def getavgmaxmin(lblnum): from averageretail import getcenter delhi = getcenter('DELHI') mumbai = getcenter('MUMBAI') lucknow = getcenter('LUCKNOW') t1, c1 = getavgmaxmin2(delhi, anomaliesdelhi, delhilabels, lblnum) t2, c2 = getavgmaxmin2(mumbai, anomaliesmumbai, mumbailabels, lblnum) t3, c3 = getavgmaxmin2(lucknow, anomalieslucknow, lucknowlabels, lblnum) return (t1 + t2 + t3) / (c1 + c2 + c3)
#anomaliesnew = get_anomalies('data/anomaly/anomalies_extended.csv') anomaliesdelhi = get_anomalies('data/anomaly/delhi_anomalies_new.csv') anomalieslucknow = get_anomalies('data/anomaly/lucknow_anomalies_new.csv') #anomalies = pd.concat([anomalies,anomaliesnew],ignore_index=True) from averagemandi import mandipriceseries from averagemandi import mandiarrivalseries from averageretail import retailpriceseries from average_export import exportseries from rainfallmonthly import rainfallmonthly from fuelprice import fuelpricemumbai from cpimonthlyseries import cpimonthlyseries from oilmonthlyseries import oilmonthlyseries from averageretail import getcenter retailpriceseriesdelhi = getcenter('DELHI') retailpriceserieslucknow = getcenter('LUCKNOW') from averagemandi import getmandi mandipriceseriesdelhi = getmandi('Azadpur',True) mandiarrivalseriesdelhi = getmandi('Azadpur',False) mandipriceserieslucknow = getmandi('Devariya',True) mandiarrivalserieslucknow = getmandi('Devariya',False) START = CONSTANTS['STARTDATE'] END = CONSTANTS['ENDDATEOLD'] retailpriceseries = retailpriceseries[START:END] mandipriceseries = mandipriceseries[START:END] mandiarrivalseries = mandiarrivalseries[START:END] retailpriceseriesdelhi = retailpriceseriesdelhi[START:END] retailpriceserieslucknow = retailpriceserieslucknow[START:END]
def whiten_series_list(list): for i in range(0, len(list)): mean = list[i].mean() list[i] -= mean temp = pd.DataFrame() for i in range(0, len(list)): temp[i] = list[i] temp = whiten(temp) newlist = [temp[i] for i in range(0, len(list))] return newlist from averageretail import getcenter retailpriceseriesmumbai = getcenter('MUMBAI') retailpriceseriesdelhi = getcenter('DELHI') retailpriceserieslucknow = getcenter('LUCKNOW') retailpriceseriesbhub = getcenter('BHUBANESHWAR') retailpriceseriespatna = getcenter('PATNA') [retailpriceseriesdelhi, retailpriceserieslucknow, retailpriceseriesmumbai] = whiten_series_list([ retailpriceseriesdelhi, retailpriceserieslucknow, retailpriceseriesmumbai ]) from averagemandi import getmandi mandipriceseriesdelhi = getmandi('Azadpur', True) mandiarrivalseriesdelhi = getmandi('Azadpur', False) mandipriceserieslucknow = getmandi('Bahraich', True) mandiarrivalserieslucknow = getmandi('Bahraich', False)
from sklearn.svm import SVC from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import confusion_matrix from averagemandi import give_avg_series import os cwd = os.getcwd() matplotlib.rcParams.update({'font.size': 26}) # from reading_timeseries import retailP, mandiP, mandiA, retailPM, mandiPM, mandiAM # retailpriceseriesmumbai = retailP[3] # retailpriceseriesdelhi = retailP[1] # retailpriceserieslucknow = retailP[2] # print retailpriceseriesmumbai from averageretail import getcenter retailpriceseriesmumbai = getcenter('MUMBAI') retailpriceseriesdelhi = getcenter('DELHI') retailpriceserieslucknow = getcenter('LUCKNOW') retailpriceseriesbangalore = getcenter('BENGALURU') avg_retailpriceseriesmumbai = give_avg_series(retailpriceseriesmumbai) avg_retailpriceseriesdelhi = give_avg_series(retailpriceseriesdelhi) avg_retailpriceserieslucknow = give_avg_series(retailpriceserieslucknow) avg_retailpriceseriesbangalore = give_avg_series(retailpriceseriesbangalore) from averagemandi import getmandi mandipriceseriesdelhi = getmandi('Azadpur', True) mandiarrivalseriesdelhi = getmandi('Azadpur', False) mandipriceserieslucknow = getmandi('Bahraich', True) mandiarrivalserieslucknow = getmandi('Bahraich', False) mandipriceseriespune = getmandi('Pune', True)