_graphviz(clf.tree_, 'SVC_Tree.dot', self.feature_names)
graph = pydot.graph_from_dot_file('SVC_Tree.dot')
graph[0].write_pdf("SVC_Tree.pdf")
clf = DecisionTreeClassifier()
clf = clf.fit(x,y)
clf.predict(x, check_input=True)
clf.predict_proba(x)
# version v1 pdf output
dot_data = tree.export_graphviz(clf, out_file='SVC_Tree_v1.dot')
graph = pydot.graph_from_dot_file('SVC_Tree_v1.dot')
graph[0].write_pdf("SVC_Tree_v1.pdf")
# version v2 pdf output
dot_data = tree.export_graphviz(clf, out_file="SVC_Tree_v2", feature_names=self.feature_names, class_names=self.target, filled=True, rounded=True, special_characters=True)
#dot_data = tree.export_graphviz(clf, out_file="Decision-Tree-Regression-v2", feature_names=feature_names, class_names=target.name, filled=True, rounded=True, special_characters=True)
graph = graphviz.Source(dot_data)
# print graph this is done correct as lang as out_file=None
graph
# save graph version 2 as pdf data file
graph = pydot.graph_from_dot_file('SVC_Tree_v2')
graph[0].write_pdf("SVC_Tree_v2.pdf")
return True
def Tree():
names = ["tarcza", "czy lata", "wiek", "zbroja", "hp", "level", "potwor"]
count = len(open('przypadki.txt', 'rU').readlines())
x = []
for i in range(1, count):
line = linecache.getline('przypadki.txt', i).split(" ")
line[6] = str(line[6][0])
x.append(line)
X = np.asarray(x)
print(X)
y = np.array([int(i) for i in linecache.getline('wyniki.txt', 1)[:-2]])
yd = [int(i) for i in linecache.getline('wyniki.txt', 1)[:-2]]
d = []
d.append(names)
d[0].append("wynik")
for i in range(0, len(yd)):
d.append(x[i] + [yd[i]])
print(d)
clf = Id3Estimator()
clf.fit(X, y, check_input=True)
#d = np.array([['0', '0', '39', '1', '9', '0','1', 't']])
#print(d)
#c = clf.predict(d)
#print(c)
export_graphviz(clf.tree_, "out.dot", names)
print(export_text(clf.tree_, names))
return clf
def main():
feature_names = ["Opponent", "Home/Away", "AP Top 25", "Media"]
X = np.array([['Texas', 'Home', 'Out', '1-NBC'],
['Virginia', 'Away', 'Out', '4-ABC'],
['GeorgiaTech', 'Home', 'In', '1-NBC'],
['UMass', 'Home', 'Out', '1-NBC'],
['Clemson', 'Away', 'In', '4-ABC'],
['Navy', 'Home', 'Out', '1-NBC'],
['USC', 'Home', 'In', '1-NBC'],
['Temple', 'Away', 'Out', '4-ABC'],
['PITT', 'Away', 'Out', '4-ABC'],
['WakeForest', 'Home', 'Out', '1-NBC'],
['BostonCollege', 'Away', 'Out', '1-NBC'],
['Stanford', 'Away', 'In', '3-FOX'],
['Texas', 'Away', 'Out', '4-ABC'],
['Nevada', 'Home', 'Out', '1-NBC'],
['MichiganState', 'Home', 'Out', '1-NBC'],
['Duke', 'Home', 'Out', '1-NBC'],
['Syracuse', 'Home', 'Out', '2-ESPN'],
['NorthCarolinaState', 'Away', 'Out', '4-ABC'],
['Stanford', 'Home', 'In', '1-NBC'],
['MiamiFlorida', 'Home', 'Out', '1-NBC'],
['Navy', 'Home', 'Out', '5-CBS'],
['Army', 'Home', 'Out', '1-NBC'],
['VirginiaTech', 'Home', 'In', '1-NBC'],
['USC', 'Away', 'In', '4-ABC']])
y = np.array([
"Win", "Win", "Win", "Win", "Lose", "Win", "Win", "Win", "Win", "Win",
"Win", "Lose", "Lose", "Win", "Lose", "Lose", "Win", "Lose", "Lose",
"Win", "Lose", "Win", "Lose", "Lose"
])
clf = Id3Estimator()
clf.fit(X, y, check_input=True)
print("Training:")
print(export_text(clf.tree_, feature_names))
testing = [
["Temple", "Home", "Out", "1-NBC"],
# ["Georgia", "Home", "In", "1-NBC"],
["BostonCollege", "Away", "Out", "2-ESPN"],
["MichiganState", "Away", "Out", "3-FOX"],
# ["MiamiOhio", "Home", "Out", "1-NBC"],
# ["NorthCarolina", "Away", "Out", "4-ABC"],
["USC", "Home", "In", "1-NBC"],
["NorthCarolinaState", "Home", "Out", "1-NBC"],
["WakeForest", "Home", "Out", "1-NBC"],
["MiamiFlorida", "Away", "In", "4-ABC"],
["Navy", "Home", "Out", "1-NBC"],
["Stanford", "Away", "In", "4-ABC"]
]
print("\n\nTesting:")
print(clf.predict(testing))
def main():
feature_names = ["home/away", "top25", "media"]
X = np.array([['home', 'out', '1-nbc'], ['home', 'in', '1-nbc'],
['away', 'out', '2-espn'], ['away', 'out', '3-fox'],
['home', 'out', '1-nbc'], ['away', 'out', '4-abc']])
y = np.array(["win", "lose", "win", "win", "win", "win"])
clf = Id3Estimator()
clf.fit(X, y, check_input=True)
print(export_text(clf.tree_, feature_names))
testing = [["home", "in", "1-nbc"], ["home", "out", "1-nbc"],
["home", "out", "1-nbc"], ["home", "in", "4-abc"],
["home", "out", "1-nbc"], ["home", "in", "4-abc"]]
print("\n\nTesting:")
print(clf.predict(testing))
def main():
feature_names = ["outlook",
"temperature",
"humidity",
"windy"]
X = np.array([['sunny', 'hot', 'high', 'false'],
['sunny', 'hot', 'high', 'true'],
['overcast', 'hot', 'high', 'false'],
['rainy', 'mild', 'high', 'false'],
['rainy', 'cool', 'normal', 'false'],
['rainy', 'cool', 'normal', 'true'],
['overcast', 'cool', 'normal', 'true'],
['sunny', 'mild', 'high', 'false'],
['sunny', 'cool', 'normal', 'false'],
['rainy', 'mild', 'normal', 'false'],
['sunny', 'mild', 'normal', 'true'],
['overcast', 'mild', 'high', 'true'],
['overcast', 'hot', 'normal', 'false'],
['rainy', 'mild', 'high', 'true']])
y = np.array(["No",
"No",
"Yes",
"Yes",
"Yes",
"No",
"Yes",
"No",
"Yes",
"Yes",
"Yes",
"Yes",
"Yes",
"No"])
clf = Id3Estimator()
clf.fit(X, y, check_input=True)
print("Training:")
print(export_text(clf.tree_, feature_names))
print("Testing: rainy, hot, high, false")
print(clf.predict([["rainy", "hot", "high", "false"]])) #Throws DeprecationWarning, ignore it
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Author: Andrew Floyd
Date: 10/18/2018
Course: CS3001: Intro to Data Science
File Description: File for basketball ID3 tree
"""
from id3 import Id3Estimator, export_text
import numpy as np
feature_names = ["home/away", "top25", "media"]
X = np.array([["home", "out", "1-NBC"], ["home", "in", "1-NBC"],
["away", "out", "2-ESPN"], ["away", "out", "3-FOX"],
["home", "out", "1-NBC"], ["away", "out", "4-ABC"]])
y = np.array(["(win)", "(lose)", "(win)", "(win)", "(win)", "(win)"])
clf = Id3Estimator()
clf.fit(X, y, check_input=True)
print(export_text(clf.tree_, feature_names))
df['windy'] = df.windy.map({'weak': 0, 'strong': 1})
print('\n+++ CSV Data Change +++')
print(df.head())
print('\n+++ Data shape +++')
print(df.shape)
data = df.values
print('\n+++ Data values +++')
print(data)
data_train = data[:, :-1]
print('\n+++ Data train +++')
print(data_train)
data_label = data[:, -1:].flatten()
print('\n+++ Data label +++')
print(data_label)
clf = Id3Estimator()
clf.fit(data_train, data_label, check_input=True)
feature_names = ["outlook", "temperature", "humidity", "windy"]
exported_text = export_text(clf.tree_, feature_names)
print(exported_text)
export_graphviz(clf.tree_, 'out.dot', feature_names)
from id3 import Id3Estimator, export_text
import numpy as np
import preprocess, csv
X = []
Y = []
feature_names = []
with open('aggregated_data.csv', mode='r') as infile:
print('opened el file correcto')
csvfile = csv.reader(infile, delimiter=',')
rows = 0
for row in csvfile:
if rows == 0:
feature_names = [
'CodedMonth', 'District', 'XCoord', 'YCoord', 'Day', 'Time'
]
else:
X.append([row[1], row[3], row[4], row[5], row[6], row[7]])
Y.append(row[2])
rows += 1
X = np.array(X)
Y = np.array(Y)
clf = Id3Estimator()
clf.fit(X, Y, check_input=True)
text = export_text(clf.tree_, feature_names)
with open('tree.txt', mode='w+') as out:
out.write(text)
out.close()
testX = []
testY = []
with open('testing.csv', mode='r') as test: