Random-ML-algorithms/Random_Forest_Ass6.py at master · tushar-31093/Random-ML-algorithms · 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
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
import pandas as pd
import time

# Grab the DLA HAR dataset from:
# http://groupware.les.inf.puc-rio.br/har
# http://groupware.les.inf.puc-rio.br/static/har/dataset-har-PUC-Rio-ugulino.zip


#
# TODO: Load up the dataset into dataframe 'X'
#
# .. your code here ..
#X = pd.read_csv('E:\DAT210x-master\DAT210x-master\test_data_rf.csv', sep = ';', decimal = ',')
X = pd.read_csv('http://groupware.les.inf.puc-rio.br/static/har/dataset-har-PUC-Rio-ugulino.zip', sep = ';', decimal = ',')
print (X.head())

#
# TODO: Encode the gender column, 0 as male, 1 as female
#
# .. your code here ..
X.gender = X.gender.map({'Man': 0, 'Woman': 1})
print (X.head(6))

#
# TODO: Clean up any column with commas in it
# so that they're properly represented as decimals instead
#
# .. your code here ..
# Done! Pd.read_csv(decimal = ',') takes care of it.

#
# INFO: Check data types
print (X.dtypes)

#
# TODO: Convert any column that needs to be converted into numeric
# use errors='raise'. This will alert you if something ends up being problematic
#
# .. your code here ..
X.z4 = pd.to_numeric(X.z4, errors = 'coerce')

#
# INFO: If you find any problematic records, drop them before calling the to_numeric methods above...
print (X.isnull().sum())
X.dropna(axis = 0, how = 'any', inplace = True)

#
# TODO: Encode your 'y' value as a dummies version of your dataset's "class" column
#
# .. your code here ..
y = X[['class']]
y = pd.get_dummies(y)

#
# TODO: Get rid of the user and class columns
#
# .. your code here ..
X.drop(labels = ['user', 'class'], axis = 1, inplace = True)
print (X.describe())

#
# INFO: An easy way to show which rows have nans in them
print (X[pd.isnull(X).any(axis=1)])

#
# TODO: Create an RForest classifier 'model' and set n_estimators = 30, the max_depth to 10, and oob_score = True, and random_state = 0
#
# .. your code here ..
from sklearn.ensemble import RandomForestClassifier
forest = RandomForestClassifier(n_estimators = 30, max_depth = 10, oob_score = True, random_state = 0)

#
# TODO: Split your data into test / train sets
# Your test size can be 30% with random_state 7
# Use variable names: X_train, X_test, y_train, y_test
#
# .. your code here ..
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 7)

print ("Fitting...")
s = time.time()
#
# TODO: train your model on your training set
#
# .. your code here ..
model = forest.fit(X_train, y_train)
print ("Fitting completed in: ", time.time() - s)

#
# INFO: Display the OOB Score of your data
score = model.oob_score_
print ("OOB Score: ", round((score*100), 3))

print ("Scoring...")
s = time.time()
#
# TODO: score your model on your test set
#
# .. your code here ..
score = model.score(X_test, y_test)
print ("Score: ", round((score*100), 3))
print ("Scoring completed in: ", time.time() - s)

#
# TODO: Answer the lab questions, then come back to experiment more

# Lab Question:
# Whichever score ended up being the lesser value, either the OOB score or the model's accuracy score, enter that figure below:
# 95.687

#
# TODO: Try playing around with the gender column
# Encode it as Male:1, Female:0
# Try encoding it to pandas dummies
# Also try dropping it. See how it affects the score
# This will be a key on how features affect your overall scoring
# and why it's important to choose good ones.

#
# TODO: After that, try messing with 'y'. Right now its encoded with
# dummies try other encoding methods to experiment with the effect.