PTGenerator.py

'''
    This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).

    PM4Py 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.

    PM4Py 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 PM4Py.  If not, see <https://www.gnu.org/licenses/>.
'''
from pm4py.objects.process_tree import obj
from pm4py.objects.process_tree import obj as pt_operator
from pm4py.objects.process_tree import exporter
from pm4py import view_process_tree
from pm4py import convert_to_petri_net
from pm4py import play_out
from pm4py import write_xes
from pm4py import write_pnml
import uuid
import sys
import argparse

from scipy.stats import triang as triangular
import string
import math
import itertools
from enum import Enum
from itertools import accumulate as _accumulate, repeat as _repeat
from bisect import bisect as _bisect
import random


def choices(population, weights=None, *, cum_weights=None, k=1):
    """Return a k sized list of population elements chosen with replacement.
    If the relative weights or cumulative weights are not specified,
    the selections are made with equal probability.
    """
    n = len(population)
    if cum_weights is None:
        if weights is None:
            _int = int
            n += 0.0    # convert to float for a small speed improvement
            return [population[_int(random.random() * n)] for i in _repeat(None, k)]
        cum_weights = list(_accumulate(weights))
    elif weights is not None:
        raise TypeError('Cannot specify both weights and cumulative weights')
    if len(cum_weights) != n:
        raise ValueError('The number of weights does not match the population')
    bisect = _bisect
    total = cum_weights[-1] + 0.0   # convert to float
    hi = n - 1
    return [population[bisect(cum_weights, random.random() * total, 0, hi)]
            for i in _repeat(None, k)]



class Parameters(Enum):
    SEQUENCE = "sequence"
    CHOICE = "choice"
    PARALLEL = "parallel"
    OR = "or"
    MODE = "mode"
    MIN = "min"
    MAX = "max"
    SILENT = "silent"
    DUPLICATE = "duplicate"
    LT_DEPENDENCY = "lt_dependency"
    INFREQUENT = "infrequent"
    NO_MODELS = "no_models"
    UNFOLD = "unfold"
    MAX_REPEAT = "max_repeat"



def apply(parameters=None):
    """
    Generate a process tree using the PTAndLogGenerator approach
    (see the paper PTandLogGenerator: A Generator for Artificial Event Data)

    Parameters
    --------------
    parameters
        Parameters of the algorithm, according to the paper:
        - Parameters.MODE: most frequent number of visible activities
        - Parameters.MIN: minimum number of visible activities
        - Parameters.MAX: maximum number of visible activities
        - Parameters.SEQUENCE: probability to add a sequence operator to tree
        - Parameters.CHOICE: probability to add a choice operator to tree
        - Parameters.PARALLEL: probability to add a parallel operator to tree
        - Parameters.LOOP: probability to add a loop operator to tree
        - Parameters.OR: probability to add an or operator to tree
        - Parameters.SILENT: probability to add silent activity to a choice or loop operator
        - Parameters.DUPLICATE: probability to duplicate an activity label
        - Parameters.LT_DEPENDENCY: probability to add a random dependency to the tree
        - Parameters.INFREQUENT: probability to make a choice have infrequent paths
        - Parameters.NO_MODELS: number of trees to generate from model population
        - Parameters.UNFOLD: whether or not to unfold loops in order to include choices underneath in dependencies: 0=False, 1=True
            if lt_dependency <= 0: this should always be 0 (False)
            if lt_dependency > 0: this can be 1 or 0 (True or False)
        - Parameters.MAX_REPEAT: maximum number of repetitions of a loop (only used when unfolding is True)
    """
    if parameters is None:
        parameters = {}

    if not "mode" in parameters:
        parameters["mode"] = 10
    if not "min" in parameters:
        parameters["min"] = 10
    if not "max" in parameters:
        parameters["max"] = 20
    if not "sequence" in parameters:
        parameters["sequence"] = 0.25
    if not "choice" in parameters:
        parameters["choice"] = 0.25
    if not "parallel" in parameters:
        parameters["parallel"] = 0.25
    if not "or" in parameters:
        parameters["or"] = 0.25
    if not "silent" in parameters:
        parameters["silent"] = 0
    if not "duplicate" in parameters:
        parameters["duplicate"] = 0
    if not "lt_depencency" in parameters:
        parameters["lt_depencency"] = 0.0
    if not "infrequent" in parameters:
        parameters["infrequent"] = 0.5
    if not "no_models" in parameters:
        parameters["no_models"] = 10
    if not "unfold" in parameters:
        parameters["unfold"] = 10
    if not "max_repeat" in parameters:
        parameters["max_repeat"] = 10

    return GeneratedTree(parameters).generate()

def assign_operator(operator):
    if operator == "choice":
        return pt_operator.Operator.XOR
    elif operator == "sequence":
        return pt_operator.Operator.SEQUENCE
    elif operator == "parallel":
        return pt_operator.Operator.PARALLEL
    elif operator == "or":
        return pt_operator.Operator.OR
    else:
        return None



class GeneratedTree(object):
    # are later used as labels
    alphabet = string.ascii_lowercase

    def calculate_activity_distribution(self, mode, min, max):
        """
        Here, the triangular function is used, since the parameters for this function are given in the paramterfile.
        However, this approach can be applied on other distribution functions as well.
        :param mode: Mode of the distribution
        :param min: Smallest number
        :param max: Highest number
        :return: Distribution object
        """
        c = (mode - min) / (max - min)
        return triangular(c, loc=min, scale=max - min)


    def draw_random_number_from_distribution(self):
        return self.activity_distribution.rvs(1)[0]


    def select_operator(self):
        # add root operator, if probabilities are high enough
        # ordering of operator computation is sequence, choice, parallel, loop, or
        operator = choices(["sequence", "choice", "parallel","or"],
                                  [self.parameters["sequence"], self.parameters["choice"], self.parameters["parallel"],
                                   self.parameters["or"]])
        return operator[0]


    def get_next_activity(self, activity):
        result = self.set_activity_labels[self.set_activity_labels.index(activity) + 1]
        return result


    def assign_root_opeartor(self):
        activity = "a"
        # is a silent activity chosen
        silent_activity = False
        if random.random() < self.parameters["silent"]:
            silent_activity = True
        root = self.tree._get_root()
        operator = self.select_operator()
        root.operator = assign_operator(operator)
        # if operator is loop, we use a special structure, otherwise 2
        if operator == "loop":
            print('POPO')
        else:
            if silent_activity and operator == "choice":
                number = random.choice([0, 1])
                if number == 0:
                    new_node = obj.ProcessTree(label=None)
                    new_node.parent = root
                    root._children.append(new_node)
                    new_node = obj.ProcessTree(label=activity)
                    new_node.parent = root
                    root._children.append(new_node)
                else:
                    new_node = obj.ProcessTree(label=activity)
                    new_node.parent = root
                    root._children.append(new_node)
                    new_node = obj.ProcessTree(label=None)
                    new_node.parent = root
                    root._children.append(new_node)
            else:
                new_node = obj.ProcessTree(label=activity)
                new_node.parent = root
                root._children.append(new_node)
                activity = self.get_next_activity(activity)
                new_node = obj.ProcessTree(label=activity)
                new_node.parent = root
                root._children.append(new_node)
        # always two children are added
        self.total_activities -= 2
        return self.get_next_activity(activity)


    def add_node(self, next_activity):
        """
        Add nodes to current tree. The general procedure is as follows:
        Select a random leaf (leaves have label). Next step, and opertor is chosen.
        The chosen operator then replaces the leaf, whereby the old label is then add as a leaf to the manipulated node.
        Then, next activity is added as a second leaf to the new operator node or a silent acticity (tau) is added.
        :return: Next activity
        """
        # Need to select random node that is not a silent activity
        leaf_silent = True
        while (leaf_silent):
            leaf = random.choice(self.tree._get_leaves())
            if leaf.label is not None:
                leaf_silent = False
        operator_nok = True
        while (operator_nok):
            operator = self.select_operator()
            if self.total_activities > 1:
                operator_nok = False
            else:
                if operator != "loop":
                    operator_nok = False
        activity = leaf._get_label()
        leaf._set_label(None)
        leaf._set_operator(assign_operator(operator))
        # Will be an tau added?
        silent_activity = False
        if random.random() < self.parameters["silent"]:
            silent_activity = True
        # add two children
        if operator == "loop":
            print('popo')
        else:
            if silent_activity and operator == "choice":
                number = random.choice([0, 1])
                if number == 0:
                    new_node = obj.ProcessTree(label=None)
                    new_node.parent = leaf
                    leaf._children.append(new_node)
                    new_node = obj.ProcessTree(label=activity)
                    new_node.parent = leaf
                    leaf._children.append(new_node)
                else:
                    new_node = obj.ProcessTree(label=activity)
                    new_node.parent = leaf
                    leaf._children.append(new_node)
                    new_node = obj.ProcessTree(label=None)
                    new_node.parent = leaf
                    leaf._children.append(new_node)
            else:
                new_node = obj.ProcessTree(label=activity)
                new_node.parent = leaf
                leaf._children.append(new_node)
                activity = next_activity
                new_node = obj.ProcessTree(label=activity)
                new_node.parent = leaf
                leaf._children.append(new_node)

        self.total_activities -= 2
        if silent_activity and operator == "choice":
            return next_activity
        else:
            return self.get_next_activity(activity)


    def add_duplicates(self):
        """
        Replaces some leaves  to add duplicated labels. Depends on parameter.
        :return:
        """
        duplication_allowed = False
        leaves = self.tree._get_leaves()
        for leaf in leaves:
            if leaf._parent != self.tree._get_root():
                duplication_allowed = True
                break
        # if there is at least a depth of two
        if duplication_allowed:
            # list that contains the leaves with a label unequal to tau
            leaves_with_label = []
            for leaf in leaves:
                if leaf.label is not None:
                    leaves_with_label.append(leaf)
            # generate random list of duplicates
            duplicates = []
            for leaf in leaves:
                if random.random() < self.parameters["duplicate"]:
                    duplicates.append(leaf)
            if len(duplicates) > 0:
                # select potential leaves to replace them by duplicates
                possible_replacements = []
                for leaf in leaves:
                    if leaf not in duplicates:
                        possible_replacements.append(leaf)
            for leaf in duplicates:
                i = 0
                siblings = []
                # determine sibling nodes (same parent)
                p = leaf._parent
                for child in p._children:
                    if child != leaf:
                        siblings.append(child)
                # TODO Skaling? Original: 30times, my idea : percentage of duplicates * len(leaves)
                while i < self.parameters["duplicate"] * len(leaves):
                    replacement = random.choice(possible_replacements)
                    if replacement not in siblings:
                        replacement._label = leaf._label
                        break


    def create_process_tree(self):
        self.tree = obj.ProcessTree()
        self.set_activity_labels = []
        p = 1
        # create labels
        while (self.total_activities > len(self.set_activity_labels)):
            # pairwise product
            l = itertools.product(self.alphabet, repeat=p)
            for item in l:
                label = ""
                for element in item:
                    label += str(element)
                self.set_activity_labels.append(label)
            p += 1
        step = 1
        activity = self.assign_root_opeartor()
        step += 1

        while (self.total_activities > 0):
            activity = self.add_node(activity)
            step += 1


    def __init__(self, parameters):
        self.parameters = {}
        for param in parameters:
            p = param if type(param) is str else param.value
            self.parameters[p] = parameters[param]
        # rescale probabilities of operators if the sum is not equal to one
        if self.parameters["sequence"] + self.parameters["choice"] + self.parameters["parallel"] + self.parameters["or"] != 1:
            sum_of_operators = self.parameters["sequence"] + self.parameters["choice"] + self.parameters["parallel"] + \
                               self.parameters["or"]
            self.parameters["sequence"] = self.parameters["sequence"] / sum_of_operators
            self.parameters["choice"] = self.parameters["choice"] / sum_of_operators
            self.parameters["parallel"] = self.parameters["parallel"] / sum_of_operators
            self.parameters["or"] = self.parameters["or"] / sum_of_operators
        # First step: Compute acivity distribution
        # Since mode, min and max are given, the triangle distribution is chosen
        self.activity_distribution = self.calculate_activity_distribution(self.parameters["mode"],
                                                                          self.parameters["min"],
                                                                          self.parameters["max"])
        # Number of total activities represented in the tree. Also, tau is counted as an activity.
        self.total_activities = int(math.ceil(self.draw_random_number_from_distribution()))


    def generate(self):
        # Create a process tree based on the given probabilities
        self.create_process_tree()
        # add duplicates
        self.add_duplicates()

        return self.tree
    
def main(mode, min, max):
    # set the parameters for the PT generation
    parameters = { "mode": int(mode), "min": int(min), "max": int(max)}
    # generate the PT
    print("Starting PT generation...")
    pt = apply(parameters)
    # view_process_tree(pt,"png")
    code = str(uuid.uuid4())
    code = code[-5:]
    # export the pt in ptml
    exporter.exporter.apply(pt, "PT_" +code + ".xml")
    print("PT generated and exported!")

    net, im, fm = convert_to_petri_net(pt)
    log = play_out(net, im, fm)
    #log = pm4py.play_out(thept)
    #log = semantics.generate_log(nl, no_traces=5)
    # generate 1000 traces from the pt, and save them in a xes file
    write_xes(log, "PT_" + code +'.xes')
    print("Traces generated and exported!")

    file1 = open("PTfiles.txt", "a")  # append mode
    file1.write("PT_"+code+"\n")
    file1.close()
    
if __name__ == "__main__":
    if(len(sys.argv) != 4):
        print("Use the following command: python PTGenerator.py mode min max")
        quit()

    #mode = sys.argv[1]
    #min = sys.argv[2]
    #max = sys.argv[3]
    #main(mode, min, max)
    main(sys.argv[1:])
    print("Done.")