From 279c4c8ecea20ec450f77d5419604c87f68e278c Mon Sep 17 00:00:00 2001
From: Baptiste Bauvin <baptiste.bauvin@lis-lab.fr>
Date: Mon, 6 Mar 2023 09:45:10 -0500
Subject: [PATCH] Pyscm boostable
---
.../monoview_classifiers/scm.py | 41 ++++++------
.../monoview_classifiers/scmboost.py | 65 +++----------------
.../multiview_platform/utils/compression.py | 3 +-
3 files changed, 30 insertions(+), 79 deletions(-)
diff --git a/summit/multiview_platform/monoview_classifiers/scm.py b/summit/multiview_platform/monoview_classifiers/scm.py
index b3096059..8db54053 100644
--- a/summit/multiview_platform/monoview_classifiers/scm.py
+++ b/summit/multiview_platform/monoview_classifiers/scm.py
@@ -64,13 +64,10 @@ class SCM(scm, BaseMonoviewClassifier):
self.weird_strings = {}
def fit(self, X, y, tiebreaker=None, iteration_callback=None, sample_weight=None, **fit_params):
- if sample_weight is not None:
- new_X, new_y = self.fake_repetitions(X, y, sample_weight, precision=4)
- else:
- new_X = X
- new_y = y
- self.n_features = new_X.shape[1]
- scm.fit(self, new_X, new_y, tiebreaker=None, iteration_callback=None, **fit_params)
+ self.n_features = X.shape[1]
+ scm.fit(self, X, y, tiebreaker=tiebreaker,
+ iteration_callback=iteration_callback,
+ sample_weight=sample_weight, **fit_params)
self.feature_importances_ = np.zeros(self.n_features)
# sum the rules importances :
# rules_importances = estim.get_rules_importances() #activate it when pyscm will implement importance
@@ -81,21 +78,21 @@ class SCM(scm, BaseMonoviewClassifier):
self.feature_importances_ /= np.sum(self.feature_importances_)
return self
- def fake_repetitions(self, X, y, sample_weight, precision=3):
- sample_repetitions = (np.round(sample_weight, precision)*10**precision).astype(np.int64)
- for ind, sample_rep in enumerate(sample_repetitions):
- if sample_rep==0:
- sample_repetitions[ind] = 1
- gcd = np.gcd.reduce(sample_repetitions)
- sample_repetitions = (sample_repetitions/gcd).astype(np.int64)
- new_X = np.zeros((X.shape[0]+ int(np.sum(sample_repetitions)-len(sample_repetitions)), X.shape[1]))
- new_y = np.zeros(X.shape[0]+ int(np.sum(sample_repetitions)-len(sample_repetitions)))
- ind=0
- for sample_index, (sample_rep, sample, label) in enumerate(zip(sample_repetitions, X, y)):
- new_X[ind:ind+sample_rep, :] = sample
- new_y[ind:ind+sample_rep] =label
- ind+=sample_rep
- return new_X, new_y
+ # def fake_repetitions(self, X, y, sample_weight, precision=3):
+ # sample_repetitions = (np.round(sample_weight, precision)*10**precision).astype(np.int64)
+ # for ind, sample_rep in enumerate(sample_repetitions):
+ # if sample_rep==0:
+ # sample_repetitions[ind] = 1
+ # gcd = np.gcd.reduce(sample_repetitions)
+ # sample_repetitions = (sample_repetitions/gcd).astype(np.int64)
+ # new_X = np.zeros((X.shape[0]+ int(np.sum(sample_repetitions)-len(sample_repetitions)), X.shape[1]))
+ # new_y = np.zeros(X.shape[0]+ int(np.sum(sample_repetitions)-len(sample_repetitions)))
+ # ind=0
+ # for sample_index, (sample_rep, sample, label) in enumerate(zip(sample_repetitions, X, y)):
+ # new_X[ind:ind+sample_rep, :] = sample
+ # new_y[ind:ind+sample_rep] =label
+ # ind+=sample_rep
+ # return new_X, new_y
diff --git a/summit/multiview_platform/monoview_classifiers/scmboost.py b/summit/multiview_platform/monoview_classifiers/scmboost.py
index 73db0419..2ed86500 100644
--- a/summit/multiview_platform/monoview_classifiers/scmboost.py
+++ b/summit/multiview_platform/monoview_classifiers/scmboost.py
@@ -25,69 +25,24 @@ class SCMboost(AdaBoostClassifier, BaseMonoviewClassifier):
"""
def __init__(self, random_state=None, n_estimators=50,
- base_estimator=SCM(p=0.49, max_rules=1, model_type="disjunction"),
- base_estimator_config=None, **kwargs):
+ base_estimator=SCM(p=0.49, max_rules=10, model_type="conjunction"),
+ **kwargs):
if "base_estimator__p" in kwargs:
base_estimator.p = kwargs["base_estimator__p"]
+ if "base_estimator__model_type" in kwargs:
+ base_estimator.model_type = kwargs["base_estimator__model_type"]
AdaBoostClassifier.__init__(self,
random_state=random_state,
n_estimators=n_estimators,
base_estimator=base_estimator,
algorithm="SAMME",)
- self.param_names = ["n_estimators", "base_estimator__p"]
+ self.param_names = ["n_estimators", "base_estimator__p", "base_estimator__model_type"]
self.classed_params = []
- self.distribs = [CustomRandint(low=1, high=100), CustomUniform(loc=0, state=1)]
+ self.distribs = [CustomRandint(low=1, high=100), CustomUniform(loc=0, state=1), ["conjunction", "disjunction"]]
self.weird_strings = {}
- self.plotted_metric = metrics.zero_one_loss
- self.plotted_metric_name = "zero_one_loss"
- self.base_estimator_config = base_estimator_config
- self.step_predictions = None
- def fit(self, X, y, sample_weight=None):
- begin = time.time()
- AdaBoostClassifier.fit(self, X, y)
- end = time.time()
- self.train_time = end - begin
- self.train_shape = X.shape
- self.base_predictions = np.array(
- [estim.predict(X) for estim in self.estimators_])
- self.metrics = np.array([self.plotted_metric.score(pred, y) for pred in
- self.staged_predict(X)])
- return self
- def predict(self, X):
- begin = time.time()
- pred = AdaBoostClassifier.predict(self, X)
- end = time.time()
- self.pred_time = end - begin
- self.step_predictions = np.array(
- [step_pred for step_pred in self.staged_predict(X)])
- return pred
-
- def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
- multi_class=False): # pragma: no cover
- interpretString = ""
- # interpretString += self.get_feature_importance(directory,
- # base_file_name,
- # feature_ids)
- # interpretString += "\n\n Estimator error | Estimator weight\n"
- # interpretString += "\n".join(
- # [str(error) + " | " + str(weight / sum(self.estimator_weights_)) for
- # error, weight in
- # zip(self.estimator_errors_, self.estimator_weights_)])
- # step_test_metrics = np.array(
- # [self.plotted_metric.score(y_test, step_pred) for step_pred in
- # self.step_predictions])
- # get_accuracy_graph(step_test_metrics, "Adaboost",
- # os.path.join(directory,
- # base_file_name + "test_metrics.png"),
- # self.plotted_metric_name, set="test")
- # np.savetxt(os.path.join(directory, base_file_name + "test_metrics.csv"),
- # step_test_metrics,
- # delimiter=',')
- # np.savetxt(
- # os.path.join(directory, base_file_name + "train_metrics.csv"),
- # self.metrics, delimiter=',')
- # np.savetxt(os.path.join(directory, base_file_name + "times.csv"),
- # np.array([self.train_time, self.pred_time]), delimiter=',')
- return interpretString
+ # def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
+ # multi_class=False): # pragma: no cover
+ # interpretString = ""
+ # return interpretString
diff --git a/summit/multiview_platform/utils/compression.py b/summit/multiview_platform/utils/compression.py
index 24b0ebc7..0a11b895 100644
--- a/summit/multiview_platform/utils/compression.py
+++ b/summit/multiview_platform/utils/compression.py
@@ -48,6 +48,5 @@ if __name__=="__main__":
# for exp in os.listdir((os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir))):
# print("\t", exp)
# explore_files(os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir, exp))
- explore_files(
- os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", "mage_dset", "debug_started_2022_12_13-10_15_20_th"))
+ explore_files("/home/baptiste/Documents/Gitwork/biobanq_covid_expes/results/")
# simplify_plotly("/home//baptiste/Documents/Gitwork/summit/results/hepatitis/debug_started_2022_03_16-15_06_55__/hepatitis-mean_on_10_iter-balanced_accuracy_p.html")
--
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