Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Documentation #379

Merged
merged 4 commits into from
Apr 23, 2024
Merged

Documentation #379

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
44b0cc0
Documentation
Likhitha-palla Apr 23, 2024
054e6ef
Documentation
Likhitha-palla Apr 23, 2024
399a4ec
Documentation
Likhitha-palla Apr 23, 2024
c2aca4a
Merge branch 'UdayLab:main' into main
pallamadhavi Apr 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
93 changes: 66 additions & 27 deletions PAMI/weightedFrequentPattern/basic/WFIM.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,14 +3,16 @@
# patterns from tree.It employs downward closure property to reduce the search space effectively.
#
# **Importing this algorithm into a python program**
# ----------------------------------------------------------
#
#
# from PAMI.weightFrequentPattern.basic import basic as alg
#
# iFile = 'sampleDB.txt'
#
# minSup = 10 # can also be specified between 0 and 1
#
# obj = alg.basic(iFile, wFile, minSup, minWeight)
#
# obj.startMine()
# obj.mine()
#
# frequentPatterns = obj.getPatterns()
#
Expand Down Expand Up @@ -48,8 +50,6 @@

You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
Copyright (C) 2021 Rage Uday Kiran

"""

from PAMI.weightedFrequentPattern.basic import abstract as _fp
Expand All @@ -74,10 +74,13 @@ class _Node:

itemId: int
storing item of a node

counter: int
To maintain the support of node

parent: node
To maintain the parent of node

children: list
To maintain the children of node

Expand All @@ -98,7 +101,7 @@ def addChild(self, node: '_Node') -> None:
Retrieving the child from the tree

:param node: Children node
:type node: Node
:type node: _Node
:return: Updates the children nodes and parent nodes
"""
self.children[node.itemId] = node
Expand All @@ -113,8 +116,10 @@ class _Tree:

root : Node
The first node of the tree set to Null.

summaries : dictionary
Stores the nodes itemId which shares same itemId

info : dictionary
frequency of items in the transactions

Expand Down Expand Up @@ -237,14 +242,15 @@ def generatePatterns(self, prefix: List[str]) -> Generator[Tuple[List[str], int]

class WFIM(_fp._weightedFrequentPatterns):
"""
:Description:
* WFMiner is one of the fundamental algorithm to discover weighted frequent patterns in a transactional database.
* It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs employs downward closure property to reduce the search space effectively.
About this algorithm
====================

:Reference :
U. Yun and J. J. Leggett, “Wfim: weighted frequent itemset mining with a weight range and a minimum weight,”
in Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 2005, pp. 636–640.
https://epubs.siam.org/doi/pdf/10.1137/1.9781611972757.76
:Description: * WFMiner is one of the fundamental algorithm to discover weighted frequent patterns in a transactional database.
* It stores the database in compressed fp-tree decreasing the memory usage and extracts the patterns from tree.It employs employs downward closure property to reduce the search space effectively.

:Reference: U. Yun and J. J. Leggett, “Wfim: weighted frequent itemset mining with a weight range and a minimum weight,”
In: Proceedings of the 2005 SIAM International Conference on Data Mining. SIAM, 2005, pp. 636–640.
https://epubs.siam.org/doi/pdf/10.1137/1.9781611972757.76

:param iFile: str :
Name of the Input file to mine complete set of weighted Frequent Patterns.
Expand All @@ -260,43 +266,56 @@ class WFIM(_fp._weightedFrequentPatterns):

iFile : file
Input file name or path of the input file

minSup: float or int 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.
Otherwise, it will be treated as float.
Example: minSup=10 will be treated as integer, while minSup=10.0 will be treated as float

minWeight: float or int or str
The user can specify minWeight either in count or proportion of database size.
If the program detects the data type of minWeight is integer, then it treats minWeight is expressed in count.
Otherwise, it will be treated as float.
Example: minWeight=10 will be treated as integer, while minWeight=10.0 will be treated as float

sep : str
This variable is used to distinguish items from one another in a transaction. The default separator is tab space or \t.
However, the users can override their default separator.

oFile : file
Name of the output file or the path of the output file

startTime:float
To record the start time of the mining process

endTime:float
To record the completion time of the mining process

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

mapSupport : Dictionary
To maintain the information of item and their frequency

lno : int
it represents the total no of transactions

tree : class
it represents the Tree class

finalPatterns : dict
it represents to store the patterns

:Methods :

startMine()
mine()
Mining process will start from here
getPatterns()
Complete set of patterns will be retrieved with this function
Expand All @@ -315,11 +334,15 @@ class WFIM(_fp._weightedFrequentPatterns):
frequentOneItem()
Extracts the one-frequent patterns from transactions

**Methods to execute code on terminal**
-------------------------------------------
.. code-block:: console
Execution methods
=================


**Terminal command**


.. code-block:: console

Format:

(.venv) $ python3 basic.py <inputFile> <weightFile> <outputFile> <minSup> <minWeight>
Expand All @@ -328,19 +351,22 @@ class WFIM(_fp._weightedFrequentPatterns):

(.venv) $ python3 basic.py sampleDB.txt weightSample.txt patterns.txt 10.0 3.4

.. note:: minSup and maxPer will be considered in support count or frequency

.. note:: minSup and maxPer will be considered in support count or frequency

**Calling from a python program**

**Importing this algorithm into a python program**
-----------------------------------------------------
.. code-block:: python

from PAMI.weightFrequentPattern.basic import basic as alg

iFile = 'sampleDB.txt'

minSup = 10 # can also be specified between 0 and 1

obj = alg.basic(iFile, wFile, minSup, minWeight)

obj.startMine()
obj.mine()

frequentPatterns = obj.getPatterns()

Expand All @@ -362,11 +388,12 @@ class WFIM(_fp._weightedFrequentPatterns):

print("Total ExecutionTime in seconds:", run)

**Credits:**
----------------------
The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.
Credits
=======

"""

The complete program was written by P.Likhitha under the supervision of Professor Rage Uday Kiran.
"""

__startTime = float()
__endTime = float()
Expand All @@ -390,6 +417,7 @@ def __init__(self, iFile: str, wFile: str, minSup: str, minWeight: int, sep: str
def __creatingItemSets(self) -> None:
"""
Storing the complete transactions of the database/input file in a database variable

:return: None
"""
self.__Database = []
Expand Down Expand Up @@ -426,6 +454,7 @@ def __creatingItemSets(self) -> None:
def _scanningWeights(self) -> None:
"""
Storing the weights of the variables in input file in a weights variable

:return: None
"""
global _weights
Expand Down Expand Up @@ -487,6 +516,7 @@ def __convert(self, value: Union[int, float, str]) -> Union[int, float]:
def __frequentOneItem(self) -> List[str]:
"""
Generating One frequent items sets

:return: list
"""
global _maxWeight
Expand All @@ -505,6 +535,7 @@ def __frequentOneItem(self) -> List[str]:
def __updateTransactions(self, itemSet: List[str]) -> List[List[int]]:
"""
Updates the items in transactions with rank of items according to their support

:Example: oneLength = {'a':7, 'b': 5, 'c':'4', 'd':3}
rank = {'a':0, 'b':1, 'c':2, 'd':3}

Expand Down Expand Up @@ -549,17 +580,20 @@ def __savePeriodic(self, itemSet: List[int]) -> str:
temp = temp + self.__rankDup[i] + "\t"
return temp

@deprecated("It is recommended to use mine() instead of startMine() for mining process")
@deprecated(
"It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.")
def startMine(self) -> None:
"""
main program to start the operation

:return: None
"""
self.mine()

def mine(self) -> None:
"""
main program to start the operation

:return: None
"""
global _minSup, _minWeight, _miniWeight, _maxWeight, _weights
Expand Down Expand Up @@ -597,6 +631,7 @@ def mine(self) -> None:

def getMemoryUSS(self) -> float:
"""

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
Expand All @@ -607,6 +642,7 @@ def getMemoryUSS(self) -> float:

def getMemoryRSS(self) -> float:
"""

Total amount of RSS memory consumed by the mining process will be retrieved from this function.

:return: returning RSS memory consumed by the mining process
Expand All @@ -617,6 +653,7 @@ def getMemoryRSS(self) -> float:

def getRuntime(self) -> float:
"""

Calculating the total amount of runtime taken by the mining process.

:return: returning total amount of runtime taken by the mining process
Expand All @@ -627,6 +664,7 @@ def getRuntime(self) -> float:

def getPatternsAsDataFrame(self) -> pd.DataFrame:
"""

Storing final frequent patterns in a dataframe.

:return: returning frequent patterns in a dataframe
Expand All @@ -642,6 +680,7 @@ def getPatternsAsDataFrame(self) -> pd.DataFrame:

def save(self, outFile: str) -> None:
"""

Complete set of frequent patterns will be loaded in to an output file.

:param outFile: name of the output file
Expand All @@ -656,6 +695,7 @@ def save(self, outFile: str) -> None:

def getPatterns(self) -> Dict[str, int]:
"""

Function to send the set of frequent patterns after completion of the mining process.

:return: returning frequent patterns
Expand All @@ -666,7 +706,6 @@ def getPatterns(self) -> Dict[str, int]:
def printResults(self) -> None:
"""
This function is used to print the results
:return: None
"""
print("Total number of Weighted Frequent Patterns:", len(self.getPatterns()))
print("Total Memory in USS:", self.getMemoryUSS())
Expand Down
Loading