#2 updated the error in readtheDocs

UdayLab · Jun 11, 2024 · 2c3e732 · 2c3e732
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diff --git a/PAMI/correlatedPattern/basic/CoMine.py b/PAMI/correlatedPattern/basic/CoMine.py
@@ -115,9 +115,7 @@ def traverse(self) -> Tuple[List[int], int]:
 
 class CoMine(_ab._correlatedPatterns):
     """
-
-    About this algorithm
-    ====================
+    **About this algorithm**
 
     :**Description**: CoMine is one of the fundamental algorithm to discover correlated  patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.
 
@@ -144,8 +142,7 @@ class CoMine(_ab._correlatedPatterns):
                         - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.*
                         - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.*
 
-    Execution methods
-    =================
+    **Execution methods**
 
     **Terminal command**
 
@@ -197,10 +194,9 @@ class CoMine(_ab._correlatedPatterns):
 
             print("Total ExecutionTime in seconds:", run)
 
-    Credits
-    =======
+    **Credits**
 
-             The complete program was written by B.Sai Chitra under the supervision of Professor Rage Uday Kiran.
+    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
 
     """
 

diff --git a/PAMI/correlatedPattern/basic/CoMinePlus.py b/PAMI/correlatedPattern/basic/CoMinePlus.py
@@ -115,8 +115,7 @@ def traverse(self) -> Tuple[List[int], int]:
 
 class CoMine(_ab._correlatedPatterns):
     """
-    About this algorithm
-    ====================
+    **About this algorithm**
 
     :**Description**: CoMinePlus is one of the fundamental algorithm to discover correlated  patterns in a transactional database. It is based on the traditional FP-Growth algorithm. This algorithm uses depth-first search technique to find all correlated patterns in a transactional database.
 
@@ -143,8 +142,7 @@ class CoMine(_ab._correlatedPatterns):
                         - **itemSetBuffer** (*list*) -- *it represents the store the items in mining.*
                         - **maxPatternLength** (*int*) -- *it represents the constraint for pattern length.*
 
-    Execution methods
-    =================
+    **Execution methods**
 
     **Terminal command**
 
@@ -196,10 +194,9 @@ class CoMine(_ab._correlatedPatterns):
 
             print("Total ExecutionTime in seconds:", run)
 
-    Credits
-    =======
+    **Credits**
 
-    The complete program was written by B.Sai Chitra and revised by Tarun Sreepads under the supervision of Professor Rage Uday Kiran.
+    The complete program was written by B.Sai Chitra and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
 
     """
 

diff --git a/PAMI/frequentPattern/basic/Apriori.py b/PAMI/frequentPattern/basic/Apriori.py
@@ -2,7 +2,7 @@
 #
 # **Importing this algorithm into a python program**
 #
-#             import PAMI1.frequentPattern.basic.Apriori as alg
+#             import PAMI.frequentPattern.basic.Apriori as alg
 #
 #             iFile = 'sampleDB.txt'
 #
@@ -58,8 +58,7 @@
 
 class Apriori(_ab._frequentPatterns):
     """
-    About this algorithm
-    ====================
+    **About this algorithm**
 
     :**Description**: Apriori is one of the fundamental algorithm to discover frequent patterns in a transactional database. This program employs apriori property (or downward closure property) to  reduce the search space effectively. This algorithm employs breadth-first search technique to find the complete set of frequent patterns in a transactional database.
 
@@ -79,8 +78,7 @@ class Apriori(_ab._frequentPatterns):
                         - **Database** (*list*) -- *To store the transactions of a database in list.*
 
 
-    Execution methods
-    =================
+    **Execution methods**
 
     **Terminal command**
 
@@ -101,7 +99,7 @@ class Apriori(_ab._frequentPatterns):
 
     .. code-block:: python
 
-            import PAMI1.frequentPattern.basic.Apriori as alg
+            import PAMI.frequentPattern.basic.Apriori as alg
 
             iFile = 'sampleDB.txt'
 
@@ -132,8 +130,7 @@ class Apriori(_ab._frequentPatterns):
             print("Total ExecutionTime in seconds:", run)
 
 
-    Credits
-    =======
+    **Credits**
 
     The complete program was written by P. Likhitha  and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
 

diff --git a/PAMI/frequentPattern/basic/Aprioribitset.py b/PAMI/frequentPattern/basic/Aprioribitset.py
@@ -1,10 +1,13 @@
 # AprioriBitset 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.AprioriBitset as alg
 #
+#             iFile = 'sampleDB.txt'
+#
+#             minSup = 10  # can also be specified between 0 and 1
+#
 #             obj = alg.AprioriBitset(iFile, minSup)
 #
 #             obj.mine()
@@ -54,43 +57,30 @@
 
 class Aprioribitset(_ab._frequentPatterns):
     """
-    :Description:  AprioriBitset 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
-
-    :param  iFile: str :
-                   Name of the Input file to mine complete set of frequent patterns
-    :param  oFile: str :
-                   Name of the output file to store complete set of frequent patterns
-    :param  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.
-    :param  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.
+    **About this algorithm**
 
-    :Attributes:
+    :**Description**:  AprioriBitset is one of the fundamental algorithm to discover frequent patterns in a transactional database.
 
-        startTime : float
-          To record the start time of the mining process
+    :**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
 
-        endTime : float
-          To record the completion time of the mining process
+    :**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. Otherwise, it will be treated as float.*
+                        - **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.*
 
-        finalPatterns : dict
-          Storing the complete set of patterns in a dictionary variable
+    :**Attributes**:    - **startTime** (*float*) -- *To record the start time of the mining process.*
+                        - **endTime** (*float*) -- *To record the completion 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.*
 
-        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**
 
-
-    **Methods to execute code on terminal**
-    ------------------------------------------
+    **Terminal command**
 
     .. code-block:: console
 
@@ -102,22 +92,26 @@ class Aprioribitset(_ab._frequentPatterns):
 
       (.venv) $ python3 AprioriBitset.py sampleDB.txt patterns.txt 10.0
 
-    .. note:: minSup will be considered in percentage of database transactions
+    .. note:: minSup can be specified  in support count or a value between 0 and 1.
+
 
+    **Calling from a python program**
 
-    **Importing this algorithm into a python program**
-    ---------------------------------------------------------
     .. code-block:: python
 
-            import PAMI.frequentPattern.basic.AprioriBitset as alg
+            import PAMI.frequentPattern.basic.Aprioribitset as alg
+
+            iFile = 'sampleDB.txt'
 
-            obj = alg.AprioriBitset(iFile, minSup)
+            minSup = 10  # can also be specified between 0 and 1
+
+            obj = alg.Aprioribitset(iFile, minSup)
 
             obj.mine()
 
-            frequentPatterns = obj.getPatterns()
+            frequentPattern = obj.getPatterns()
 
-            print("Total number of Frequent Patterns:", len(frequentPatterns))
+            print("Total number of Frequent Patterns:", len(frequentPattern))
 
             obj.save(oFile)
 
@@ -135,10 +129,10 @@ class Aprioribitset(_ab._frequentPatterns):
 
             print("Total ExecutionTime in seconds:", run)
 
-    **Credits:**
-    -------------------
 
-               The complete program was written by Yudai Masu under the supervision of Professor Rage Uday Kiran.
+    **Credits**
+
+    The complete program was written by Yudai Masu and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.
 
     """
 
@@ -160,11 +154,8 @@ 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:
@@ -216,28 +207,19 @@ def _creatingItemSets(self):
                     print("File Not Found")
         self._minSup = self._convert(self._minSup)
 
-    @deprecated(
-        "It is recommended to use 'mine()' instead of 'startMine()' for mining process. Starting from January 2025, 'startMine()' will be completely terminated.")
+    @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):
-        """
-        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
-
         """
         packed_bits = 0
         for i in data:
@@ -248,7 +230,6 @@ def _bitPacker(self, data, maxIndex):
     def mine(self) -> None:
         """
         Frequent pattern mining process will start from here
-        # Bitset implementation
         """
         self._startTime = _ab._time.time()
 
@@ -307,6 +288,7 @@ def mine(self) -> None:
     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
         """
@@ -316,6 +298,7 @@ def getMemoryUSS(self):
     def getMemoryRSS(self):
         """
         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
         :rtype: float
         """
@@ -325,6 +308,7 @@ def getMemoryRSS(self):
     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
         """
@@ -333,11 +317,9 @@ def getRuntime(self):
 
     def getPatternsAsDataFrame(self) -> _ab._pd.DataFrame:
         """
-
         Storing final frequent patterns in a dataframe
 
         :return: returning frequent patterns in a dataframe
-
         :rtype: pd.DataFrame
 
         """
@@ -358,7 +340,6 @@ def getPatternsAsDataFrame(self) -> _ab._pd.DataFrame:
 
     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
@@ -379,6 +360,7 @@ def save(self, outFile: str, seperator = "\t" ) -> None:
     def getPatterns(self):
         """
         Function to send the set of frequent patterns after completion of the mining process
+
         :return: returning frequent patterns
         :rtype: dict
         """

diff --git a/PAMI/frequentPattern/basic/ECLAT.py b/PAMI/frequentPattern/basic/ECLAT.py
@@ -56,8 +56,7 @@
 
 class ECLAT(_ab._frequentPatterns):
     """
-    About this algorithm
-    ====================
+    **About this algorithm**
 
     :**Description**: ECLAT is one of the fundamental algorithm to discover frequent patterns in a transactional database.
 
@@ -76,8 +75,7 @@ class ECLAT(_ab._frequentPatterns):
                         - **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
-    =================
+    **Execution methods**
 
     **Terminal command**
 
@@ -129,8 +127,7 @@ class ECLAT(_ab._frequentPatterns):
             print("Total ExecutionTime in seconds:", run)
 
 
-    Credits:
-    ========
+    **Credits:**
 
     The complete program was written by Kundai and revised by Tarun Sreepada under the supervision of Professor Rage Uday Kiran.