# Data from CSV file workflow

**URL:** <https://openturns.discourse.group/t/data-from-csv-file-workflow/108>\
**Category:** Python usage\
**Created:** [April 9, 2021, 9:49am UTC](https://openturns.discourse.group/t/data-from-csv-file-workflow/108 "2021-04-09T09:49:45Z")\
**Posts on this page:** 1\
**Page:** 2

<div class="post-metadata">

**Author:** ![josephmure](https://yyz2.discourse-cdn.com/free1/user_avatar/openturns.discourse.group/josephmure/32/114_2.png) [@josephmure](https://openturns.discourse.group/u/josephmure)\
**Post date:** [May 10, 2021, 2:41pm UTC](https://openturns.discourse.group/t/data-from-csv-file-workflow/108/21 "2021-05-10T14:41:26Z")

</div>

You could use the [TensorizedCovarianceModel](https://openturns.github.io/openturns/1.17/user_manual/_generated/openturns.TensorizedCovarianceModel.html) class for that.

As far as optimization goes, it is best to do it one output at a time, because it makes the solver’s life easier.

However, after the optimization, you can get the optimized 1D covariance models and feed them to a `TensorizedCovarianceModel`.

## Implementation

At the start of your script, define an empty list:

```auto
list_covmodels = []

```

Inside your `for` loop, add the following line after `result = algo.getResult()`

```auto
    list_covmodels.append(result.getCovarianceModel())

```

After the loop, insert the following code:

```auto
# Create a TensorizedCovarianceModel from the list of covariance models
tencov = ot.TensorizedCovarianceModel(list_covmodels)

# Get the "marginal" sample corresponding to the outputs
y = sample.getMarginal(range(dim, sample.getDimension()))[0:Test_sample]

# Create basis
basis = ot.LinearBasisFactory(dim).build()

# Create the KrigingAlgorithm
algo = ot.KrigingAlgorithm(x, y, tencov, basis)

# Do NOT optimize the covariance parameters (they are already optimal!)
algo.setOptimizeParameters(False)

# Run the algorithm and get the result as before
algo.run()
result = algo.getResult()

# The kriging metamodel is multidimensional
krigingMetamodel = result.getMetaModel()

# Validate the metamodel as before
X_test = sample.getMarginal(range(0,dim))[Test_sample:]
Y_test = sample.getMarginal(range(dim, sample.getDimension()))[Test_sample:]
val = ot.MetaModelValidation(X_test, Y_test, krigingMetamodel)
Q2 = val.computePredictivityFactor()

```

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