diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index 86fc876202b90c8400978e0c35222249957d0523..65c4abb43c2173d5af4b935965ab4ba6d42a5b34 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -9,14 +9,15 @@ from sklearn.base import BaseEstimator, ClassifierMixin
import time
import matplotlib.pyplot as plt
-from .BoostUtils import StumpsClassifiersGenerator, sign, BaseBoost, getInterpretBase, get_accuracy_graph
+from .BoostUtils import StumpsClassifiersGenerator, sign, BaseBoost, \
+ getInterpretBase, get_accuracy_graph
from ... import Metrics
class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
def __init__(self, n_max_iterations=None, estimators_generator=None,
random_state=42, self_complemented=True, twice_the_same=False,
- c_bound_choice = True, random_start = True,
+ c_bound_choice=True, random_start=True,
n_stumps_per_attribute=None, use_r=True,
plotted_metric=Metrics.zero_one_loss):
super(ColumnGenerationClassifierQar, self).__init__()
@@ -59,7 +60,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
if n_stumps_per_attribute:
self.n_stumps = n_stumps_per_attribute
self.use_r = use_r
- self.printed_args_name_list = ["n_max_iterations", "self_complemented", "twice_the_same",
+ self.printed_args_name_list = ["n_max_iterations", "self_complemented",
+ "twice_the_same",
"c_bound_choice", "random_start",
"n_stumps", "use_r"]
@@ -79,7 +81,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.init_info_containers()
- m,n,y_kernel_matrix = self.init_hypotheses(formatted_X, formatted_y)
+ m, n, y_kernel_matrix = self.init_hypotheses(formatted_X, formatted_y)
self.n_total_hypotheses_ = n
self.n_total_examples = m
@@ -87,12 +89,19 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.init_boosting(m, formatted_y, y_kernel_matrix)
self.break_cause = " the maximum number of iterations was attained."
- for k in range(min(n-1, self.n_max_iterations-1 if self.n_max_iterations is not None else np.inf)):
+ for k in range(min(n - 1,
+ self.n_max_iterations - 1 if self.n_max_iterations is not None else np.inf)):
# Print dynamically the step and the error of the current classifier
- print("Resp. bound : {}, {}/{}, eps :{}".format(self.respected_bound, k+2, self.n_max_iterations, self.voter_perfs[-1]), end="\r")
+ print(
+ "Resp. bound : {}, {}/{}, eps :{}".format(self.respected_bound,
+ k + 2,
+ self.n_max_iterations,
+ self.voter_perfs[-1]),
+ end="\r")
- sol, new_voter_index = self.choose_new_voter(y_kernel_matrix, formatted_y)
+ sol, new_voter_index = self.choose_new_voter(y_kernel_matrix,
+ formatted_y)
if type(sol) == str:
self.break_cause = new_voter_index #
@@ -108,12 +117,12 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.update_info_containers(formatted_y, voter_perf, k)
-
self.nb_opposed_voters = self.check_opposed_voters()
- self.estimators_generator.estimators_ = self.estimators_generator.estimators_[self.chosen_columns_]
+ self.estimators_generator.estimators_ = \
+ self.estimators_generator.estimators_[self.chosen_columns_]
self.weights_ = np.array(self.weights_)
- self.weights_/= np.sum(self.weights_)
+ self.weights_ /= np.sum(self.weights_)
formatted_y[formatted_y == -1] = 0
formatted_y = formatted_y.reshape((m,))
@@ -129,7 +138,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
logging.warning('Converting sparse matrix to dense matrix.')
X = np.array(X.todense())
classification_matrix = self._binary_classification_matrix(X)
- margins = np.squeeze(np.asarray(np.matmul(classification_matrix, self.weights_)))
+ margins = np.squeeze(
+ np.asarray(np.matmul(classification_matrix, self.weights_)))
signs_array = np.array([int(x) for x in sign(margins)])
signs_array[signs_array == -1] = 0
end = time.time()
@@ -141,7 +151,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.example_weights_.append(self.example_weights)
self.previous_vote = np.matmul(
self.classification_matrix[:, self.chosen_columns_],
- np.array(self.weights_).reshape((k + 2, 1))).reshape((self.n_total_examples, 1))
+ np.array(self.weights_).reshape((k + 2, 1))).reshape(
+ (self.n_total_examples, 1))
self.previous_votes.append(self.previous_vote)
self.previous_margins.append(
@@ -150,7 +161,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
if self.use_r:
bound = self.bounds[-1] * math.sqrt(1 - voter_perf ** 2)
else:
- bound = np.prod(np.sqrt(1-4*np.square(0.5-np.array(self.voter_perfs))))
+ bound = np.prod(
+ np.sqrt(1 - 4 * np.square(0.5 - np.array(self.voter_perfs))))
if train_metric > bound:
self.respected_bound = False
@@ -193,7 +205,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
y_kernel_matrix)
return sol, new_voter_index
-
def init_boosting(self, m, y, y_kernel_matrix):
"""THis initialization corressponds to the first round of boosting with equal weights for each examples and the voter chosen by it's margin."""
self.example_weights = self._initialize_alphas(m).reshape((m, 1))
@@ -220,7 +231,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
epsilon = self._compute_epsilon(y)
self.voter_perfs.append(epsilon)
-
if self.use_r:
self.q = 0.5 * math.log((1 + r) / (1 - r))
else:
@@ -234,11 +244,11 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.previous_margins.append(
np.multiply(y, self.previous_vote))
- train_metric =self.plotted_metric.score(y, np.sign(self.previous_vote))
+ train_metric = self.plotted_metric.score(y, np.sign(self.previous_vote))
if self.use_r:
bound = math.sqrt(1 - r ** 2)
else:
- bound = np.prod(np.sqrt(1-4*np.square(0.5-np.array(epsilon))))
+ bound = np.prod(np.sqrt(1 - 4 * np.square(0.5 - np.array(epsilon))))
if train_metric > bound:
self.respected_bound = False
@@ -260,14 +270,15 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
def init_hypotheses(self, X, y):
"""Inintialization for the hyptotheses used to build the boosted vote"""
if self.estimators_generator is None:
- self.estimators_generator = StumpsClassifiersGenerator(n_stumps_per_attribute=self.n_stumps,
- self_complemented=self.self_complemented)
+ self.estimators_generator = StumpsClassifiersGenerator(
+ n_stumps_per_attribute=self.n_stumps,
+ self_complemented=self.self_complemented)
self.estimators_generator.fit(X, y)
self.classification_matrix = self._binary_classification_matrix(X)
m, n = self.classification_matrix.shape
y_kernel_matrix = np.multiply(y, self.classification_matrix)
- return m,n,y_kernel_matrix
+ return m, n, y_kernel_matrix
def init_info_containers(self):
"""Initialize the containers that will be collected at each iteration for the analysis"""
@@ -283,31 +294,41 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.previous_margins = []
self.respected_bound = True
- def _compute_epsilon(self,y):
+ def _compute_epsilon(self, y):
"""Updating the error variable, the old fashioned way uses the whole majority vote to update the error"""
ones_matrix = np.zeros(y.shape)
- ones_matrix[np.multiply(y, self.new_voter.reshape(y.shape)) < 0] = 1 # can np.divide if needed
+ ones_matrix[np.multiply(y, self.new_voter.reshape(
+ y.shape)) < 0] = 1 # can np.divide if needed
epsilon = np.average(ones_matrix, weights=self.example_weights, axis=0)
return epsilon
def _compute_r(self, y):
ones_matrix = np.ones(y.shape)
- ones_matrix[np.multiply(y, self.new_voter.reshape(y.shape)) < 0] = -1 # can np.divide if needed
+ ones_matrix[np.multiply(y, self.new_voter.reshape(
+ y.shape)) < 0] = -1 # can np.divide if needed
r = np.average(ones_matrix, weights=self.example_weights, axis=0)
return r
def update_example_weights(self, y):
"""Old fashioned exaple weights update uses the whole majority vote, the other way uses only the last voter."""
- new_weights = self.example_weights.reshape((self.n_total_examples, 1))*np.exp(-self.q*np.multiply(y,self.new_voter))
- self.example_weights = new_weights/np.sum(new_weights)
+ new_weights = self.example_weights.reshape(
+ (self.n_total_examples, 1)) * np.exp(
+ -self.q * np.multiply(y, self.new_voter))
+ self.example_weights = new_weights / np.sum(new_weights)
- def _find_best_weighted_margin(self, y_kernel_matrix, upper_bound=1.0, lower_bound=0.0):
+ def _find_best_weighted_margin(self, y_kernel_matrix, upper_bound=1.0,
+ lower_bound=0.0):
"""Finds the new voter by choosing the one that has the best weighted margin between 0.5 and 0.55
to avoid too god voters that will get all the votes weights"""
- weighted_kernel_matrix = np.multiply(y_kernel_matrix, self.example_weights.reshape((self.n_total_examples, 1)))
- pseudo_h_values = ma.array(np.sum(weighted_kernel_matrix, axis=0), fill_value=-np.inf)
+ weighted_kernel_matrix = np.multiply(y_kernel_matrix,
+ self.example_weights.reshape(
+ (self.n_total_examples, 1)))
+ pseudo_h_values = ma.array(np.sum(weighted_kernel_matrix, axis=0),
+ fill_value=-np.inf)
pseudo_h_values[self.chosen_columns_] = ma.masked
- acceptable_indices = np.where(np.logical_and(np.greater(upper_bound, pseudo_h_values), np.greater(pseudo_h_values, lower_bound)))[0]
+ acceptable_indices = np.where(
+ np.logical_and(np.greater(upper_bound, pseudo_h_values),
+ np.greater(pseudo_h_values, lower_bound)))[0]
if acceptable_indices.size > 0:
worst_h_index = self.random_state.choice(acceptable_indices)
return worst_h_index, [0]
@@ -336,9 +357,11 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
possible_sols = []
indices = []
causes = []
- for hypothese_index, hypothese in enumerate(y_kernel_matrix.transpose()):
- if (hypothese_index not in self.chosen_columns_ or self.twice_the_same) \
- and set(self.chosen_columns_)!={hypothese_index} \
+ for hypothese_index, hypothese in enumerate(
+ y_kernel_matrix.transpose()):
+ if (
+ hypothese_index not in self.chosen_columns_ or self.twice_the_same) \
+ and set(self.chosen_columns_) != {hypothese_index} \
and self._is_not_too_wrong(hypothese, y):
w = self._solve_one_weight_min_c(hypothese, y)
if w[0] != "break":
@@ -363,18 +386,26 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
No precalc because longer ; see the "derivee" latex document for more precision"""
m = next_column.shape[0]
zero_diag = np.ones((m, m)) - np.identity(m)
- weighted_previous_sum = np.multiply(y, self.previous_vote.reshape((m, 1)))
- weighted_next_column = np.multiply(next_column.reshape((m,1)), self.example_weights.reshape((m,1)))
+ weighted_previous_sum = np.multiply(y,
+ self.previous_vote.reshape((m, 1)))
+ weighted_next_column = np.multiply(next_column.reshape((m, 1)),
+ self.example_weights.reshape((m, 1)))
self.B2 = np.sum(weighted_next_column ** 2)
self.B1 = np.sum(2 * weighted_next_column * weighted_previous_sum)
self.B0 = np.sum(weighted_previous_sum ** 2)
- M2 = np.sum(np.multiply(np.matmul(weighted_next_column, np.transpose(weighted_next_column)), zero_diag))
- M1 = np.sum(np.multiply(np.matmul(weighted_previous_sum, np.transpose(weighted_next_column)) +
- np.matmul(weighted_next_column, np.transpose(weighted_previous_sum))
+ M2 = np.sum(np.multiply(
+ np.matmul(weighted_next_column, np.transpose(weighted_next_column)),
+ zero_diag))
+ M1 = np.sum(np.multiply(np.matmul(weighted_previous_sum,
+ np.transpose(weighted_next_column)) +
+ np.matmul(weighted_next_column,
+ np.transpose(weighted_previous_sum))
, zero_diag))
- M0 = np.sum(np.multiply(np.matmul(weighted_previous_sum, np.transpose(weighted_previous_sum)), zero_diag))
+ M0 = np.sum(np.multiply(np.matmul(weighted_previous_sum,
+ np.transpose(weighted_previous_sum)),
+ zero_diag))
self.A2 = self.B2 + M2
self.A1 = self.B1 + M1
@@ -386,8 +417,9 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
if C2 == 0:
if C1 == 0:
return ['break', "the derivate was constant"]
- else :
- is_acceptable, sol = self._analyze_solutions_one_weight(np.array(float(C0)/C1).reshape((1,1)))
+ else:
+ is_acceptable, sol = self._analyze_solutions_one_weight(
+ np.array(float(C0) / C1).reshape((1, 1)))
if is_acceptable:
return np.array([sol])
try:
@@ -421,7 +453,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
def _cbound(self, sol):
"""Computing the objective function"""
- return 1 - (self.A2*sol**2 + self.A1*sol + self.A0)/(self.B2*sol**2 + self.B1*sol + self.B0)/self.n_total_examples
+ return 1 - (self.A2 * sol ** 2 + self.A1 * sol + self.A0) / (
+ self.B2 * sol ** 2 + self.B1 * sol + self.B0) / self.n_total_examples
def _best_sol(self, sols):
"""Return the best min in the two possible sols"""
@@ -437,12 +470,14 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
path = "/".join(directory.split("/")[:-1])
try:
import os
- os.makedirs(path+"/gif_images")
+ os.makedirs(path + "/gif_images")
except:
raise
- filenames=[]
- max_weight = max([np.max(examples_weights) for examples_weights in self.example_weights_])
- min_weight = min([np.max(examples_weights) for examples_weights in self.example_weights_])
+ filenames = []
+ max_weight = max([np.max(examples_weights) for examples_weights in
+ self.example_weights_])
+ min_weight = min([np.max(examples_weights) for examples_weights in
+ self.example_weights_])
for iterIndex, examples_weights in enumerate(self.example_weights_):
r = np.array(examples_weights)
theta = np.arange(self.n_total_examples)
@@ -451,7 +486,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
ax = fig.add_subplot(111)
c = ax.scatter(theta, r, c=colors, cmap='RdYlGn', alpha=0.75)
ax.set_ylim(min_weight, max_weight)
- filename = path+"/gif_images/"+str(iterIndex)+".png"
+ filename = path + "/gif_images/" + str(iterIndex) + ".png"
filenames.append(filename)
plt.savefig(filename)
plt.close()
@@ -461,21 +496,22 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
logging.getLogger("PIL").setLevel(logging.WARNING)
for filename in filenames:
images.append(imageio.imread(filename))
- imageio.mimsave(path+'/weights.gif', images, duration=1. / 2)
+ imageio.mimsave(path + '/weights.gif', images, duration=1. / 2)
import shutil
- shutil.rmtree(path+"/gif_images")
- get_accuracy_graph(self.voter_perfs, self.__class__.__name__, directory + 'voter_perfs.png', "Errors")
- interpretString = getInterpretBase(self, directory, "QarBoost", self.weights_, self.break_cause)
-
- args_dict = dict((arg_name, str(self.__dict__[arg_name])) for arg_name in self.printed_args_name_list)
- interpretString += "\n \n With arguments : \n"+u'\u2022 '+ ("\n"+u'\u2022 ').join(['%s: \t%s' % (key, value)
- for (key, value) in args_dict.items()])
+ shutil.rmtree(path + "/gif_images")
+ get_accuracy_graph(self.voter_perfs, self.__class__.__name__,
+ directory + 'voter_perfs.png', "Errors")
+ interpretString = getInterpretBase(self, directory, "QarBoost",
+ self.weights_, self.break_cause)
+
+ args_dict = dict(
+ (arg_name, str(self.__dict__[arg_name])) for arg_name in
+ self.printed_args_name_list)
+ interpretString += "\n \n With arguments : \n" + u'\u2022 ' + (
+ "\n" + u'\u2022 ').join(['%s: \t%s' % (key, value)
+ for (key, value) in
+ args_dict.items()])
if not self.respected_bound:
interpretString += "\n\n The bound was not respected"
return interpretString
-
-
-
-
-