Module fri.model.ordinal_regression

View Source
import cvxpy as cvx

import numpy as np

from sklearn.metrics import make_scorer

from sklearn.utils import check_X_y

from .base_cvxproblem import Relevance_CVXProblem

from .base_initmodel import InitModel

from .base_type import ProblemType

class OrdinalRegression(ProblemType):

    @classmethod

    def parameters(cls):

        return ["C"]

    @property

    def get_initmodel_template(cls):

        return OrdinalRegression_SVM

    @property

    def get_cvxproblem_template(cls):

        return OrdinalRegression_Relevance_Bound

    def relax_factors(cls):

        return ["loss_slack", "w_l1_slack"]

    def preprocessing(self, data, **kwargs):

        X, y = data

        # Check that X and y have correct shape

        X, y = check_X_y(X, y)

        if np.min(y) > 0:

            print("First ordinal class has index > 0. Shifting index...")

            y = y - np.min(y)

        return X, y

class OrdinalRegression_SVM(InitModel):

    HYPERPARAMETER = ["C"]

    def __init__(self, C=1):

        super().__init__()

        self.C = C

    def fit(self, X, y, **kwargs):

        (n, d) = X.shape

        C = self.get_params()["C"]

        self.classes_ = np.unique(y)

        original_bins = sorted(self.classes_)

        n_bins = len(original_bins)

        bins = np.arange(n_bins)

        get_old_bin = dict(zip(bins, original_bins))

        w = cvx.Variable(shape=(d), name="w")

        # For ordinal regression we use two slack variables, we observe the slack in both directions

        slack_left = cvx.Variable(shape=(n), name="slack_left")

        slack_right = cvx.Variable(shape=(n), name="slack_right")

        # We have an offset for every bin boundary

        b_s = cvx.Variable(shape=(n_bins - 1), name="bias")

        objective = cvx.Minimize(cvx.norm(w, 1) + C * cvx.sum(slack_left + slack_right))

        constraints = [slack_left >= 0, slack_right >= 0]

        # Add constraints for slack into left neighboring bins

        for i in range(n_bins - 1):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w - slack_left[indices] <= b_s[i] - 1)

        # Add constraints for slack into right neighboring bins

        for i in range(1, n_bins):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w + slack_right[indices] >= b_s[i - 1] + 1)

        # Add explicit constraint, that all bins are ascending

        for i in range(n_bins - 2):

            constraints.append(b_s[i] <= b_s[i + 1])

        # Solve problem.

        problem = cvx.Problem(objective, constraints)

        problem.solve(**self.SOLVER_PARAMS)

        w = w.value

        b_s = b_s.value

        slack_left = np.asarray(slack_left.value).flatten()

        slack_right = np.asarray(slack_right.value).flatten()

        self.model_state = {"w": w, "b_s": b_s, "slack": (slack_left, slack_right)}

        loss = np.sum(slack_left + slack_right)

        w_l1 = np.linalg.norm(w, ord=1)

        self.constraints = {"loss": loss, "w_l1": w_l1}

        return self

    def predict(self, X):

        w = self.model_state["w"]

        b_s = self.model_state["b_s"]

        scores = np.dot(X, w.T)[np.newaxis]

        bin_thresholds = np.append(b_s, np.inf)

        # If thresholds are smaller than score the value belongs to the bigger bin

        # after subtracting we check for positive elements

        indices = np.sum(scores.T - bin_thresholds >= 0, -1)

        return self.classes_[indices]

    def score(self, X, y, error_type="mmae", return_error=False, **kwargs):

        X, y = check_X_y(X, y)

        prediction = self.predict(X)

        score = ordinal_scores(y, prediction, error_type, return_error=return_error)

        return score

    def make_scorer(self):

        # Use multiple scores for ordinal regression

        mze = make_scorer(ordinal_scores, error_type="mze")

        mae = make_scorer(ordinal_scores, error_type="mae")

        mmae = make_scorer(ordinal_scores, error_type="mmae")

        scorer = {"mze": mze, "mae": mae, "mmae": mmae}

        return scorer, "mmae"

def ordinal_scores(y, prediction, error_type, return_error=False):

    """Score function for ordinal problems.

    Parameters

    ----------

    y : target class vector

        Truth vector

    prediction : prediction class vector

        Predicted classes

    error_type : str

        Error type "mze","mae","mmae"

    return_error : bool, optional

        Return error (lower is better) or score (inverted, higher is better)

    Returns

    -------

    float

        Error or score depending on 'return_error'

    Raises

    ------

    ValueError

        When using wrong error_type

    """

    n = len(y)

    classes = np.unique(y)

    n_bins = len(classes)

    max_dist = n_bins - 1

    # If only one class available, we dont need to average

    if max_dist == 0:

        error_type = "mze"

    def mze(prediction, y):

        return np.sum(prediction != y)

    def mae(prediction, y):

        return np.sum(np.abs(prediction - y))

    # Score based on mean zero-one error

    if error_type == "mze":

        error = mze(prediction, y) / n

        score = 1 - error

    # Score based on mean absolute error

    elif error_type == "mae":

        error = mae(prediction, y) / n

        score = (max_dist - error) / max_dist

    # Score based on macro-averaged mean absolute error

    elif error_type == "mmae":

        sum = 0

        for i in range(n_bins):

            samples = y == i

            n_samples = np.sum(samples)

            if n_samples > 0:

                bin_error = mae(prediction[samples], y[samples]) / n_samples

                sum += bin_error

        error = sum / n_bins

        score = (max_dist - error) / max_dist

    else:

        raise ValueError("error_type {} not available!'".format(error_type))

    if return_error:

        return error

    else:

        return score

class OrdinalRegression_Relevance_Bound(Relevance_CVXProblem):

    def init_objective_UB(self, sign=None, **kwargs):

        self.add_constraint(

            self.feature_relevance <= sign * self.w[self.current_feature]

        )

        self._objective = cvx.Maximize(self.feature_relevance)

    def init_objective_LB(self, **kwargs):

        self.add_constraint(

            cvx.abs(self.w[self.current_feature]) <= self.feature_relevance

        )

        self._objective = cvx.Minimize(self.feature_relevance)

    def _init_constraints(self, parameters, init_model_constraints):

        n_bins = len(np.unique(self.y))

        # Upper constraints from initial model

        l1_w = init_model_constraints["w_l1"]

        init_loss = init_model_constraints["loss"]

        C = parameters["C"]

        # New Variables

        self.w = cvx.Variable(shape=(self.d), name="w")

        # For ordinal regression we use two slack variables, we observe the slack in both directions

        self.slack_left = cvx.Variable(shape=(self.n), name="slack_left", nonneg=True)

        self.slack_right = cvx.Variable(shape=(self.n), name="slack_right", nonneg=True)

        # We have an offset for every bin boundary

        self.b_s = cvx.Variable(shape=(n_bins - 1), name="bias")

        # New Constraints

        self.loss = cvx.sum(self.slack_left + self.slack_right)

        self.weight_norm = cvx.norm(self.w, 1)

        for i in range(n_bins - 1):

            indices = np.where(self.y == i)

            self.add_constraint(

                self.X[indices] * self.w - self.slack_left[indices] <= self.b_s[i] - 1

            )

        for i in range(1, n_bins):

            indices = np.where(self.y == i)

            self.add_constraint(

                self.X[indices] * self.w + self.slack_right[indices]

                >= self.b_s[i - 1] + 1

            )

        for i in range(n_bins - 2):

            self.add_constraint(self.b_s[i] <= self.b_s[i + 1])

        self.add_constraint(self.weight_norm <= l1_w)

        self.add_constraint(C * self.loss <= C * init_loss)

        self.feature_relevance = cvx.Variable(nonneg=True, name="Feature Relevance")

Functions

ordinal_scores

def ordinal_scores(
    y,
    prediction,
    error_type,
    return_error=False
)

Score function for ordinal problems.

Parameters

y : target class vector Truth vector prediction : prediction class vector Predicted classes error_type : str Error type "mze","mae","mmae" return_error : bool, optional Return error (lower is better) or score (inverted, higher is better)

Returns

float Error or score depending on 'return_error'

Raises

ValueError When using wrong error_type

View Source
def ordinal_scores(y, prediction, error_type, return_error=False):

    """Score function for ordinal problems.

    Parameters

    ----------

    y : target class vector

        Truth vector

    prediction : prediction class vector

        Predicted classes

    error_type : str

        Error type "mze","mae","mmae"

    return_error : bool, optional

        Return error (lower is better) or score (inverted, higher is better)

    Returns

    -------

    float

        Error or score depending on 'return_error'

    Raises

    ------

    ValueError

        When using wrong error_type

    """

    n = len(y)

    classes = np.unique(y)

    n_bins = len(classes)

    max_dist = n_bins - 1

    # If only one class available, we dont need to average

    if max_dist == 0:

        error_type = "mze"

    def mze(prediction, y):

        return np.sum(prediction != y)

    def mae(prediction, y):

        return np.sum(np.abs(prediction - y))

    # Score based on mean zero-one error

    if error_type == "mze":

        error = mze(prediction, y) / n

        score = 1 - error

    # Score based on mean absolute error

    elif error_type == "mae":

        error = mae(prediction, y) / n

        score = (max_dist - error) / max_dist

    # Score based on macro-averaged mean absolute error

    elif error_type == "mmae":

        sum = 0

        for i in range(n_bins):

            samples = y == i

            n_samples = np.sum(samples)

            if n_samples > 0:

                bin_error = mae(prediction[samples], y[samples]) / n_samples

                sum += bin_error

        error = sum / n_bins

        score = (max_dist - error) / max_dist

    else:

        raise ValueError("error_type {} not available!'".format(error_type))

    if return_error:

        return error

    else:

        return score

Classes

OrdinalRegression

class OrdinalRegression(
    **kwargs
)

Helper class that provides a standard way to create an ABC using inheritance.

View Source
class OrdinalRegression(ProblemType):

    @classmethod

    def parameters(cls):

        return ["C"]

    @property

    def get_initmodel_template(cls):

        return OrdinalRegression_SVM

    @property

    def get_cvxproblem_template(cls):

        return OrdinalRegression_Relevance_Bound

    def relax_factors(cls):

        return ["loss_slack", "w_l1_slack"]

    def preprocessing(self, data, **kwargs):

        X, y = data

        # Check that X and y have correct shape

        X, y = check_X_y(X, y)

        if np.min(y) > 0:

            print("First ordinal class has index > 0. Shifting index...")

            y = y - np.min(y)

        return X, y

Ancestors (in MRO)

  • fri.model.base_type.ProblemType
  • abc.ABC

Static methods

parameters
def parameters(

)
View Source
    @classmethod

    def parameters(cls):

        return ["C"]

Instance variables

get_cvxproblem_template
get_initmodel_template

Methods

get_all_parameters
def get_all_parameters(
    self
)
View Source
    def get_all_parameters(self):

        return {p: self.get_chosen_parameter(p) for p in self.parameters()}
get_all_relax_factors
def get_all_relax_factors(
    self
)
View Source
    def get_all_relax_factors(self):

        return {p: self.get_chosen_relax_factors(p) for p in self.relax_factors()}
get_chosen_parameter
def get_chosen_parameter(
    self,
    p
)
View Source
    def get_chosen_parameter(self, p):

        try:

            return [

                self.chosen_parameters_[p]

            ]  # We return list for param search function

        except:

            # # TODO: rewrite the parameter logic

            # # TODO: move this to subclass

            if p == "scaling_lupi_w":

                # return [0.1, 1, 10, 100, 1000]

                return scipy.stats.reciprocal(a=1e-15, b=1e10)

            # if p == "scaling_lupi_loss":

            #    # value 0>p<1 causes standard svm solution

            #    # p>1 encourages usage of lupi function

            #    return scipy.stats.reciprocal(a=1e-15, b=1e15)

            if p == "C":

                return scipy.stats.reciprocal(a=1e-5, b=1e5)

            if p == "epsilon":

                return [0, 0.001, 0.01, 0.1, 1, 10, 100]

            else:

                return scipy.stats.reciprocal(a=1e-10, b=1e10)
get_chosen_relax_factors
def get_chosen_relax_factors(
    self,
    p
)
View Source
    def get_chosen_relax_factors(self, p):

        try:

            factor = self.relax_factors_[p]

        except KeyError:

            try:

                factor = self.relax_factors_[p + "_slack"]

            except KeyError:

                factor = 0.1

        if factor < 0:

            raise ValueError("Slack Factor multiplier is positive!")

        return factor
get_relaxed_constraints
def get_relaxed_constraints(
    self,
    constraints
)
View Source
    def get_relaxed_constraints(self, constraints):

        return {c: self.relax_constraint(c, v) for c, v in constraints.items()}
postprocessing
def postprocessing(
    self,
    bounds
)
View Source
    def postprocessing(self, bounds):

        return bounds
preprocessing
def preprocessing(
    self,
    data,
    **kwargs
)
View Source
    def preprocessing(self, data, **kwargs):

        X, y = data

        # Check that X and y have correct shape

        X, y = check_X_y(X, y)

        if np.min(y) > 0:

            print("First ordinal class has index > 0. Shifting index...")

            y = y - np.min(y)

        return X, y
relax_constraint
def relax_constraint(
    self,
    key,
    value
)
View Source
    def relax_constraint(self, key, value):

        return value * (1 + self.get_chosen_relax_factors(key))
relax_factors
def relax_factors(
    cls
)
View Source
    def relax_factors(cls):

        return ["loss_slack", "w_l1_slack"]

OrdinalRegression_Relevance_Bound

class OrdinalRegression_Relevance_Bound(
    current_feature: int,
    data: tuple,
    hyperparameters,
    best_model_constraints,
    preset_model=None,
    best_model_state=None,
    probeID=-1,
    **kwargs
)

Helper class that provides a standard way to create an ABC using inheritance.

View Source
class OrdinalRegression_Relevance_Bound(Relevance_CVXProblem):

    def init_objective_UB(self, sign=None, **kwargs):

        self.add_constraint(

            self.feature_relevance <= sign * self.w[self.current_feature]

        )

        self._objective = cvx.Maximize(self.feature_relevance)

    def init_objective_LB(self, **kwargs):

        self.add_constraint(

            cvx.abs(self.w[self.current_feature]) <= self.feature_relevance

        )

        self._objective = cvx.Minimize(self.feature_relevance)

    def _init_constraints(self, parameters, init_model_constraints):

        n_bins = len(np.unique(self.y))

        # Upper constraints from initial model

        l1_w = init_model_constraints["w_l1"]

        init_loss = init_model_constraints["loss"]

        C = parameters["C"]

        # New Variables

        self.w = cvx.Variable(shape=(self.d), name="w")

        # For ordinal regression we use two slack variables, we observe the slack in both directions

        self.slack_left = cvx.Variable(shape=(self.n), name="slack_left", nonneg=True)

        self.slack_right = cvx.Variable(shape=(self.n), name="slack_right", nonneg=True)

        # We have an offset for every bin boundary

        self.b_s = cvx.Variable(shape=(n_bins - 1), name="bias")

        # New Constraints

        self.loss = cvx.sum(self.slack_left + self.slack_right)

        self.weight_norm = cvx.norm(self.w, 1)

        for i in range(n_bins - 1):

            indices = np.where(self.y == i)

            self.add_constraint(

                self.X[indices] * self.w - self.slack_left[indices] <= self.b_s[i] - 1

            )

        for i in range(1, n_bins):

            indices = np.where(self.y == i)

            self.add_constraint(

                self.X[indices] * self.w + self.slack_right[indices]

                >= self.b_s[i - 1] + 1

            )

        for i in range(n_bins - 2):

            self.add_constraint(self.b_s[i] <= self.b_s[i + 1])

        self.add_constraint(self.weight_norm <= l1_w)

        self.add_constraint(C * self.loss <= C * init_loss)

        self.feature_relevance = cvx.Variable(nonneg=True, name="Feature Relevance")

Ancestors (in MRO)

  • fri.model.base_cvxproblem.Relevance_CVXProblem
  • abc.ABC

Descendants

  • fri.model.lupi_ordinal_regression.LUPI_OrdinalRegression_Relevance_Bound

Static methods

aggregate_max_candidates
def aggregate_max_candidates(
    max_problems_candidates
)
View Source
    @classmethod

    def aggregate_max_candidates(cls, max_problems_candidates):

        vals = [candidate.solved_relevance for candidate in max_problems_candidates]

        max_value = max(vals)

        return max_value
aggregate_min_candidates
def aggregate_min_candidates(
    min_problems_candidates
)
View Source
    @classmethod

    def aggregate_min_candidates(cls, min_problems_candidates):

        vals = [candidate.solved_relevance for candidate in min_problems_candidates]

        min_value = min(vals)

        return min_value
generate_lower_bound_problem
def generate_lower_bound_problem(
    best_hyperparameters,
    init_constraints,
    best_model_state,
    data,
    di,
    preset_model,
    probeID=-1
)
View Source
    @classmethod

    def generate_lower_bound_problem(

        cls,

        best_hyperparameters,

        init_constraints,

        best_model_state,

        data,

        di,

        preset_model,

        probeID=-1,

    ):

        problem = cls(

            di,

            data,

            best_hyperparameters,

            init_constraints,

            preset_model=preset_model,

            best_model_state=best_model_state,

            probeID=probeID,

        )

        problem.init_objective_LB()

        problem.isLowerBound = True

        yield problem
generate_upper_bound_problem
def generate_upper_bound_problem(
    best_hyperparameters,
    init_constraints,
    best_model_state,
    data,
    di,
    preset_model,
    probeID=-1
)
View Source
    @classmethod

    def generate_upper_bound_problem(

        cls,

        best_hyperparameters,

        init_constraints,

        best_model_state,

        data,

        di,

        preset_model,

        probeID=-1,

    ):

        for sign in [-1, 1]:

            problem = cls(

                di,

                data,

                best_hyperparameters,

                init_constraints,

                preset_model=preset_model,

                best_model_state=best_model_state,

                probeID=probeID,

            )

            problem.init_objective_UB(sign=sign)

            problem.isLowerBound = False

            yield problem

Instance variables

accepted_status
constraints
cvx_problem
isProbe
is_solved
objective
probeID
solved_relevance
solver_kwargs

Methods

add_constraint
def add_constraint(
    self,
    new
)
View Source
    def add_constraint(self, new):

        self._constraints.append(new)
init_objective_LB
def init_objective_LB(
    self,
    **kwargs
)
View Source
    def init_objective_LB(self, **kwargs):

        self.add_constraint(

            cvx.abs(self.w[self.current_feature]) <= self.feature_relevance

        )

        self._objective = cvx.Minimize(self.feature_relevance)
init_objective_UB
def init_objective_UB(
    self,
    sign=None,
    **kwargs
)
View Source
    def init_objective_UB(self, sign=None, **kwargs):

        self.add_constraint(

            self.feature_relevance <= sign * self.w[self.current_feature]

        )

        self._objective = cvx.Maximize(self.feature_relevance)
preprocessing_data
def preprocessing_data(
    self,
    data,
    best_model_state
)
View Source
    def preprocessing_data(self, data, best_model_state):

        X, y = data

        self.n = X.shape[0]

        self.d = X.shape[1]

        self.X = X

        self.y = np.array(y)
solve
def solve(
    self
) -> object
View Source
    def solve(self) -> object:

        # We init cvx problem here because pickling LP solver objects is problematic

        # by deferring it to here, worker threads do the problem building themselves and we spare the serialization

        self._cvx_problem = cvx.Problem(

            objective=self.objective, constraints=self.constraints

        )

        try:

            # print("Solve", self)

            self._cvx_problem.solve(**self.solver_kwargs)

        except SolverError:

            # We ignore Solver Errors, which are common with our framework:

            # We solve multiple problems per bound and choose a feasible solution later (see '_create_interval')

            pass

        self._solver_status = self._cvx_problem.status

        # self._cvx_problem = None

        return self

OrdinalRegression_SVM

class OrdinalRegression_SVM(
    C=1
)

Helper class that provides a standard way to create an ABC using inheritance.

View Source
class OrdinalRegression_SVM(InitModel):

    HYPERPARAMETER = ["C"]

    def __init__(self, C=1):

        super().__init__()

        self.C = C

    def fit(self, X, y, **kwargs):

        (n, d) = X.shape

        C = self.get_params()["C"]

        self.classes_ = np.unique(y)

        original_bins = sorted(self.classes_)

        n_bins = len(original_bins)

        bins = np.arange(n_bins)

        get_old_bin = dict(zip(bins, original_bins))

        w = cvx.Variable(shape=(d), name="w")

        # For ordinal regression we use two slack variables, we observe the slack in both directions

        slack_left = cvx.Variable(shape=(n), name="slack_left")

        slack_right = cvx.Variable(shape=(n), name="slack_right")

        # We have an offset for every bin boundary

        b_s = cvx.Variable(shape=(n_bins - 1), name="bias")

        objective = cvx.Minimize(cvx.norm(w, 1) + C * cvx.sum(slack_left + slack_right))

        constraints = [slack_left >= 0, slack_right >= 0]

        # Add constraints for slack into left neighboring bins

        for i in range(n_bins - 1):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w - slack_left[indices] <= b_s[i] - 1)

        # Add constraints for slack into right neighboring bins

        for i in range(1, n_bins):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w + slack_right[indices] >= b_s[i - 1] + 1)

        # Add explicit constraint, that all bins are ascending

        for i in range(n_bins - 2):

            constraints.append(b_s[i] <= b_s[i + 1])

        # Solve problem.

        problem = cvx.Problem(objective, constraints)

        problem.solve(**self.SOLVER_PARAMS)

        w = w.value

        b_s = b_s.value

        slack_left = np.asarray(slack_left.value).flatten()

        slack_right = np.asarray(slack_right.value).flatten()

        self.model_state = {"w": w, "b_s": b_s, "slack": (slack_left, slack_right)}

        loss = np.sum(slack_left + slack_right)

        w_l1 = np.linalg.norm(w, ord=1)

        self.constraints = {"loss": loss, "w_l1": w_l1}

        return self

    def predict(self, X):

        w = self.model_state["w"]

        b_s = self.model_state["b_s"]

        scores = np.dot(X, w.T)[np.newaxis]

        bin_thresholds = np.append(b_s, np.inf)

        # If thresholds are smaller than score the value belongs to the bigger bin

        # after subtracting we check for positive elements

        indices = np.sum(scores.T - bin_thresholds >= 0, -1)

        return self.classes_[indices]

    def score(self, X, y, error_type="mmae", return_error=False, **kwargs):

        X, y = check_X_y(X, y)

        prediction = self.predict(X)

        score = ordinal_scores(y, prediction, error_type, return_error=return_error)

        return score

    def make_scorer(self):

        # Use multiple scores for ordinal regression

        mze = make_scorer(ordinal_scores, error_type="mze")

        mae = make_scorer(ordinal_scores, error_type="mae")

        mmae = make_scorer(ordinal_scores, error_type="mmae")

        scorer = {"mze": mze, "mae": mae, "mmae": mmae}

        return scorer, "mmae"

Ancestors (in MRO)

  • fri.model.base_initmodel.InitModel
  • abc.ABC
  • sklearn.base.BaseEstimator

Class variables

HYPERPARAMETER
SOLVER_PARAMS

Instance variables

L1_factor

Methods

fit
def fit(
    self,
    X,
    y,
    **kwargs
)
View Source
    def fit(self, X, y, **kwargs):

        (n, d) = X.shape

        C = self.get_params()["C"]

        self.classes_ = np.unique(y)

        original_bins = sorted(self.classes_)

        n_bins = len(original_bins)

        bins = np.arange(n_bins)

        get_old_bin = dict(zip(bins, original_bins))

        w = cvx.Variable(shape=(d), name="w")

        # For ordinal regression we use two slack variables, we observe the slack in both directions

        slack_left = cvx.Variable(shape=(n), name="slack_left")

        slack_right = cvx.Variable(shape=(n), name="slack_right")

        # We have an offset for every bin boundary

        b_s = cvx.Variable(shape=(n_bins - 1), name="bias")

        objective = cvx.Minimize(cvx.norm(w, 1) + C * cvx.sum(slack_left + slack_right))

        constraints = [slack_left >= 0, slack_right >= 0]

        # Add constraints for slack into left neighboring bins

        for i in range(n_bins - 1):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w - slack_left[indices] <= b_s[i] - 1)

        # Add constraints for slack into right neighboring bins

        for i in range(1, n_bins):

            indices = np.where(y == get_old_bin[i])

            constraints.append(X[indices] * w + slack_right[indices] >= b_s[i - 1] + 1)

        # Add explicit constraint, that all bins are ascending

        for i in range(n_bins - 2):

            constraints.append(b_s[i] <= b_s[i + 1])

        # Solve problem.

        problem = cvx.Problem(objective, constraints)

        problem.solve(**self.SOLVER_PARAMS)

        w = w.value

        b_s = b_s.value

        slack_left = np.asarray(slack_left.value).flatten()

        slack_right = np.asarray(slack_right.value).flatten()

        self.model_state = {"w": w, "b_s": b_s, "slack": (slack_left, slack_right)}

        loss = np.sum(slack_left + slack_right)

        w_l1 = np.linalg.norm(w, ord=1)

        self.constraints = {"loss": loss, "w_l1": w_l1}

        return self
get_params
def get_params(
    self,
    deep=True
)

Get parameters for this estimator.

Parameters

deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params : mapping of string to any Parameter names mapped to their values.

View Source
    def get_params(self, deep=True):

        """

        Get parameters for this estimator.

        Parameters

        ----------

        deep : bool, default=True

            If True, will return the parameters for this estimator and

            contained subobjects that are estimators.

        Returns

        -------

        params : mapping of string to any

            Parameter names mapped to their values.

        """

        out = dict()

        for key in self._get_param_names():

            try:

                value = getattr(self, key)

            except AttributeError:

                warnings.warn('From version 0.24, get_params will raise an '

                              'AttributeError if a parameter cannot be '

                              'retrieved as an instance attribute. Previously '

                              'it would return None.',

                              FutureWarning)

                value = None

            if deep and hasattr(value, 'get_params'):

                deep_items = value.get_params().items()

                out.update((key + '__' + k, val) for k, val in deep_items)

            out[key] = value

        return out
make_scorer
def make_scorer(
    self
)
View Source
    def make_scorer(self):

        # Use multiple scores for ordinal regression

        mze = make_scorer(ordinal_scores, error_type="mze")

        mae = make_scorer(ordinal_scores, error_type="mae")

        mmae = make_scorer(ordinal_scores, error_type="mmae")

        scorer = {"mze": mze, "mae": mae, "mmae": mmae}

        return scorer, "mmae"
predict
def predict(
    self,
    X
)
View Source
    def predict(self, X):

        w = self.model_state["w"]

        b_s = self.model_state["b_s"]

        scores = np.dot(X, w.T)[np.newaxis]

        bin_thresholds = np.append(b_s, np.inf)

        # If thresholds are smaller than score the value belongs to the bigger bin

        # after subtracting we check for positive elements

        indices = np.sum(scores.T - bin_thresholds >= 0, -1)

        return self.classes_[indices]
score
def score(
    self,
    X,
    y,
    error_type='mmae',
    return_error=False,
    **kwargs
)
View Source
    def score(self, X, y, error_type="mmae", return_error=False, **kwargs):

        X, y = check_X_y(X, y)

        prediction = self.predict(X)

        score = ordinal_scores(y, prediction, error_type, return_error=return_error)

        return score
set_params
def set_params(
    self,
    **params
)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object.

Parameters

**params : dict Estimator parameters.

Returns

self : object Estimator instance.

View Source
    def set_params(self, **params):

        """

        Set the parameters of this estimator.

        The method works on simple estimators as well as on nested objects

        (such as pipelines). The latter have parameters of the form

        ``<component>__<parameter>`` so that it's possible to update each

        component of a nested object.

        Parameters

        ----------

        **params : dict

            Estimator parameters.

        Returns

        -------

        self : object

            Estimator instance.

        """

        if not params:

            # Simple optimization to gain speed (inspect is slow)

            return self

        valid_params = self.get_params(deep=True)

        nested_params = defaultdict(dict)  # grouped by prefix

        for key, value in params.items():

            key, delim, sub_key = key.partition('__')

            if key not in valid_params:

                raise ValueError('Invalid parameter %s for estimator %s. '

                                 'Check the list of available parameters '

                                 'with `estimator.get_params().keys()`.' %

                                 (key, self))

            if delim:

                nested_params[key][sub_key] = value

            else:

                setattr(self, key, value)

                valid_params[key] = value

        for key, sub_params in nested_params.items():

            valid_params[key].set_params(**sub_params)

        return self