import logging

import numpy as np
from sklearn.cluster import MiniBatchKMeans
from sklearn.utils import check_random_state

from csrank.discretechoice.likelihoods import create_weight_dictionary
from csrank.discretechoice.likelihoods import fit_pymc3_model
from csrank.learner import Learner
import csrank.numpy_util as npu
import csrank.theano_util as ttu
from csrank.util import print_dictionary
from .discrete_choice import DiscreteObjectChooser
from .likelihoods import likelihood_dict
from .likelihoods import LogLikelihood

try:
import pymc3 as pm
from pymc3.variational.callbacks import CheckParametersConvergence
except ImportError:
from csrank.util import MissingExtraError

raise MissingExtraError("pymc3", "probabilistic")

try:
import theano
from theano import tensor as tt
except ImportError:
from csrank.util import MissingExtraError

raise MissingExtraError("theano", "probabilistic")

class NestedLogitModel(DiscreteObjectChooser, Learner):
def __init__(
self,
n_nests=None,
loss_function="",
regularization="l1",
alpha=1e-2,
random_state=None,
**kwd,
):
"""
Create an instance of the Nested Logit model for learning the discrete choice function. This model divides
objects into disjoint subsets called nests,such that the objects which are similar to each other are in same
nest. This model structure is 1-layer of hierarchy and the :math:\\lambda for each nest :math:B_k signifies
the degree of independence and  :math:1-\\lambda signifies the correlations between the object in it. We
learn two weight vectors and the  :math:\\lambda s.

The probability of choosing an object :math:x_i from the given query set :math:Q is defined by product
of choosing the nest in which :math:x_i exists and then choosing the the object from the nest.

.. math::

P(x_i \\lvert Q) = P_i = P_{i \\lvert B_k} P_{B_k} \\enspace ,

The discrete choice for the given query set :math:Q is defined as:

.. math::

dc(Q) := \\operatorname{argmax}_{x_i \\in Q }  \\; P(x_i \\lvert Q)

Parameters
----------
n_nests : int range : [2,n_objects/2]
The number of nests/subsets in which the objects are divided.
This may not surpass half the amount of objects this model will
be trained on.
loss_function : string , {‘categorical_crossentropy’, ‘binary_crossentropy’, ’categorical_hinge’}
Loss function to be used for the discrete choice decision from the query set
regularization : string, {‘l1’, ‘l2’}, string
Regularizer function (L1 or L2) applied to the kernel weights matrix
alpha: float (range : [0,1])
The lower bound of the correlations between the objects in a nest
random_state : int or object
Numpy random state
**kwargs
Keyword arguments for the algorithms

References
----------
[1] Kenneth E Train. „Discrete choice methods with simulation“. In: Cambridge university press, 2009. Chap GEV, pp. 87–111.

[2] Kenneth Train. Qualitative choice analysis. Cambridge, MA: MIT Press, 1986

[3] Kenneth Train and Daniel McFadden. „The goods/leisure tradeoff and disaggregate work trip mode choice models“. In: Transportation research 12.5 (1978), pp. 349–353
"""
self.logger = logging.getLogger(NestedLogitModel.__name__)
self.n_nests = n_nests
self.alpha = alpha
self.random_state = random_state
self.loss_function = loss_function
known_regularization_functions = {"l1", "l2"}
if regularization not in known_regularization_functions:
raise ValueError(
f"Regularization function {regularization} is unknown. Must be one of {known_regularization_functions}"
)
self.regularization = regularization
self._config = None
self.cluster_model = None
self.features_nests = None
self.model = None
self.trace = None
self.trace_vi = None
self.Xt = None
self.Yt = None
self.p = None
self.y_nests = None
self.threshold = 5e6

@property
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Weights to evaluates the utility of the objects
* **weights_k** : Weights to evaluates the utility of the nests

For l1 regularization the priors are:

.. math::

\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)

For l2 regularization the priors are:

.. math::

\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)

Returns
-------
configuration : dict
Dictionary containing the priors applies on the weights
"""
if self._config is None:
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
self._config = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
"weights_k": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
}
self.logger.info(
"Creating model with config {}".format(print_dictionary(self._config))
)
return self._config

def create_nests(self, X):
"""
For allocating the objects to different nests we use the clustering algorithm with number of clusters
:math:k and allocate the similar objects in query set :math:Q.

Parameters
----------
X : numpy array
(n_instances, n_objects, n_features)
Feature vectors of the objects in the query sets

Returns
-------
Yn : numpy array
(n_instances, n_objects) Values for each object implying the nest it belongs to. For example for :math:2 nests the value 0 implies that object is allocated to nest 1 and value 1 implies it is allocated to nest 2.

"""
self.random_state_ = self.random_state_
n, n_obj, n_dim = X.shape
objects = X.reshape(n * n_obj, n_dim)
if self.cluster_model is None:
self.cluster_model = MiniBatchKMeans(
n_clusters=self.n_nests, random_state=self.random_state_
).fit(objects)
self.features_nests = self.cluster_model.cluster_centers_
prediction = self.cluster_model.labels_
else:
prediction = self.cluster_model.predict(objects)
Yn = []
for i in np.arange(0, n * n_obj, step=n_obj):
nest_ids = prediction[i : i + n_obj]
Yn.append(nest_ids)
Yn = np.array(Yn)
return Yn

def _eval_utility(self, weights):
utility = tt.zeros(tuple(self.y_nests.shape))
for i in range(self.n_nests):
rows, cols = tt.eq(self.y_nests, i).nonzero()
utility = tt.set_subtensor(
utility[rows, cols], tt.dot(self.Xt[rows, cols], weights[i])
)
return utility

def get_probabilities(self, utility, lambda_k, utility_k):
"""
This method calculates the probability of choosing an object from the query set using the following parameters of the model which are used:

* **weights** (:math:w): Weights to get the utility of the object :math:Y_i = U(x_i) = w \\cdot x_i
* **weights_k** (:math:w_k): Weights to get the utility of the next  :math:W_k = U_k(x) = w_k \\cdot c_k, where :math:c_k is the center of the object space of nest :math:B_k
* **lambda_k** (:math:\\lambda_k): Lambda is the measure of independence amongst the obejcts in the nest :math:B_k

The probability of choosing the object  :math:x_i from the query set :math:Q:

.. math::
P_i = \\frac{\\boldsymbol{e}^{ ^{Y_i} /_{\\lambda_k}}}{\\sum_{j \\in B_k} \\boldsymbol{e}^{^{Y_j} /_{\\lambda_k}}} \\frac {\\boldsymbol{e}^{W_k + \\lambda_k I_k}} {\\sum_{\\ell = 1}^{K} \\boldsymbol{e}^{ W_{\\ell } + \\lambda_{\\ell} I_{\\ell}}} \\quad i \\in B_k  \\enspace , \\\\
where,\\enspace I_k = \\ln \\sum_{ j \\in B_k} \\boldsymbol{e}^{^{Y_j} /_{\\lambda_k}}

Parameters
----------
utility : theano tensor
(n_instances, n_objects)
Utility :math:Y_i of the objects :math:x_i \\in Q in the query sets
lambda_k : theano tensor (range : [alpha, 1.0])
(n_nests)
Measure of independence amongst the obejcts in each nests
utility_k : theano tensor
(n_instances, n_nests)
Utilities of the nests :math:B_k \\in \\mathcal{B}

Returns
-------
p : theano tensor
(n_instances, n_objects)
Choice probabilities :math:P_i of the objects :math:x_i \\in Q in the query sets

"""
n_instances, n_objects = self.y_nests.shape
pni_k = tt.zeros((n_instances, n_objects))
ivm = tt.zeros((n_instances, self.n_nests))
for i in range(self.n_nests):
rows, cols = tt.neq(self.y_nests, i).nonzero()
sub_tensor = tt.set_subtensor(utility[rows, cols], -1e50)
ink = ttu.logsumexp(sub_tensor)
rows, cols = tt.eq(self.y_nests, i).nonzero()
pni_k = tt.set_subtensor(
pni_k[rows, cols], tt.exp(sub_tensor - ink)[rows, cols]
)
ivm = tt.set_subtensor(ivm[:, i], lambda_k[i] * ink[:, 0] + utility_k[i])
pk = tt.exp(ivm - ttu.logsumexp(ivm))
pn_k = tt.zeros((n_instances, n_objects))
for i in range(self.n_nests):
rows, cols = tt.eq(self.y_nests, i).nonzero()
p = tt.ones((n_instances, n_objects)) * pk[:, i][:, None]
pn_k = tt.set_subtensor(pn_k[rows, cols], p[rows, cols])
p = pni_k * pn_k
return p

def _eval_utility_np(self, x_t, y_nests, weights):
utility = np.zeros(tuple(y_nests.shape))
for i in range(self.n_nests):
rows, cols = np.where(y_nests == i)
utility[rows, cols] = np.dot(x_t[rows, cols], weights[i])
return utility

def _get_probabilities_np(self, Y_n, utility, lambda_k, utility_k):
n_instances, n_objects = Y_n.shape
pni_k = np.zeros((n_instances, n_objects))
ivm = np.zeros((n_instances, self.n_nests))
for i in range(self.n_nests):
sub_tensor = np.copy(utility)
sub_tensor[np.where(Y_n != i)] = -1e50
ink = npu.logsumexp(sub_tensor)
pni_k[np.where(Y_n == i)] = np.exp(sub_tensor - ink)[np.where(Y_n == i)]
ivm[:, i] = lambda_k[i] * ink[:, 0] + utility_k[i]
pk = np.exp(ivm - npu.logsumexp(ivm))
pn_k = np.zeros((n_instances, n_objects))
for i in range(self.n_nests):
rows, cols = np.where(Y_n == i)
p = np.ones((n_instances, n_objects)) * pk[:, i][:, None]
pn_k[rows, cols] = p[rows, cols]
p = pni_k * pn_k
return p

def construct_model(self, X, Y):
"""
Constructs the nested logit model by applying priors on weight vectors **weights** and **weights_k** as per
:meth:model_configuration. Then we apply a uniform prior to the :math:\\lambda s, i.e.
:math:\\lambda s \\sim Uniform(\\text{alpha}, 1.0).The probability of choosing the object :math:x_i from
the query set :math:Q = \\{x_1, \\ldots ,x_n\\} is evaluated in :meth:get_probabilities.

Parameters
----------
X : numpy array
(n_instances, n_objects, n_features)
Feature vectors of the objects
Y : numpy array
(n_instances, n_objects)
Preferences in the form of discrete choices for given objects

Returns
-------
model : pymc3 Model :class:pm.Model
"""
self.loss_function_ = likelihood_dict.get(self.loss_function, None)
if np.prod(X.shape) > self.threshold:
upper_bound = int(self.threshold / np.prod(X.shape[1:]))
indices = self.random_state_.choice(X.shape[0], upper_bound, replace=False)
X = X[indices, :, :]
Y = Y[indices, :]
self.logger.info(
"Train Set instances {} objects {} features {}".format(*X.shape)
)
y_nests = self.create_nests(X)
with pm.Model() as self.model:
self.Xt = theano.shared(X)
self.Yt = theano.shared(Y)
self.y_nests = theano.shared(y_nests)
shapes = {
"weights": self.n_object_features_fit_,
"weights_k": self.n_object_features_fit_,
}

weights_dict = create_weight_dictionary(self.model_configuration, shapes)
lambda_k = pm.Uniform("lambda_k", self.alpha, 1.0, shape=self.n_nests)
weights = weights_dict["weights"] / lambda_k[:, None]
utility = self._eval_utility(weights)
utility_k = tt.dot(self.features_nests, weights_dict["weights_k"])
self.p = self.get_probabilities(utility, lambda_k, utility_k)

LogLikelihood(
"yl", loss_func=self.loss_function_, p=self.p, observed=self.Yt
)
self.logger.info("Model construction completed")

def fit(
self,
X,
Y,
sampler="variational",
tune=500,
draws=500,
vi_params={
"n": 20000,
"callbacks": [CheckParametersConvergence()],
},
**kwargs,
):
"""
Fit a nested logit model on the provided set of queries X and choices Y of those objects. The provided
queries and corresponding preferences are of a fixed size (numpy arrays). For learning this network the
categorical cross entropy loss function for each object :math:x_i \\in Q is defined as:

.. math::

C_{i} =  -y(i)\\log(P_i) \\enspace,

where :math:y is ground-truth discrete choice vector of the objects in the given query set :math:Q.
The value :math:y(i) = 1 if object :math:x_i is chosen else :math:y(i) = 0.

Parameters
----------
X : numpy array (n_instances, n_objects, n_features)
Feature vectors of the objects
Y : numpy array (n_instances, n_objects)
Choices for given objects in the query
sampler : {‘variational’, ‘metropolis’, ‘nuts’}, string
The sampler used to estimate the posterior mean and mass matrix from the trace

* **variational** : Run inference methods to estimate posterior mean and diagonal mass matrix
* **metropolis** : Use the MAP as starting point and Metropolis-Hastings sampler
* **nuts** : Use the No-U-Turn sampler
vi_params : dict
The parameters for the **variational** inference method
draws : int
The number of samples to draw. Defaults to 500. The number of tuned samples are discarded by default
tune : int
Number of iterations to tune, defaults to 500. Ignored when using 'SMC'. Samplers adjust
the step sizes, scalings or similar during tuning. Tuning samples will be drawn in addition
to the number specified in the draws argument, and will be discarded unless
discard_tuned_samples is set to False.
**kwargs :
Keyword arguments for the fit function of :meth:pymc3.fitor :meth:pymc3.sample
"""
_n_instances, self.n_objects_fit_, self.n_object_features_fit_ = X.shape
if self.n_nests is None:
self.n_nests = int(self.n_objects_fit_ / 2)
self.random_state_ = check_random_state(self.random_state)
self.construct_model(X, Y)
fit_pymc3_model(self, sampler, draws, tune, vi_params, **kwargs)

def _predict_scores_fixed(self, X, **kwargs):
y_nests = self.create_nests(X)
mean_trace = dict(pm.summary(self.trace)["mean"])
weights = np.array(
[
mean_trace["weights[{}]".format(i)]
for i in range(self.n_object_features_fit_)
]
)
weights_k = np.array(
[
mean_trace["weights_k[{}]".format(i)]
for i in range(self.n_object_features_fit_)
]
)
lambda_k = np.array(
[mean_trace["lambda_k[{}]".format(i)] for i in range(self.n_nests)]
)
weights = weights / lambda_k[:, None]
utility_k = np.dot(self.features_nests, weights_k)
utility = self._eval_utility_np(X, y_nests, weights)
scores = self._get_probabilities_np(y_nests, utility, lambda_k, utility_k)
return scores

def predict(self, X, **kwargs):
return super().predict(X, **kwargs)

def predict_scores(self, X, **kwargs):
return super().predict_scores(X, **kwargs)

def predict_for_scores(self, scores, **kwargs):
return DiscreteObjectChooser.predict_for_scores(self, scores, **kwargs)

def set_tunable_parameters(
self, alpha=None, n_nests=None, loss_function=None, regularization="l1", **point
):
"""
Set tunable parameters of the Nested Logit model to the values provided.

Parameters
----------
alpha: float (range : [0,1])
The lower bound of the correlations between the objects in a nest
n_nests: int (range : [2,n_objects])
The number of nests in which the objects are divided
loss_function : string , {‘categorical_crossentropy’, ‘binary_crossentropy’, ’categorical_hinge’}
Loss function to be used for the discrete choice decision from the query set
regularization : string, {‘l1’, ‘l2’}, string
Regularizer function (L1 or L2) applied to the kernel weights matrix
point: dict
Dictionary containing parameter values which are not tuned for the network
"""
if alpha is not None:
self.alpha = alpha
if n_nests is None:
self.n_nests = int(self.n_objects_fit_ / 2)
else:
self.n_nests = n_nests
self.regularization = regularization
if loss_function is not None:
if loss_function not in likelihood_dict:
raise ValueError(
f"Loss function {loss_function} is unknown. Must be one of {set(likelihood_dict.keys())}"
)
self.loss_function = loss_function
self.cluster_model = None
self.features_nests = None
self.model = None
self.trace = None
self.trace_vi = None
self.Xt = None
self.Yt = None
self.p = None
self.y_nests = None
self._config = None
if len(point) > 0:
self.logger.warning(
"This ranking algorithm does not support tunable parameters"
" called: {}".format(print_dictionary(point))
)