cbrlib 1.0.0

Library for projects to use with Case Based Reasoning

PyCBR

Installation

pip install pycbr

Sample for the NumberInterpolationEvaluator

import matplotlib.pyplot as plt
%matplotlib inline

from evaluation.NumberInterpolationEvaluator import NumberInterpolationEvaluator, NumberInterpolationMetrics, \
    NumberInterpolation
from model.AssemblyClass import AssemblyClass
from model.Attribute import Attribute
from model.EnumerationPredicate import EnumerationPredicate
from model.FloatClass import FloatClass
from model.IntegerClass import IntegerClass
from model.RangePredicate import RangePredicate
from model.StringClass import StringClass

#plt.style.use('ggplot')

# Define the price class with minimum value of 10 and maximum value of 100000
price_class = FloatClass('Price')
min_ = price_class.create_object(10)
max_ = price_class.create_object(10000)
price_class.set_predicate(RangePredicate(min_, max_))

# Define inperpolation metrics with a tolerance if case is lesser than query of 0
# -> Every case lower than the query will have 0 similarity
metrics = NumberInterpolationMetrics()
metrics.tolerance_if_more = 0.0
less_is_good = NumberInterpolationEvaluator('LessIsGood', min_.get_value(), max_.get_value(), metrics)

# Define the same as above but use an origin value
# The lower the query the smaller is the vicinity
metrics = NumberInterpolationMetrics()
metrics.tolerance_if_more = 0.0
metrics.origin = 10
metrics.use_origin = True
metrics.tolerance_if_more = 0.0
less_is_good_with_origin = NumberInterpolationEvaluator('LessIsGoodUseOrigin',
                                                        min_.get_value(), max_.get_value(), metrics)

metrics = NumberInterpolationMetrics()
metrics.tolerance_if_less = 0.0
metrics.origin = min_.get_value()
metrics.use_origin = True
metrics.tolerance_if_less = 0.3
metrics.tolerance_if_more = 0.1
metrics.linearity_if_less = 0.5
metrics.linearity_if_more = 3
metrics.set_interpolation_if_less(NumberInterpolation.Sigmoid)
metrics.set_interpolation_if_more(NumberInterpolation.Sigmoid)
real_behaviour = NumberInterpolationEvaluator('RealBehaviour',
                                                        min_.get_value(), max_.get_value(), metrics)

query = price_class.read_object(500)

less_is_good_values = list()
less_is_good_with_origin_values = list()
real_behaviour_values = list()
steps = list()
for i in range(300, 600):
    steps.append(i)
    case = price_class.read_object(i)
    less_is_good_values.append(
        less_is_good.evaluate(query, case))
    less_is_good_with_origin_values.append(
        less_is_good_with_origin.evaluate(query, case))
    real_behaviour_values.append(real_behaviour.evaluate(query, case))

figure = plt.figure(2, figsize=(20, 9.6))
plt1 = figure.add_subplot(221)
plt2 = figure.add_subplot(222)
plt3 = figure.add_subplot(223)

plt1.plot(
    steps, less_is_good_values
)
plt1.set_title('Less is good')
plt1.set_xlabel(price_class.get_id())
plt1.set_ylabel('Similarity')
plt2.plot(
    steps, less_is_good_with_origin_values
)
plt2.set_title('Less is good with use of an origin')
plt2.set_xlabel(price_class.get_id())
plt2.set_ylabel('Similarity')

plt3.plot(
    steps, real_behaviour_values
)
plt3.set_title('Real behaviour')
plt3.set_xlabel(price_class.get_id())
plt3.set_ylabel('Similarity')

plt.show()