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PatternFinder.py
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PatternFinder.py
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#!/home3/lafontai/python/Python-2.7.2/python
# -*- coding: UTF-8 -*-
from __future__ import division
print "Content-Type: text/plain; charset=utf-8"
print
import cgi
import cgitb
cgitb.enable(display=1,logdir="/home3/lafontai/public_html/logs/pythonlog.txt")
import datetime as dt
import numpy as np
import scipy as sp
from numpy.fft import *
import calendar
from scipy import signal
from sklearn.cluster import DBSCAN
import itertools
def transformCalendar(startDate,endDate,fuzzyness,cycle,cycleFreq):
if cycle == 1:
dateList = [startDate + dt.timedelta(days=x*cycleFreq) for x in range(0, 1+np.floor((endDate-startDate).days/cycleFreq).astype('int'))]
elif cycle == 2:
dateList = [dt.datetime(startDate.year+int((startDate.month+x*cycleFreq-1)//12), int((startDate.month+x*cycleFreq-1)%12+1), min(startDate.day,(calendar.monthrange(startDate.year+int((startDate.month+x*cycleFreq-1)//12),int((startDate.month+x*cycleFreq-1)%12+1)))[1])) for x in range(0, 1+np.floor(round((endDate-startDate).days/30)/cycleFreq).astype('int'))]
elif cycle == 3:
dateList = [dt.datetime(startDate.year+int(x*cycleFreq), startDate.month, startDate.day) for x in range(0, 1+np.floor(round((endDate-startDate).days/365)/cycleFreq).astype('int'))]
return dateList
def add2Calendar(startDate,endDate,fuzzyness,cycle,cycleFreq):
if cycle == 1:
dateList = [startDate + dt.timedelta(days=x*cycleFreq) for x in range(0, 2+np.floor((endDate-startDate).days/cycleFreq).astype('int'))]
elif cycle == 2:
dateList = [dt.datetime(startDate.year+int((startDate.month+x*cycleFreq-1)//12), int((startDate.month+x*cycleFreq-1)%12+1), min(startDate.day,calendar.monthrange(startDate.year+int((startDate.month+x*cycleFreq-1)//12),int((startDate.month+x*cycleFreq-1)%12+1))[1] )) for x in range(0, 2+np.floor(round((endDate-startDate).days/30)/cycleFreq).astype('int'))]
elif cycle == 3:
dateList = [dt.datetime(startDate.year+int(x*cycleFreq), startDate.month, startDate.day) for x in range(0, 2+np.floor(round((endDate-startDate).days/365)/cycleFreq).astype('int'))]
return dateList[-1]
def patternScore2(realCalendar,startDate,endDate,fuzzyness,cycle,cycleFreq):
w = 1 # Penalty for missing a prediction
if cycle==1 and cycleFreq-fuzzyness <= 2: #Periods cannot overlap
return 0
noCycles = np.floor(SubDate(startDate,endDate)[cycle]/cycleFreq).astype('int')
hitDays = 1+noCycles*(1+2*fuzzyness) #Total days there might be events including fuzzyness
totalDays = (endDate-startDate).days
precision = cycle*cycle/fuzzyness
patternCalendar = transformCalendar(startDate,endDate,fuzzyness,cycle,cycleFreq)
predictions = [(np.min(np.abs((date-realCalendar)))).days<=fuzzyness for date in patternCalendar]
trueMfalse = (1+w)*np.sum(predictions)-w*len(predictions) -1 # True positives - False positives - 1
# because the first one is meaningless
return (precision*trueMfalse)
def SubDate(date1, date2):
differences = [0 ,
(date2-date1).days,
date2.month - date1.month + (date2.year-date1.year)*12,
date2.year-date1.year]
return differences
def calendar2Signal(calendar):
signals = np.zeros((max(calendar)-min(calendar)).days+1)
for date in calendar:
signals[(date-min(calendar)).days] = 1
return signals
def findPeriod_Fourier(realCalendar):
signal=calendar2Signal(realCalendar)
Transform = np.fft.rfft(signal)
n = len(signal)
peakind = sp.signal.find_peaks_cwt(np.square(np.real(Transform)), np.arange(1,10))
Periods = np.unique(np.round((1/np.fft.rfftfreq(n,1))[peakind]))
#print Periods
#plt.plot(1/np.fft.rfftfreq(n,1), np.square(np.real(Transform)))
return Periods
#return plotScatter(1/np.fft.rfftfreq(n,1), np.square(np.real(Transform)))
def findPeriod_Differences(realCalendar):
DateCombinations = list(itertools.combinations(realCalendar,2))
return np.unique(np.abs([(a-b).days for a,b in DateCombinations]))
def monthDivider(curDate, iniDate, period):
MonthDiff = curDate.month - iniDate.month + (curDate.year-iniDate.year)*12
monthMod = MonthDiff%period
monthPer = np.floor(MonthDiff/period)
Mod = min(curDate.day, 28) + 28*monthMod #We may want to deal with February better
return [Mod/(28*period*2), monthPer/12]
def yearDivider(curDate, iniDate, period):
yearDiff = curDate.year-iniDate.year
yearMod = yearDiff%period
yearPer = np.floor(yearDiff/period)
Mod = curDate.timetuple().tm_yday + 365*yearMod
return [Mod/(365*period*2), yearPer/12]
def findPattern(realCalendar):
# Function to step through time making predictions but blind to the future
# Algorithm will always make a prediction based on a pattern score
# Function will return all predictions made, the score that made it and if it was correct
endDate = realCalendar[-1]
BestScore = 0
BestSet = (None,None,None,None,None,None)
if len(realCalendar)>10:
Periods = findPeriod_Fourier(realCalendar)
else:
Periods = findPeriod_Differences(realCalendar)
#Always try a yearly cycle, fourier isn't good at checking this with the given parameters
#changing parameters will just slow it down so it's not worth it (from observation)
CycleFreq = 1
X = np.array([yearDivider(date, realCalendar[0], CycleFreq) for date in realCalendar])
xShift = np.zeros(X.shape)
xShift[:,0] = .5
X = np.vstack((X,X+xShift))
#X = StandardScaler().fit_transform(X) # I'm normalizing in yearDivider
DB = DBSCAN(eps=.4, min_samples=1).fit(X)
#return X,CycleFreq,DB
mainLabel = (sp.stats.mode(DB.labels_))[0]
realCalendar2 = np.hstack((realCalendar, realCalendar))
startDate = np.min(realCalendar2[DB.labels_==mainLabel])
# Find fuzziness. A perfect pattern is currently not allowed/ min(fuzziness) = 1
mods = X[DB.labels_==mainLabel,0]
cyclePos = X[DB.labels_==mainLabel,1]
meanMod = np.mean(mods)
modDiff = np.abs(mods-meanMod)
trimMods = []
#Correct for two dates in same cycle
for pos in np.unique(cyclePos):
trimMods.append((mods[cyclePos==pos])[np.argmin(modDiff[cyclePos==pos])])
meanMod = np.mean(trimMods)
# Start date should be at the mean modulus
startDate = startDate+dt.timedelta(days=365*CycleFreq*2*(meanMod-(yearDivider(startDate, realCalendar[0], CycleFreq))[0]))
fuzziness = max(np.ceil((np.max(trimMods) - np.min(trimMods))*365*CycleFreq*2/2),1)
if fuzziness<180: #Limit on fuzziness is half a year
Score = patternScore2(realCalendar,startDate, endDate,fuzziness,3,max(CycleFreq,1))
if Score>BestScore:
BestScore = Score
BestSet = (BestScore,startDate, endDate,fuzziness,3,CycleFreq)
if np.any(Periods):
for Period in Periods:
if Period>20: # If relevant cycle through months
CycleFreq = round(Period/30)
X = np.array([monthDivider(date, realCalendar[0], CycleFreq) for date in realCalendar])
xShift = np.zeros(X.shape)
xShift[:,0] = .5
X = np.vstack((X,X+xShift))
#X = StandardScaler().fit_transform(X) # I'm normalizing in monthDivider
DB = DBSCAN(eps=.1, min_samples=1).fit(X)
mainLabel = (sp.stats.mode(DB.labels_))[0]
realCalendar2 = np.hstack((realCalendar, realCalendar))
startDate = np.min(realCalendar2[DB.labels_==mainLabel])
# Find fuzziness. A perfect pattern is currently not allowed/ min(fuzziness) = 1
mods = X[DB.labels_==mainLabel,0]
cyclePos = X[DB.labels_==mainLabel,1]
u, indices,counts = np.unique(cyclePos, return_index=True, return_counts=True)
if np.min(counts)==2:
counts = counts/2
#print 'Labels='
#print DB.labels_
#print 'Indices ='
#print indices
#print 'Counts ='
#print counts
#print 'Period ='
#print Period
meanMod = np.mean(mods[indices[counts==1]])
modDiff = np.abs(mods-meanMod)
trimMods = []
#Correct for two dates in same cycle
for pos in np.unique(cyclePos):
trimMods.append((mods[cyclePos==pos])[np.argmin(modDiff[cyclePos==pos])])
meanMod = (np.max(trimMods)+np.min(trimMods))/2
# Start date should be at the mean modulus
startDate = startDate+dt.timedelta(days=28*CycleFreq*2*(meanMod-(monthDivider(startDate, realCalendar[0], CycleFreq))[0]))
fuzziness = max(np.ceil((np.max(trimMods) - np.min(trimMods))*28*CycleFreq*2/2),1)
if fuzziness<Period:
Score = patternScore2(realCalendar,startDate, endDate,fuzziness,2,max(CycleFreq,1))
if Score>BestScore:
BestScore = Score
BestSet = (BestScore,startDate, endDate,fuzziness,2,CycleFreq)
#Always cycle through days (for a given period)
DateDiffs = [(date-realCalendar[0]).days for date in realCalendar]
Mods = np.array(DateDiffs%Period)
Mods = np.hstack((Mods,Period+Mods))
Floors = np.array(np.floor(DateDiffs/Period))
Floors = np.hstack((Floors,Floors))
X = (np.vstack((Mods,Floors))).T
X[:,0] = X[:,0]/(2*Period)
X[:,1] = X[:,1]/24 # At the day scale every cycle becomes less significant? 24>12
#X = StandardScaler().fit_transform(X)
DB = DBSCAN(eps=.2, min_samples=1).fit(X)
#return X, Period, DB
mainLabel = (sp.stats.mode(DB.labels_))[0]
realCalendar2 = np.hstack((realCalendar, realCalendar))
startDate = np.min(realCalendar2[DB.labels_==mainLabel])
# Find fuzziness. A perfect pattern is currently not allowed/ min(fuzziness) = 1
mods = X[DB.labels_==mainLabel,0]
cyclePos = X[DB.labels_==mainLabel,1]
meanMod = np.mean(mods)
modDiff = np.abs(mods-meanMod)
trimMods = []
#Correct for two dates in same cycle
for pos in np.unique(cyclePos):
trimMods.append((mods[cyclePos==pos])[np.argmin(modDiff[cyclePos==pos])])
meanMod = np.median(trimMods)
# Start date should be at the mean modulus
startDate = startDate+dt.timedelta(days=(Period*2*meanMod-(startDate-realCalendar[0]).days%Period))
fuzziness = max(np.ceil((np.max(trimMods) - np.min(trimMods))*Period*2/2),1)
if fuzziness<Period:
Score = patternScore2(realCalendar,startDate, endDate,fuzziness,1,max(Period,1))
if Score>BestScore:
BestScore = Score
BestSet = (BestScore,startDate, endDate,fuzziness,1,Period)
return BestSet
postData = cgi.FieldStorage()
textCalendar=np.array(postData.getvalue("realCalendar").split(','))
realCalendar=np.array(sorted([dt.datetime.strptime(date,'%Y-%m-%d') for date in textCalendar]))
#json_data=open("/media/Libraries/Documents/Insight/Data/user0.json").read()
#userData = json.loads(json_data)
#flightData = pd.DataFrame([Datum['data'] for Datum in userData])
#flightData['end_dt'] = pd.to_datetime(flightData['end_dt'])
#flightData['start_dt'] = pd.to_datetime(flightData['start_dt'])
#realCalendar = np.array(sorted(flightData[flightData.end_airport=='SNA'].start_dt))
BestSet = findPattern(realCalendar)
if BestSet[0]:
nextDate = add2Calendar(*BestSet[1:])
else:
nextDate = BestSet[0]
print BestSet[0]
print nextDate.strftime('%Y-%m-%d')
print BestSet[1].strftime('%Y-%m-%d')
print BestSet[3]
print BestSet[4]
print BestSet[5]