diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index b535464288994d61076a0c1d054a22a991649cdb..52f8093c10867275018ac8c6e474b3fbd777fe89 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -14,6 +14,8 @@ from .BoostUtils import StumpsClassifiersGenerator, sign, BaseBoost, \
from ... import Metrics
+# Used for QarBoost and CGreed
+
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,
@@ -51,6 +53,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
else:
self.random_state = random_state
self.train_time = 0
+ self.train_shape = None
+ self.step_decisions = None
self.n_max_iterations = n_max_iterations
self.estimators_generator = estimators_generator
self.self_complemented = self_complemented
@@ -68,6 +72,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"twice_the_same",
"c_bound_choice", "random_start",
"n_stumps", "use_r", "c_bound_sol"]
+ self.matrix_compute = False
def set_params(self, **params):
self.self_complemented = params["self_complemented"]
@@ -79,6 +84,8 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.use_r = params["use_r"]
def fit(self, X, y):
+
+
start = time.time()
formatted_X, formatted_y = self.format_X_y(X, y)
@@ -97,6 +104,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
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
+ self.it = k
print(
"Resp. bound : {}, {}; {}/{}, eps :{}".format(self.respected_bound,
self.bounds[-1] > self.train_metrics[-1],
@@ -127,6 +135,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
if self.save_train_data:
self.X_train = self.classification_matrix[:, self.chosen_columns_]
+ self.raw_weights = self.weights_
self.y_train = formatted_y
self.weights_ = np.array(self.weights_)
@@ -146,8 +155,7 @@ 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)
- # if self.test_graph:
- # self.make_test_graph(classification_matrix)
+ self.step_predict(classification_matrix)
margins = np.squeeze(
np.asarray(np.matmul(classification_matrix, self.weights_)))
signs_array = np.array([int(x) for x in sign(margins)])
@@ -156,6 +164,16 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.predict_time = end - start
return signs_array
+ def step_predict(self, classification_matrix):
+ if classification_matrix.shape != self.train_shape:
+ self.step_decisions = np.zeros(classification_matrix.shape)
+ for weight_index in range(self.weights_.shape[0]-1):
+ margins = np.squeeze(
+ np.asarray(np.matmul(classification_matrix[:, :weight_index+1], self.weights_[:weight_index+1])))
+ signs_array = np.array([int(x) for x in sign(margins)])
+ signs_array[signs_array == -1] = 0
+ self.step_decisions[:, weight_index] = signs_array
+
def update_info_containers(self, y, voter_perf, k):
"""Is used at each iteration to compute and store all the needed quantities for later analysis"""
self.example_weights_.append(self.example_weights)
@@ -293,6 +311,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self_complemented=self.self_complemented)
self.estimators_generator.fit(X, y)
self.classification_matrix = self._binary_classification_matrix(X)
+ self.train_shape = self.classification_matrix.shape
m, n = self.classification_matrix.shape
y_kernel_matrix = np.multiply(y, self.classification_matrix)
@@ -349,21 +368,18 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
fill_value=-np.inf)
pseudo_h_values[self.chosen_columns_] = ma.masked
return np.argmax(pseudo_h_values), [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]
- # else:
- # return " no margin over random and acceptable", ""
def _is_not_too_wrong(self, hypothese, y):
"""Check if the weighted margin is better than random"""
- ones_matrix = np.zeros(y.shape)
- ones_matrix[hypothese.reshape(y.shape) < 0] = 1
- epsilon = np.average(ones_matrix, weights=self.example_weights, axis=0)
- return epsilon < 0.5
+ if self.c_bound_sol:
+ return np.sum(hypothese) > 0
+ else:
+ # ones_matrix = np.zeros(y.shape)
+ # ones_matrix[hypothese.reshape(y.shape) < 0] = 1
+ print(np.average(hypothese.reshape(y.shape), weights=self.example_weights))
+ quit()
+ weighted_margin = np.average(hypothese.reshape(y.shape), weights=self.example_weights)#ondes matrix, axis=0
+ return weighted_margin > 0
def get_possible(self, y_kernel_matrix, y):
"""Get all the indices of the hypothesis that are good enough to be chosen"""
@@ -380,117 +396,61 @@ 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} \
- and self._is_not_too_wrong(hypothese, y):
- w = self._solve_one_weight_min_c(hypothese, y)
- if w[0] != "break":
- c_bounds.append(self._cbound(w[0]))
- possible_sols.append(w)
- indices.append(hypothese_index)
- else:
- causes.append(w[1])
- if not causes:
- causes = ["no feature was better than random and acceptable"]
- if c_bounds:
- min_c_bound_index = ma.argmin(c_bounds)
- self.c_bounds.append(c_bounds[min_c_bound_index])
- selected_sol = possible_sols[min_c_bound_index]
- self.margins.append(self.margin(selected_sol))
- self.disagreements.append(self.disagreement(selected_sol))
- selected_voter_index = indices[min_c_bound_index]
- return selected_sol, selected_voter_index #selected_sol/(1+selected_sol)
- else:
- return "break", " and ".join(set(causes))
-
- def _solve_one_weight_min_c(self, next_column, y):
- """Here we solve the min C-bound problem for two voters using one weight only and return the best weight
- 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)
+ m = y_kernel_matrix.shape[0]
weighted_previous_sum = np.multiply(y,
self.previous_vote.reshape((m, 1)))
+ margin_old = np.sum(weighted_previous_sum)
if self.c_bound_sol:
- weighted_next_column = next_column.reshape((m, 1))
+ weighted_hypothesis = y_kernel_matrix
else:
- weighted_next_column = np.multiply(next_column.reshape((m, 1)),
- self.example_weights.reshape((m, 1)))
+ weighted_hypothesis = y_kernel_matrix * self.example_weights.reshape((m, 1))
+
+ bad_margins = np.where(np.sum(weighted_hypothesis, axis=0)<=0.0)[0]
- self.B2 = np.sum(weighted_next_column ** 2)
- self.B1 = np.sum(2 * weighted_next_column * weighted_previous_sum)
+ self.B2 = m
+ self.B1s = np.sum(2 * (weighted_previous_sum * weighted_hypothesis),
+ axis=0)
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))
- , 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
- self.A0 = self.B0 + M0
-
- C2 = (M1 * self.B2 - M2 * self.B1)
- C1 = 2 * (M0 * self.B2 - M2 * self.B0)
- C0 = M0 * self.B1 - M1 * self.B0
- 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)))
- if is_acceptable:
- return np.array([sol])
- if C1*C1 < 4*C2*C0:
- return ['break', "no roots"]
- if C2 > 0 and C1*C1 == 4*C2*C0:
- return ['break', "maximum"]
-
- if C1*C1-4*C2*C0<=0:
- try:
- sols = np.roots(np.array([C2, C1, C0]))
- except:
- return ["break", "nan"]
- else:
- sols = np.array([(-C1-math.sqrt(C1*C1-4*C2*C0))/(2*C2), (-C1+math.sqrt(C1*C1-4*C2*C0))/(2*C2)])
+ self.A2s = np.sum(weighted_hypothesis, axis=0) ** 2
+ self.A1s = np.sum(weighted_hypothesis, axis=0) * margin_old * 2
+ self.A0 = margin_old**2
- is_acceptable, sol = self._analyze_solutions_one_weight(sols)
- if is_acceptable:
- return np.array([sol])
- else:
- return ["break", sol]
-
- def _analyze_solutions_one_weight(self, sols):
- """"We just check that the solution found by np.roots is acceptable under our constraints
- (real, a minimum and over 0)"""
- if sols.shape[0] == 1:
- if self._cbound(sols[0]) < self._cbound(sols[0] + 1) and sols[0] > 0:
- best_sol = sols[0]
- else:
- if sols[0] > 0:
- return False, "the only solution was a maximum."
- else:
- return False, "the only solution was negative"
- elif sols.shape[0] == 2 and sols[0] > 0 and sols[1] > 1:
- best_sol = self._best_sol(sols)
- elif np.greater(sols, np.zeros(2)).any():
- return self._analyze_solutions_one_weight(np.array([np.max(sols)]))
- else:
- return False, "no solution were found"
+ C2s = (self.A1s * self.B2 - self.A2s * self.B1s)
+ C1s = 2 * (self.A0 * self.B2 - self.A2s * self.B0)
+ C0s = self.A0 * self.B1s - self.A1s * self.B0
+
+ sols = (-C1s + np.sqrt(C1s * C1s - 4 * C2s * C0s)) / (2 * C2s)
+ sols[np.where(C2s == 0)[0]] = C0s[np.where(C2s == 0)[0]] / C1s[np.where(C2s == 0)[0]]
+
+ masked_c_bounds = self.make_masked_c_bounds(sols, bad_margins)
+ best_hyp_index = np.argmin(masked_c_bounds)
+
+ self.c_bounds.append(masked_c_bounds[best_hyp_index])
+ self.margins.append(math.sqrt(self.A2s[best_hyp_index]/m))
+ self.disagreements.append(0.5*self.B1s[best_hyp_index]/m)
+
+ return sols[best_hyp_index], best_hyp_index
+
+
+ def make_masked_c_bounds(self, sols, bad_margins):
+ c_bounds = self.compute_c_bounds(sols)
+ trans_c_bounds = self.compute_c_bounds(sols + 1)
+ masked_c_bounds = ma.array(c_bounds, fill_value=np.inf)
+ # Masing Maximums
+ masked_c_bounds[c_bounds >= trans_c_bounds] = ma.masked
+ # Masking magrins <= 0
+ masked_c_bounds[bad_margins] = ma.masked
+ # Masking weights < 0 (because self-complemented)
+ masked_c_bounds[sols < 0] = ma.masked
+ # Masking nan c_bounds
+ masked_c_bounds[np.isnan(c_bounds)] = ma.masked
+ return masked_c_bounds
+
+ def compute_c_bounds(self, sols):
+ return 1 - (self.A2s * sols ** 2 + self.A1s * sols + self.A0) / ((
+ self.B2 * sols ** 2 + self.B1s * sols + self.B0) * self.n_total_examples)
- if isinstance(best_sol, complex):
- return False, "the sol was complex"
- else:
- return True, best_sol
def _cbound(self, sol):
"""Computing the objective function"""
@@ -512,53 +472,20 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"""Initialize the examples wieghts"""
return 1.0 / n_examples * np.ones((n_examples,))
- # def make_test_graph(self, classification_matrix):
- # signs_arrays = np.array([self.predict_classification_matrix(classification_matrix, index+1) for index in range(self.weights_.shape[0])]).reshape((classification_matrix.shape))
- # np.savetxt("/home/baptiste/signs_arrays.csv",
- # signs_arrays.astype(np.int16), delimiter=",")
- #
- # def predict_classification_matrix(self, classification_matrix, index):
- # margins = np.squeeze(
- # np.asarray(np.matmul(classification_matrix[:, :index], self.weights_[:index])))
- # signs_array = np.array([int(x) for x in sign(margins)])
- # signs_array[signs_array == -1] = 0
- # return signs_array
-
- def getInterpretQar(self, directory):
+ def get_step_decision_test_graph(self, directory, y_test):
+ np.savetxt(directory + "y_test_step.csv", self.step_decisions, delimiter=',')
+ step_metrics = []
+ for step_index in range(self.step_decisions.shape[1]-1):
+ step_metrics.append(self.plotted_metric.score(y_test, self.step_decisions[:, step_index]))
+ step_metrics = np.array(step_metrics)
+ get_accuracy_graph(step_metrics, self.__class__.__name__,
+ directory + 'step_test_metrics.png', self.plotted_metric)
+
+ def getInterpretQar(self, directory, y_test=None):
self.directory = directory
"""Used to interpret the functionning of the algorithm"""
- path = "/".join(directory.split("/")[:-1])
- # try:
- # import os
- # 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_])
- # for iterIndex, examples_weights in enumerate(self.example_weights_):
- # r = np.array(examples_weights)
- # theta = np.arange(self.n_total_examples)
- # colors = np.sign(self.previous_margins[iterIndex])
- # fig = plt.figure(figsize=(5, 5), dpi=80)
- # 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"
- # filenames.append(filename)
- # plt.savefig(filename)
- # plt.close()
- #
- # import imageio
- # images = []
- # logging.getLogger("PIL").setLevel(logging.WARNING)
- # for filename in filenames:
- # images.append(imageio.imread(filename))
- # imageio.mimsave(path + '/weights.gif', images, duration=1. / 2)
- # import shutil
- # shutil.rmtree(path + "/gif_images")
+ if self.step_decisions is not None:
+ self.get_step_decision_test_graph(directory, y_test)
get_accuracy_graph(self.voter_perfs[:20], self.__class__.__name__,
directory + 'voter_perfs.png', "Rs")
get_accuracy_graph(self.weights_, self.__class__.__name__,
@@ -574,11 +501,13 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
directory + 'disagreements.png', "disagreements")
get_accuracy_graph(self.train_metrics[:-1], self.__class__.__name__,
directory + 'c_bounds_train_metrics.png', self.plotted_metric, self.c_bounds, "C-Bound", self.bounds[:-1])
- interpretString = getInterpretBase(self, directory, "QarBoost",
+ interpretString = getInterpretBase(self, directory, self.__class__.__name__,
self.weights_, self.break_cause)
if self.save_train_data:
np.savetxt(directory+"x_train.csv", self.X_train, delimiter=',')
np.savetxt(directory+"y_train.csv", self.y_train, delimiter=',')
+ np.savetxt(directory + "raw_weights.csv", self.raw_weights, delimiter=',')
+ np.savetxt(directory + "c_bounds.csv", self.c_bounds, delimiter=',')
args_dict = dict(
(arg_name, str(self.__dict__[arg_name])) for arg_name in
self.printed_args_name_list)
@@ -590,3 +519,135 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
interpretString += "\n\n The bound was not respected"
return interpretString
+
+
+# def _solve_one_weight_min_c(self, next_column, y, margin_old, m):
+ # """Here we solve the min C-bound problem for two voters using one weight only and return the best weight
+ # No precalc because longer ; see the "derivee" latex document for more precision"""
+ # # zero_diag = np.ones((m, m)) - np.identity(m)
+ # weighted_previous_sum = np.multiply(y,
+ # self.previous_vote.reshape(
+ # (m, 1)))
+ # if self.c_bound_sol:
+ # weighted_next_column = next_column.reshape((m, 1))
+ # else:
+ # weighted_next_column = np.multiply(next_column.reshape((m, 1)),
+ # self.example_weights.reshape((m, 1)))
+ #
+ # self.B1 = np.sum(2 * weighted_next_column * weighted_previous_sum)
+ # self.B0 = np.sum(weighted_previous_sum ** 2)
+ # self.B2 = m
+ #
+ # # M2 = np.sum(np.multiply(
+ # # np.matmul(weighted_next_column, np.transpose(weighted_next_column)),
+ # # zero_diag))
+ # # plif = np.sum(np.multiply(weighted_next_column, y))**2 - m
+ # # print(M2, plif, "plouf")
+ # # 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))
+ # # self.A2 = self.B2 + M2
+ # # self.A1 = self.B1 + M1
+ # # self.A0 = self.B0 + M0
+ #
+ # # C2 = (M1 * self.B2 - M2 * self.B1)
+ # # C1 = 2 * (M0 * self.B2 - M2 * self.B0)
+ # # C0 = M0 * self.B1 - M1 * self.B0
+ #
+ # margin_new = np.sum(weighted_next_column)
+ # # margin_old = np.sum(weighted_previous_sum)
+ #
+ # self.A2 = margin_new**2
+ # self.A1 = 2*margin_new*margin_old
+ # self.A0 = margin_old**2
+ #
+ # C2 = (self.A1 * self.B2 - self.A2 * self.B1)
+ # C1 = 2 * (self.A0 * self.B2 - self.A2 * self.B0)
+ # C0 = self.A0 * self.B1 - self.A1 * self.B0
+ #
+ #
+ # det = C1*C1-4*C2*C0
+ #
+ # if C2 == 0:
+ # if C1 == 0:
+ # return ['break', "the derivate was constant"]
+ # else:
+ # sols = np.array([float(C0) / C1])
+ # elif det < 0:
+ # return ['break', "no real roots"]
+ # elif det == 0 :
+ # sols = np.array([-C1/(2*C2)])
+ # else:
+ # # print('yes')
+ # sols = np.array([(-C1 + math.sqrt(C1 * C1 - 4 * C2 * C0)) / (2 * C2)])
+ #
+ # is_acceptable, sol = self._analyze_solutions_one_weight(sols)
+ # if is_acceptable:
+ # return np.array([sol])
+ # else:
+ # return ["break", sol]
+ #
+ # def _analyze_solutions_one_weight(self, sols):
+ # """"We just check that the solution found by np.roots is acceptable under our constraints
+ # (real, a minimum and over 0)"""
+ # if sols.shape[0] == 2:
+ # best_sol = self._best_sol(sols)
+ # if best_sol > 0:
+ # if best_sol == sols[0]:
+ # print("moins")
+ # return True, best_sol
+ # else:
+ # return False, "sol < 0"
+ # elif sols.shape[0] == 1:
+ # if self._cbound(sols[0]) < self._cbound(sols[0] + 1) and sols[0] > 0:
+ # return True, sols[0]
+ # else:
+ # return False, "sol was max"
+ # else:
+ # return False, "no sol"
+
+ # elif sols.shape[0] == 2 and sols[0] > 0 and sols[1] > 1:
+ # best_sol = self._best_sol(sols)
+ # elif np.greater(sols, np.zeros(2)).any():
+ # return self._analyze_solutions_one_weight(np.array([np.max(sols)]))
+ # else:
+ # return False, "no solution were found"
+ #
+ # if isinstance(best_sol, complex):
+ # return False, "the sol was complex"
+ # else:
+ # return True, best_sol
+
+ #
+ # else:
+ # for hypothese_index, hypothese in enumerate(
+ # weighted_hypothesis.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, margin_old, m)
+ # if w[0] != "break":
+ # c_bounds.append(self._cbound(w[0]))
+ # possible_sols.append(w)
+ # indices.append(hypothese_index)
+ # else:
+ # causes.append(w[1])
+ # if not causes:
+ # causes = ["no feature was better than random and acceptable"]
+ # if c_bounds:
+ # min_c_bound_index = ma.argmin(c_bounds)
+ # self.c_bounds.append(c_bounds[min_c_bound_index])
+ # selected_sol = possible_sols[min_c_bound_index]
+ # self.margins.append(self.margin(selected_sol))
+ # self.disagreements.append(self.disagreement(selected_sol))
+ # selected_voter_index = indices[min_c_bound_index]
+ # return selected_sol, selected_voter_index #selected_sol/(1+selected_sol)
+ # else:
+ # return "break", " and ".join(set(causes))
\ No newline at end of file
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/SVCClassifier.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/SVCClassifier.py
index 746ad0d09c5fbad3a553b368c97352301b9cd8aa..6778caad9d7ee9a6795e88d0cbf74b96be3a8dc2 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/SVCClassifier.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/SVCClassifier.py
@@ -19,6 +19,6 @@ class SVCClassifier(SVC):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
return interpretString
\ No newline at end of file
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/analyzeResult.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/analyzeResult.py
index b81e84af5593ec70708596ddf4e712098d9274ec..daf8dd09f7e58a5bdf70d1bd0d536f0b59847076 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/analyzeResult.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/analyzeResult.py
@@ -16,14 +16,14 @@ def getDBConfigString(name, feat, classificationIndices, shape, classLabelsNames
return dbConfigString
-def getClassifierConfigString(gridSearch, nbCores, nIter, clKWARGS, classifier, directory):
+def getClassifierConfigString(gridSearch, nbCores, nIter, clKWARGS, classifier, directory, y_test):
classifierConfigString = "Classifier configuration : \n"
classifierConfigString += "\t- " + classifier.getConfig()[5:] + "\n"
classifierConfigString += "\t- Executed on " + str(nbCores) + " core(s) \n"
if gridSearch:
classifierConfigString += "\t- Got configuration using randomized search with " + str(nIter) + " iterations \n"
classifierConfigString += "\n\n"
- classifierInterpretString = classifier.getInterpret(directory)
+ classifierInterpretString = classifier.getInterpret(directory, y_test)
return classifierConfigString, classifierInterpretString
@@ -52,7 +52,7 @@ def execute(name, learningRate, KFolds, nbCores, gridSearch, metrics, nIter, fea
stringAnalysis += metrics[0][0] + " on train : " + str(trainScore) + "\n" + metrics[0][0] + " on test : " + str(
testScore) + "\n\n"
stringAnalysis += getDBConfigString(name, feat, learningRate, shape, classLabelsNames, KFolds)
- classifierConfigString, classifierIntepretString = getClassifierConfigString(gridSearch, nbCores, nIter, clKWARGS, classifier, directory)
+ classifierConfigString, classifierIntepretString = getClassifierConfigString(gridSearch, nbCores, nIter, clKWARGS, classifier, directory, y_test)
stringAnalysis += classifierConfigString
for metric in metrics:
metricString, metricScore = getMetricScore(metric, y_train, y_train_pred, y_test, y_test_pred)
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
index 19352ef3d0d4d55d8ec122543fd5dcc398643a58..0bdc49779defbcd502fc5f4a3f45563791e87659 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/Adaboost.py
@@ -40,7 +40,7 @@ class Adaboost(AdaBoostClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
interpretString += self.getFeatureImportance(directory)
interpretString += "\n\n Estimator error | Estimator weight\n"
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
similarity index 82%
rename from multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py
rename to multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
index 2ea8b44664323f7656ab1840774f31a4a5b77a65..d534e54a95084a458c761c9c245486d7d75fcbb2 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
@@ -3,10 +3,10 @@ from ..Monoview.Additions.BoostUtils import getInterpretBase
from ..Monoview.Additions.QarBoostUtils import ColumnGenerationClassifierQar
-class QarBoostNC(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
+class CGreed(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
def __init__(self, random_state=None, **kwargs):
- super(QarBoostNC, self).__init__(n_max_iterations=300,
+ super(CGreed, self).__init__(n_max_iterations=500,
random_state=random_state,
self_complemented=True,
twice_the_same=True,
@@ -25,11 +25,11 @@ class QarBoostNC(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
- return "QBNC"
+ return "CGr"
def formatCmdArgs(args):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
index 290bbbb0815150ca5a885c7600be994643e5cbe4..3d57fe81f33c75423a1ab8518c39b57ae9528e64 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
@@ -21,8 +21,8 @@ class CQBoost(ColumnGenerationClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return getInterpretBase(self, directory, "CQBoost", self.weights_,)
+ def getInterpret(self, directory, y_test):
+ return getInterpretBase(self, directory, "CQBoost", self.weights_, y_test)
def formatCmdArgs(args):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py
index 98a9d6534a39219ecadede1f4524923f03cee4fe..28f121c917375a2f3aca517c3996efed56d48d84 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv2.py
@@ -76,7 +76,7 @@ class CQBoostv2(ColumnGenerationClassifierv2, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
return getInterpretBase(self, directory, "CQBoostv2", self.weights_,)
def get_name_for_fusion(self):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv21.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv21.py
index 9274d9dc11ecaff6c5d107c20f305176650626f7..cf808f01e1b662ed6241c3a4694128e632b5d3cc 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv21.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoostv21.py
@@ -258,7 +258,7 @@ class CQBoostv21(CqBoostClassifierv21, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
return getInterpretBase(self, directory, "CQBoostv21", self.weights_, self.break_cause)
def get_name_for_fusion(self):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/DecisionTree.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/DecisionTree.py
index b9dbf42be758f1d7290b15d35741a1b4178060aa..b511fdf28668db404b411827605dd98ec894be7d 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/DecisionTree.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/DecisionTree.py
@@ -28,7 +28,7 @@ class DecisionTree(DecisionTreeClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
interpretString += self.getFeatureImportance(directory)
return interpretString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/KNN.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/KNN.py
index e7de1a29a46c6e416b5f591fb9aefc9977108461..a8cb8fc6447a5ffe1ef3c97930931cbb1b6165d5 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/KNN.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/KNN.py
@@ -28,7 +28,7 @@ class KNN(KNeighborsClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
return interpretString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoost.py
index 1dcc048616ab7e2e041f3bb89bd6e39221461ad9..e4380ff4281b513de8766d324088003683021235 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoost.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoost.py
@@ -6,13 +6,18 @@ from ..Monoview.Additions.QarBoostUtils import ColumnGenerationClassifierQar
class QarBoost(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
def __init__(self, random_state=None, **kwargs):
- super(QarBoost, self).__init__(
- random_state=random_state)
-
- self.param_names = ["self_complemented", "twice_the_same", "old_fashioned", "previous_vote_weighted",
- "c_bound_choice", "random_start", "two_wieghts_problem"]
- self.distribs = [[True, False], [True, False], [True, False], [True, False],
- [True, False], [True, False], [True, False]]
+ super(QarBoost, self).__init__(n_max_iterations=500,
+ random_state=random_state,
+ self_complemented=True,
+ twice_the_same=True,
+ c_bound_choice=True,
+ random_start=False,
+ n_stumps_per_attribute=1,
+ use_r=True,
+ c_bound_sol=False
+ )
+ self.param_names = []
+ self.distribs = []
self.classed_params = []
self.weird_strings = {}
@@ -20,8 +25,11 @@ class QarBoost(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
+
+ def get_name_for_fusion(self):
+ return "QB"
def formatCmdArgs(args):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC2.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC2.py
index 9b07ade50a40f220a210fbfa1bc047002dc97c30..f5bf8416163edf7adb66b766bc0562e81b7e2af5 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC2.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC2.py
@@ -25,8 +25,8 @@ class QarBoostNC2(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
return "QBN2"
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC3.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC3.py
index 7133f80620bbae532461b5f7b053d3a8b440157a..d407d12efce0edc85313b47e792f4bfa1366b905 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC3.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostNC3.py
@@ -25,8 +25,8 @@ class QarBoostNC3(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
return "QBN3"
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv2.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv2.py
index 934e07ad329bf0f6e11546caab967e71163eb798..1829f8c435d2cb465422192a4271a030727f8335 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv2.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv2.py
@@ -25,8 +25,8 @@ class QarBoostv2(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
return "QBv2"
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv3.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv3.py
index aca342a5ed3fd6483581a1eb86df97e1cea3ad9c..8b99a7a5841abbc2ef479deb3be58c580b1f2873 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv3.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/QarBoostv3.py
@@ -28,8 +28,8 @@ class QarBoostv3(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
- return self.getInterpretQar(directory)
+ def getInterpret(self, directory, y_test):
+ return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
return "QBv3"
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/RandomForest.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/RandomForest.py
index 671fa1af52baf30c89d3b43fc373bfc5b9940eb6..5aeb0f9a5afc1e5b3355379bf57dc2101d817326 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/RandomForest.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/RandomForest.py
@@ -28,7 +28,7 @@ class RandomForest(RandomForestClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
interpretString += self.getFeatureImportance(directory)
return interpretString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCM.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCM.py
index 8b1de8447dd98ea9c69d6cbd26a1119db3ad9ad0..0543be7723e165bc1ff72503dd24a31e92f72ec5 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCM.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCM.py
@@ -62,7 +62,7 @@ class SCM(scm, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = "Model used : " + str(self.model_)
return interpretString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SGD.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SGD.py
index c2ce6b9e20f48b7242b8e8c1f60541a7c6e68aa0..0cfb7f9305cb81c43ea5004a18281e985bd988b3 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SGD.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SGD.py
@@ -28,7 +28,7 @@ class SGD(SGDClassifier, BaseMonoviewClassifier):
"""Used to know if the classifier can return label probabilities"""
return True
- def getInterpret(self, directory):
+ def getInterpret(self, directory, y_test):
interpretString = ""
return interpretString
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SVMLinear.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SVMLinear.py
index 88e7d40bce8d5c085a4af70bf231dc48ac2464fe..80a0179470b05f17e442ba35b0332dccf05494e4 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SVMLinear.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SVMLinear.py
@@ -19,6 +19,7 @@ class SVMLinear(SVCClassifier, BaseMonoviewClassifier):
self.distribs = [CustomUniform(loc=0, state=1), ]
+
def formatCmdArgs(args):
"""Used to format kwargs for the parsed args"""
kwargsDict = {"C": args.SVML_C, }