My data is zero inflated so I'm running a zero-inflated model using glmmamdb:
Model3z <- glmmadmb(Count3 ~ Light3 + (1|Site3), zeroInflation = T, family= "poisson", data = dframe3)
However, when I try and do pairwise comparisons of the different light types in this model pwcs3 <- lsmeans(Model3z, "Light") I get the error message:
Error in ref_grid(object, ...) :
Can't handle an object of class “glmmadmb”
Use help("models", package = "emmeans") for information on supported models.
When I go on the emmeans package website it says that glmmadmb is no longer supported.
I've switched to pscl and the zeroinfl function but am unsure on how to restructure my code to fit the pscl format. Typing in P <- zeroinfl(Count3 ~ Light3 + (1|Site3), family = poisson, data = dframe3) gets the error message:
Error in contrasts<-(*tmp*, value = contr.funs[1 + isOF[nn]]) :
contrasts can be applied only to factors with 2 or more levels
In addition: Warning message:
In Ops.factor(1, Site3) : ‘|’ not meaningful for factors
Is there another way of using glmmadmb with lsmeans? If not, does anyone know how a zero-inflated model code in pscl is supposed to look? Thanks.
Related
Using Binance Futures API I am trying to get a proper form of my position regarding cryptocurrencies.
Using the code
from binance_f import RequestClient
request_client = RequestClient(api_key= my_key, secret_key=my_secet_key)
result = request_client.get_position()
I get the following result
[{"symbol":"BTCUSDT","positionAmt":"0.000","entryPrice":"0.00000","markPrice":"5455.13008723","unRealizedProfit":"0.00000000","liquidationPrice":"0","leverage":"20","maxNotionalValue":"5000000","marginType":"cross","isolatedMargin":"0.00000000","isAutoAddMargin":"false"}]
The type command indicates it is a list, however adding at the end of the code print(result) yields:
[<binance_f.model.position.Position object at 0x1135cb670>]
Which is baffling because it seems not to be the list (in fact, debugging it indicates object of type Position). Using PrintMix.print_data(result) yields:
data number 0 :
entryPrice:0.0
isAutoAddMargin:True
isolatedMargin:0.0
json_parse:<function Position.json_parse at 0x1165af820>
leverage:20.0
liquidationPrice:0.0
marginType:cross
markPrice:5442.28502271
maxNotionalValue:5000000.0
positionAmt:0.0
symbol:BTCUSDT
unrealizedProfit:0.0
Now it seems like a JSON format... But it is a list. I am confused - any ideas how I can convert result to a proper DataFrame? So that columns are Symbol, PositionAmt, entryPrice, etc.
Thanks!
Your main question remains as you wrote on the header you should not be confused. In your case you have a list of Position object, you can see the structure of Position in the GitHub of this library
Anyway to answer the question please use the following:
df = pd.DataFrame([t.__dict__ for t in result])
For more options and information please read the great answers on this question
Good Luck!
you can use that
df = pd.DataFrame([t.__dict__ for t in result])
klines=df.values.tolist()
open = [float(entry[1]) for entry in klines]
high = [float(entry[2]) for entry in klines]
low = [float(entry[3]) for entry in klines]
close = [float(entry[4]) for entry in klines]
I'm starting with pyomo and I have some questions.
I create an abstract model and the correspondenting data file. I would like to solve it in the script in order to use the solutions of the variables later.
I tried to do it with the next code:
data = DataPortal()
data.load(filename="Datos\Datos_reactor2.dat")
instance = model.create_instance(data)
opt = SolverFactory("ipopt")
results = opt.solve(instance)
And it solve it perfectly, but I had some problems with the data. In my model there aren't set, so the data are simple constants defined as:
param qv := 2.832;
param ci := 14.46;
...
However if I use this data file python tell me an error:
ValueError: Parameter 'qv' defined with '1' dimensions, but data has '1' values: [2.832]. Are you missing a value for a 1-dimensional index?
When I change my data file defining the parameters like:
param qv := 1 2.832;
param ci := 1 14.46;
...
I got another error:
RuntimeError: Failed to set value for param=qv, index=1, value=2.832. source error message="Error setting parameter value: Cannot treat the scalar Param 'qv' as an array"
Finally, to can solve the problem I have to define the constants as tables (see beolw), but I don't understand the previous errors.
table qv := 2.832;
table ci := 14.46;
...
To highlight just say that if I try to solve the problem with:
!pyomo solve Reactor2.py Datos_reactor.dat --solver=ipopt --summary
With the data defined as in the first case (param qv := 2.832; ...) I don't have any problem and I got the same solution.
Can anyone tell me the difference and explain me the errors?
Thank you!!
Maria
I haven't seen this specific scenario in my research for this error in Numba. This is my first time using the package so it might be something obvious.
I have a function that calculates engineered features in a data set by adding, multiplying and/or dividing each column in a dataframe called data and I wanted to test whether numba would speed it up
#jit
def engineer_features(engineer_type,features,joined):
#choose which features to engineer (must be > 1)
engineered = features
if len(engineered) > 1:
if 'Square' in engineer_type:
sq = data[features].apply(np.square)
sq.columns = map(lambda s:s + '_^2',features)
for c1,c2 in combinations(engineered,2):
if 'Add' in engineer_type:
data['{0}+{1}'.format(c1,c2)] = data[c1] + data[c2]
if 'Multiply' in engineer_type:
data['{0}*{1}'.format(c1,c2)] = data[c1] * data[c2]
if 'Divide' in engineer_type:
data['{0}/{1}'.format(c1,c2)] = data[c1] / data[c2]
if 'Square' in engineer_type and len(sq) > 0:
data= pd.merge(data,sq,left_index=True,right_index=True)
return data
When I call it with lists of features, engineer_type and the dataset:
engineer_type = ['Square','Add','Multiply','Divide']
df = engineer_features(engineer_type,features,joined)
I get the error: Failed at object (analyzing bytecode)
'DataFlowAnalysis' object has no attribute 'op_MAKE_FUNCTION'
Same question here. I think the problem might be the lambda function since numba does not support function creation.
I had this same error. Numba doesnt support pandas. I converted important columns from my pandas df into bunch of arrays and it worked successfully under #JIT.
Also arrays are much faster then pandas df, incase you need it for processing large data.
In fact, I'm trying to implement the very simple model formulation:
min sum_i(y_i*f_i) + sum_i(sum_j(x_ij*c_ij))
s.t. sum_i(x_ij) = 1 for all j
x_ij <= y_i for all i,j
x_ij, y_i are binary
But I simply cannot figure out how the Python API works. They suggest creating variables like this:
model.variables.add(obj = fixedcost,
lb = [0] * num_facilities,
ub = [1] * num_facilities,
types = ["B"] * num_facilities)
# Create one binary variable for each facility/client pair. The variables
# model whether a client is served by a facility.
for c in range(num_clients):
model.variables.add(obj = cost[c],
lb = [0] * num_facilities,
ub = [1] * num_facilities,
types = ["B"] * num_facilities)
# Create corresponding indices for later use
supply = []
for c in range(num_clients):
supply.append([])
for f in range(num_facilities):
supply[c].append((c+1)*(num_facilities)+f)
# Constraint no. 1:
for c in range(num_clients):
assignment_constraint = cplex.SparsePair(ind = [supply[c][f] for f in \
range(num_facilities)],
val = [1] * num_facilities)
model.linear_constraints.add(lin_expr = [assignment_constraint],
senses = ["L"],
rhs = [1])
For this constraints I have no idea how the variables from above are refered to since it only mentions an auxiliary list of lists. Can anyone explain to me how that should work? The problem is easy, I also know how to do it in C++ but the Python API is a closed book to me.
The problem is the uncapacitated facility location problem and I want to adapt the example file facility.py
EDIT: One idea for constraint no.2 is to create one-dimensional vectors and use vector addition to create the final constraint. But that tells me that this is an unsupported operand for SparsePairs
for f in range(num_facilities):
index = [f]
value = [-1.0]
for c in range(num_clients):
open_constraint = cplex.SparsePair(ind = index, val = value) + cplex.SparsePair(ind = [supply[c][f]], val = [1.0])
model.linear_constraints.add(lin_expr=[open_constraint],
senses = ["L"],
rhs = [0])
The Python API is closer to the C Callable Library in nature than the C++/Concert API. Variables are indexed from 0 to model.variables.get_num() - 1 and can be referred to by index, for example, when creating constraints. They can also be referred to by name (the add method has an optional names argument). See the documentation for the VariablesInterface here (this is for version 12.5.1, which I believe you are using, given your previous post).
It may help to start looking at the most simple examples like lpex1.py (and read the comments). Finally, I highly recommend playing with the Python API from the interactive Python prompt (aka the REPL). You can read the help there and type things in to see what they do.
You also might want to take a look at the docplex package. This is a modeling layer built on top of the CPLEX Python API (or which can solve on the cloud if you don't have a local installation of CPLEX installed).
c1 = CallistoSpectrogram.read('BIR_20110922_101500_01.fit')
c2 = CallistoSpectrogram.read('BIR_20110922_103000_01.fit')
d = CallistoSpectrogram.join_many([c1, c2])
If I want to join approximately 40 files like this, it is throwing the following error
ValueError: Too large gap.
Is there any limit in number?
This error is an internal error of the sunpy package that you are using. Really your question is not to do with python but to do with that package. You need to tag it with that.
But we can see what's going on by looking at the source, eg here. It shows that the ValueError is thrown when two adjacent spectra are separated by more than the maxgap parameter which defaults to zero.
So one fix might be simply to pass in maxgap = None
d = CallistoSpectrogram.join_many([c1, c2],maxgap = None)
That assumes you don't mind the gaps, of course.