diff --git a/multimodal/boosting/boost.py b/multimodal/boosting/boost.py
index 7b039e6f94f37d16cafe9e4999ed51b7c4bda9bd..5ef38ddfc95e86acac032fbc67176e03438196c5 100644
--- a/multimodal/boosting/boost.py
+++ b/multimodal/boosting/boost.py
@@ -26,14 +26,22 @@ class UBoosting(metaclass=ABCMeta):
else:
check_array(X, accept_sparse=['csr', 'csc'])
if X.ndim < 2:
- mes = "Reshape your data"
- raise ValueError(mes)
- if X.ndim > 1:
+ X = X[np.newaxis, :]
if X.shape[1] != self.n_features_:
- mes = "Reshape your data"
raise ValueError("Number of features of the model must "
- "match the input. Model n_features is %s and "
- "input n_features is %s " % (self.n_features_, X.shape[1]))
+ "match the input. Model n_features is %s and "
+ "input n_features is %s " % (self.n_features_, X.shape[1]))
+ else:
+ mes = "Reshape your data"
+ raise ValueError(mes)
+ if X.ndim > 1:
+ if X.shape[1] != self.n_features_:
+ if X.shape[0] == self.n_features_ and X.shape[1] > 1:
+ raise ValueError("Reshape your data")
+ else:
+ raise ValueError("Number of features of the model must "
+ "match the input. Model n_features is %s and "
+ "input n_features is %s " % (self.n_features_, X.shape[1]))
#
diff --git a/multimodal/datasets/__pycache__/__init__.cpython-36.pyc b/multimodal/datasets/__pycache__/__init__.cpython-36.pyc
deleted file mode 100644
index 78203c1c83371c7711d386af2cea6da83faec7cb..0000000000000000000000000000000000000000
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diff --git a/multimodal/datasets/__pycache__/base.cpython-36.pyc b/multimodal/datasets/__pycache__/base.cpython-36.pyc
deleted file mode 100644
index 5d08b5d131a62ac27ac9450f9ba3386c212792bd..0000000000000000000000000000000000000000
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diff --git a/multimodal/datasets/__pycache__/data_sample.cpython-36.pyc b/multimodal/datasets/__pycache__/data_sample.cpython-36.pyc
deleted file mode 100644
index aad5511d634098208d0e9aa1cb41329d923a21d6..0000000000000000000000000000000000000000
Binary files a/multimodal/datasets/__pycache__/data_sample.cpython-36.pyc and /dev/null differ
diff --git a/multimodal/datasets/data_sample.py b/multimodal/datasets/data_sample.py
index ed3ab6d2e95a17b95a758e84db1d71396a090963..9f6d730d4ac6d7fecd851cef66d5a52b6e2c420e 100644
--- a/multimodal/datasets/data_sample.py
+++ b/multimodal/datasets/data_sample.py
@@ -33,7 +33,6 @@ class MultiModalData(metaclass=ABCMeta):
def _first_validate_views_ind(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.")
@@ -79,7 +78,6 @@ 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.")
@@ -228,129 +226,6 @@ class MultiModalSparseArray(sp.csr_matrix, sp.csc_matrix, MultiModalSparseInfo,
sp.csc_matrix.__init__(self, *arg, **kwargs)
-# class MultiModalSparseArray(sp.csr_matrix, sp.csc_matrix, MultiModalData):
-# """
-# MultiModalArray inherit from numpy ndarray
-#
-#
-# Parameters
-# ----------
-#
-# data : can be
-# - dictionary of multiview array with shape = (n_samples, n_features) for multi-view
-# for each view.
-# {0: array([[]],
-# 1: array([[]],
-# ...}
-# - numpy array like with shape = (n_samples, n_features) for multi-view
-# for each view.
-# [[[...]],
-# [[...]],
-# ...]
-# - {array like} with (n_samples, nviews * n_features) with 'views_ind' diferent to 'None'
-# for Multi-view input samples.
-#
-#
-#
-#
-# views_ind : array-like (default= None ) if None
-# [0, n_features//2, n_features]) is constructed (2 views)
-# Paramater specifying how to extract the data views from X:
-#
-# - views_ind is a 1-D array of sorted integers, the entries
-# indicate the limits of the slices used to extract the views,
-# where view ``n`` is given by
-# ``X[:, views_ind[n]:views_ind[n+1]]``.
-#
-# Attributes
-# ----------
-#
-# view_ind : list of views' indice (may be None)
-#
-# n_views : int number of views
-#
-# shapes_int: list of int numbers of feature for each views
-#
-# keys : name of key, where data come from a dictionary
-#
-#
-# :Example:
-#
-# >>> from multimodal.datasets.base import load_dict
-# >>> from multimodal.tests.datasets.get_dataset_path import get_dataset_path
-# >>> from multimodal.datasets.data_sample import DataSample
-# >>> file = 'input_x_dic.pkl'
-# >>> data = load_dict(get_dataset_path(file))
-# >>> print(data.__class__)
-# <class 'dict'>
-# >>> multiviews = MultiModalArray(data)
-# >>> multiviews.shape
-# (120, 240)
-# >>> multiviews.keys
-# dict_keys([0, 1])
-# >>> multiviews.shapes_int
-# [120, 120]
-# >>> multiviews.n_views
-# 2
-#
-#
-# """
-#
-# def __init__(self, data, view_ind=None, shape=None, dtype=None, copy=False):
-# """Constructor of Metriclearn_array"""
-# shapes_int = []
-# index = 0
-# new_data = np.ndarray([])
-# n_views = 1
-# thekeys = None
-# # view_ind_self = None
-# view_mode = 'slices'
-# if isinstance(data, tuple) and len(data) == 3:
-# data_data = data[0]
-# indices = data[1]
-# indptr = data[2]
-# data_shape = shape
-# else:
-# if shape is None:
-# data_shape = data.shape
-# if dtype is None:
-# dtype = data.dtype
-# data_data = data.data
-# data_indices = data.indices
-# data_indptr = data.indptr
-# if (sp.issparse(data)) and data.ndim > 1:
-# if view_ind is not None:
-# try:
-# view_ind = np.asarray(view_ind)
-# except :
-# raise TypeError("n_views should be list or nparray")
-# elif view_ind is None:
-# if data.shape[1] > 1:
-# view_ind = np.array([0, data.shape[1]//2, data.shape[1]])
-# else:
-# view_ind = np.array([0, data.shape[1]])
-#
-# new_data = data
-# # view_ind_self = view_ind
-# view_ind, n_views, view_mode = self._first_validate_views_ind(view_ind,
-# data_shape[1])
-# if view_ind.ndim == 1 and view_mode.startswith("slicing"):
-# shapes_int = [in2 - in1 for in1, in2 in zip(view_ind, view_ind[1:])]
-# if isinstance(data, sp.csr_matrix) :
-# sp.csr_matrix.__init__(self, (data_data, data_indices, data_indptr), shape=data_shape)
-# #sp.csr_matrix.__init__(self, data)
-# elif isinstance(data, sp.csc_matrix):
-# sp.csc_matrix.__init__(self, (data_data, data_indices, data_indptr), shape=data_shape)
-# #sp.csc_matrix.__init__(self, data)
-# else:
-# raise TypeError("This sparse format is not supported")
-# if self.shape[0] < 1 or self.shape[1] < 1:
-# raise ValueError("input data shouldbe not empty")
-# self.view_mode_ = view_mode
-# self.views_ind = view_ind
-# self.shapes_int = shapes_int
-# self.n_views = n_views
-
class MultiModalArray(np.ndarray, MultiModalData):
"""
@@ -420,7 +295,7 @@ class MultiModalArray(np.ndarray, MultiModalData):
"""
def __new__(cls, data, view_ind=None):
- """Constructor of Metriclearn_array"""
+ """Constructor of MultiModalArray_array"""
shapes_int = []
index = 0
new_data = np.ndarray([])
@@ -430,24 +305,30 @@ class MultiModalArray(np.ndarray, MultiModalData):
view_mode = 'slices'
if isinstance(data, dict):
n_views = len(data)
+ view_ind = [0]
for key, dat_values in data.items():
new_data = cls._populate_new_data(index, dat_values, new_data)
shapes_int.append(dat_values.shape[1])
+ view_ind.append(dat_values.shape[1] + view_ind[index])
index += 1
thekeys = data.keys()
- if isinstance(data, np.ndarray) and view_ind is None and data.ndim == 1:
+
+ elif isinstance(data, np.ndarray) and view_ind is None and data.ndim == 1:
+ try:
+ dat0 = np.array(data[0])
+ except Exception:
+ raise TypeError("input format is not supported")
+
+ if dat0.ndim < 2:
+ data = data[np.newaxis, ...]
+ if data.shape[1] > 1:
+ view_ind = np.array([0, data.shape[1]//2, data.shape[1]])
+ else:
+ view_ind = np.array([0, data.shape[1]])
+ new_data = data
+ else:
+ new_data, shapes_int, view_ind = cls._for_data(cls, data)
n_views = data.shape[0]
- view_ind = np.empty(n_views+1)
- view_ind[0] = 0
- for dat_values in data:
- try:
- dat_values = np.array(dat_values)
- except:
- raise TypeError("input format is not supported")
- shapes_int.append(dat_values.shape[1])
- view_ind[index+1] = dat_values.shape[1] + view_ind[index]
- new_data = cls._populate_new_data(index, dat_values, new_data)
- index += 1
elif (isinstance(data, np.ndarray) ) and data.ndim > 1:
try:
data = np.asarray(data)
@@ -468,12 +349,16 @@ class MultiModalArray(np.ndarray, MultiModalData):
else:
try:
new_data = np.asarray(data)
- if new_data.ndim == 1:
- new_data = new_data.reshape(1, new_data.shape[0])
- view_ind = np.array([0, new_data.shape[1]])
+ # if new_data.ndim == 1:
+ # new_data = new_data.reshape(1, new_data.shape[0])
+ if view_ind is None:
+ view_ind = np.array([0, new_data.shape[1]])
except Exception as e:
raise ValueError('Reshape your data')
+ if new_data.ndim < 2 or new_data.shape == (1, 1) or view_ind[-1] > new_data.shape[1]:
+ raise ValueError('Reshape your data')
+
# view_ind_self = view_ind
# if new_data.shape[1] < 1:
# msg = ("%d feature\(s\) \\(shape=\%s\) while a minimum of \\d* "
@@ -482,7 +367,7 @@ class MultiModalArray(np.ndarray, MultiModalData):
# raise ValueError(msg)
view_ind, n_views, view_mode = cls._first_validate_views_ind(view_ind,
new_data.shape[1])
- if view_ind.ndim == 1 and view_mode.startswith("slicing"):
+ if view_ind.ndim == 1 and view_mode.startswith("slices"):
shapes_int = [in2 - in1 for in1, in2 in zip(view_ind, view_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)
@@ -501,6 +386,25 @@ class MultiModalArray(np.ndarray, MultiModalData):
obj.keys = thekeys
return obj
+ @staticmethod
+ def _for_data(cls, data):
+ n_views = data.shape[0]
+ index = 0
+ view_ind = np.empty(n_views + 1, dtype=np.int)
+ view_ind[0] = 0
+ shapes_int = []
+ new_data = np.ndarray([])
+ for dat_values in data:
+ try:
+ dat_values = np.array(dat_values)
+ except Exception:
+ raise TypeError("input format is not supported")
+ new_data = cls._populate_new_data(index, dat_values, new_data)
+ view_ind[index + 1] = dat_values.shape[1] + view_ind[index]
+ shapes_int.append(dat_values.shape[1])
+ index += 1
+ return new_data, shapes_int, view_ind
+
@staticmethod
def _populate_new_data(index, dat_values, new_data):
if index == 0:
diff --git a/multimodal/tests/__pycache__/__init__.cpython-36.pyc b/multimodal/tests/__pycache__/__init__.cpython-36.pyc
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index 1e99420542d93339ad39b73b8ba0b9c48bc020ac..0000000000000000000000000000000000000000
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diff --git a/multimodal/tests/data/__pycache__/get_dataset_path.cpython-36.pyc b/multimodal/tests/data/__pycache__/get_dataset_path.cpython-36.pyc
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index a2c8d7aba3ba76053fed1475db7036e08f9fc6bd..0000000000000000000000000000000000000000
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deleted file mode 100644
index 72e442b9eaa3fe3d85f23eac5c5fcf430c5cedf9..0000000000000000000000000000000000000000
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diff --git a/multimodal/tests/test.py b/multimodal/tests/test.py
deleted file mode 100644
index 9a68d84f26be86e9fa5ac7f7791070d22b63c10f..0000000000000000000000000000000000000000
--- a/multimodal/tests/test.py
+++ /dev/null
@@ -1,224 +0,0 @@
-
-from abc import ABCMeta
-import numpy as np
-import numpy.ma as ma
-import scipy.sparse as sp
-
-from multimodal.boosting.mumbo import MumboClassifier
-
-class MultiModalData(metaclass=ABCMeta):
-
- @staticmethod
- def _first_validate_views_ind(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 np.any(views_ind[:-1] >= views_ind[1:]):
- raise ValueError("Values in views_ind must be sorted.")
- if views_ind[0] < 0 or views_ind[-1] > n_features:
- raise ValueError("Values in views_ind are not in a correct "
- + "range for the provided data.")
- view_mode_ = "slices"
- n_views = views_ind.shape[0]-1
- else:
- if views_ind.ndim == 1:
- if not views_ind.dtype == np.object:
- raise ValueError("The format of views_ind is not "
- + "supported.")
- for ind, val in enumerate(views_ind):
- views_ind[ind] = np.array(val)
- if not np.issubdtype(views_ind[ind].dtype, np.integer):
- raise ValueError("Values in views_ind must be "
- + "integers.")
- if views_ind[ind].min() < 0 \
- or views_ind[ind].max() >= n_features:
- raise ValueError("Values in views_ind are not in a "
- + "correct range for the provided "
- + "data.")
- elif views_ind.ndim == 2:
- if not np.issubdtype(views_ind.dtype, np.integer):
- raise ValueError("Values in views_ind must be integers.")
- if views_ind.min() < 0 or views_ind.max() >= n_features:
- raise ValueError("Values in views_ind are not in a "
- + "correct range for the provided data.")
- else:
- raise ValueError("The format of views_ind is not supported.")
- view_mode_ = "indices"
- n_views = views_ind.shape[0]
- return (views_ind, n_views, view_mode_)
-
- def _extract_view(self, ind_view):
- """Extract the view for the given index ind_view from the dataset X."""
- if self.view_mode_ == "indices":
- return self[:, self.views_ind[ind_view]]
- else:
- return self[:, self.views_ind[ind_view]:self.views_ind[ind_view+1]]
-
- 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 np.any(views_ind[:-1] >= views_ind[1:]):
- raise ValueError("Values in views_ind must be sorted.")
- if views_ind[0] < 0 or views_ind[-1] > n_features:
- raise ValueError("Values in views_ind are not in a correct "
- + "range for the provided data.")
- self.view_mode_ = "slices"
- n_views = views_ind.shape[0]-1
- else:
- if views_ind.ndim == 1:
- if not views_ind.dtype == np.object:
- raise ValueError("The format of views_ind is not "
- + "supported.")
- for ind, val in enumerate(views_ind):
- views_ind[ind] = np.array(val)
- if not np.issubdtype(views_ind[ind].dtype, np.integer):
- raise ValueError("Values in views_ind must be "
- + "integers.")
- if views_ind[ind].min() < 0 \
- or views_ind[ind].max() >= n_features:
- raise ValueError("Values in views_ind are not in a "
- + "correct range for the provided "
- + "data.")
- elif views_ind.ndim == 2:
- if not np.issubdtype(views_ind.dtype, np.integer):
- raise ValueError("Values in views_ind must be integers.")
- if views_ind.min() < 0 or views_ind.max() >= n_features:
- raise ValueError("Values in views_ind are not in a "
- + "correct range for the provided data.")
- else:
- raise ValueError("The format of views_ind is not supported.")
- self.view_mode_ = "indices"
- n_views = views_ind.shape[0]
- self.views_ind = views_ind
- self.n_views = n_views
- return (views_ind, n_views)
-
-class MultiModalSparseInfo():
-
- def __init__(self, data, view_ind=None):
- """Constructor of Metriclearn_array"""
- shapes_int = []
- index = 0
- new_data = np.ndarray([])
- n_views = data.size
- thekeys = None
- # view_ind_self = None
- view_mode = 'slices'
-
- if (sp.issparse(data)) and data.ndim > 1:
- if view_ind is not None:
- try:
- view_ind = np.asarray(view_ind)
- except :
- raise TypeError("n_views should be list or nparray")
- elif view_ind is None:
- if data.shape[1] > 1:
- view_ind = np.array([0, data.shape[1]//2, data.shape[1]])
- else:
- view_ind = np.array([0, data.shape[1]])
-
- new_data = data
- # view_ind_self = view_ind
- view_ind, n_views, view_mode = self._first_validate_views_ind(view_ind,
- data.shape[1])
- if view_ind.ndim == 1 and view_mode.startswith("slicing"):
- shapes_int = [in2 - in1 for in1, in2 in zip(view_ind, view_ind[1:])]
-
- if data.shape[0] < 1 or data.shape[1] < 1:
- raise ValueError("input data shouldbe not empty")
- self.view_mode_ = view_mode
- self.views_ind = view_ind
- self.shapes_int = shapes_int
- self.n_views = n_views
-
-
-class MultiModalSparseArray(sp.csr_matrix, sp.csc_matrix, MultiModalSparseInfo, MultiModalData):
- """
- MultiModalArray inherit from numpy ndarray
-
-
- Parameters
- ----------
-
- data : can be
- - dictionary of multiview array with shape = (n_samples, n_features) for multi-view
- for each view.
- {0: array([[]],
- 1: array([[]],
- ...}
- - numpy array like with shape = (n_samples, n_features) for multi-view
- for each view.
- [[[...]],
- [[...]],
- ...]
- - {array like} with (n_samples, nviews * n_features) with 'views_ind' diferent to 'None'
- for Multi-view input samples.
-
-
-
-
- views_ind : array-like (default= None ) if None
- [0, n_features//2, n_features]) is constructed (2 views)
- Paramater specifying how to extract the data views from X:
-
- - views_ind is a 1-D array of sorted integers, the entries
- indicate the limits of the slices used to extract the views,
- where view ``n`` is given by
- ``X[:, views_ind[n]:views_ind[n+1]]``.
-
- Attributes
- ----------
-
- view_ind : list of views' indice (may be None)
-
- n_views : int number of views
-
- shapes_int: list of int numbers of feature for each views
-
- keys : name of key, where data come from a dictionary
-
-
- :Example:
-
- >>> from multimodal.datasets.base import load_dict
- >>> from multimodal.tests.datasets.get_dataset_path import get_dataset_path
- >>> from multimodal.datasets.data_sample import DataSample
- >>> file = 'input_x_dic.pkl'
- >>> data = load_dict(get_dataset_path(file))
-
- """
-
- def __init__(self, *arg, **kwargs ):
- """Constructor of Metriclearn_array"""
- if sp.issparse(arg[0]):
- MultiModalSparseInfo.__init__(self, *arg)
- if isinstance(arg[0], sp.csr_matrix) :
- sp.csr_matrix.__init__(self, arg[0])
- elif isinstance(arg[0], sp.csc_matrix):
- sp.csc_matrix.__init__(self, arg[0])
- else:
- raise TypeError("This sparse format is not supported")
- else:
- if isinstance(self,sp.csr_matrix):
- sp.csr_matrix.__init__(self, *arg, **kwargs)
- elif isinstance(self, sp.csc_matrix):
- sp.csc_matrix.__init__(self, *arg, **kwargs)
-
-
-
-
-if __name__ == '__main__':
- rng = np.random.RandomState(0)
- X = rng.rand(40, 10)
- X[X < .8] = 0
- X_csr = sp.csr_matrix(X)
- y = (4 * rng.rand(40)).astype(np.int)
- X_ = MultiModalSparseArray(X_csr)
- print(X_.shape)
- print(X_[:,0:1])
-
- X = np.array([[3, 0], [0, 1], [0, 2], [1, 1], [1, 2], [2, 1]])
- y = [1, 1, 1, 2, 2, 2]
- clf = MumboClassifier()
- clf.fit(X, y)
\ No newline at end of file
diff --git a/multimodal/tests/test_data_sample.py b/multimodal/tests/test_data_sample.py
index 04c6b49e39fc637eddd5e238c323a3802ccf586b..b21d4afd6e3bdde764ae3f1c495ead0d8a7ac59f 100644
--- a/multimodal/tests/test_data_sample.py
+++ b/multimodal/tests/test_data_sample.py
@@ -33,7 +33,7 @@ class UnitaryTest(unittest.TestCase):
np.testing.assert_almost_equal(a.get_view(0), self.kernel_dict[0], 8)
np.testing.assert_almost_equal(a.get_view(1), self.kernel_dict[1], 8)
- def test_init_Metriclearn_array(self):
+ def test_init_Multimodal_array(self):
a = MultiModalArray(self.kernel_dict)
self.assertEqual(a.shape, (120, 240))
self.assertEqual(a.shapes_int, [120, 120])
@@ -48,3 +48,5 @@ class UnitaryTest(unittest.TestCase):
np.testing.assert_equal(b.views_ind, np.array([0, 120, 240]))
+if __name__ == '__main__':
+ unittest.main()
\ No newline at end of file
diff --git a/multimodal/tests/test_mumbo.py b/multimodal/tests/test_mumbo.py
index 978244fa63a653500330d0430903ceef21cf61dd..f22f5ba72ea6d747c9f80be486830198c3f2ddb4 100644
--- a/multimodal/tests/test_mumbo.py
+++ b/multimodal/tests/test_mumbo.py
@@ -705,8 +705,8 @@ class TestMuCumboClassifier(unittest.TestCase):
def test_classifier(self):
- X_zero_features = np.empty(0).reshape(3, 0)
- y = np.array([1, 0, 1])
+ # X_zero_features = np.empty(0).reshape(3, 0)
+ # y = np.array([1, 0, 1])
# e = MumboClassifier()
# e.fit(X_zero_features, y)
# print(e.predict(X_zero_features))