We Keep Coding

Pyomo: KeyError: "Index '(0, 1, 1)' is not valid for indexed component 'x_ijl'" - python

With reference to the images in the attached files, i want to model using pyomo.
What I have done so far.
from pyomo.environ import *
from pyomo.opt import SolverFactory
import pyomo.environ
n=13
distanceMatrix=[[0,8,4,10,12,9,15,8,11,5,9,4,10],
[8,0,7,6,8,6,7,10,12,9,8,7,5],
[4,7,0,7,9,5,8,5,4,8,6 ,10,8],
[10,6 ,7,0,6,11,5 ,9,8,12,11,6,9],
[12,8 ,9,6, 0,7,9,6,9,8,4,11,10],
[9,6,5,11,7,0,10,4,3,10,6,5,7],
[15,7 ,8,5,9,10,0,10,9,8,5,9,10],
[8,10 ,5,9,6,4,10,0,11,5,9,6,7],
[11,12,4,8, 9,3,9,11,0, 9,11,11,6],
[5,9,8,12,8,10,8,5,9,0,6,7,5],
[9,8,6,11,4,6,5,9,11,6,0,10,7],
[4,7,10,6,11,5,9,6,11,7,10,0,9],
[10,5,8,9,10,7,10,7,6,5,7,9,0]]
travel_time=[[0,8,4,10,12,9,15,8,11,5,9,4,10],
[8,0,7,6,8,6,7,10,12,9,8,7,5],
[4,7,0,7,9,5,8,5,4,8,6 ,10,8],
[10,6 ,7,0,6,11,5 ,9,8,12,11,6,9],
[12,8 ,9,6, 0,7,9,6,9,8,4,11,10],
[9,6,5,11,7,0,10,4,3,10,6,5,7],
[15,7 ,8,5,9,10,0,10,9,8,5,9,10],
[8,10 ,5,9,6,4,10,0,11,5,9,6,7],
[11,12,4,8, 9,3,9,11,0, 9,11,11,6],
[5,9,8,12,8,10,8,5,9,0,6,7,5],
[9,8,6,11,4,6,5,9,11,6,0,10,7],
[4,7,10,6,11,5,9,6,11,7,10,0,9],
[10,5,8,9,10,7,10,7,6,5,7,9,0]]
Time_windows = [(1400,1500), (0000,2400), (0000,2400),(0700,2400),(0000,2400),(0000,0700),(0700,2400),(0700,2400),(0000,0700),(0000,2400),\
(0000,2400),(0000,2400),(0700,2400)]
Service_time = [0000, 1600,1600,180,30,120,120,60,30,30,90,120,330]
demand = [9999.00, 9999.00,9999.00,12.00, 4.00, 6.00, 8.00,16.00,6.00,16.00,12.00,24.00,8.00]
K = 4 # no. of vehicles
C = 280; # capacity
speed = 40; # default speed
M = 200;
startCity = 0
model = ConcreteModel()
# sets
#model.M = Set(initialize=range(1, n+1))
model.N = Set(initialize=range(1, n+1))
model.K = Set(initialize=range(1, K+1))
model.Nc = Set(initialize=range(3, n+1)) # set of customers
# Param
model.cost = Param(model.N, model.N, initialize=lambda model, i, j: distanceMatrix[i-1][j-1])
model.travel_time = Param(model.N, model.N,initialize=lambda model, i,j: travel_time[i-1][j-1])
model.Time_windows = Param(model.N, initialize=lambda model, i: travel_time[i-1]) # time_windows
model.Service_time = Param(model.N, initialize=lambda model, i: Service_time[i-1]) # Service time
model.demand = Param(model.N, initialize=lambda model, i: demand[i-1])
model.M = Param(initialize=M)
model.C = Param(initialize=C)
# variables
model.x_ijl = Var(model.N, model.N, model.K, within=Binary) # decision variable = 1 iff vehicle l in K uses arc (i,j) in A
model.d_il = Var(model.N, model.K, bounds=(0,None)) # the accumulative demand at node i in V for vehicle l in K
model.w_il = Var(model.N, model.K, bounds=(0,None)) # start time of service at node i in V for vehicle l in K
"""
Constriants
"""
# All l vehicles must leave the depot
def leave_depot(model,l):
return sum(model.x_ijl[0,j,l] for j in model.N) == 1
model.leave_depot = Constraint(model.K, rule=leave_depot)
# All l vehicles must return to the depot
def return_depot(model,l):
return sum(model.x_ijl[i,0,l] for i in model.N) == 1
model.return_depot = Constraint(model.K, rule=return_depot)
# ensures that all customers are serviced exactly once.
def customer_service(model, j):
return sum(sum(model.x_ijl[i,j,l] for l in model.K) for i in model.N) ==1
model.customer_service1 = Constraint(model.Nc, rule=customer_service)
# Inflow and outflow must be equal except for the depot nodes
def flow(model,j,l):
return sum(model.x_ijl[i,j,l] for i in model.N if i < j) == sum(model.x_ijl[j,i,l] for i in model.N if j < i)
model.flow1 = Constraint(model.N,model.K, rule=flow)
# Time windows
def time_windows1(model,i,l):
return model.Time_windows[i][0] <=model.w_il[i,l] <= model.Time_windows[i][1]
model.time_windows = Constraint(model.N,model.K, rule=time_windows1)
# service time
def service_time(model,i,j,l):
return model.w_il[i,l] + model.Service_time[i] + model.travel_time[i,j] <= model.w_il[j,l] + (1 - model.x_ijl[i,j,l])*200
model.service_time = Constraint(model.N, model.N, model.K, rule=service_time)
# vehicle must be empty at start and end of routes
def empty(model, l):
return model.d_il[0,l] + model.d_il[-1,l] == 0
model.empty = Constraint(model.K, rule=empty)
# accumulative demand for all nodes except disposal sites
def demands_forall_nodes(model,i,j,l):
return model.d_il[i,l] + model.demand[i] <= model.d_il[j,l]+(1 - model.x_ijl[i,j,l]*200)
model.demands_forall_nodes = Constraint(model.Nc, model.N,model.K,rule=demands_forall_nodes)
# Capacity contraints
def vehicle_capacity(model, i,l):
return model.d_il[i,l] <= model.C
model.vehicle_capacity = Constraint(model.N, model.K, rule=vehicle_capacity)
# Objective Function
def objective(model):
return sum(model.cost[i,j]*model.x_ijl[i,j,l] for i in model.N for j in model.N for l in model.K)
model.obj = Objective(rule=objective)
opt = SolverFactory("glpk")
results = opt.solve(model, tee=True)
results.write()
However, I got an error with constriant 2 (from image 2) which I know similar will apply to constraint 3 and constriant 9. The error is:
ERROR: Rule failed when generating expression for constraint leave_depot with
index 1: KeyError: "Index '(0, 1, 1)' is not valid for indexed component
'x_ijl'"
ERROR: Constructing component 'leave_depot' from data=None failed: KeyError:
"Index '(0, 1, 1)' is not valid for indexed component 'x_ijl'"
Traceback (most recent call last):
File "vrptwModel.py", line 81, in <module>
model.leave_depot = Constraint(model.K, rule=leave_depot)
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/block.py", line 540, in __setattr__
self.add_component(name, val)
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/block.py", line 980, in add_component
val.construct(data)
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/constraint.py", line 793, in construct
ndx)
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/misc.py", line 61, in apply_indexed_rule
return rule(model, index)
File "vrptwModel.py", line 80, in leave_depot
return sum(model.x_ijl[0,j,l] for j in model.N) == 1
File "vrptwModel.py", line 80, in <genexpr>
return sum(model.x_ijl[0,j,l] for j in model.N) == 1
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/indexed_component.py", line 543, in __getitem__
index = self._validate_index(index)
File "/usr/local/lib/python2.7/dist-packages/pyomo/core/base/indexed_component.py", line 695, in _validate_index
% ( idx, self.name, ))
KeyError: "Index '(0, 1, 1)' is not valid for indexed component 'x_ijl'"
My problem is modeling constraint 2 and 3.
Please could someone help me to write these constraints correctly

The problem is that you started your indexing sets at 1 not 0. Change m.x_ijl[0,j,l] to m.x_ijl[1,j,l].

Related

I am defining a main_meal_model function to list a lunch plan that takes in a day value. I have previously defined a random_main_meal_dataset function that creates a database of all the foods that are to be eaten at lunch which also takes in the same day value. For some reason i can't omit a forced declaration of the day value in the random_main_meal_dataset function, else it either gives a KeyError or NameError
I have tried every day of the week and it seems perfect as much as my forced declaration is the same as the day value i send when calling the main_meal_model function but as soon as i try to make this an automatic correspondance it sends KeyError: 'Monday' or NameError: name 'day' is not defined per day_data = data[day]
Error messages:
Full Errorr messages:
Traceback (most recent call last):
File "c:\Users\Leonix\Desktop\CS50 Final Project\test.py", line 104, in <module>
print(main_meal_model('Monday', 70, 2000, data, 'Lunch'))
File "c:\Users\Leonix\Desktop\CS50 Final Project\test.py", line 72, in main_meal_model
day_data = data[day]
KeyError: 'Monday
or
Traceback (most recent call last):
File "c:\Users\Leonix\Desktop\CS50 Final Project\test.py", line 103, in <module>
print(main_meal_model('Monday', 70, 2000, data, 'Lunch')) File "c:\Users\Leonix\Desktop\CS50 Final Project\test.py", line 71, in main_meal_model
day_data = data[day] NameError: name 'day' is not defined
Here is the part of the code I suppose is causing the problem
https://pastebin.com/w8XQ8rTn
split_values_day = np.linspace(0, len(data), 8).astype(int)
split_values_day[-1] = split_values_day[-1]-1
def random_main_meal_dataset(data, day):
data = data[data['meal'].str.contains('Main Dishes|Condiments|Side Dishes', na=False)]
frac_data = data.sample(frac=1).reset_index().drop('index', axis=1)
day_data = []
for s in range(len(split_values_day)-1):
day_data.append(
frac_data.loc[split_values_day[s]:split_values_day[s+1]])
return dict(zip(day, day_data))
# define a lunch / dinner model that takes in prob, kg, calories, data and makes a lunch / dinner plan for the day
def main_meal_model(day, kg, calories, data, meal):
data = random_main_meal_dataset(data, day=['Monday'])
G = extract_gram(build_nutritional_values(kg, calories))
E = G['Carbohydrates Grams']
F = G['Fat Grams']
P = G['Protein Grams']
day_data = data[day]
day_data = day_data[day_data.calories != 0]
food = day_data.name.tolist()
c = day_data.calories.tolist()
x = pulp.LpVariable.dicts(
"x", indices=food, lowBound=0, upBound=1.5, cat='Continuous', indexStart=[])
e = day_data.carbohydrate.tolist()
f = day_data.total_fat.tolist()
p = day_data.protein.tolist()
div_meal = meal_split[meal]
prob = pulp.LpProblem("Diet", LpMinimize)
prob += pulp.lpSum([x[food[i]]*c[i] for i in range(len(food))])
prob += pulp.lpSum([x[food[i]]*e[i] for i in range(len(x))]) >= E*0.35
prob += pulp.lpSum([x[food[i]]*f[i] for i in range(len(x))]) >= F*0.35
prob += pulp.lpSum([x[food[i]]*p[i] for i in range(len(x))]) >= P*0.35
prob.solve(PULP_CBC_CMD(msg=0))
variables = []
values = []
for v in prob.variables():
variable = v.name
value = v.varValue
variables.append(variable)
values.append(value)
values = np.array(values).round(2).astype(float)
sol = pd.DataFrame(np.array([food, values]).T,
columns=['Food', 'Quantity'])
sol['Quantity'] = sol.Quantity.astype(float)
sol = sol[sol['Quantity'] != 0.0]
sol.Quantity = sol.Quantity*100
sol = sol.rename(columns={'Quantity': 'Quantity (g)'})
return sol
print(main_meal_model('Monday', 70, 2000, data, 'Lunch'))`

Hi I'm trying to create an AI inspired NEAT but I ran into a problem after performing the calculation, the data from the device can't be moved to the host.
I'm trying to use cuda.synchronize () before moving the data, but in this case the error has already occurred on this line.
I tried the cuda features before and they worked without problems until now.
I'm attaching the code with an error.
Please can you help me?
code:
import os
# needs to appear before cuda import
os.environ["NUMBA_ENABLE_CUDASIM"] = "0"
# set to "1" for more debugging, but slower performance
os.environ["NUMBA_CUDA_DEBUGINFO"] = "0"
from numba import cuda
import numpy as np
from random import uniform
from pprint import pprint
#cuda.jit(device=True)
def LeakyRelu(x):
return max(x * 0.1, x)
#cuda.jit
def Calculate(set_io, many_neurons, many_inputs, reindex_io, memory_io, weights_io, biases_io, nets_info_io, auxmemort_io):
x = cuda.grid(1)
shape = set_io.shape
if x < shape[0]:
netidx = reindex_io[x, 0]
neuidx = reindex_io[x, 1]
weiidx = reindex_io[x, 2]
result = 0
for i in range(nets_info_io[netidx, 1]):
result += memory_io[nets_info_io[netidx, 0] + i] * weights_io[weiidx + i]
result += biases_io[x]
result = LeakyRelu(result)
auxmemort_io[neuidx] = result
def CalculateSlow():
pass
class ANET:
def __init__(self, many_inputs, many_outputs, many_networks, info = True, activation_function = LeakyRelu):
self.many_inputs = many_inputs
self.many_outputs = many_outputs
self.many_networks = many_networks
self.activation_function = activation_function
self.info = info
self.netid = -1
self.cuda_many_input = cuda.to_device(self.many_inputs)
if self.info:
print("starting Setup:\n|")
device = str(cuda.get_current_device()).split("'b'")[1].split("''")[0]
print(f"| cuda run on: {device}")
print(f"| generate genomes")
self.networks_genomes = [self._GenerateGenome() for _ in range(self.many_networks)]
self._BuildPopulation()
def _CudaPre(self, block, array):
griddim = tuple(np.array(array.shape) // block + 1)
blockdim = tuple(np.full_like(griddim, block))
return griddim, blockdim
def _GenerateGenome(self):
neurons_genome = [[self.activation_function, uniform(-1,1), i + self.many_inputs] for i in range(self.many_outputs)]
synapses_genome = [[[i, ii], uniform(-1,1), True] for i in range(self.many_inputs) for ii in range(self.many_inputs, self.many_outputs + self.many_inputs)]
self.netid += 1
return [neurons_genome, synapses_genome, self.netid]
def _BuildPopulation(self):
memory_len = sum([(len(i[0]) + self.many_inputs) for i in self.networks_genomes])
memory = np.zeros(memory_len, dtype=np.float64)
auxmemory = np.copy(memory)
weights_len = sum([(len(i[1]) + self.many_inputs + 1) for i in self.networks_genomes])
weights = np.zeros(weights_len, dtype=np.float64)
biases_len = sum([len(i[0]) for i in self.networks_genomes])
biases = np.zeros(biases_len, dtype=np.float64)
nets_info = np.zeros([self.many_networks, 5], dtype=np.int64)
movmem = 0
movwei = 0
movbia = 0
for idx,i in enumerate(self.networks_genomes):
nets_info[idx] = (movmem, len(i[0]) + self.many_inputs, movwei, movbia, i[2])
movmem += len(i[0]) + self.many_inputs
movwei += len(i[1]) + self.many_inputs + 1
movbia += len(i[0])
biaidx = 0
for genome, net_info in zip(self.networks_genomes, nets_info):
for gen, biagen in zip(genome[1], genome[0]):
if gen[2]:
if gen[0][0] < self.many_inputs:
target = gen[0][0]
else:
target = genome[0][gen[0][0] - self.many_inputs - self.many_outputs][2] + self.many_inputs
weights[(gen[0][1] - self.many_inputs) * net_info[1] + net_info[2] + target] = gen[1]
biases[biaidx] = biagen[1]
biaidx += 1
reindex = np.empty([sum([(i[1] - self.many_inputs) for i in nets_info]), 3], dtype=np.int64)
write = 0
weiidx = 0
for idx,inf in enumerate(nets_info):
for i in range(inf[1] - self.many_inputs):
reindex[write][0] = idx
reindex[write][1] = inf[0] + i + self.many_inputs
reindex[write][2] = weiidx
weiidx += inf[1]
write += 1
if self.info:
print("| move arrays to device")
self.memory = memory
self.cuda_auxmemory = cuda.to_device(auxmemory)
print(self.cuda_auxmemory.copy_to_host())
self.cuda_weights = cuda.to_device(weights)
self.cuda_biases = cuda.to_device(biases)
self.nets_info = nets_info
self.cuda_nets_info = cuda.to_device(nets_info)
self.many_neurons = np.sum(nets_info, axis=0)[1]
self.cuda_many_neurons = cuda.to_device(self.many_neurons)
self.cuda_reindex = cuda.to_device(reindex)
# print(nets_info)
# print(f"{memory_len} | {weights_len} | {biases_len}")
def MathPopulation(self, inputs):
for idx, inp in enumerate(inputs):
self.memory[self.nets_info[idx][0]: self.nets_info[idx][0] + self.many_inputs] = inp
self.cuda_memory = cuda.to_device(self.memory)
setlen = cuda.to_device(np.zeros(self.many_neurons))
Calculate[self._CudaPre(32, np.empty([self.many_neurons]))](setlen, self.cuda_many_neurons, self.cuda_many_input, self.cuda_reindex, self.cuda_memory, self.cuda_weights, self.cuda_biases, self.cuda_nets_info, self.cuda_auxmemory)
# cuda.synchronize()
arr = self.cuda_auxmemory.copy_to_host()
print(arr)
if __name__ == '__main__':
anet = ANET(many_inputs = 3, many_outputs = 2, many_networks = 5)
# pprint(anet._GenerateGenome())
anet.MathPopulation([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
error:
Traceback (most recent call last):
File "c:\Users\Ondra\Documents\Pythonporno\ANET\ANET.py", line 144, in <module>
anet.MathPopulation([[1,2,3], [4,5,6], [7,8,9], [10,11,12], [13,14,15]])
File "c:\Users\Ondra\Documents\Pythonporno\ANET\ANET.py", line 138, in MathPopulation
arr = self.cuda_auxmemory.copy_to_host()
File "C:\Users\Ondra\AppData\Local\Programs\Python\Python310\lib\site-packages\numba\cuda\cudadrv\devices.py", line 232, in _require_cuda_context
return fn(*args, **kws)
File "C:\Users\Ondra\AppData\Local\Programs\Python\Python310\lib\site-packages\numba\cuda\cudadrv\devicearray.py", line 277, in copy_to_host
_driver.device_to_host(hostary, self, self.alloc_size,
File "C:\Users\Ondra\AppData\Local\Programs\Python\Python310\lib\site-packages\numba\cuda\cudadrv\driver.py", line 2998, in device_to_host
fn(host_pointer(dst), device_pointer(src), size, *varargs)
File "C:\Users\Ondra\AppData\Local\Programs\Python\Python310\lib\site-packages\numba\cuda\cudadrv\driver.py", line 319, in safe_cuda_api_call
self._check_ctypes_error(fname, retcode)
File "C:\Users\Ondra\AppData\Local\Programs\Python\Python310\lib\site-packages\numba\cuda\cudadrv\driver.py", line 384, in _check_ctypes_error
raise CudaAPIError(retcode, msg)
numba.cuda.cudadrv.driver.CudaAPIError: [700] Call to cuMemcpyDtoH results in UNKNOWN_CUDA_ERROR

def simulate(self, timesteps, **kwargs):
pos = {comp: i for i, comp in enumerate(kwargs)}
population = np.zeros(len(pos), dtype='int')
for comp in pos:
population[pos[comp]] = kwargs[comp] # line where the error is
values = []
values.append(population)
comps = list(self.transitions.nodes)
time = np.arange(1, timesteps, 1, dtype='int')
for t in time:
pop = values[-1]
new_pop = values[-1].copy()
N = np.sum(pop)
I am having this value error and I'm not sure on how to fix it. Any suggestion
Error happens when I call this,
if population is None:
population = SIR1.values_.iloc[-1].copy()
else:
population = pd.concat([population, SIR1.values_.iloc[-1]])
S0 = population.S
I00 = population.I
R0 = population.R
Quarantine.simulate(365 - 74, S=S0, I=I00, R=R0) ### Line causing the error

# __________________________________________________________________________
#
# Pyomo: Python Optimization Modeling Objects
# Copyright 2017 National Technology and Engineering Solutions of Sandia, LLC
# Under the terms of Contract DE-NA0003525 with National Technology and
# Engineering Solutions of Sandia, LLC, the U.S. Government retains certain
# rights in this software.
# This software is distributed under the 3-clause BSD License.
# ___________________________________________________________________________
from pyomo.contrib.pynumero.sparse import BlockSymMatrix
from pyomo.contrib.pynumero.interfaces import PyomoNLP
import matplotlib.pylab as plt
import pyomo.environ as aml
import pyomo.dae as dae
def create_problem(begin, end):
m = aml.ConcreteModel()
m.t = dae.ContinuousSet(bounds=(begin, end))
m.x = aml.Var([1, 2], m.t, initialize=1.0)
m.u = aml.Var(m.t, bounds=(None, 0.8), initialize=0)
m.xdot = dae.DerivativeVar(m.x)
def _x1dot(M, i):
if i == M.t.first():
return aml.Constraint.Skip
return M.xdot[1, i] == (1-M.x[2, i] ** 2) * M.x[1, i] - M.x[2, i] + M.u[i]
m.x1dotcon = aml.Constraint(m.t, rule=_x1dot)
def _x2dot(M, i):
if i == M.t.first():
return aml.Constraint.Skip
return M.xdot[2, i] == M.x[1, i]
m.x2dotcon = aml.Constraint(m.t, rule=_x2dot)
def _init(M):
t0 = M.t.first()
yield M.x[1, t0] == 0
yield M.x[2, t0] == 1
yield aml.ConstraintList.End
m.init_conditions = aml.ConstraintList(rule=_init)
def _int_rule(M, i):
return M.x[1, i] ** 2 + M.x[2, i] ** 2 + M.u[i] ** 2
m.integral = dae.Integral(m.t, wrt=m.t, rule=_int_rule)
m.obj = aml.Objective(expr=m.integral)
m.init_condition_names = ['init_conditions']
return m
instance = create_problem(0.0, 10.0)
# Discretize model using Orthogonal Collocation
discretizer = aml.TransformationFactory('dae.collocation')
discretizer.apply_to(instance, nfe=100, ncp=3, scheme='LAGRANGE-RADAU')
discretizer.reduce_collocation_points(instance, var=instance.u, ncp=1, contset=instance.t)
# Interface pyomo model with nlp
nlp = PyomoNLP(instance)
x = nlp.create_vector_x()
lam = nlp.create_vector_y()
# Evaluate jacobian
jac_c = nlp.jacobian_g(x)
plt.spy(jac_c)
plt.title('Jacobian of the constraints\n')
plt.show()
# Evaluate hessian of the lagrangian
hess_lag = nlp.hessian_lag(x, lam)
plt.spy(hess_lag)
plt.title('Hessian of the Lagrangian function\n')
plt.show()
# Build KKT matrix
kkt = BlockSymMatrix(2)
kkt[0, 0] = hess_lag
kkt[1, 0] = jac_c
plt.spy(kkt.tocoo())
plt.title('KKT system\n')
plt.show()
Error:
Traceback (most recent call last):
File "C:/Users/user/Google Drive/k_aug_M/test/CSTR_ex_declare/check_para_k_aug(KKT)/check.py", line 66, in <module>
nlp = PyomoNLP(instance)
File "C:\Users\user\.conda\envs\env_name\lib\site-packages\pyomo\contrib\pynumero\interfaces\nlp.py", line 1484, in __init__
super(PyomoNLP, self).__init__(nl_file)
File "C:\Users\user\.conda\envs\env_name\lib\site-packages\pyomo\contrib\pynumero\interfaces\nlp.py", line 858, in __init__
self._asl = _asl.AmplInterface(self._model)
File "C:\Users\user\.conda\envs\env_name\lib\site-packages\pyomo\contrib\pynumero\extensions\asl.py", line 34, in __init__
% (os.name,) )
RuntimeError: ASL interface is not supported on this platform (nt)
Exception AttributeError: "'AmplInterface' object has no attribute 'ASLib'" in <bound method AmplInterface.__del__ of <pyomo.contrib.pynumero.extensions.asl.AmplInterface object at 0x000000000E48BA88>> ignored
How can I use pyomoNLP on window?

I'm quite new with python and pandas, and I'm stuck with an error. I'm working with audiofiles, and I need to extract some features from audiostreams.
def load_features(self, audio_file):
def stereo_to_mono(x):
# stereo to mono
if len(x.shape) > 1 and x.shape[1] > 1:
print('Converting stereo to mono')
x = x.mean(axis=1)
return x
def cut_or_pad_to_length(x, duration, fs):
desired_length = int(round(duration * fs))
length = len(x)
diff = length - desired_length
abs_diff = abs(diff)
if diff < 0:
print('Padding')
# put the short signal in the middle
pad_before = abs_diff // 2
pad_after = abs_diff - pad_before
x = np.lib.pad(x, (pad_before, pad_after), 'constant')
elif diff > 1:
print('Cutting')
# cut the beginning
x = x[0:desired_length]
return x
def adjust_input(x, fs):
x = stereo_to_mono(x)
x = cut_or_pad_to_length(x, 2.0, fs)
return x
#x is data, fs is samplerate
x, fs = sf.read(audio_file)
x0 = adjust_input(x, fs)
# pitchgram
x_features = self.ch.transform(x0)
if self.scaler is not None:
x_features = self.scaler.transform(x_features.reshape(1, -1)) \
# 1 data point with 2D features
x_features = x_features.reshape(1, *x_features.shape)
return x_features
def predict_class_label(self, audio_file):
x_features = self.load_features(audio_file)
instrument_class = np_utils.probas_to_classes(self.model.predict(x_features, verbose=0))[0]
label = self.instr_family_le.inverse_transform(instrument_class)
return label
This gives me following error:
File "C:/dipl0m/ml-master/instrument-classification/predict.py", line 104, in predict_probabilities
x_features = self.load_features(audio_file)
File "C:/dipl0m/ml-master/instrument-classification/predict.py", line 87, in load_features
x_features = self.ch.transform(x0)
AttributeError: 'dict' object has no attribute 'transform'
But x and x0 don't seems like dictionary, because I operate with them like lists, but transform gives me that error ... Or am I wrong somewhere? Can't figure it out for a long time.