Let's say I have a dataset that has 9 continuous columns of data and 4 columns of categorical data. In Matlab, I separate the columns into two groups and do training/testing (naïve bayes) on them separately and determine that the continuous columns have an error rate of 0.45 and the categorical columns have an error 0.33. My question is - how do I determine the combined error?
EDIT - Simple pseudocode overview added:
for x = 1:num_iterations
Mdl_NB1 = fitcnb(TrainingSet_Con,TrainingTargets,'Distribution','normal');
Mdl_NB2 = fitcnb(TrainingSet_Dis,TrainingTargets,'Distribution','mn');
[NB1_label,NB1_Posterior,NB1_Cost] = predict(Mdl_NB1,TestPoint_Con);
[NB2_label,NB2_Posterior,NB2_Cost] = predict(Mdl_NB2,TestPoint_Dis);
NB1_cumulLoss = NB1_cumulLoss + resubLoss(Mdl_NB1);
NB2_cumulLoss = NB2_cumulLoss + resubLoss(Mdl_NB2);
end
NB1_avg_score = NB1_cumulLoss/num_iterations
NB2_avg_score = NB2_cumulLoss/num_iterations
total_avg_score = ???
The three obvious choices, in principle, are:
(A+B) / 2
A * B
(A*(CountA/TotalCount)) + (B*(CountB/TotalCount))
But not sure if any of these are right, in this case.
This does not make sense; you are effectively building two separate models. So either build one model with all columns (maybe with 'Distribution','mvmn') or combine both models into one with something like
Mdl_Ens = fitcnb([NB1_Posterior; NB2_Posterior],TrainingTargets,'Distribution','normal');
NEns_cumulLoss = NEns_cumulLoss + resubLoss(Mdl_Ens);
to actually build a single model out of the output of the two models based on a subset of the columns each.
Related
When trying to create a table with the conditional random effects in r using the gtsummary function tbl_regression from a glmmTMB mixed effects negative-binomial zero-inflated model, I get duplicate random effects rows.
Example (using Mollie Brooks' Zero-Inflated GLMMs on Salamanders Dataset):
data(Salamanders)
head(Salamanders)
library(glmmTMB)
zinbm2 = glmmTMB(count~spp + mined +(1|site), zi=~spp + mined + (1|site), Salamanders, family=nbinom2)
zinbm2_table_cond <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "cond"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_cond
Output:
Random Effects Output (cond)
When extracting the random effects from de zero-inflated part of the model I get the same problem.
Example:
zinbm2_table_zi <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "zi"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_zi
Output:
Random Effects Output (zi)
The problem persists if I specify the effects argument in broom.mixed.
tidy_fun = function(...) broom.mixed::tidy(..., effects = "ran_pars", component = "cond"),
Looking at confidence intervals in both outputs it seems that somehow it is extracting random effects from both parts of the model and changing the estimate of the zero-inflated random effects (in 1st image; opposite in the 2nd image) to match the conditional part estimate while keeping the CI.
I am not knowledgeable enough to understand why this is happening. Since both rows have the same label I am having difficulty removing the wrong one.
Any tips on how to avoid this problem or a workaround to remove the undesired rows?
If you need more info, let me know.
Thank you in advance.
PS: Output images were changed to link due to insufficient reputation.
I have a data set that contains the following columns: outcome (this is the outcome that we want to predict), and raw (a column that consists of text). I want to develop an ML model that will predict the outcome from the raw column. I have trained an ML model in Databricks using the following pipeline:
regexTokenizer = RegexTokenizer(inputCol="raw", outputCol="words", pattern="\\W")
countVec = CountVectorizer(inputCol="words", outputCol="features")
indexer = StringIndexer(inputCol="outcome", outputCol="label").setHandleInvalid("skip").fit(trainDF)
inverter = IndexToString(inputCol="prediction", outputCol="prediction_label", labels=indexer.labels)
nb = NaiveBayes(labelCol="label", featuresCol="features", smoothing=1.0, modelType="multinomial")
pipeline = Pipeline(stages=[regexTokenizer, indexer, countVec, nb, inverter])
model = pipeline.fit(trainDF)
model.write().overwrite().save("/FileStore/project")
In another notebook, I load the model and try to predict the values for a new data set. This data set does not contain the outcome variable ("outcome" in this case):
model = PipelineModel.load("/FileStore/project")
score_output_df = model.transform(score_this)
When I try to predict the values for the new data set, I get an error message that the column "outcome" cannot be found. I suspect that this is due to the fact that some stages in the pipeline transform this column (the indexer and inverter stages are used to convert the outcome column to numbers and then back to string labels.).
My question is this, how can I load a saved model and use it to predict values when the original pipeline contains stages that have this column as an input.
instead of using
model.write().overwrite().save("/FileStore/project")
you have to write it like this
model.write().overwrite().save("/FileStore/project/model.sav")
and then for loading you will use this
model = PipelineModel.load("/FileStore/project/model.sav")
score_output_df = model.transform(score_this)
I have found a solution to the problem and will post it here so that if someone faces the same problem they can benefit from it. The solution was simply to extract the stages that I want to use in the prediction and save them to the model as such:
model = PipelineModel.load("/FileStore/project")
stages1 = []
stages1 += [model.stages[0]]
stages1 += [model.stages[2]]
stages1 += [model.stages[3]]
stages1 += [model.stages[4]]
model.stages = stages1
score_output_df = model.transform(score_this)
In this code, I exclude the second step ([1]) because it contains the indexer. Once I do this, I can predict values when the "outcome" column is not available.
I am trying to fir a partial db-RDA with field.ID to correct for the repeated measurements character of the samples. However including Condition(field.ID) leads to Disappearance of the centroids of the main factor of interest from the plot (left plot below).
The Design: 12 fields have been sampled for species data in two consecutive years, repeatedly. Additionally every year 3 samples from reference fields have been sampled. These three fields have been changed in the second year, due to unavailability of the former fields.
Additionally some environmental variables have been sampled (Nitrogen, Soil moisture, Temperature). Every field has an identifier (field.ID).
Using field.ID as Condition seem to erroneously remove the F1 factor. However using Sampling campaign (SC) as Condition does not. Is the latter the rigth way to correct for repeated measurments in partial db-RDA??
set.seed(1234)
df.exp <- data.frame(field.ID = factor(c(1:12,13,14,15,1:12,16,17,18)),
SC = factor(rep(c(1,2), each=15)),
F1 = factor(rep(rep(c("A","B","C","D","E"),each=3),2)),
Nitrogen = rnorm(30,mean=0.16, sd=0.07),
Temp = rnorm(30,mean=13.5, sd=3.9),
Moist = rnorm(30,mean=19.4, sd=5.8))
df.rsp <- data.frame(Spec1 = rpois(30, 5),
Spec2 = rpois(30,1),
Spec3 = rpois(30,4.5),
Spec4 = rpois(30,3),
Spec5 = rpois(30,7),
Spec6 = rpois(30,7),
Spec7 = rpois(30,5))
data=cbind(df.exp, df.rsp)
dbRDA <- capscale(df.rsp ~ F1 + Nitrogen + Temp + Moist + Condition(SC), df.exp); ordiplot(dbRDA)
dbRDA <- capscale(df.rsp ~ F1 + Nitrogen + Temp + Moist + Condition(field.ID), df.exp); ordiplot(dbRDA)
You partial out variation due to ID and then you try to explain variable aliased to this ID, but it was already partialled out. The key line in the printed output was this:
Some constraints were aliased because they were collinear (redundant)
And indeed, when you ask for details, you get
> alias(dbRDA, names=TRUE)
[1] "F1B" "F1C" "F1D" "F1E"
The F1? variables were constant within ID which already was partialled out, and nothing was left to explain.
From a Monte-Carlo simulation I have a range of files, say: file_1.mat, file_2.mat,...,file_n.mat, where n is large. Each file contains one or several (maximum 3 if it matters) large 1D arrays in time of interest, say var1, var2, var3.
I am now as always interested in finding the mean value of these variables. My question is now, how do I do this in the most efficient way? The keyword here is efficiency. Below you will find the MWE which is done the standard way, but this is quite time consuming as the files are large and there are many.
I am programming in Matlab, however ideas presented in pseudo code is also very well received.
MWE:(The standard way)
meanVar1 = zeros(1,1e6); %I do not remember the exact size, just use 1e6
meanVar2 = zeros(1,1e6);
meanVar3 = zeros(1,1e6);
for i 1=1:n
load(strcat('file_',int2str(i)),'var1','var2','var3')
meanVar1 = meanVar1 + var1;
meanVar2 = meanVar2 + var2;
meanVar3 = meanVar3 + var3;
end
meanVar1 = meanVar1/n;
meanVar2 = meanVar2/n;
meanVar3 = meanVar3/n;
I am new to Mathematica and I am having difficulties with one thing. I have this Table that generates 10 000 times 13 numbers (12 numbers + 1 that is a starting number). I need to create a Histogram from all 10 000 13th numbers. I hope It's quite clear, quite tricky to explain.
This is the table:
F = Table[(Xi = RandomVariate[NormalDistribution[], 12];
Mu = -0.00644131;
Sigma = 0.0562005;
t = 1/12; s = 0.6416;
FoldList[(#1*Exp[(Mu - Sigma^2/2)*t + Sigma*Sqrt[t]*#2]) &, s,
Xi]), {SeedRandom[2]; 10000}]
The result for the following histogram could be a table that will take all the 13th numbers to one table - than It would be quite easy to create an histogram. Maybe with "select"? Or maybe you know other ways to solve this.
You can access different parts of a list using Part or (depending on what parts you need) some of the more specialised commands, such as First, Rest, Most and (the one you need) Last. As noted in comments, Histogram[Last/#F] or Histogram[F[[All,-1]]] will work fine.
Although it wasn't part of your question, I would like to note some things you could do for your specific problem that will speed it up enormously. You are defining Mu, Sigma etc 10,000 times, because they are inside the Table command. You are also recalculating Mu - Sigma^2/2)*t + Sigma*Sqrt[t] 120,000 times, even though it is a constant, because you have it inside the FoldList inside the Table.
On my machine:
F = Table[(Xi = RandomVariate[NormalDistribution[], 12];
Mu = -0.00644131;
Sigma = 0.0562005;
t = 1/12; s = 0.6416;
FoldList[(#1*Exp[(Mu - Sigma^2/2)*t + Sigma*Sqrt[t]*#2]) &, s,
Xi]), {SeedRandom[2]; 10000}]; // Timing
{4.19049, Null}
This alternative is ten times faster:
F = Module[{Xi, beta}, With[{Mu = -0.00644131, Sigma = 0.0562005,
t = 1/12, s = 0.6416},
beta = (Mu - Sigma^2/2)*t + Sigma*Sqrt[t];
Table[(Xi = RandomVariate[NormalDistribution[], 12];
FoldList[(#1*Exp[beta*#2]) &, s, Xi]), {SeedRandom[2];
10000}] ]]; // Timing
{0.403365, Null}
I use With for the local constants and Module for the things that are other redefined within the Table (Xi) or are calculations based on the local constants (beta). This question on the Mathematica StackExchange will help explain when to use Module versus Block versus With. (I encourage you to explore the Mathematica StackExchange further, as this is where most of the Mathematica experts are hanging out now.)
For your specific code, the use of Part isn't really required. Instead of using FoldList, just use Fold. It only retains the final number in the folding, which is identical to the last number in the output of FoldList. So you could try:
FF = Module[{Xi, beta}, With[{Mu = -0.00644131, Sigma = 0.0562005,
t = 1/12, s = 0.6416},
beta = (Mu - Sigma^2/2)*t + Sigma*Sqrt[t];
Table[(Xi = RandomVariate[NormalDistribution[], 12];
Fold[(#1*Exp[beta*#2]) &, s, Xi]), {SeedRandom[2];
10000}] ]];
Histogram[FF]
Calculating FF in this way is even a little faster than the previous version. On my system Timing reports 0.377 seconds - but such a difference from 0.4 seconds is hardly worth worrying about.
Because you are setting the seed with SeedRandom, it is easy to verify that all three code examples produce exactly the same results.
Making my comment an answer:
Histogram[Last /# F]