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Commit be23b7d3 authored by Lani Jung's avatar Lani Jung
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add random forest tree image

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experiment/random forest/RandomForest.ipynb
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%% Cell type:code id: tags:
``` python
from sklearn import tree
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
df = pd.read_csv("../../dataset/DM_data.csv")
df.info()
```
%% Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 25192 entries, 0 to 25191
Data columns (total 40 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 duration 25192 non-null int64
1 protocol_type 25192 non-null int64
2 service 25192 non-null int64
3 flag 25192 non-null int64
4 src_bytes 25192 non-null int64
5 dst_bytes 25192 non-null int64
6 land 25192 non-null int64
7 wrong_fragment 25192 non-null int64
8 hot 25192 non-null int64
9 num_failed_logins 25192 non-null int64
10 logged_in 25192 non-null int64
11 num_compromised 25192 non-null int64
12 root_shell 25192 non-null int64
13 su_attempted 25192 non-null int64
14 num_root 25192 non-null int64
15 num_file_creations 25192 non-null int64
16 num_shells 25192 non-null int64
17 num_access_files 25192 non-null int64
18 is_guest_login 25192 non-null int64
19 count 25192 non-null int64
20 srv_count 25192 non-null int64
21 serror_rate 25192 non-null float64
22 srv_serror_rate 25192 non-null float64
23 rerror_rate 25192 non-null float64
24 srv_rerror_rate 25192 non-null float64
25 same_srv_rate 25192 non-null float64
26 diff_srv_rate 25192 non-null float64
27 srv_diff_host_rate 25192 non-null float64
28 dst_host_count 25192 non-null int64
29 dst_host_srv_count 25192 non-null int64
30 dst_host_same_srv_rate 25192 non-null float64
31 dst_host_diff_srv_rate 25192 non-null float64
32 dst_host_same_src_port_rate 25192 non-null float64
33 dst_host_srv_diff_host_rate 25192 non-null float64
34 dst_host_serror_rate 25192 non-null float64
35 dst_host_srv_serror_rate 25192 non-null float64
36 dst_host_rerror_rate 25192 non-null float64
37 dst_host_srv_rerror_rate 25192 non-null float64
38 class 25192 non-null int64
39 index_num 25192 non-null int64
dtypes: float64(15), int64(25)
memory usage: 7.7 MB
%% Cell type:code id: tags:
``` python
X, y = df.drop(columns=["class", "index_num"]), df['class']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=True, random_state=42)
X_train.reset_index(drop=True, inplace=True)
X_test.reset_index(drop=True, inplace=True)
y_train.reset_index(drop=True, inplace=True)
X_test.reset_index(drop=True, inplace=True)
X_train.info()
```
%% Output
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20153 entries, 0 to 20152
Data columns (total 38 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 duration 20153 non-null int64
1 protocol_type 20153 non-null int64
2 service 20153 non-null int64
3 flag 20153 non-null int64
4 src_bytes 20153 non-null int64
5 dst_bytes 20153 non-null int64
6 land 20153 non-null int64
7 wrong_fragment 20153 non-null int64
8 hot 20153 non-null int64
9 num_failed_logins 20153 non-null int64
10 logged_in 20153 non-null int64
11 num_compromised 20153 non-null int64
12 root_shell 20153 non-null int64
13 su_attempted 20153 non-null int64
14 num_root 20153 non-null int64
15 num_file_creations 20153 non-null int64
16 num_shells 20153 non-null int64
17 num_access_files 20153 non-null int64
18 is_guest_login 20153 non-null int64
19 count 20153 non-null int64
20 srv_count 20153 non-null int64
21 serror_rate 20153 non-null float64
22 srv_serror_rate 20153 non-null float64
23 rerror_rate 20153 non-null float64
24 srv_rerror_rate 20153 non-null float64
25 same_srv_rate 20153 non-null float64
26 diff_srv_rate 20153 non-null float64
27 srv_diff_host_rate 20153 non-null float64
28 dst_host_count 20153 non-null int64
29 dst_host_srv_count 20153 non-null int64
30 dst_host_same_srv_rate 20153 non-null float64
31 dst_host_diff_srv_rate 20153 non-null float64
32 dst_host_same_src_port_rate 20153 non-null float64
33 dst_host_srv_diff_host_rate 20153 non-null float64
34 dst_host_serror_rate 20153 non-null float64
35 dst_host_srv_serror_rate 20153 non-null float64
36 dst_host_rerror_rate 20153 non-null float64
37 dst_host_srv_rerror_rate 20153 non-null float64
dtypes: float64(15), int64(23)
memory usage: 5.8 MB
%% Cell type:code id: tags:
``` python
print(f"Shape of X_train: {X_train.shape}\nShape of X_test: {X_test.shape}")
print(f"Shape of y_train: {y_train.shape}\nShape of y_test: {y_test.shape}")
```
%% Output
Shape of X_train: (20153, 38)
Shape of X_test: (5039, 38)
Shape of y_train: (20153,)
Shape of y_test: (5039,)
%% Cell type:code id: tags:
``` python
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=20, max_depth=5,random_state=0)
clf.fit(X_train, y_train)
```
%% Output
RandomForestClassifier(max_depth=5, n_estimators=20, random_state=0)
%% Cell type:code id: tags:
``` python
from IPython import display
import graphviz
import os
os.environ["PATH"] += os.pathsep + 'C:\\Users\\s\\anaconda3\\pkgs\\graphviz-2.38-hfd603c8_2\\Library\\bin\\graphviz'
# 시각화
dot_data1 = tree.export_graphviz(clf.estimators_[5],
out_file = None,
feature_names = X_train.columns,
class_names = ["0", "1"],
filled = True,
rounded = True,
special_characters = True)
graph = graphviz.Source(dot_data1)
graph
```
%% Cell type:code id: tags:
``` python
from sklearn.metrics import precision_recall_fscore_support
clf_y_pred = clf.predict(X_test)
precision_recall_fscore_support(y_test, clf_y_pred, average="binary")
```
%% Output
(0.9974059662775616, 0.9754756871035941, 0.9863189397178281, None)
%% Cell type:code id: tags:
``` python
from sklearn.metrics import accuracy_score
accuracy_score(y_test ,clf_y_pred)
```
%% Output
0.9872990672752531
%% Cell type:code id: tags:
``` python
clf_vector = list()
for m_depth_iter in range(1, 21):
clf_item = RandomForestClassifier(n_estimators=20, max_depth=m_depth_iter, random_state=0, criterion='entropy')
clf_item.fit(X_train, y_train)
clf_vector.append(clf_item)
```
%% Cell type:code id: tags:
``` python
for clf_item in clf_vector:
clf_y_pred = clf_item.predict(X_test)
p, r, f, _ = precision_recall_fscore_support(y_test, clf_y_pred, average="binary")
print(f"depth: {clf_item.max_depth} {p} {r} {f}")
```
%% Output
depth: 1 0.9866186309830159 0.810570824524313 0.8899721448467967
depth: 2 0.9861751152073732 0.904862579281184 0.9437706725468578
depth: 3 0.9902998236331569 0.9496828752642706 0.9695661558385495
depth: 4 0.9969365426695842 0.9632135306553912 0.9797849462365591
depth: 5 0.9978326831382748 0.9733615221987315 0.9854452054794521
depth: 6 0.9991379310344828 0.9801268498942918 0.9895410885805763
depth: 7 0.9978687127024722 0.9898520084566597 0.993844194438548
depth: 8 0.9987261146496815 0.9945031712473573 0.9966101694915255
depth: 9 0.9978768577494692 0.9936575052854123 0.9957627118644068
depth: 10 0.9983050847457627 0.9961945031712474 0.9972486772486773
depth: 11 0.9978840457046128 0.9970401691331924 0.9974619289340102
depth: 12 0.9987293519695044 0.9970401691331924 0.9978840457046128
depth: 13 0.9974630021141649 0.9974630021141649 0.9974630021141649
depth: 14 0.9978867286559594 0.99830866807611 0.9980976537729866
depth: 15 0.9978813559322034 0.9957716701902748 0.996825396825397
depth: 16 0.9974619289340102 0.9970401691331924 0.9972510044406852
depth: 17 0.9970376639864579 0.9961945031712474 0.9966159052453468
depth: 18 0.996196111580727 0.9966173361522199 0.9964066793489749
depth: 19 0.9966159052453468 0.9961945031712474 0.9964051596532035
depth: 20 0.9970376639864579 0.9961945031712474 0.9966159052453468
%% Cell type:code id: tags:
``` python
```
......
experiment/random forest/rnf.png 0 → 100644
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View file @ be23b7d3
experiment/random forest/rnf.png

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