Merge pull request #304 from zStupan/remove_scipy_dep

Removed scipy dependency

.flake8

@@ -12,6 +12,6 @@ per-file-ignores =
     NiaPy/tests/*:B007,D100,D101,D102,D107,D208,D211,D413,E117,E302,E701,E704,F841,S110,W191,W293,D204,RST210,E101,D204,D205,D206,E741
     NiaPy/algorithms/*:B305,B007,D100,D101,D102,D107,D204,D206,D413,E101,E117,E126,E302,E305,E701,E704,E711,E741,F812,RST201,RST203,RST204,RST210,RST301,W191
     NiaPy/task/*:E701
-    NiaPy/util/*:W191,E117,E305,E302,E704,E701,RST203,D204,E306
+    NiaPy/util/*:W191,E117,E305,E302,E704,E701,RST203,D204,E306,D100
     NiaPy/benchmarks/*:W191,E117,E305,E302,E704,E701,RST203,D204,E306,D206,D208
 

CONTRIBUTING.md

@@ -11,14 +11,14 @@ Before creating bug reports, please check existing issues list as you might find
 
 ### Suggesting Enhancements
 - Open new issue
-- Write in details what enhancement whould you like to see in the future
+- Write in details what enhancement would you like to see in the future
 - If you have technical knowledge, propose solution on how to implement enhancement
 
 ### Pull requests
 
 If you are not so familiar with Git or/and GitHub, we suggest you take a look at our [beginner's guide](.github/beginners_guide.md).
 
-- Fill in the [reqired template](.github/pull_request_template.md)
+- Fill in the [required template](.github/pull_request_template.md)
 - Document new code
 - Make sure all the code goes through Flake8 without problems (run ```make check``` command)
 - Make sure PR builds goes through
@@ -43,11 +43,10 @@ List of NiaPy's dependencies:
 
 | Package    | Version | Platform |
 | ---------- |:-------:|:--------:|
-| click      | Any     | All      |
 | numpy      | 1.17.0  | All      |
-| scipy      | 1.0.0   | All      |
-| xlsxwriter | 1.0.2   | All      |
-| matplotlib | Any     | All      |
+| matplotlib | 2.2.4   | All      |
+| pandas     | 0.24.2  | All      |
+| openpyxl   | 3.0.3   | All      |
 
 
 List of development dependencies:
@@ -65,20 +64,11 @@ List of development dependencies:
 |freezegun                      | Any     | Any      |
 |coverage-space                 | Any     | Any      |
 |docutils                       | Any     | Any      |
-|pygments                       | Any     | Any      |
+|Pygments                       | Any     | Any      |
 |wheel                          | Any     | Any      |
-|pyinstaller                    | Any     | Any      |
 |twine                          | Any     | Any      |
 |sniffer                        | Any     | Any      |
-|macfsevents                    | Any     | darwin   |
-|enum34                         | Any     | Any      |
-|singledispatch                 | Any     | Any      |
-|backports.functools-lru-cache  | Any     | Any      |
-|configparser                   | Any     | Any      |
 |sphinx                         | Any     | Any      |
-|sphinx-rtd-theme               | Any     | Any      |
-|funcsigs                       | Any     | Any      |
-|futures                        | Any     | Any      |
 |autopep8                       | Any     | Any      |
 |sphinx-autobuild               | Any     | Any      |
 

NiaPy/__init__.py

@@ -2,9 +2,6 @@
 
 """Python micro framework for building nature-inspired algorithms."""
 
-from __future__ import print_function
-
-
 from NiaPy import util, algorithms, benchmarks, task
 from NiaPy.runner import Runner
 

NiaPy/algorithms/algorithm.py

@@ -5,8 +5,7 @@
 
 from numpy import random as rand, inf, ndarray, asarray, array_equal, argmin, apply_along_axis
 
-from NiaPy.util import FesException, GenException, TimeException, RefException
-from NiaPy.util.utility import objects2array
+from NiaPy.util import FesException, GenException, TimeException, RefException, objects_to_array
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.util.utility')
@@ -53,7 +52,7 @@ def defaultIndividualInit(task, NP, rnd=rand, itype=None, **kwargs):
 			1. Initialized individuals.
 			2. Initialized individuals function/fitness values.
 	"""
-	pop = objects2array([itype(task=task, rnd=rnd, e=True) for _ in range(NP)])
+	pop = objects_to_array([itype(task=task, rnd=rnd, e=True) for _ in range(NP)])
 	return pop, asarray([x.f for x in pop])
 
 class Algorithm:

NiaPy/algorithms/basic/ca.py

@@ -4,7 +4,7 @@
 from numpy import exp, random as rand, asarray
 
 from NiaPy.algorithms.algorithm import Algorithm, Individual
-from NiaPy.util.utility import objects2array
+from NiaPy.util import objects_to_array
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')
@@ -239,7 +239,7 @@ def initPop(self, task, NP, rnd, itype, **kwargs):
 				1. Initialize population of camels.
 				2. Initialized populations function/fitness values.
 		"""
-		caravan = objects2array([itype(E_init=self.E_init, S_init=self.S_init, task=task, rnd=rnd, e=True) for _ in range(NP)])
+		caravan = objects_to_array([itype(E_init=self.E_init, S_init=self.S_init, task=task, rnd=rnd, e=True) for _ in range(NP)])
 		return caravan, asarray([c.f for c in caravan])
 
 	def walk(self, c, cb, task):
@@ -324,9 +324,9 @@ def runIteration(self, task, caravan, fcaravan, cb, fcb, **dparams):
 				4. New global best fitness/objective value
 				5. Additional arguments
 		"""
-		ncaravan = objects2array([self.walk(c, cb, task) for c in caravan])
-		ncaravan = objects2array([self.oasis(c, self.rand(), self.alpha) for c in ncaravan])
-		ncaravan = objects2array([self.lifeCycle(c, self.mu, task) for c in ncaravan])
+		ncaravan = objects_to_array([self.walk(c, cb, task) for c in caravan])
+		ncaravan = objects_to_array([self.oasis(c, self.rand(), self.alpha) for c in ncaravan])
+		ncaravan = objects_to_array([self.lifeCycle(c, self.mu, task) for c in ncaravan])
 		fncaravan = asarray([c.f for c in ncaravan])
 		cb, fcb = self.getBest(ncaravan, fncaravan, cb, fcb)
 		return ncaravan, fncaravan, cb, fcb, {}

NiaPy/algorithms/basic/cro.py

@@ -1,8 +1,8 @@
 # encoding=utf8
 import logging
-from scipy.spatial.distance import euclidean
 from numpy import apply_along_axis, argsort, where, random as rand, asarray, delete, sqrt, sum, unique, append
 from NiaPy.algorithms.algorithm import Algorithm
+from NiaPy.util import euclidean
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')

NiaPy/algorithms/basic/cs.py

@@ -1,8 +1,8 @@
 # encoding=utf8
 import logging
 from numpy import apply_along_axis, argsort
-from scipy.stats import levy
 from NiaPy.algorithms.algorithm import Algorithm
+from NiaPy.util import levy_flight
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')
@@ -154,7 +154,7 @@ def runIteration(self, task, pop, fpop, xb, fxb, pa_v, **dparams):
 					* pa_v (float): TODO
 		"""
 		i = self.randint(self.NP)
-		Nn = task.repair(pop[i] + self.alpha * levy.rvs(size=[task.D], random_state=self.Rand), rnd=self.Rand)
+		Nn = task.repair(pop[i] + levy_flight(self.alpha, size=task.D, rng=self.Rand), rnd=self.Rand)
 		Nn_f = task.eval(Nn)
 		j = self.randint(self.NP)
 		while i == j: j = self.randint(self.NP)

NiaPy/algorithms/basic/de.py

@@ -3,10 +3,9 @@
 import math
 
 from numpy import random as rand, argmin, argmax, mean, cos, asarray, append, sin, isfinite
-from scipy.spatial.distance import euclidean
 
 from NiaPy.algorithms.algorithm import Algorithm, Individual, defaultIndividualInit
-from NiaPy.util.utility import objects2array
+from NiaPy.util import objects_to_array, euclidean
 
 __all__ = ['DifferentialEvolution', 'DynNpDifferentialEvolution', 'AgingNpDifferentialEvolution', 'CrowdingDifferentialEvolution', 'MultiStrategyDifferentialEvolution', 'DynNpMultiStrategyDifferentialEvolution', 'AgingNpMultiMutationDifferentialEvolution', 'AgingIndividual', 'CrossRand1', 'CrossBest2', 'CrossBest1', 'CrossBest2', 'CrossCurr2Rand1', 'CrossCurr2Best1', 'multiMutations']
 
@@ -317,7 +316,7 @@ def evolve(self, pop, xb, task, **kwargs):
 		Returns:
 			numpy.ndarray: New evolved populations.
 		"""
-		return objects2array([self.itype(x=self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand), task=task, rnd=self.Rand, e=True) for i in range(len(pop))])
+		return objects_to_array([self.itype(x=self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand), task=task, rnd=self.Rand, e=True) for i in range(len(pop))])
 
 	def selection(self, pop, npop, xb, fxb, task, **kwargs):
 		r"""Operator for selection.
@@ -336,7 +335,7 @@ def selection(self, pop, npop, xb, fxb, task, **kwargs):
 				2. New global best solution.
 				3. New global best solutions fitness/objective value.
 		"""
-		arr = objects2array([e if e.f < pop[i].f else pop[i] for i, e in enumerate(npop)])
+		arr = objects_to_array([e if e.f < pop[i].f else pop[i] for i, e in enumerate(npop)])
 		xb, fxb = self.getBest(arr, asarray([e.f for e in arr]), xb, fxb)
 		return arr, xb, fxb
 
@@ -545,7 +544,7 @@ def postSelection(self, pop, task, xb, fxb, **kwargs):
 		"""
 		Gr = task.nFES // (self.pmax * len(pop)) + self.rp
 		nNP = len(pop) // 2
-		if (task.Iters + 1) == Gr and len(pop) > 3: pop = objects2array([pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP] for i in range(nNP)])
+		if (task.Iters + 1) == Gr and len(pop) > 3: pop = objects_to_array([pop[i] if pop[i].f < pop[i + nNP].f else pop[i + nNP] for i in range(nNP)])
 		return pop, xb, fxb
 
 def proportional(Lt_min, Lt_max, mu, x_f, avg, **args):
@@ -743,7 +742,7 @@ def aging(self, task, pop):
 			x.age += 1
 			Lt = round(self.age(Lt_min=self.Lt_min, Lt_max=self.Lt_max, mu=self.mu, x_f=x.f, avg=avg, x_gw=x_w.f, x_gb=x_b.f))
 			if x.age <= Lt: npop.append(x)
-		if len(npop) == 0: npop = objects2array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(self.NP)])
+		if len(npop) == 0: npop = objects_to_array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(self.NP)])
 		return npop
 
 	def popIncrement(self, pop, task):
@@ -757,7 +756,7 @@ def popIncrement(self, pop, task):
 			numpy.ndarray[Individual]: Increased population.
 		"""
 		deltapop = int(round(max(1, self.NP * self.deltaPopE((task.Iters + 1)))))
-		return objects2array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(deltapop)])
+		return objects_to_array([self.itype(task=task, rnd=self.Rand, e=True) for _ in range(deltapop)])
 
 	def popDecrement(self, pop, task):
 		r"""Decrement population.
@@ -776,7 +775,7 @@ def popDecrement(self, pop, task):
 		for i, e in enumerate(pop):
 			if i not in ni: npop.append(e)
 			elif self.rand() >= self.omega: npop.append(e)
-		return objects2array(npop)
+		return objects_to_array(npop)
 
 	def selection(self, pop, npop, xb, fxb, task, **kwargs):
 		r"""Select operator for individuals with aging.
@@ -929,7 +928,7 @@ def evolve(self, pop, xb, task, **kwargs):
 		Returns:
 			numpy.ndarray: New population of individuals.
 		"""
-		return objects2array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])
+		return objects_to_array([self.CrossMutt(pop, i, xb, self.F, self.CR, self.Rand, task, self.itype, self.strategies) for i in range(len(pop))])
 
 class DynNpMultiStrategyDifferentialEvolution(MultiStrategyDifferentialEvolution, DynNpDifferentialEvolution):
 	r"""Implementation of Dynamic population size Differential evolution algorithm with dynamic population size that is defined by the quality of population.

NiaPy/algorithms/basic/es.py

@@ -6,7 +6,7 @@
 from numpy.linalg import norm, cholesky as chol, eig, solve, lstsq
 
 from NiaPy.algorithms.algorithm import Algorithm, Individual, defaultIndividualInit
-from NiaPy.util.utility import objects2array
+from NiaPy.util import objects_to_array
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')
@@ -175,7 +175,7 @@ def runIteration(self, task, c, fpop, xb, fxb, ki, **dparams):
 					* ki (int): Number of successful rho update.
 		"""
 		if (task.Iters + 1) % self.k == 0: c.rho, ki = self.updateRho(c.rho, ki), 0
-		cn = objects2array([task.repair(self.mutate(c.x, c.rho), self.Rand) for _i in range(self.mu)])
+		cn = objects_to_array([task.repair(self.mutate(c.x, c.rho), self.Rand) for _i in range(self.mu)])
 		cn_f = asarray([task.eval(cn[i]) for i in range(len(cn))])
 		ib = argmin(cn_f)
 		if cn_f[ib] < c.f:
@@ -384,9 +384,9 @@ def runIteration(self, task, c, fpop, xb, fxb, ki, **dparams):
 					* ki (int): Number of successful mutations.
 		"""
 		if (task.Iters + 1) % self.k == 0: _, ki = self.updateRho(c, ki), 0
-		cn = objects2array([IndividualES(x=self.mutateRand(c, task), task=task, rnd=self.Rand) for _ in range(self.lam)])
+		cn = objects_to_array([IndividualES(x=self.mutateRand(c, task), task=task, rnd=self.Rand) for _ in range(self.lam)])
 		cn = append(cn, c)
-		cn = objects2array([cn[i] for i in argsort([i.f for i in cn])[:self.mu]])
+		cn = objects_to_array([cn[i] for i in argsort([i.f for i in cn])[:self.mu]])
 		ki += self.changeCount(c, cn)
 		fcn = asarray([x.f for x in cn])
 		xb, fxb = self.getBest(cn, fcn, xb, fxb)
@@ -441,10 +441,10 @@ def newPop(self, pop):
 			numpy.ndarray: New population.
 		"""
 		pop_s = argsort([i.f for i in pop])
-		if self.mu < self.lam: return objects2array([pop[i] for i in pop_s[:self.mu]])
+		if self.mu < self.lam: return objects_to_array([pop[i] for i in pop_s[:self.mu]])
 		npop = list()
 		for i in range(int(ceil(float(self.mu) / self.lam))): npop.extend(pop[:self.lam if (self.mu - i * self.lam) >= self.lam else self.mu - i * self.lam])
-		return objects2array(npop)
+		return objects_to_array(npop)
 
 	def initPopulation(self, task):
 		r"""Initialize starting population.
@@ -483,7 +483,7 @@ def runIteration(self, task, c, fpop, xb, fxb, **dparams):
 				4. New global best solutions fitness/objective value.
 				5. Additional arguments.
 		"""
-		cn = objects2array([IndividualES(x=self.mutateRand(c, task), task=task, rand=self.Rand) for _ in range(self.lam)])
+		cn = objects_to_array([IndividualES(x=self.mutateRand(c, task), task=task, rand=self.Rand) for _ in range(self.lam)])
 		c = self.newPop(cn)
 		fc = asarray([x.f for x in c])
 		xb, fxb = self.getBest(c, fc, xb, fxb)

NiaPy/algorithms/basic/foa.py

@@ -2,7 +2,7 @@
 import logging
 from numpy import where, apply_along_axis, zeros, append, ndarray, delete, arange, argmin, absolute, int32
 from NiaPy.algorithms.algorithm import Algorithm
-from NiaPy.util import limit_repair
+from NiaPy.util import repair
 
 __all__ = ['ForestOptimizationAlgorithm']
 
@@ -131,7 +131,7 @@ def localSeeding(self, task, trees):
 		perms = self.rand([deltas.shape[0], deltas.shape[1]]).argsort(1)
 		deltas = deltas[arange(deltas.shape[0])[:, None], perms]
 		trees += deltas
-		trees = apply_along_axis(limit_repair, 1, trees, task.Lower, task.Upper)
+		trees = apply_along_axis(repair.limit, 1, trees, task.Lower, task.Upper)
 		return trees
 
 	def globalSeeding(self, task, candidates, size):

NiaPy/algorithms/basic/fpa.py

@@ -1,10 +1,10 @@
 # encoding=utf8
 import logging
 
-from scipy.special import gamma as Gamma
-from numpy import where, sin, fabs, pi, zeros
+from numpy import where, zeros
 
 from NiaPy.algorithms.algorithm import Algorithm
+from NiaPy.util import levy_flight
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')
@@ -105,15 +105,6 @@ def repair(self, x, task):
 		x[ir] = task.Lower[ir] + x[ir] % task.bRange[ir]
 		return x
 
-	def levy(self, D):
-		r"""Levy function.
-
-		Returns:
-			float: Next Levy number.
-		"""
-		sigma = (Gamma(1 + self.beta) * sin(pi * self.beta / 2) / (Gamma((1 + self.beta) / 2) * self.beta * 2 ** ((self.beta - 1) / 2))) ** (1 / self.beta)
-		return 0.01 * (self.normal(0, 1, D) * sigma / fabs(self.normal(0, 1, D)) ** (1 / self.beta))
-
 	def initPopulation(self, task):
 		pop, fpop, d = Algorithm.initPopulation(self, task)
 		d.update({'S': zeros((self.NP, task.D))})
@@ -139,7 +130,7 @@ def runIteration(self, task, Sol, Sol_f, xb, fxb, S, **dparams):
 				5. Additional arguments.
 		"""
 		for i in range(self.NP):
-			if self.uniform(0, 1) > self.p: S[i] += self.levy(task.D) * (Sol[i] - xb)
+			if self.uniform(0, 1) > self.p: S[i] += levy_flight(beta=self.beta, size=task.D, rng=self.Rand) * (Sol[i] - xb)
 			else:
 				JK = self.Rand.permutation(self.NP)
 				S[i] += self.uniform(0, 1) * (Sol[JK[0]] - Sol[JK[1]])

NiaPy/algorithms/basic/fss.py

@@ -1,7 +1,7 @@
 # encoding=utf8
 from numpy import nan, asarray, zeros, full
 
-from NiaPy.util.utility import objects2array
+from NiaPy.util import objects_to_array
 from NiaPy.algorithms.algorithm import Algorithm, Individual
 
 class Fish(Individual):
@@ -170,7 +170,7 @@ def init_school(self, task):
 			school.append(fish)
 			curr_weight_school += fish.weight
 		prev_weight_school = curr_weight_school
-		return curr_step_individual, curr_step_volitive, curr_weight_school, prev_weight_school, objects2array(school)
+		return curr_step_individual, curr_step_volitive, curr_weight_school, prev_weight_school, objects_to_array(school)
 
 	def max_delta_cost(self, school):
 		r"""Find maximum delta cost - return 0 if none of the fishes moved.

NiaPy/algorithms/basic/fwa.py

@@ -2,7 +2,7 @@
 import logging
 from numpy import apply_along_axis, argmin, argmax, sum, sqrt, round, argsort, fabs, asarray, where
 from NiaPy.algorithms.algorithm import Algorithm
-from NiaPy.util import fullArray
+from NiaPy.util import full_array
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')
@@ -185,7 +185,7 @@ def initAmplitude(self, task):
 		Returns:
 			numpy.ndarray[float]: Starting amplitudes.
 		"""
-		return fullArray(self.A, task.D)
+		return full_array(self.A, task.D)
 
 	def SparsksNo(self, x_f, xw_f, Ss):
 		r"""Calculate number of sparks based on function value of individual.
@@ -446,7 +446,7 @@ def initRanges(self, task):
 				2. Final amplitude values over dimensions.
 				3. uAmin.
 		"""
-		Ainit, Afinal = fullArray(self.Ainit, task.D), fullArray(self.Afinal, task.D)
+		Ainit, Afinal = full_array(self.Ainit, task.D), full_array(self.Afinal, task.D)
 		return Ainit, Afinal, self.uAmin(Ainit, Afinal, task)
 
 	def uAmin(self, Ainit, Afinal, task):

NiaPy/algorithms/basic/gso.py

@@ -1,10 +1,10 @@
 # encoding=utf8
 import logging
 
-from scipy.spatial.distance import euclidean
 from numpy import full, apply_along_axis, copy, sum, fmax, pi, where
 
 from NiaPy.algorithms.algorithm import Algorithm
+from NiaPy.util import euclidean
 
 logging.basicConfig()
 logger = logging.getLogger('NiaPy.algorithms.basic')