# ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
#
# **Importing this algorithm into a python program**
#
# import PAMI.frequentPattern.basic.ECLATbitset as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# obj = alg.ECLATbitset(iFile, minSup)
#
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
# print("Total number of Frequent Patterns:", len(frequentPatterns))
#
# obj.save(oFile)
#
# Df = obj.getPatternInDataFrame()
#
# memUSS = obj.getMemoryUSS()
#
# print("Total Memory in USS:", memUSS)
#
# memRSS = obj.getMemoryRSS()
#
# print("Total Memory in RSS", memRSS)
#
# run = obj.getRuntime()
#
# print("Total ExecutionTime in seconds:", run)
#
__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 <https://www.gnu.org/licenses/>.
"""
from PAMI.frequentPattern.basic import abstract as _ab
from deprecated import deprecated
[docs]
class ECLATbitset(_ab._frequentPatterns):
"""
**About this algorithm**
:*Description*: ECLATbitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
:*Reference*: Mohammed Javeed Zaki: Scalable Algorithms for Association Mining. IEEE Trans. Knowl. Data Eng. 12(3):
372-390 (2000), https://ieeexplore.ieee.org/document/846291
:**Parameters**: - **iFile** (*str or URL or dataFrame*) -- *Name of the Input file to mine complete set of frequent patterns.*
- **oFile** (*str*) -- *Name of the output file to store complete set of frequent patterns*
- **minSup** (*int or float or str*) -- *The user can specify minSup either in count or proportion of database size. If the program detects the data type of minSup is integer, then it treats minSup is expressed in count.*
- **sep** (*str*) -- *This variable is used to distinguish items from one another in a transaction. The default seperator is tab space. However, the users can override their default separator.*
:**Attributes**: - **startTime** (*float*) -- *To record the start time of the mining process.*
- **endTime** (*float*) -- *To record the end time of the mining process.*
- **finalPatterns** (*dict*) -- *Storing the complete set of patterns in a dictionary variable.*
- **memoryUSS** (*float*) -- *To store the total amount of USS memory consumed by the program.*
- **memoryRSS** *(float*) -- *To store the total amount of RSS memory consumed by the program.*
- **Database** (*list*) -- *To store the transactions of a database in list.*
**Execution methods**
**Terminal command**
.. code-block:: console
Format:
(.venv) $ python3 ECLATbitset.py <inputFile> <outputFile> <minSup>
Example Usage:
(.venv) $ python3 ECLATbitset.py sampleDB.txt patterns.txt 10.0
.. note:: minSup can be specified in support count or a value between 0 and 1.
**Calling from a python program**
.. code-block:: python
import PAMI.frequentPattern.basic.ECLATbitset as alg
iFile = 'sampleDB.txt'
minSup = 10 # can also be specified between 0 and 1
obj = alg.ECLATbitset(iFile, minSup)
obj.mine()
frequentPatterns = obj.getPatterns()
print("Total number of Frequent Patterns:", len(frequentPatterns))
obj.save(oFile)
Df = obj.getPatternInDataFrame()
memUSS = obj.getMemoryUSS()
print("Total Memory in USS:", memUSS)
memRSS = obj.getMemoryRSS()
print("Total Memory in RSS", memRSS)
run = obj.getRuntime()
print("Total ExecutionTime in seconds:", run)
**Credits:**
The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
"""
_startTime = float()
_endTime = float()
_finalPatterns = {}
_iFile = " "
_oFile = " "
_sep = " "
_minSup = str()
_memoryUSS = float()
_memoryRSS = float()
_Database = []
_mapSupport = {}
_lno = 0
def _convert(self, value):
"""
To convert the user specified minSup value
:param value: user specified minSup value
:type value: int
:return: converted type
:rtype: int or float or string
"""
if type(value) is int:
value = int(value)
if type(value) is float:
value = (len(self._Database) * value)
if type(value) is str:
if '.' in value:
value = float(value)
value = (len(self._Database) * value)
else:
value = int(value)
return value
def _creatingItemSets(self):
"""
Storing the complete transactions of the database/input file in a database variable
"""
self._Database = []
self._mapSupport = {}
if isinstance(self._iFile, _ab._pd.DataFrame):
if self._iFile.empty:
print("its empty..")
i = self._iFile.columns.values.tolist()
if 'Transactions' in i:
self._Database = self._iFile['Transactions'].tolist()
self._Database = [x.split(self._sep) for x in self._Database]
else:
print("The column name should be Transactions and each line should be separated by tab space or a seperator specified by the user")
if isinstance(self._iFile, str):
if _ab._validators.url(self._iFile):
data = _ab._urlopen(self._iFile)
for line in data:
line.strip()
line = line.decode("utf-8")
temp = [i.rstrip() for i in line.split(self._sep)]
temp = [x for x in temp if x]
self._Database.append(temp)
else:
try:
with open(self._iFile, 'r') as f:
for line in f:
self._lno += 1
splitter = [i.rstrip() for i in line.split(self._sep)]
splitter = [x for x in splitter if x]
self._Database.append(splitter)
except IOError:
print("File Not Found")
self._minSup = self._convert(self._minSup)
[docs]
@deprecated("It is recommended to use 'mine()' instead of 'mine()' for mining process. Starting from January 2025, 'mine()' will be completely terminated.")
def startMine(self):
"""
Frequent pattern mining process will start from here
We start with the scanning the itemSets and store the bitsets respectively.
We form the combinations of single items and check with minSup condition to check the frequency of patterns
"""
self.mine()
def _bitPacker(self, data, maxIndex):
"""
It takes the data and maxIndex as input and generates integer as output value.
:param data: it takes data as input.
:type data: int or float
:param maxIndex: It converts the data into bits By taking the maxIndex value as condition.
:type maxIndex: int
"""
s = ['0'] * (maxIndex + 1)
for i in data:
s[i] = '1'
return int(''.join(s), 2)
@staticmethod
def _popcount(x):
"""
Count set bits in a Python big int
"""
try:
return x.bit_count()
except AttributeError:
return bin(x).count('1')
def __recursive(self, items, cands, memorySaver):
"""
This function generates new candidates by taking input as original candidates.
:param items: A dictionary containing items and their corresponding support values.
:type items: dict
:param cands: A list of candidate itemsets.
:type cands: list
:return: None
"""
if memorySaver:
for i in range(len(cands)):
newCands = []
for j in range(i + 1, len(cands)):
newCand = tuple(cands[i] + tuple([cands[j][-1]]))
intersection = items[tuple([newCand[0]])]
for k in newCand[1:]:
intersection &= items[tuple([k])]
count = self._popcount(intersection)
if count >= self._minSup:
newCands.append(newCand)
self._finalPatterns[newCand] = count
if len(newCands) > 1:
self.__recursive(items, newCands, memorySaver)
else:
for i in range(len(cands)):
newCands = []
for j in range(i + 1, len(cands)):
newCand = tuple(cands[i] + tuple([cands[j][-1]]))
intersection = items[cands[i]] & items[cands[j]]
count = self._popcount(intersection)
if count >= self._minSup:
newCands.append(newCand)
self._finalPatterns[newCand] = count
items[newCand] = intersection
if len(newCands) > 1:
self.__recursive(items, newCands, memorySaver)
[docs]
def mine(self, memorySaver=True) -> None:
"""
Frequent pattern mining process will start from here
# Bitset implementation
"""
self._startTime = _ab._time.time()
self._finalPatterns = {}
self._Database = []
self._creatingItemSets()
items = {}
index = 0
for line in self._Database:
for item in line:
if tuple([item]) in items:
items[tuple([item])].append(index)
else:
items[tuple([item])] = [index]
index += 1
# sort by length in descending order
items = dict(sorted(items.items(), key=lambda x: len(x[1]), reverse=True))
cands = []
for key in list(items.keys()):
if len(items[key]) >= self._minSup:
self._finalPatterns[key] = len(items[key])
cands.append(key)
items[key] = self._bitPacker(items[key], index)
else:
break
self.__recursive(items, cands, memorySaver)
self._endTime = _ab._time.time()
process = _ab._psutil.Process(_ab._os.getpid())
self._memoryUSS = float()
self._memoryRSS = float()
self._memoryUSS = process.memory_full_info().uss
self._memoryRSS = process.memory_info().rss
print("Frequent patterns were generated successfully using ECLAT algorithm ")
[docs]
def getMemoryUSS(self):
"""
Total amount of USS memory consumed by the mining process will be retrieved from this function
:return: returning USS memory consumed by the mining process
:rtype: float
"""
return self._memoryUSS
[docs]
def getRuntime(self):
"""
Calculating the total amount of runtime taken by the mining process
:return: returning total amount of runtime taken by the mining process
:rtype: float
"""
return self._endTime - self._startTime
[docs]
def getPatternsAsDataFrame(self):
"""
Storing final frequent patterns in a dataframe
:return: returning frequent patterns in a dataframe
:rtype: pd.DataFrame
"""
# dataFrame = {}
# data = []
# for a, b in self._finalPatterns.items():
# data.append([a.replace('\t', ' '), b])
# dataFrame = _ab._pd.DataFrame(data, columns=['Patterns', 'Support'])
# dataFrame = _ab._pd.DataFrame(list([self._sep.join(x)dataFrame = _ab._pd.DataFrame(list([self._sep.join(x), y] for x,y in self._finalPatterns.items()), columns=['Patterns', 'Support']), y] for x,y in self._finalPatterns.items()]), columns=['Patterns', 'Support'])
dataFrame = _ab._pd.DataFrame(list([self._sep.join(x), y] for x,y in self._finalPatterns.items()), columns=['Patterns', 'Support'])
return dataFrame
[docs]
def save(self, outFile: str, seperator = "\t" ) -> None:
"""
Complete set of frequent patterns will be loaded in to an output file
:param outFile: name of the output file
:type outFile: csvfile
:param seperator: variable to store separator value
:type seperator: string
:return: None
"""
# self._oFile = outFile
# writer = open(self._oFile, 'w+')
# for x, y in self._finalPatterns.items():
# patternsAndSupport = x.strip() + ":" + str(y[0])
# writer.write("%s \n" % patternsAndSupport)
with open(outFile, 'w') as f:
for x, y in self._finalPatterns.items():
x = seperator.join(x)
f.write(f"{x}:{y}\n")
[docs]
def getPatterns(self):
"""
Function to send the set of frequent patterns after completion of the mining process
:return: returning frequent patterns
:rtype: dict
"""
return self._finalPatterns
[docs]
def printResults(self):
"""
This function is used to print the result
"""
print("Total number of Frequent Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
print("Total Memory in RSS", self.getMemoryRSS())
print("Total ExecutionTime in ms:", self.getRuntime())
if __name__ == "__main__":
_ap = str()
if len(_ab._sys.argv) == 4 or len(_ab._sys.argv) == 5:
if len(_ab._sys.argv) == 5:
_ap = ECLATbitset(_ab._sys.argv[1], _ab._sys.argv[3], _ab._sys.argv[4])
if len(_ab._sys.argv) == 4:
_ap = ECLATbitset(_ab._sys.argv[1], _ab._sys.argv[3])
_ap.mine()
_ap.mine()
print("Total number of Frequent Patterns:", len(_ap.getPatterns()))
_ap.save(_ab._sys.argv[2])
print("Total Memory in USS:", _ap.getMemoryUSS())
print("Total Memory in RSS", _ap.getMemoryRSS())
print("Total ExecutionTime in ms:", _ap.getRuntime())
else:
print("Error! The number of input parameters do not match the total number of parameters provided")