I am trying to do sequential feature selection in Python and would like to select the number of features through GridsearchCV, can someone help me with understanding why this happens?
X_SFS=X_train_normed
numbers = [x for x in range(1,19)]
svc = SVC(kernel='rbf', gamma=0.1, C = 10)
sfs = SequentialFeatureSelector(estimator=svc)
param_grid = [{'k_features':numbers}]
search = GridSearchCV(sfs, param_grid, cv=StratifiedKFold(5, shuffle=False), scoring='f1_macro')
search.fit(X_SFS, y_train)
svm_params.append(search.best_params_)
features_bool.append(search.best_estimator_.named_steps['sfs'].get_support())
scores.append(search.best_score_)
The above throws the error:
AttributeError: 'SequentialFeatureSelector' object has no attribute 'predict'
Related
I'm dealing with a problem with the update function in Matlab.
conn=database('MySQL','user','password');)
selectquery_select = 'SELECT * FROM inputs WHERE i_read = 0';
data_select = select(conn,selectquery_select);
for j=1:size(data_select)
id_data = data_select(j,1);
id_data = string(id_data.(1));
time_data = data_select(j,4);
time_data = string(time_data.(1));
time_dataform = datetime(time_data,'InputFormat','yyyy-MM-dd HH:mm:ss');
y0=data_select(j,2);
y0 = str2num(string(y0.(1)));
r0=data_select(j,3);
r0 = str2num(string(r0.(1)));
if id_data == "115"
run("C:\Users\...\uu.m")
update(conn,'inputs','i_read',1,'WHERE (ID_code = "115") AND WHERE (i_Time = time_data)');
end
end
Basically, I'm taking some value from the database when i_read is equal to 0 (i_read is a boolean variable in the database that should give 1 if the value is already processed and 0 if not). After a value is read, we want to change the i_read in the database from 0 to 1. We decide to use the update function, but this gave us the following error:
Error using database.odbc.connection/update
Too many input arguments.
Error in Patient_Identification (line 57)
update(conn,'inputs','i_read',1,'WHERE (ID_code = "112") AND WHERE (i_Time = ', time_data,')');
Someone is able to help us with this problem? Thank you.
I am learning Bender's decomposition method and I want to use that in a small instance. I started from "bendersatsp.py" example in CPLEX. When I run this example with my problem, I got the following error. Could you please let me know what the problem is and how I can fix it? In the following you can see the modifications in lazy constraints function. I have two decision variables in master problem "z_{ik}" and "u_{k}" that would incorporate as constant in the workerLp.
class BendersLazyConsCallback(LazyConstraintCallback):
def __call__(self):
print("shoma")
v = self.v
u = self.u
z = self.z
print ("u:", u)
print ("z:", z)
workerLP = self.workerLP
boxty = len(u)
#scenarios=self.scenarios2
ite=len(z)
print ("ite:", ite)
print ("boxty:", boxty)
# Get the current x solution
sol1 = []
sol2 = []
sol3 = []
print("okkkk")
for k in range(1, boxty+1):
sol1.append([])
sol1[k-1]= [self.get_values(u[k-1])];
print ("sol1:", sol1[k-1])
for i in range(1, ite+1):
sol2.append([])
sol2[i-1]= self.get_values(z[i-1]);
print ("sol2:", sol2[i-1])
for i in range(1, ite+1):
sol3.append([])
sol3[i-1]= self.get_values(v[i-1]);
#print ("sol3:", sol3[i-1])
# Benders' cut separation
if workerLP.separate(sol3,sol1,sol2,v,u,z):
self.add(cut = workerLP.cutLhs, sense = "G", rhs = workerLP.cutRhs)
CPLEX Error 1006: Error during callback.
benders(sys.argv[1][0], datafile)
cpx.solve()
_proc.mipopt(self._env._e, self._lp)
check_status(env, status)
raise callback_exception
TypeError: unsupported operand type(s) for +: 'int' and 'list'
I'm using some codes that called specific function.
in line 17 of this function I get the error
Index exceeds matrix dimensions.
Error in generateExpReport (line 17)
checkPValuesField = subjFiles(1).name;
the function up to line 20 is like this:
function [] = generateExpReport(copyDir,resultDir,params)
% Syntax
methodNames = fieldnames(params.methods);
numMethods = length(methodNames);
for i = 1 : numMethods
cd(resultDir)
numTargets = length(params.methods.(methodNames{i,1}).idTargets);
idDrivers = params.methods.(methodNames{i,1}).idDrivers;
nameFiles = [methodNames{i,1} '*.mat'];
subjFiles = dir(nameFiles);
numSubj = length(subjFiles);
significanceOnDrivers = zeros(numSubj,numTargets);
matrixTransferEntropy = zeros(numSubj,(numTargets)+1);
% check if the pValues matrix is present
checkPValuesField = load(subjFiles(1).name);
fields = fieldnames(checkPValuesField);
nameFields = checkPValuesField.(fields{1,1});
I can't find the problem
please help me:(( what's wrong with
checkPValuesField = load(subjFiles(1).name);
I am quite new to Matlab and I am trying to use this code I found online.
I am trying to fit a graph described by the HydrodynamicSpectrum. But instead of having it fit after inputting fvA and fmA, I am trying to obtain the fitted parameters for this value also.
I have tried removing them, changing them. But none is working. I was wondering if any one here will be able to point me into the right direction of fixing this.
specFunc = #(f, para)HydrodynamicSpectrum(f, [para fvA fmA]);
[fit.AXfc, fit.AXD] = NonLinearFit(fit.f(indXY), fit.AXSpec(indXY), specFunc, [iguess_AXfc iguess_AXD]);
[fit.AYfc, fit.AYD] = NonLinearFit(fit.f(indXY), fit.AYSpec(indXY), specFunc, [iguess_AYfc iguess_AYD]);
[fit.ASumfc, fit.ASumD] = NonLinearFit(fit.f(indSum), fit.ASumSpec(indSum), specFunc, [iguess_ASumfc iguess_ASumD]);
predictedAX = HydrodynamicSpectrum(fit.f, [fit.AXfc fit.AXD fvA fmA]);
predictedAY = HydrodynamicSpectrum(fit.f, [fit.AYfc fit.AYD fvA fmA]);
predictedASum = HydrodynamicSpectrum(fit.f, [fit.ASumfc fit.ASumD fvA fmA]);
function spec = HydrodynamicSpectrum(f, para);
fc = para(1);
D = para(2);
fv = para(3);
fm = para(4);
f = abs(f); %Kludge!
spec = D/pi^2*(1+sqrt(f/fv))./((fc - f.*sqrt(f./fv) - (f.^2)/fm).^2 + (f + f.*sqrt(f./fv)).^2);
function [fc, D, sfc, sD] = NonLinearFit(f, spec, specFunc, init);
func = #(para, f)spec./specFunc(f, para);
[paraFit, resid, J] = nlinfit(f, ones(1, length(spec)), func, init);
fc = paraFit(1);
D = paraFit(2);
ci = nlparci(real(paraFit), real(resid), real(J)); % Kludge!!
sfc = (ci(1,2) - ci(1,1))/4;
sD = (ci(2,2) - ci(2,1))/4;
[paraFit, resid, J] = nlinfit(f, ones(1, length(spec)), func, init);
It looks like you get your fitted parameter using this line. And you are further processing them to get other stuff out from the function. You can modify your second function to get them out as well.
As there are very few comments, and questions seems to be application specific, there is not much help I can give with what you have presented.
class Matrix:
def __init__(self, nr, nc):
self.NRows = nr
self.NCols = nc
self.data = [ [0]*self.NCols for r in range(self.NRows) ]
def max(self, other):
""" return: a matrix with as many rows as the shorter of self and other and as many columns as the narrower of self and other.
Each entry of the returned matrix should be the larger (the max) of the corresponding entries in self and other.
"""
minrows = min(other.NRows, self.NRows)
mincols = min(other.NCols, self.NCols)
M = Matrix(minrows, mincols)
for i in range(minrows):
for j in range(mincols):
M.data[i][j] = max(self.data[i][j], other[i][j])
return M
This code give Traceback in max when tested and output said:
...in max
M.data[i][j] = max(self.data[i][j], other[i][j])
AttributeError: Matrix instance has no attribute '__getitem__'
How to get rid of this error? Where I made mistake?. Please help someone.
You should use this :
M.data[i][j] = max(self.data[i][j], other.data[i][j])
instead of
M.data[i][j] = max(self.data[i][j], other[i][j])