From 40c7ee5a50a991be14a167e06f400c9b3c557fdb Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Thu, 28 Feb 2019 16:52:27 -0500
Subject: [PATCH] Added get_params and removed mincq tracking
---
.../Monoview/Additions/BoostUtils.py | 3 --
.../Monoview/Additions/QarBoostUtils.py | 13 +++---
.../Monoview/ExportResults.py | 2 +-
.../Monoview/MonoviewUtils.py | 2 +-
.../MonoviewClassifiers/CGDesc.py | 3 +-
.../MonoviewClassifiers/CGreed.py | 2 +-
.../MonoviewClassifiers/CQBoost.py | 3 +-
.../MonoviewClassifiers/GradientBoosting.py | 46 +++++++++++++++++++
8 files changed, 58 insertions(+), 16 deletions(-)
create mode 100644 multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/GradientBoosting.py
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/BoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/BoostUtils.py
index 5c7c042b..ca2cf704 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/BoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/BoostUtils.py
@@ -696,9 +696,6 @@ def get_accuracy_graph(plotted_data, classifier_name, file_name, name="Accuracie
class BaseBoost(object):
- def __init__(self):
- self.n_stumps = 10
-
def _collect_probas(self, X):
return np.asarray([clf.predict_proba(X) for clf in self.estimators_generator.estimators_])
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
index eee18a47..7e51a32f 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/Additions/QarBoostUtils.py
@@ -19,9 +19,9 @@ 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,
- n_stumps_per_attribute=None, use_r=True, c_bound_sol=True,
+ n_stumps_per_attribute=1, use_r=True, c_bound_sol=True,
plotted_metric=Metrics.zero_one_loss, save_train_data=True,
- test_graph=True, mincq_tracking=True):
+ test_graph=True, mincq_tracking=False):
super(ColumnGenerationClassifierQar, self).__init__()
r"""
@@ -46,7 +46,6 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
plotted_metric : Metric module
The metric that will be plotted for each iteration of boosting.
"""
-
if type(random_state) is int:
self.random_state = np.random.RandomState(random_state)
else:
@@ -62,8 +61,7 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
self.c_bound_choice = c_bound_choice
self.random_start = random_start
self.plotted_metric = plotted_metric
- if n_stumps_per_attribute:
- self.n_stumps = n_stumps_per_attribute
+ self.n_stumps = n_stumps_per_attribute
self.use_r = use_r
self.c_bound_sol = c_bound_sol
self.save_train_data = save_train_data
@@ -74,15 +72,16 @@ class ColumnGenerationClassifierQar(BaseEstimator, ClassifierMixin, BaseBoost):
"n_stumps", "use_r", "c_bound_sol"]
self.mincq_tracking = mincq_tracking
+ def get_params(self, deep=True):
+ return {"random_state":self.random_state, "n_max_iterations":self.n_max_iterations}
+
def set_params(self, **params):
self.n_max_iterations = params["n_max_iterations"]
return self
def fit(self, X, y):
-
start = time.time()
-
formatted_X, formatted_y = self.format_X_y(X, y)
self.init_info_containers()
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/ExportResults.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/ExportResults.py
index dedcf5e6..a5e34dda 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/ExportResults.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/ExportResults.py
@@ -23,7 +23,7 @@ from sklearn import metrics # For stastics on classification
# Author-Info
__author__ = "Nikolas Huelsmann"
__status__ = "Prototype" # Production, Development, Prototype
-__date__ = 2016 - 03 - 25
+__date__ = 2016_03_25
#### Export Features to CSV
diff --git a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
index f67aac06..64ecb3ff 100644
--- a/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
+++ b/multiview_platform/MonoMultiViewClassifiers/Monoview/MonoviewUtils.py
@@ -28,7 +28,7 @@ def randomizedSearch(X_train, y_train, randomState, outputFileName, classifierMo
scorer = metricModule.get_scorer(**metricKWARGS)
nb_possible_combinations = compute_possible_combinations(params_dict)
min_list = np.array([min(nb_possible_combination, nIter) for nb_possible_combination in nb_possible_combinations])
- randomSearch = RandomizedSearchCV(estimator, n_iter=np.sum(min_list), param_distributions=params_dict, refit=True,
+ randomSearch = RandomizedSearchCV(estimator, n_iter=int(np.sum(min_list)), param_distributions=params_dict, refit=True,
n_jobs=nbCores, scoring=scorer, cv=KFolds, random_state=randomState)
detector = randomSearch.fit(X_train, y_train)
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGDesc.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGDesc.py
index 6b0f045e..6773cc5c 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGDesc.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGDesc.py
@@ -16,7 +16,6 @@ class CGDesc(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
use_r=True,
c_bound_sol=True
)
-
self.param_names = ["n_max_iterations"]
self.distribs = [CustomRandint(low=2, high=1000)]
self.classed_params = []
@@ -30,7 +29,7 @@ class CGDesc(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
return self.getInterpretQar(directory, y_test)
def get_name_for_fusion(self):
- return "CGr"
+ return "CGD"
def formatCmdArgs(args):
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
index 351698c2..bb0a6ca0 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CGreed.py
@@ -5,7 +5,7 @@ from ..Monoview.Additions.QarBoostUtils import ColumnGenerationClassifierQar
class CGreed(ColumnGenerationClassifierQar, BaseMonoviewClassifier):
- def __init__(self, random_state=None, n_max_iterations=500, n_stumps_per_attribute=10, **kwargs):
+ def __init__(self, random_state=None, n_max_iterations=500, n_stumps_per_attribute=1, **kwargs):
super(CGreed, self).__init__(n_max_iterations=n_max_iterations,
random_state=random_state,
self_complemented=True,
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
index 99618aff..9556dd1c 100644
--- a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/CQBoost.py
@@ -7,7 +7,7 @@ import os
class CQBoost(ColumnGenerationClassifier, BaseMonoviewClassifier):
- def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, **kwargs):
+ def __init__(self, random_state=None, mu=0.01, epsilon=1e-06, n_stumps=10, **kwargs):
super(CQBoost, self).__init__(
random_state=random_state,
mu=mu,
@@ -18,6 +18,7 @@ class CQBoost(ColumnGenerationClassifier, BaseMonoviewClassifier):
CustomRandint(low=1, high=15, multiplier="e-")]
self.classed_params = []
self.weird_strings = {}
+ self.n_stumps = n_stumps
if "nbCores" not in kwargs:
self.nbCores = 1
else:
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/GradientBoosting.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/GradientBoosting.py
new file mode 100644
index 00000000..40eb5145
--- /dev/null
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/GradientBoosting.py
@@ -0,0 +1,46 @@
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import GradientBoostingClassifier
+
+from ..Monoview.MonoviewUtils import CustomRandint, BaseMonoviewClassifier
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+
+class GradientBoosting(GradientBoostingClassifier, BaseMonoviewClassifier):
+
+ def __init__(self, random_state=None, loss="exponential", max_depth=1.0,
+ n_estimators=100, init=DecisionTreeClassifier(max_depth=1), **kwargs):
+ super(GradientBoosting, self).__init__(
+ loss=loss,
+ max_depth=max_depth,
+ n_estimators=n_estimators,
+ init=init,
+ random_state=random_state
+ )
+ self.param_names = ["n_estimators",]
+ self.classed_params = []
+ self.distribs = [CustomRandint(low=50, high=500),]
+ self.weird_strings = {}
+
+ def canProbas(self):
+ """Used to know if the classifier can return label probabilities"""
+ return True
+
+ def getInterpret(self, directory, y_test):
+ interpretString = ""
+ return interpretString
+
+
+def formatCmdArgs(args):
+ """Used to format kwargs for the parsed args"""
+ kwargsDict = {"n_estimators": args.GB_n_est,}
+ return kwargsDict
+
+
+def paramsToSet(nIter, randomState):
+ paramsSet = []
+ for _ in range(nIter):
+ paramsSet.append({"n_estimators": randomState.randint(50, 500),})
+ return paramsSet
\ No newline at end of file
--
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