Commit 3200adbb authored by Your Name's avatar Your Name
Browse files


parent dd99200a
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from scipy.signal import lfilter
from tslearn.clustering import TimeSeriesKMeans
from tslearn.preprocessing import TimeSeriesScalerMeanVariance
from tslearn.utils import to_time_series_dataset
#define functions
'''brauchbare Werte werden in Integer umgewandelt, Messfehler [Height > 190 & Temperatur > 100] werden bereineigt.
Wobei Messfehler in Bezug auf die Höhe gelöscht werden und Temperatur Messfehler mit NaN Werten überschrieben werden '''
def cleaning_del(df):
new = pd.DataFrame()
#setting device id to join multiple bins later in the process
new['device_id'] = df['deveui']
#seeting time stamp
new['time_stamp'] = pd.to_datetime(df['created_at'], format='%Y-%m-%d')
#deleting rows with values height > 190 -> Measurement errors
new['Height'] = df['Height'].str.replace('cm', '').astype(int)
new = new[new.Height < 190]
#casting temperature to int and replace values > 100 with NaN
new['Temperature'] = df['Temperature'].str.replace('C', '').astype(int)
new.loc[(new.Temperature > 100), 'Temperature'] = np.nan
#casting Tilt to int
new['Tilt'] = df['Tilt'].str.replace('Degree', '').astype(int)
#sorting values acording to time_stamp
new.sort_values(by=['time_stamp'], ascending=True, inplace = True)
return new
''' unterschiedliche Smoothing verfahren werden angewandt - einmal auf die ungruppierten Daten und einmal auf die gruppierten Daten. Als aggregationsfunktion wird .mean() verwendet. Als Level wird auf eintägig guppiert.
NaN Temperaturwerte werden mit dem durchschnittlichen Temperaturwert überschrieben - gleiches gilt für Tilt.
beim mov_avg auf height werden NaN durch Interpolation gefüllt
def smoothing_fillingNaN(df):
#smooth with lfilter
n = 60 # the larger n is, the smoother curve will be
b = [1.0 / n] * n
a = 1
df['lfilter'] = lfilter(b,a, df['Height'])
#moving average smoothing
df['mov_avg'] = df['Height'].rolling(30).mean()
#minimum moving average
df['min_avg'] = df['Height'].rolling(30).min()
#creating new DataFrame on Level Daily with aggregation max()
daily = df.groupby(pd.Grouper(key='time_stamp', axis=0,
freq='1D', sort=True)).mean()
#add device_id
daily['device_id'] = df.iloc[1, 0]
#further smoothing on daily level with rolling mean window 2
daily['mov_avg'] = daily['Height'].rolling(2).mean()
#fill missing values with interpolation
df['inter_pol'] = df['mov_avg'].interpolate(limit_direction = 'both')
daily['inter_pol'] = daily['mov_avg'].interpolate(limit_direction = 'both')
daily.inter_pol.fillna(method = 'backfill', inplace = True)
#fill missing NaN in DeviceID
daily['device_id'].fillna(method = 'bfill', inplace=True)
#fill Temperature NaN with average
daily.Temperature.fillna(value = df.Temperature.mean(), inplace = True)
#fill Tilt NaNs with mean
daily.Tilt.fillna(value = daily.Tilt.mean(), inplace = True)
return df, daily
def kmeans_cluster():
csv_folder = '../data/preprocessed/good_csv/'
csv_files = [csv for csv in os.listdir(csv_folder) if csv.endswith('.csv')]
count = 1
all_series = pd.DataFrame()
#df_list = list()
for file in csv_files:
# import DataFrame
df = pd.read_csv('../data/preprocessed/good_csv/' + file)
df = cleaning_del(df)
df, grouped = smoothing_fillingNaN(df)
#write the interpolated hight into columns
all_series[file] = grouped['inter_pol']
count = count + 1
#transform dataframe to numpy array
time_series = to_time_series_dataset(all_series)
#turn into float
time_series = time_series.astype(float)
#roate array into correct shape
a = np.rot90(time_series, k = 3)
#scale values
train = TimeSeriesScalerMeanVariance().fit_transform(a)
#initialise cluster algorithm
dba_km = TimeSeriesKMeans(n_clusters = 3, metric = "dtw", max_iter = 50, random_state=1)
#predict clusters and save
pred = dba_km.fit_predict(train)
sz = train.shape[1]
#print sample of the different clusters
for yi in range(3):
plt.subplot(3, 3, 4 + yi)
for xx in a[pred == yi]:
plt.plot(xx.ravel(), "k-", alpha=.2)
plt.plot(dba_km.cluster_centers_[yi].ravel(), "r-")
plt.xlim(0, sz)
plt.ylim(-4, 4)
plt.text(0.55, 0.85,'Cluster %d' % (yi + 1),
if yi == 1:
plt.title("DBA $k$-means")
cluster_count = [0, 1, 2]
path_name = '../data/preprocessed/clusters/'
for i in cluster_count:
dirName = path_name + str(i)
#creating folders based on clusters and export files
# Create target Directory if don't exist
if not os.path.exists(dirName):
print("Directory " , dirName , " Created ")
print("Directory " , dirName , " already exists")
print('Cluster ' + str(i))
for series, clust in clusters.items():
if clust == i:
df = pd.read_csv('../data/preprocessed/good_csv/' + series)
df = cleaning_del(df)
df, grouped = smoothing_fillingNaN(df)
df.to_csv(dirName + '/' + series)
grouped.to_csv(dirName + '/g_' + series)
plt.xticks(fontsize=8, rotation=90)
plt.yticks(fontsize=10, fontweight='bold')
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