"""
Kriging: metamodel with continuous and categorical variables
============================================================
"""
# %%
# We consider here the surrogate modeling of an analytical function characterized by
# continuous and categorical variables
#

# %%
import openturns as ot
import openturns.experimental as otexp
import numpy as np
import matplotlib.pyplot as plt

# Seed chosen in order to obtain a visually nice plot
ot.RandomGenerator.SetSeed(5)

# %%
# We first show the advantage of modeling the various levels of a mixed
# continuous / categorical function through a single surrogate model
# on a simple test-case taken from [pelamatti2020]_, defined below.


# %%
def illustrativeFunc(inp):
    x, z = inp
    y = np.cos(7 * x) + 0.5 * z
    return [y]


dim = 2
fun = ot.PythonFunction(dim, 1, illustrativeFunc)
numberOfZLevels = 2  # Number of categorical levels for z
# Input distribution
dist = ot.ComposedDistribution(
    [ot.Uniform(0, 1), ot.UserDefined(ot.Sample.BuildFromPoint(range(numberOfZLevels)))]
)

# %%
# In this example, we compare the performances of the :class:`~openturns.experimental.LatentVariableModel`
# with a naive approach, which would consist in modeling each combination of categorical
# variables through a separate and independent Gaussian process.

# %%
# In order to deal with mixed continuous / categorical problems we can rely on the
# :class:`~openturns.ProductCovarianceModel` class. We start here by defining the product kernel,
# which combines :class:`~openturns.SquaredExponential` kernels for the continuous variables, and
# :class:`~openturns.experimental.LatentVariableModel` for the categorical ones.

# %%
latDim = 1  # Dimension of the latent space
activeCoord = 1 + latDim * (
    numberOfZLevels - 2
)  # Nb of active coordinates in the latent space
kx = ot.SquaredExponential(1)
kz = otexp.LatentVariableModel(numberOfZLevels, latDim)
kLV = ot.ProductCovarianceModel([kx, kz])
kLV.setNuggetFactor(1e-6)
# Bounds for the hyperparameter optimization
lowerBoundLV = [1e-4] * dim + [-10.0] * activeCoord
upperBoundLV = [2.0] * dim + [10.0] * activeCoord
boundsLV = ot.Interval(lowerBoundLV, upperBoundLV)
# Distribution for the hyperparameters initialization
initDistLV = ot.DistributionCollection()
for i in range(len(lowerBoundLV)):
    initDistLV.add(ot.Uniform(lowerBoundLV[i], upperBoundLV[i]))
initDistLV = ot.ComposedDistribution(initDistLV)

# %%
# As a reference, we consider a purely continuous kernel for independent Gaussian processes.
# One for each combination of categorical variables levels.

# %%
kIndependent = ot.SquaredExponential(1)
lowerBoundInd = [1e-4]
upperBoundInd = [20.0]
boundsInd = ot.Interval(lowerBoundInd, upperBoundInd)
initDistInd = ot.DistributionCollection()
for i in range(len(lowerBoundInd)):
    initDistInd.add(ot.Uniform(lowerBoundInd[i], upperBoundInd[i]))
initDistInd = ot.ComposedDistribution(initDistInd)
initSampleInd = initDistInd.getSample(10)
optAlgInd = ot.MultiStart(ot.NLopt("LN_COBYLA"), initSampleInd)

# %%
# Generate the training data set
x = dist.getSample(10)
y = fun(x)

# And the plotting data set
xPlt = dist.getSample(200)
xPlt = xPlt.sort()
yPlt = fun(xPlt)

# %%
# Initialize  and parameterize the optimization algorithm
initSampleLV = initDistLV.getSample(30)
optAlgLV = ot.MultiStart(ot.NLopt("LN_COBYLA"), initSampleLV)

# %%
# Create and train the Gaussian process models
basis = ot.ConstantBasisFactory(2).build()
algoLV = ot.KrigingAlgorithm(x, y, kLV, basis)
algoLV.setOptimizationAlgorithm(optAlgLV)
algoLV.setOptimizationBounds(boundsLV)
algoLV.run()
resLV = algoLV.getResult()

algoIndependentList = []
for z in range(2):
    # Select the training samples corresponding to the correct combination
    # of categorical levels
    ind = np.where(np.all(np.array(x[:, 1]) == z, axis=1))[0]
    xLoc = x[ind][:, 0]
    yLoc = y[ind]

    # Create and train the Gaussian process models
    basis = ot.ConstantBasisFactory(1).build()
    algoIndependent = ot.KrigingAlgorithm(xLoc, yLoc, kIndependent, basis)
    algoIndependent.setOptimizationAlgorithm(optAlgInd)
    algoIndependent.setOptimizationBounds(boundsInd)
    algoIndependent.run()
    algoIndependentList.append(algoIndependent.getResult())

# %%
# Plot the prediction of the mixed continuous / categorical GP,
# as well as the one of the two separate continuous GPs
fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(15, 10))
for z in range(numberOfZLevels):
    # Select the training samples corresponding to the correct combination
    # of categorical levels
    ind = np.where(np.all(np.array(x[:, 1]) == z, axis=1))[0]
    xLoc = x[ind][:, 0]
    yLoc = y[ind]

    # Compute the models predictive performances on a validation data set.
    # The predictions are computed independently for each level of z,
    # i.e., by only considering the values of z corresponding to the
    # target level.
    ind = np.where(np.all(np.array(xPlt[:, 1]) == z, axis=1))[0]
    xPltInd = xPlt[ind]
    yPltInd = yPlt[ind]

    predMeanLV = resLV.getConditionalMean(xPltInd)
    predMeanInd = algoIndependentList[z].getConditionalMean(xPltInd[:, 0])
    predSTDLV = np.sqrt(resLV.getConditionalMarginalVariance(xPltInd))
    predSTDInd = np.sqrt(
        algoIndependentList[z].getConditionalMarginalVariance(xPltInd[:, 0])
    )

    (trainingData,) = ax1.plot(xLoc[:, 0], yLoc, "r*")
    (trueFunction,) = ax1.plot(xPltInd[:, 0], yPltInd, "k--")
    (prediction,) = ax1.plot(xPltInd[:, 0], predMeanLV, "b-")
    stdPred = ax1.fill_between(
        xPltInd[:, 0].asPoint(),
        (predMeanLV - predSTDLV).asPoint(),
        (predMeanLV + predSTDLV).asPoint(),
        alpha=0.5,
        color="blue",
    )
    ax2.plot(xLoc[:, 0], yLoc, "r*")
    ax2.plot(xPltInd[:, 0], yPltInd, "k--")
    ax2.plot(xPltInd[:, 0], predMeanInd, "b-")
    ax2.fill_between(
        xPltInd[:, 0].asPoint(),
        (predMeanInd - predSTDInd).asPoint(),
        (predMeanInd + predSTDInd).asPoint(),
        alpha=0.5,
        color="blue",
    )
ax1.legend(
    [trainingData, trueFunction, prediction, stdPred],
    ["Training data", "True function", "Prediction", "Prediction standard deviation"],
)
ax1.set_title("Mixed continuous-categorical modeling")
ax2.set_title("Separate modeling")
ax2.set_xlabel("x", fontsize=15)
ax1.set_ylabel("y", fontsize=15)
ax2.set_ylabel("y", fontsize=15)

# %%
# It can be seen that the joint modeling of categorical and continuous variables
# improves the overall prediction accuracy, as the Gaussian process model is
# able to exploit the information provided by the entire training data set.

# %%
# We now consider a more complex function which is a modified version of the Goldstein function,
# taken from [pelamatti2020]_. This function depends on 2 continuous variables and 2 categorical ones.
# Each categorical variable is characterized by 3 levels.


# %%
def h(x1, x2, x3, x4):
    y = (
        53.3108
        + 0.184901 * x1
        - 5.02914 * x1**3 * 1e-6
        + 7.72522 * x1**4 * 1e-8
        - 0.0870775 * x2
        - 0.106959 * x3
        + 7.98772 * x3**3 * 1e-6
        + 0.00242482 * x4
        + 1.32851 * x4**3 * 1e-6 * 0.00146393 * x1 * x2
        - 0.00301588 * x1 * x3
        - 0.00272291 * x1 * x4
        + 0.0017004 * x2 * x3
        + 0.0038428 * x2 * x4
        - 0.000198969 * x3 * x4
        + 1.86025 * x1 * x2 * x3 * 1e-5
        - 1.88719 * x1 * x2 * x4 * 1e-6
        + 2.50923 * x1 * x3 * x4 * 1e-5
        - 5.62199 * x2 * x3 * x4 * 1e-5
    )
    return y


def Goldstein(inp):
    x1, x2, z1, z2 = inp
    x1 = 100 * x1
    x2 = 100 * x2

    if z1 == 0:
        x3 = 80
    elif z1 == 1:
        x3 = 20
    elif z1 == 2:
        x3 = 50
    else:
        print("error, no matching category z1")

    if z2 == 0:
        x4 = 20
    elif z2 == 1:
        x4 = 80
    elif z2 == 2:
        x4 = 50
    else:
        print("error, no matching category z2")

    return [h(x1, x2, x3, x4)]


dim = 4
fun = ot.PythonFunction(dim, 1, Goldstein)
numberOfZLevels1 = 3  # Number of categorical levels for z1
numberOfZLevels2 = 3  # Number of categorical levels for z2
# Input distribution
dist = ot.ComposedDistribution(
    [
        ot.Uniform(0, 1),
        ot.Uniform(0, 1),
        ot.UserDefined(ot.Sample.BuildFromPoint(range(numberOfZLevels1))),
        ot.UserDefined(ot.Sample.BuildFromPoint(range(numberOfZLevels2))),
    ]
)

# %%
# As in the previous example, we start here by defining the product kernel,
# which combines :class:`~openturns.SquaredExponential` kernels for the continuous variables, and
# :class:`~openturns.experimental.LatentVariableModel` for the categorical ones.

# %%
latDim = 2  # Dimension of the latent space
activeCoord = (
    2 + latDim * (numberOfZLevels1 - 2) + latDim * (numberOfZLevels2 - 2)
)  # Nb ative coordinates in the latent space
kx1 = ot.SquaredExponential(1)
kx2 = ot.SquaredExponential(1)
kz1 = otexp.LatentVariableModel(numberOfZLevels1, latDim)
kz2 = otexp.LatentVariableModel(numberOfZLevels2, latDim)
kLV = ot.ProductCovarianceModel([kx1, kx2, kz1, kz2])
kLV.setNuggetFactor(1e-6)
# Bounds for the hyperparameter optimization
lowerBoundLV = [1e-4] * dim + [-10] * activeCoord
upperBoundLV = [3.0] * dim + [10.0] * activeCoord
boundsLV = ot.Interval(lowerBoundLV, upperBoundLV)
# Distribution for the hyperparameters initialization
initDistLV = ot.DistributionCollection()
for i in range(len(lowerBoundLV)):
    initDistLV.add(ot.Uniform(lowerBoundLV[i], upperBoundLV[i]))
initDistLV = ot.ComposedDistribution(initDistLV)

# %%
# Alternatively, we consider a purely continuous kernel for independent Gaussian processes.
# one for each combination of categorical variables levels.

# %%
kIndependent = ot.SquaredExponential(2)
lowerBoundInd = [1e-4, 1e-4]
upperBoundInd = [3.0, 3.0]
boundsInd = ot.Interval(lowerBoundInd, upperBoundInd)
initDistInd = ot.DistributionCollection()
for i in range(len(lowerBoundInd)):
    initDistInd.add(ot.Uniform(lowerBoundInd[i], upperBoundInd[i]))
initDistInd = ot.ComposedDistribution(initDistInd)
initSampleInd = initDistInd.getSample(10)
optAlgInd = ot.MultiStart(ot.NLopt("LN_COBYLA"), initSampleInd)

# %%
# In order to assess their respective robustness with regards to the training data set,
# we repeat the experiments 10 times with different training of size 72,
# and compute each time the normalized prediction Root Mean Squared Error (RMSE) on a
# test data set of size 1000.

# %%
rmseLVList = []
rmseIndList = []
for rep in range(5):
    # Generate the normalized training data set
    x = dist.getSample(72)
    y = fun(x)
    yMax = y.getMax()
    yMin = y.getMin()
    y = (y - yMin) / (yMin - yMax)

    # Initialize  and parameterize the optimization algorithm
    initSampleLV = initDistLV.getSample(10)
    optAlgLV = ot.MultiStart(ot.NLopt("LN_COBYLA"), initSampleLV)

    # Create and train the Gaussian process models
    basis = ot.ConstantBasisFactory(dim).build()
    algoLV = ot.KrigingAlgorithm(x, y, kLV, basis)
    algoLV.setOptimizationAlgorithm(optAlgLV)
    algoLV.setOptimizationBounds(boundsLV)
    algoLV.run()
    resLV = algoLV.getResult()

    # Compute the models predictive performances on a validation data set
    xVal = dist.getSample(1000)
    yVal = fun(xVal)
    yVal = (yVal - yMin) / (yMin - yMax)

    valLV = ot.MetaModelValidation(xVal, yVal, resLV.getMetaModel())
    rmseLV = valLV.getResidualSample().computeStandardDeviation()[0]
    rmseLVList.append(rmseLV)

    error = ot.Sample(0, 1)
    for z1 in range(numberOfZLevels1):
        for z2 in range(numberOfZLevels2):
            # Select the training samples corresponding to the correct combination
            # of categorical levels
            ind = np.where(np.all(np.array(x[:, 2:]) == [z1, z2], axis=1))[0]
            xLoc = x[ind][:, :2]
            yLoc = y[ind]

            # Create and train the Gaussian process models
            basis = ot.ConstantBasisFactory(2).build()
            algoIndependent = ot.KrigingAlgorithm(xLoc, yLoc, kIndependent, basis)
            algoIndependent.setOptimizationAlgorithm(optAlgInd)
            algoIndependent.setOptimizationBounds(boundsInd)
            algoIndependent.run()
            resInd = algoIndependent.getResult()

            # Compute the models predictive performances on a validation data set
            ind = np.where(np.all(np.array(xVal[:, 2:]) == [z1, z2], axis=1))[0]
            xValInd = xVal[ind][:, :2]
            yValInd = yVal[ind]
            valInd = ot.MetaModelValidation(xValInd, yValInd, resInd.getMetaModel())
            error.add(valInd.getResidualSample())
    rmseInd = error.computeStandardDeviation()[0]
    rmseIndList.append(rmseInd)

plt.figure()
plt.boxplot([rmseLVList, rmseIndList])
plt.xticks([1, 2], ["Mixed continuous-categorical GP", "Independent GPs"])
plt.ylabel("RMSE")

# %%
# The obtained results show, for this test-case, a better modeling performance
# when modeling the function as a mixed categorical/continuous function, rather
# than relying on multiple purely continuous Gaussian processes.
