documentation update

PAMI/partialPeriodicPatternInMultipleTimeSeries/PPGrowth.py

@@ -1,8 +1,6 @@
 # PPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database.
 #
 # **Importing this algorithm into a python program**
-# --------------------------------------------------------
-#
 #
 #     from PAMI.periodicFrequentPattern.basic import PPGrowth as alg
 #
@@ -27,6 +25,8 @@
 #     print("Total Memory in RSS", memRSS)
 #
 #     run = obj.getRuntime()
+#
+#     print("Total ExecutionTime in seconds:", run)
 
 
 __copyright__ = """
@@ -44,7 +44,7 @@
 
      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
+
 
 """
 
@@ -312,6 +312,9 @@ def generatePatterns(self, prefix):
 
 class PPGrowth(_ab._partialPeriodicPatterns):
     """
+    About this algorithm
+    ====================
+
     :Description:   PPGrowth is one of the fundamental algorithm to discover periodic-frequent patterns in a transactional database.
 
     :Reference:   C. Saideep, R. Uday Kiran, K. Zettsu, P. Fournier-Viger, M. Kitsuregawa and P. Krishna Reddy,
@@ -393,11 +396,15 @@ class PPGrowth(_ab._partialPeriodicPatterns):
         convert()
             to convert the user specified value
 
-    **Executing the code on terminal:**
-    -------------------------------------
-    .. code-block:: console
+    Execution methods
+    =================
+
+
+    **Terminal command**
 
 
+    .. code-block:: console
+
        Format:
 
        (.venv) $ python3 PPGrowth.py <inputFile> <outputFile> <minSup> <maxPer>
@@ -437,7 +444,9 @@ class PPGrowth(_ab._partialPeriodicPatterns):
 
     **Credits:**
     --------------
-        The complete program was written by P.Likhitha  under the supervision of Professor Rage Uday Kiran.\n
+
+
+            The complete program was written by P.Likhitha  under the supervision of Professor Rage Uday Kiran.
 
     """
     _startTime = float()
@@ -495,7 +504,6 @@ def _creatingItemSets(self):
                     print("File Not Found")
                     quit()
 
-
     def _periodicFrequentOneItem(self):
         """
         Calculates the support of each item in the database and assign ranks to the items

PAMI/partialPeriodicPatternInMultipleTimeSeries/abstract.py

@@ -1,3 +1,18 @@
+#  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 abc import ABC as _ABC, abstractmethod as _abstractmethod
 import time as _time
 import math as _math
@@ -15,6 +30,9 @@
 
 class _partialPeriodicPatterns(_ABC):
     """
+    About this algorithm
+    ====================
+
     :Description:   This abstract base class defines the variables and methods that every frequent pattern mining algorithm must
                     employ in PAMI
 
@@ -83,6 +101,7 @@ def iFile(self):
     def periodicSupport(self):
         """Variable to store the user-specified minimum support value"""
         pass
+    @abstractmethod
     def period(self):
         """Variable to store the user specified maximum periodicity value"""
         pass
@@ -162,6 +181,6 @@ def getRuntime(self):
 
     @_abstractmethod
     def printResults(self):
-        """ To print all the results of execution"""
+        """ To print the results of execution"""
 
         pass

PAMI/periodicCorrelatedPattern/basic/EPCPGrowth.py

@@ -646,7 +646,8 @@ def startMine(self) -> None:
         print("Correlated Periodic-Frequent patterns were generated successfully using EPCPGrowth algorithm ")
 
     def getMemoryUSS(self) -> float:
-        """Total amount of USS memory consumed by the mining process will be retrieved from this function
+        """
+        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
@@ -655,7 +656,8 @@ def getMemoryUSS(self) -> float:
         return self._memoryUSS
 
     def getMemoryRSS(self) -> float:
-        """Total amount of RSS memory consumed by the mining process will be retrieved from this function
+        """
+        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
@@ -664,7 +666,8 @@ def getMemoryRSS(self) -> float:
         return self._memoryRSS
 
     def getRuntime(self) -> float:
-        """Calculating the total amount of runtime taken by the mining process
+        """
+        Calculating the total amount of runtime taken by the mining process
 
 
         :return: returning total amount of runtime taken by the mining process
@@ -674,7 +677,8 @@ def getRuntime(self) -> float:
         return self._endTime - self._startTime
 
     def getPatternsAsDataFrame(self) -> pd.DataFrame:
-        """Storing final periodic-frequent patterns in a dataframe
+        """
+        Storing final periodic-frequent patterns in a dataframe
 
         :return: returning periodic-frequent patterns in a dataframe
         :rtype: pd.DataFrame
@@ -688,7 +692,8 @@ def getPatternsAsDataFrame(self) -> pd.DataFrame:
         return dataFrame
 
     def save(self, outFile: str) -> None:
-        """Complete set of periodic-frequent patterns will be loaded in to an output file
+        """
+        Complete set of periodic-frequent patterns will be loaded in to an output file
 
         :param outFile: name of the output file
         :type outFile: csv file

PAMI/periodicCorrelatedPattern/basic/abstract.py

@@ -30,6 +30,9 @@
 
 class _periodicCorrelatedPatterns(_ABC):
     """
+    About this algorithm
+    ====================
+
     :Description:   This abstract base class defines the variables and methods that every periodic-frequent pattern mining algorithm must
         employ in PAMI
 

PAMI/periodicFrequentPattern/basic/abstract.py

@@ -157,6 +157,6 @@ def getRuntime(self):
 
     @_abstractmethod
     def printResults(self):
-        """ To print results of the execution."""
+        """ To print the results of the execution."""
 
         pass

PAMI/periodicFrequentPattern/closed/CPFPMiner.py

@@ -59,9 +59,11 @@
 
 from PAMI.periodicFrequentPattern.closed import abstract as _ab
 
-
 class CPFPMiner(_ab._periodicFrequentPatterns):
-    """ 
+    """
+    About this algorithm
+    ====================
+
     :Description:   CPFPMiner algorithm is used to discover the closed periodic frequent patterns in temporal databases.
                     It uses depth-first search.
 
@@ -127,10 +129,15 @@ class CPFPMiner(_ab._periodicFrequentPatterns):
         getRuntime()
             Total amount of runtime taken by the mining process will be retrieved from this function
 
-    **Methods to execute code on terminal**
-    --------------------------------------------
-    .. code-block:: console
 
+    Execution methods
+    =================
+
+
+    **Terminal command**
+
+
+    .. code-block:: console
 
        Format:
 
@@ -140,14 +147,13 @@ class CPFPMiner(_ab._periodicFrequentPatterns):
 
        (.venv) $ python3 CPFPMiner.py sampleTDB.txt patterns.txt 0.3 0.4
 
-
-               .. note:: minSup will be considered in percentage of database transactions
+     .. note:: minSup will be considered in percentage of database transactions
 
 
-    **Importing this algorithm into a python program**
-    -------------------------------------------------------
+    **Calling from a python program**
+
     .. code-block:: python
-        
+
                     from PAMI.periodicFrequentPattern.closed import CPFPMiner as alg
 
                     obj = alg.CPFPMiner("../basic/sampleTDB.txt", "2", "6")
@@ -224,6 +230,7 @@ def _convert(self, value):
     def _scanDatabase(self):
         """
         To scan the database and extracts the 1-length periodic-frequent items
+
         :return:   Returns the 1-length periodic-frequent items
         """
         Database = []
@@ -381,7 +388,7 @@ def _save(self, prefix, suffix, tidSetX):
 
     def _processEquivalenceClass(self, prefix, itemSets, tidSets):
         """
-
+        identifies and saves closed periodic patterns of length more than 2 in a dataset, by processing equivalence classes of item sets that satisfy a minimum support condition.
         :param prefix: Prefix class of an itemSet
         :param itemSets: suffix items in periodicFrequentItems that satisfies the minSup condition
         :param tidSets: timeStamps of items in itemSets respectively

PAMI/periodicFrequentPattern/closed/abstract.py

@@ -157,6 +157,6 @@ def getRuntime(self):
 
     @_abstractmethod
     def printResults(self):
-        """ TO print the results of execution """
+        """ To print the results of execution """
 
         pass