-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathmain.py
195 lines (174 loc) · 8.35 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
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
import os
import pandas as pd
import matplotlib.pyplot as plt
import openpyxl
import seaborn as sns
import numpy as np
from sklearn.mixture import GaussianMixture
from sklearn.datasets import load_digits
from sklearn.decomposition import FastICA
from sklearn.svm import SVC
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler, RobustScaler
from utils import *
def testClassifier(classifier, X, y, split=0.7, ntrials=100):
means = np.zeros(ntrials, )
for trial in range(ntrials):
xTr, yTr, xTe, yTe, trIdx, teIdx = trteSplitEven(X, y, split, trial)
# Train
scaler = RobustScaler()
scaler.fit(xTr)
xTr = scaler.transform(xTr)
xTe = scaler.transform(xTe)
trained_classifier = classifier.trainClassifier(xTr, yTr)
# Predict
yPr = trained_classifier.classify(xTe)
# Compute classification error
#if trial % 10 == 0:
print("Trial:", trial, "Accuracy", "%.3g" % (100 * np.mean((yPr == yTe).astype(float))))
means[trial] = 100 * np.mean((yPr == yTe).astype(float))
print("Final mean classification accuracy ", "%.3g" % (np.mean(means)), "with standard deviation",
"%.3g" % (np.std(means)))
def trteSplitEven(X, y, pcSplit, seed=None):
labels = np.unique(y)
xTr = np.zeros((0, X.shape[1]))
xTe = np.zeros((0, X.shape[1]))
yTe = np.zeros((0,), dtype=int)
yTr = np.zeros((0,), dtype=int)
trIdx = np.zeros((0,), dtype=int)
teIdx = np.zeros((0,), dtype=int)
np.random.seed(seed)
for label in labels:
classIdx = np.where(y == label)[0]
NPerClass = len(classIdx)
Ntr = int(np.rint(NPerClass * pcSplit))
idx = np.random.permutation(NPerClass)
trClIdx = classIdx[idx[:Ntr]]
teClIdx = classIdx[idx[Ntr:]]
trIdx = np.hstack((trIdx, trClIdx))
teIdx = np.hstack((teIdx, teClIdx))
# Split data
xTr = np.vstack((xTr, X[trClIdx, :]))
yTr = np.hstack((yTr, y[trClIdx]))
xTe = np.vstack((xTe, X[teClIdx, :]))
yTe = np.hstack((yTe, y[teClIdx]))
return xTr, yTr, xTe, yTe, trIdx, teIdx
def label_race(row):
if (df_room3_outliers.index == row['Time stamp']).any():
return 1
elif (absent_on.index == row['Time stamp']).any():
return 2
else:
return 0
def fix_date(column):
column = pd.to_datetime(column)
change = True
i = 0
while i < column.shape[0]:
for c in range(13):
if i + c >= column.shape[0]:
break
if c != 0 and column[i + c].hour >= 12:
change = not change
break
else:
if change:
column[i + c] = (
"""%s%s""" % (column[i + c], " AM"))
else:
column[i + c] = (
"""%s%s""" % (column[i + c], " PM"))
i = i + c
return column
a = os.getcwd()
os.chdir("Elena_202109 - per hour")
df_room3_lightning_toilet = pd.read_excel('Rum&Toilett Y/Belysning Lägenhet C - toilet.xlsx', engine='openpyxl')
df_room3_lightning_toilet['Time stamp'] = pd.to_datetime(df_room3_lightning_toilet['Time stamp'])
grouped_by_presence = df_room3_lightning_toilet.groupby('Lägenhet 3 - Närvaroindikering Badrum')[
['Time stamp', 'Ljusstyrka Badrum']].mean()
df_room3_temperature_toilet = pd.read_excel('Rum&Toilett Y/Temperatur 3 - Toilet.xlsx')
df_room3_temperature_toilet['Time stamp'] = pd.to_datetime(df_room3_temperature_toilet['Time stamp'])
df_room3_toilet_merged = df_room3_lightning_toilet.merge(df_room3_temperature_toilet, on='Time stamp')
df_room3_living_area = pd.read_excel('Rum&Toilett Y/Belysning Lägenhet C room.xlsx')
df_room3_living_area['Time stamp'] = pd.to_datetime(df_room3_living_area['Time stamp'])
df_room3 = df_room3_living_area.merge(df_room3_toilet_merged, on='Time stamp')
absent_bathroom_lighton = df_room3[
(df_room3['Lägenhet 3 - Närvaroindikering Badrum'] == 0.0) & (df_room3['Ljusstyrka Badrum'] > 0.0)]
absent_bathroom_lighoff = df_room3[(df_room3['Lägenhet 3 - Närvaroindikering Badrum'] == 0.0) & (
df_room3['Lägenhet 3 - Närvaroindikering rum'] == 0.0)]
absent_room_lighton = df_room3[
(df_room3['Lägenhet 3 - Närvaroindikering rum'] == 0.0) & (df_room3['Ljusstyrka srum'] > 0.0)]
df_room3 = df_room3.assign(Rounded_TS=df_room3['Time stamp'].dt.round('H'))
df_room3_water2020 = pd.read_csv('Rum&Toilett Y/water labtrino/Lgh 3 2020.csv')
df_room3_water2021 = pd.read_csv('Rum&Toilett Y/water labtrino/Lgh 3 2021.csv')
# Water is in 12 hour format but AM PM is missing
df_room3_water2021['hour'] = fix_date(df_room3_water2021['hour'])
df_room3_water2020['hour'] = fix_date(df_room3_water2020['hour'])
df_room3_water = df_room3_water2020.append(df_room3_water2021)
df_room3 = df_room3.merge(df_room3_water, left_on="Rounded_TS", right_on="hour")
df_room3 = df_room3.drop(columns=['Rounded_TS', 'hour'])
df_room3[' Total Volume m3'].describe()
df_room3[' Total Volume m3'].hist(bins=100, log=True)
df_room3['Hour'] = df_room3['Time stamp'].dt.hour
df_room3['Weekday'] = df_room3['Time stamp'].dt.weekday
df_agg = df_room3.groupby([df_room3['Time stamp'].dt.date]).sum()
df_agg[' Total Volume m3'].describe()
corr = df_room3.drop(columns=['Time stamp', ' Total Volume m3']).corr()
sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns)
#plt.show()
df_room3_relevant = df_room3.drop(columns=['Time stamp', 'Temperatur Badrum', ' Cold Water m3', ' Hot Water m3'])
df_room3_electricity = pd.read_excel('Rum&Toilett Y/Electricity total.xlsx')
df_room3_electricity['Time stamp'] = pd.to_datetime(df_room3_electricity['Time stamp'])
df_room3 = df_room3.merge(df_room3_electricity, on='Time stamp')
df_agg = df_room3.groupby([df_room3['Time stamp'].dt.date]).sum()
df_agg.boxplot('Lägenhet 3 - Energiförbrukning föregående timme')
IN_BATHROOM = df_room3['Lägenhet 3 - Närvaroindikering Badrum'] == 1.0
IN_ROOM = df_room3['Lägenhet 3 - Närvaroindikering rum'] == 1.0
df_agg = df_room3.groupby([df_room3['Time stamp'].dt.date]).sum()
std_water = df_agg[' Total Volume m3'].std()
avg_water = df_agg[' Total Volume m3'].mean()
df_agg['EXCESSIVE_WATER'] = 0
#df_agg[df_agg[' Total Volume m3'] > avg_water + std_water]['EXCESSIVE_WATER'] = 1
X = df_room3.drop(columns=['Time stamp', ' Total Volume m3']).dropna().to_numpy()
gm = GaussianMixture(n_components=2, random_state=0).fit(X)
mean = gm.means_
transformer = FastICA(n_components=2, random_state=0)
X_transformed = transformer.fit_transform(X)
x = X_transformed[:, 0]
y = X_transformed[:, 1]
stds = (x.std(), y.std())
df_room3['Hour'] = df_room3['Time stamp'].dt.hour
df_room3['Weekday'] = df_room3['Time stamp'].dt.weekday
_ = sns.displot(x=X_transformed[:, 0], y=X_transformed[:, 1], kind="kde",
hue=df_room3.drop(columns=['Time stamp', ' Total Volume m3']).dropna()['Weekday'])
#plt.show()
sns.scatterplot(x=X_transformed[:, 0], y=X_transformed[:, 1],
hue=df_room3.drop(columns=['Time stamp', ' Total Volume m3']).dropna()['Weekday'])
#plt.show()
outliers = [True if abs(x[0] - x.mean()) > x.std() and abs(x[1] - y.mean()) > y.std() else False for x in X_transformed]
out = X_transformed[outliers]
df_room3_outliers = df_room3.dropna()[outliers]
sns.scatterplot(x=out[:, 0], y=out[:, 1],
hue=df_room3_outliers.drop(columns=['Time stamp', ' Total Volume m3']).dropna()['Weekday'])
#plt.show()
absent_on = df_room3.loc[
((df_room3['Lägenhet 3 - Närvaroindikering Badrum'] == 0.0) & (df_room3['Ljusstyrka Badrum'] > 0.0)) | (
(df_room3['Lägenhet 3 - Närvaroindikering rum'] == 0.0) & (df_room3['Ljusstyrka srum'] > 0.0))]
labels=[]
df_room3= df_room3.dropna()
for index, row in df_room3.iterrows():
vals= absent_on['Time stamp'].values
if row['Time stamp'] in absent_on['Time stamp'].values:
labels.append(1)
elif row['Time stamp'] in df_room3_outliers['Time stamp'].values:
labels.append(1)
else:
labels.append(0)
df_room3['Label']= labels
#inputs= np.concatenate((X_transformed,np.reshape(np.array(labels),(-1,1))),axis=1)
labels= np.array(labels,dtype=int)
print("Random Forest: ")
testClassifier(RandForestClassifier(), X_transformed, labels, split=0.7)
print("SVM: ")
testClassifier(SVMClassifier(), X_transformed, labels, split=0.7)
#testClassifier(BoostClassifier(RandForestClassifier(), T=10), X_transformed, labels, split=0.7)