diff --git a/summit/multiview_platform/monoview_classifiers/lasso.py b/summit/multiview_platform/monoview_classifiers/lasso.py
index 288bf1dbf75b9bf5d5990c41d240fefd956015d9..aadfce61157be1bfafd0e0ef3692657e9f81bace 100644
--- a/summit/multiview_platform/monoview_classifiers/lasso.py
+++ b/summit/multiview_platform/monoview_classifiers/lasso.py
@@ -1,5 +1,6 @@
import numpy as np
from sklearn.linear_model import Lasso as LassoSK
+from sklearn.preprocessing import RobustScaler
from ..monoview.monoview_utils import BaseMonoviewClassifier
from summit.multiview_platform.utils.hyper_parameter_search import CustomUniform, CustomRandint
@@ -19,28 +20,65 @@ class Lasso(LassoSK, BaseMonoviewClassifier):
"""
def __init__(self, random_state=None, alpha=1.0,
- max_iter=10, warm_start=False, **kwargs):
+ max_iter=10, warm_start=False, scale=False, **kwargs):
LassoSK.__init__(self,
alpha=alpha,
max_iter=max_iter,
warm_start=warm_start,
random_state=random_state
)
- self.param_names = ["max_iter", "alpha", "random_state"]
+ self.scale = scale
+ self.param_names = ["max_iter", "alpha", "scale", "random_state"]
self.classed_params = []
self.distribs = [CustomRandint(low=1, high=300),
- CustomUniform(), [random_state]]
+ CustomUniform(), [True, False], [random_state]]
self.weird_strings = {}
def fit(self, X, y, check_input=True):
neg_y = np.copy(y)
neg_y[np.where(neg_y == 0)] = -1
+ if self.scale:
+ self.scaler = RobustScaler()
+ X = self.scaler.fit_transform(X, neg_y)
LassoSK.fit(self, X, neg_y)
- # self.feature_importances_ = self.coef_/np.sum(self.coef_)
+ if np.sum(self.coef_)!=0:
+ self.feature_importances_ = np.abs(self.coef_/np.sum(self.coef_))
+ else:
+ self.feature_importances_ = np.abs(self.coef_)
return self
def predict(self, X):
+ if self.scale:
+ X = self.scaler.transform(X)
prediction = LassoSK.predict(self, X)
signed = np.sign(prediction)
signed[np.where(signed == -1)] = 0
return signed
+
+ 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
\ No newline at end of file
diff --git a/summit/multiview_platform/monoview_classifiers/linear_tree.py b/summit/multiview_platform/monoview_classifiers/linear_tree.py
new file mode 100644
index 0000000000000000000000000000000000000000..bbadbcf3ba6e314e9d2bef93322299736bc1ea79
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/linear_tree.py
@@ -0,0 +1,43 @@
+import os
+import time
+
+import numpy as np
+from sklearn.ensemble import AdaBoostClassifier
+from sklearn.linear_model import RidgeClassifier, LogisticRegression
+from lineartree import LinearTreeClassifier
+
+
+from ..monoview.monoview_utils import BaseMonoviewClassifier
+from summit.multiview_platform.utils.hyper_parameter_search import CustomRandint
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+classifier_class_name = "LinearLeafsTree"
+
+class LinearLeafsTree(LinearTreeClassifier, BaseMonoviewClassifier):
+ """
+ This class is an adaptation of scikit-learn's `AdaBoostClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier>`_
+
+ """
+
+ def __init__(self, base_estimator=None,
+ base_estimator_config=None, max_depth=None, **kwargs):
+ base_estimator = BaseMonoviewClassifier.get_base_estimator(self,
+ base_estimator,
+ base_estimator_config)
+ LinearTreeClassifier.__init__(self,
+ base_estimator=base_estimator,
+ max_depth=max_depth
+ )
+ self.param_names = [ "base_estimator", "max_depth"]
+ self.classed_params = ["base_estimator"]
+ self.distribs = [[LogisticRegression(), RidgeClassifier()], CustomRandint(low=1, high=20)]
+ self.weird_strings = {"base_estimator": "class_name"}
+ self.base_estimator_config = base_estimator_config
+
+ 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/monoview_classifiers/samba_dt.py b/summit/multiview_platform/monoview_classifiers/samba_dt.py
new file mode 100644
index 0000000000000000000000000000000000000000..78d8239e51938e515f3bb39e404b6948a020fe05
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/samba_dt.py
@@ -0,0 +1,81 @@
+from SamBA.samba import NeighborHoodClassifier, ExpTrainWeighting
+import numpy as np
+from sklearn.tree import DecisionTreeClassifier
+from SamBA.relevances import *
+from SamBA.distances import *
+from sklearn.preprocessing import RobustScaler
+
+from ..monoview.monoview_utils import BaseMonoviewClassifier
+from ..utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+
+# class Decis
+classifier_class_name = "SamBADTClf"
+
+class SamBADTClf(NeighborHoodClassifier, BaseMonoviewClassifier):
+
+ def __init__(self, base_estimator=DecisionTreeClassifier(max_depth=3,
+ splitter='best',
+ criterion='gini'),
+ n_estimators=2,
+ estimator_params=tuple(),
+ relevance=ExpRelevance(),
+ distance=EuclidianDist(),
+ difficulty=ExpTrainWeighting(),
+ keep_selected_features=True,
+ normalizer=RobustScaler(),
+ b=2, a=0.01,
+ pred_train=False,
+ forced_diversity=False,
+ normalize_dists=False,
+ class_weight="balanced",
+ **kwargs):
+ """
+
+ Parameters
+ ----------
+ random_state
+ model_type
+ max_rules
+ p
+ kwargs
+ """
+ super(SamBADTClf, self).__init__(base_estimator=base_estimator,
+ n_estimators=n_estimators,
+ estimator_params=estimator_params,
+ relevance=relevance,
+ distance=distance,
+ difficulty=difficulty,
+ keep_selected_features=keep_selected_features,
+ normalizer=normalizer,
+ forced_diversity=forced_diversity,
+ b=b, a=a, pred_train=pred_train,
+ normalize_dists=normalize_dists,
+ class_weight=class_weight)
+ self.param_names = ["n_estimators",
+ "relevance",
+ "distance",
+ "difficulty", "b", "pred_train", "normalizer",
+ "normalize_dists", "a", "class_weight",]
+ self.distribs = [CustomRandint(low=1, high=70),
+ [ExpRelevance()],
+ [EuclidianDist(), PolarDist(), ExpEuclidianDist(), Jaccard()],
+ [ExpTrainWeighting()],
+ CustomUniform(0.1, 6,),
+ [True, False],
+ [RobustScaler()],
+ [True], CustomRandint(0, 10, 'e-'),
+ ["balanced", None],
+ ]
+ self.classed_params = []
+ self.weird_strings = {}
+
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
+ multi_class=False):
+ interpret_string = self.get_feature_importance(directory, base_file_name, feature_ids)
+ return interpret_string
diff --git a/summit/multiview_platform/monoview_classifiers/xgboost.py b/summit/multiview_platform/monoview_classifiers/xgboost.py
new file mode 100644
index 0000000000000000000000000000000000000000..2b44f2116a3cdfd4462b7abe09c6cd0745ac42b7
--- /dev/null
+++ b/summit/multiview_platform/monoview_classifiers/xgboost.py
@@ -0,0 +1,71 @@
+import os
+import time
+
+import numpy as np
+from xgboost import XGBClassifier
+from sklearn.tree import DecisionTreeClassifier
+
+from .. import metrics
+from ..monoview.monoview_utils import BaseMonoviewClassifier, get_accuracy_graph
+from summit.multiview_platform.utils.hyper_parameter_search import CustomRandint, CustomUniform
+
+# Author-Info
+__author__ = "Baptiste Bauvin"
+__status__ = "Prototype" # Production, Development, Prototype
+
+classifier_class_name = "XGB"
+
+
+
+class XGB(XGBClassifier, BaseMonoviewClassifier):
+ """
+ This class is an adaptation of scikit-learn's `GradientBoostingClassifier <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.GradientBoostingClassifier.html>`_
+
+
+ """
+
+ def __init__(self, random_state=None, learning_rate=0.3, max_depth=1.0,
+ n_estimators=100, objective='binary:logistic',
+ **kwargs):
+ XGBClassifier.__init__(self, learning_rate=learning_rate, max_depth=max_depth,
+ n_estimators=n_estimators, objective=objective,
+ random_state=random_state)
+ self.param_names = ["n_estimators", "learning_rate", "max_depth", "objective"]
+ self.classed_params = []
+ self.distribs = [CustomRandint(low=10, high=500),
+ CustomUniform(),
+ CustomRandint(low=1, high=10),
+ ['binary:logistic', 'binary:hinge', ],]
+ self.weird_strings = {}
+
+
+ def get_interpretation(self, directory, base_file_name, y_test, feature_ids,
+ multi_class=False):
+ interpretString = ""
+ if multi_class:
+ return interpretString
+ else:
+ interpretString += self.get_feature_importance(directory,
+ base_file_name,
+ feature_ids)
+ # 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, "AdaboostClassic",
+ # directory + "test_metrics.png",
+ # self.plotted_metric_name, set="test")
+ # get_accuracy_graph(self.metrics, "AdaboostClassic",
+ # directory + "metrics.png",
+ # self.plotted_metric_name)
+ # 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
diff --git a/summit/multiview_platform/utils/compression.py b/summit/multiview_platform/utils/compression.py
index 7ea525f81862a9561fb343cb045fd0ce13c467ae..6fb7cb49a4ec2a934273c38253af289685957337 100644
--- a/summit/multiview_platform/utils/compression.py
+++ b/summit/multiview_platform/utils/compression.py
@@ -43,14 +43,14 @@ def remove_compressed(exp_path):
if __name__=="__main__":
- for dir in os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"):
- print(dir)
- for exp in os.listdir((os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir))):
- print("\t", exp)
- if os.path.isdir(os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir, exp)):
- explore_files(os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir, exp))
+ # for dir in os.listdir("/home/baptiste/Documents/Gitwork/summit/results/"):
+ # print(dir)
+ # for exp in os.listdir((os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir))):
+ # print("\t", exp)
+ # if os.path.isdir(os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir, exp)):
+ # explore_files(os.path.join("/home/baptiste/Documents/Gitwork/summit/results/", dir, exp))
# # explore_files("/home/baptiste/Documents/Gitwork/biobanq_covid_expes/results/")
- # explore_files("/home/baptiste/Documents/Gitwork/summit/results/tnbc_mazid/debug_started_2023_03_24-11_27_46_thesis")
+ explore_files("/home/baptiste/Documents/Gitwork/summit/results/clinical/debug_started_2023_04_05-08_23_00_bal_acc")
# explore_files(
# "/home/baptiste/Documents/Gitwork/summit/results/lives_thesis_EMF/debug_started_2023_03_24-10_02_21_thesis")
# # 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")