-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
150 lines (127 loc) · 5.74 KB
/
main.py
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
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import math
from sklearn.ensemble import RandomForestClassifier
class passanger:
def __init__(self, id, is_survived, pclass, sex, age, embarked):
self.id = id
self.is_survived = is_survived
self.pclass = pclass
self.sex = sex
self.age = age
self.embarked = embarked
class Data_set:
def __init__(self, id, is_survived, pclass, sex, age, embarked):
self.data_dict = {}
self.id_list = id
self.survived_list = is_survived
self.pclass_list = pclass
self.sex_list = sex
self.age_list = age
self.embarked_list = embarked
def solve(train, test):
y = train["Survived"]
features = ["Pclass", "Age", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train[features])
X_test = pd.get_dummies(test[features])
model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
model.fit(X, y)
predictions = model.predict(X_test)
output = pd.DataFrame({'PassengerId': test.PassengerId, 'Survived': predictions})
output.to_csv('./Data/model_+_age.csv', index=False)
def draw_2D_graphics_of_one_param(title: str, ox: list, oy: list, x_label: str, y_label: str, label_description: str,
output_file: str):
plt.figure()
plt.grid()
plt.title(title)
# plt.plot(survived_list, age_list, label=label_description)
plt.scatter(ox, oy, label=label_description)
plt.xlabel(x_label)
plt.ylabel(y_label)
plt.legend(loc='best')
# plt.show()
plt.savefig(output_file)
plt.close()
def check_results():
kagle = pd.read_csv('./Data/gender_submission.csv')
model = pd.read_csv('./Data/model_without_age.csv')
model_with_age = pd.read_csv('./Data/model_+_age.csv')
id_list = kagle["PassengerId"].values.tolist()
survived_list_kagle = kagle["Survived"].values.tolist()
survived_list_m = model["Survived"].values.tolist()
survived_list_ma = model_with_age["Survived"].values.tolist()
couter_1 = 0
couter_2 = 0
couter_3 = 0
couter_4 = 0
for i in range(len(id_list)):
if survived_list_kagle[i] == survived_list_m[i] == survived_list_ma[i]:
# print(f"{id_list[i]} correct modelling")
couter_1 += 1
elif survived_list_kagle[i] == survived_list_m[i]:
# print(f"{id_list[i]} good result for modeling without age")
couter_2 += 1
elif survived_list_kagle[i] == survived_list_ma[i]:
# print(f"{id_list[i]} good result for modeling with age")
couter_3 += 1
elif survived_list_kagle[i] != survived_list_m[i] and survived_list_kagle[i] != survived_list_ma[i]:
# print(f"{id_list[i]} bad model result")
couter_4 += 1
print(f"Counters : {couter_1}-{couter_2}-{couter_3}-{couter_4}")
print(f"Both models gave the same correct result {couter_1} times")
print(f"Model without age gave the correct result when model with age gave wrong result {couter_2} times")
print(f"Model with age gave the correct result when model without age gave wrong result {couter_3} times")
print(f"Both models gave the same wrong result {couter_4} times")
if __name__ == "__main__":
df = pd.read_csv('./Data/gender_submission.csv')
test = pd.read_csv('./Data/test.csv')
train = pd.read_csv('./Data/train.csv')
age_mean = train["Age"].mean()
print("mean value of all ages {",age_mean,"} used to replace nan to this value")
# remove_nans(age_mean)
train_without_nans_in_age = pd.read_csv('./Data/train.csv')
ages_list_mode = train_without_nans_in_age["Age"].values.tolist()
train_without_nans_in_age["Age"] = train_without_nans_in_age["Age"].fillna(29)
test["Age"] = test["Age"].fillna(29)
id_list = train["PassengerId"].values.tolist()
survived_list = train["Survived"].values.tolist()
pclass_list = train["Pclass"].values.tolist()
sex_list = train["Sex"].values.tolist()
age_list = train["Age"].values.tolist()
# sib_list = train["SibSp"].values.tolist()
# parch_list = train["Parch"].values.tolist()
# ticket_list = train["Ticket"].values.tolist()
# fare_list = train["Fare"].values.tolist()
# cabin_list = train["Cabin"].values.tolist()
embarked_list = train["Embarked"].values.tolist()
for i in range(len(age_list)):
if math.isnan(age_list[i]):
# print(i)
age_list[i] = age_mean
for i in range(len(embarked_list)):
if type(embarked_list[i]) is not str:
embarked_list[i] = "S"
# print(id_list)
# print(survived_list)
# print(pclass_list)
# print(sex_list)
# print(age_list)
# # print(sib_list) # hard to find correlation
# # print(parch_list) # hard to find correlation
# # print(ticket_list) # hard to interpretative
# # print(fare_list) # too many nan values
# # print(cabin_list) # too many nan values
# print(embarked_list)
# init
ds = Data_set(id=id_list, is_survived=survived_list, pclass=pclass_list, sex=sex_list,
age=age_list, embarked=embarked_list)
for i in range(len(id_list)):
human = passanger(id=id_list[i], is_survived=survived_list[i], pclass=pclass_list[i], sex=sex_list[i],
age=age_list[i], embarked=embarked_list[i])
ds.data_dict[id_list[i]] = human
solve(train_without_nans_in_age, test)
check_results()
# draw_2D_graphics_of_one_param('Graphic', survived_list[:100], age_list[:100], "Is survived", 'Age', 'graphic',
# './Data/model')