From 6b23786fdc365432e1b3429f19983028e369c2f7 Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Sun, 31 Mar 2019 14:41:46 -0400
Subject: [PATCH] add res dir and scmsparsity
---
.../MonoMultiViewClassifiers/ExecClassif.py | 2 +-
.../MonoviewClassifiers/SCMSparsity.py | 110 ++++++++++++++++++
.../utils/execution.py | 21 +++-
3 files changed, 129 insertions(+), 4 deletions(-)
create mode 100644 multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMSparsity.py
diff --git a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
index 64c9f085..2e50963c 100644
--- a/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
+++ b/multiview_platform/MonoMultiViewClassifiers/ExecClassif.py
@@ -453,7 +453,7 @@ def execClassif(arguments):
monoviewAlgos = args.CL_algos_monoview
multiviewAlgos = args.CL_algos_multiview
- directory = execution.initLogFile(args.name, args.views, args.CL_type, args.log, args.debug, args.label)
+ directory = execution.initLogFile(args.name, args.views, args.CL_type, args.log, args.debug, args.label, args.res_dir)
randomState = execution.initRandomState(args.randomState, directory)
statsIterRandomStates = execution.initStatsIterRandomStates(statsIter,randomState)
diff --git a/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMSparsity.py b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMSparsity.py
new file mode 100644
index 00000000..1544ce6c
--- /dev/null
+++ b/multiview_platform/MonoMultiViewClassifiers/MonoviewClassifiers/SCMSparsity.py
@@ -0,0 +1,110 @@
+from sklearn.externals.six import iteritems
+from pyscm.scm import SetCoveringMachineClassifier as scm
+from sklearn.base import BaseEstimator, ClassifierMixin
+import numpy as np
+import time
+
+from ..Monoview.MonoviewUtils import CustomRandint, CustomUniform, BaseMonoviewClassifier
+from ..Metrics import zero_one_loss
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+# class DecisionStumpSCMNew(scm, BaseEstimator, ClassifierMixin):
+# """docstring for SCM
+# A hands on class of SCM using decision stump, built with sklearn format in order to use sklearn function on SCM like
+# CV, gridsearch, and so on ..."""
+#
+# def __init__(self, model_type='conjunction', p=0.1, max_rules=10, random_state=42):
+# super(DecisionStumpSCMNew, self).__init__(model_type=model_type, max_rules=max_rules, p=p, random_state=random_state)
+# # self.model_type = model_type
+# # self.p = p
+# # self.max_rules = max_rules
+# # self.random_state = random_state
+# # self.clf = scm(model_type=self.model_type, max_rules=self.max_rules, p=self.p, random_state=self.random_state)
+#
+# # def fit(self, X, y):
+# # print(self.clf.model_type)
+# # self.clf.fit(X=X, y=y)
+# #
+# # def predict(self, X):
+# # return self.clf.predict(X)
+# #
+# # def set_params(self, **params):
+# # for key, value in iteritems(params):
+# # if key == 'p':
+# # self.p = value
+# # if key == 'model_type':
+# # self.model_type = value
+# # if key == 'max_rules':
+# # self.max_rules = value
+#
+# # def get_stats(self):
+# # return {"Binary_attributes": self.clf.model_.rules}
+
+
+class SCMSparsity(BaseMonoviewClassifier):
+
+ def __init__(self, random_state=None, model_type="conjunction",
+ max_rules=10, p=0.1, **kwargs):
+ self.scm_estimators = [scm(
+ random_state=random_state,
+ model_type=model_type,
+ max_rules=max_rule,
+ p=p
+ ) for max_rule in range(max_rules)]
+ self.model_type = model_type
+ self.p = p
+ self.random_state = random_state
+ self.max_rules = max_rules
+ self.param_names = ["model_type", "max_rules", "p", "random_state"]
+ self.distribs = [["conjunction", "disjunction"],
+ CustomRandint(low=1, high=15),
+ CustomUniform(loc=0, state=1), [random_state]]
+ self.classed_params = []
+ self.weird_strings = {}
+
+ def get_params(self):
+ return {"model_type":self.model_type, "p":self.p, "max_rules":self.max_rules, "random_state":self.random_state}
+
+ def fit(self, X, y, tiebreaker=None, iteration_callback=None, **fit_params):
+ for scm_estimator in self.scm_estimators:
+ beg = time.time()
+ scm_estimator.fit(X, y, tiebreaker=None, iteration_callback=None, **fit_params)
+ end = time.time()
+ self.times = np.array([end-beg, 0])
+ self.train_metrics = [zero_one_loss.score(y, scm_estimator.predict(X)) for scm_estimator in self.scm_estimators]
+ return self.scm_estimators[-1]
+
+ def predict(self, X):
+ self.preds = [scm_estimator.predict(X) for scm_estimator in self.scm_estimators]
+ return self.preds[-1]
+
+ def canProbas(self):
+ """Used to know if the classifier can return label probabilities"""
+ return True
+
+ def getInterpret(self, directory, y_test):
+ interpretString = ""
+ np.savetxt(directory+"test_metrics.csv", np.array([zero_one_loss.score(y_test, pred) for pred in self.preds]))
+ np.savetxt(directory + "times.csv", self.times)
+ np.savetxt(directory + "train_metrics.csv", self.train_metrics)
+ return interpretString
+
+
+def formatCmdArgs(args):
+ """Used to format kwargs for the parsed args"""
+ kwargsDict = {"model_type": args.SCS_model_type,
+ "p": args.SCS_p,
+ "max_rules": args.SCS_max_rules}
+ return kwargsDict
+
+
+def paramsToSet(nIter, randomState):
+ paramsSet = []
+ for _ in range(nIter):
+ paramsSet.append({"model_type": randomState.choice(["conjunction", "disjunction"]),
+ "max_rules": randomState.randint(1, 15),
+ "p": randomState.random_sample()})
+ return paramsSet
diff --git a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
index 2bfdbe1a..3dce0a8f 100644
--- a/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
+++ b/multiview_platform/MonoMultiViewClassifiers/utils/execution.py
@@ -51,6 +51,9 @@ def parseTheArgs(arguments):
groupStandard.add_argument('--noise_std', metavar='FLOAT', action='store',
help='The std of the gaussian noise that will be added to the data.',
type=float, default=0.15)
+ groupStandard.add_argument('--res_dir', metavar='STRING', action='store',
+ help='The path to the result directory',
+ default="../Results/")
groupClass = parser.add_argument_group('Classification arguments')
@@ -192,6 +195,18 @@ def parseTheArgs(arguments):
action='store',
help='Number of stumps per attribute', default=1)
+ groupSCMSparsity = parser.add_argument_group('SCMSparsity arguments')
+ groupSCMSparsity.add_argument('--SCS_max_rules', metavar='INT', type=int,
+ action='store',
+ help='Max number of rules for SCM', default=1)
+ groupSCMSparsity.add_argument('--SCS_p', metavar='FLOAT', type=float,
+ action='store',
+ help='Max number of rules for SCM', default=1.0)
+ groupSCMSparsity.add_argument('--SCS_model_type', metavar='STRING',
+ action='store',
+ help='Max number of rules for SCM',
+ default="conjunction")
+
groupCQBoost = parser.add_argument_group('CQBoost arguments')
groupCQBoost.add_argument('--CQB_mu', metavar='FLOAT', type=float, action='store',
help='Set the mu parameter for CQBoost', default=0.001)
@@ -455,7 +470,7 @@ def getDatabaseFunction(name, type):
return getDatabase
-def initLogFile(name, views, CL_type, log, debug, label):
+def initLogFile(name, views, CL_type, log, debug, label, result_directory):
r"""Used to init the directory where the preds will be stored and the log file.
First this function will check if the result directory already exists (only one per minute is allowed).
@@ -479,9 +494,9 @@ def initLogFile(name, views, CL_type, log, debug, label):
Reference to the main results directory for the benchmark.
"""
if debug:
- resultDirectory = "../Results/" + name + "/debug_started_" + time.strftime("%Y_%m_%d-%H_%M_%S") + "_" + label + "/"
+ resultDirectory = result_directory + name + "/debug_started_" + time.strftime("%Y_%m_%d-%H_%M_%S") + "_" + label + "/"
else:
- resultDirectory = "../Results/" + name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "_" + label + "/"
+ resultDirectory = result_directory + name + "/started_" + time.strftime("%Y_%m_%d-%H_%M") + "_" + label + "/"
logFileName = time.strftime("%Y_%m_%d-%H_%M") + "-" + ''.join(CL_type) + "-" + "_".join(
views) + "-" + name + "-LOG"
if os.path.exists(os.path.dirname(resultDirectory)):
--
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