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How to use step_init method in autotest

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st-DBSCAN.py

Source:st-DBSCAN.py

1import numpy as np2from scipy.spatial.distance import pdist, squareform3from sklearn.cluster import DBSCAN4from sklearn.utils import check_array5from ML import calculate_rand_score, stock_labels6from utils import get_dataset_steps_positions_velocities_headings7def get_mean_score(name_file):8    try:9        with open(name_file):10            score = np.mean(np.array(np.loadtxt(name_file), dtype=float))11            return score12    except IOError:13        print("Could not open file {0}".format(name_file))14        exit()15class ST_DBSCAN:16    """17        ST_DBSCAN class for clustering18        ref19        - ST-DBSCAN: An algorithm for clustering spatialâtemporal data20        Derya Birant, Alp Kut21        ----------22        :param eps1: float, the density threshold for spatial neighborhood23        :param eps2: float, The temporal threshold for temporal neighborhood24        :param min_samples: The number of samples required for an object to be a core point.25        :param metric_1: string, metric for spatial neighborhood26        :param metric_2: string, metric for temporal neighborhood27        string default='euclidean', can also be a custom function28        The used distance metric - more options are29        âbraycurtisâ, âcanberraâ, âchebyshevâ, âcityblockâ, âcorrelationâ,30        âcosineâ, âdiceâ, âeuclideanâ, âhammingâ, âjaccardâ, âjensenshannonâ,31        âkulsinskiâ, âmahalanobisâ, âmatchingâ, ârogerstanimotoâ, âsqeuclideanâ,32        ârussellraoâ, âseuclideanâ, âsokalmichenerâ, âsokalsneathâ, âyuleâ.33        :param indices_1: list of column indices where spatial attributes are situated in the data34        :param indices_2: list of column indices where non-spatial attributes are situated in the data35    """36    def __init__(self,37                 eps1,38                 eps2,39                 min_samples,40                 indices_1,41                 indices_2,42                 metric_1='euclidean',43                 metric_2='euclidean',44                 ):45        self.eps1 = eps146        self.eps2 = eps247        self.indices_1 = indices_148        self.indices_2 = indices_249        self.min_samples = min_samples50        self.metric_1 = metric_151        self.metric_2 = metric_252        self.labels = None53        assert self.eps1 > 0, 'eps1 must be positive'54        assert self.eps2 > 0, 'eps2 must be positive'55        assert type(self.min_samples) == int, 'min_samples must be a positive integer'56        assert self.min_samples > 0, 'min_samples must be a positive integer'57    def fit(self, X):58        # check if input is correct59        X = check_array(X)60        if not self.eps1 > 0.0 or not self.eps2 > 0.0 or not self.min_samples > 0.0:61            raise ValueError('eps1, eps2, minPts must be positive')62        # Compute squared distance matrix for63        non_spatial_square_dist_matrix = pdist(X[:, self.indices_1], metric=self.metric_1)64        spatial_square_dist_matrix = pdist(X[:, self.indices_2], metric=self.metric_2)65        # filter the euc_dist matrix using the time_dist66        dist = np.where(non_spatial_square_dist_matrix <= self.eps1, spatial_square_dist_matrix, 10 * self.eps2)67        db = DBSCAN(eps=self.eps2,68                    min_samples=self.min_samples,69                    metric='precomputed')70        db.fit(squareform(dist))71        self.labels = db.labels_72    def stock_labels_to_directory(self, directory, nb_obs, step_init, step_end):73        true_steps = np.arange(step_init, step_end)74        steps = np.arange(0, step_end - step_init)75        ind_init = steps[0]76        # for each step77        for (step, step_true) in zip(steps, true_steps):78            # get all the observations of step i79            ind_to_get = np.arange(ind_init, ind_init + nb_obs)80            label_step = self.labels[ind_to_get]81            stock_labels(label_step, step_true, repository=directory,82                         filename="ST_DBSCAN_eps1=" + str(self.eps1) + "eps2="83                                  + str(self.eps2) + "Nsample="84                                  + str(self.min_samples) + "label")85            ind_init += nb_obs86    def generate_results(self, directory, step_init, step_end):87        steps = list(np.arange(step_init, step_end))  # steps to take into account in the calculation88        filename_true = "ground_truth_label"  # file name for ground-truth (see for example file_name89        # argument in stock_file function in build_ground_truth function in module ML.py)90        filename_pred = "ST_DBSCAN_eps1=" + str(self.eps1) + "eps2=" + \91                        str(self.eps2) + "Nsample=" + str(self.min_samples) + "label"92        score_mean = calculate_rand_score(steps, directory, filename_true, filename_pred)93        return score_mean94def split_data(data, n_indiv, time_step):95    list_data = list()96    for i in np.arange(0, data.shape[0], n_indiv * time_step):97        list_data.append(data[i:i + time_step * n_indiv, :])98    return list_data99if __name__ == "__main__":100    n_indiv = 120101    n_time_step = 3102    directory = "simulation_data/"103    step_init = 300104    step_end = 500105    split = False  # True if we split the data in sequences of n_time_step, False else106    # build the dataset107    data = get_dataset_steps_positions_velocities_headings(step_init, step_end, n_indiv, directory)108    # split the dateset to have series of n_time_step times-steps with positions for each boids109    list_data = split_data(data, n_indiv, n_time_step)110    # parameters111    eps1 = [3]112    eps2 = [85]113    min_samples = [5]114    # eps1 = 2 -> eps2=80, min_sample=5 best found115    # eps1 = 1 -> eps2=80, min_sample=5 best found116    # eps1 = 3 -> eps2=80, min_sample=5 best found117    results = list()118    param_list = list()119    # test each parameters120    if split:121        for eps_1 in eps1:122            for eps_2 in eps2:123                for min_sample in min_samples:124                    # we test on 10 sets of n_time_frame samples125                    mean_res = list()126                    for i in range(40):127                        st_dbscan = ST_DBSCAN(eps_1, eps_2, min_sample, [0], [1, 2])128                        st_dbscan.fit(list_data[i])129                        st_dbscan.stock_labels_to_directory(directory=directory,130                                                            nb_obs=n_indiv,131                                                            step_init=step_init + i * n_time_step,132                                                            step_end=step_init + (i + 1) * n_time_step)133                        ari_score = st_dbscan.generate_results(directory=directory,134                                                               step_init=step_init + i * n_time_step,135                                                               step_end=step_init + (i + 1) * n_time_step)136                        mean_res.append(ari_score)137                    print("eps1: ", eps_1)138                    print("eps2: ", eps_2)139                    print("min_sample", min_sample)140                    results.append(np.mean(mean_res))141                    param_list.append([eps_1, eps_2, min_sample])142    else:143        for eps_1 in eps1:144            for eps_2 in eps2:145                for min_sample in min_samples:146                    st_dbscan = ST_DBSCAN(eps_1, eps_2, min_sample, [0], [1, 2])147                    st_dbscan.fit(data)148                    st_dbscan.stock_labels_to_directory(directory=directory,149                                                        nb_obs=n_indiv,150                                                        step_init=step_init,151                                                        step_end=step_end)152                    print("eps1: ", eps_1)153                    print("eps2: ", eps_2)154                    print("min_sample", min_sample)155                    ari_score = st_dbscan.generate_results(directory=directory,156                                                           step_init=step_init,157                                                           step_end=step_end)158                    results.append(ari_score)159                    param_list.append([eps_1, eps_2, min_sample])160    print("best score from all {0} trial(s): {1}".format(len(results),161                                                         results[np.argmax(results)]))162    print("best parameters from all {0} "...

opti.py

Source:opti.py

1#!/usr/bin/env python2# -*- coding: utf-8 -*-3"""4This module is an simple optimization module that helps find the point of 5maximum value for a given function.6"""7from sage.all import *8def optimize(func, args_init, step_init, step_min, iter_max, verbose=False):9  r"""10  Optimization function finding the maximum reaching coordinates for ``func``11  with a random walk. 12  13  :param function func: The function to be optimized, each of its arguments must 14    be numerical and will be tweaked to find ``func``'s maximum. 15  :param tuple args_init: Initial coordinates for the random walk. 16  :param real step_init: The step size will vary with time in this function, so17    this is the initial value for the step size. 18  :param real step_min: The limit size for the step.19  :param int iter_max: Upper iterations bound for each loop to avoid infinite 20    loops.21  :param bool verbose: If ``verbose`` then extra run information will be 22    displayed in terminal.23  :returns: vector, complex -- The coordinates of the optimum found for 24    ``func`` and the value of ``func`` at this point.25  """26  solution = vector(CC, args_init).normalized()27  solution_temp = solution28  value = func(solution)29  value_temp = value30  step_current = step_init31  if verbose:32    print("Initial solution : " + func.__name__ + 33      str(solution) + " = " + str(value))34  iter_nb = 035  while step_current > step_min:36    count = 137    while value_temp <= value and count < iter_max:38      direction = vector(39        [uniform(-1,1) for _ in range(len(args_init))]40        ).normalized()41      solution_temp = solution + direction*step_current42      value_temp = func(solution_temp)43      count += 144    if value_temp > value:45      solution = solution_temp46      value = value_temp47    else:48      step_current = step_current/249  50  if verbose:51    print("Final solution : " + func.__name__ + 52      str(solution) + " = " + str(value))53  return (solution, value)54def optimize_normalized(func, normalizer_func, args_init, step_init, step_min, iter_max, verbose=False):55  r"""56  Optimization function finding the maximum reaching coordinates for ``func``57  with a random walk. 58  59  :param function func: The function to be optimized, each of its arguments must 60    be numerical and will be tweaked to find ``func``'s maximum. 61  :param tuple args_init: Initial coordinates for the random walk. 62  :param real step_init: The step size will vary with time in this function, so63    this is the initial value for the step size. 64  :param real step_min: The limit size for the step.65  :param int iter_max: Upper iterations bound for each loop to avoid infinite 66    loops.67  :param bool verbose: If ``verbose`` then extra run information will be 68    displayed in terminal. 69  :returns: vector, complex -- The coordinates of the optimum found for 70    ``func`` and the value of ``func`` at this point.71  """72  solution = vector(CC, normalizer_func(args_init))73  solution_temp = solution74  value = func(solution)75  value_temp = value76  step_current = step_init77  if verbose:78    print("Initial solution : " + func.__name__ + 79      str(solution) + " = " + str(value))80  iter_nb = 081  while step_current > step_min:82    count = 183    while value_temp <= value and count < iter_max:84      direction = vector(85        [uniform(-1,1) for _ in range(len(args_init))]86        ).normalized()87      solution_temp = vector(normalizer_func(solution + direction*step_current))88      value_temp = func(solution_temp)89      count += 190    if value_temp > value:91      solution = solution_temp92      value = value_temp93    else:94      step_current = step_current/295      if verbose:96        print("optimization, current step: " + str(step_current))97  98  if verbose:99    print("Final solution : " + func.__name__ + 100      str(solution) + " = " + str(value))101  return (solution, value)102def optimize_2spheres(func, args_init, step_init, step_min, iter_max, radius=1, verbose = False):103  r"""104  Optimization function finding the maximum reaching coordinates for ``func``105  with a random walk on a two sphere of dimension half the size of 106  ``args_init``. (Work in progress !)107    For now, this function is in project and is not used, it can be ignored.108  109  :param function func: The function to be optimized, each of its arguments must be 110    numerical and will be tweaked to find ``func``'s maximum. 111  :param tuple args_init: Initial coordinates for the random walk.112  :param real step_init: The step size will vary with time in this function, so113    this is the initial value for the step size. 114  :param real step_min: The limit size for the step.115  :param int iter_max: Upper iterations bound for each loop to avoid infinite 116    loops.117  :param real radius: Sphere radius.118  :param bool verbose: If ``verbose`` then extra run information will be displayed in 119    terminal.120  :returns: vector, complex -- The coordianates of the optimum found for 121    ``func`` and the value of ``func`` at this point.122  """123  def unwrap(vector_tuple):124    """Takes in vectors and returns a list of their coefficients125    """126    arguments_unwraped = []127    for vector_instance in vector_tuple:128      for coefficient in vector_instance:129        arguments_unwraped.append(coefficient)130    return arguments_unwraped131  def point_on_cone(cone_center, cone_spherical_radius, sphere_radius): # TODO132    dimension = len(cone_center)133    rotation_cone_center_to_Z = rotation_to_Z(cone_center) # matrix134    point_random_angles = [uniform(-pi,pi) for _ in dimension - 2]135    cone_angle = cone_spherical_radius/sphere_radius136    point = vector_from_pherical(sphere_radius, cone_angle, *point_random_angles)137    point = rotation_cone_center_to_Z.inverse() * point138    return point139  dimension = len(args_init)/2140  solution = vector(CC, args_init[:dimension]).normalized(), \141    vector(CC, args_init[dimension:]).normalized()142  value = func(unwrap(solution))143  value_temp = value144  step_current = step_init145  if verbose:146    print("Initial solution : " + func.__name__ + 147      str(solution) + " = " + str(value))148  iter_nb = 0149  while step_current > step_min:150    solution_temp = solution # initialization151    count = 1152    while CC(value_temp) <= CC(value) and count < iter_max:153      solution_temp = point_on_cone(solution, step_current, 1)154      value_temp = func(unwrap(solution_temp))155      count += 1156    if CC(value_temp) > CC(value):157      solution = solution_temp158      value = value_temp159    else:160      step_current = step_current/2161  162  if verbose:163    print("Final solution : " + func.__name__ + 164      str(solution) + " = " + str(value))...

python_sgd.py

Source:python_sgd.py

1import time2import numpy as np3from benchopt.base import BaseSolver4class Solver(BaseSolver):5    name = 'Python-SGD'  # stochastic gradient descent6    # any parameter defined here is accessible as a class attribute7    parameters = {'step_init': [1.]}8    def set_objective(self, X, y, lmbd):9        self.X, self.y, self.lmbd = X, y, lmbd10    def init(self):11        # print("---------------------------------- Initializing SGD weights")12        time.sleep(.2)13        n_samples, n_features = self.X.shape14        self.w = np.zeros(n_features)15    def run(self, n_iter):16        n_samples, n_features = self.X.shape17        # w = np.zeros(n_features)18        w = self.w19        # self.step_init = 1e3 # TODO: set as parameter ?20        # idx_samples = np.random.choice(n_samples, n_iter)21        # steps = self.step_init / np.sqrt(1 + np.arange(1, n_iter + 1)) # which decreasing rule for the step size ?22        t_new = 123        for i in range(n_iter):24            # When n_iter is known in advance:25            # idx = idx_samples[i]26            # step = steps[i]27            # When n_iter is NOT known in advance:28            idx = np.random.choice(n_samples)29            step = self.step_init / np.sqrt(1 + i)30            # SGD step31            w -= step * self.grad_i_logreg_l2(w, self.X, self.y, self.lmbd, idx)32        self.w = w33    def grad_i_logreg_l2(self, w, X, y, lmbd, i):34        return self.grad_i_logreg(w, X, y, i) + lmbd * w35    def grad_i_logreg(self, w, X, y, i):36        return - X[i] * y[i] / (1. + np.exp(y[i] * (X[i] @ w)))37    def get_result(self):...

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