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data_dd2.csv
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data_dd3.csv
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dt.py
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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
class GadId3Classifier:
def fit(self, input, output):
data = input.copy()
data[output.name] = output
self.tree = self.decision_tree(data, data, input.columns, output.name)
def predict(self, input):
# convert input data into a dictionary of samples
samples = input.to_dict(orient='records')
predictions = []
# make a prediction for every sample
for sample in samples:
predictions.append(self.make_prediction(sample, self.tree, 1.0))
return predictions
def entropy(self, attribute_column):
# find unique values and their frequency counts for the given attribute
values, counts = np.unique(attribute_column, return_counts=True)
# calculate entropy for each unique value
entropy_list = []
for i in range(len(values)):
probability = counts[i]/np.sum(counts)
entropy_list.append(-probability*np.log2(probability))
# calculate sum of individual entropy values
total_entropy = np.sum(entropy_list)
return total_entropy
def information_gain(self, data, feature_attribute_name, target_attribute_name):
# find total entropy of given subset
total_entropy = self.entropy(data[target_attribute_name])
# find unique values and their frequency counts for the attribute to be split
values, counts = np.unique(data[feature_attribute_name], return_counts=True)
# calculate weighted entropy of subset
weighted_entropy_list = []
for i in range(len(values)):
subset_probability = counts[i]/np.sum(counts)
subset_entropy = self.entropy(data.where(data[feature_attribute_name]==values[i]).dropna()[target_attribute_name])
weighted_entropy_list.append(subset_probability*subset_entropy)
total_weighted_entropy = np.sum(weighted_entropy_list)
# calculate information gain
information_gain = total_entropy - total_weighted_entropy
return information_gain
def decision_tree(self, data, orginal_data, feature_attribute_names, target_attribute_name, parent_node_class=None):
# base cases:
# if data is pure, return the majority class of subset
unique_classes = np.unique(data[target_attribute_name])
if len(unique_classes) <= 1:
return unique_classes[0]
# if subset is empty, ie. no samples, return majority class of original data
elif len(data) == 0:
majority_class_index = np.argmax(np.unique(original_data[target_attribute_name], return_counts=True)[1])
return np.unique(original_data[target_attribute_name])[majority_class_index]
# if data set contains no features to train with, return parent node class
elif len(feature_attribute_names) == 0:
return parent_node_class
# if none of the above are true, construct a branch:
else:
# determine parent node class of current branch
majority_class_index = np.argmax(np.unique(data[target_attribute_name], return_counts=True)[1])
parent_node_class = unique_classes[majority_class_index]
# determine information gain values for each feature
# choose feature which best splits the data, ie. highest value
ig_values = [self.information_gain(data, feature, target_attribute_name) for feature in feature_attribute_names]
best_feature_index = np.argmax(ig_values)
best_feature = feature_attribute_names[best_feature_index]
# create tree structure, empty at first
tree = {best_feature: {}}
# remove best feature from available features, it will become the parent node
feature_attribute_names = [i for i in feature_attribute_names if i != best_feature]
# create nodes under parent node
parent_attribute_values = np.unique(data[best_feature])
for value in parent_attribute_values:
sub_data = data.where(data[best_feature] == value).dropna()
# call the algorithm recursively
subtree = self.decision_tree(sub_data, orginal_data, feature_attribute_names, target_attribute_name, parent_node_class)
# add subtree to original tree
tree[best_feature][value] = subtree
return tree
def make_prediction(self, sample, tree, default=1):
# map sample data to tree
for attribute in list(sample.keys()):
# check if feature exists in tree
if attribute in list(tree.keys()):
try:
result = tree[attribute][sample[attribute]]
except:
return default
result = tree[attribute][sample[attribute]]
# if more attributes exist within result, recursively find best result
if isinstance(result, dict):
return self.make_prediction(sample, result)
else:
return result
#data_url = "https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data"
#df = pd.read_csv(data_url, header=None)
df = pd.read_csv("data_dd3.csv", header=None)
# rename known columns
columns = ['p_strength','p_agility','p_wisdom','p_health','p_melee_damage','p_ranged_damage','p_magic_damage',
'p_armor_defence','p_armor_magic_protection','e_strength','e_agility','e_wisdom','e_health','e_melee_damage',
'e_ranged_damage','e_magic_damage','e_armor_defence','e_armor_magic_protection','e_attack_type','strategy']
#columns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg',
#'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'disease_present']
df.columns = columns
# convert disease_present feature to binary
# df['disease_present'] = df.disease_present.replace([1,2,3,4], 1)
# drop rows with missing values, missing = ?
df = df.replace("?", np.nan)
df = df.dropna()
# organize data into input and output
#X = df.drop(columns="disease_present")
#y = df["disease_present"]
X = df.drop(columns="strategy")
y = df["strategy"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
# initialize and fit model
model = GadId3Classifier()
model.fit(X_train, y_train)
# return accuracy score
y_pred = model.predict(X_test)
a = accuracy_score(y_test, y_pred)
print(a)
#print(y_pred)
#print(y_test)

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tree_data_gen.py
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import random
from os import urandom
import statistics
import csv
def nominalizeOld(val, max_val):
return_value = "NONE"
if val > 0.8 * max_val:
return_value = "VERY_HIGH"
elif val > 0.6 * max_val:
return_value = "HIGH"
elif val > 0.4 * max_val:
return_value = "MEDIUM"
elif val > 0.2 * max_val:
return_value = "LOW"
elif val > 0:
return_value = "VERY_LOW"
return return_value
def nominalize(val, max_val):
return_value = "NONE"
if val > 0.66 * max_val:
return_value = "HIGH"
elif val > 0.33 * max_val:
return_value = "MEDIUM"
elif val > 0:
return_value = "LOW"
return return_value
class Stats:
def __init__(self):
self.strength = random.randint(1, 10)
self.agility = random.randint(1, 10)
self.wisdom = random.randint(1, 10)
self.health = random.randint(1, 50)
self.melee_wep_damage = random.randint(1, 10)
self.ranged_wep_damage = random.randint(1, 10)
self.magic_wep_damage = random.randint(1, 10)
self.armor_defence = random.randint(0, 5)
self.armor_magic_protection = random.randint(0, 5)
self.damage = 0
def meleeAttack(self, opponent):
attackValue = self.strength + random.randint(1, 6)
defenseValue = opponent.strength + opponent.armor_defence
damage = attackValue - defenseValue
if damage > 0:
opponent.damage += (damage + self.melee_wep_damage)
def rangeAttack(self, opponent):
attackValue = self.agility + random.randint(1, 6)
defenseValue = opponent.agility
damage = attackValue - defenseValue
if (damage > 0) and (damage + self.ranged_wep_damage - opponent.armor_defence > 0):
opponent.damage += (damage + self.ranged_wep_damage - opponent.armor_defence)
def magicAttack(self, opponent):
attackValue = self.wisdom + random.randint(1, 6)
defenseValue = opponent.wisdom
damage = attackValue - defenseValue
if (damage > 0) and (damage + self.magic_wep_damage - opponent.armor_magic_protection > 0):
opponent.damage += (damage + self.magic_wep_damage - opponent.armor_magic_protection)
def reset(self):
self.damage = 0
FIELDNAMES = ["p_strength",
"p_agility",
"p_wisdom",
"p_health",
"p_melee_damage",
"p_ranged_damage",
"p_magic_damage",
"p_armor_defence",
"p_armor_magic_protection",
"e_strength",
"e_agility",
"e_wisdom",
"e_health",
"e_damage",
"e_armor_defence",
"e_armor_magic_protection",
"e_attack_type",
"strategy"]
RESULT_FILE = open('data.csv', 'w', newline='')
FILE_WRITER = csv.writer(RESULT_FILE, dialect='excel', delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
FILE_WRITER.writerow(FIELDNAMES)
SETUP_RESULTS = [[], [], []]
MAX_COMBAT_TIME = 20
def try_combat(my_seed, p, e, player_att_type, enemy_att_type):
random.seed(my_seed)
current_iteration = 0
while True:
if player_att_type == 0:
p.meleeAttack(e)
elif player_att_type == 1:
p.rangeAttack(e)
else:
p.magicAttack(e)
if e.damage >= e.health:
SETUP_RESULTS[player_att_type].append(p.health - p.damage)
break
if enemy_att_type == 0:
e.meleeAttack(p)
elif enemy_att_type == 1:
e.rangeAttack(p)
else:
e.magicAttack(p)
if p.damage >= p.health:
SETUP_RESULTS[player_att_type].append(0)
break
current_iteration += 1
if current_iteration >= MAX_COMBAT_TIME:
SETUP_RESULTS[player_att_type].append(0)
break
p.reset()
e.reset()
for trial in range(10000):
stat_seed = urandom(16)
random.seed(stat_seed)
player = Stats()
enemy = Stats()
enemy_attack_type = random.randint(0, 2) # Enemy weapon choice
for i in range(30):
combat_seed = urandom(16)
try_combat(combat_seed, player, enemy, 0, enemy_attack_type)
try_combat(combat_seed, player, enemy, 1, enemy_attack_type)
try_combat(combat_seed, player, enemy, 2, enemy_attack_type)
for i, series in enumerate(SETUP_RESULTS):
SETUP_RESULTS[i] = statistics.mean(series)
strategy = "PASS"
if any(SETUP_RESULTS):
max_index = SETUP_RESULTS.index(max(SETUP_RESULTS))
if max_index == 0:
strategy = "MELEE"
elif max_index == 1:
strategy = "RANGED"
elif max_index == 2:
strategy = "MAGIC"
enemy_damage = 0
if enemy_attack_type == 0:
enemy_attack_type = "MELEE"
enemy_damage = enemy.melee_wep_damage
elif enemy_attack_type == 1:
enemy_attack_type = "RANGED"
enemy_damage = enemy.ranged_wep_damage
elif enemy_attack_type == 2:
enemy_attack_type = "MAGIC"
enemy_damage = enemy.magic_wep_damage
FILE_WRITER.writerow([nominalize(player.strength, 10),
nominalize(player.agility, 10),
nominalize(player.wisdom, 10),
nominalize(player.health, 50),
nominalize(player.melee_wep_damage, 10),
nominalize(player.ranged_wep_damage, 10),
nominalize(player.magic_wep_damage, 10),
nominalize(player.armor_defence, 5),
nominalize(player.armor_magic_protection, 5),
nominalize(enemy.strength, 10),
nominalize(enemy.agility, 10),
nominalize(enemy.wisdom, 10),
nominalize(enemy.health, 50),
nominalize(enemy_damage, 10),
nominalize(enemy.armor_defence, 5),
nominalize(enemy.armor_magic_protection, 5),
enemy_attack_type,
strategy])
SETUP_RESULTS = [[], [], []]
if trial%100 == 0:
print("Trials done: " + str(trial))