diff --git a/PAMI/extras/syntheticDataGenerator/GeoReferentialSequentialDatabase.py b/PAMI/extras/syntheticDataGenerator/GeoReferentialSequentialDatabase.py new file mode 100644 index 00000000..5f7c3dd1 --- /dev/null +++ b/PAMI/extras/syntheticDataGenerator/GeoReferentialSequentialDatabase.py @@ -0,0 +1,243 @@ +# generateSequentialDatabase is a code used to generate sequential database. +# +# **Importing this algorithm into a python program** +# -------------------------------------------------------- +# from PAMI.extras.generateDatabase import generateSequentialDatabase as db +# obj = db(10,10, 5, 10) +# obj.create() +# obj.save('db.txt') +# print(obj.getTransactions()) to get the transactional database as a pandas dataframe + +# **Running the code from the command line** +# -------------------------------------------------------- +# python generateDatabase.py 10 5 10 db.txt +# cat db.txt +# + + +__copyright__ = """ +Copyright (C) 2024 Rage Uday Kiran + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import math + +import numpy as np +import pandas as pd +import sys + + +class GeoReferentialSequentialDatabase: + """ + :Description Generate a sequential database with the given number of lines, average number of items per line, and total number of items + + :Attributes: + numSeq: int + - number of sequences in database + avgItemsetPerSeq:int + - avarage number of itemset in one sequence + avgItemsPeritemset: int + - average number of items per itemset + numItems: int + - total kind of items + maxItem: int(default:numItems) + - maximum number of items per itemset + maxItemset: int(default:avgItemsetPerSeq * 2) + - maximum number of itemset per sequence + seqSep: str + - Separator for each item set + + :Methods: + create: + Generate the transactional database + save: + Save the sequential database to a file + getTransactions: + Get the sequential database + + + + + """ + + def __init__(self, numSeq, avgItemsetPerSeq, avgItemsPerItemset, numItems,x1=0,y1=0,x2=100,y2=100, maxItem=0, maxItemset=0, + seqSep="-1") -> None: + """ + Initialize the transactional database with the given parameters + + """ + + self.numSeq = numSeq + self.avgItemsetPerSeq = avgItemsetPerSeq + self.avgItemsPerItemset = avgItemsPerItemset + self.numItems = numItems + if maxItem == 0: + self.maxItem = numItems + else: + self.maxItem = maxItem + if maxItemset == 0: + self.maxItemset = avgItemsetPerSeq * 2 + else: + self.maxItemset = maxItemset + self.seqSep = seqSep + self.db = [] + numPoints = (x2 - x1) * (y2 - y1) + if numItems > numPoints: + raise ValueError("Number of points is less than the number of lines * average items per line") + + self.itemPoint = {} + + + for i in (range(1, numItems + 1)): + # self.itemPoint[i] = (np.random.randint(x1, x2), np.random.randint(y1, y2)) + point = self.getPoint(x1, y1, x2, y2) + while point in self.itemPoint: + point = self.getPoint(x1, y1, x2, y2) + self.itemPoint[i] = point + + def tuning(self, array, sumRes) -> list: + """ + Tune the array so that the sum of the values is equal to sumRes + + :param array: list of values + + :type array: list + + :param sumRes: the sum of the values in the array to be tuned + + :type sumRes: int + + :return: list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes + + :rtype: list + """ + + while np.sum(array) != sumRes: + # get index of largest value + randIndex = np.random.randint(0, len(array)) + # if sum is too large, decrease the largest value + if np.sum(array) > sumRes: + array[randIndex] -= 1 + # if sum is too small, increase the smallest value + else: + minIndex = np.argmin(array) + array[randIndex] += 1 + return array + + def generateArray(self, nums, avg, maxItems) -> list: + """ + Generate a random array of length nums whose values average to avg + + :param nums: number of values + + :type nums: list + + :param avg: average value + + :type avg: float + + :param maxItems: maximum value + + :type maxItems: int + + :return: random array + + :rtype: list + """ + + # generate n random values + values = np.random.randint(1, maxItems, nums) + sumRes = nums * avg + + values = self.tuning(values, sumRes) + + # if any value is less than 1, increase it and tune the array again + while np.any(values < 1): + for i in range(nums): + if values[i] < 1: + values[i] += 1 + values = self.tuning(values, sumRes) + + while np.any(values > maxItems): + for i in range(nums): + if values[i] > maxItems: + values[i] -= 1 + values = self.tuning(values, sumRes) + + # if all values are same then randomly increase one value and decrease another + while np.all(values == values[0]): + values[np.random.randint(0, nums)] += 1 + values = self.tuning(values, sumRes) + + return values + + def create(self, item="") -> None: + """ + :param item: list (default:generate random numItems items) + item list to make database + Generate the sequential database + :return: None + """ + if item == "": + item=self.itemPoint + + db = set() + sequences = self.generateArray(self.numSeq, self.avgItemsetPerSeq - 1, self.maxItemset) + + for numItemset in sequences: + seq = [] + values = self.generateArray(numItemset + 1, self.avgItemsPerItemset, self.maxItem) + + for value in values: + line = list(set(np.random.choice(item, value, replace=False))) + seq = seq + line + seq = seq + [self.seqSep] + seq.pop() + + self.db.append(seq) + + def save(self, filename, sep="\t") -> None: + """ + Save the transactional database to a file + + :param filename: name of the file + + :type filename: str + + :return: None + """ + + with open(filename, 'w') as f: + for line in self.db: + f.write(sep.join(map(str, line)) + '\n') + + def getSequence(self) -> pd.DataFrame: + """ + Get the sequential database + + :return: the sequential database + + :rtype: pd.DataFrame + """ + df = pd.DataFrame(self.db) + return df + + +if __name__ == "__main__": + # test the class + db = GeoReferentialSequentialDatabase(10, 5, 5, 10) + db.create() + db.save('db.txt') + print(db.getTransactions()) diff --git a/PAMI/extras/syntheticDataGenerator/GeoreferentialTemporalDatabase.py b/PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabase.py similarity index 86% rename from PAMI/extras/syntheticDataGenerator/GeoreferentialTemporalDatabase.py rename to PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabase.py index 99984ba6..f9306247 100644 --- a/PAMI/extras/syntheticDataGenerator/GeoreferentialTemporalDatabase.py +++ b/PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabase.py @@ -7,6 +7,7 @@ import tqdm import pandas as pd + class GeoReferentialTemporalDatabase: """ This class create synthetic geo-referential temporal database. @@ -57,6 +58,7 @@ def __init__( self.seperator = sep self.occurrenceProbabilityOfSameTimestamp = occurrenceProbabilityOfSameTimestamp self.occurrenceProbabilityToSkipSubsequentTimestamp = occurrenceProbabilityToSkipSubsequentTimestamp + self.current_timestamp=int() self._startTime = float() self._endTime = float() self._memoryUSS = float() @@ -76,7 +78,7 @@ def __init__( def getPoint(self, x1, y1, x2, y2): - return (np.random.randint(x1, x2), np.random.randint(y1, y2)) + return (np.random.randint(x1, x2),np.random.randint(y1, y2)) def performCoinFlip(self, probability: float) -> bool: """ @@ -86,7 +88,7 @@ def performCoinFlip(self, probability: float) -> bool: :return: True if the coin lands heads, False otherwise. """ result = np.random.choice([0, 1], p=[1 - probability, probability]) - return result == 1 + return result def tuning(self, array, sumRes) -> np.ndarray: """ @@ -106,15 +108,14 @@ def tuning(self, array, sumRes) -> np.ndarray: """ while np.sum(array) != sumRes: - # get index of largest value - randIndex = np.random.randint(0, len(array)) # if sum is too large, decrease the largest value if np.sum(array) > sumRes: - array[randIndex] -= 1 + maxIndex = np.argmax(array) + array[maxIndex] -= 1 # if sum is too small, increase the smallest value else: minIndex = np.argmin(array) - array[randIndex] += 1 + array[minIndex] += 1 return array def generateArray(self, nums, avg, maxItems) -> np.ndarray: @@ -139,7 +140,7 @@ def generateArray(self, nums, avg, maxItems) -> np.ndarray: """ # generate n random values - values = np.random.randint(1, maxItems, nums) + values = np.random.randint(1, avg*1.5, nums) sumRes = nums * avg @@ -172,15 +173,15 @@ def create(self) -> None: """ self._startTime = time.time() db = set() - lineSize = [] #may be error. need to check it. - sumRes = self.databaseSize * self.avgItemsPerTransaction # Total number of items + values = self.generateArray(self.databaseSize, self.avgItemsPerTransaction, self.numItems) + for i in range(self.databaseSize): # Determine the timestamp if self.performCoinFlip(self.occurrenceProbabilityOfSameTimestamp): timestamp = self.current_timestamp else: - if self.performCoinFlip(self.occurrenceProbabilityToSkipSubsequentTimestamp): + if self.performCoinFlip(self.occurrenceProbabilityToSkipSubsequentTimestamp)==1: self.current_timestamp += 2 else: self.current_timestamp += 1 @@ -188,23 +189,16 @@ def create(self) -> None: self.db.append([timestamp]) # Start the transaction with the timestamp - lineSize.append([i, 0]) # Initialize lineSize with 0 for each transaction - - # Adjust lineSize to ensure sum of sizes equals sumRes - lineSize = self.tuning(lineSize, sumRes) + # For each transaction, generate items - for i in tqdm.tqdm(range(len(lineSize))): - transaction_index = lineSize[i][0] - num_items = lineSize[i][1] + for i in tqdm.tqdm(range(self.databaseSize)): - if num_items > self.numItems: - raise ValueError( - "Error: Either increase numItems or decrease avgItemsPerTransaction or modify percentage") - items = np.random.choice(range(1, self.numItems + 1), num_items, replace=False) - self.db[transaction_index].extend(items) + items = np.random.choice(range(1, self.numItems + 1), values[i], replace=False) + nline = [self.itemPoint[i] for i in items] + self.db[i].extend(nline) - self._runTime = time.time() - self._startTime + self._endTime = time.time() process = psutil.Process(os.getpid()) self._memoryUSS = process.memory_full_info().uss self._memoryRSS = process.memory_info().rss diff --git a/PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabaseByTriangle.py b/PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabaseByTriangle.py new file mode 100644 index 00000000..caea785c --- /dev/null +++ b/PAMI/extras/syntheticDataGenerator/GeoReferentialTemporalDatabaseByTriangle.py @@ -0,0 +1,245 @@ +import random as _rd +import sys as _sys +import time +import os +import psutil +import numpy as np +import tqdm +import pandas as pd + + +class GeoReferentialTemporalDatabaseByTriangle: + """ + This class create synthetic geo-referential temporal database. + + :Attribute: + + totalTransactions : int + No of transactions + noOfItems : int or float + No of items + avgTransactionLength : str + The length of average transaction + outputFile: str + Name of the output file. + + :Methods: + + GeoReferentialTemporalDatabase(outputFile) + Create geo-referential temporal database and store into outputFile + + **Credits:** + --------------- + The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran. + + """ + + def __init__( + self, + databaseSize: int, + avgItemsPerTransaction: int, + numItems: int, + maxDis:int=1, + sep: str = '\t', + occurrenceProbabilityOfSameTimestamp: float = 0, + occurrenceProbabilityToSkipSubsequentTimestamp: float = 0, + ) -> None: + self.databaseSize = databaseSize + self.avgItemsPerTransaction = avgItemsPerTransaction + self.numItems = numItems + self.__tx = [0] + self.__ty = [0] + self.maxDis = maxDis + self.seperator = sep + self.occurrenceProbabilityOfSameTimestamp = occurrenceProbabilityOfSameTimestamp + self.occurrenceProbabilityToSkipSubsequentTimestamp = occurrenceProbabilityToSkipSubsequentTimestamp + self.current_timestamp=int() + self._startTime = float() + self._endTime = float() + self._memoryUSS = float() + self._memoryRSS = float() + numPoints = numItems * 10 + + self.itemPoint = {} + + self.itemPoint = self.draw(numPoints) + + def performCoinFlip(self, probability: float) -> bool: + """ + Perform a coin flip with the given probability. + + :param probability: Probability of the coin landing heads (i.e., the event occurring). + :return: True if the coin lands heads, False otherwise. + """ + result = np.random.choice([0, 1], p=[1 - probability, probability]) + return result + + def tuning(self, array, sumRes) -> list: + """ + Tune the array so that the sum of the values is equal to sumRes + + :param array: list of values + + :type array: list + + :param sumRes: the sum of the values in the array to be tuned + + :type sumRes: int + + :return: list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes + + :rtype: list + """ + + while np.sum(array) != sumRes: + # if sum is too large, decrease the largest value + if np.sum(array) > sumRes: + maxIndex = np.argmax(array) + array[maxIndex] -= 1 + # if sum is too small, increase the smallest value + else: + minIndex = np.argmin(array) + array[minIndex] += 1 + return array + + def generateArray(self, nums, avg, maxItems) -> list: + """ + Generate a random array of length n whose values average to m + + :param nums: number of values + + :type nums: list + + :param avg: average value + + :type avg: float + + :param maxItems: maximum value + + :type maxItems: int + + :return: random array + + :rtype: list + """ + + # generate n random values + values = np.random.randint(1, avg*1.5, nums) + + sumRes = nums * avg + + self.tuning(values, sumRes) + + # if any value is less than 1, increase it and tune the array again + while np.any(values < 1): + for i in range(nums): + if values[i] < 1: + values[i] += 1 + self.tuning(values, sumRes) + + while np.any(values > maxItems): + for i in range(nums): + if values[i] > maxItems: + values[i] -= 1 + self.tuning(values, sumRes) + + # if all values are same then randomly increase one value and decrease another + while np.all(values == values[0]): + values[np.random.randint(0, nums)] += 1 + self.tuning(values, sumRes) + + return values + + def create(self) -> None: + """ + Generate the Temporal database + :return: None + """ + self._startTime = time.time() + db = set() + + values = self.generateArray(self.databaseSize, self.avgItemsPerTransaction, self.numItems) + + for i in range(self.databaseSize): + # Determine the timestamp + if self.performCoinFlip(self.occurrenceProbabilityOfSameTimestamp): + timestamp = self.current_timestamp + else: + if self.performCoinFlip(self.occurrenceProbabilityToSkipSubsequentTimestamp)==1: + self.current_timestamp += 2 + else: + self.current_timestamp += 1 + timestamp = self.current_timestamp + + self.db.append([timestamp]) # Start the transaction with the timestamp + + + + # For each transaction, generate items + for i in tqdm.tqdm(range(self.databaseSize)): + + items = np.random.choice(range(1, self.numItems + 1), values[i], replace=False) + nline = [self.itemPoint[i] for i in items] + self.db[i].extend(nline) + + self._endTime = time.time() + process = psutil.Process(os.getpid()) + self._memoryUSS = process.memory_full_info().uss + self._memoryRSS = process.memory_info().rss + + def save(self,filename, sep='\t') -> None: + """ + Save the Temporal database to a file + + :param filename: name of the file + + :type filename: str + + :param sep: seperator for the items + + :type sep: str + + :return: None + """ + + with open(filename, 'w') as f: + for line in self.db: + # f.write(','.join(map(str, line)) + '\n') + line = list(map(str, line)) + f.write(sep.join(line) + '\n') + def getTransactions(self) -> pd.DataFrame: + """ + Get the Temporal database + + :return: the Temporal database + + :rtype: pd.DataFrame + """ + df = pd.DataFrame(['\t'.join(map(str, line)) for line in self.db], columns=['Transactions']) + return df + + + def getRuntime(self) -> float: + """ + Get the runtime of the transactional database + + :return: the runtime of the transactional database + + + :rtype: float + """ + return self._endTime - self._startTime + + def getMemoryUSS(self) -> float: + + return self._memoryUSS + + def getMemoryRSS(self) -> float: + + return self._memoryRSS +# if __name__ == "__main__": +# _ap = str() +# _ap = createSyntheticGeoreferentialTemporal(100000, 870, 10) +# _ap.GeoreferentialTemporalDatabase("T10_geo_temp.txt") +# else: +# print("Error! The number of input parameters do not match the total number of parameters provided") diff --git a/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabase.py b/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabase.py index 3d888b17..d0e88d0f 100644 --- a/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabase.py +++ b/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabase.py @@ -121,15 +121,14 @@ def tuning(self, array, sumRes) -> np.ndarray: """ while np.sum(array) != sumRes: - # get index of largest value - randIndex = np.random.randint(0, len(array)) # if sum is too large, decrease the largest value if np.sum(array) > sumRes: - array[randIndex] -= 1 + maxIndex = np.argmax(array) + array[maxIndex] -= 1 # if sum is too small, increase the smallest value else: minIndex = np.argmin(array) - array[randIndex] += 1 + array[minIndex] += 1 return array def generateArray(self, nums, avg, maxItems) -> np.ndarray: @@ -154,7 +153,7 @@ def generateArray(self, nums, avg, maxItems) -> np.ndarray: """ # generate n random values - values = np.random.randint(1, maxItems, nums) + values = np.random.randint(1, avg*1.5, nums) sumRes = nums * avg diff --git a/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabaseByTriangle.py b/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabaseByTriangle.py new file mode 100644 index 00000000..bd0cb1e6 --- /dev/null +++ b/PAMI/extras/syntheticDataGenerator/GeoReferentialTransactionalDatabaseByTriangle.py @@ -0,0 +1,357 @@ +# generateTransactionalDatabase is a code used to convert the database into Temporal database. +# +# **Importing this algorithm into a python program** +# -------------------------------------------------------- +# from PAMI.extras.generateDatabase import generateTransactionalDatabase as db +# obj = db(10, 5, 10) +# obj.create() +# obj.save('db.txt') +# print(obj.getTransactions()) to get the transactional database as a pandas dataframe + +# **Running the code from the command line** +# -------------------------------------------------------- +# python generateDatabase.py 10 5 10 db.txt +# cat db.txt +# + + +__copyright__ = """ +Copyright (C) 2021 Rage Uday Kiran + + This program is free software: you can redistribute it and/or modify + it under the terms of the GNU General Public License as published by + the Free Software Foundation, either version 3 of the License, or + (at your option) any later version. + + This program is distributed in the hope that it will be useful, + but WITHOUT ANY WARRANTY; without even the implied warranty of + MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the + GNU General Public License for more details. + + You should have received a copy of the GNU General Public License + along with this program. If not, see . +""" + +import numpy as np +import pandas as pd +import time +import sys, psutil, os, time, tqdm +from matplotlib import pyplot as plt +import random +class GeoReferentialTransactionalDatabaseByTriangle: + """ + :Description Generate a transactional database with the given number of lines, average number of items per line, and total number of items + + :Attributes: + numLines: int + - number of lines + avgItemsPerTransaction: int + - average number of items per line + numItems: int + - total number of items + + :Methods: + create: + Generate the transactional database + save: + Save the transactional database to a file + getTransactions: + Get the transactional database + + + + + """ + def __tran_1(self, p): + """ + To set a harf point + :param + p:list + point(x,y) + :return + x1 :float + new x-coordinate + y1 :float + new y-coordinate + """ + x = p[0] + y = p[1] + x1 = 0.5 * x + y1 = 0.5 * y + + return x1, y1 + + def __tran_2(self, p): + """ + to get harf point starts from center + :param p:list + point(x,y) + :return: + x1 :float + new x-coordinate + y1 :float + new y-coordinate + """ + x = p[0] + y = p[1] + x1 = 0.5 * x + self.maxDis/4 + y1 = 0.5 * y + self.maxDis/4 + return x1, y1 + + def __tran_3(self, p): + """ + To get a harf point start from top + :param p:list + point(x,y) + :return: + x1 :float + new x-coordinate + y1 :float + new y-coordinate + """ + x = p[0] + y = p[1] + x1 = 0.5 * x + self.maxDis/2 + y1 = 0.5 * y + return x1, y1 + + def __get_index(self): + """ + To get index + :return: int + the number of point + """ + prob = [0.333, 0.333, 0.333] + r = random.random() + c = 0 + sump = [] + for p in prob: + c += p + sump.append(c) + for item, sp in enumerate(sump): + if(r <= sp): + return item + return len(prob) - 1 + + def __tran(self, p): + """ + To set transactioon + :param p: list + (point(x,y)) + :return: + x:float + new x-coordinates + y:float + new y-coordinate + """ + trans = [self.__tran_1, self.__tran_2, self.__tran_3] + tindex = self.__get_index() + t = trans[tindex] + x, y = t(p) + return x, y + + def __draw(self, n): + """ + To set points + :param n: int + the number of points + :return: + """ + x1 = 0 + y1 = 0 + for i in range(n): + x1, y1 = self.__tran((x1, y1)) + self.__tx.append(x1) + self.__ty.append(y1) + return self.__tx, self.__ty + + def draw(self, n=5000): + """ + To grow a graph + :param n:int + the number of point + :return:float + points + """ + x, y = self.__draw(n) + point=[(x[i],y[i])for i in range(n)] + return point + def __init__(self, databaseSize, avgItemsPerTransaction, numItems, maxDis, sep='\t') -> None: + """ + Initialize the transactional database with the given parameters + + Parameters: + databaseSize: int - number of lines + avgItemsPerTransaction: int - average number of items per line + numItems: int - total number of items + """ + + self.databaseSize = databaseSize + self.avgItemsPerTransaction = avgItemsPerTransaction + self.numItems = numItems + self.db = [] + self.__tx = [0] + self.__ty = [0] + self.maxDis=maxDis + self.seperator = sep + + numPoints = numItems*10 + + self.itemPoint = {} + + self.itemPoint= self.draw(numPoints) + self._startTime = float() + self._endTime = float() + self._memoryUSS = float() + self._memoryRSS = float() + def tuning(self, array, sumRes) -> list: + """ + Tune the array so that the sum of the values is equal to sumRes + + :param array: list of values + + :type array: list + + :param sumRes: the sum of the values in the array to be tuned + + :type sumRes: int + + :return: list of values with the tuned values and the sum of the values in the array to be tuned and sumRes is equal to sumRes + + :rtype: list + """ + + while np.sum(array) != sumRes: + # if sum is too large, decrease the largest value + if np.sum(array) > sumRes: + maxIndex = np.argmax(array) + array[maxIndex] -= 1 + # if sum is too small, increase the smallest value + else: + minIndex = np.argmin(array) + array[minIndex] += 1 + return array + + def generateArray(self, nums, avg, maxItems) -> list: + """ + Generate a random array of length n whose values average to m + + :param nums: number of values + + :type nums: list + + :param avg: average value + + :type avg: float + + :param maxItems: maximum value + + :type maxItems: int + + :return: random array + + :rtype: list + """ + + # generate n random values + values = np.random.randint(1, avg*1.5, nums) + + sumRes = nums * avg + + self.tuning(values, sumRes) + + # if any value is less than 1, increase it and tune the array again + while np.any(values < 1): + for i in range(nums): + if values[i] < 1: + values[i] += 1 + self.tuning(values, sumRes) + + while np.any(values > maxItems): + for i in range(nums): + if values[i] > maxItems: + values[i] -= 1 + self.tuning(values, sumRes) + + # if all values are same then randomly increase one value and decrease another + while np.all(values == values[0]): + values[np.random.randint(0, nums)] += 1 + self.tuning(values, sumRes) + + return values + + def create(self) -> None: + """ + Generate the transactional database + :return: None + """ + self._startTime = time.time() + db = set() + + values = self.generateArray(self.databaseSize, self.avgItemsPerTransaction, self.numItems) + + for value in tqdm.tqdm(values): + line = np.random.choice(range(1, self.numItems + 1), value, replace=False) + nline = [self.itemPoint[i] for i in line] + # print(line, nline) + # for i in range(len(line)): + # print(line[i], self.itemPoint[line[i]]) + # line[i] = self.itemPoint[line[i]] + self.db.append(nline) + # self.db.append(line) + self._endTime = time.time() + + def save(self,filename, sep='\t') -> None: + """ + Save the transactional database to a file + + :param filename: name of the file + + :type filename: str + + :param sep: seperator for the items + + :type sep: str + + :return: None + """ + + with open(filename, 'w') as f: + for line in self.db: + # f.write(','.join(map(str, line)) + '\n') + line = list(map(str, line)) + f.write(sep.join(line) + '\n') + + def getTransactions(self) -> pd.DataFrame: + """ + Get the transactional database + + :return: the transactional database + + :rtype: pd.DataFrame + """ + df = pd.DataFrame(['\t'.join(map(str, line)) for line in self.db], columns=['Transactions']) + return df + + def getRuntime(self) -> float: + """ + Get the runtime of the transactional database + + :return: the runtime of the transactional database + + + :rtype: float + """ + return self._endTime - self._startTime + + def getMemoryUSS(self) -> float: + + process = psutil.Process(os.getpid()) + self._memoryUSS = process.memory_full_info().uss + return self._memoryUSS + + def getMemoryRSS(self) -> float: + + process = psutil.Process(os.getpid()) + self._memoryRSS = process.memory_info().rss + return self._memoryRSS \ No newline at end of file diff --git a/PAMI/extras/syntheticDataGenerator/utilityDatabase.py b/PAMI/extras/syntheticDataGenerator/UtilityDatabase.py similarity index 60% rename from PAMI/extras/syntheticDataGenerator/utilityDatabase.py rename to PAMI/extras/syntheticDataGenerator/UtilityDatabase.py index ef88ea64..94888b5b 100644 --- a/PAMI/extras/syntheticDataGenerator/utilityDatabase.py +++ b/PAMI/extras/syntheticDataGenerator/UtilityDatabase.py @@ -1,43 +1,65 @@ import numpy as np import pandas as pd import random +import psutil, os, time -class UtilityDataGenerator: - def __init__(self, databaseSize, numberOfItems, averageLengthOfTransaction, - minimumInternalUtilityValue, maximumInternalUtilityValue, - minimumExternalUtilityValue, maximumExternalUtilityValue): +class UtilityDatabase: + def __init__(self, databaseSize, numItems, avgItemsPerTransaction, + minInternalUtilityValue, maxInternalUtilityValue, + minExternalUtilityValue, maxExternalUtilityValue): self.databaseSize = databaseSize - self.numberOfItems = numberOfItems - self.averageLengthOfTransaction = averageLengthOfTransaction - self.minInternalUtilityValue = minimumInternalUtilityValue - self.maxInternalUtilityValue = maximumInternalUtilityValue - self.minExternalUtilityValue = minimumExternalUtilityValue - self.maxExternalUtilityValue = maximumExternalUtilityValue + self.numItems = numItems + self.avgItemsPerTransaction = avgItemsPerTransaction + self.minInternalUtilityValue = minInternalUtilityValue + self.maxInternalUtilityValue = maxInternalUtilityValue + self.minExternalUtilityValue = minExternalUtilityValue + self.maxExternalUtilityValue = maxExternalUtilityValue self.entries = [] self.ExternalUtilityData = self.GenerateExternalUtilityData() + self._startTime = float() + self._endTime = float() + self._memoryUSS = float() + self._memoryRSS = float() def GenerateExternalUtilityData(self): - items = range(1, self.numberOfItems + 1) + items = range(1, self.numItems + 1) ExternalUtilityData = {f'item{item}': random.randint(100, 900) for item in items} return ExternalUtilityData - def Generate(self): + def create(self): + self._startTime = time.time() for entry_id in range(1, self.databaseSize + 1): - entry_length = np.random.randint(1, self.averageLengthOfTransaction * 2) + entry_length = np.random.randint(1, self.avgItemsPerTransaction * 2) entry = np.random.randint(self.minInternalUtilityValue, self.maxInternalUtilityValue + 1, - size=self.numberOfItems) + size=self.numItems) entry_sum = entry.sum() self.entries.append((entry, entry_sum)) + self._endTime = time.time() - def Save(self, fileName): + def save(self, fileName): with open(fileName, 'w') as file: for idx, (entry, entry_sum) in enumerate(self.entries, start=1): entry_str = '\t'.join(map(str, entry)) file.write(f'{idx}\t{entry_str}\t{entry_sum}\n') + def getMemoryUSS(self) -> float: + + process = psutil.Process(os.getpid()) + self._memoryUSS = process.memory_full_info().uss + return self._memoryUSS + + def getMemoryRSS(self) -> float: + + process = psutil.Process(os.getpid()) + self._memoryRSS = process.memory_info().rss + return self._memoryRSS + + def getRuntime(self) -> float: + return self._endTime - self._startTime + def SaveItemsInternalUtilityValues(self, fileName): - items = random.sample(range(1, self.numberOfItems + 1), self.numberOfItems) + items = random.sample(range(1, self.numItems + 1), self.numItems) internal_utility_data = [np.random.randint(self.minInternalUtilityValue, self.maxInternalUtilityValue + 1) for _ in items] data = {'Item': items, 'Internal Utility Value': internal_utility_data} @@ -45,7 +67,7 @@ def SaveItemsInternalUtilityValues(self, fileName): df.to_csv(fileName, sep='\t', index=False) def Saveitemsexternalutilityvalues(self, fileName): - items = random.sample(range(1, self.numberOfItems + 1), self.numberOfItems) + items = random.sample(range(1, self.numItems + 1), self.numItems) data = {'Item': [f'item{item}' for item in items], 'External Utility Value': list(self.ExternalUtilityData.values())} df = pd.DataFrame(data) @@ -59,7 +81,7 @@ def GetUtilityData(self): return df def GetInternalUtilityData(self): - items = random.sample(range(1, self.numberOfItems + 1), self.numberOfItems) + items = random.sample(range(1, self.numItems + 1), self.numItems) InternalUtilityData = [np.random.randint(self.minInternalUtilityValue, self.maxInternalUtilityValue + 1) for _ in items] data = {'Item': items, 'Internal Utility Value': InternalUtilityData} @@ -67,14 +89,14 @@ def GetInternalUtilityData(self): return df def GetExternalUtilityData(self): - items = random.sample(range(1, self.numberOfItems + 1), self.numberOfItems) + items = random.sample(range(1, self.numItems + 1), self.numItems) data = {'Item': [f'item{item}' for item in items], 'External Utility Value': list(self.ExternalUtilityData.values())} df = pd.DataFrame(data) return df def GenerateAndPrintItemPairs(self): - items = random.sample(range(1, self.numberOfItems + 1), 2) + items = random.sample(range(1, self.numItems + 1), 2) item1_id = f'item{items[0]}' item2_id = f'item{items[1]}' item1_value = self.ExternalUtilityData[item1_id] @@ -87,12 +109,15 @@ def GenerateAndPrintItemPairs(self): if __name__ == "__main__": - data_generator = UtilityDataGenerator(100000, 2000, 10, 1, 100, 1, 10) - data_generator.Generate() - data_generator.Save("utility_data-6.csv") + data_generator = UtilityDatabase(100000, 2000, 10, 1, 100, 1, 10) + data_generator.create() + data_generator.save("utility_data-6.csv") data_generator.SaveItemsInternalUtilityValues("items_internal_utility.csv") data_generator.Saveitemsexternalutilityvalues("items_external_utility.csv") - utility_data = data_generator.GetUtilityData() + utilityDataFrame = data_generator.GetUtilityData() + print('Runtime: ' + str(data_generator.getRuntime())) + print('Memory (RSS): ' + str(data_generator.getMemoryRSS())) + print('Memory (USS): ' + str(data_generator.getMemoryUSS())) InternalUtilityData = data_generator.GetInternalUtilityData() ExternalUtilityData = data_generator.GetExternalUtilityData() diff --git a/PAMI/geoReferencedPeriodicFrequentPattern/basic/GPFPMiner.py b/PAMI/geoReferencedPeriodicFrequentPattern/basic/GPFPMiner.py index 75720efd..4b0d218e 100644 --- a/PAMI/geoReferencedPeriodicFrequentPattern/basic/GPFPMiner.py +++ b/PAMI/geoReferencedPeriodicFrequentPattern/basic/GPFPMiner.py @@ -328,7 +328,7 @@ def _save(self, prefix, suffix, tidSetX): """ - if prefix == None: + if prefix is None: prefix = suffix else: prefix = prefix + suffix @@ -354,7 +354,7 @@ def _Generation(self, prefix, itemSets, tidSets): return for i in range(len(itemSets)): itemX = itemSets[i] - if itemX == None: + if itemX is None: continue tidSetX = tidSets[i] classItemSets = [] diff --git a/PAMI/georeferencedFrequentSequencePattern/basic/GFSP_Miner.py b/PAMI/georeferencedFrequentSequencePattern/basic/GFSP_Miner.py index b8394de0..bd2d09b7 100644 --- a/PAMI/georeferencedFrequentSequencePattern/basic/GFSP_Miner.py +++ b/PAMI/georeferencedFrequentSequencePattern/basic/GFSP_Miner.py @@ -223,7 +223,7 @@ def _creatingItemSets(self): if 'Transactions' in i: temp = self._iFile['Transactions'].tolist() if "tid" in i: - temp2 = self._iFile[''].tolist() + temp2 = self._iFile('').tolist() addList = [] addList.append(temp[0]) for k in range(len(temp) - 1): @@ -403,7 +403,7 @@ def make2LenDatabase(self): keyNumber += 1 for key2 in keyList[keyNumber:]: if key1 != key2: - if (key1 in self._NeighboursMap.keys() and key2 in self._NeighboursMap.keys()): + if key1 in self._NeighboursMap.keys() and key2 in self._NeighboursMap.keys(): if key1 in self._NeighboursMap[key2]: if len(self._Database[key1].keys()) >= len(self._Database[key1].keys()): diff --git a/PAMI/georeferencedFrequentSequencePattern/basic/GFSPminer.py b/PAMI/georeferencedFrequentSequencePattern/basic/GFSPminer.py index 95b186d3..a6502f19 100644 --- a/PAMI/georeferencedFrequentSequencePattern/basic/GFSPminer.py +++ b/PAMI/georeferencedFrequentSequencePattern/basic/GFSPminer.py @@ -428,7 +428,7 @@ def make2LenDatabase(self): keyNumber += 1 for key2 in keyList[keyNumber:]: if key1 != key2: - if (key1 in self._NeighboursMap.keys() and key2 in self._NeighboursMap.keys()): + if key1 in self._NeighboursMap.keys() and key2 in self._NeighboursMap.keys(): if key1 in self._NeighboursMap[key2]: if len(self._Database[key1].keys()) >= len(self._Database[key1].keys()): diff --git a/PAMI/graphTransactionalCoveragePattern/basic/GTCP.py b/PAMI/graphTransactionalCoveragePattern/basic/GTCP.py index 0c765c46..02a4bf53 100644 --- a/PAMI/graphTransactionalCoveragePattern/basic/GTCP.py +++ b/PAMI/graphTransactionalCoveragePattern/basic/GTCP.py @@ -109,7 +109,7 @@ def OverlapRatio(self,pattern): intersection=lastcoverage & lastbutcoverage cs= (lastcoverage | lastbutcoverage).count()/len(self.Sf) - return (intersection.count()/self.Df[lastitem].count(),cs) + return intersection.count()/self.Df[lastitem].count(),cs def GetFIDBasedFlatTransactions(self): @@ -150,16 +150,16 @@ def join(self,l1,l2): newpattern=[] for i in range(len(l1)): for j in range(i+1,len(l2)): - if(l1[i][:-1]==l2[j][:-1]): - if(self.Coverage(l1[i][-1])>=self.Coverage(l2[j][-1])): + if l1[i][:-1]==l2[j][:-1]: + if self.Coverage(l1[i][-1])>=self.Coverage(l2[j][-1]): newpattern= l1[i]+[l2[j][-1]] else: newpattern=l2[j]+[l1[i][-1]] ov,cs=self.OverlapRatio(newpattern) - if(ov<=self.maxOR): - if(cs>=self.minGTPC): + if ov<=self.maxOR: + if cs>=self.minGTPC: self.L.append((newpattern,cs)) else: self.Nol.append(newpattern) @@ -187,7 +187,7 @@ def Cmine(self): self.L=[] self.Nol_1_temp=[] for g in self.Nol_1: - if(self.Coverage(g[0])>=self.minGTPC): + if self.Coverage(g[0])>=self.minGTPC: self.L.append((g,self.Coverage(g[0]))) else: self.Nol_1_temp.append(g) @@ -196,7 +196,7 @@ def Cmine(self): self.Nol=[] - while(len(self.Nol_1)>0): + while len(self.Nol_1)>0: self.Nol=[] # print(len(self.Nol_1)) self.join(self.Nol_1,self.Nol_1) diff --git a/PAMI/highUtilityFrequentPattern/basic/HUFIM.py b/PAMI/highUtilityFrequentPattern/basic/HUFIM.py index f58aa86a..1982a0d5 100644 --- a/PAMI/highUtilityFrequentPattern/basic/HUFIM.py +++ b/PAMI/highUtilityFrequentPattern/basic/HUFIM.py @@ -640,7 +640,7 @@ def _backTrackingHUFIM(self, transactionsOfP: List[_Transaction], itemsToKeep: L else: projectedTransaction = transaction.projectTransaction(positionE) utilityPe += projectedTransaction.prefixUtility - if previousTransaction == []: + if previousTransaction is []: previousTransaction = projectedTransaction elif self._isEqual(projectedTransaction, previousTransaction): if consecutiveMergeCount == 0: @@ -679,7 +679,7 @@ def _backTrackingHUFIM(self, transactionsOfP: List[_Transaction], itemsToKeep: L previousTransaction = projectedTransaction consecutiveMergeCount = 0 transaction.offset = positionE - if previousTransaction != []: + if previousTransaction is not []: transactionsPe.append(previousTransaction) supportPe += previousTransaction.getSupport() # print("support is", supportPe) diff --git a/PAMI/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.py b/PAMI/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.py index c9e3cc3a..d7da9a47 100644 --- a/PAMI/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.py +++ b/PAMI/highUtilityGeoreferencedFrequentPattern/basic/SHUFIM.py @@ -298,7 +298,7 @@ def createTransaction(self, items, utilities, utilitySum, pmustring): utilities = [] pmus = [] for idx, item in enumerate(itemsString): - if (self.strToInt).get(item) is None: + if self.strToInt.get(item) is None: self.strToInt[item] = self.cnt self.intToStr[self.cnt] = item self.cnt += 1 @@ -307,9 +307,9 @@ def createTransaction(self, items, utilities, utilitySum, pmustring): self.maxItem = itemInt items.append(itemInt) utilities.append(int(utilityString[idx])) - if pmuString != None: + if pmuString is not None: pmus.append(int(pmuString[idx])) - if pmuString == None: + if pmuString is None: pmus = None return _Transaction(items, utilities, transactionUtility, pmus) @@ -643,7 +643,7 @@ def _backtrackingEFIM(self, transactionsOfP, itemsToKeep, itemsToExplore, prefix else: projectedTransaction = transaction.projectTransaction(positionE) utilityPe += projectedTransaction.prefixUtility - if previousTransaction == []: + if previousTransaction is []: previousTransaction = projectedTransaction elif self._isEqual(projectedTransaction, previousTransaction): if consecutiveMergeCount == 0: @@ -682,7 +682,7 @@ def _backtrackingEFIM(self, transactionsOfP, itemsToKeep, itemsToExplore, prefix previousTransaction = projectedTransaction consecutiveMergeCount = 0 transaction.offset = positionE - if previousTransaction != []: + if previousTransaction is not []: transactionsPe.append(previousTransaction) supportPe += previousTransaction.getSupport() self._temp[prefixLength] = self._newNamesToOldNames[e] diff --git a/PAMI/highUtilityPattern/basic/HMiner.py b/PAMI/highUtilityPattern/basic/HMiner.py index 1ea84c1a..8cd44018 100644 --- a/PAMI/highUtilityPattern/basic/HMiner.py +++ b/PAMI/highUtilityPattern/basic/HMiner.py @@ -370,7 +370,7 @@ def mine(self): for i in range(0, len(items_str)): item = items_str[i] twu = self._mapOfTWU.get(item) - if twu == None: + if twu is None: twu = transUtility else: twu += transUtility @@ -431,7 +431,7 @@ def mine(self): for i in range(len(revisedTrans) - 1, -1, -1): pair = revisedTrans[i] mapFMAPItem = self._mapFMAP.get(pair.item) - if mapFMAPItem == None: + if mapFMAPItem is None: mapFMAPItem = {} self._mapFMAP[pair.item] = mapFMAPItem for j in range(i + 1, len(revisedTrans)): @@ -503,9 +503,9 @@ def _construcCUL(self, x, culs, st, minutil, length): exSZ = sz for j in range(st + 1, len(culs)): mapOfTWUF = self._mapFMAP[x.item] - if mapOfTWUF != None: + if mapOfTWUF is not None: twuf = mapOfTWUF.get(culs[j].item) - if twuf != None and twuf < minutil: + if twuf is not None and twuf < minutil: excul[j] = None exSZ = sz - 1 else: diff --git a/PAMI/highUtilityPattern/basic/UPGrowth.py b/PAMI/highUtilityPattern/basic/UPGrowth.py index e85c0340..9dab91c1 100644 --- a/PAMI/highUtilityPattern/basic/UPGrowth.py +++ b/PAMI/highUtilityPattern/basic/UPGrowth.py @@ -492,7 +492,7 @@ def _creatingItemSets(self) -> None: if 'UtilitySum' in i: data = self._iFile['UtilitySum'].tolist() for i in range(len(data)): - tr = [timeStamp[i]] + tr = timeStamp[i] tr.append(data[i]) self._Database.append(tr) if isinstance(self._iFile, str): diff --git a/PAMI/highUtilityPattern/basic/efimParallel.py b/PAMI/highUtilityPattern/basic/efimParallel.py index 25c5a743..fa484109 100644 --- a/PAMI/highUtilityPattern/basic/efimParallel.py +++ b/PAMI/highUtilityPattern/basic/efimParallel.py @@ -411,7 +411,7 @@ def _search(self, collections): :type collections: list """ - if (self.threads > 1): + if self.threads > 1: with Parallel(n_jobs=self.threads) as parallel: while len(collections) > 0: new_collections = [] diff --git a/PAMI/highUtilityPatternsInStreams/HUPMS.py b/PAMI/highUtilityPatternsInStreams/HUPMS.py index 519ebf92..2852797d 100644 --- a/PAMI/highUtilityPatternsInStreams/HUPMS.py +++ b/PAMI/highUtilityPatternsInStreams/HUPMS.py @@ -360,25 +360,25 @@ def removeBatchUtility(self, tempNode): curBatchUtility = tempNode.utility[0] tempNode.shiftUtility() - if(sum(tempNode.utility) == 0): - if(tempNode.itemName in self.headerTable.table): + if sum(tempNode.utility) == 0: + if tempNode.itemName in self.headerTable.table: curNode = self.headerTable.table[tempNode.itemName][1] - if(curNode == tempNode): + if curNode == tempNode: self.headerTable.table[tempNode.itemName][1] = tempNode.next else: - while(curNode != None and curNode.next != tempNode): + while curNode is not None and curNode.next is not tempNode: curNode = curNode.next - if(curNode != None): + if curNode is not None: curNode.next = tempNode.next self.headerTable.removeUtility(tempNode.itemName, curBatchUtility) curChilds = list(tempNode.children.keys()) for child in curChilds: - if(sum(tempNode.children[child].utility) == 0): + if sum(tempNode.children[child].utility) == 0: del tempNode.children[child] @@ -604,7 +604,7 @@ def createPrefixBranch(self, root): """ stack = [] - while(root is not None): + while root is not None: stack.append(root) root = root.parent @@ -621,10 +621,10 @@ def fixUtility(self, root): :type root: Node """ - if(root is None): + if root is None: return - if(len(root.utility) > 1): + if len(root.utility) > 1: root.utility = [sum(root.utility)] for child in root.children: @@ -654,12 +654,12 @@ def createConditionalTree(self, root, transactions, minUtil): for transaction in transactions: for item in transaction["transaction"]: - if(root.headerTable.table[item][0] < minUtil): + if root.headerTable.table[item][0] < minUtil: transaction["transaction"].remove(item) tempTree = _HUSTree(1, 1) for transaction in transactions: - if(len(transaction["transaction"]) != 0): + if len(transaction["transaction"]) != 0: tempTree.addTransaction(transaction["transaction"], transaction["utility"]) self.fixUtility(tempTree.root) @@ -685,7 +685,7 @@ def contains(self, superset, subset): return reduce(and_, [i in superset for i in subset]) - def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): + def treeGenerations(self, root, netUtil, candidatePattern, curItem = None): """ Generates the tree of the high utility patterns @@ -706,12 +706,12 @@ def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): :type curItem: list """ - if(root is None): + if root is None: return for item in reversed(root.headerTable.orderedItems): - if(root.headerTable.table[item][0] >= netUtil): + if root.headerTable.table[item][0] >= netUtil: prefixBranches = [] tempNode = root.headerTable.table[item][1] @@ -738,13 +738,13 @@ def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): newItemset = curItem.copy() newItemset.append(item) - if(len(newItemset) not in candidatePattern): + if len(newItemset) not in candidatePattern: candidatePattern[len(newItemset)] = [newItemset] else: candidatePattern[len(newItemset)].append(newItemset) - if(len(conditionalTree.headerTable.table) != 0): + if len(conditionalTree.headerTable.table) != 0: self.treeGenerations(conditionalTree, netUtil, candidatePattern, newItemset) @deprecated("It is recommended to use 'mine()' instead of 'mine()' for mining process. Starting from January 2025, 'mine()' will be completely terminated.") @@ -792,7 +792,7 @@ def mine(self): startIndex = 0 endIndex = self.__windowSize * self.__paneSize - while (endIndex <= len(self._transactions)): + while endIndex <= len(self._transactions): filteredItemsets = {} @@ -808,12 +808,12 @@ def mine(self): for item in itemSet: itemSetUtility += transactionwiseUtility[transId][item] - if (itemSetUtility >= self._minUtil): + if itemSetUtility >= self._minUtil: results.append([itemSet, itemSetUtility]) self.__finalPatterns[(startIndex, endIndex)] = results - if (endIndex >= len(self._transactions)): + if endIndex >= len(self._transactions): break self.__tree.removeBatch() diff --git a/PAMI/highUtilityPatternsInStreams/SHUGrowth.py b/PAMI/highUtilityPatternsInStreams/SHUGrowth.py index aa743462..75519215 100644 --- a/PAMI/highUtilityPatternsInStreams/SHUGrowth.py +++ b/PAMI/highUtilityPatternsInStreams/SHUGrowth.py @@ -645,7 +645,7 @@ def minPathUtil(self, nodeIndex, stack): activeBatch = [i for i, e in enumerate(stack[0].utility) if e != 0] for batch in activeBatch: - if(stack[nodeIndex + 1].tail == None or stack[nodeIndex + 1].tail[batch] == False): + if stack[nodeIndex + 1].tail is None or stack[nodeIndex + 1].tail[batch] is False: minUtil += (stack[nodeIndex].utility[batch] - stack[nodeIndex + 1].utility[batch]) return minUtil @@ -669,7 +669,7 @@ def createPrefixBranch(self, root): """ stack = [] - while(root is not None): + while root is not None: stack.append(root) root = root.parent @@ -699,10 +699,10 @@ def fixUtility(self, root): :type root: _Node """ - if(root is None): + if root is None: return - if(len(root.utility) > 1): + if len(root.utility) > 1: root.utility = [sum(root.utility)] for child in root.children: @@ -731,14 +731,14 @@ def createConditionalTree(self, root, transactions, minUtil): for transaction in transactions: for item in transaction["transaction"]: - if(root.headerTable.table[item][0] < minUtil): + if root.headerTable.table[item][0] < minUtil: itemIndex = transaction["transaction"].index(item) transaction["transaction"].remove(item) del transaction["itemwiseUtility"][itemIndex] tempTree = _SHUTree(1, 1, True) for transaction in transactions: - if(len(transaction["transaction"]) != 0): + if len(transaction["transaction"]) != 0: tempTree.addTransaction(transaction["transaction"], transaction["utility"], transaction["itemwiseUtility"]) @@ -763,7 +763,7 @@ def contains(self, superset, subset): return reduce(and_, [i in superset for i in subset]) - def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): + def treeGenerations(self, root, netUtil, candidatePattern, curItem =None): """ Generates the tree of the high utility patterns @@ -784,12 +784,12 @@ def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): :type curItem: list """ - if(root is None): + if root is None: return for item in reversed(root.headerTable.orderedItems): - if(root.headerTable.table[item][0] >= netUtil): + if root.headerTable.table[item][0] >= netUtil: prefixBranches = [] tempNode = root.headerTable.table[item][1] @@ -818,13 +818,13 @@ def treeGenerations(self, root, netUtil, candidatePattern, curItem = []): newItemset = curItem.copy() newItemset.append(item) - if(len(newItemset) not in candidatePattern): + if len(newItemset) not in candidatePattern: candidatePattern[len(newItemset)] = [newItemset] else: candidatePattern[len(newItemset)].append(newItemset) - if(len(conditionalTree.headerTable.table) != 0): + if len(conditionalTree.headerTable.table) != 0: self.treeGenerations(conditionalTree, netUtil, candidatePattern, newItemset) @deprecated("It is recommended to use 'mine()' instead of 'mine()' for mining process. Starting from January 2025, 'mine()' will be completely terminated.") @@ -873,7 +873,7 @@ def mine(self): startIndex = 0 endIndex = self.__windowSize * self.__paneSize - while (endIndex <= len(self._transactions)): + while endIndex <= len(self._transactions): filteredItemsets = {} @@ -885,16 +885,16 @@ def mine(self): for itemSet in filteredItemsets[itemSetLen]: itemSetUtility = 0 for transId in range(startIndex, endIndex): - if (self.contains(list(transactionwiseUtility[transId].keys()), itemSet)): + if self.contains(list(transactionwiseUtility[transId].keys()), itemSet): for item in itemSet: itemSetUtility += transactionwiseUtility[transId][item] - if (itemSetUtility >= self._minUtil): + if itemSetUtility >= self._minUtil: results.append([itemSet, itemSetUtility]) self.__finalPatterns[(startIndex, endIndex)] = results - if (endIndex >= len(self._transactions)): + if endIndex >= len(self._transactions): break self.__tree.removeBatch() @@ -928,7 +928,7 @@ def printTree(self, root, level = 0): print(' ' * level, level, root.itemName, root.utility, root.parent.itemName if root.parent else None ) - if(root.tail is not None): + if root.tail is not None: print(' ' * (level + 1), level + 1, root.tail) for child in root.children.values(): diff --git a/notebooks/neuroSymbolicAI.ipynb b/notebooks/neuroSymbolicAI.ipynb new file mode 100644 index 00000000..b2584f6e --- /dev/null +++ b/notebooks/neuroSymbolicAI.ipynb @@ -0,0 +1,5768 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# **Step1: Read the csv File**" + ], + "metadata": { + "id": "QZEzyFMS9B55" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 617 + }, + "id": "dp1DOhSi8YQf", + "outputId": "ceede90d-a923-45df-e8b2-0503add0607a" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " TimeStamp 3207010 29209010 11222030 14215010 33202110 \\\n", + "0 2018-01-01 00:00:00 4.0 14.0 26.0 8.0 13.0 \n", + "1 2018-01-01 01:00:00 4.0 14.0 26.0 8.0 13.0 \n", + "2 2018-01-01 02:00:00 12.0 10.0 27.0 0.0 21.0 \n", + "3 2018-01-01 03:00:00 5.0 13.0 19.0 2.0 16.0 \n", + "4 2018-01-01 04:00:00 7.0 13.0 18.0 2.0 12.0 \n", + "... ... ... ... ... ... ... \n", + "52603 2024-01-01 19:00:00 3.0 4.0 14.0 18.0 15.0 \n", + "52604 2024-01-01 20:00:00 3.0 4.0 14.0 18.0 15.0 \n", + "52605 2024-01-01 21:00:00 3.0 4.0 14.0 18.0 15.0 \n", + "52606 2024-01-01 22:00:00 3.0 4.0 14.0 18.0 15.0 \n", + "52607 2024-01-01 23:00:00 3.0 4.0 14.0 18.0 15.0 \n", + "\n", + " 14104030 8344010 43202020 28216010 ... 13103010 13105010 \\\n", + "0 17.0 9.0 6.0 12.0 ... 24.0 31.0 \n", + "1 17.0 9.0 6.0 12.0 ... 24.0 31.0 \n", + "2 23.0 1.0 13.0 12.0 ... 27.0 34.0 \n", + "3 12.0 6.0 3.0 12.0 ... 23.0 31.0 \n", + "4 15.0 2.0 7.0 19.0 ... 22.0 21.0 \n", + "... ... ... ... ... ... ... ... \n", + "52603 9.0 4.0 25.0 5.0 ... 9.0 7.0 \n", + "52604 9.0 4.0 25.0 5.0 ... 9.0 7.0 \n", + "52605 9.0 4.0 25.0 5.0 ... 9.0 7.0 \n", + "52606 9.0 4.0 25.0 5.0 ... 9.0 7.0 \n", + "52607 9.0 4.0 25.0 5.0 ... 9.0 7.0 \n", + "\n", + " 13108010 15201150 27201030 26210510 20202050 5203020 34207090 \\\n", + "0 34.0 8.0 13.0 20.0 6.0 17.0 14.0 \n", + "1 34.0 8.0 13.0 20.0 6.0 17.0 14.0 \n", + "2 43.0 6.0 15.0 21.0 7.0 12.0 13.0 \n", + "3 35.0 7.0 13.0 20.0 7.0 4.0 16.0 \n", + "4 32.0 9.0 10.0 22.0 6.0 2.0 17.0 \n", + "... ... ... ... ... ... ... ... \n", + "52603 17.0 2.0 13.0 0.0 1.0 9.0 6.0 \n", + "52604 17.0 2.0 13.0 0.0 1.0 9.0 6.0 \n", + "52605 17.0 2.0 13.0 0.0 1.0 9.0 6.0 \n", + "52606 17.0 2.0 13.0 0.0 1.0 9.0 6.0 \n", + "52607 17.0 2.0 13.0 0.0 1.0 9.0 6.0 \n", + "\n", + " 44214010 \n", + "0 6.0 \n", + "1 6.0 \n", + "2 6.0 \n", + "3 9.0 \n", + "4 7.0 \n", + "... ... \n", + "52603 4.0 \n", + "52604 4.0 \n", + "52605 4.0 \n", + "52606 4.0 \n", + "52607 4.0 \n", + "\n", + "[52608 rows x 992 columns]" + ], + "text/html": [ + "\n", + "
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RkpJSZ5mYmBjExMTUWYYcn/HlynnlciKi2nhbB/N47yoiIiJySgw5REREDozdVeYx5JCiyWdXsUmWiOhO7K4yjyGHiIiInBJDDhERkQNjd5V5DDmkaMatsGyRpYZinU/NCburzGPIISKnwzqfiACGHCJFYMMDEd0tdleZx5BDCidM/OZ8nPm92QPrfGpO2F1lHkMOETkd1vlEBDDkECkCGx6IiKyPIYcUTT67ynm/njvvOyMish+GHCIiIgfGgcfmMeSQoslu68DmDiKiWpy5lbuxGHKIiIjIKTHkEBEROTB2V5nHkEOKxlZYuhus86k5YXeVeQw5ROR0WOcTEcCQQ0RE5NDYXWUeQw4pmnEzLL+dU0OxzqfmhN1V5jHkEJHTYZ1PRABDDhERkUNjd5V5DDmkaLKLAfLmB9RArPOpOWF3lXkMOUTkdFjnExHAkEOkCGx4IKK7xe4q8xhySNHkdyG333Y0NSd+a3bBOp+aE3ZXmceQQ0ROh3U+EQEMOUSKwIYHIrpb7K4yjyGHFM14RpUzfzl35vdmD6zziQhgyCEiIiInxZBDpABseCAisj6GHFI22ewq5+3Ucd53RkRkPww5RERE5JQYckjRhJnfnQ27q4jobjlzK3djMeQQKQCrKCIi62PIISIicmC8To55DDmkaM3ltg5ERHeL3VXmMeQQKQwrrMbjLiQigCGHiIjIobG7yjyGHFI00WzmV5E1sc6n5oStv+Y1KuR8+umncHFxwZQpU6THysvLMXnyZLRq1Qp+fn4YNWoUcnJyZM/LysrCiBEj4OPjg6CgIEybNg1VVVWyMnv27EH//v3h6emJzp07Y9WqVbVePy4uDp06dYKXlxciIiJw5MiRxrwdIkVgfUVEZB13HXKOHj2Kb775Bn369JE9PnXqVPzyyy/YsGED9u7di2vXruHZZ5+Vluv1eowYMQI6nQ6HDh3Cd999h1WrVmHGjBlSmczMTIwYMQJDhgxBamoqpkyZgtdffx3bt2+Xyqxbtw6xsbGYOXMmjh8/jr59+yI6Ohq5ubl3+5aI7IYND0R0t9hdZd5dhZzi4mKMGTMG3377LVq2bCk9XlhYiOXLl2PevHl4/PHHER4ejpUrV+LQoUM4fPgwAGDHjh1IT0/Hv//9b/Tr1w/Dhw/H7NmzERcXB51OBwBYsmQJwsLC8MUXX6BHjx6IiYnBc889h/nz50uvNW/ePEyYMAHjxo1Dz549sWTJEvj4+GDFihWN2R+kMM1ldpUTvzUiIru5q5AzefJkjBgxAlFRUbLHk5OTUVlZKXu8e/fu6NChAxITEwEAiYmJ6N27N4KDg6Uy0dHR0Gq1OH36tFTmznVHR0dL69DpdEhOTpaVcXV1RVRUlFTGlIqKCmi1WtkPkdIw8BARWYe7pU9Yu3Ytjh8/jqNHj9ZaptFooFKpEBAQIHs8ODgYGo1GKmMccGqW1yyrq4xWq0VZWRlu3boFvV5vsszZs2fNbvucOXPw4YcfNuyNEtkQG5uJiKzPopac7OxsvPPOO1i9ejW8vLyaapuazPvvv4/CwkLpJzs7296bRPVoLnOrnPm9ERHZi0UhJzk5Gbm5uejfvz/c3d3h7u6OvXv34ssvv4S7uzuCg4Oh0+lQUFAge15OTg5CQkIAACEhIbVmW9X8XV8ZtVoNb29vtG7dGm5ubibL1KzDFE9PT6jVatkPkdJwOigRkXVYFHKeeOIJpKWlITU1VfoZMGAAxowZI/3u4eGBhIQE6TkZGRnIyspCZGQkACAyMhJpaWmyWVA7d+6EWq1Gz549pTLG66gpU7MOlUqF8PBwWRmDwYCEhASpDJEjYXcVEZH1WTQmp0WLFrj//vtlj/n6+qJVq1bS4+PHj0dsbCwCAwOhVqvx1ltvITIyEg899BAAYOjQoejZsydefvllzJ07FxqNBtOnT8fkyZPh6ekJAJg4cSK++uorvPvuu3jttdewa9curF+/Hlu2bJFeNzY2FmPHjsWAAQMwcOBALFiwACUlJRg3blyjdggpi3GrhjM3cDjxWyMishuLBx7XZ/78+XB1dcWoUaNQUVGB6OhoLF68WFru5uaGzZs3Y9KkSYiMjISvry/Gjh2LWbNmSWXCwsKwZcsWTJ06FQsXLkT79u2xbNkyREdHS2VGjx6NGzduYMaMGdBoNOjXrx/i4+NrDUYmcjQMPERE1tHokLNnzx7Z315eXoiLi0NcXJzZ53Ts2BFbt26tc72DBw9GSkpKnWViYmIQExPT4G0lUip2VxERWR/vXUWKJp9d5bxtHM77zoiI7Ichh0hhnHnsERGRLTHkECkAu6uI6G7xshPmMeSQsjWTe1cZc+ZuOSIiW2LIISIicmC8C7l5DDmkaMatGs7ckuPEb42IyG4YcogUxpnDHBGRLTHkECkAG5uJiKyPIYcUzbhVw5kH5DrvOyMish+GHCITGDqIiBwfQw6RArC7iojI+hhySNGEna6TY+vQwZYjIiLrY8ghUhjOriIisg6GHCIFYHcVEZH1MeSQojWXRo3m8j6JiGyJIYdIYZx5qjwRkS0x5BApALuriIisjyGHFE0I3ruKiIjuDkMOkcI4c5gjIrIlhhwiBWB3FRGR9THkkKIJ2e/No4mjebxLIqKmx5BDRERETokhh4iIiJwSQw4pmr3uXWVPorm8USKiJsaQQ0RERE6JIYcUjq0aRER0dxhyyGHYMu7YM1ox1jUep+QTEcCQQ0ROiEGRiACGHFI4+cBj2526bN0SwJMyEZH1MeQQmWDX7iomnkZjdxURAQw5RIrAk7J1MScSEcCQQwonzPze1NhdRUTk+BhyiEywa+hg4mk0towREcCQQ6QIPClbF3MiEQEMOaRwtrytg/HsLXZXERE5PoYcIhPsezFARp7GYssYEQEMOUSKwJOydTEmEhHAkEMKJ2w4v8q4O8yeoYPXySEisg6GHCITmDMcG1vGiAhgyCEiIiInxZBDimbT2VVGv9u1u8qOr01E5EwsCjlff/01+vTpA7VaDbVajcjISGzbtk1aXl5ejsmTJ6NVq1bw8/PDqFGjkJOTI1tHVlYWRowYAR8fHwQFBWHatGmoqqqSldmzZw/69+8PT09PdO7cGatWraq1LXFxcejUqRO8vLwQERGBI0eOWPJWiMiJMSgSEWBhyGnfvj0+/fRTJCcn49ixY3j88cfx9NNP4/Tp0wCAqVOn4pdffsGGDRuwd+9eXLt2Dc8++6z0fL1ejxEjRkCn0+HQoUP47rvvsGrVKsyYMUMqk5mZiREjRmDIkCFITU3FlClT8Prrr2P79u1SmXXr1iE2NhYzZ87E8ePH0bdvX0RHRyM3N7ex+4OIiIichEUh56mnnsKTTz6JLl26oGvXrvj444/h5+eHw4cPo7CwEMuXL8e8efPw+OOPIzw8HCtXrsShQ4dw+PBhAMCOHTuQnp6Of//73+jXrx+GDx+O2bNnIy4uDjqdDgCwZMkShIWF4YsvvkCPHj0QExOD5557DvPnz5e2Y968eZgwYQLGjRuHnj17YsmSJfDx8cGKFSusuGtICWx57yrjiwHa9y7kbIdoLA48JmsrLKtErrbc3ptBFrrrMTl6vR5r165FSUkJIiMjkZycjMrKSkRFRUllunfvjg4dOiAxMREAkJiYiN69eyM4OFgqEx0dDa1WK7UGJSYmytZRU6ZmHTqdDsnJybIyrq6uiIqKksqYU1FRAa1WK/shIufDmEjW1vfDHRj4SQIKSnX23hSygMUhJy0tDX5+fvD09MTEiROxceNG9OzZExqNBiqVCgEBAbLywcHB0Gg0AACNRiMLODXLa5bVVUar1aKsrAx5eXnQ6/Umy9Ssw5w5c+bA399f+gkNDbX07VMzwZYAIjLlXE6xvTeBLGBxyOnWrRtSU1ORlJSESZMmYezYsUhPT2+KbbO6999/H4WFhdJPdna2vTeJ6iHrQrLh7CpbtwTY87WdEUMqEQGAu6VPUKlU6Ny5MwAgPDwcR48excKFCzF69GjodDoUFBTIWnNycnIQEhICAAgJCak1C6pm9pVxmTtnZOXk5ECtVsPb2xtubm5wc3MzWaZmHeZ4enrC09PT0rdMRA6GQZGIACtcJ8dgMKCiogLh4eHw8PBAQkKCtCwjIwNZWVmIjIwEAERGRiItLU02C2rnzp1Qq9Xo2bOnVMZ4HTVlatahUqkQHh4uK2MwGJCQkCCVIefU1ANylXJbByIisg6LWnLef/99DB8+HB06dEBRURHWrFmDPXv2YPv27fD398f48eMRGxuLwMBAqNVqvPXWW4iMjMRDDz0EABg6dCh69uyJl19+GXPnzoVGo8H06dMxefJkqYVl4sSJ+Oqrr/Duu+/itddew65du7B+/Xps2bJF2o7Y2FiMHTsWAwYMwMCBA7FgwQKUlJRg3LhxVtw11JzZvCXAht1yzQFDKhEBFoac3NxcvPLKK7h+/Tr8/f3Rp08fbN++Hb///e8BAPPnz4erqytGjRqFiooKREdHY/HixdLz3dzcsHnzZkyaNAmRkZHw9fXF2LFjMWvWLKlMWFgYtmzZgqlTp2LhwoVo3749li1bhujoaKnM6NGjcePGDcyYMQMajQb9+vVDfHx8rcHIRERE1HxZFHKWL19e53IvLy/ExcUhLi7ObJmOHTti69atda5n8ODBSElJqbNMTEwMYmJi6ixDjk92W4emfi2jV7B1SwAbb4iIrI/3riIywa4XA2TkISKyCoYcIiIickoMOaRoxq0aTX6dHDvOrhK8UA4RkdUx5BCZwJxBROT4GHKIiIjqwJvmOi6GHFI0+ewq21U0tp9dpYw7oBNRbcw4joshh8gE1mlERI6PIYeIiKgO/NLjuBhySNFsOevInk3Ssm451qhEisIxOY6LIYeIiKgOjDiOiyGHiIiInBJDDimaLa+RZ8/bKcjfJ783EikJe6scF0MOERFRHfjFw3Ex5BAREdWBLTmOiyGHFM14VoMt711la5xdRURkfQw5RERE5JQYcohMsPVtHYhIudi66rgYckjRbDnryJYzuWq/Nu9dRaRUHHjsuBhyiIiIyCkx5JCy2XBArvEgZ5t3V8neJ781EikJP5KOiyGHyATWaURUg/WB42LIISIiqgNbVx0XQw4pmi0H5Bqv39bdVbJBz6xPiYisgiGHyATmDCKqwfrAcTHkEBER1YGtq46LIYcUTdhw1pF9b+vAWpRIsfjxdFgMOUREROSUGHKIiIjqwCseOy6GHFI0m95qgXchJyIT+Jl0XAw5REREdWDGcVwMOURERA7Mxeb3oXEcDDmkaMKG/VX27He35d3WicgySp/9qPDNsyuGHCIiojowQzguhhwiIqI62PJ6XXeD3VXmMeSQosnvXeXMFwNUxnYQkeNhnWEeQw4ROR0XfrUlK7LljYLJuhhyiBSGlWjjKbFLgRwYW1odFkMOKZotu3HsWXdxRhWRcnH2o+NiyCEip8PuKrImtt44LoYcUrTmWLewq6XxuA+pyfDQcigWhZw5c+bgwQcfRIsWLRAUFISRI0ciIyNDVqa8vByTJ09Gq1at4Ofnh1GjRiEnJ0dWJisrCyNGjICPjw+CgoIwbdo0VFVVycrs2bMH/fv3h6enJzp37oxVq1bV2p64uDh06tQJXl5eiIiIwJEjRyx5O+Rgmry7yo4nRp6TiZSLA48dl0UhZ+/evZg8eTIOHz6MnTt3orKyEkOHDkVJSYlUZurUqfjll1+wYcMG7N27F9euXcOzzz4rLdfr9RgxYgR0Oh0OHTqE7777DqtWrcKMGTOkMpmZmRgxYgSGDBmC1NRUTJkyBa+//jq2b98ulVm3bh1iY2Mxc+ZMHD9+HH379kV0dDRyc3Mbsz+IyAmwu4qsiZd4cFzulhSOj4+X/b1q1SoEBQUhOTkZjz32GAoLC7F8+XKsWbMGjz/+OABg5cqV6NGjBw4fPoyHHnoIO3bsQHp6On799VcEBwejX79+mD17Nv7617/igw8+gEqlwpIlSxAWFoYvvvgCANCjRw8cOHAA8+fPR3R0NABg3rx5mDBhAsaNGwcAWLJkCbZs2YIVK1bgvffea/SOIYUQtvsGpZS6Synb4cjYXUXWxKPJcTVqTE5hYSEAIDAwEACQnJyMyspKREVFSWW6d++ODh06IDExEQCQmJiI3r17Izg4WCoTHR0NrVaL06dPS2WM11FTpmYdOp0OycnJsjKurq6IioqSyphSUVEBrVYr+yEiImoozq5yLHcdcgwGA6ZMmYKHH34Y999/PwBAo9FApVIhICBAVjY4OBgajUYqYxxwapbXLKurjFarRVlZGfLy8qDX602WqVmHKXPmzIG/v7/0ExoaavkbJyKiZsW4ZVCJjYTsnTXvrkPO5MmTcerUKaxdu9aa29Ok3n//fRQWFko/2dnZ9t4kqofs+hRNXLvY8/40Sq9EiZozWd1gv80wi3WGeRaNyakRExODzZs3Y9++fWjfvr30eEhICHQ6HQoKCmStOTk5OQgJCZHK3DkLqmb2lXGZO2dk5eTkQK1Ww9vbG25ubnBzczNZpmYdpnh6esLT09PyN0xEDoUDj4kIsLAlRwiBmJgYbNy4Ebt27UJYWJhseXh4ODw8PJCQkCA9lpGRgaysLERGRgIAIiMjkZaWJpsFtXPnTqjVavTs2VMqY7yOmjI161CpVAgPD5eVMRgMSEhIkMoQUfPFgcfUVJR4bDHTm2dRS87kyZOxZs0a/PTTT2jRooU0/sXf3x/e3t7w9/fH+PHjERsbi8DAQKjVarz11luIjIzEQw89BAAYOnQoevbsiZdffhlz586FRqPB9OnTMXnyZKmVZeLEifjqq6/w7rvv4rXXXsOuXbuwfv16bNmyRdqW2NhYjB07FgMGDMDAgQOxYMEClJSUSLOtyDnYspnYntfCEHX8RUT2xe4qx2VRyPn6668BAIMHD5Y9vnLlSrz66qsAgPnz58PV1RWjRo1CRUUFoqOjsXjxYqmsm5sbNm/ejEmTJiEyMhK+vr4YO3YsZs2aJZUJCwvDli1bMHXqVCxcuBDt27fHsmXLpOnjADB69GjcuHEDM2bMgEajQb9+/RAfH19rMDIRNT/sriJrks2oYqBwKBaFnIY003l5eSEuLg5xcXFmy3Ts2BFbt26tcz2DBw9GSkpKnWViYmIQExNT7zYRUfOixC4FclxKP5yY6c3jvatI0Ww668iOVzXlFVWJHIMSr5PDOsM8hhwicjrsriJrkl/Kwm6bQXeBIYdIYViHNh67q8ialH4dK2Z68xhySNFsOetImPndFpTYBE5E1ZT+6VRi8FIKhhwicjrsrqKmwjzhWBhyiBSG38oaj91VZE32vOVLQzDTm8eQQ4pmy1lH9r13lU1fjogsYr8LhTYE6w/zGHKIyOmwu4qsiZd4cFwMOUQm2LMe4yBkIiLrYMghRbPljCd7hgvGGiLl4r3lHBdDDhERUR2U3l3F3lnzGHJI0Wx5ES5hzwvlGL+0AitRouZMcOCxw2LIIVIAVlJERNbHkENERFQHdlc5LoYcchhNPTBY3ltl+xs7SL8psBIlas5kIUeBHVasM8xjyCEiIqqD4JcQh8WQQ6QwSvymSETKxe4q8xhySNFse1sH+31b47dDIuWSd1cpD+sP8xhyiIiIGkiJN+gk8xhyiBSGdah18aREjaX0Q4jdVeYx5JCi2fIiXPacJqr0SpSIlIv1h3kMOURERHXg7CrHxZBDRE6NJyVqLKVfJ4fdVeYx5JCiybuQbFe52LoiU2LFSUTVZBcK5UfVoTDkEJFT4zmJGouD1x0XQw6RCfas01ifEikXP5+OhSGHFM2W9QmDDRGZIsz8TsrHkENETo1dDdRY9hobSI3HkENkgj2rMQ5CJlIaZX8mmbvMY8ghRbPpvavsWJGxjiJyDPysOhaGHCJyajwpUWMJhQ/K4XVyzGPIITKBg5CJqIY84yjvA8o6wzyGHFI0+b2rmvaTzGBDRKbw8+m4GHKIFIb1qXXxBEXWpMTjid1V5jHkkKLZduCx+b+amhKbwImomvG0cSV+UpUYvJSCIYeIiKgOvHeV42LIIVIYXmzMuthKRo2l9I8ku6vMY8ghh9Hk3VXGTdK2rtQUXokSUTWlhGZ+GWoYhhwicmo8F1BjyWZ58nhyKAw5RArDOpRIYYTJX8kBWBxy9u3bh6eeegrt2rWDi4sLNm3aJFsuhMCMGTPQtm1beHt7IyoqCufPn5eVyc/Px5gxY6BWqxEQEIDx48ejuLhYVubkyZN49NFH4eXlhdDQUMydO7fWtmzYsAHdu3eHl5cXevfuja1bt1r6dkjhbDmrwZ4XNWXFSaRcSvx8skWpYSwOOSUlJejbty/i4uJMLp87dy6+/PJLLFmyBElJSfD19UV0dDTKy8ulMmPGjMHp06exc+dObN68Gfv27cMbb7whLddqtRg6dCg6duyI5ORkfPbZZ/jggw+wdOlSqcyhQ4fw0ksvYfz48UhJScHIkSMxcuRInDp1ytK3RERE1DBMFw7F3dInDB8+HMOHDze5TAiBBQsWYPr06Xj66acBAN9//z2Cg4OxadMmvPjiizhz5gzi4+Nx9OhRDBgwAACwaNEiPPnkk/j888/Rrl07rF69GjqdDitWrIBKpUKvXr2QmpqKefPmSWFo4cKFGDZsGKZNmwYAmD17Nnbu3ImvvvoKS5YsuaudQVTDnoP6WIcSKYtgd5XDsuqYnMzMTGg0GkRFRUmP+fv7IyIiAomJiQCAxMREBAQESAEHAKKiouDq6oqkpCSpzGOPPQaVSiWViY6ORkZGBm7duiWVMX6dmjI1r2NKRUUFtFqt7IeUTX4xQGe+rQOrTiKlUuLAY4VshuJZNeRoNBoAQHBwsOzx4OBgaZlGo0FQUJBsubu7OwIDA2VlTK3D+DXMlalZbsqcOXPg7+8v/YSGhlr6FonIwSjlpESOi8eQ42pWs6vef/99FBYWSj/Z2dn23iRSKPvWaaxRiZSKra6OxaohJyQkBACQk5MjezwnJ0daFhISgtzcXNnyqqoq5Ofny8qYWofxa5grU7PcFE9PT6jVatkPKZttZzzZcRyO3V6ZiOpjz5mX5jBsNYxVQ05YWBhCQkKQkJAgPabVapGUlITIyEgAQGRkJAoKCpCcnCyV2bVrFwwGAyIiIqQy+/btQ2VlpVRm586d6NatG1q2bCmVMX6dmjI1r0NEBCjnCrXkuOx6NXRqFItDTnFxMVJTU5GamgqgerBxamoqsrKy4OLigilTpuCjjz7Czz//jLS0NLzyyito164dRo4cCQDo0aMHhg0bhgkTJuDIkSM4ePAgYmJi8OKLL6Jdu3YAgD/96U9QqVQYP348Tp8+jXXr1mHhwoWIjY2VtuOdd95BfHw8vvjiC5w9exYffPABjh07hpiYmMbvFWr27DsI2X6vTUS1KfEjqcRtUiKLp5AfO3YMQ4YMkf6uCR5jx47FqlWr8O6776KkpARvvPEGCgoK8MgjjyA+Ph5eXl7Sc1avXo2YmBg88cQTcHV1xahRo/Dll19Ky/39/bFjxw5MnjwZ4eHhaN26NWbMmCG7ls6gQYOwZs0aTJ8+HX/729/QpUsXbNq0Cffff/9d7QhSJmHDdmIGGyKqDz+qjsXikDN48OA6+wJdXFwwa9YszJo1y2yZwMBArFmzps7X6dOnD/bv319nmeeffx7PP/983RtM5GBYiVoXAyQ1mg0vZWEvQgi4OOHtzJvV7CqihnLOaoyI7oYSx3XJryHWuHWdua5F/9k78d2hy41bkQIx5JCiyS7C1cQVjT2rMeVVoc6D+5Yay0kbbyR//c9J3CqtxMyfT9t7U6yOIYdIYZy9QiVyZEr8fDa2l8mgxDdlJQw5pGj2+uw5a787Ob7MvBJsSrkKg4HHqK3I712ljP2ulO1QOosHHhPZS1PnDt67yjk5274d8vke6feRD9xjvw1pRmSTPJ3rcHJ6bMkhUhhnOylT00i+csvem9BsyC4GaMftIMsx5JCi2fJy6vZs/mXFSaRcSvx88rtQwzDkEJFT47mArMkZw4UzvqcaDDlEJth1fI79XpqITFDiwGNqGIYcUjYb3hjPrt9mWG8SKZhz36DTCS90LGHIISKn5ownJbItZz+GnPn9MeQQmWDXQchOXOEQEdkSQw4pmnx2VRPf1sGu43CYbMgyztzFoDTy6+Qo47OqkM1QPIYcInJuPBlQI1nzZphK5MyBmSGHyAS26hBRDSV+Jq25Tc4Y3Gow5JCi2fIbFMfhOCclnqDIcfFociwMOURKw1qUSFGcvbvKmTHkEJnAioyIathyAkRDsY5qGIYcUjTjCqXJ711l17uQ2++1nV1T7ltdlaHpVq4Qb/7rGF5cmgiDofkepEqZUUWWY8ghUhhWp44hJesWuv1jG75MOG/vTWkyVXoDtp/OweFL+biUV2LvzVEE5h3HwpBDZIJSmqTt6WpBGXafzeW3WDNm/HQaQgDzdp6z96Y0mSqj1hu90e/5JTrkaMvtsUl2p5RPg1K2Q+kYckjRZOdXJz7ZNnWoulFUgWX7L+FWia7Bz3n4010Yt+oo9mTcaMIta3rOe9Q0XHZ+Kf4Zfxa5FgYTUyFHCIH+s3ci4pMElFRUNWg9+SU6lFfqLXptWxBCyMKb+XLm/iClY8ghUpimqEPHf3cUH205gynrUi1+7uFLN62/QU7AHhdQu9txMaO/ScTXey4iZk2KRc+r0t8ec2T474FpHAquFpTVu46bxRXoP3snHv50l0WvbQtT16XikX/uQnE9YY0tu46LIYfIBGf7snbyt0IAwN5zd9Eq48RXQ3U0eqMD05J/y7XC6hacI5fzLXq9Sn3tlpwqC4PW0cu3AAA3LWhFtJVNqddwvbAc29KuN/g5Sqka2I3cMAw5pGjCzO+NdTzrFv62MQ0FpbcrXs6uck7OdDJoSNdKQzS0Rcj49XT/bdWp1Fs6o8zx9z+vk+O4GHKoWXp28SGsScrC7M1nTC63Zz3GpnEyx2CFM+zpa4XoO2sHlu2/VG9Z40BTUVn9e5VR605DNscZQoEs5PDz6VAYckjRmvob1IXcotvrb2Tl9e/DV+ocv6KrMiDp0s1mcW0Ve7hZXIHCskp7b0ajVVTpUaozPUbEGi05f994CkXlVfhoy+2AX6qrwpWbtaeIV8lacqoHDhsHnwYN2jX63VotUbamxK1W4jYpEUMONW9WGj2aePEmpm86hReXHjZbZtbm0xi99DBm/ny61jJWWI1TqqtC+Ee/ou+HO2otc6R9K4TAo//cjQEf/WpyNpLBCvnYVPdd1Bd78bvP9iD9mlb2uN7oBcsrDcgv0aHSKKhUNWCDjF/O8q6upnO33ZjO0DLVnDDkUPNmrsaysCIz9S34Tv8+nAUA+OFIluzxeTsysDM9p95NAoBjl/PxvxtO4GZxhWUb6OSy8kul36sUdCK1VFmlHrlFFSjV6WXvqYa+ic6wNQOTfz2TI3vceODxX1YfR//ZO3HwQp7J5eYYt5AqKeRY0qpkHIiaS8YxGASKyh2/ZZQhh+okhED6Na3dulhkt3Vo4q9Q9viGpiksx5e7LjS4/HNLEvF/yb9h9ub0OstlaIrw6bazKCx1/ErKFCEEVhzIxIHzebUCX0WVQX5ScqCzUr7RDCQPt9rVs3HLybXCcny0OR1ZN2uHIWupMhFiFu26fYXn3WdzcS6nqFYZY8b739T6atj6OjrGXXH1HSJKvExOU2/HKyuOoPcHO5BtImw7EoYcqtO/k7Lw5Jf78dYPx+29KTZ15HI+1iRl1V+wke42PF68UXfLUfSCfViy9yJm1ROGzFH6jKTESzcxa3M6/rw8CeEf/Yr/WX9CWlbhwGOebpXcDqWmjg3j3qGd6TlYdiATL31rvou0sUx1RxkHla92X8DQ+fukvzccy8bWO6ZjG6/DXEtO/Knr6P6PeJt85mroLGlVUvbHodFMfdwP/LfFblPKVRtvjXUx5FCdlu67CADYfjqnnpLO528b0+7qeZYEBFOzZaxZn574reCunmfptVBs7c5vl6eNxpJUVCnvyroNlW90SYOakGPc/Waqu6ohF+QzZsl/1tRxYK6LqqBUh2n/dxJ/WX1cdiVk47BWaea4mvjv6i9Rd/uZM2d3Ri6eWXwQF3KLay2rq1WpLoqZXdVEm6H0LziWYshpAvklOnyy9QzO19OMSw0gTP7aFKu32vot+YZ4t60ODa1o77bCutsTgK3U9bYqKg13dC8o+70Yyy+53fWm0+sx65d09J+9E9f+G2RsfSdwU8eBucHGJbrb4TLDqO7TGa2j0satbONWHkVKVgHe/qH2lZ4tmSUmmroiMvWaQmDtkSyc1WjrL9xIxvMv7twX9riytzUx5DSB6ZvSsHTfJYz48oC9N4XswJIuKFOtDtY8Kd/tmiqtMY2nCdX1vu4Mjo7UfWXcXVVRZcCKg5nQlldh6b7qa9pYOgU7bvcFvPBNouyxO89Zxsfbncvq666qYTAI2ZiaM9dvn5hlLTl2GnisMXHPLuNtqW+wurB9xsHmk9fx3o9pGLZgv41esVpTDW63F4acJpB8pfoy5hb1+RIAIEdbLrsKsbVU6g1N+o3eeM2WhZy7bMkx8VZOXS1s8GyISr0BezJyzd6zp6lbcrLzSxs1oLHOlpw7gmOpznG6r4y7eZL/ezsEY5aegD7bnoEjmfJbOdy5hrrqKVPHgamgUlFlkIWcvKLa3W7Vz7XPCdRUiDF+b7p6tsseW30iu6DO5U3VbWYwNPyLVnmlHmc1WkW3ljLkKNDFG8WY+K9knLpaaO9NsSlteSUiPklAv1k7pccaOqvhakGZ2ab8vOIKPPjxr/jrf07WWmb8zbUxH1Tjit+ScNuQQHToQh6W7b9U5/btO3cDf1h0AM8sPiRfYOYpXyacx6srj+IdE834wB1N+VY+MZVUVOHRubvx6NzdTfLNvnp21e2/y5po1k5TtOKXGm3rFzvP1VreFN1VdR2DpsbkmHqsokqP8srb6ymtvB3WjP/H9mrJMT22qOEtOcaUfEK3lMEgsODXc7IxbQYhGhxGX1l+BMMW7EfCmdym2sRGY8hRoL/8+zjiT2vw7NeH6i/sRC4ZzRiypDVkW9p1PPzpLnz4S+2L7AHA2iNZKCitxPpjv9VaZrVxOEbb2+juqjv+/tOyJHy05UydN9f8KfUaANQaYHkprwTvmQh3qw5dBgAknDVdOclOAFY+sdbcLBTAXV+hWF9Hd1pFpXyZuasH1zh9rRCjv0lE8hXLbl7ZFON+yuppdWqKrgTj4/XOtZs6+ZvqMiuvNKDCKKB9s/cShny+BzeKKmTrb8jFA5tCfQOo6wtfxrv92/2ZFt3QU8l2pOdgwa/nZY/phZDVSy51DMqpueHrndf+UhKGnCbQ2Hro3H9vNdCcL/9vyUWoPtueAQD4LvGKyeV1fUitpeJuQ05lw8teMboeiiXH2Nqj2bUeq2uPrEnKkk0btfa3b+MWyhIz3WX1Ka9jv90ZHOsLDuNWHkVSZj6eX5JYZ7m63M3tCjI0RfjDov341ehCkPUFMktep6FldXW0tJibDXWniio9yu/Y75l5Jfh2/yXZ+nVVyumukrcwWTDwGMCk1ba9pIapEG2N7x6muoyrx1cZZH/XR8ltWww5CuTuWvsUpDeIeitAR1BXs7Dx+ysqr/69IVcaVXt71PmarkYhR28QZj+0jfmgGoccS8bZ1FfWeH9ZM6u5mjjGAOC3W6X428Y0fL7jdldJzdiFKhPjmqr0BotD0PXC24NAa/7PlqrrwnEVVQbZSam+7qrcouoZTQYBLNt/qdYYloZoaGuX3iCkfTh1XSpOXdXi9e+PSctL6glkljSENKS+EELIgvadAb2uFjNj1WNyapctr9Q3+cDjhnQ1mfr31Hf9np9SryLik19xPOuWyS8VjX0v9W238dWnTXWBG38RdL3LysHUuB6DkH9RaEj3u5K78BhyFMjd9fa/5fHP9+BCbhFe+CYRPWdsR9zuuq+OK4TAX1YnY+DHv9Z5s8j6CCGw/mg2svNNX4PjfzecwItLE1GlN2DZ/ksYtmAfbhTVfauBrJul6DdrJ2ZvTkeV3oAXlyai03tb8MQXe3C1oAwlFbc/WHWd/PQGgQnfH8PUdakQQshCjqmKw/h8XlReWetDO23DCbywJBHrjtRu8ajvw/uPTafw7OKDKDba3oZUCl/vuYjhC/ebnPVhPAbGeD/M+Ol2d5zxViWcycF/jt/uimtIhXNnpViqq4IQAjeLaw/6rjQYUFxRhUf+uRtv/itZevyHI1no/Pdt6PL3bfWOH9uY8hui5+/DpRvFsmnS5lpyyiv1+D7xstmr+d7ZamDsL6uPI0d7+zUsGXj80ZYzeOGbRBgMAuNXHcWE74+Z3Z/Ge7C+E96NogqUV+rx+3l7pdlOuUXy//1Xu85jy8m6u0HWmOkWSDLxWW/I+67UC9nxemfobujYjPJKvdngqZN1fVq/VbD3BzvqrRdNqau7Sm8QeGdtKnK0FViy56LJL0DJV25h8Ge70em9LXj9u6MWnehrtvvLhPMmlx84n4fLRsd+SYUeLy9PQt8Pd+DtH1Jwq0SHggZczXzzyWv4aHO62VY9U5usNwgs2XtR+rshX9qUG3EAd3tvgDO623/4/J3nsP5Ytuyb56W8Evztx1PSjK3PtmdgRO+2iPnhOEb2uwevP3qvbB2FZZXYmqYBAPxy4hoeurfV7e0SosFdNzvTc/CuibEcQHWF9n/J1SfVtKuF0t2Mv95zETOe6ml2nd/su4jiiiosP5CJJ3u3xeFL1d+YL94owefbM/BY19ZSWW0d3VUZmiLpXk+Th9wHL/fbofB6YTlCA33u2N7bH9LCskr5nZWrDNjw3/dS079srKLKAC8PN5PbYTAI/OtwdReZ8eX4G9Jd9c/4swDkU21rGK+rrv1QY/x3x2R/19dycSK7QBb8rtwswdD5+/CHPu3wx37tapWv0gvsPpsLjbYcmvRyFJZVYlPKVdmNRmf9ko71EyPNvubUddVXJJ7582nZMVhiprVh6rpUbDulwdCeeVj6yoBay8t0DT9Z3s3tAi7llUjjlfKKdWjTwrPO8lV6gZvFFfBWucFHVV2tXi8sw7Y0DbqFtMCYZUnoHOSHS3kluJRXgpvFFbU+i8atZ7XW/99wYG7sw66zuRAAzucWY8zADvh2/yWzrUvGJzad3iAfT3bHyb6hs+zMteQIAdltNxrTXZVfosNHm9PxwoOhUr328ZYzKKvU47PtGZg8pHOdzxdC4OKNEuiqDOjZTl1nd9XVW7e/3Pl6mj5NvvVDivTF7tczucjRViDE38tk2Uq9QXabjsV7LqCsUo95O8/h7Se6yMquO5qFv/5HflHEU1cLsf989RWIfz5xDW1aeOKxrm2k5TX75t1h3aFyd8Wpq4X45eQ1fLO3+tIDgzq3wuPdg2ttl6ljxCCEdJ89wPTnp1RXhR2nTd9vr7xSb7bOtAeHDzlxcXH47LPPoNFo0LdvXyxatAgDBw606zbdbcvdQjOp/s4T4T9+OoVTV7XVTd13hBzjloE8o8pla9p1TFmbirnP9cGTvdtC5V53I97xrIJaj1XpDXB3c4XGTHfDioOZmBbdDd4q0we48bcJ7R0DTq8WlMku6V7TFCu7PoUQ0JZX4vS1260GezJuyKZBH7uSjxe+ScTQnsH48On7AQDFFbdfS1tWhTlbz0p/59bT+mTqA5uZV4LNJ65hRJ+20mMFZfLL8V8vLEOVXtQKXA1h/A2/JkxawlRrjLGn4w7KTtrf7r+EiioD/nP8N5MDoasMBtk38KcWHah188iiBo6tydGW41zO7cHRReVV0FUZoNMb4Gd0Mtl2qjqo70jPwaELeVh+IBMznuqJjq18AdTdknOnulo0zI3XOZ51e/r2oYt5eLrfPbXKGA8C/u1WGV5cmih1Nw3p1gZnNUWyrjnjQeEXcostmtlX1xgkAPhm3yV8899r6aRcuYUfG3gp/jKdXvZN/dCFPDy5cD9mj+yFfefycOFG7SsFm94+0y05BiFwOc/o5qkNaMmp1BsQt/sCHuvaBv07tJQe/2x7Bn5MuYofU67i2PQotPbzrLMF7c7Wi7xiHUbGHURxRRUOvfe4LMDduR7j4/tWqQ5lJsL4nS3X1wrL4OvpBm15FfxU7vD3qW5hTs0uwPNLDuHVQZ3w9xHVXwK93G/XKdrySqi9brdG3xlwgNo3AD6SmY/lBzJljy07kAm1twfefqILpm86hVSjKehrkrLxj02nMfz+ELz2SBjaBXhDU1gujWc0dudV2E2NG/zw53SsO1a75XveznNYtOs8/vVaBB7p0rrWcntw6JCzbt06xMbGYsmSJYiIiMCCBQsQHR2NjIwMBAUF2XvzAFTfy+XwpXy89kgneLi54uRvhRh+fwj0QkBTWA4flRvO5xTjcKb5rqU7TyA1iR6obv2ZEtUFLi4u0BsENhjNINp+OgdL913E2EGd8Nf/nIROb8CUdalYduASfte1DU5d1eLN392LELUXQgN9sCnlKorKq/BAhwCT3QjlVQacv1oou2/SxTsqwRGL9uOHCQ/hys1SXL5ZgmC1F24WV+DpfvfIPjwbkuUfkNTsAtk3ykmrjyNzzghZmWOXb+HTbWdl3z6SMvNlLR81LQbfJV5BtxA1rhaU4qbR8iOX8xF/WiP9bfxcU0Z8eQAf/LEXfFRueOjeVnAB8OdlSbhaUCab4mvcdLzq0GXsOpuLFl7u+HHSIExZl2rRB/7ghZt447H7sOpQJhaZuXnnb/ml+N8NJ2RBq8Y3+y6aeIaccQW92aiLZLOJ7pKtaRqkGIVeU3fHPnNdi4n/Ssagzq3w0sAOcHd1QWFZJQxCfnI3DjgAsPLgZfxwJEtq1QOAz57rIyvzp2VJAICzmiIcfO9xLNt/yaJ7HH267Sz6d2iJczlFuP8ef/S5xx9llXqo3F3xydYzJp+z22jW2TtrU7H/fB56tVPDy8MN205p8N6w7rKT5FNfyS/8uTvD/Ew4ADiXUyQL2H0/3FFn+RxtOZ5r4GzL+gJOmlHX4kNzEmRhoKaLZNTXlg3CPpFdYHKcV8KZXNwyuu5VzfWqUrIL4OfpDr1BYNXBy7LnjFmWhCOZ+Vjw63lc/ORJ7Dt3A3nFFbLZb8MW7MPSVwYg+9btYzEl6xbiT1cfqw90CMCbj90nW++yA5ekL0RDPt+D2SPvl5YVV1QhZs1xhKi98KeIDsjMu32c7sm4gT31/D8BIOlSPj7delZqEX77iS6I/X1X/JCUhUq9wLf7MxEa6IPQlj6yYLn+aDaiegRD7e2BQF+VyXWfv2PWZJqZ7uF/Hb6CMREdZAEHuD2+Z9mBTGxJu47Pn+9r8pIaALD5hLwOWHcsGx8+3QtVBgFvDze4ubrUCjg3SypwJDNf6n77639OokdbNSLCAjHu4U5wN3GzWVtxEUoeMVSPiIgIPPjgg/jqq68AAAaDAaGhoXjrrbfw3nvv1ft8rVYLf39/FBYWQq1WW227Bnz0q6wVxRR3VxerTs0NDfRGSYW+3pO2I7knwNvi+/I0JWv/z8i+XF2sM0OFLKO0z7WSdAtuIbslhtIEqz2h9vKoFbrqMui+Vljx6oNW78Jq6PnbYQce63Q6JCcnIyoqSnrM1dUVUVFRSEw0/S2koqICWq1W9tMUWvuZTuPGLD1ZhgZ6o3tIC7PLs/PLnCrgAHXfeDCohSc6tbK8K6gx7BVwGnI83Y2n+rZDVI/a/fSWerhzq/oL3cHPzDgHa2trZowE0LQBJzTQu+lWbsTT3RW92lVX8E+bGE+lBC4uwLBeIdLfpj7XTXWM30llQYtCfeOwmkJ9AeeZB2p3m/4pogP8PN3RLbgF7mvj21SbBgDI0VZYFHCA6q61O1uWbMlhu6vy8vKg1+sRHCyvpIODg3H27FmTz5kzZw4+/PDDJt+2+CmPobiiCmeua1FUXok+7QPg5eEGd1cXXMgtxllNEdxcAV+VO3q2U6N9Sx/szsjFzWIdHuvSGjnaCgT4eMDN1QUabTncXV3Qp30AbhZXYEvadUTe2wpdglvg0o1ipGYXoKC0Eu0CvBCk9kJoSx8cupiHYLUXCkorUWUwwNvDDe5urujUygcnfitE3/b+OJdTjCq9AeVVemjLqhDoq0KgrwrllXpcyC2Gn5c72rf0QUlFFcp0evh5uVfPjuoQgFslOri5uqBSb8DNEh0CfVQI79QSCWdyEdbaF1k3S+GtckOHQB+4ubrA090V53OL4eHmigEdW0Lt7YFdZ3ORW1QObw83dA9R4+CFPFQZBMJa++Che1vhzPUi5BaVI69Yh06tfBDoq0J+iQ7Bai9k55eif8eW8Fa54fiVW+jVzh8tvNzh4eaK1OwCtPZTISkzH2U6PR7oEIAjmfkIa+2Lnu3U2HE6B0IIdA1pAbWXBwpKK9GxlQ/yiivg7uqKYLUnWnh5QKc34ER2AdoFeMHT3Q0luiocvXwLbi4uaOvvhc5BfrhysxQ3SyrQsZUvzmmKEOzvhVa+qv+uT4fMvGJk3SxF+5Y+8Fa5wd/bA12C/XAiuxAdAn2QW1QOVxcXGIRAx1bV+62VnwrXC8tQVF6Fe1v7oaJKjy7BLXC9sAzJV27hsa5t0MLTXbqgXklFFe4L8kNQC0/kl+hw6OJNuLhUPx7eMRAXbxTD39sDV2+VoZWfCvcEeCP7Vil8Ve54oENLqNxdUVJRhSs3S3G1oAz+3h7o3yEAJ34rhKe7K/KKK9DazxOBviqcz61e1+W8EpRX6lGq0yO8Y0v0ae+P7adz0KNtC/h5uuPQxZvQllfi8e5B8PZwQ36JDgLVwaZMp4ebqwvUXh7Ycy4XD4S2xKW8YvRq549WvipUVBlwVqOF2tsD97XxQ6XeAG1ZJUp1ehy9nA+DqD4hVukFurdtgVNXCzG4WxB2pOegrb8XHggNwPbTObhZUoE+7QPQLzQAZzXV49eG3x+CvOIKnL6mRStfFar+e7+ljq184O7qiqsFZSgorUSJrgraskoMuq819AaB8io9bhRVoGuwH26VVkKI6nFTvp7ucHNxQZsWnricV4Lcogq8ODAUnu5u0FUZsO/cDQzo1BLp17Tw9HDFtYJy/K5bG9ws1uHsdS0MAgjx98L996iRfPkW/H080OG/Y7gu3ihBSUUVjmTmQwiBYH8vPNgpEBmaIvRp7w8PN1dk5ZeitZ8nOrbywYXcYnQPaYGn+7XD1VtliLyvNTq28sHRzHxczCuBp5srBnVuhcKySrQP8MGtUh2yb5WiQ6APsvPL4OoK6X/a554AaLTlcHOtvsaUj8oNKjdXpF0txLWCcpTqqtCnfQBCA72h9vLAkcx8BKk9UakXqKjUI9BXhcs3S9H7Hn8cvnQTPdupMaBjS+w5dwMFpTq4urigZ1s13FxdUKrTIzTQB/7eHqio0uPX9Fzo9Hp0bOWLzBslcHdzgb+3B7oGt8DJ3wrg5eGG/h1b4ufUawgN9IELgGC1F3RVBri7VddJKndXFJVXoai8Eio3NzwY1hLHLt/CpbwSPNWnLQwCuHyzBLnachRX6NGnvT9KKqrgo3KHiwvg4eaKSr0B97XxQ3F5FX49kwM/L3c8dG8r7D13A17ursgv0WHQfa3h6eGK4ooqhLXyxRmNFnqDwJnrWtwTUF335WjLUV6px8CwQNwoqsADHVri4o1inMguQNeQFtCWVcLVxQVF5VVo4eWOKoMBxRV65BVVoG+oPzoHtcDBC3koKK1Ep1Y+GNS5NSYP6YzrhWW4XlAOfx8PDO0ZjL8/2QM+KjeU6vTYfloDbw83DOgUiEBfFQ5fuonWfp5o4eUOLw833CqtnpmVqy3H1YIy3NfGDwYh0DW4BS7llSC8Y0tcuVmCDoE+uJxXKn0edVUGuLq4oERXBS8PNzzePQg3iioghEDa1UKU6vRo6++FLsEtkKMtR7fgFki7WogynV42AcbWHLa76tq1a7jnnntw6NAhREbentXx7rvvYu/evUhKSqr1nIqKClRU3O5G0mq1CA0NtXp3FRERETWdhnZXOWxLTuvWreHm5oacnBzZ4zk5OQgJCTH5HE9PT3h62r4JkoiIiGzPYcfkqFQqhIeHIyEhQXrMYDAgISFB1rJDREREzZPDtuQAQGxsLMaOHYsBAwZg4MCBWLBgAUpKSjBu3Dh7bxoRERHZmUOHnNGjR+PGjRuYMWMGNBoN+vXrh/j4+FqDkYmIiKj5cdiBx9bQVNfJISIioqbj9NfJISIiIqoLQw4RERE5JYYcIiIickoMOUREROSUGHKIiIjIKTHkEBERkVNiyCEiIiKnxJBDRERETsmhr3jcWDXXQdRqtXbeEiIiImqomvN2fdczbtYhp6ioCAAQGhpq5y0hIiIiSxUVFcHf39/s8mZ9WweDwYBr166hRYsWcHFxsdp6tVotQkNDkZ2dzdtFNDHua9vhvrYd7mvb4v62HWvtayEEioqK0K5dO7i6mh9506xbclxdXdG+ffsmW79areYHxka4r22H+9p2uK9ti/vbdqyxr+tqwanBgcdERETklBhyiIiIyCkx5DQBT09PzJw5E56envbeFKfHfW073Ne2w31tW9zftmPrfd2sBx4TERGR82JLDhERETklhhwiIiJySgw5RERE5JQYcoiIiMgpNYuQ8/XXX6NPnz7SxYciIyOxbds2AEB+fj7eeustdOvWDd7e3ujQoQPefvttFBYWytaRlZWFESNGwMfHB0FBQZg2bRqqqqqk5a+++ipcXFxq/fTq1Uu2nri4OHTq1AleXl6IiIjAkSNHZMuXLl2KwYMHQ61Ww8XFBQUFBbXeT35+PsaMGQO1Wo2AgACMHz8excXFVtpbDVfXfgUAjUaDl19+GSEhIfD19UX//v3xn//8R1p++fJljB8/HmFhYfD29sZ9992HmTNnQqfTyV7n5MmTePTRR+Hl5YXQ0FDMnTtXtvzHH3/EgAEDEBAQAF9fX/Tr1w//+te/ZGWEEJgxYwbatm0Lb29vREVF4fz587IyH3/8MQYNGgQfHx8EBASYfM/1HQdNad++fXjqqafQrl07uLi4YNOmTbLlP/74I4YOHYpWrVrBxcUFqampsuXWOtYBYPXq1ejbty98fHzQtm1bvPbaa7h586aszIYNG9C9e3d4eXmhd+/e2Lp1a633dObMGfzxj3+Ev78/fH198eCDDyIrK0taXl5ejsmTJ6NVq1bw8/PDqFGjkJOTcxd7r+HmzJmDBx98EC1atEBQUBBGjhyJjIyMWuUSExPx+OOPw9fXF2q1Go899hjKysqk5X/84x/RoUMHeHl5oW3btnj55Zdx7do12TqEEPj888/RtWtXeHp64p577sHHH38sLXf2eqUh+/rNN9/EfffdB29vb7Rp0wZPP/00zp49Ky0/ceIEXnrpJYSGhsLb2xs9evTAwoULa73Wnj170L9/f3h6eqJz585YtWqVbHl99RnQsOPx7bffRnh4ODw9PdGvXz+T77u+Oo2sSDQDP//8s9iyZYs4d+6cyMjIEH/729+Eh4eHOHXqlEhLSxPPPvus+Pnnn8WFCxdEQkKC6NKlixg1apT0/KqqKnH//feLqKgokZKSIrZu3Spat24t3n//falMQUGBuH79uvSTnZ0tAgMDxcyZM6Uya9euFSqVSqxYsUKcPn1aTJgwQQQEBIicnBypzPz588WcOXPEnDlzBABx69atWu9n2LBhom/fvuLw4cNi//79onPnzuKll15qkn1Xl7r2qxBC/P73vxcPPvigSEpKEhcvXhSzZ88Wrq6u4vjx40IIIbZt2yZeffVVsX37dnHx4kXx008/iaCgIPE///M/0msUFhaK4OBgMWbMGHHq1Cnxww8/CG9vb/HNN99IZXbv3i1+/PFHkZ6eLi5cuCAWLFgg3NzcRHx8vFTm008/Ff7+/mLTpk3ixIkT4o9//KMICwsTZWVlUpkZM2aIefPmidjYWOHv71/r/TbkOGhKW7duFX//+9/Fjz/+KACIjRs3ypZ///334sMPPxTffvutACBSUlJky611rB84cEC4urqKhQsXikuXLon9+/eLXr16iWeeeUYqc/DgQeHm5ibmzp0r0tPTxfTp04WHh4dIS0uTyly4cEEEBgaKadOmiePHj4sLFy6In376SfZ5mDhxoggNDRUJCQni2LFj4qGHHhKDBg2y0h41LTo6WqxcuVKcOnVKpKamiieffFJ06NBBFBcXS2UOHTok1Gq1mDNnjjh16pQ4e/asWLdunSgvL5fKzJs3TyQmJorLly+LgwcPisjISBEZGSl7rbfeekt069ZN/PTTT+LSpUvi2LFjYseOHdJyZ69XGrKvv/nmG7F3716RmZkpkpOTxVNPPSVCQ0NFVVWVEEKI5cuXi7ffflvs2bNHXLx4UfzrX/8S3t7eYtGiRdI6Ll26JHx8fERsbKxIT08XixYtqlVH1FefCdGw4/Gtt94SX331lXj55ZdF3759a73nhtRpZD3NIuSY0rJlS7Fs2TKTy9avXy9UKpWorKwUQlSfXFxdXYVGo5HKfP3110KtVouKigqT69i4caNwcXERly9flh4bOHCgmDx5svS3Xq8X7dq1E3PmzKn1/N27d5usjNLT0wUAcfToUemxbdu2CRcXF3H16tX633gTM96vvr6+4vvvv5ctDwwMFN9++63Z58+dO1eEhYVJfy9evFi0bNlStp//+te/im7dutW5HQ888ICYPn26EEIIg8EgQkJCxGeffSYtLygoEJ6enuKHH36o9dyVK1eaDDl3cxw0FVMhp0ZmZqbJkGPK3Rzrn332mbj33ntl6/nyyy/FPffcI/39wgsviBEjRsjKREREiDfffFP6e/To0eLPf/6z2W0rKCgQHh4eYsOGDdJjZ86cEQBEYmJive/NWnJzcwUAsXfvXumxiIgI6fhqqJ9++km4uLgInU4nhKj+LLu7u4uzZ882eB3OXq+Y2td3OnHihAAgLly4YLbMX/7yFzFkyBDp73fffVf06tVLVmb06NEiOjq6zu0xrs8sPR5nzpxpMuTcbZ1Gd6dZdFcZ0+v1WLt2LUpKShAZGWmyTGFhIdRqNdzdq2/tlZiYiN69eyM4OFgqEx0dDa1Wi9OnT5tcx/LlyxEVFYWOHTsCAHQ6HZKTkxEVFSWVcXV1RVRUFBITExu8/YmJiQgICMCAAQOkx6KiouDq6oqkpKQGr8faTO3XQYMGYd26dcjPz4fBYMDatWtRXl6OwYMHm11PYWEhAgMDpb8TExPx2GOPQaVSSY9FR0cjIyMDt27dqvV8IQQSEhKQkZGBxx57DACQmZkJjUYj2/f+/v6IiIiweN9behwo3d0c65GRkcjOzsbWrVshhEBOTg7+7//+D08++aT0nMTERNn+rllPzf42GAzYsmULunbtiujoaAQFBSEiIkLWBZecnIzKykrZerp3744OHTpY9H9rrJruvJrjMjc3F0lJSQgKCsKgQYMQHByM3/3udzhw4IDZdeTn52P16tUYNGgQPDw8AAC//PIL7r33XmzevBlhYWHo1KkTXn/9deTn55tdj7PXK3fu6zuVlJRg5cqVCAsLQ2hoaJ3rubMeqet4vJOp+sxax6OldRo1TrMJOWlpafDz84OnpycmTpyIjRs3omfPnrXK5eXlYfbs2XjjjTekxzQajazSByD9rdFoaq3j2rVr2LZtG15//XXZevV6vcn1mFqHORqNBkFBQbLH3N3dERgYaNF6rKWu/bp+/XpUVlaiVatW8PT0xJtvvomNGzeic+fOJtd14cIFLFq0CG+++ab0WEP3fWFhIfz8/KBSqTBixAgsWrQIv//972XlrLHvLTkOlO5uj/WHH34Yq1evxujRo6FSqRASEgJ/f3/ExcXVu56adeTm5qK4uBiffvophg0bhh07duCZZ57Bs88+i71790rrUKlUtcZHWfp/awyDwYApU6bg4Ycfxv333w8AuHTpEgDggw8+wIQJExAfH4/+/fvjiSeeqDXO669//St8fX3RqlUrZGVl4aeffpKWXbp0CVeuXMGGDRvw/fffY9WqVUhOTsZzzz1nclucvV4xta9rLF68GH5+fvDz88O2bduwc+dOWUgwdujQIaxbt65Bx7VWq5WNo6qrPrPW8ehs9YjSNZuQ061bN6SmpiIpKQmTJk3C2LFjkZ6eLiuj1WoxYsQI9OzZEx988MFdv9Z3332HgIAAjBw5snEb7QDq2q//+Mc/UFBQgF9//RXHjh1DbGwsXnjhBaSlpdVaz9WrVzFs2DA8//zzmDBhgsXb0aJFC6SmpuLo0aP4+OOPERsbiz179jT27Tmtxhzr6enpeOeddzBjxgwkJycjPj4ely9fxsSJExu8DoPBAAB4+umnMXXqVPTr1w/vvfce/vCHP2DJkiUWbU9Tmjx5Mk6dOoW1a9dKj9Vs+5tvvolx48bhgQcewPz589GtWzesWLFC9vxp06YhJSUFO3bsgJubG1555RWI/15k3mAwoKKiAt9//z0effRRDB48GMuXL8fu3btNDnR29nrF1L6uMWbMGKSkpGDv3r3o2rUrXnjhBZSXl9cqd+rUKTz99NOYOXMmhg4davE2NOQ8QY7F3d4bYCsqlUpqQQgPD8fRo0excOFCfPPNNwCAoqIiDBs2DC1atMDGjRulJmUACAkJqTVboWZEfUhIiOxxIQRWrFiBl19+WfZNo3Xr1nBzc6s1Ej8nJ6fWOuoSEhKC3Nxc2WNVVVXIz8+3aD3WYm6/vvvuu/jqq69w6tQpaSZI3759sX//fsTFxclOZNeuXcOQIUMwaNAgLF26VLb+kJAQk/usZlkNV1dXaTv69euHM2fOYM6cORg8eLBULicnB23btpWtx9zsB1MsOQ6UrLHH+pw5c/Dwww9j2rRpAIA+ffrA19cXjz76KD766CO0bdvW7P+tZh2tW7eGu7t7rdbUHj16SN0+ISEh0Ol0KCgokH17tvQzc7diYmKwefNm7Nu3D+3bt5cerzmGTG278cwwoPp9tm7dGl27dkWPHj0QGhqKw4cPIzIyEm3btoW7uzu6du0qWwdQPcOtW7du0uPOXq+Y29c1/P394e/vjy5duuChhx5Cy5YtsXHjRrz00ktSmfT0dDzxxBN44403MH36dNnzzR2ParUa3t7e0mN1nSesdTw2tE4j62g2LTl3qvkWBVR/qx06dChUKhV+/vlneHl5ycpGRkYiLS1NVgns3LkTarW6VkW3d+9eXLhwAePHj5c9rlKpEB4ejoSEBNk2JCQkmB0bZEpkZCQKCgqQnJwsPbZr1y4YDAZEREQ0eD1NpWa/lpaWAqgOH8bc3Nykb8JAdQvO4MGDER4ejpUrV9YqHxkZiX379qGyslJ6bOfOnejWrRtatmxZ73YAQFhYGEJCQmT7XqvVIikpyeJ939DjQKmscayXlpaa/L8CkFopIiMjZfu7Zj01+1ulUuHBBx+s1WJx7tw5abxJeHg4PDw8ZOvJyMhAVlaWRf83SwkhEBMTg40bN2LXrl0ICwuTLe/UqRPatWtX57abUnPc1xyXDz/8MKqqqnDx4kXZOgDUWo+z1iv17WtzzxFCSPsRAE6fPo0hQ4Zg7Nixsin4Neo7Hs0xrkesdTzebZ1Gd8leI55t6b333pOmIJ48eVK89957wsXFRezYsUMUFhaKiIgI0bt3b3HhwgXZdM2aKYo102qHDh0qUlNTRXx8vGjTpo3JqcN//vOfRUREhMntWLt2rfD09BSrVq0S6enp4o033hABAQGymSzXr18XKSkp0jTgffv2iZSUFHHz5k2pzLBhw8QDDzwgkpKSxIEDB0SXLl3sMoW8rv2q0+lE586dxaOPPiqSkpLEhQsXxOeffy5cXFzEli1bhBBC/Pbbb6Jz587iiSeeEL/99pts39coKCgQwcHB4uWXXxanTp0Sa9euFT4+PrLplp988onYsWOHuHjxokhPTxeff/65cHd3l83i+vTTT0VAQID46aefxMmTJ8XTTz9dawr5lStXREpKivjwww+Fn5+fSElJESkpKaKoqEgIYdlx0BSKioqkbQIg5s2bJ1JSUsSVK1eEEELcvHlTpKSkiC1btggAYu3atSIlJUXan9Y61leuXCnc3d3F4sWLxcWLF8WBAwfEgAEDxMCBA6UyBw8eFO7u7uLzzz8XZ86cETNnzqw1hfzHH38UHh4eYunSpeL8+fPStN79+/dLZSZOnCg6dOggdu3aJY4dO2ZyGra1TZo0Sfj7+4s9e/bI9lFpaalUZv78+UKtVosNGzaI8+fPi+nTpwsvLy9pxs/hw4fFokWLREpKirh8+bJISEgQgwYNEvfdd580zVyv14v+/fuLxx57TBw/flwcO3ZMREREiN///ve1tslZ65X69vXFixfFJ598Io4dOyauXLkiDh48KJ566ikRGBgoTZFPS0sTbdq0EX/+859l68jNzZVep2YK+bRp08SZM2dEXFxcrSnkddVnNRpyPJ4/f16kpKSIN998U3Tt2lX6zNbMpmpInUbW0yxCzmuvvSY6duwoVCqVaNOmjXjiiSekA7dmSqWpn8zMTGkdly9fFsOHDxfe3t6idevW4n/+53+kabc1CgoKhLe3t1i6dKnZbVm0aJHo0KGDUKlUYuDAgeLw4cOy5TNnzjS5LStXrpTK3Lx5U7z00kvCz89PqNVqMW7cOOlEbEt17VchhDh37px49tlnRVBQkPDx8RF9+vSRTSlfuXKl2X1v7MSJE+KRRx4Rnp6e4p577hGffvqpbPnf//530blzZ+Hl5SVatmwpIiMjxdq1a2VlDAaD+Mc//iGCg4OFp6eneOKJJ0RGRoaszNixY01uy+7du6UyDTkOmoq5Y3Xs2LFCCPP7s+aaKtY81r/88kvRs2dP4e3tLdq2bSvGjBkjfvvtN1mZ9evXi65duwqVSiV69eolhVtjy5cvl/53ffv2FZs2bZItLysrE3/5y19Ey5YthY+Pj3jmmWdkIbgpmNtHxp9BIYSYM2eOaN++vfDx8RGRkZGycHby5EkxZMgQERgYKDw9PUWnTp3ExIkTa+2jq1evimeffVb4+fmJ4OBg8eqrr8qChxDOXa/Ut6+vXr0qhg8fLoKCgoSHh4do3769+NOf/iSbdm/uvXXs2FH2Wrt37xb9+vUTKpVK3HvvvbX+n/XVZ0I07Hj83e9+V+9nrL46jazHRYj/ti8TEREROZFmOyaHiIiInBtDDhERETklhhwiIiJySgw5RERE5JQYcoiIiMgpMeQQERGRU2LIISIiIqfEkENEREROiSGHiIiInBJDDhERETklhhwiIiJySgw5RERE5JT+H2XzU4JpVMtJAAAAAElFTkSuQmCC\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 4: Replacing Extremely High Values with NaN**" + ], + "metadata": { + "id": "haYz2_Bi64iY" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset <=250,np.nan, inplace=True)\n", + "dataset.max().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "koX0JO5Lhxsx", + "outputId": "be4c4d47-3dea-4ffe-b880-c5e34fa18c68" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 39 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# **Step 5: Identifying Minimum Values**" + ], + "metadata": { + "id": "_Epu-WpR7bWK" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset >= 0, np.nan, inplace=True)\n", + "dataset.min().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "FrqsjeCqAjex", + "outputId": "2de3268c-0b63-457d-dae9-63444b522eab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 40 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step6:Calculate the percentage of NaN values in each column and droping columns with more than 80% NaN values**" + ], + "metadata": { + "id": "EbrC-CQs75hH" + } + }, + { + "cell_type": "code", + "source": [ + "nan_threshold = 0.8 * len(dataset)\n", + "dataset = dataset.dropna(axis=1, thresh=nan_threshold)\n", + "dataset" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 + }, + "id": "wv43s_LCjCrG", + "outputId": "eef4a1df-e901-495c-da0e-2ae71d8c874f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 3207010 29209010 11222030 14215010 33202110 14104030 8344010 \\\n", + "0 4.0 14.0 26.0 8.0 13.0 17.0 9.0 \n", + "1 4.0 14.0 26.0 8.0 13.0 17.0 9.0 \n", + "2 12.0 10.0 27.0 0.0 21.0 23.0 1.0 \n", + "3 5.0 13.0 19.0 2.0 16.0 12.0 6.0 \n", + "4 7.0 13.0 18.0 2.0 12.0 15.0 2.0 \n", + "... ... ... ... 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "dataset.min().plot()" + ], + "metadata": { + "id": "Jw8iXp1Bmlm2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "outputId": "c941ed36-069b-4434-cd78-882271169680" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 45 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step9: Saving the Processed Dataset After Applying Preprocessing Techniques**" + ], + "metadata": { + "id": "AIFYlZoe9CIR" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.to_csv('/content/updated_dataset.csv', index=False)\n", + "\n", + "# Print confirmation\n", + "print(\"Updated dataset saved to 'updated_dataset.csv'\")" + ], + "metadata": { + "id": "NM36izmEe-Hu", + "outputId": "5ef1e0c9-306d-4f12-e1cc-e612337ef901", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Updated dataset saved to 'updated_dataset.csv'\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step10: Read the Updated Dataset**" + ], + "metadata": { + "id": "oksNUDFq9cbX" + } + }, + { + "cell_type": "code", + "source": [ + "updated_dataset = 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When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 10ms/step - loss: 0.0020\n", + "Epoch 2/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 11ms/step - loss: 0.0011\n", + "Epoch 3/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 10ms/step - loss: 0.0011\n", + "Epoch 4/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 5/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 13ms/step - loss: 0.0011\n", + "Epoch 6/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 14ms/step - loss: 0.0011\n", + "Epoch 7/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 8/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 17ms/step - loss: 0.0011\n", + "Epoch 9/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 10/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 13ms/step - loss: 0.0011\n", + "\n", + "Overall Evaluation Metrics:\n", + "Overall RMSE: 3.8277\n", + "Overall MAE: 2.6643\n", + "Overall R²: 0.7188\n", + "\n", + "Predictions for the next 240 hours saved to predictions240hours.csv\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":85: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " index=pd.date_range(start=\"2023-10-31 23:00:00\", periods=240, freq='H') # Next 240 hours\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 12: Reading the Predicted Air Pollution Values for the Next 10 Days and 10 Columns**" + ], + "metadata": { + "id": "Mjz8KZh-986h" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "future_predictions_df=pd.read_csv(\"/content/predictions240hours.csv\")\n", + "future_predictions_df" + ], + "metadata": { + "id": "91Ogk-nHSl-9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "outputId": "b6bda5dd-b47b-4052-eb3c-fa634fdc2374" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0 29209010 11222030 14215010 33202110 14104030 \\\n", + "0 2023-10-31 23:00:00 5.345446 12.991408 13.275642 13.347651 9.517594 \n", + "1 2023-11-01 00:00:00 6.293464 12.215577 10.512154 12.283895 9.353355 \n", + "2 2023-11-01 01:00:00 6.888478 11.579747 8.811533 11.570954 8.992475 \n", + "3 2023-11-01 02:00:00 7.233640 11.040293 7.716928 11.069855 8.615580 \n", + "4 2023-11-01 03:00:00 7.414830 10.574987 6.981767 10.697695 8.278315 \n", + ".. ... ... ... ... ... ... \n", + "235 2023-11-10 18:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "236 2023-11-10 19:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "237 2023-11-10 20:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "238 2023-11-10 21:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "239 2023-11-10 22:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "\n", + " 8344010 43202020 28216010 28209010 24202530 \n", + "0 6.046752 22.072032 6.818363 5.402340 8.351093 \n", + "1 6.849514 19.619124 7.997456 5.982958 10.052781 \n", + "2 7.080735 17.630834 8.718381 6.535463 10.948564 \n", + "3 7.065603 16.051361 9.125044 6.989049 11.426456 \n", + "4 6.953997 14.811003 9.323415 7.330277 11.675499 \n", + ".. ... ... ... ... ... \n", + "235 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "236 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "237 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "238 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "239 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "\n", + "[240 rows x 11 columns]" + ], + "text/html": [ + "\n", + "
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+ ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step13: Drop the frist column it means timestamp column**" + ], + "metadata": { + "id": "9si47Uv8-Lbp" + } + }, + { + "cell_type": "code", + "source": [ + "future_predictions_df= future_predictions_df.drop(future_predictions_df.columns[0], axis=1)\n", + "future_predictions_df" + ], + "metadata": { + "id": "bI3g3hNzYs8P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "outputId": "d6532d94-6363-41cd-82d9-334da1c61ff2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 29209010 11222030 14215010 33202110 14104030 8344010 43202020 \\\n", + "0 5.345446 12.991408 13.275642 13.347651 9.517594 6.046752 22.072032 \n", + "1 6.293464 12.215577 10.512154 12.283895 9.353355 6.849514 19.619124 \n", + "2 6.888478 11.579747 8.811533 11.570954 8.992475 7.080735 17.630834 \n", + "3 7.233640 11.040293 7.716928 11.069855 8.615580 7.065603 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\"28209010\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.34746151129365044,\n \"min\": 5.402340084314346,\n \"max\": 7.846320018172264,\n \"num_unique_values\": 189,\n \"samples\": [\n 6.340882107615471,\n 6.340900577604771,\n 7.323002323508263\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"24202530\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4471713364257031,\n \"min\": 8.351093418896198,\n \"max\": 11.83024924993515,\n \"num_unique_values\": 188,\n \"samples\": [\n 9.85799190402031,\n 9.858012929558754,\n 10.956023439764977\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 51 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 14:Installing the Pami Library**" + ], + "metadata": { + "id": "SeBq0i5k-Zk8" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami" + ], + "metadata": { + "id": "ZICrUGlBTu3_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1013287d-3675-4f8e-df11-480bb1e9fa8f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2024.12.10.1-py3-none-any.whl.metadata (80 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (2.2.2)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.24.1)\n", + "Requirement already 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] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 15: Converting the DataFrame into Transactional Form**" + ], + "metadata": { + "id": "VB5fejB4-peW" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.convert import denseDF2DB as db\n", + "obj = db.denseDF2DB(future_predictions_df)\n", + "obj.convert2TransactionalDatabase('TDB.csv', '>=',6)" + ], + "metadata": { + "id": "2QM3VdAPZEEc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step16 Statistics**" + ], + "metadata": { + "id": "6GZfcSmBoNhZ" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.dbStats import TransactionalDatabase as tds\n", + "obj = tds.TransactionalDatabase('TDB.csv')\n", + "obj.run()\n", + "obj.printStats()\n", + "obj.plotGraphs()" + ], + "metadata": { + "id": "LqJ0GjQ_oPzD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "3df2474f-7539-42ef-ef4e-0ef2f72b1554" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size (total no of transactions) : 240\n", + "Number of items : 10\n", + "Minimum Transaction Size : 6\n", + "Average Transaction Size : 6.3\n", + "Maximum Transaction Size : 10\n", + "Standard Deviation Transaction Size : 0.8475454756727413\n", + "Variance in Transaction Sizes : 0.7213389121338912\n", + "Sparsity : 0.37\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 17: Extracting Patterns from Predicted Data Using the FP-Growth Algorithm**" + ], + "metadata": { + "id": "_CBreLxc_O77" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.frequentPattern.basic import FPGrowth as ab\n", + "obj = ab.FPGrowth('TDB.csv', 120)\n", + "obj.mine()\n", + "obj.printResults()\n", + "obj.save('10days_frequentPatterns.txt')" + ], + "metadata": { + "id": "hVfXDTa6VCjs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "71dba615-9aad-4e83-8ea2-f92927c9c341" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n", + "Total number of Frequent Patterns: 63\n", + "Total Memory in USS: 6328397824\n", + "Total Memory in RSS 6350974976\n", + "Total ExecutionTime in ms: 0.007161617279052734\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 18:Display the Frequent Patterns**" + ], + "metadata": { + "id": "YjN4QIDD_Jov" + } + }, + { + "cell_type": "code", + "source": [ + "!head /content/10days_frequentPatterns.txt" + ], + "metadata": { + "id": "xjUqMnHF4a7c", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7a7c80d6-9f5f-44d0-9c60-b31c27edc676" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "28209010:238\n", + "11222030\t28209010:238\n", + "33202110\t28209010:238\n", + "43202020\t28209010:238\n", + "28216010\t28209010:238\n", + "24202530\t28209010:238\n", + "11222030\t33202110\t28209010:238\n", + "11222030\t43202020\t28209010:238\n", + "11222030\t28216010\t28209010:238\n", + "11222030\t24202530\t28209010:238\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/notebooks/neurosymbolicai.ipynb b/notebooks/neurosymbolicai.ipynb new file mode 100644 index 00000000..510ee89c --- /dev/null +++ b/notebooks/neurosymbolicai.ipynb @@ -0,0 +1,5878 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Neuro-Symbolic AI**" + ], + "metadata": { + "id": "A8X39-fu2945" + } + }, + { + "cell_type": "markdown", + "source": [ + "## **Step 0: Download the dataset**" + ], + "metadata": { + "id": "XCDLK64u-CaK" + } + }, + { + "cell_type": "code", + "source": [ + "!wget -nc https://www.dropbox.com/scl/fi/vf5hoasomkbulr9xartz5/pm25_20180101_20231231.csv?rlkey=5m900gyo1f97uvevgnvar575b&st=vb1dwhtw&dl=1" + ], + "metadata": { + "id": "kCKj6ati-Hn_", + "outputId": "a5580a7b-4378-4ff3-c17b-0f01cfcf927e", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-12-27 07:19:01-- https://www.dropbox.com/scl/fi/vf5hoasomkbulr9xartz5/pm25_20180101_20231231.csv?rlkey=5m900gyo1f97uvevgnvar575b\n", + "Resolving www.dropbox.com (www.dropbox.com)... 162.125.5.18, 2620:100:601d:18::a27d:512\n", + "Connecting to www.dropbox.com (www.dropbox.com)|162.125.5.18|:443... connected.\n", + "HTTP request sent, awaiting response... 302 Found\n", + "Location: https://uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com/cd/0/inline/ChDLVbfjp1j9PgE9MHYjNN_QXaj8OYweD-CZTTTQS6XzmqMkrzC-hI1Zrhut0QobFoK6-dHbpKLybOAl45RP5iFaUjXTNfBRtQBBp1zaicEUXomzeyc4MI-w9CUoHl4Z9Y3gnmmJCOpmZWrUmYat85d2/file# [following]\n", + "--2024-12-27 07:19:01-- https://uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com/cd/0/inline/ChDLVbfjp1j9PgE9MHYjNN_QXaj8OYweD-CZTTTQS6XzmqMkrzC-hI1Zrhut0QobFoK6-dHbpKLybOAl45RP5iFaUjXTNfBRtQBBp1zaicEUXomzeyc4MI-w9CUoHl4Z9Y3gnmmJCOpmZWrUmYat85d2/file\n", + "Resolving uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com (uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com)... 162.125.5.15, 2620:100:601d:15::a27d:50f\n", + "Connecting to uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com (uc5524b9600a52eeb125186c9ec5.dl.dropboxusercontent.com)|162.125.5.15|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 233654053 (223M) [text/plain]\n", + "Saving to: ‘pm25_20180101_20231231.csv?rlkey=5m900gyo1f97uvevgnvar575b’\n", + "\n", + "pm25_20180101_20231 100%[===================>] 222.83M 55.4MB/s in 4.1s \n", + "\n", + "2024-12-27 07:19:06 (55.0 MB/s) - ‘pm25_20180101_20231231.csv?rlkey=5m900gyo1f97uvevgnvar575b’ saved [233654053/233654053]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "!mv pm25_20180101_20231231.csv?rlkey=5m900gyo1f97uvevgnvar575b pm25_20180101_20231231.csv" + ], + "metadata": { + "id": "Og3Hz2G3-zbE" + }, + "execution_count": 8, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!head pm25_20180101_20231231.csv" + ], + "metadata": { + "id": "GeC6gfSN-MXH", + "outputId": "974af4a0-4230-4751-f167-a6234ef20f70", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "TimeStamp,3207010,29209010,11222030,14215010,33202110,14104030,8344010,43202020,28216010,28209010,24202530,11202040,13114510,20206010,13212520,4101020,11240010,13120010,28203060,37201080,1102510,14109040,21220010,7201200,47211050,30201300,11237020,33209010,17201310,22201060,13211010,40132530,18202020,12225090,13110010,12219060,15102510,43201180,13104540,14211510,40214010,11346510,37205010,2205010,12344020,27218010,3211070,20485950,13209010,14112020,27367010,30203010,33202530,11201510,5205010,25212010,38208030,13201040,14106010,24207510,30205010,33202400,11239010,28105010,25206510,15108010,9204020,14201050,30201270,28217010,28214510,22202150,23110040,27209510,35202020,12208510,37202010,13123010,14203040,18204010,34369010,13203510,43211050,21213020,7206050,13221010,8406050,24212010,36402010,35211010,8541010,28108010,40223510,14101010,19201011,25201540,33202420,28101520,15216010,28108510,31202020,30322010,12327010,4205020,1107020,14212020,5201010,28203020,27217510,1213510,23202030,28207030,10425010,8217010,13108520,11208070,34212030,40134510,6203010,44212010,2203510,14103510,21218010,23103020,43201510,13109030,29202010,16201021,28214010,42205010,30202140,27228010,33423010,23226010,34207510,17206010,24213010,14135530,1202080,12226010,3201070,28204520,14131530,33201040,14204530,27227020,23201030,38202100,25201080,29363010,38204010,13123030,34201520,13111520,7368010,22201090,41210010,36361010,23227010,11234010,29201020,7203130,28108020,39381030,45441010,46201520,35204010,17207010,4421010,1101540,11218510,43201150,32202040,28201530,8215010,13209020,4201210,11203530,13111540,14105020,45206030,23216110,15201060,38201090,42201300,14132060,27229510,11201040,40132010,8405010,38401010,27146010,11326010,30201080,34202010,40206010,32209010,45205010,7205050,38212040,38203010,23101010,6206010,42392020,43444010,46212010,30201130,14115010,45403010,22205010,33205020,41201030,11212020,37386010,22202040,43531010,40220010,22219010,26110510,36202010,6205010,21203010,28210180,25206030,13202010,15207020,40106010,23201060,14113510,43205160,13119510,41206010,27216010,27202060,38201080,16408060,42209010,22220010,2201080,4201540,43403010,28204570,16342060,26362020,42309010,23107020,12201050,20421030,9205010,13204510,4201560,10210010,25204050,8211010,11219020,23427510,14207510,14110510,33210010,34105010,16201110,23208530,31203020,5206060,22213510,26213010,6209010,14133010,20216510,18441010,28110010,13113510,10205010,43201520,28205010,23202510,40103010,11221510,12481160,41202160,13121020,12408010,17361010,28213010,27201090,14209010,35202360,28206090,14136010,39212010,42210010,13114010,14321010,27201600,22204090,21212510,24205010,11211010,22132540,28202540,11209010,14209050,34207040,39201350,23113010,26201040,27216520,29451010,32207060,42314020,23210040,42201030,2203050,28382010,12219110,17201320,12219010,13108020,27143510,28201050,19202030,14131100,22204100,27210020,38380010,32203040,15206050,13121510,35321010,18361010,43201190,1103520,21214510,13101010,26104510,28102010,22207010,1204050,12221020,27113510,44206010,1102010,13208020,40621010,23223010,12217030,7204210,13108530,17208010,47201140,3201550,24204010,20202510,4201080,37203010,14217020,23206530,17201650,14153510,12206010,27207030,4211010,4105530,4201050,7542010,38209050,9203010,45202020,46201130,11202510,34209020,9201580,39203010,17201090,27203030,11205530,45201050,43201160,13111530,22214010,35203010,33204040,10203010,28109010,27106010,33503010,16202520,11203060,13102510,3202050,37201010,28211010,28102040,14206010,12203080,21201010,43443010,27104010,30207010,12224040,11208060,27321010,45204010,34201510,11229510,27115010,12201260,27301010,30201020,45201530,26206010,23104510,14135070,23302010,13222510,36342010,13203010,34203010,1102020,15224010,40135020,12204110,7203520,14131020,13104010,27201080,3204010,15201270,28216520,14134010,19205020,37201090,25201510,43208010,19201510,1204150,8216010,13112010,40205010,33202090,9211010,36205010,13218010,35201030,28212010,11421010,27225010,4541010,41203010,18207010,42211010,33202140,8407010,13109520,23211020,36483010,42322060,42208040,37201550,44202020,29208010,27123010,11216010,13112520,14136510,14131540,26212010,22203010,34211200,11232010,26203010,27211010,27206030,13110510,12481520,37322010,26366030,47301950,35207090,4210010,11205120,1207090,35201040,44201370,26110010,12220010,40105010,12201570,28208010,23215010,22202520,30301030,14102010,26104060,13217510,42201280,39206020,8441010,12212040,33211060,11238010,16201400,11343010,8213010,14212520,23110050,29205510,14213510,35208010,22218010,43202520,2202510,11232510,11203040,24323010,42213010,38201030,23621071,14131030,21211010,24206020,33201210,40135510,13105530,42202160,11362010,11408510,17201110,22202080,23111021,38201020,28107010,40101020,28202020,28101030,23201540,6202010,13303520,8402010,1206290,28201060,11212520,10207010,29204010,27209010,11208530,26102510,12213010,14107010,46208010,1213220,40107020,43204200,22201030,33202180,26303510,17201290,22206510,24209210,37201100,33208010,7204110,4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+ "2018-01-01 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+ "2018-01-01 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+ "2018-01-01 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+ "2018-01-01 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+ "2018-01-01 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+ "2018-01-01 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 4: Replacing Extremely High Values with NaN**" + ], + "metadata": { + "id": "haYz2_Bi64iY" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset <=250,np.nan, inplace=True)\n", + "dataset.max().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "koX0JO5Lhxsx", + "outputId": "be4c4d47-3dea-4ffe-b880-c5e34fa18c68" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 39 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "# **Step 5: Identifying Minimum Values**" + ], + "metadata": { + "id": "_Epu-WpR7bWK" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.where(dataset >= 0, np.nan, inplace=True)\n", + "dataset.min().plot()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "id": "FrqsjeCqAjex", + "outputId": "2de3268c-0b63-457d-dae9-63444b522eab" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 40 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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+ }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step6:Calculate the percentage of NaN values in each column and droping columns with more than 80% NaN values**" + ], + "metadata": { + "id": "EbrC-CQs75hH" + } + }, + { + "cell_type": "code", + "source": [ + "nan_threshold = 0.8 * len(dataset)\n", + "dataset = dataset.dropna(axis=1, thresh=nan_threshold)\n", + "dataset" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 443 + }, + "id": "wv43s_LCjCrG", + "outputId": "eef4a1df-e901-495c-da0e-2ae71d8c874f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 3207010 29209010 11222030 14215010 33202110 14104030 8344010 \\\n", + "0 4.0 14.0 26.0 8.0 13.0 17.0 9.0 \n", + "1 4.0 14.0 26.0 8.0 13.0 17.0 9.0 \n", + "2 12.0 10.0 27.0 0.0 21.0 23.0 1.0 \n", + "3 5.0 13.0 19.0 2.0 16.0 12.0 6.0 \n", + "4 7.0 13.0 18.0 2.0 12.0 15.0 2.0 \n", + "... ... ... ... 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [ + "dataset.min().plot()" + ], + "metadata": { + "id": "Jw8iXp1Bmlm2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 447 + }, + "outputId": "c941ed36-069b-4434-cd78-882271169680" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ] + }, + "metadata": {}, + "execution_count": 45 + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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Vwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABW8Vq4OXjwoIYPH67g4GCFhoYqNTVVhw8frnWZ48ePa+zYsWrTpo1atmypIUOGqLCw0D3/m2++0bBhwxQdHa2goCD17NlT06dP99YmAACAJshr4Wb48OHKy8vTihUrtGTJEq1evVpjxoypdZkHH3xQH330kRYuXKhVq1apoKBAN954o3t+Tk6O2rVrp7feekt5eXl69NFHlZ6erhkzZnhrMwAAQBPjMMaYhm5027Zt6tWrl9atW6d+/fpJkpYtW6brr79e3333naKioqosU1xcrPDwcM2bN0833XSTJGn79u3q2bOnsrOzdfnll1e7rrFjx2rbtm1auXJlnftXUlKikJAQFRcXKzg4+Cy2EAAANLa6fn575cpNdna2QkND3cFGkhITE+Xj46O1a9dWu0xOTo7Ky8uVmJjontajRw/FxMQoOzu7xnUVFxcrLCys4ToPAACaNF9vNOpyudSuXTvPFfn6KiwsTC6Xq8Zl/P39FRoa6jE9IiKixmXWrFmjBQsW6OOPP661P6WlpSotLXW/LykpqcNWAACApqheV24mTpwoh8NR62v79u3e6quHLVu2aPDgwcrIyNDAgQNrrc3MzFRISIj7FR0d3Sh9BAAAja9eV27Gjx+v22+/vdaaCy64QJGRkTpw4IDH9BMnTujgwYOKjIysdrnIyEiVlZWpqKjI4+pNYWFhlWW2bt2qhIQEjRkzRpMmTTpjv9PT05WWluZ+X1JSQsABAMBS9Qo34eHhCg8PP2Od0+lUUVGRcnJyFBcXJ0lauXKlKisrFR8fX+0ycXFx8vPzU1ZWloYMGSJJys/P1969e+V0Ot11eXl5uvbaazVq1Cg99dRTdep3QECAAgIC6lQLAACaNq88LSVJ1113nQoLCzVr1iyVl5dr9OjR6tevn+bNmydJ2r9/vxISEvTmm2+qf//+kqR77rlHS5cu1dy5cxUcHKxx48ZJOnlvjXTyq6hrr71WSUlJeu6559zratasWZ1C1yk8LQUAQNNT189vr9xQLElvv/227r33XiUkJMjHx0dDhgzRSy+95J5fXl6u/Px8HT161D1t2rRp7trS0lIlJSXp5Zdfds9/99139cMPP+itt97SW2+95Z7eqVMn7d6921ubAgAAmhCvXbk5n3HlBgCApuec/p0bAACAc4VwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYxWvh5uDBgxo+fLiCg4MVGhqq1NRUHT58uNZljh8/rrFjx6pNmzZq2bKlhgwZosLCwmprf/rpJ3Xs2FEOh0NFRUVe2AIAANAUeS3cDB8+XHl5eVqxYoWWLFmi1atXa8yYMbUu8+CDD+qjjz7SwoULtWrVKhUUFOjGG2+stjY1NVV9+vTxRtcBAEAT5jDGmIZudNu2berVq5fWrVunfv36SZKWLVum66+/Xt99952ioqKqLFNcXKzw8HDNmzdPN910kyRp+/bt6tmzp7Kzs3X55Ze7a1955RUtWLBAkydPVkJCgn7++WeFhobWuX8lJSUKCQlRcXGxgoODf93GAgCARlHXz2+vXLnJzs5WaGioO9hIUmJionx8fLR27dpql8nJyVF5ebkSExPd03r06KGYmBhlZ2e7p23dulVPPPGE3nzzTfn41K37paWlKikp8XgBAAA7eSXcuFwutWvXzmOar6+vwsLC5HK5alzG39+/yhWYiIgI9zKlpaUaNmyYnnvuOcXExNS5P5mZmQoJCXG/oqOj67dBAACgyahXuJk4caIcDketr+3bt3urr0pPT1fPnj1122231Xu54uJi92vfvn1e6iEAADjXfOtTPH78eN1+++211lxwwQWKjIzUgQMHPKafOHFCBw8eVGRkZLXLRUZGqqysTEVFRR5XbwoLC93LrFy5Ups3b9a7774rSTp1u1Dbtm316KOP6vHHH6+27YCAAAUEBNRlEwEAQBNXr3ATHh6u8PDwM9Y5nU4VFRUpJydHcXFxkk4Gk8rKSsXHx1e7TFxcnPz8/JSVlaUhQ4ZIkvLz87V37145nU5J0nvvvadjx465l1m3bp3uuOMOffHFF7rwwgvrsykAAMBS9Qo3ddWzZ08NGjRId955p2bNmqXy8nLde++9uvXWW91PSu3fv18JCQl688031b9/f4WEhCg1NVVpaWkKCwtTcHCwxo0bJ6fT6X5S6pcB5scff3Svrz5PSwEAAHt5JdxI0ttvv617771XCQkJ8vHx0ZAhQ/TSSy+555eXlys/P19Hjx51T5s2bZq7trS0VElJSXr55Ze91UUAAGAhr/ydm/Mdf+cGAICm55z+nRsAAIBzhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABWIdwAAACrEG4AAIBVCDcAAMAqhBsAAGAVwg0AALAK4QYAAFiFcAMAAKxCuAEAAFYh3AAAAKsQbgAAgFUINwAAwCqEGwAAYBXCDQAAsArhBgAAWIVwAwAArOJ7rjtwLhhjJEklJSXnuCcAAKCuTn1un/ocr8lvMtwcOnRIkhQdHX2OewIAAOrr0KFDCgkJqXG+w5wp/liosrJSBQUFatWqlRwOR4O2XVJSoujoaO3bt0/BwcEN2jY8MdaNi/FuPIx142K8G9evGW9jjA4dOqSoqCj5+NR8Z81v8sqNj4+POnbs6NV1BAcH80PSSBjrxsV4Nx7GunEx3o3rbMe7tis2p3BDMQAAsArhBgAAWIVw08ACAgKUkZGhgICAc90V6zHWjYvxbjyMdeNivBtXY4z3b/KGYgAAYC+u3AAAAKsQbgAAgFUINwAAwCqEGwAAYBWrw80rr7yiPn36uP9QkNPp1CeffCJJOnjwoMaNG6fu3bsrKChIMTExuu+++1RcXOzRxt69e5WcnKzmzZurXbt2mjBhgk6cOOGef/vtt8vhcFR5XXTRRR7tzJw5U507d1ZgYKDi4+P19ddfe8yfPXu2rr76agUHB8vhcKioqKjK9hw8eFDDhw9XcHCwQkNDlZqaqsOHDzfQaNVPbWMrSS6XSyNGjFBkZKRatGihSy+9VO+99557/u7du5WamqouXbooKChIF154oTIyMlRWVuaxnk2bNukPf/iDAgMDFR0drWeffdZj/vvvv69+/fopNDRULVq0UGxsrP7nf/7Ho8YYo8mTJ6t9+/YKCgpSYmKivv32W4+ap556SgMGDFDz5s0VGhpa7Taf6VhoLFOmTJHD4dADDzzgnnb11VdXOQbvvvtu9/xvvvlGw4YNU3R0tIKCgtSzZ09Nnz69Stuff/65Lr30UgUEBKhr166aO3duvfohScePH9fYsWPVpk0btWzZUkOGDFFhYaFHzX333ae4uDgFBAQoNja22vbPtO+9afXq1brhhhsUFRUlh8OhxYsXe8x///33NXDgQLVp00YOh0MbN26s0sZdd92lCy+8UEFBQQoPD9fgwYO1fft2j5p169YpISFBoaGhat26tZKSkvTNN9+453/++ecaPHiw2rdv7z6+33777SrrWrhwoXr06KHAwED17t1bS5curXd/67LfvOVM4326u+++Ww6HQy+++KJ7WkOdT+bOnVvl5ygwMNCjpi7nk86dO1dpZ8qUKfXqC34dq8NNx44dNWXKFOXk5Gj9+vW69tprNXjwYOXl5amgoEAFBQWaOnWqtmzZorlz52rZsmVKTU11L19RUaHk5GSVlZVpzZo1+tvf/qa5c+dq8uTJ7prp06fr+++/d7/27dunsLAw3Xzzze6aBQsWKC0tTRkZGdqwYYP69u2rpKQkHThwwF1z9OhRDRo0SI888kiN2zN8+HDl5eVpxYoVWrJkiVavXq0xY8Y08KjVTW1jK0kjR45Ufn6+PvzwQ23evFk33nijbrnlFuXm5kqStm/frsrKSr366qvKy8vTtGnTNGvWLI/tLykp0cCBA9WpUyfl5OToueee01/+8hfNnj3bXRMWFqZHH31U2dnZ2rRpk0aPHq3Ro0dr+fLl7ppnn31WL730kmbNmqW1a9eqRYsWSkpK0vHjx901ZWVluvnmm3XPPfdUu711ORYaw7p16/Tqq6+qT58+VebdeeedHsfi6SfLnJwctWvXTm+99Zby8vL06KOPKj09XTNmzHDX7Nq1S8nJybrmmmu0ceNGPfDAA/qP//gPj7GsSz8efPBBffTRR1q4cKFWrVqlgoIC3XjjjVXq7rjjDg0dOrTa7azLvvemI0eOqG/fvpo5c2aN86+44go988wzNbYRFxenOXPmaNu2bVq+fLmMMRo4cKAqKiokSYcPH9agQYMUExOjtWvX6p///KdatWqlpKQklZeXS5LWrFmjPn366L333nMf3yNHjtSSJUvc61mzZo2GDRum1NRU5ebmKiUlRSkpKdqyZUu9+lvX/eYNZxrvUxYtWqSvvvpKUVFRHtMb6nwinfyruaf/HO3Zs8djfl3OJ5L0xBNPeLQzbty4evcFv4L5jWndurX57//+72rnvfPOO8bf39+Ul5cbY4xZunSp8fHxMS6Xy13zyiuvmODgYFNaWlptG4sWLTIOh8Ps3r3bPa1///5m7Nix7vcVFRUmKirKZGZmVln+s88+M5LMzz//7DF969atRpJZt26de9onn3xiHA6H2b9//5k3vBGcPrYtWrQwb775psf8sLAw89prr9W4/LPPPmu6dOnifv/yyy+b1q1be4z1ww8/bLp3715rPy655BIzadIkY4wxlZWVJjIy0jz33HPu+UVFRSYgIMD8/e9/r7LsnDlzTEhISJXpZ3MsNLRDhw6Zbt26mRUrVpirrrrK3H///e55v3xfF3/+85/NNddc437/0EMPmYsuusijZujQoSYpKanO/SgqKjJ+fn5m4cKF7mnbtm0zkkx2dnaVPmRkZJi+fftWmX62+94bJJlFixZVO2/Xrl1GksnNzT1jO998842RZHbs2GGMMWbdunVGktm7d6+7ZtOmTUaS+fbbb2ts5/rrrzejR492v7/llltMcnKyR018fLy566676tzf+u43b6ppvL/77jvToUMHs2XLFtOpUyczbdq0Wts5m/NJTT//p9T1fHKm/p1Px7etrL5yc7qKigrNnz9fR44ckdPprLamuLhYwcHB8vU9+U9uZWdnq3fv3oqIiHDXJCUlqaSkxH2F4pdef/11JSYmqlOnTpJOXhHIyclRYmKiu8bHx0eJiYnKzs6uc/+zs7MVGhqqfv36uaclJibKx8dHa9eurXM73lDd2A4YMEALFizQwYMHVVlZqfnz5+v48eO6+uqra2ynuLhYYWFh7vfZ2dm68sor5e/v756WlJSk/Px8/fzzz1WWN8YoKytL+fn5uvLKKyWdvBrhcrk8xj8kJETx8fH1Hv/6HgsNbezYsUpOTvbYltO9/fbbatu2rS6++GKlp6fr6NGjtbZX3Xj/su2kpKQq41RbP3JyclReXu4xr0ePHoqJian3eNdn35/vjhw5ojlz5qhLly6Kjo6WJHXv3l1t2rTR66+/rrKyMh07dkyvv/66evbsqc6dO9fY1tnut9o01H7zlsrKSo0YMUITJkyo8pV/Tc72fHL48GF16tRJ0dHRHlejpfqdT6ZMmaI2bdrokksu0XPPPefxFbZtx/f5yPp/OHPz5s1yOp06fvy4WrZsqUWLFqlXr15V6n788Uc9+eSTHl/zuFwujw8zSe73LperShsFBQX65JNPNG/ePI92Kyoqqm3nl9+/18blcqldu3Ye03x9fRUWFlZtXxpDbWP7zjvvaOjQoWrTpo18fX3VvHlzLVq0SF27dq22rR07duivf/2rpk6d6p7mcrnUpUsXj7rTx79169aSTp7EOnTooNLSUjVr1kwvv/yy/vjHP7rrTl/u9HbqM271PRYa2vz587VhwwatW7eu2vl/+tOf1KlTJ0VFRWnTpk16+OGHlZ+fr/fff7/a+jVr1mjBggX6+OOP3dNq2saSkhIdO3ZMQUFBZ+yHy+WSv79/lfuWzma867Lvz3cvv/yyHnroIR05ckTdu3fXihUr3B9orVq10ueff66UlBQ9+eSTkqRu3bpp+fLl7l+wfumdd95xfyV4Sk37rb7j3RD7zVueeeYZ+fr66r777qtT/dmeT7p376433nhDffr0UXFxsaZOnaoBAwYoLy9PHTt2rPP55L777tOll16qsLAwrVmzRunp6fr+++/1wgsv1Lkv+HWsDzfdu3fXxo0bVVxcrHfffVejRo3SqlWrPAJOSUmJkpOT1atXL/3lL38563X97W9/U2hoqFJSUn59x5uA2sb2scceU1FRkf7xj3+obdu2Wrx4sW655RZ98cUX6t27t0c7+/fv16BBg3TzzTfrzjvvrHc/WrVqpY0bN+rw4cPKyspSWlqaLrjgglqvEjUl+/bt0/33368VK1ZUubnxlNNDee/evdW+fXslJCRo586duvDCCz1qt2zZosGDBysjI0MDBw5s0H7A0/Dhw/XHP/5R33//vaZOnapbbrlFX375pQIDA3Xs2DGlpqbq97//vf7+97+roqJCU6dOVXJystatW6egoCCPtj777DONHj1ar732Wp2vXtggJydH06dP14YNG+RwOM5Y/2vOJ06n0+PK/oABA9SzZ0+9+uqr7gBaF2lpae7/79Onj/z9/XXXXXcpMzOTf+KhkVj/tZS/v7+6du2quLg4ZWZmqm/fvh5PiRw6dEiDBg1Sq1attGjRIvn5+bnnRUZGVnla4NT7yMhIj+nGGL3xxhsaMWKEx6XGtm3bqlmzZtW288s2ahMZGelxA7IknThxQgcPHqxXOw2pprHduXOnZsyYoTfeeEMJCQnq27evMjIy1K9fvyo3DBYUFOiaa67RgAEDqtxMV9fx9/HxUdeuXRUbG6vx48frpptuUmZmpkddQ4x/XY+FhpaTk6MDBw7o0ksvla+vr3x9fbVq1Sq99NJL8vX1dd+gerr4+HhJJ3+DPd3WrVuVkJCgMWPGaNKkSR7zatrG4OBgBQUF1akfkZGRKisrq/K0X1Ma74YUEhKibt266corr9S7776r7du3a9GiRZKkefPmaffu3ZozZ44uu+wyXX755Zo3b5527dqlDz74wKOdVatW6YYbbtC0adM0cuRIj3k1jVV9x7sh9ps3fPHFFzpw4IBiYmLcx92ePXs0fvz4Kl/fNcT55HR+fn665JJL3D9HZ3s+iY+P14kTJ7R79+6z7gvqx/pw80uVlZUqLS2V9K871v39/fXhhx9W+W3U6XRq8+bNHqFixYoVCg4OrvLV1qpVq7Rjxw6Pp62kkwEgLi5OWVlZHn3Iysqq8d6f6jidThUVFSknJ8c9beXKlaqsrHR/kJ1rp8b21L0ePj6eh1ezZs1UWVnpfr9//35dffXV7qdKflnvdDq1evVq95Mj0snx7969e62XbU/fx126dFFkZKTH+JeUlGjt2rX1Hv+6HgsNLSEhQZs3b9bGjRvdr379+mn48OHauHGjmjVrVmWZU4/6tm/f3j0tLy9P11xzjUaNGqWnnnqqyjJOp9NjnKST23hqnOrSj7i4OPn5+Xm0k5+fr71799Z7vM9m35/PjDEyxriPzaNHj8rHx8fjasSp96f/nHz++edKTk7WM888U+3TkWfab3XRUPvNG0aMGKFNmzZ5HHdRUVGaMGGCx5N83jifVFRUaPPmze6fo7M9n2zcuFE+Pj7uWwtsPL7PO+f2fmbvmjhxolm1apXZtWuX2bRpk5k4caJxOBzm008/NcXFxSY+Pt707t3b7Nixw3z//ffu14kTJ4wxxpw4ccJcfPHFZuDAgWbjxo1m2bJlJjw83KSnp1dZ12233Wbi4+Or7cf8+fNNQECAmTt3rtm6dasZM2aMCQ0N9Xjy5vvvvze5ubnmtddeM5LM6tWrTW5urvnpp5/cNYMGDTKXXHKJWbt2rfnnP/9punXrZoYNG9bAo1Y3tY1tWVmZ6dq1q/nDH/5g1q5da3bs2GGmTp1qHA6H+fjjj40xJ5986Nq1q0lISDDfffedx/ifUlRUZCIiIsyIESPMli1bzPz5803z5s3Nq6++6q55+umnzaeffmp27txptm7daqZOnWp8fX09nsqaMmWKCQ0NNR988IHZtGmTGTx4sOnSpYs5duyYu2bPnj0mNzfXPP7446Zly5YmNzfX5ObmmkOHDhlj6ncsNIbTn1LasWOHeeKJJ8z69evNrl27zAcffGAuuOACc+WVV7rrN2/ebMLDw81tt93mMdYHDhxw1/zf//2fad68uZkwYYLZtm2bmTlzpmnWrJlZtmxZnfpxyt13321iYmLMypUrzfr1643T6TROp9Oj5ttvvzW5ubnmrrvuMr/73e/c433q6ZG67HtvOnTokLtPkswLL7xgcnNzzZ49e4wxxvz0008mNzfXfPzxx0aSmT9/vsnNzXUfvzt37jRPP/20Wb9+vdmzZ4/58ssvzQ033GDCwsJMYWGhMebk00gBAQHmnnvuMVu3bjVbtmwxt912mwkJCTEFBQXGGGNWrlxpmjdvbtLT0z322+nnhS+//NL4+vqaqVOnmm3btpmMjAzj5+dnNm/e7K45U3+Nqdt+85Yzjfcv/fJppIY6nzz++ONm+fLlZufOnSYnJ8fceuutJjAw0OTl5blrznQ+WbNmjZk2bZrZuHGj2blzp3nrrbdMeHi4GTlyZL36gl/H6nBzxx13mE6dOhl/f38THh5uEhISzKeffmqM+dcj19W9du3a5W5j9+7d5rrrrjNBQUGmbdu2Zvz48e5HxU8pKioyQUFBZvbs2TX25a9//auJiYkx/v7+pn///uarr77ymJ+RkVFtX+bMmeOu+emnn8ywYcNMy5YtTXBwsBk9erT7w7ex1Ta2xhjzv//7v+bGG2807dq1M82bNzd9+vTxeDR8zpw5NY7/6b755htzxRVXmICAANOhQwczZcoUj/mPPvqo6dq1qwkMDDStW7c2TqfTzJ8/36OmsrLSPPbYYyYiIsIEBASYhIQEk5+f71EzatSoavvy2WefuWvqciw0ltNDxd69e82VV15pwsLCTEBAgOnatauZMGGCKS4udtfXdHx16tTJo93PPvvMxMbGGn9/f3PBBRd4HH9n6scpx44dM3/+859N69atTfPmzc2///u/e3zInFruTD97Z9r33lTT+WHUqFHGmJqP34yMDGOMMfv37zfXXXedadeunfHz8zMdO3Y0f/rTn8z27ds91vPpp5+a3//+9yYkJMS0bt3aXHvttR6PXtd0XF511VUe7bzzzjvmd7/7nfH39zcXXXSR+5eIU87UX2Pqtt+85Uzj/Uu/DDcNdT554IEH3OfpiIgIc/3115sNGzZ41JzpfJKTk2Pi4+NNSEiICQwMND179jRPP/20OX78eL36gl/HYYwxv+7aDwAAwPnjN3fPDQAAsBvhBgAAWIVwAwAArEK4AQAAViHcAAAAqxBuAACAVQg3AADAKoQbAABgFcINAACwCuEGAABYhXADAACsQrgBAABW+X9vYz2LUnCAhQAAAABJRU5ErkJggg==\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step9: Saving the Processed Dataset After Applying Preprocessing Techniques**" + ], + "metadata": { + "id": "AIFYlZoe9CIR" + } + }, + { + "cell_type": "code", + "source": [ + "dataset.to_csv('/content/updated_dataset.csv', index=False)\n", + "\n", + "# Print confirmation\n", + "print(\"Updated dataset saved to 'updated_dataset.csv'\")" + ], + "metadata": { + "id": "NM36izmEe-Hu", + "outputId": "5ef1e0c9-306d-4f12-e1cc-e612337ef901", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Updated dataset saved to 'updated_dataset.csv'\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step10: Read the Updated Dataset**" + ], + "metadata": { + "id": "oksNUDFq9cbX" + } + }, + { + "cell_type": "code", + "source": [ + "updated_dataset = 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When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 10ms/step - loss: 0.0020\n", + "Epoch 2/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 11ms/step - loss: 0.0011\n", + "Epoch 3/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 10ms/step - loss: 0.0011\n", + "Epoch 4/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 5/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 13ms/step - loss: 0.0011\n", + "Epoch 6/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 14ms/step - loss: 0.0011\n", + "Epoch 7/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 8/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 17ms/step - loss: 0.0011\n", + "Epoch 9/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 12ms/step - loss: 0.0011\n", + "Epoch 10/10\n", + "\u001b[1m1316/1316\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 13ms/step - loss: 0.0011\n", + "\n", + "Overall Evaluation Metrics:\n", + "Overall RMSE: 3.8277\n", + "Overall MAE: 2.6643\n", + "Overall R²: 0.7188\n", + "\n", + "Predictions for the next 240 hours saved to predictions240hours.csv\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + ":85: FutureWarning: 'H' is deprecated and will be removed in a future version, please use 'h' instead.\n", + " index=pd.date_range(start=\"2023-10-31 23:00:00\", periods=240, freq='H') # Next 240 hours\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 12: Reading the Predicted Air Pollution Values for the Next 10 Days and 10 Columns**" + ], + "metadata": { + "id": "Mjz8KZh-986h" + } + }, + { + "cell_type": "code", + "source": [ + "import pandas as pd\n", + "future_predictions_df=pd.read_csv(\"/content/predictions240hours.csv\")\n", + "future_predictions_df" + ], + "metadata": { + "id": "91Ogk-nHSl-9", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "outputId": "b6bda5dd-b47b-4052-eb3c-fa634fdc2374" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " Unnamed: 0 29209010 11222030 14215010 33202110 14104030 \\\n", + "0 2023-10-31 23:00:00 5.345446 12.991408 13.275642 13.347651 9.517594 \n", + "1 2023-11-01 00:00:00 6.293464 12.215577 10.512154 12.283895 9.353355 \n", + "2 2023-11-01 01:00:00 6.888478 11.579747 8.811533 11.570954 8.992475 \n", + "3 2023-11-01 02:00:00 7.233640 11.040293 7.716928 11.069855 8.615580 \n", + "4 2023-11-01 03:00:00 7.414830 10.574987 6.981767 10.697695 8.278315 \n", + ".. ... ... ... ... ... ... \n", + "235 2023-11-10 18:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "236 2023-11-10 19:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "237 2023-11-10 20:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "238 2023-11-10 21:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "239 2023-11-10 22:00:00 5.610075 6.577713 3.655871 6.942476 5.424214 \n", + "\n", + " 8344010 43202020 28216010 28209010 24202530 \n", + "0 6.046752 22.072032 6.818363 5.402340 8.351093 \n", + "1 6.849514 19.619124 7.997456 5.982958 10.052781 \n", + "2 7.080735 17.630834 8.718381 6.535463 10.948564 \n", + "3 7.065603 16.051361 9.125044 6.989049 11.426456 \n", + "4 6.953997 14.811003 9.323415 7.330277 11.675499 \n", + ".. ... ... ... ... ... \n", + "235 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "236 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "237 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "238 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "239 4.833715 9.176862 6.368562 6.340881 9.857989 \n", + "\n", + "[240 rows x 11 columns]" + ], + "text/html": [ + "\n", + "
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+ ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step13: Drop the frist column it means timestamp column**" + ], + "metadata": { + "id": "9si47Uv8-Lbp" + } + }, + { + "cell_type": "code", + "source": [ + "future_predictions_df= future_predictions_df.drop(future_predictions_df.columns[0], axis=1)\n", + "future_predictions_df" + ], + "metadata": { + "id": "bI3g3hNzYs8P", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "outputId": "d6532d94-6363-41cd-82d9-334da1c61ff2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " 29209010 11222030 14215010 33202110 14104030 8344010 43202020 \\\n", + "0 5.345446 12.991408 13.275642 13.347651 9.517594 6.046752 22.072032 \n", + "1 6.293464 12.215577 10.512154 12.283895 9.353355 6.849514 19.619124 \n", + "2 6.888478 11.579747 8.811533 11.570954 8.992475 7.080735 17.630834 \n", + "3 7.233640 11.040293 7.716928 11.069855 8.615580 7.065603 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\"28209010\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.34746151129365044,\n \"min\": 5.402340084314346,\n \"max\": 7.846320018172264,\n \"num_unique_values\": 189,\n \"samples\": [\n 6.340882107615471,\n 6.340900577604771,\n 7.323002323508263\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"24202530\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4471713364257031,\n \"min\": 8.351093418896198,\n \"max\": 11.83024924993515,\n \"num_unique_values\": 188,\n \"samples\": [\n 9.85799190402031,\n 9.858012929558754,\n 10.956023439764977\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 51 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 14:Installing the Pami Library**" + ], + "metadata": { + "id": "SeBq0i5k-Zk8" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install -U pami" + ], + "metadata": { + "id": "ZICrUGlBTu3_", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1013287d-3675-4f8e-df11-480bb1e9fa8f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting pami\n", + " Downloading pami-2024.12.10.1-py3-none-any.whl.metadata (80 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n", + "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (2.2.2)\n", + "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.24.1)\n", + "Requirement already 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] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 15: Converting the DataFrame into Transactional Form**" + ], + "metadata": { + "id": "VB5fejB4-peW" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.convert import denseDF2DB as db\n", + "obj = db.denseDF2DB(future_predictions_df)\n", + "obj.convert2TransactionalDatabase('TDB.csv', '>=',6)" + ], + "metadata": { + "id": "2QM3VdAPZEEc" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step16 Statistics**" + ], + "metadata": { + "id": "6GZfcSmBoNhZ" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.extras.dbStats import TransactionalDatabase as tds\n", + "obj = tds.TransactionalDatabase('TDB.csv')\n", + "obj.run()\n", + "obj.printStats()\n", + "obj.plotGraphs()" + ], + "metadata": { + "id": "LqJ0GjQ_oPzD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "outputId": "3df2474f-7539-42ef-ef4e-0ef2f72b1554" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Database size (total no of transactions) : 240\n", + "Number of items : 10\n", + "Minimum Transaction Size : 6\n", + "Average Transaction Size : 6.3\n", + "Maximum Transaction Size : 10\n", + "Standard Deviation Transaction Size : 0.8475454756727413\n", + "Variance in Transaction Sizes : 0.7213389121338912\n", + "Sparsity : 0.37\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
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\n" 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iIiKrkYQNTSLJy8tDYGAgtm/fjqFDh0Kn0yEgIADffPMN7r77bgDAiRMn0KNHD+zZswe33norNmzYgIkTJyIzMxNBQdVnTz799FM8//zzyMvLg0qluuH7FhYWQqvVQqfTQaPRWKVv+ioD+r+RiKLyKnz/eAwGdvC1yvsQERE5i6Z+f9vUnB2dTgcA8PWtDgJJSUmorKzEqFGjTG26d++Odu3aYc+ePQCAPXv2oHfv3qagAwCxsbEoLCzEsWPH6n0fvV6PwsJCs4e1qV2UGNMzGACw7jBXZREREbUWmwk7RqMR8+fPx+DBgxEREQEAyM7Ohkqlgo+Pj1nboKAgZGdnm9rUDjo1+2v21Sc+Ph5ardb0CAsLa+He1G9iVAgAYMPRLFQZjK3ynkRERM7OZsLOnDlzcPToUaxcudLq77Vo0SLodDrT4/z581Z/TwC4rbM/fDxccam4AnvT8lvlPYmIiJydTYSduXPnIiEhAVu3bkXbtm1N24ODg1FRUYGCggKz9jk5OQgODja1uX51Vs3zmjbXU6vV0Gg0Zo/W4KpUYFxEdU0JvMAgERFRq5A17AghMHfuXPz444/YsmULOnToYLa/X79+cHV1xebNm03bTp48iYyMDMTExAAAYmJikJKSgtzcXFObTZs2QaPRoGfPnq3TEQvUrMracDQblRzKIiIisjoXOd98zpw5+Oabb/DTTz/B29vbNMdGq9XC3d0dWq0WM2fOxIIFC+Dr6wuNRoOnnnoKMTExuPXWWwEAY8aMQc+ePfHwww/j3XffRXZ2Nv7xj39gzpw5UKvVcnavXtEdfOHvpcKl4grsSr2E4d0C5S6JiIjIocl6ZueTTz6BTqfD8OHDERISYnp89913pjbvv/8+Jk6ciClTpmDo0KEIDg7GmjVrTPuVSiUSEhKgVCoRExODhx56CNOmTcNrr70mR5duyEWpwLiI6onK63iBQSIiIquzqevsyKU1rrNT296zl3HfZ3/A280FB/4xCmoXpdXfk4iIyNHY5XV2nMWAcF8EadQoKq/CjlOX5C6HiIjIoTHsyEChkDCh99U7oXNVFhERkVUx7Mik5gKDiX/moLzSIHM1REREjothRyZ9wnxwi487SioM2Hoi98YvICIiomZh2JGJJEmYGHl1VRaHsoiIiKyGYUdGNRcY3HIiFyX6KpmrISIickwMOzKKuEWDcD8PlFcakXg858YvICIiIosx7MioeiirZlUWLzBIRERkDQw7MqtZlbX9ZB4KyytlroaIiMjxMOzIrFuQNzoHeqHCYMRvxziURURE1NIYdmRWe1UWLzBIRETU8hh2bEDNvJ2dpy/hSkmFzNUQERE5FoYdG9A50As9QjSoMgr8eixb7nKIiIgcCsOOjbg2lMVVWURERC2JYcdGxF0dytp95hIuFetlroaIiMhxMOzYiHZ+Hohsq4VRABtSeHaHiIiopTDs2JBr98pi2CEiImopDDs2ZMLVoaz96fnIKSyXuRoiIiLHwLBjQ27xcUe/9m0gBLCeZ3eIiIhaBMOOjeEFBomIiFoWw46NGd87BJIEHMwowIUrpXKXQ0REZPcYdmxMkMYNA8N9AXAoi4iIqCUw7NigiVHVE5V5gUEiIqKbx7Bjg8ZFBEOpkJByUYf0SyVyl0NERGTXGHZskL+XGoM6+QEA1vMCg0RERDeFYcdGmS4weJirsoiIiG4Gw46Niu0VDBeFhBPZRUjNLZK7HCIiIrvFsGOjfDxUGNLFHwCw7jCHsoiIiJqLYceGTYysWZWVCSGEzNUQERHZJ4YdGza6VxBULgqcySvBiWwOZRERETWHrGFnx44diIuLQ2hoKCRJwtq1a832S5JU72Px4sWmNuHh4XX2v/32263cE+vQuLlieNcAALx9BBERUXPJGnZKSkoQFRWFZcuW1bs/KyvL7PH5559DkiRMmTLFrN1rr71m1u6pp55qjfJbRe0LDHIoi4iIyHIucr75uHHjMG7cuAb3BwcHmz3/6aefMGLECHTs2NFsu7e3d522jmJk90C4uSpw7nIpUi7qENnWR+6SiIiI7IrdzNnJycnB+vXrMXPmzDr73n77bfj5+aFPnz5YvHgxqqqqZKjQOjzVLhjZPQgAbx9BRETUHLKe2bHEF198AW9vb0yePNls+7x589C3b1/4+vpi9+7dWLRoEbKysrB06dIGj6XX66HX603PCwsLrVZ3S5gYGYL1KVlYfyQLi8Z1hyRJcpdERERkN+wm7Hz++eeYOnUq3NzczLYvWLDA9HNkZCRUKhUef/xxxMfHQ61W13us+Ph4vPrqq1attyWN6B4IT5USFwvKcDCjAP3at5G7JCIiIrthF8NYv//+O06ePIm//vWvN2wbHR2NqqoqpKenN9hm0aJF0Ol0psf58+dbsNqW5+aqxOieNUNZXJVFRERkCbsIO//973/Rr18/REVF3bBtcnIyFAoFAgMDG2yjVquh0WjMHrau5gKD649kwWDkqiwiIqKmknUYq7i4GKmpqabnaWlpSE5Ohq+vL9q1awegej7NqlWrsGTJkjqv37NnD/bu3YsRI0bA29sbe/bswTPPPIOHHnoIbdo41lDPkK7+8HZzQW6RHvvT83FrRz+5SyIiIrILsp7ZOXDgAPr06YM+ffoAqJ5/06dPH7z00kumNitXroQQAg888ECd16vVaqxcuRLDhg1Dr1698Oabb+KZZ57BZ5991mp9aC1qFyVie1Uvr+dQFhERUdNJgleqQ2FhIbRaLXQ6nU0PaW0/lYfpn++Dn6cKe//fSLgo7WIUkoiIyCqa+v3Nb0s7MqiTH9p4uOJySQX+OJsvdzlERER2gWHHjrgqFRgbEQKAQ1lERERNxbBjZ+Iiq8POhqPZqKgyylwNERGR7WPYsTPRHf3g76WGrqwSu1IvyV0OERGRzWPYsTNKhYTxvatXZa3jUBYREdENMezYobio6gsMbjqWg/JKg8zVEBER2TaGHTvUr10bBGvcUKSvwo5TeXKXQ0REZNMYduyQQiFhQmTNqqwsmashIiKybQw7dmri1bCTeDwHZRUcyiIiImoIw46d+kuYD9q2cUdphQFbTuTKXQ4REZHNYtixU5JUeyiLq7KIiIgawrBjx+Iiq1dlbTmRi2J9lczVEBER2SaGHTvWK1SDDv6e0FcZsfl4jtzlEBER2SSGHTsmSZJpovK6w1yVRUREVB+GHTs38epQ1vZTudCVVcpcDRERke1h2LFz3YK90SXQC5UGgd+OZctdDhERkc1h2HEANbeP4AUGiYiI6mLYcQA183Z2pV7ClZIKmashIiKyLQw7DqBjgBd6hmhQZRTYyKEsIiIiMww7DmJiVM2qLF5gkIiIqDaGHQcxsXf1vJ0/zl5GXpFe5mqIiIhsB8OOg2jn54GotloYBbDhKCcqExER1WDYcSCmVVm8wCAREZEJw44DGd+7et7O/nP5yNaVy1wNERGRbWDYcSChPu7o374NhADWp/DsDhEREcCw43Cu3SuLq7KIiIgAhh2HM753CCQJSD5fgPP5pXKXQ0REJDuGHQcTqHFDdAdfABzKIiIiAhh2HNK1e2VxKIuIiIhhxwGNiwiBUiHh6MVCpF0qkbscIiIiWTHsOCBfTxUGdfIDACRwojIRETk5WcPOjh07EBcXh9DQUEiShLVr15rtnzFjBiRJMnuMHTvWrE1+fj6mTp0KjUYDHx8fzJw5E8XFxa3YC9sUF1kzlMV5O0RE5NxkDTslJSWIiorCsmXLGmwzduxYZGVlmR7ffvut2f6pU6fi2LFj2LRpExISErBjxw489thj1i7d5sX2CoarUsLJnCKczimSuxwiIiLZuMj55uPGjcO4ceMabaNWqxEcHFzvvuPHj2Pjxo3Yv38/+vfvDwD48MMPMX78eLz33nsIDQ1t8ZrthdbDFUO7BGDziVysO5KFBaO95S6JiIhIFjY/Z2fbtm0IDAxEt27dMHv2bFy+fNm0b8+ePfDx8TEFHQAYNWoUFAoF9u7d2+Ax9Xo9CgsLzR6OaGJU9QUGE45kQgghczVERETysOmwM3bsWPzvf//D5s2b8c4772D79u0YN24cDAYDACA7OxuBgYFmr3FxcYGvry+ys7MbPG58fDy0Wq3pERYWZtV+yGVUjyCoXBQ4m1eC41kcyiIiIudk02Hn/vvvxx133IHevXtj0qRJSEhIwP79+7Ft27abOu6iRYug0+lMj/Pnz7dMwTbG280VI7oFAADW8Zo7RETkpGw67FyvY8eO8Pf3R2pqKgAgODgYubm5Zm2qqqqQn5/f4DwfoHoekEajMXs4qomR1y4wyKEsIiJyRnYVdi5cuIDLly8jJKR6LkpMTAwKCgqQlJRkarNlyxYYjUZER0fLVaZNGdkjEO6uSpzPL8ORCzq5yyEiImp1soad4uJiJCcnIzk5GQCQlpaG5ORkZGRkoLi4GM899xz++OMPpKenY/PmzbjzzjvRuXNnxMbGAgB69OiBsWPHYtasWdi3bx927dqFuXPn4v7773fqlVi1eahcMLJH9bwm3j6CiIickaxh58CBA+jTpw/69OkDAFiwYAH69OmDl156CUqlEkeOHMEdd9yBrl27YubMmejXrx9+//13qNVq0zG+/vprdO/eHSNHjsT48eNx22234bPPPpOrSzapZihr/ZEsGI0cyiIiIuciCU7kQGFhIbRaLXQ6nUPO3ymvNKD/G4ko1ldh9ewY9GvvK3dJREREN62p3992NWeHmsfNVYnRPYMAAOsO8/YRRETkXBh2nMTEyOpJ3etTsmDgUBYRETkRhh0nMaRLADRuLsgr0mNfWr7c5RAREbUahh0noXJRYGxE9bWHuCqLiIicCcOOE6lZlbXhaDaqDEaZqyEiImodDDtOZFAnP/h6qpBfUoHdZy7f+AVEREQOgGHHibgoOZRFRETOh2HHydSsytp4NBsVVRzKIiIix8ew42SiO/ghwFuNwvIq7EzNk7scIiIiq2PYcTJKhYQJvavP7iTwAoNEROQEGHacUM1Q1m9/5qC80iBzNURERNbFsOOE+rZrgxCtG4r1Vdh2kkNZRETk2Bh2nJCi9lAWV2UREZGDY9hxUhOjqi8wuPl4LkorqmSuhoiIyHoYdpxUVFstwnzdUVZpwJYTuXKXQ0REZDUMO05KkiTT7SO4KouIiBwZw44Tq1mVtfVkLorKK2WuhoiIyDoYdpxYzxANOvp7Ql9lROLxHLnLISIisgqGHSdWPZTFCwwSEZFjY9hxcjWrsnaczoOulENZRETkeBh2nFzXIG90C/JGpUHg1z+z5S6HiIioxTHs0LWhrCMcyiIiIsfDsEOmoaxdqZdwuVgvczVEREQti2GH0MHfE71CNTAYBTYe41AWERE5FoYdAgBeYJCIiBwWww4BuDZvZ2/aZeQWlctcDRERUcth2CEAQJivB/4S5gOjADakcCiLiIgcB8MOmVxblZUpcyVEREQth2GHTCZcDTv7068gs6BM5mqIiIhaBsMOmYRo3TEgvA0A4JcUTlQmIiLHIGvY2bFjB+Li4hAaGgpJkrB27VrTvsrKSjz//PPo3bs3PD09ERoaimnTpiEz03yIJTw8HJIkmT3efvvtVu6J46hZlbWOFxgkIiIHIWvYKSkpQVRUFJYtW1ZnX2lpKQ4ePIgXX3wRBw8exJo1a3Dy5Enccccdddq+9tpryMrKMj2eeuqp1ijfIY3rHQyFBBw+X4Dz+aVyl0NERHTTXCx9wdmzZ9GxY8cWefNx48Zh3Lhx9e7TarXYtGmT2baPPvoIAwcOREZGBtq1a2fa7u3tjeDg4BapydkFervh1o5+2H3mMhKOZGH28E5yl0RERHRTLD6z07lzZ4wYMQJfffUVystb93osOp0OkiTBx8fHbPvbb78NPz8/9OnTB4sXL0ZVVVWjx9Hr9SgsLDR70DWmCwxyVRYRETkAi8POwYMHERkZiQULFiA4OBiPP/449u3bZ43azJSXl+P555/HAw88AI1GY9o+b948rFy5Elu3bsXjjz+Ot956C3/7298aPVZ8fDy0Wq3pERYWZu3y7crYiGAoFRKOZRbibF6x3OUQERHdFEkIIZrzwqqqKvz8889YsWIFNm7ciK5du+LRRx/Fww8/jICAAMsLkST8+OOPmDRpUp19lZWVmDJlCi5cuIBt27aZhZ3rff7553j88cdRXFwMtVpdbxu9Xg+9/toNLwsLCxEWFgadTtfosZ3JtM/3YcepPCwY3RXzRnaRuxwiIqI6CgsLodVqb/j93ewJyi4uLpg8eTJWrVqFd955B6mpqVi4cCHCwsIwbdo0ZGW1zGqeyspK3HvvvTh37hw2bdp0wzASHR2NqqoqpKenN9hGrVZDo9GYPcgcLzBIRESOotlh58CBA3jyyScREhKCpUuXYuHChThz5gw2bdqEzMxM3HnnnTddXE3QOX36NBITE+Hn53fD1yQnJ0OhUCAwMPCm39+ZxfYKhqtSwqmcYpzKKZK7HCIiomazeDXW0qVLsXz5cpw8eRLjx4/H//73P4wfPx4KRXVu6tChA1asWIHw8PAbHqu4uBipqamm52lpaUhOToavry9CQkJw99134+DBg0hISIDBYEB2dvU9m3x9faFSqbBnzx7s3bsXI0aMgLe3N/bs2YNnnnkGDz30ENq0aWNp16gWrbsrhnUNQOLxXCQczsSCMd3kLomIiKhZLJ6z06VLFzz66KOYMWMGQkJC6m1TUVGBb7/9FtOnT2/0WNu2bcOIESPqbJ8+fTpeeeUVdOjQod7Xbd26FcOHD8fBgwfx5JNP4sSJE9Dr9ejQoQMefvhhLFiwoMH5OvVp6pifs1l76CLmf5eMDv6e2PLsMEiSJHdJREREJk39/m72BGVHwrBTv2J9Ffq9vgn6KiMSnroNEbdo5S6JiIjIxGoTlJcvX45Vq1bV2b5q1Sp88cUXlh6ObJiX2gUjulXPfUrg7SOIiMhOWRx24uPj4e/vX2d7YGAg3nrrrRYpimzHxKhrq7J4EpCIiOyRxWEnIyOj3rk07du3R0ZGRosURbbj9u6B8FApceFKGQ5f0MldDhERkcUsDjuBgYE4cuRIne2HDx9u0tJwsi8eKheM7BEEAEg4zGvuEBGR/bE47DzwwAOYN28etm7dCoPBAIPBgC1btuDpp5/G/fffb40aSWbXLjCYBaORQ1lERGRfLL7Ozuuvv4709HSMHDkSLi7VLzcajZg2bRrn7DioYV0D4K12QXZhOZIyrmBAuK/cJRERETWZxWFHpVLhu+++w+uvv47Dhw/D3d0dvXv3Rvv27a1RH9kAN1clRvcMwppDF5FwOJNhh4iI7IrFYadG165d0bVr15ashWxYXFQo1hy6iF+OZuOluF5QKniBQSIisg8Whx2DwYAVK1Zg8+bNyM3NhdFoNNu/ZcuWFiuObMfgzv7Qursir0iPvWmXMahT3csPEBER2SKLw87TTz+NFStWYMKECYiIiOAtBJyEykWBsb2C8d2B80g4ksWwQ0REdsPisLNy5Up8//33GD9+vDXqIRs2MSoE3x04jw0pWXj1jl5wVVq8mI+IiKjVWfxtpVKp0LlzZ2vUQjYupqMf/DxVuFJaid1nLstdDhERUZNYHHaeffZZfPDBB7x1gBNyUSowNiIYAC8wSERE9sPiYaydO3di69at2LBhA3r16gVXV1ez/WvWrGmx4sj2xEWF4uu9Gfj1WDbevKs3VC4cyiIiIttmcdjx8fHBXXfdZY1ayA4MCPdFoLcauUV6/H46z3QrCSIiIltlcdhZvny5NeogO6FUSBjfOwQrdqdj3eFMhh0iIrJ5zRqDqKqqQmJiIv7973+jqKgIAJCZmYni4uIWLY5sU1xU9b2yNv2Zg/JKg8zVEBERNc7iMzvnzp3D2LFjkZGRAb1ej9GjR8Pb2xvvvPMO9Ho9Pv30U2vUSTakT1gbhGrdkKkrx7aTuRgbESJ3SURERA2y+MzO008/jf79++PKlStwd3c3bb/rrruwefPmFi2ObJNCIWHC1TuhrzuSJXM1REREjbM47Pz+++/4xz/+AZVKZbY9PDwcFy9ebLHCyLbFRYUCALYcz0VpRZXM1RARETXM4rBjNBphMNSdp3HhwgV4e3u3SFFk+3rfokU7Xw+UVRqw+Xiu3OUQERE1yOKwM2bMGPzzn/80PZckCcXFxXj55Zd5CwknIkkSJtYMZfECg0REZMMsDjtLlizBrl270LNnT5SXl+PBBx80DWG988471qiRbNTEyOqhrG2n8lBUXilzNURERPWzeDVW27ZtcfjwYaxcuRJHjhxBcXExZs6cialTp5pNWCbH1yPEGx0DPHE2rwSb/szB5L5t5S6JiIioDovDDgC4uLjgoYceaulayM5IkoS4yFB8sPk0Eo5kMewQEZFNsjjs/O9//2t0/7Rp05pdDNmfuKgQfLD5NH4/nQddaSW0Hq43fhEREVErsjjsPP3002bPKysrUVpaCpVKBQ8PD4YdJ9M50Bvdg71xIrsIvx7Lxr0DwuQuiYiIyIzFE5SvXLli9iguLsbJkydx22234dtvv7VGjWTjTKuyjnBVFhER2Z5m3Rvrel26dMHbb79d56wPOYeaVVm7z1zG5WK9zNUQERGZa5GwA1RPWs7M5L/snVG4vycibtHAYBTYcDRb7nKIiIjMWBx2fv75Z7PHTz/9hE8//RQPPfQQBg8ebNGxduzYgbi4OISGhkKSJKxdu9ZsvxACL730EkJCQuDu7o5Ro0bh9OnTZm3y8/MxdepUaDQa+Pj4YObMmbz7ugzirp7dSeBQFhER2RiLJyhPmjTJ7LkkSQgICMDtt9+OJUuWWHSskpISREVF4dFHH8XkyZPr7H/33Xfxr3/9C1988QU6dOiAF198EbGxsfjzzz/h5uYGAJg6dSqysrKwadMmVFZW4pFHHsFjjz2Gb775xtKu0U2YEBmC+A0nsDctH7mF5QjUuMldEhEREQBAEkIIuYsAqkPTjz/+aApTQgiEhobi2WefxcKFCwEAOp0OQUFBWLFiBe6//34cP34cPXv2xP79+9G/f38AwMaNGzF+/HhcuHABoaGhTXrvwsJCaLVa6HQ6aDQaq/TPGdz18S4cyijAy3E98cjgDnKXQ0REDq6p398tNmenpaWlpSE7OxujRo0ybdNqtYiOjsaePXsAAHv27IGPj48p6ADAqFGjoFAosHfv3lav2dlNNA1lZclcCRER0TUWD2MtWLCgyW2XLl1q6eFNsrOrJ7oGBQWZbQ8KCjLty87ORmBgoNl+FxcX+Pr6mtrUR6/XQ6+/tmqosLCw2XXSNRN6h+CN9X8i6dwVZBaUIdSHtw8hIiL5WRx2Dh06hEOHDqGyshLdunUDAJw6dQpKpRJ9+/Y1tZMkqeWqbGHx8fF49dVX5S7D4QRr3TCgvS/2pedj/ZEszBraUe6SiIiILB/GiouLw9ChQ3HhwgUcPHgQBw8exPnz5zFixAhMnDgRW7duxdatW7Fly5abKiw4OBgAkJOTY7Y9JyfHtC84OBi5ublm+6uqqpCfn29qU59FixZBp9OZHufPn7+pWumauKjqCwxyVRYREdkKi8POkiVLEB8fjzZt2pi2tWnTBm+88YbFq7Ea06FDBwQHB2Pz5s2mbYWFhdi7dy9iYmIAADExMSgoKEBSUpKpzZYtW2A0GhEdHd3gsdVqNTQajdmDWsbYiBAoJODwBR0yLpfKXQ4REZHlYaewsBB5eXl1tufl5aGoqMiiYxUXFyM5ORnJyckAqiclJycnIyMjA5IkYf78+XjjjTfw888/IyUlBdOmTUNoaKhpxVaPHj0wduxYzJo1C/v27cOuXbswd+5c3H///U1eiUUtK8BbjZhOfgB4+wgiIrINFoedu+66C4888gjWrFmDCxcu4MKFC1i9ejVmzpxZ77VyGnPgwAH06dMHffr0AVA9+blPnz546aWXAAB/+9vf8NRTT+Gxxx7DgAEDUFxcjI0bN5qusQMAX3/9Nbp3746RI0di/PjxuO222/DZZ59Z2i1qQVyVRUREtsTi6+yUlpZi4cKF+Pzzz1FZWQmgegXUzJkzsXjxYnh6elqlUGvidXZa1pWSCgx4MxFVRoHNzw5DpwAvuUsiIiIHZLXr7Hh4eODjjz/G5cuXTSuz8vPz8fHHH9tl0KGW18ZThcGd/QEACYd5doeIiOTV7IsKZmVlISsrC126dIGnpyds5ELMZCPionivLCIisg0Wh53Lly9j5MiR6Nq1K8aPH4+srOp/uc+cORPPPvtsixdI9mlMryColAqczi3GyWzLJq4TERG1JIvDzjPPPANXV1dkZGTAw8PDtP2+++7Dxo0bW7Q4sl8aN1cM7RoAAFh3mGd3iIhIPhaHnd9++w3vvPMO2rZta7a9S5cuOHfuXIsVRvav9gUGOcxJRERysTjslJSUmJ3RqZGfnw+1Wt0iRZFjGNkjCGoXBdIvl+JYJu8/RkRE8rA47AwZMgT/+9//TM8lSYLRaMS7776LESNGtGhxZN+81C4Y2aP6Rq28wCAREcnF4huBvvvuuxg5ciQOHDiAiooK/O1vf8OxY8eQn5+PXbt2WaNGsmMTI0PxS0o2Eg5n4YWx3W36BrFEROSYLD6zExERgVOnTuG2227DnXfeiZKSEkyePBmHDh1Cp06drFEj2bER3QLhoVLiYkEZDp0vkLscIiJyQhad2amsrMTYsWPx6aef4u9//7u1aiIH4q5SYlSPIPx8OBMJh7PQt12bG7+IiIioBVl0ZsfV1RVHjhyxVi3koCZGVq/K+iUlC0YjV2UREVHrsngY66GHHsJ///tfa9RCDmpYtwB4q12QXViOA+euyF0OERE5GYsnKFdVVeHzzz9HYmIi+vXrV+d+WEuXLm2x4sgxqF2UGNMrGKsPXkDCkUwM7OArd0lEROREmhR2jhw5goiICCgUChw9ehR9+/YFAJw6dcqsHVfaUEMmRoVg9cEL+CUlCy9N7AkXZbNvy0ZERGSRJoWdPn36ICsrC4GBgTh37hz2798PPz8/a9dGDuS2zv7w8XDFpeIK7E3LN90VnYiIyNqa9M9rHx8fpKWlAQDS09NhNBqtWhQ5HlelAmN7BQPgndCJiKh1NenMzpQpUzBs2DCEhIRAkiT0798fSqWy3rZnz55t0QLJcUyMDMXK/eex4Wg2XrszAq4cyiIiolbQpLDz2WefYfLkyUhNTcW8efMwa9YseHt7W7s2cjC3dvSFn6cKl0sqsCv1EoZ3C5S7JCIicgJNXo01duxYAEBSUhKefvpphh2ymItSgfG9Q/DlH+eQcCSLYYeIiFqFxeMIy5cvZ9ChZqu5wOCvx7KhrzLIXA0RETkDTpqgVjUg3BdBGjWKyquw49QlucshIiInwLBDrUqhkDC+d/XZHa7KIiKi1sCwQ61uYmQoACDxzxyUV3Ioi4iIrIthh1pd33Y+uMXHHSUVBmw9kSt3OURE5OAYdqjVSZJkmqiccCRL5mqIiMjRMeyQLGqGsjafyEGJvkrmaoiIyJEx7JAsIm7RoL2fB8orjUg8niN3OURE5MAYdkgWHMoiIqLWwrBDsqkZytp+Mg+F5ZUyV0NERI6KYYdk0z3YG50DvVBhMGLTMQ5lERGRdTDskGzMh7J4gUEiIrIOmw874eHhkCSpzmPOnDkAgOHDh9fZ98QTT8hcNTVVzVDW76cv4UpJhczVEBGRI2ryXc/lsn//fhgM166ye/ToUYwePRr33HOPadusWbPw2muvmZ57eHi0ao3UfJ0DvdA92Bsnsovw67Fs3D+wndwlERGRg7H5MzsBAQEIDg42PRISEtCpUycMGzbM1MbDw8OsjUajkbFislRcVPXZHa7KIiIia7D5sFNbRUUFvvrqKzz66KOQJMm0/euvv4a/vz8iIiKwaNEilJaWNnocvV6PwsJCswfJp2bezu4zl3CpWC9zNURE5GjsKuysXbsWBQUFmDFjhmnbgw8+iK+++gpbt27FokWL8OWXX+Khhx5q9Djx8fHQarWmR1hYmJUrp8a09/NEZFstjALYcDRb7nKIiMjBSEIIIXcRTRUbGwuVSoV169Y12GbLli0YOXIkUlNT0alTp3rb6PV66PXXziAUFhYiLCwMOp2OQ2Ay+WzHGbz1ywlEd/DFd4/HyF0OERHZgcLCQmi12ht+f9vNmZ1z584hMTERf/3rXxttFx0dDQBITU1tsI1arYZGozF7kLwmXF2VtS89HzmF5TJXQ0REjsRuws7y5csRGBiICRMmNNouOTkZABASEtIKVVFLucXHHX3b+UAIYD0nKhMRUQuyi7BjNBqxfPlyTJ8+HS4u11bLnzlzBq+//jqSkpKQnp6On3/+GdOmTcPQoUMRGRkpY8XUHDXX3OEFBomIqCXZRdhJTExERkYGHn30UbPtKpUKiYmJGDNmDLp3745nn30WU6ZMaXROD9muCZEhkCTgYEYBLhaUyV0OERE5CJu/qCAAjBkzBvXNow4LC8P27dtlqIisIUjjhoHhvtiblo/1RzLx2ND6J5gTERFZwi7O7JDzmHj1AoPrDnPeDhERtQyGHbIp4yKCoZCAlIs6pF8qkbscIiJyAAw7ZFP8vdQY1MkfALA+hWd3iIjo5jHskM2puX3EusNclUVERDePYYdsztiIYLgoJJzILkJqbrHc5RARkZ1j2CGb4+OhwpAu1UNZvOYOERHdLIYdskk1Fxhcdziz3ssOEBERNRXDDtmk0b2CoFIqcCavBCeyi+Quh4iI7BjDDtkkjZsrhnULAMChLCIiujkMO2SzalZlJRzJ4lAWERE1G8MO2axRPYLg5qrAuculOHqxUO5yiIjITjHskM3yVLtgZPcgABzKIiKi5mPYIZvGoSwiIrpZDDtk00Z0D4SnSomLBWU4mFEgdzlERGSHGHbIprm5KjGqJ4eyiIio+Rh2yObFXb3A4C8pWTAaOZRFRESWYdghmzekqz+83VyQU6jH/vR8ucshIiI7w7BDNk/tokRsr2AAwDoOZRERkYUYdsgu1KzK2pCSjSqDUeZqiIjInjDskF0Y3NkfbTxccbmkAn+c5VAWERE1HcMO2QVXpQJjI6qHsrgqi4iILMGwQ3ajZlXWxmPZqORQFhERNRHDDtmN6I5+8PdSo6C0EjtTL8ldDhER2QmGHbIbSoWE8b2vrso6zKEsIiJqGoYdsisTrw5lbTqWg/JKg8zVEBGRPWDYIbvSv30bBGvcUKSvwo5TeXKXQ0REdoBhh+yKQiFhfO9rd0InIiK6EYYdsjtxUdVhJ/F4DsoqOJRFRESNY9ghu/OXMB+0beOO0goDtp7MlbscIiKycQw7ZHckScKEq7eP4KosIiK6EZsOO6+88gokSTJ7dO/e3bS/vLwcc+bMgZ+fH7y8vDBlyhTk5OTIWDG1lpoLDG45kYtifZXM1RARkS2z6bADAL169UJWVpbpsXPnTtO+Z555BuvWrcOqVauwfft2ZGZmYvLkyTJWS62lV6gG4X4e0FcZsfk4Ay4RETXM5sOOi4sLgoODTQ9/f38AgE6nw3//+18sXboUt99+O/r164fly5dj9+7d+OOPP2SumqxNkiTTNXfWHeaqLCIiapjNh53Tp08jNDQUHTt2xNSpU5GRkQEASEpKQmVlJUaNGmVq2717d7Rr1w579uxp9Jh6vR6FhYVmD7I/cVHVYWfHqTzoyiplroaIiGyVTYed6OhorFixAhs3bsQnn3yCtLQ0DBkyBEVFRcjOzoZKpYKPj4/Za4KCgpCdnd3ocePj46HVak2PsLAwK/aCrKVbsDe6BHqhwmDEb8ca/8yJiMh52XTYGTduHO655x5ERkYiNjYWv/zyCwoKCvD999/f1HEXLVoEnU5nepw/f76FKqbWVjOUxQsMEhFRQ2w67FzPx8cHXbt2RWpqKoKDg1FRUYGCggKzNjk5OQgODm70OGq1GhqNxuxB9mni1QsM7kq9hCslFTJXQ0REtsiuwk5xcTHOnDmDkJAQ9OvXD66urti8ebNp/8mTJ5GRkYGYmBgZq6TW1CnACz1CNKgyCmzkUBYREdXDpsPOwoULsX37dqSnp2P37t246667oFQq8cADD0Cr1WLmzJlYsGABtm7diqSkJDzyyCOIiYnBrbfeKnfp1Ipqbh+RcIQXGCQiorpc5C6gMRcuXMADDzyAy5cvIyAgALfddhv++OMPBAQEAADef/99KBQKTJkyBXq9HrGxsfj4449lrppa28TeoXh340nsOXMZeUV6BHir5S6JiIhsiCSEEHIXIbfCwkJotVrodDrO37FTd360E4cv6PDanb0wLSZc7nKIiKgVNPX726aHsYiayrQqixcYJCKi6zDskEOouTHo/nP5yNaVy1wNERHZEoYdcgihPu7o174NhADWp/DsDhERXcOwQw4jLpKrsoiIqC6GHXIY43uHQJKAQxkFOJ9fKnc5RERkIxh2yGEEatwQ3cEXAIeyiIjoGoYdcijX7pXFoSwiIqrGsEMOZVxEMJQKCUcvFiL9Uonc5RARkQ1g2CGH4uelxqBOfgB4doeIiKox7JDDiTMNZXHeDhERMeyQA4rtFQxXpYQT2UU4nVMkdzlERCQzhh1yOFoPVwzpUn2z2HU8u0NE5PQYdsghTax1gUHe65aIyLkx7JBDGt0zCCoXBc7mleB4FoeyiIicGcMOOSRvN1eM6FY9lMVVWUREzo1hhxzWxFqrsjiURUTkvBh2yGGN7BEId1clMvJLceSCTu5yiIhIJgw75LA8VC64vUcgAA5lERE5M4YdcmhxV1dlrT+SBaORQ1lERM6IYYcc2vBugfBUKZGpK8eh81fkLoeIiGTAsEMOzc1ViTG9ggEA6w7zAoNERM6IYYccXs0FBtenZMHAoSwiIqfDsEMOb0iXAGjcXJBXpMe+tHy5yyEiolbGsEMOT+WiQOzVoSyuyiIicj4MO+QUJkZVX2Bw49FsVBmMMldDREStiWGHnMKgTn5o4+GKyyUV2HP2stzlEBFRK2LYIafgqlRgXO+rd0LnqiwiIqfCsENOo2ZV1oajWaio4lAWEZGzYNghpxHdwQ8B3moUlldhZ2qe3OUQEVErYdghp6FUSBgfcXVVFoeyiIicBsMOOZWaVVm//ZmD8kqDzNUQEVFrsOmwEx8fjwEDBsDb2xuBgYGYNGkSTp48adZm+PDhkCTJ7PHEE0/IVDHZun7t2iBE64ZifRW2n+JQFhGRM7DpsLN9+3bMmTMHf/zxBzZt2oTKykqMGTMGJSUlZu1mzZqFrKws0+Pdd9+VqWKydQqFhAk1q7KOcCiLiMgZuMhdQGM2btxo9nzFihUIDAxEUlIShg4datru4eGB4ODg1i6P7NTEqFD83840JP6Zg9KKKniobPrXgIiIbpJNn9m5nk6nAwD4+vqabf/666/h7++PiIgILFq0CKWlpY0eR6/Xo7Cw0OxBziOqrRZhvu4oqzRgy4lcucshIiIrs5uwYzQaMX/+fAwePBgRERGm7Q8++CC++uorbN26FYsWLcKXX36Jhx56qNFjxcfHQ6vVmh5hYWHWLp9siCRJmNC7eqIyV2URETk+SQgh5C6iKWbPno0NGzZg586daNu2bYPttmzZgpEjRyI1NRWdOnWqt41er4derzc9LywsRFhYGHQ6HTQaTYvXTrbn6EUdJn64E2oXBZJeHA0vNYeyiIjsTWFhIbRa7Q2/v+3izM7cuXORkJCArVu3Nhp0ACA6OhoAkJqa2mAbtVoNjUZj9iDn0itUg47+ntBXGZH4Z47c5RARkRXZdNgRQmDu3Ln48ccfsWXLFnTo0OGGr0lOTgYAhISEWLk6smeSJJluH7HucKbM1RARkTXZdNiZM2cOvvrqK3zzzTfw9vZGdnY2srOzUVZWBgA4c+YMXn/9dSQlJSE9PR0///wzpk2bhqFDhyIyMlLm6snW1VxgcMfpPOhKK2WuhoiIrMWmw84nn3wCnU6H4cOHIyQkxPT47rvvAAAqlQqJiYkYM2YMunfvjmeffRZTpkzBunXrZK6c7EHXIG90DfJCpUHg1z+z5S6HiIisxKZnZd5o7nRYWBi2b9/eStWQI5oYGYqlm04h4UgW7u3PVXlERI7Ips/sEFlbzbydXamXkF9SIXM1RERkDQw75NQ6BnihV6gGBqPAxqMcyiIickQMO+T0JkZWT1TmqiwiIsfEsENOr2Yoa2/aZeQWlctcDRERtTSGHXJ6Yb4eiArzgVEAG1I4lEVE5GgYdogAxF09u5NwhENZRESOhmGHCMCEq2Fnf/oVZOnKZK6GiIhaEsMOEYAQrTsGhLcBAKw/wjuhExE5EoYdoqtMq7IYdoiIHArDDtFV43oHQyEBh88X4Hx+qdzlEBFRC2HYIboq0NsN0R38AAAJPLvjMLJ0Zdh95hLnYhE5MZu+NxZRa5sYFYI9Zy8j4UgmZg/vJHc5ZCEhBArLq1BQWoErpZX4KfkiVuxKhwAgScCLE3rikcHhkCRJ7lKJqBVJ4kZ323QChYWF0Gq10Ol00Gg0cpdDMsovqcCANxNhMApseXYYOgZ4yV2S0yqvNOBKaQWulFSioLQCBWWVuFJagYLSSlwpqX5eE2pqtuvKKmEwNv6/NFeFhCCtG4I0bgjWuCFQo673Z081/y1IZOua+v3N32aiWnw9VRjc2R87TuUh4UgW5o3sIndJdq/KYISurBJXSiuhK6sOL6bQUlortNTaXlBWgfJKY7Pf091VCQ+VApdLKuvsqzQKXLhShgtXGh/W8lK7IFCjRrCmOhjV/jlIo0agd/U2tYuy2XUSUetg2CG6zsTIkKthJ5NhpxYhBIr1VdVh5GpQMYUT09mV6jMtpjMxJRUoLK9q9nsqFRLaeLjCx0MFH/fqP9t4uKKNpwo+Hq7wcVeZ9rfxdEUbDxW07q5wc1UiS1eGwW9vQe0TPQoJ+OGJGAgAOYV65BSWI7uwHLnX/Vysr6p+5FXhbF5JozX6eqoQ6K1GsNYNQd7VQSjI9LMbgrRq+HmqoVRw6IxILgw7RNeJ7RmMvytTcCqnGKdyitA1yFvuklpcRZXxuiGgmjMtNUND1ft0plBTfVam0tD8UW9vNxe08bgaUmpCS81z95oAczW8uKvg4+kKb7VLs+fXhGjdET+5N/7fmqMwCAGlJOGtyRHo2973hq8t1lchp7AcOfUEodo/VxiMyC+pQH5JBU5kFzV4PKVCQoBXTQhSm84OBZnOFFUPnWncm99fImoYww7RdbQerhjaJQCbT+Qi4XAmFozpJndJDTIaBQrLK02hpXY4Kaj3zEv1n6UVhma/p8pFYRZUqv+s+bkmyNSccal+rnV3hauy9Rd/3jegHYZ2DUD6pVKE+3sgROvepNd5qV3gFeCFTo3M2RJCoKC0Etm1QlHNzzmFeuQWlSNbV45LxXoYjALZV0NSY9QuihvOJQrSuMFdxaEzIksw7BDVY2JUSHXYOZKFZ0Z3tfq/toUQKKs0XBsCqh1aSq6bnGsKMBXQlVXiBvNxG6SQAK27eWjRXv2zdmjxuRpa2lx9bm9ftCFa9yaHHEtIkoQ2niq08VShR0jDEyOrDEZcLqlAtu5qECrSI+f6n4vKUVBaCX2VERn5pci4wXWevN1czOYSXQtCagRe/TnAWy1LwCSyRQw7RPUY1SMIahcFzl4qwbf7MjCie2CTvzArr07INQ0TlVybdGs643J1Mq6u7Fqoqahq/oRcT5XSNG/Fx11VK5y41jrrojILLd5uLlBwHonVuSgVpqGqxpRXGqqHyYqunR3KMZ0pqn6erStHWaUBReVVKCovxunc4gaPJ0mAn6eq1lCZ2uznQG83BGvd4Ouh4t8Dcnhceg4uPaf6TfzX7ziaWQig+otjxqBw/CXMB1dKag8TVdZaAl2BgpJKFOmbPyHXVSmZz1upCSlXJ9/WN0lX6+7KFUFOomaSeO0wVN9cotyi8ibPr3JRSAj0rj2p2nyCdbC2+mzRzcyfIrIWLj0nuglZujIcuxp0AEAIYPmudIuOoXV3bWAy7tUzMLW2a69O0PVUKfmFQg2SJAnebq7wdnNF58CGJ84bjQJXSivqBKGcQj1ya/18uUSPKqNApq4cmbrG5xO5uyoRrHWrDkYaN7Ofa88tcnNl8Cbbw7BDVI+0SyWo79/FvUI1CPfzvDq35drk3NqTcWvCC5cak1wUCgl+Xmr4eanRK7ThdpUGI/KK9PUMm12bYJ1TWI7C8iqUVRqQdqkEaZcaX4qvdXetZ1L1tblEQRo3+Hup4NLE+URZujKkXSpBB39Pq8y9IufAsENUjw7+nlBIMJv8q5Qk/N/0/vwfLjkMV6UCoT7uCPVp/O90WYXhWhBqYIJ1tq4c+qrq+Wq6skqczGl4Kb4kAf5e6nqCkHko+u1YNv7fjykwiuoJ9fGTe+O+Ae1a+j8DOQHO2QHn7FD9vtufUecaLfwfLVH9hBAoLKtqcIJ19tUhtNwi/Q1v6dGYAeFt4KFygVIhQamQ4HL1T/PnCigVgItCYdqmqNXW/Lmi3v3XH6/xY0hX3wumtnVrqv1cAYUEpxmytubZuaZ+fzPsgGGHGpalK7P4Gi1E1DCDUeByib7RuUS5heW4XFIhd6lWV18Yqh2eFNcHNkmCi1Kq57ni2nOFBKXy6p/S1bZXX1P9XFH3GLVec+25opFjXHvfxva7KCRsPJqNpZtOWe3sHMOOBRh2iIhsS8blEgx/b5vZULIkAS9P7AlvN1cYjAJVRgGD0Vjr5+o/jdc/FwJVhuq2NdsNtfab/2y87jXXH0OgymiEUQBVRiMMhlr7jcL03GAUMAhxU2exHJFSkrDzhREt9o9HrsYiIiK71c7Ps97bfdjbULIQ1wWmWoHI/LnR9LwmVNWEJfPnxkZC2NU2BqN54KongNWEv+vfo6rOa4wwCJi9b/11CVP4q3leVmlAid78au0GIZB+qbTVz5Qz7BARkU1q7u0+bIl0dajJGS+FVd/NeJWShHB/j1avhdcSJyIimxWidUdMJz+7DDrOruZmvMqrE7Frzs7J8VnyzA4RERFZha2cnXOYMzvLli1DeHg43NzcEB0djX379sldEhERkdOzhbNzDhF2vvvuOyxYsAAvv/wyDh48iKioKMTGxiI3N1fu0oiIiEhmDhF2li5dilmzZuGRRx5Bz5498emnn8LDwwOff/653KURERGRzOw+7FRUVCApKQmjRo0ybVMoFBg1ahT27NlT72v0ej0KCwvNHkREROSY7D7sXLp0CQaDAUFBQWbbg4KCkJ2dXe9r4uPjodVqTY+wsLDWKJWIiIhkYPdhpzkWLVoEnU5nepw/f17ukoiIiMhK7H7pub+/P5RKJXJycsy25+TkIDg4uN7XqNVqqNXq1iiPiIiIZGb3Z3ZUKhX69euHzZs3m7YZjUZs3rwZMTExMlZGREREtsDuz+wAwIIFCzB9+nT0798fAwcOxD//+U+UlJTgkUcekbs0IiIikplDhJ377rsPeXl5eOmll5CdnY2//OUv2LhxY51Jy0REROR8JCGE099/vqm3iCciIiLb0dTvb7ufs0NERETUGIcYxrpZNSe3eHFBIiIi+1HzvX2jQSqGHQBFRUUAwIsLEhER2aGioiJotdoG93PODqqXqmdmZsLb2xuSJLXYcQsLCxEWFobz58877FwgR+8j+2f/HL2P7J/9c/Q+WrN/QggUFRUhNDQUCkXDM3N4ZgfV99Jq27at1Y6v0Wgc8i9wbY7eR/bP/jl6H9k/++fofbRW/xo7o1ODE5SJiIjIoTHsEBERkUNj2LEitVqNl19+2aHvw+XofWT/7J+j95H9s3+O3kdb6B8nKBMREZFD45kdIiIicmgMO0REROTQGHaIiIjIoTHsEBERkUNj2LlJy5YtQ3h4ONzc3BAdHY19+/Y12n7VqlXo3r073Nzc0Lt3b/zyyy+tVGnzWNK/FStWQJIks4ebm1srVmuZHTt2IC4uDqGhoZAkCWvXrr3ha7Zt24a+fftCrVajc+fOWLFihdXrvBmW9nHbtm11PkNJkpCdnd06BVsoPj4eAwYMgLe3NwIDAzFp0iScPHnyhq+zl9/D5vTP3n4PP/nkE0RGRpouOBcTE4MNGzY0+hp7+fwAy/tnb5/f9d5++21IkoT58+c32q61P0OGnZvw3XffYcGCBXj55Zdx8OBBREVFITY2Frm5ufW23717Nx544AHMnDkThw4dwqRJkzBp0iQcPXq0lStvGkv7B1RfITMrK8v0OHfuXCtWbJmSkhJERUVh2bJlTWqflpaGCRMmYMSIEUhOTsb8+fPx17/+Fb/++quVK20+S/tY4+TJk2afY2BgoJUqvDnbt2/HnDlz8Mcff2DTpk2orKzEmDFjUFJS0uBr7On3sDn9A+zr97Bt27Z4++23kZSUhAMHDuD222/HnXfeiWPHjtXb3p4+P8Dy/gH29fnVtn//fvz73/9GZGRko+1k+QwFNdvAgQPFnDlzTM8NBoMIDQ0V8fHx9ba/9957xYQJE8y2RUdHi8cff9yqdTaXpf1bvny50Gq1rVRdywIgfvzxx0bb/O1vfxO9evUy23bfffeJ2NhYK1bWcprSx61btwoA4sqVK61SU0vLzc0VAMT27dsbbGNvv4e1NaV/9vx7WKNNmzbi//7v/+rdZ8+fX43G+mevn19RUZHo0qWL2LRpkxg2bJh4+umnG2wrx2fIMzvNVFFRgaSkJIwaNcq0TaFQYNSoUdizZ0+9r9mzZ49ZewCIjY1tsL2cmtM/ACguLkb79u0RFhZ2w3+92Bt7+vxu1l/+8heEhIRg9OjR2LVrl9zlNJlOpwMA+Pr6NtjGnj/HpvQPsN/fQ4PBgJUrV6KkpAQxMTH1trHnz68p/QPs8/ObM2cOJkyYUOezqY8cnyHDTjNdunQJBoMBQUFBZtuDgoIanN+QnZ1tUXs5Nad/3bp1w+eff46ffvoJX331FYxGIwYNGoQLFy60RslW19DnV1hYiLKyMpmqalkhISH49NNPsXr1aqxevRphYWEYPnw4Dh48KHdpN2Q0GjF//nwMHjwYERERDbazp9/D2praP3v8PUxJSYGXlxfUajWeeOIJ/Pjjj+jZs2e9be3x87Okf/b4+a1cuRIHDx5EfHx8k9rL8RnyrufUYmJiYsz+tTJo0CD06NED//73v/H666/LWBk1Vbdu3dCtWzfT80GDBuHMmTN4//338eWXX8pY2Y3NmTMHR48exc6dO+UuxSqa2j97/D3s1q0bkpOTodPp8MMPP2D69OnYvn17g4HA3ljSP3v7/M6fP4+nn34amzZtsumJ1Aw7zeTv7w+lUomcnByz7Tk5OQgODq73NcHBwRa1l1Nz+nc9V1dX9OnTB6mpqdYosdU19PlpNBq4u7vLVJX1DRw40OYDxNy5c5GQkIAdO3agbdu2jba1p9/DGpb073r28HuoUqnQuXNnAEC/fv2wf/9+fPDBB/j3v/9dp609fn6W9O96tv75JSUlITc3F3379jVtMxgM2LFjBz766CPo9XoolUqz18jxGXIYq5lUKhX69euHzZs3m7YZjUZs3ry5wbHYmJgYs/YAsGnTpkbHbuXSnP5dz2AwICUlBSEhIdYqs1XZ0+fXkpKTk232MxRCYO7cufjxxx+xZcsWdOjQ4YavsafPsTn9u549/h4ajUbo9fp699nT59eQxvp3PVv//EaOHImUlBQkJyebHv3798fUqVORnJxcJ+gAMn2GVpv67ARWrlwp1Gq1WLFihfjzzz/FY489Jnx8fER2drYQQoiHH35YvPDCC6b2u3btEi4uLuK9994Tx48fFy+//LJwdXUVKSkpcnWhUZb279VXXxW//vqrOHPmjEhKShL333+/cHNzE8eOHZOrC40qKioShw4dEocOHRIAxNKlS8WhQ4fEuXPnhBBCvPDCC+Lhhx82tT979qzw8PAQzz33nDh+/LhYtmyZUCqVYuPGjXJ14YYs7eP7778v1q5dK06fPi1SUlLE008/LRQKhUhMTJSrC42aPXu20Gq1Ytu2bSIrK8v0KC0tNbWx59/D5vTP3n4PX3jhBbF9+3aRlpYmjhw5Il544QUhSZL47bffhBD2/fkJYXn/7O3zq8/1q7Fs4TNk2LlJH374oWjXrp1QqVRi4MCB4o8//jDtGzZsmJg+fbpZ+++//1507dpVqFQq0atXL7F+/fpWrtgylvRv/vz5prZBQUFi/Pjx4uDBgzJU3TQ1y6yvf9T0afr06WLYsGF1XvOXv/xFqFQq0bFjR7F8+fJWr9sSlvbxnXfeEZ06dRJubm7C19dXDB8+XGzZskWe4pugvr4BMPtc7Pn3sDn9s7ffw0cffVS0b99eqFQqERAQIEaOHGkKAkLY9+cnhOX9s7fPrz7Xhx1b+AwlIYSw3nkjIiIiInlxzg4RERE5NIYdIiIicmgMO0REROTQGHaIiIjIoTHsEBERkUNj2CEiIiKHxrBDREREDo1hh4gcxrZt2yBJEgoKCqz6PsOHD8f8+fOt+h5N1Vp9JrJnDDtETsKWvqBbQn39GTRoELKysqDVauUpysoc7TMkai0MO0RkIoRAVVWV3GU0m0qlQnBwMCRJkrsUIrIhDDtETmDGjBnYvn07PvjgA0iSBEmSkJ6ebhoC2bBhA/r16we1Wo2dO3fizJkzuPPOOxEUFAQvLy8MGDAAiYmJZscMDw/HW2+9hUcffRTe3t5o164dPvvsM9P+iooKzJ07FyEhIXBzc0P79u0RHx9v2r906VL07t0bnp6eCAsLw5NPPoni4mKz99i1axeGDx8ODw8PtGnTBrGxsbhy5coN+1N7SGf16tXo1asX1Go1wsPDsWTJEov60RR6vR4LFy7ELbfcAk9PT0RHR2Pbtm2m/StWrICPjw9+/fVX9OjRA15eXhg7diyysrJMbaqqqjBv3jz4+PjAz88Pzz//PKZPn45JkyY1+hnWSEpKQv/+/eHh4YFBgwbh5MmTFvWByKFZ9c5bRGQTCgoKRExMjJg1a5bpztlVVVWmG4VGRkaK3377TaSmporLly+L5ORk8emnn4qUlBRx6tQp8Y9//EO4ubmZ7pYuhBDt27cXvr6+YtmyZeL06dMiPj5eKBQKceLECSGEEIsXLxZhYWFix44dIj09Xfz+++/im2++Mb3+/fffF1u2bBFpaWli8+bNolu3bmL27Nmm/YcOHRJqtVrMnj1bJCcni6NHj4oPP/xQ5OXl3bA/V65cEUIIceDAAaFQKMRrr70mTp48KZYvXy7c3d3NbqR5o37U5/obHf71r38VgwYNEjt27BCpqali8eLFQq1Wi1OnTgkhhFi+fLlwdXUVo0aNEvv37xdJSUmiR48e4sEHHzQd44033hC+vr5izZo14vjx4+KJJ54QGo1G3HnnnU36DKOjo8W2bdvEsWPHxJAhQ8SgQYMs/ntC5KgYdoicxPVf0EJcuyv62rVrb/j6Xr16iQ8//ND0vH379uKhhx4yPTcajSIwMFB88sknQgghnnrqKXH77bcLo9HYpPpWrVol/Pz8TM8feOABMXjw4Gb1pybsPPjgg2L06NFmbZ577jnRs2fPJvfjRu997tw5oVQqxcWLF83ajBw5UixatEgIUR12AIjU1FTT/mXLlomgoCDT86CgILF48WLT86qqKtGuXTtT2LlRnxMTE03b1q9fLwCIsrKyBvtA5Ew4jEVE6N+/v9nz4uJiLFy4ED169ICPjw+8vLxw/PhxZGRkmLWLjIw0/SxJEoKDg5GbmwugetglOTkZ3bp1w7x58/Dbb7+ZvTYxMREjR47ELbfcAm9vbzz88MO4fPkySktLAQDJyckYOXLkTfXr+PHjGDx4sNm2wYMH4/Tp0zAYDE3qx42kpKTAYDCga9eu8PLyMj22b9+OM2fOmNp5eHigU6dOpuchISGm99DpdMjJycHAgQNN+5VKJfr169fkvtbuQ0hICAA0uQ9Ejs5F7gKISH6enp5mzxcuXIhNmzbhvffeQ+fOneHu7o67774bFRUVZu1cXV3NnkuSBKPRCADo27cv0tLSsGHDBiQmJuLee+/FqFGj8MMPPyA9PR0TJ07E7Nmz8eabb8LX1xc7d+7EzJkzUVFRAQ8PD7i7u1u3003sx40UFxdDqVQiKSkJSqXSbJ+Xl1ej7yGEaGbFddU+fs0E7ab2gcjR8cwOkZNQqVRmZzMas2vXLsyYMQN33XUXevfujeDgYLPJsE2l0Whw33334T//+Q++++47rF69Gvn5+UhKSoLRaMSSJUtw6623omvXrsjMzDR7bWRkJDZv3nxT/enRowd27dpVp29du3atE0yaq0+fPjAYDMjNzUXnzp3NHsHBwU06hlarRVBQEPbv32/aZjAYcPDgQbN2lnyGRHQNz+wQOYnw8HDs3bsX6enp8PLygq+vb4Ntu3TpgjVr1iAuLg6SJOHFF1+0+CzB0qVLERISgj59+kChUGDVqlUIDg6Gj48POnfujMrKSnz44YeIi4vDrl278Omnn5q9ftGiRejduzeefPJJPPHEE1CpVNi6dSvuuece+Pv7N6k/zz77LAYMGIDXX38d9913H/bs2YOPPvoIH3/8sUV9aUzXrl0xdepUTJs2DUuWLEGfPn2Ql5eHzZs3IzIyEhMmTGjScZ566inEx8ejc+fO6N69Oz788ENcuXLFbBm9JZ8hEV3DMztETmLhwoVQKpXo2bMnAgIC6sy/qW3p0qVo06YNBg0ahLi4OMTGxqJv374WvZ+3tzfeffdd9O/fHwMGDEB6ejp++eUXKBQKREVFYenSpXjnnXcQERGBr7/+2mxZOlAdIn777TccPnwYAwcORExMDH766Se4uLg0uT99+/bF999/j5UrVyIiIgIvvfQSXnvtNcyYMcOivtzI8uXLMW3aNDz77LPo1q0bJk2ahP3796Ndu3ZNPsbzzz+PBx54ANOmTUNMTAy8vLwQGxsLNzc3UxtLPkMiukYSLTloTERELcJoNKJHjx6499578frrr8tdDpFd4zAWEZENOHfuHH777TcMGzYMer0eH330EdLS0vDggw/KXRqR3eMwFhGRDVAoFFixYgUGDBiAwYMHIyUlBYmJiejRo4fcpRHZPQ5jERERkUPjmR0iIiJyaAw7RERE5NAYdoiIiMihMewQERGRQ2PYISIiIofGsENEREQOjWGHiIiIHBrDDhERETk0hh0iIiJyaP8f+/sXdVeKgtIAAAAASUVORK5CYII=\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 17: Extracting Patterns from Predicted Data Using the FP-Growth Algorithm**" + ], + "metadata": { + "id": "_CBreLxc_O77" + } + }, + { + "cell_type": "code", + "source": [ + "from PAMI.frequentPattern.basic import FPGrowth as ab\n", + "obj = ab.FPGrowth('TDB.csv', 120)\n", + "obj.mine()\n", + "obj.printResults()\n", + "obj.save('10days_frequentPatterns.txt')" + ], + "metadata": { + "id": "hVfXDTa6VCjs", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "71dba615-9aad-4e83-8ea2-f92927c9c341" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n", + "Total number of Frequent Patterns: 63\n", + "Total Memory in USS: 6328397824\n", + "Total Memory in RSS 6350974976\n", + "Total ExecutionTime in ms: 0.007161617279052734\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# **Step 18:Display the Frequent Patterns**" + ], + "metadata": { + "id": "YjN4QIDD_Jov" + } + }, + { + "cell_type": "code", + "source": [ + "!head /content/10days_frequentPatterns.txt" + ], + "metadata": { + "id": "xjUqMnHF4a7c", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7a7c80d6-9f5f-44d0-9c60-b31c27edc676" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "28209010:238\n", + "11222030\t28209010:238\n", + "33202110\t28209010:238\n", + "43202020\t28209010:238\n", + "28216010\t28209010:238\n", + "24202530\t28209010:238\n", + "11222030\t33202110\t28209010:238\n", + "11222030\t43202020\t28209010:238\n", + "11222030\t28216010\t28209010:238\n", + "11222030\t24202530\t28209010:238\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/setup.py b/setup.py index d4661410..3a350e2c 100644 --- a/setup.py +++ b/setup.py @@ -5,7 +5,7 @@ setuptools.setup( name='pami', - version='2024.12.10.1', + version='2024.12.20.1', author='Rage Uday Kiran', author_email='uday.rage@gmail.com', description='This software is being developed at the University of Aizu, Aizu-Wakamatsu, Fukushima, Japan',