"""
Perform stepwise regression
===========================
In this example we perform the selection of the most suitable function basis for a linear regression model with the help of the stepwise algorithm.

We consider te so-called Linthurst data set, which contains measures of aerial biomass (BIO) as well as 5 five physicochemical properties of the soil: salinity (SAL), pH, K, Na, and Zn.

The data set is taken from the book [rawlings2001]_
and is provided below:
"""

# %%
import openturns as ot
from openturns.viewer import View
import numpy as np
import matplotlib.pyplot as plt
from openturns.usecases import linthurst

# %%
# We define the data.
#

# %%
ds = linthurst.Linthurst()
dimension = ds.data.getDimension() - 1

input_sample = ds.data[:, 1: dimension + 1]
print("Input :")
print(input_sample[:5])
output_sample = ds.data[:, 0]
print("Output :")
print(output_sample[:5])

input_description = input_sample.getDescription()
output_description = output_sample.getDescription()

n = input_sample.getSize()
print("n = ", n)
dimension = ds.data.getDimension() - 1
print("dimension = ", dimension)


# %%
# Complete linear model
# ---------------------
#
# We consider a linear model with the purpose of predicting the aerial biomass as a function of the soil physicochemical properties,
# and we wish to identify the predictive variables which result in the most simple and precise linear regression model.
#
# We start by creating a linear model which takes into account all of the physicochemical variables present within the Linthrust data set.
#
# Let us consider the following linear model :math:`\tilde{Y} = a_0 + \sum_{i = 1}^{d} a_i X_i + \epsilon`. If all of the predictive variables
# are considered, the regression can be performed with the help of the :class:`~openturns.LinearModelAlgorithm` class.

# %%
algo_full = ot.LinearModelAlgorithm(input_sample, output_sample)
algo_full.run()
result_full = ot.LinearModelResult(algo_full.getResult())
print("R-squared = ", result_full.getRSquared())
print("Adjusted R-squared = ", result_full.getAdjustedRSquared())

# %%
# Forward stepwise regression
# ---------------------------
#
# We now wish to perform the selection of the most important predictive variables through a stepwise algorithm.
#
# It is first necessary to define a suitable function basis for the regression. Each variable is associated to a univariate basis
# and an additional basis is used in order to represent the constant term :math:`a_0`.

# %%
functions = []
functions.append(ot.SymbolicFunction(input_description, ["1.0"]))
for i in range(dimension):
    functions.append(ot.SymbolicFunction(input_description, [input_description[i]]))
basis = ot.Basis(functions)
# %%
# Plese note that this example uses a linear basis with respect to the various predictors for the sake of clarity.
# However, this is not a necessity, and more complex and non linear relations between predictors may be considered
# (e.g., polynomial bases).


# %%
# We now perform a forward stepwise regression. We suppose having no information regarding the given data set, and therefore the set of minimal
# indices only contains the constant term (indexed by 0).
#
# The first regression is performed by relying on the Akaike Information Criterion (AIC), which translates into a penalty term equal to 2.
# In practice, the algorithm selects the functional basis subset that minimizes the AIC by iteratively adding the single function which provides
# the largest improvement until convergence is reached.

# %%
minimalIndices = [0]
direction = ot.LinearModelStepwiseAlgorithm.FORWARD
penalty = 2.0
algo_forward = ot.LinearModelStepwiseAlgorithm(
    input_sample, output_sample, basis, minimalIndices, direction
)
algo_forward.setPenalty(penalty)
algo_forward.run()
result_forward = algo_forward.getResult()
print("Selected basis: ", result_forward.getCoefficientsNames())
print("R-squared = ", result_forward.getRSquared())
print("Adjusted R-squared = ", result_forward.getAdjustedRSquared())

# %%
# With this first forward stepwise regression, the results show that the selected optimal basis contains a constant term,
# plus two linear terms depending respectively on the pH value (pH) and on the sodium concentration (Na).
#
# As can be expected, the R-squared value diminishes if compared to the regression on the entire basis, as the stepwise
# regression results in a lower number of predictive variables.  However, it can also be seen that the adjusted R-squared,
# which is a metric that also takes into account the ratio  between the amount of training data and the number of explanatory
# variables, is improved if compared to the complete model.

# %%
# Backward stepwise regression
# ----------------------------
#
# We now perform a backward stepwise regression, meaning that rather than iteratively adding predictive variables, we will be removing them,
# starting from the complete model.
# This regression is performed by relying on the Bayesian Information Criterion (BIC), which translates into a penalty term equal to :math:`log(n)`.

# %%
minimalIndices = [0]
direction = ot.LinearModelStepwiseAlgorithm.BACKWARD
penalty = np.log(n)
algo_backward = ot.LinearModelStepwiseAlgorithm(
    input_sample, output_sample, basis, minimalIndices, direction
)
algo_backward.setPenalty(penalty)
algo_backward.run()
result_backward = algo_backward.getResult()
print("Selected basis: ", result_backward.getCoefficientsNames())
print("R-squared = ", result_backward.getRSquared())
print("Adjusted R-squared = ", result_backward.getAdjustedRSquared())

# %%
# It is interesting to point out that although both approaches converge towards a model characterized by 2 predictive variables,
# the selected variables do not coincide.

# %%
# Both directions stepwise regression
# -----------------------------------
#
# A third available option consists in performing a stepwise regression in both directions, meaning that at each iteration the predictive variables
# can be either added or removed. In this case, a set of starting indices must be provided to the algorithm.

# %%
minimalIndices = [0]
startIndices = [0, 2, 3]
penalty = np.log(n)
direction = ot.LinearModelStepwiseAlgorithm.BOTH
algo_both = ot.LinearModelStepwiseAlgorithm(
    input_sample, output_sample, basis, minimalIndices, direction, startIndices
)
algo_both.setPenalty(penalty)
algo_both.run()
result_both = algo_both.getResult()
print("Selected basis: ", result_both.getCoefficientsNames())
print("R-squared = ", result_both.getRSquared())
print("Adjusted R-squared = ", result_both.getAdjustedRSquared())

# %%
# It is interesting to note that the basis varies depending on the selected set of starting indices, as is shown below.
# An informed initialization might therefore improve the model selection and the resulting regression

# %%
minimalIndices = [0]
startIndices = [0, 1]
penalty = np.log(n)
direction = ot.LinearModelStepwiseAlgorithm.BOTH
algo_both = ot.LinearModelStepwiseAlgorithm(
    input_sample, output_sample, basis, minimalIndices, direction, startIndices
)
algo_both.setPenalty(penalty)
algo_both.run()
result_both = algo_both.getResult()
print("Selected basis: ", result_both.getCoefficientsNames())
print("R-squared = ", result_both.getRSquared())
print("Adjusted R-squared = ", result_both.getAdjustedRSquared())

# %%
# Graphical analyses
# ------------------
#
# Finally, we can rely on the LinearModelAnalysis class in order to analyse
# the predictive differences between the obtained models.

# %%
analysis_full = ot.LinearModelAnalysis(result_full)
analysis_full.setName("Full model")
analysis_forward = ot.LinearModelAnalysis(result_forward)
analysis_forward.setName("Forward selection")
analysis_backward = ot.LinearModelAnalysis(result_backward)
analysis_backward.setName("Backward selection")
fig = plt.figure(figsize=(12, 8))
for k, analysis in enumerate([analysis_full, analysis_forward, analysis_backward]):
    graph = analysis.drawModelVsFitted()
    ax = fig.add_subplot(3, 1, k + 1)
    ax.set_title(analysis.getName(), fontdict={"fontsize": 16})
    graph.setXTitle("Exact values")
    ax.xaxis.label.set_size(12)
    ax.yaxis.label.set_size(14)
    graph.setTitle("")
    v = View(graph, figure=fig, axes=[ax])
plt.tight_layout()


# %%
# For illustrative purposes, we show the Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) values
# which are computed during the iterations of the forward step-wise regression. Please note that in order to do
# so, we set the penalty parameter to a negligible value (meaning that the basis selection only takes into account the model likelihood,
# and not the number of parameters characterizing the linear model).

# %%
minimalIndices = [0]
penalty = 1e-10
direction = ot.LinearModelStepwiseAlgorithm.FORWARD

BIC = []
AIC = []
for iterations in range(1, 6):
    algo_forward = ot.LinearModelStepwiseAlgorithm(
        input_sample, output_sample, basis, minimalIndices, direction
    )
    algo_forward.setPenalty(penalty)
    algo_forward.setMaximumIterationNumber(iterations)
    algo_forward.run()
    result_forward = algo_forward.getResult()

    RSS = np.sum(
        np.array(result_forward.getSampleResiduals()) ** 2
    )  # Residual sum of squares
    LL = n * np.log(RSS / n)  # Log-likelihood
    BIC.append(LL + iterations * np.log(n))  # Bayesian Information Criterion
    AIC.append(LL + iterations * 2)  # Akaike Information Criterion
    print("Selected basis: ", result_forward.getCoefficientsNames())


plt.figure()
plt.plot(np.arange(1, 6), BIC, label="BIC")
plt.plot(np.arange(1, 6), AIC, label="AIC")
plt.xticks(np.arange(1, 6))
plt.xlabel("Basis size", fontsize=14)
plt.ylabel("Information criterion", fontsize=14)
plt.legend(fontsize=14)
plt.tight_layout()

# %%
# The graphic above shows that the optimal linear model in terms of compromise between prediction likelihood and model complexity
# should take into account the influence of 2 regession variables as well as the constant term. This is coherent with the results previously
# obtained
