Module ortools_utils.metric

Abstract class that must be used to associate different kinds of metrics to a specific model.

"""
Abstract class that must be used to associate different kinds of metrics to a specific model.
"""
from typing import *
from abc import ABC, abstractmethod

from ortools.sat.python import cp_model


class Metric(ABC):
    """
    Abstract class for Metric.

    **Example**

    ```
    import numpy as np

    class MetricVariance(Metric):
        def __init__(self, persons, tasks):
            self.persons = persons
            self.tasks = tasks
            self.id = "variance"

        def sentence_producer(self, value):
            return "The variance for number of task is : %d" % value

        def run(self, variable_assigment):
            nb_of_task_by_person = [sum(variable_assigment[i, j] for j in self.tasks) for i in self.persons]
            value = np.var(nb_of_task_by_person)
            return {
                "id_metrics" : self.id,
                "value" : value,
                "sentence" : self.sentence_producer(value)
            }
    ```
    """
    @abstractmethod
    def run(self, variable_assigment: Dict[cp_model.IntVar, int]) -> Dict:
        """
        Returns a dict
        ```
        {
            "id" : str - id of the metric
            "value" : int - value of the metrics
            "sentence": str - setence to print
        )
        ```
        """
        pass


class BasicMetric(Metric):
    """
    Class that enables an easier generation of a Metric, if it is rather simple

    **Example**
    ```
    m = BasicMetric(idx= "plain_sum",
        value_function= lambda dic_values : sum(dic_values[k] for k in dic_values),
        str_function= lambda value : "The sum of all variables is {}".format(value))
    ```

    """
    def __init__(self, idx: str, function_for_value: callable, function_for_sentence: callable):
        self.idx = idx
        self.f_sentence = function_for_sentence
        self.f_value = function_for_value

    def run(self, variable_assignment: Dict[cp_model.IntVar, int]) -> Dict:
        return {"id": self.idx,
                "value": self.f_value(variable_assignment),
                "sentence": self.f_sentence(variable_assignment)
                }

Classes

class BasicMetric (idx: str, function_for_value: , function_for_sentence: )

Class that enables an easier generation of a Metric, if it is rather simple

Example

m = BasicMetric(idx= "plain_sum",
    value_function= lambda dic_values : sum(dic_values[k] for k in dic_values),
    str_function= lambda value : "The sum of all variables is {}".format(value))

Expand source code

class BasicMetric(Metric):
    """
    Class that enables an easier generation of a Metric, if it is rather simple

    **Example**
    ```
    m = BasicMetric(idx= "plain_sum",
        value_function= lambda dic_values : sum(dic_values[k] for k in dic_values),
        str_function= lambda value : "The sum of all variables is {}".format(value))
    ```

    """
    def __init__(self, idx: str, function_for_value: callable, function_for_sentence: callable):
        self.idx = idx
        self.f_sentence = function_for_sentence
        self.f_value = function_for_value

    def run(self, variable_assignment: Dict[cp_model.IntVar, int]) -> Dict:
        return {"id": self.idx,
                "value": self.f_value(variable_assignment),
                "sentence": self.f_sentence(variable_assignment)
                }

Ancestors

• Metric
• abc.ABC

Inherited members

• Metric:
  • run

class Metric

Abstract class for Metric.

Example

import numpy as np

class MetricVariance(Metric):
    def __init__(self, persons, tasks):
        self.persons = persons
        self.tasks = tasks
        self.id = "variance"

    def sentence_producer(self, value):
        return "The variance for number of task is : %d" % value

    def run(self, variable_assigment):
        nb_of_task_by_person = [sum(variable_assigment[i, j] for j in self.tasks) for i in self.persons]
        value = np.var(nb_of_task_by_person)
        return {
            "id_metrics" : self.id,
            "value" : value,
            "sentence" : self.sentence_producer(value)
        }

Expand source code

class Metric(ABC):
    """
    Abstract class for Metric.

    **Example**

    ```
    import numpy as np

    class MetricVariance(Metric):
        def __init__(self, persons, tasks):
            self.persons = persons
            self.tasks = tasks
            self.id = "variance"

        def sentence_producer(self, value):
            return "The variance for number of task is : %d" % value

        def run(self, variable_assigment):
            nb_of_task_by_person = [sum(variable_assigment[i, j] for j in self.tasks) for i in self.persons]
            value = np.var(nb_of_task_by_person)
            return {
                "id_metrics" : self.id,
                "value" : value,
                "sentence" : self.sentence_producer(value)
            }
    ```
    """
    @abstractmethod
    def run(self, variable_assigment: Dict[cp_model.IntVar, int]) -> Dict:
        """
        Returns a dict
        ```
        {
            "id" : str - id of the metric
            "value" : int - value of the metrics
            "sentence": str - setence to print
        )
        ```
        """
        pass

Ancestors

• abc.ABC

Subclasses

• BasicMetric

Methods

def run(self, variable_assigment: Dict[ortools.sat.python.cp_model.IntVar, int]) ‑> Dict[~KT, ~VT]

Returns a dict

{
    "id" : str - id of the metric
    "value" : int - value of the metrics
    "sentence": str - setence to print
)

Expand source code

@abstractmethod
def run(self, variable_assigment: Dict[cp_model.IntVar, int]) -> Dict:
    """
    Returns a dict
    ```
    {
        "id" : str - id of the metric
        "value" : int - value of the metrics
        "sentence": str - setence to print
    )
    ```
    """
    pass