RandomForest/randomForestClassification.py at master · amandaw95/RandomForest · GitHub

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
from parsl import load, python_app
from parsl.configs.local_threads import config
load(config)

import pandas as pd
import numpy as np
import time


df = pd.read_csv("/home/amanda/Downloads/bill_authentication.csv")

from parsl.config import Config
from parsl.executors.threads import ThreadPoolExecutor

maxThreads = 10
local_threads = Config(
    executors=[
        ThreadPoolExecutor(
            max_threads=maxThreads,
            label='local_threads'
        )
    ]
)


@python_app
def rfClassifier(estimators):

	import pandas as pd
	import numpy as np


	dataset = df

	dataset.head()


	X = dataset.iloc[:, 0:4].values
	y = dataset.iloc[:, 4].values

	from sklearn.model_selection import train_test_split

	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

	# Feature Scaling
	from sklearn.preprocessing import StandardScaler

	sc = StandardScaler()
	X_train = sc.fit_transform(X_train)
	X_test = sc.transform(X_test)

	from sklearn.ensemble import RandomForestClassifier

	classifier = RandomForestClassifier(n_estimators=estimators, random_state=0)
	classifier.fit(X_train, y_train)
	y_pred = classifier.predict(X_test)

	from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

	#print(confusion_matrix(y_test,y_pred))
	#print(classification_report(y_test,y_pred))
	#print(accuracy_score(y_test, y_pred))

	return str(confusion_matrix(y_test,y_pred)) + '\n' +(classification_report(y_test,y_pred)) + '\n' + str(accuracy_score(y_test, y_pred))


#print(rfClassifier().result())