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  • baptiste.bauvin/summit
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summit/multiview_platform/monoview_classifiers/bagged_spkm.py
View file @ 2b939e00
......@@ -3,7 +3,7 @@ from sklearn.preprocessing import LabelBinarizer
from imblearn.under_sampling import RandomUnderSampler
from spkm.spkm_wrapper import SPKMlikeSklearn
from spkm.spkm_wrapper import SPKMl2l1likeSklearn
from spkm.kernels_and_gradients import RBFKernel, PolyKernel
from ..monoview.monoview_utils import BaseMonoviewClassifier
......@@ -13,28 +13,30 @@ from ..monoview_classifiers.spkm import SPKM
classifier_class_name = "BaggedSPKM"
proto=True
class BaggedSPKM(BaseMonoviewClassifier, SPKMlikeSklearn):
class BaggedSPKM(BaseMonoviewClassifier, SPKMl2l1likeSklearn):
def __init__(self, random_state=42, n_u=2, kernel=RBFKernel(0.5),
spkmregP=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2], **kwargs):
spkmregP_01=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2],
spkm_l1_regstyle="l1ball", **kwargs):
SPKM.__init__(self, random_state=random_state,
n_u=n_u,
kernel=kernel,
spkmregP=spkmregP,
spkmregP_01=spkmregP_01,
spkminit=spkminit,
nspkminits=nspkminits,
preprocessinglist=preprocessinglist)
preprocessinglist=preprocessinglist,
spkm_l1_regstyle=spkm_l1_regstyle)
self.rus = RandomUnderSampler(random_state=random_state)
def fit(self, X, y):
self.lb = LabelBinarizer(pos_label=1, neg_label=-1)
y = self.lb.fit_transform(y)
return SPKMlikeSklearn.fit(self, X, y[:,0],)
return SPKMl2l1likeSklearn.fit(self, X, y[:,0],)
def predict(self, X, preprocess=True):
return self.lb.inverse_transform(np.sign(SPKMlikeSklearn.predict(self, X)))
return self.lb.inverse_transform(np.sign(SPKMl2l1likeSklearn.predict(self, X)))
def get_interpretation(self, directory, base_file_name, labels, multiclass=False):
u = self.feature_interpretability()
......
summit/multiview_platform/monoview_classifiers/scmboost.py
View file @ 2b939e00
......@@ -25,20 +25,22 @@ class SCMboost(AdaBoostClassifier, BaseMonoviewClassifier):
"""
def __init__(self, random_state=None, n_estimators=50,
base_estimator=SCM(p=0.49, max_rules=10, model_type="conjunction"),
base_estimator=SCM(p=0.49, max_rules=1, 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"]
if "base_estimator__max_rules" in kwargs:
base_estimator.max_rules = kwargs["base_estimator__max_rules"]
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", "base_estimator__model_type"]
self.param_names = ["n_estimators", "base_estimator__p", "base_estimator__model_type", "base_estimator__max_rules"]
self.classed_params = []
self.distribs = [CustomRandint(low=1, high=100), CustomUniform(loc=0, state=1), ["conjunction", "disjunction"]]
self.distribs = [CustomRandint(low=1, high=100), CustomUniform(loc=0, state=1), ["conjunction", "disjunction"], CustomRandint(low=1, high=20)]
self.weird_strings = {}
......
summit/multiview_platform/monoview_classifiers/spkm.py
View file @ 2b939e00
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from spkm.spkm_wrapper import SPKMlikeSklearn
from spkm.spkm_wrapper import SPKMl2l1likeSklearn
from spkm.kernels_and_gradients import RBFKernel, PolyKernel
from ..monoview.monoview_utils import BaseMonoviewClassifier
from ..utils.hyper_parameter_search import CustomRandint
from ..utils.hyper_parameter_search import CustomUniform
from ..utils.dataset import get_samples_views_indices
classifier_class_name = "SPKM"
proto=True
class SPKM(BaseMonoviewClassifier, SPKMlikeSklearn):
class SPKM(BaseMonoviewClassifier, SPKMl2l1likeSklearn):
def __init__(self, random_state=42, n_u=2, kernel=RBFKernel(0.5),
spkmregP=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2], **kwargs):
spkmregP_01=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2],
spkm_l1_regstyle="l1ball",**kwargs):
SPKMlikeSklearn.__init__(self, random_state=random_state,
SPKMl2l1likeSklearn.__init__(self, random_state=random_state,
n_u=n_u,
kernel=kernel,
spkmregP=spkmregP,
spkmregP_01=spkmregP_01,
spkminit=spkminit,
nspkminits=nspkminits,
preprocessinglist=preprocessinglist)
self.param_names = ["n_u", "kernel", "spkmregP",
preprocessinglist=preprocessinglist,
spkm_l1_regstyle=spkm_l1_regstyle)
self.param_names = ["n_u", "kernel", "spkmregP_01",
"spkminit", "nspkminits", "preprocessinglist",
"spkm_l1_regstyle",
"random_state"]
self.distribs = [[2], [PolyKernel({"d":3, "r":1}), RBFKernel(0.5)],
CustomRandint(-4,4, multiplier='e'),
CustomUniform(),
["data"], [10],
[[0,1],],
[[0,1],[0,1,2]], ["l1ball", "proxgd"]
[random_state],]
self.more_than_two_views = False
self.weird_strings = []
......@@ -39,10 +42,10 @@ class SPKM(BaseMonoviewClassifier, SPKMlikeSklearn):
def fit(self, X, y):
self.lb = LabelBinarizer(pos_label=1, neg_label=-1)
y = self.lb.fit_transform(y)
return SPKMlikeSklearn.fit(self, X, y[:,0],)
return SPKMl2l1likeSklearn.fit(self, X, y[:,0],)
def predict(self, X, preprocess=True):
return self.lb.inverse_transform(np.sign(SPKMlikeSklearn.predict(self, X)))
return self.lb.inverse_transform(np.sign(SPKMl2l1likeSklearn.predict(self, X)))
def get_interpretation(self, directory, base_file_name, labels, multiclass=False):
u = self.feature_interpretability()
......
summit/multiview_platform/multiview_classifiers/bagged_spkm_pw.py
View file @ 2b939e00
......@@ -3,7 +3,7 @@ from sklearn.preprocessing import LabelBinarizer
from imblearn.under_sampling import RandomUnderSampler
from spkm.spkm_wrapper import pairwiseSPKMlikeSklearn
from spkm.spkm_wrapper import pairwiseSPKMl2l1likeSklearn
from spkm.kernels_and_gradients import RBFKernel, PolyKernel
from ..multiview.multiview_utils import BaseMultiviewClassifier
......@@ -16,16 +16,18 @@ classifier_class_name = "SampledPWSPKM"
class SampledPWSPKM(PWSPKM,):
def __init__(self, random_state=42, n_u=2, kernel1=RBFKernel(0.5),
kernel2=RBFKernel(0.5), spkmregP=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2], **kwargs):
kernel2=RBFKernel(0.5), spkmregP_01=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2], spkm_l1_regstyle="l1ball",
**kwargs):
PWSPKM.__init__(self, random_state=random_state,
n_u=n_u,
kernel1=kernel1,
kernel2=kernel2,
spkmregP=spkmregP,
spkmregP_01=spkmregP_01,
spkminit=spkminit,
nspkminits=nspkminits,
preprocessinglist=preprocessinglist)
preprocessinglist=preprocessinglist,
spkm_l1_regstyle=spkm_l1_regstyle)
self.rus = RandomUnderSampler(random_state=random_state)
def fit(self, X, y, train_indices=None, view_indices=None):
......@@ -46,4 +48,4 @@ class SampledPWSPKM(PWSPKM,):
for view_index in view_indices]
self.rus.fit_resample(view_list[0], y[train_indices])
resampled_list = [X[self.rus.sample_indices_, :] for X in view_list]
return pairwiseSPKMlikeSklearn.fit(self, resampled_list, y[train_indices,0][self.rus.sample_indices_],)
return pairwiseSPKMl2l1likeSklearn.fit(self, resampled_list, y[train_indices,0][self.rus.sample_indices_],)
summit/multiview_platform/multiview_classifiers/spkm_pw.py
View file @ 2b939e00
import numpy as np
from sklearn.preprocessing import LabelBinarizer
from spkm.spkm_wrapper import pairwiseSPKMlikeSklearn
from spkm.spkm_wrapper import pairwiseSPKMl2l1likeSklearn
from spkm.kernels_and_gradients import RBFKernel, PolyKernel
from ..multiview.multiview_utils import BaseMultiviewClassifier
from ..utils.hyper_parameter_search import CustomRandint
from ..utils.hyper_parameter_search import CustomUniform
from ..utils.dataset import get_samples_views_indices
classifier_class_name = "PWSPKM"
class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMlikeSklearn):
class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMl2l1likeSklearn):
def __init__(self, random_state=42, n_u=2, kernel1=RBFKernel(0.5),
kernel2=RBFKernel(0.5), spkmregP=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2], **kwargs):
kernel2=RBFKernel(0.5), spkmregP_01=1, spkminit="randn",
nspkminits=10, preprocessinglist=[0,1,2],
spkm_l1_regstyle="l1ball", **kwargs):
BaseMultiviewClassifier.__init__(self, random_state)
pairwiseSPKMlikeSklearn.__init__(self, random_state=random_state,
pairwiseSPKMl2l1likeSklearn.__init__(self, random_state=random_state,
n_u=n_u,
kernel1=kernel1,
kernel2=kernel2,
spkmregP=spkmregP,
spkmregP_01=spkmregP_01,
spkminit=spkminit,
nspkminits=nspkminits,
preprocessinglist=preprocessinglist)
self.param_names = ["n_u", "kernel1", "kernel2", "spkmregP",
preprocessinglist=preprocessinglist,
spkm_l1_regstyle=spkm_l1_regstyle)
self.param_names = ["n_u", "kernel1", "kernel2", "spkmregP_01",
"spkminit", "nspkminits", "preprocessinglist",
"spkm_l1_regstyle",
"random_state"]
self.distribs = [[2], [PolyKernel({"d":3, "r":1}), RBFKernel(0.5)],
[PolyKernel({"d":3, "r":1}), RBFKernel(0.5)],
CustomRandint(-4,4, multiplier='e'),
CustomUniform(),
["data"], [10],
[[0,1],],
[[0,1],[0,1,2]], ["l1ball", "proxgd"]
[random_state],]
self.more_than_two_views = False
self.random_state = random_state
......@@ -40,7 +43,6 @@ class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMlikeSklearn):
def fit(self, X, y, train_indices=None, view_indices=None):
self.lb = LabelBinarizer(pos_label=1, neg_label=-1)
y = self.lb.fit_transform(y)
print(np.unique(y))
train_indices, view_indices = get_samples_views_indices(X,
train_indices,
view_indices)
......@@ -53,7 +55,7 @@ class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMlikeSklearn):
for view_index in view_indices]
view_list = [X.get_v(view_index)[train_indices, :]
for view_index in view_indices]
return pairwiseSPKMlikeSklearn.fit(self, view_list, y[train_indices,0],)
return pairwiseSPKMl2l1likeSklearn.fit(self, view_list, y[train_indices,0],)
def predict(self, X, sample_indices=None, view_indices=None):
if self.more_than_two_views:
......@@ -65,7 +67,7 @@ class PWSPKM(BaseMultiviewClassifier, pairwiseSPKMlikeSklearn):
view_list = [X.get_v(view_index)[sample_indices, :]
for view_index in view_indices]
return self.lb.inverse_transform(np.sign(pairwiseSPKMlikeSklearn.predict(self, view_list)))
return self.lb.inverse_transform(np.sign(pairwiseSPKMl2l1likeSklearn.predict(self, view_list)))
def get_interpretation(self, directory, base_file_name, labels, multiclass=False):
u, v = self.feature_interpretability()
......