def build_all_corpora_multithread(path):
from multiprocessing import Pool
pool = Pool(7)
folders = sorted(get_folders(path))
pool.map(build_corpus_for_folder, folders)
for folder in get_folders(path):
build_corpus_for_folder(folder)
return
def build_all_corpora(path):
for folder in get_folders(path):
build_corpus_for_folder(folder)
return
def saxify_all(path, alphabet=(3, 21)):
for folder in get_folders(path):
saxify_folder(folder, alphabet)
return
data = df['datum'].values.tolist()
ret = []
for i in range(nrows/l):
if len(np.unique(data[i*l:(i+1)*l])) < 5:
continue
entry = {0: label}
for j in range(l):
entry[j+1] = data[i*l+j]
ret.append(entry)
adf = pd.DataFrame(ret)
return adf
from mylib import get_folders
dirs = get_folders('/Users/daoyuan.li/Documents/Smart.Buildings/Dataset/DECC/popular_appliances/within_household/')
for root in dirs:
csvfs = ['%s.csv' % x for x in ids]
train = []
test = []
for i in range(len(csvfs)):
try:
df = create_dataset(join(root, csvfs[i]), i)
nrows = df.shape[0]
train.append(df.iloc[:nrows/2, :])
test.append(df.iloc[nrows/2:, :])
except Exception as e:
pass
# print e
def generate_parameters(folder,
alphabet_range,
reset=False,
pfile='params.txt'):
df = pd.DataFrame.from_csv(join(folder, 'train', 'saxified_10.csv'),
index_col=False)
klasses = sorted(df['label'].unique())
mod = 'a'
if reset:
mod = 'w'
with open(join(root, pfile), mod) as f:
for k in klasses:
for a in range(alphabet_range[0], alphabet_range[1] + 1):
f.write('%s %s %s\n' % (folder[len(
'/Users/daoyuan.li/Documents/Smart.Buildings/Dataset/DECC/popular_appliances/'
):], a, k))
return
# generate_parameters('NewlyAddedDatasets/ElectricDevices/', (5, 10), reset=True, pfile='params.txt')
# generate_parameters('NewlyAddedDatasets/LargeKitchenAppliances', (3, 20), reset=True, pfile='params1.txt')
# generate_parameters('NewlyAddedDatasets/SmallKitchenAppliances', (3, 20), reset=False, pfile='params1.txt')
# generate_parameters('NewlyAddedDatasets/RefrigerationDevices', (3, 20), reset=False, pfile='params1.txt')
# generate_parameters('NewlyAddedDatasets/ECG5000/', (5, 10), reset=True, pfile='params.txt')
# generate_parameters('Pre_Summer_2015_Datasets/OSULeaf/', (5, 10), reset=True, pfile='params_leaf')
# generate_parameters('Pre_Summer_2015_Datasets/SwedishLeaf/', (5, 10), reset=True, pfile='params_leaf')
for folder in get_folders(root):
generate_parameters(folder, (20, 20))
traindf = pd.DataFrame.from_csv(traincsv, index_col=False)
klasses = sorted(traindf['label'].unique())
df = get_dataframe(folder, resultsdir, a, klasses)
df = predict(df)
df.to_csv(clsssifiedf, index=False)
print folder, len(klasses), a, accuracy_score(df['label'].values.tolist(), df['predicted'].values.tolist())
# print classification_report(df['label'].values.tolist(), df['predicted'].values.tolist())
except Exception as e:
print e
pass
print
return
root = '/Users/daoyuan.li/Documents/Smart.Buildings/Dataset/DECC/popular_appliances/combinations/'
for folder in get_folders(root):
try:
process_results(folder, resultsdir='final_results_wl_2_to_20')
except:
print folder
# In[ ]:
from sklearn.metrics import accuracy_score
from sklearn.metrics import classification_report
from os.path import join
import os
import pandas as pd
from mylib import get_folders
def build_all_corpora(path):
for folder in get_folders(path):
build_corpus_for_folder(folder)
return
def saxify_all_multithread(path):
from multiprocessing import Pool
pool = Pool(7)
folders = sorted(get_folders(path))[:3]
pool.map(saxify_folder, folders)
return
def saxify_all(path, alphabet=(20, 21)):
for folder in get_folders(path):
saxify_folder(folder, alphabet)
return
nrows, _ = df.shape
cols = range(l+1)
data = df['datum'].values.tolist()
ret = []
for i in range(nrows/l):
if len(np.unique(data[i*l:(i+1)*l])) < 3:
continue
entry = {0: label}
for j in range(l):
entry[j+1] = data[i*l+j]
ret.append(entry)
adf = pd.DataFrame(ret)
return adf
dirs = get_folders('/Users/daoyuan.li/Documents/Smart.Buildings/Dataset/DECC/popular_appliances/combinations/')
for root in dirs:
csvfs = ["17_18.csv", "17_160.csv", "17_51.csv", "18_160.csv", "18_51.csv", "51_160.csv", "160_161.csv", "17_161.csv", "51_161.csv", "18_183.csv", "6_17.csv", "6_18.csv", "6_51.csv", "6_160.csv", "6_161.csv"]
train = []
test = []
for i in range(len(csvfs)):
try:
df = create_dataset(join(root, csvfs[i]), i)
nrows = df.shape[0]
train.append(df.iloc[:nrows/2, :])
test.append(df.iloc[nrows/2:, :])
except Exception as e:
pass
# print e
