When I try to running survival randomforest model in mlr3 using codes as below:
data(veteran)
veteran.xgb <- veteran[, !(names(veteran) %in% c("celltype"))]
set.seed(1369)
train = sample(nrow(veteran.xgb), 0.5 * nrow(veteran.xgb))
test = setdiff(seq(nrow(veteran.xgb)), train)
task = TaskSurv$new("RFSCRC_VET", backend = veteran.xgb, time = "time", event = "trt")
learn = lrn("surv.rfsrc")
learn$train(task, row_ids = train)
And the errors happened, Error: pv$codes %??% -1L, and suggests that there is no %??% in R. Was it a bug?
Related
I am having an issue with Geopandas and PyProj.
I am loading a premade shp file from GeoPandas
borough = gpd.read_file(gpd.datasets.get_path('nybb'))
When trying to do a crs transformation with the following code:
borough = borough.to_crs({'init': 'epsg:4326'})
I get the error:
ProjError: Error creating Transformer from CRS.: (Internal Proj Error: proj_create_operations: SQLite error on SELECT source_crs_auth_name, source_crs_code, target_crs_auth_name, target_crs_code, cov.auth_name, cov.code, cov.table_name, area.south_lat, area.west_lon, area.north_lat, area.east_lon, ss.replacement_auth_name, ss.replacement_code FROM coordinate_operation_view cov JOIN area ON cov.area_of_use_auth_name = area.auth_name AND cov.area_of_use_code = area.code LEFT JOIN supersession ss ON ss.superseded_table_name = cov.table_name AND ss.superseded_auth_name = cov.auth_name AND ss.superseded_code = cov.code AND ss.superseded_table_name = ss.replacement_table_name AND ss.same_source_target_crs = 1 WHERE ((source_crs_auth_name = ? AND source_crs_code = ? AND target_crs_auth_name = ? AND target_crs_code = ?) OR (source_crs_auth_name = ? AND source_crs_code = ? AND target_crs_auth_name = ? AND target_crs_code = ?)) AND cov.deprecated = 0 AND cov.auth_name = ? ORDER BY pseudo_area_from_swne(south_lat, west_lon, north_lat, east_lon) DESC, (CASE WHEN accuracy is NULL THEN 1 ELSE 0 END), accuracy: no such column: ss.same_source_target_crs)
I really am totally clueless about what to do here.
PS: This is my first stackoverflow post, so I apologize in advanced for the poor layout of this post.
I have recently started exploring the QuantLib option pricing libraries for python and have come across an error that I don't seem to understand. Basically, I am trying to price an Up&Out Barrier option using the Heston model. The code that I have written has been taken from examples found online and adapted to my specific case. Essentially, the problem is that when I run the code below I get an error that I believe is triggered at the last line of the code, i.e. the european_option.NPV() function
*** RuntimeError: wrong argument type
Can someone please explain me what I am doing wrong?
# option inputs
maturity_date = ql.Date(30, 6, 2020)
spot_price = 969.74
strike_price = 1000
volatility = 0.20
dividend_rate = 0.0
option_type = ql.Option.Call
risk_free_rate = 0.0016
day_count = ql.Actual365Fixed()
calculation_date = ql.Date(26, 6, 2020)
ql.Settings.instance().evaluationDate = calculation_date
# construct the option payoff
european_option = ql.BarrierOption(ql.Barrier.UpOut, Barrier, Rebate,
ql.PlainVanillaPayoff(option_type, strike_price),
ql.EuropeanExercise(maturity_date))
# set the Heston parameters
v0 = volatility*volatility # spot variance
kappa = 0.1
theta = v0
hsigma = 0.1
rho = -0.75
spot_handle = ql.QuoteHandle(ql.SimpleQuote(spot_price))
# construct the Heston process
flat_ts = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
risk_free_rate, day_count))
dividend_yield = ql.YieldTermStructureHandle(ql.FlatForward(calculation_date,
dividend_rate, day_count))
heston_process = ql.HestonProcess(flat_ts, dividend_yield,
spot_handle, v0, kappa,
theta, hsigma, rho)
# run the pricing engine
engine = ql.AnalyticHestonEngine(ql.HestonModel(heston_process),0.01, 1000)
european_option.setPricingEngine(engine)
h_price = european_option.NPV()
The problem is that the AnalyticHestonEngine is not able to price Barrier options.
Check here https://www.quantlib.org/reference/group__barrierengines.html for a list of Barrier Option pricing engines.
Given that we could use self-defined metric in LightGBM and use parameter 'feval' to call it during training.
And for given metric, we could define it in the parameter dict like metric:(l1, l2)
My question is that how call several self-defined metric at the same time? I cannot use feval=(my_metric1, my_metric2) to get the result
params = {}
params['learning_rate'] = 0.003
params['boosting_type'] = 'goss'
params['objective'] = 'multiclassova'
params['metric'] = ['multi_error', 'multi_logloss']
params['sub_feature'] = 0.8
params['num_leaves'] = 15
params['min_data'] = 600
params['tree_learner'] = 'voting'
params['bagging_freq'] = 3
params['num_class'] = 3
params['max_depth'] = -1
params['max_bin'] = 512
params['verbose'] = -1
params['is_unbalance'] = True
evals_result = {}
aa = lgb.train(params,
d_train,
valid_sets=[d_train, d_dev],
evals_result=evals_result,
num_boost_round=4500,
feature_name=f_names,
verbose_eval=10,
categorical_feature = f_names,
learning_rates=lambda iter: (1 / (1 + decay_rate * iter)) * params['learning_rate'])
Lets' discuss on the code I share here. d_train is my training set. d_dev is my validation set (I have a different test set.) evals_result will record our multi_error and multi_logloss per iteration as a list. verbose_eval = 10 will make LightGBM print multi_error and multi_logloss of both training set and validation set at every 10 iterations. If you want to plot multi_error and multi_logloss as a graph:
lgb.plot_metric(evals_result, metric='multi_error')
plt.show()
lgb.plot_metric(evals_result, metric='multi_logloss')
plt.show()
You can find other useful functions from LightGBM documentation. If you can't find what you need, go to XGBoost documentation, a simple trick. If there is something missing, please do not hesitate to ask more.
I am trying to use Brightway's ParallelMonteCarloand MultiMonteCarloclass but have run into a KeyError. I am in a Brightway project with an LCI database:
In [1] bw.databases
Out [1] Brightway2 databases metadata with 2 objects:
biosphere3
ecoinvent 3_2 CutOff
Selecting an activity and a method:
In [2] db = bw.Database('ecoinvent 3_2 CutOff')
act = db.random()
method = ('CML 2001', 'climate change', 'GWP 100a')
My code is as follows:
In [3] ParallelMC_LCA = bw.ParallelMonteCarlo({act:1},
method = myMethod,
iterations=1000,
cpus=mp.cpu_count())
results = np.array(ParallelMC_LCA.calculate())
and
In [4] act1 = db.random()
act2 = db.random()
multiMC_LCA = bw.MultiMonteCarlo(demands = [{act1:1}, {act2:1}],
method = myMethod,
iterations = 10)
results = np.array(ParallelMC_LCA.calculate())
Both give me a KeyError: 'ecoinvent 3_2 CutOff'.
My question is: why?
This is a known issue due to differences in how multiprocessing works on Windows and Unix. Specifically, on Windows the project is not set correctly, causing a KeyError. As such, it isn't a Stack Overflow question.
I have made some changes to the lmer. It works as it should but I could not get rid of the warning message that pops when I run the program. I have added the following options which allows the program run without stopping but with the warning message. I believe it is the check.nobs.vs.rankZ = "warningSmall" part. How could I get rid of this, any suggestions? Thank you.
lmerControl(check.nobs.vs.nlev = "ignore",check.nobs.vs.rankZ =
"warningSmall",check.nlev.gtreq.5 = "ignore",check.nobs.vs.nRE="ignore",
check.rankX = c("ignore"),check.scaleX = "ignore",check.formula.LHS="ignore",
## convergence checking options
check.conv.grad = .makeCC("warning", tol = 1e-3, relTol = NULL),
check.conv.singular = .makeCC(action = "ignore", tol = 1e-4),
check.conv.hess = .makeCC(action = "warning", tol = 1e-6)
Warning Message from R:
Warning message:
In checkZrank(reTrms$Zt, n = n, control, nonSmall = 1e+06) :
number of observations (=300) <= rank(Z) (=300); the random-effects parameters and the
residual variance (or scale parameter) are probably unidentifiable
You should try check.nobs.vs.rankZ="ignore".
lmerControl doesn't need to specify anything other than the non-default options: at a quick glance, these are your non-default values:
lmerControl(check.nobs.vs.nlev = "ignore",check.nobs.vs.rankZ =
"ignore",check.nlev.gtreq.5 = "ignore",check.nobs.vs.nRE="ignore",
check.rankX = c("ignore"),
check.scaleX = "ignore",
check.formula.LHS="ignore",
check.conv.grad = .makeCC("warning", tol = 1e-3, relTol = NULL))
In general I would suggest it's wise to turn off only the specific warnings and errors you know you want to override -- the settings above look like they could get you in trouble.
I haven't checked this since you didn't give a reproducible example ...