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Commit 122d71c1 authored by Baptiste Bauvin's avatar Baptiste Bauvin
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multimodal/__init__.py
+ 1
− 1
View file @ 122d71c1
__version__ = "0.0.dev0"
__version__ = "0.0.0"
multimodal/boosting/boost.py
+ 0
− 7
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......@@ -110,17 +110,10 @@ class UBoosting(metaclass=ABCMeta):
return X
def _global_X_transform(self, X, views_ind=None):
X_ = None
if isinstance(X, MultiModalData):
X_ = X
elif isinstance(X, sp.spmatrix):
X_ = MultiModalSparseArray(X, views_ind)
else:
X_ = MultiModalArray(X, views_ind)
# if not isinstance(X_, MultiModalData):
# try:
# X_ = np.asarray(X)
# X_ = MultiModalArray(X_)
# except Exception as e:
# raise TypeError('Reshape your data')
return X_
multimodal/boosting/combo.py
+ 1
− 1
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......@@ -53,7 +53,7 @@ from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree._tree import DTYPE
from sklearn.tree import BaseDecisionTree
from sklearn.utils import check_array, check_X_y, check_random_state
from sklearn.utils import check_X_y, check_random_state
from sklearn.utils.multiclass import check_classification_targets
from sklearn.utils.validation import check_is_fitted, has_fit_parameter
from cvxopt import solvers, matrix, spdiag, exp, spmatrix, mul, div
......
multimodal/datasets/data_sample.py
+ 3
− 41
View file @ 122d71c1
......@@ -133,7 +133,6 @@ class MultiModalData(metaclass=ABCMeta):
def _validate_views_ind(self, views_ind, n_features):
"""Ensure proper format for views_ind and return number of views."""
# views_ind = np.array(views_ind)
if np.issubdtype(views_ind.dtype, np.integer) and views_ind.ndim == 1:
if len(views_ind) > 2 and np.any(views_ind[:-1] >= views_ind[1:]):
raise ValueError("Values in views_ind must be sorted.")
......@@ -180,7 +179,6 @@ class MultiModalSparseInfo():
new_data = np.ndarray([])
n_views = data.size
thekeys = None
# views_ind_self = None
view_mode = 'slices'
if (sp.issparse(data)) and data.ndim > 1:
......@@ -196,7 +194,6 @@ class MultiModalSparseInfo():
views_ind = np.array([0, data.shape[1]])
new_data = data
# views_ind_self = views_ind
views_ind, n_views, view_mode = self._first_validate_views_ind(views_ind,
data.shape[1])
if views_ind.ndim == 1 and view_mode.startswith("slicing"):
......@@ -365,8 +362,6 @@ class MultiModalArray(np.ndarray, MultiModalData):
views_ind.append(dat_values.shape[1] + views_ind[index])
index += 1
thekeys = data.keys()
# if new_data.ndim < 2 :
# raise ValueError('Reshape your data')
if new_data.ndim > 1 and (new_data.shape == (1, 1) or new_data.shape == ()):
raise ValueError('Reshape your data')
elif isinstance(data, np.ndarray) and views_ind is None and data.ndim == 1:
......@@ -421,21 +416,11 @@ class MultiModalArray(np.ndarray, MultiModalData):
raise ValueError('Reshape your data')
if new_data.ndim > 1 and (new_data.shape == (1, 1) or new_data.shape == ()):
raise ValueError('Reshape your data')
# if views_ind.ndim < 2 and new_data.ndim < 2 and views_ind[-1] > new_data.shape[1]:
# raise ValueError('Reshape your data')
# views_ind_self = views_ind
# if new_data.shape[1] < 1:
# msg = ("%d feature\(s\) \\(shape=\%s\) while a minimum of \\d* "
# "is required.") % (new_data.shape[1], str(new_data.shape))
# # "%d feature\(s\) \(shape=\(%d, %d\)\) while a minimum of \d* is required." % (new_data.shape[1], new_data.shape[0], new_data.shape[1])
# raise ValueError(msg)
views_ind, n_views, view_mode = cls._first_validate_views_ind(views_ind,
new_data.shape[1])
if views_ind.ndim == 1 and view_mode.startswith("slices"):
shapes_int = [in2 - in1 for in1, in2 in zip(views_ind, views_ind[1:])]
# obj = ma.MaskedArray.__new(new_data) # new_data.view() a.MaskedArray(new_data, mask=new_data.mask).view(cls)
# bj = super(Metriclearn_array, cls).__new__(cls, new_data.data, new_data.mask)
if hasattr(new_data, "mask"): # pragma: no cover
obj = ma.masked_array(new_data.data, new_data.mask).view(cls)
......@@ -448,7 +433,6 @@ class MultiModalArray(np.ndarray, MultiModalData):
obj.views_ind = views_ind
obj.shapes_int = shapes_int
obj.n_views = n_views
# obj.keys = thekeys
return obj
@staticmethod
......@@ -478,8 +462,7 @@ class MultiModalArray(np.ndarray, MultiModalData):
isinstance(dat_values, np.ndarray) or sp.issparse(dat_values):
new_data = dat_values
else:
new_data = dat_values.view(np.ndarray) # ma.masked_array(dat_values, mask=ma.nomask) dat_values.view(ma.MaskedArray) #(
# new_data.mask = ma.nomask
new_data = dat_values.view(np.ndarray)
else:
if isinstance(dat_values, np.ndarray):
new_data = np.hstack((new_data, dat_values))
......@@ -488,15 +471,13 @@ class MultiModalArray(np.ndarray, MultiModalData):
elif sp.issparse(dat_values):
new_data = sp.hstack((new_data, dat_values))
else:
new_data = np.hstack((new_data, dat_values.view(np.ndarray) ) ) # ma.masked_array(dat_values, mask=ma.nomask
new_data = np.hstack((new_data, dat_values.view(np.ndarray) ) )
return new_data
def __array_finalize__(self, obj):
if obj is None: return
# super(MultiModalArray, self).__array_finalize__(obj)
self.shapes_int = getattr(obj, 'shapes_int', None)
self.n_views = getattr(obj, 'n_views', None)
# self.keys = getattr(obj, 'keys', None)
self.views_ind = getattr(obj, 'views_ind', None)
self.view_mode_ = getattr(obj, 'view_mode_', None)
......@@ -537,22 +518,6 @@ class MultiModalArray(np.ndarray, MultiModalData):
stop = int(np.sum(np.asarray(self.shapes_int[0: view+1])))
return self[row, start:stop]
# def add_view(self, data):
# if len(self.shape) > 0:
# if data.shape[0] == self.data.shape[0]:
# print(self.data.shape, data.shape)
# new_data = np.hstack((self.data, data))
# self.shapes_int.append(data.shape[1])
# self.n_views +=1
# print(new_data.shape)
#
# else:
# raise ValueError("New view can't initialazed")
# # self.shapes_int= [data.shape[1]]
# # self.data.reshape(data.shape[0],)
# # np.insert(self.data, data, 0)
# # self.n_views = 1
def _todict(self):
dico = {}
for view in range(self.n_views):
......@@ -560,8 +525,6 @@ class MultiModalArray(np.ndarray, MultiModalData):
return dico
class DataSample(dict):
"""
A DataSample instance
......@@ -596,7 +559,6 @@ class DataSample(dict):
def __init__(self, data=None, **kwargs):
# The dictionary that contains the sample
super(DataSample, self).__init__(kwargs)
self._data = None # Metriclearn_arrayMultiModalArray(np.zeros((0,0)))
......
multimodal/kernels/lpMKL.py
+ 1
− 9
View file @ 122d71c1
......@@ -176,7 +176,6 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
self.regression_ = True
else:
raise ValueError("MKL algorithms is a binary classifier")
# " or performs regression with float target")
self.y_ = y
n = self.K_.shape[0]
self._calc_nystrom(self.K_, n)
......@@ -233,7 +232,7 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
# alpha fixed -> calculate gammas
weights_old = weights.copy()
# first the ||f_t||^2 todo wtf is the formula used here????
# first the ||f_t||^2 todo what is the formula used here ?
ft2 = np.zeros(views)
for v in range(0, views):
if self.nystrom_param < 1 and self.use_approx:
......@@ -274,16 +273,9 @@ class MKL(BaseEstimator, ClassifierMixin, MKernel):
stuck = True
max_diff = np.max([max_diff_gamma, diff_alpha])
# print([max_diff_gamma, diff_alpha]) # print if convergence is interesting
C_old = C.copy()
rounds = rounds + 1
# print("\nlearned the weights:")
# np.set_printoptions(precision=3, suppress=True)
# print(weights)
# print("")
# print if resulting convergence is of interest
# print("convergence of ", max_diff, " at step ", rounds, "/500")
if stuck:
return C_old, weights_old
else:
......
multimodal/kernels/mkernel.py
+ 0
− 14
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......@@ -82,7 +82,6 @@ class MKernel(metaclass=ABCMeta):
elif isinstance(self.kernel, list):
ind = min(v, len(self.kernel) - 1)
met = self.kernel[ind]
# Y,
return pairwise_kernels(X, Y, metric=met,
filter_params=True, **params)
......@@ -113,7 +112,6 @@ class MKernel(metaclass=ABCMeta):
"""
kernel_dict = {}
flag_sparse = False
X_ = None
y = None
if Y is None:
y = Y
......@@ -124,22 +122,10 @@ class MKernel(metaclass=ABCMeta):
X = X_
if isinstance(X, MultiModalArray):
X_ = X
# if not isinstance(X_, MultiModalArray):
# try:
# X_ = np.asarray(X)
# X_ = MultiModalArray(X_, views_ind)
# except Exception as e:
# pass
# raise TypeError('Reshape your data')
if isinstance(X_, MultiModalArray):
for v in range(X.n_views):
if Y is not None: y = Y.get_view(v) # y = self._global_check_pairwise(X, Y, v)
kernel_dict[v] = self._get_kernel(X_.get_view(v), y)
# if not isinstance(X_, MultiModalArray):
# if sp.sparse.issparse(X):
# raise TypeError("Nonsensical Error: no sparse data are allowed as input")
# raise TypeError('Reshape your data')
K_ = MultiModalArray(kernel_dict)
return X_, K_
......
multimodal/kernels/mvml.py
+ 4
− 14
View file @ 122d71c1
......@@ -166,7 +166,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
self.nystrom_param = nystrom_param
self.lmbda = lmbda
self.eta = eta
# self.regression_params = regression_params
self.learn_A = learn_A
self.learn_w = learn_w
self.n_loops = n_loops
......@@ -226,10 +225,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
self.regression_ = False
self.X_, self.K_= self._global_kernel_transform(X, views_ind=views_ind)
check_X_y(self.X_, y)
# if type_of_target(y) not in "binary":
# raise ValueError("target should be binary")
if type_of_target(y) in "binary":
check_classification_targets(y)
......@@ -244,7 +239,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
" or performs regression with float target")
self.y_ = y
# n = X[0].shape[0]
n = self.K_.shape[0]
self.n_approx = int(np.floor(self.nystrom_param * n)) # number of samples in approximation, equals n if no approx.
if self.nystrom_param < 1:
......@@ -257,7 +251,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
if self.warning_message:
import logging
logging.warning("warning appears during fit process" + str(self.warning_message))
# print("warning appears during fit process", self.warning_message)
return self
def _learn_mvml(self, learn_A=1, learn_w=0, n_loops=6):
......@@ -335,11 +328,13 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
# first invert A
try:
# Changed because of numerical instability
# A_inv = np.linalg.pinv(A + 1e-09 * np.eye(views * self.n_approx))
cond_A = np.linalg.cond(A + 1e-08 * np.eye(views * self.n_approx))
if cond_A < 10:
A_inv = spli.pinv(A + 1e-8 * np.eye(views * self.n_approx))
else:
# Changed because of numerical instability
# A_inv = self._inverse_precond_LU(A + 1e-8 * np.eye(views * self.n_approx), pos="precond_A") # self._inverse_precond_jacobi(A + 1e-8 * np.eye(views * self.n_approx), pos="precond_A")
A_inv = self._inv_best_precond(A + 1e-8 * np.eye(views * self.n_approx), pos="precond_A")
except spli.LinAlgError: # pragma: no cover
......@@ -355,7 +350,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
except ValueError: # pragma: no cover
self.warning_message["ValueError"] = self.warning_message.get("ValueError", 0) + 1
return A_prev, g_prev, w_prev
# print("A_inv ",np.sum(A_inv))
# then calculate g (block-sparse multiplications in loop) using A_inv
for v in range(views):
for vv in range(views):
......@@ -365,11 +359,13 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
vv * self.n_approx:(vv + 1) * self.n_approx]
g[v * self.n_approx:(v + 1) * self.n_approx, 0] = np.dot(w[v] * np.transpose(self.U_dict[v]), self.y_)
try:
# Changed because of numerical instability
# minA_inv = np.min(np.absolute(A_inv)) , rcond=self.r_cond*minA_inv
# here A_inv isn't actually inverse of A (changed in above loop)
if np.linalg.cond(A_inv) < 10:
g = np.dot(spli.pinv(A_inv), g)
else:
# Changed because of numerical instability
# g = np.dot(self._inverse_precond_LU(A_inv, pos="precond_A_1"), g)
g = np.dot(self._inv_best_precond(A_inv, pos="precond_A_1"), g)
except spli.LinAlgError: # pragma: no cover
......@@ -413,9 +409,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
Pm, L, U = spli.lu(A)
M = spli.inv(np.dot(L, U))
Pre_lu = np.dot(M, A)
# print("cond a", np.linalg.cond(A))
# print("cond Pre_J", np.linalg.cond(Pre_J))
# print("cond Pre_lu", np.linalg.cond(Pre_lu))
if np.linalg.cond(A) > np.linalg.cond(Pre_J) and np.linalg.cond(Pre_J) <= np.linalg.cond(Pre_lu):
P_inv = spli.pinv(Pre_J)
A_inv = np.dot(P_inv, J_1)
......@@ -430,7 +423,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
def _inverse_precond_jacobi(self, A, pos="precond_A"): # pragma: no cover
J_1 = np.diag(1.0/np.diag(A))
# J_1 = np.linalg.inv(J)
P = np.dot(J_1, A)
if np.linalg.cond(A) > np.linalg.cond(P):
P_inv = spli.pinv(P)
......@@ -532,7 +524,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
"""
views = len(self.U_dict)
# t = test_kernels[0].shape[0]
t = test_kernels.shape[0]
K = np.zeros((t, views * self.n_approx))
for v in range(views):
......@@ -580,7 +571,6 @@ class MVML(MKernel, BaseEstimator, ClassifierMixin, RegressorMixin):
rounds = 0
L = lmbda * np.linalg.norm(np.dot(g, g.T))
# print("L ", L)
while not converged and rounds < 100:
# no line search - this has worked well enough experimentally
......
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