def solveProximal(self, data, rho, master_Z):
inputDF, outputDF = data
n, d = inputDF.shape
n, m = outputDF.shape
features = inputDF.columns
outputs = outputDF.columns
#betas = SparseDataFrame(np.zeros(m,d), index = outputs, columns = features)
#consolidate input data with master Z (features and/or outputs may be missing from either tables)
#ghost_Z = SparseDataFrame(np.zeros((m,d)),index = outputs, columns = features)
#target_Z = ghost_Z + master_Z
#the columns (rows) of target_Z are a union of the columns (rows) of ghost_Z and master_Z; zeros are added as necessary
target_Z = reconstructDataFrame(master_Z)
betas = SparseDataFrame(target_Z.copy())
grad_norm_sum = 0.0
converged_sum = 0.0
it_sum = 0.0
count = 0.0
for out in outputDF.columns:
beta_target = np.array(target_Z.loc[out, features])
y = np.array(outputDF.loc[:, out])
X = np.array(inputDF)
beta, grad_norm, converged, it = self.solveSingle(
X, y, rho, beta_target)
count += 1.0
grad_norm_sum += grad_norm
converged_sum += converged
it_sum += it
logging.info(
str(datetime.datetime.now()) + '- Converged: ' +
str(converged) + ' Gradient norm: ' + str(grad_norm))
logging.info(
str(datetime.datetime.now()) + '- Beta learned: ' + str(beta))
betas.loc[out, features] = beta
stats = {}
stats['Grad'] = grad_norm_sum / (1. * count)
stats['It'] = it_sum / (1. * count)
stats['Conv'] = converged_sum / (1. * count)
return unravelDataFrame(betas), stats
def solveProximal(self,data,rho,master_Z):
inputDF,outputDF = data
n,d = inputDF.shape
n,m = outputDF.shape
features = inputDF.columns
outputs = outputDF.columns
#betas = SparseDataFrame(np.zeros(m,d), index = outputs, columns = features)
#consolidate input data with master Z (features and/or outputs may be missing from either tables)
#ghost_Z = SparseDataFrame(np.zeros((m,d)),index = outputs, columns = features)
#target_Z = ghost_Z + master_Z
#the columns (rows) of target_Z are a union of the columns (rows) of ghost_Z and master_Z; zeros are added as necessary
target_Z = reconstructDataFrame(master_Z)
betas = SparseDataFrame(target_Z.copy())
grad_norm_sum = 0.0
converged_sum = 0.0
it_sum = 0.0
count = 0.0
for out in outputDF.columns:
beta_target = np.array(target_Z.loc[out,features])
y = np.array(outputDF.loc[:,out])
X = np.array(inputDF)
beta, grad_norm, converged, it = self.solveSingle(X,y,rho,beta_target)
count += 1.0
grad_norm_sum +=grad_norm
converged_sum += converged
it_sum += it
logging.info(str(datetime.datetime.now())+ '- Converged: ' + str(converged)+' Gradient norm: '+ str(grad_norm) )
logging.info( str(datetime.datetime.now())+'- Beta learned: '+ str(beta))
betas.loc[out,features] = beta
stats = {}
stats['Grad']=grad_norm_sum/(1.*count)
stats['It']=it_sum/(1.*count)
stats['Conv']=converged_sum/(1.*count)
return unravelDataFrame(betas),stats
#d = 4
#m = 3
rho = 0.1
#inputDF,outputDF,true_beta = generateData(n,d,m)
#saveFileOutput(inputDF,outputDF,'LR_example.txt')
LR = LogisticRegressionSolver()
(inputDF, outputDF), keys, stats = LR.readPointBatch(sys.stdin)
#fudged_betas = true_beta + pandas.DataFrame(0.1* np.random.random( (m,d)) ,index= true_beta.index, columns =true_beta.columns)
#logging.info(str(datetime.datetime.now())+'True Beta \n'+str(true_beta))
zeros = reconstructDataFrame(dict(zip(keys, [0.0] * len(keys))))
logging.info(str(zeros))
betas = reconstructDataFrame(
LR.solveProximal((inputDF, outputDF), rho, unravelDataFrame(zeros)))
betas = pandas.DataFrame(normalize_row(betas),
index=betas.index,
columns=betas.columns)
logging.info(
str(datetime.datetime.now()) + ' Estimated Betas \n' + str(betas))
#betas = LR.solveProximal( (inputDF,outputDF),rho,fudged_betas)
#betas = SparseDataFrame(normalize_row(betas),index= true_beta.index, columns =true_beta.columns)
#logging.info(str(true_beta))
#logging.info(str(betas))
logging.basicConfig(level=logging.DEBUG) #n = 10000 #d = 4 #m = 3 rho = 0.1 #inputDF,outputDF,true_beta = generateData(n,d,m) #saveFileOutput(inputDF,outputDF,'LR_example.txt') LR = LogisticRegressionSolver() (inputDF,outputDF), keys,stats = LR.readPointBatch(sys.stdin) #fudged_betas = true_beta + pandas.DataFrame(0.1* np.random.random( (m,d)) ,index= true_beta.index, columns =true_beta.columns) #logging.info(str(datetime.datetime.now())+'True Beta \n'+str(true_beta)) zeros = reconstructDataFrame(dict(zip(keys, [0.0]*len(keys)))) logging.info(str(zeros)) betas = reconstructDataFrame(LR.solveProximal( (inputDF,outputDF),rho, unravelDataFrame(zeros))) betas = pandas.DataFrame(normalize_row(betas) , index=betas.index,columns = betas.columns) logging.info(str(datetime.datetime.now())+' Estimated Betas \n'+str(betas)) #betas = LR.solveProximal( (inputDF,outputDF),rho,fudged_betas) #betas = SparseDataFrame(normalize_row(betas),index= true_beta.index, columns =true_beta.columns) #logging.info(str(true_beta)) #logging.info(str(betas))