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If not, see . + +Also add information on how to contact you by electronic and paper mail. + + If the program does terminal interaction, make it output a short +notice like this when it starts in an interactive mode: + + Copyright (C) + This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. + This is free software, and you are welcome to redistribute it + under certain conditions; type `show c' for details. + +The hypothetical commands `show w' and `show c' should show the appropriate +parts of the General Public License. Of course, your program's commands +might be different; for a GUI interface, you would use an "about box". + + You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU GPL, see +. + + The GNU General Public License does not permit incorporating your program +into proprietary programs. If your program is a subroutine library, you +may consider it more useful to permit linking proprietary applications with +the library. If this is what you want to do, use the GNU Lesser General +Public License instead of this License. But first, please read +. diff --git a/doc/source/conf.py b/doc/source/conf.py index 31f1b3e..c5c3d9b 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -37,7 +37,8 @@ 'sphinx.ext.doctest', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', - 'sphinx.ext.viewcode'] + 'sphinx.ext.viewcode', + 'sphinxcontrib.bibtex'] # Add any paths that contain templates here, relative to this directory. templates_path = ['ytemplates'] diff --git a/doc/source/reference.rst b/doc/source/reference.rst index cb24e82..1495538 100644 --- a/doc/source/reference.rst +++ b/doc/source/reference.rst @@ -36,12 +36,3 @@ Utilities :undoc-members: :inherited-members: :show-inheritance: - -Logging -------- - -.. automodule:: graphtools.logging - :members: - :undoc-members: - :inherited-members: - :show-inheritance: \ No newline at end of file diff --git a/doc/source/requirements.txt b/doc/source/requirements.txt index a023da3..88f6bff 100644 --- a/doc/source/requirements.txt +++ b/doc/source/requirements.txt @@ -5,3 +5,4 @@ scikit-learn>=0.19.1 future sphinx sphinxcontrib-napoleon +sphinxcontrib-bibtex diff --git a/graphtools/api.py b/graphtools/api.py index a372802..ede4f39 100644 --- a/graphtools/api.py +++ b/graphtools/api.py @@ -1,10 +1,9 @@ import numpy as np import warnings +import tasklogger -from .logging import (set_logging, - log_debug) -from .base import PyGSPGraph -from .graphs import kNNGraph, TraditionalGraph, MNNGraph, LandmarkGraph +from . import base +from . import graphs def Graph(data, @@ -138,7 +137,7 @@ def Graph(data, ------ ValueError : if selected parameters are incompatible. """ - set_logging(verbose) + tasklogger.set_level(verbose) if sample_idx is not None and len(np.unique(sample_idx)) == 1: warnings.warn("Only one unique sample. " "Not using MNNGraph") @@ -159,7 +158,7 @@ def Graph(data, # set base graph type if graphtype == "knn": - base = kNNGraph + basegraph = graphs.kNNGraph if precomputed is not None: raise ValueError("kNNGraph does not support precomputed " "values. Use `graphtype='exact'` or " @@ -170,13 +169,13 @@ def Graph(data, "`sample_idx=None`") elif graphtype == "mnn": - base = MNNGraph + basegraph = graphs.MNNGraph if precomputed is not None: raise ValueError("MNNGraph does not support precomputed " "values. Use `graphtype='exact'` and " "`sample_idx=None` or `precomputed=None`") elif graphtype == "exact": - base = TraditionalGraph + basegraph = graphs.TraditionalGraph if sample_idx is not None: raise ValueError("TraditionalGraph does not support batch " "correction. Use `graphtype='mnn'` or " @@ -186,32 +185,24 @@ def Graph(data, "['knn', 'mnn', 'exact', 'auto']") # set add landmarks if necessary - parent_classes = [base] + parent_classes = [basegraph] msg = "Building {} graph".format(graphtype) if n_landmark is not None: - parent_classes.append(LandmarkGraph) + parent_classes.append(graphs.LandmarkGraph) msg = msg + " with landmarks" if use_pygsp: - parent_classes.append(PyGSPGraph) + parent_classes.append(base.PyGSPGraph) if len(parent_classes) > 2: msg = msg + " with PyGSP inheritance" else: msg = msg + " and PyGSP inheritance" - log_debug(msg) - - # Python3 syntax only - # class Graph(*parent_classes): - # pass - if len(parent_classes) == 1: - Graph = parent_classes[0] - elif len(parent_classes) == 2: - class Graph(parent_classes[0], parent_classes[1]): - pass - elif len(parent_classes) == 3: - class Graph(parent_classes[0], parent_classes[1], parent_classes[2]): - pass - else: + tasklogger.log_debug(msg) + + class_names = [p.__name__.replace("Graph", "") for p in parent_classes] + try: + Graph = eval("graphs." + "".join(class_names) + "Graph") + except NameError: raise RuntimeError("unknown graph classes {}".format(parent_classes)) params = kwargs @@ -224,7 +215,7 @@ class Graph(parent_classes[0], parent_classes[1], parent_classes[2]): pass # build graph and return - log_debug("Initializing {} with arguments {}".format( + tasklogger.log_debug("Initializing {} with arguments {}".format( parent_classes, ", ".join(["{}='{}'".format(key, value) for key, value in params.items() diff --git a/graphtools/base.py b/graphtools/base.py index 5427041..edd678a 100644 --- a/graphtools/base.py +++ b/graphtools/base.py @@ -9,6 +9,8 @@ from scipy import sparse import warnings import numbers +import tasklogger + try: import pandas as pd except ImportError: @@ -24,10 +26,6 @@ from .utils import (elementwise_minimum, elementwise_maximum, set_diagonal) -from .logging import (set_logging, - log_start, - log_complete, - log_debug) class Base(object): @@ -67,6 +65,9 @@ def _get_param_names(cls): return parameters + def set_params(self, **kwargs): + return self + class Data(Base): """Parent class that handles the import and dimensionality reduction of data @@ -152,7 +153,7 @@ def _reduce_data(self): Reduced data matrix """ if self.n_pca is not None and self.n_pca < self.data.shape[1]: - log_start("PCA") + tasklogger.log_start("PCA") if sparse.issparse(self.data): if isinstance(self.data, sparse.coo_matrix) or \ isinstance(self.data, sparse.lil_matrix) or \ @@ -166,7 +167,7 @@ def _reduce_data(self): random_state=self.random_state) self.data_pca.fit(self.data) data_nu = self.data_pca.transform(self.data) - log_complete("PCA") + tasklogger.log_complete("PCA") return data_nu else: data_nu = self.data @@ -204,6 +205,7 @@ def set_params(self, **params): raise ValueError("Cannot update n_pca. Please create a new graph") if 'random_state' in params: self.random_state = params['random_state'] + super().set_params(**params) return self def transform(self, Y): @@ -342,10 +344,10 @@ def __init__(self, kernel_symm='+', self._check_symmetrization(kernel_symm, gamma) if initialize: - log_debug("Initializing kernel...") + tasklogger.log_debug("Initializing kernel...") self.K else: - log_debug("Not initializing kernel.") + tasklogger.log_debug("Not initializing kernel.") super().__init__(**kwargs) def _check_symmetrization(self, kernel_symm, gamma): @@ -363,7 +365,8 @@ def _check_symmetrization(self, kernel_symm, gamma): warnings.warn("kernel_symm='gamma' but gamma not given. " "Defaulting to gamma=0.5.") self.gamma = gamma = 0.5 - elif not isinstance(gamma, numbers.Number) or gamma < 0 or gamma > 1: + elif not isinstance(gamma, numbers.Number) or \ + gamma < 0 or gamma > 1: raise ValueError("gamma {} not recognized. Expected " "a float between 0 and 1".format(gamma)) @@ -392,18 +395,18 @@ def _build_kernel(self): def symmetrize_kernel(self, K): # symmetrize if self.kernel_symm == "+": - log_debug("Using addition symmetrization.") + tasklogger.log_debug("Using addition symmetrization.") K = (K + K.T) / 2 elif self.kernel_symm == "*": - log_debug("Using multiplication symmetrization.") + tasklogger.log_debug("Using multiplication symmetrization.") K = K.multiply(K.T) elif self.kernel_symm == 'gamma': - log_debug( + tasklogger.log_debug( "Using gamma symmetrization (gamma = {}).".format(self.gamma)) K = self.gamma * elementwise_minimum(K, K.T) + \ (1 - self.gamma) * elementwise_maximum(K, K.T) elif self.kernel_symm is None: - log_debug("Using no symmetrization.") + tasklogger.log_debug("Using no symmetrization.") pass else: # this should never happen @@ -438,9 +441,11 @@ def set_params(self, **params): """ if 'gamma' in params and params['gamma'] != self.gamma: raise ValueError("Cannot update gamma. Please create a new graph") - if 'kernel_symm' in params and params['kernel_symm'] != self.kernel_symm: + if 'kernel_symm' in params and \ + params['kernel_symm'] != self.kernel_symm: raise ValueError( "Cannot update kernel_symm. Please create a new graph") + super().set_params(**params) return self @property @@ -462,6 +467,31 @@ def P(self): self._diff_op = normalize(self.kernel, 'l1', axis=1) return self._diff_op + @property + def diff_aff(self): + """Symmetric diffusion affinity matrix + + Return or calculate the symmetric diffusion affinity matrix + + .. math:: A(x,y) = K(x,y) (d(x) d(y))^{-1/2} + + where :math:`d` is the degrees (row sums of the kernel.) + + Returns + ------- + + diff_aff : array-like, shape=[n_samples, n_samples] + symmetric diffusion affinity matrix defined as a + doubly-stochastic form of the kernel matrix + """ + row_degrees = np.array(self.kernel.sum(axis=1)).reshape(-1, 1) + col_degrees = np.array(self.kernel.sum(axis=0)).reshape(1, -1) + if sparse.issparse(self.kernel): + return self.kernel.multiply(1 / np.sqrt(row_degrees)).multiply( + 1 / np.sqrt(col_degrees)) + else: + return (self.kernel / np.sqrt(row_degrees)) / np.sqrt(col_degrees) + @property def diff_op(self): """Synonym for P @@ -597,7 +627,7 @@ def __init__(self, data, # kwargs are ignored self.n_jobs = n_jobs self.verbose = verbose - set_logging(verbose) + tasklogger.set_level(verbose) super().__init__(data, **kwargs) def get_params(self): diff --git a/graphtools/graphs.py b/graphtools/graphs.py index 1a5ed93..a6b5974 100644 --- a/graphtools/graphs.py +++ b/graphtools/graphs.py @@ -9,16 +9,13 @@ from scipy import sparse import numbers import warnings +import tasklogger from .utils import (set_diagonal, elementwise_minimum, elementwise_maximum, set_submatrix) -from .logging import (log_start, - log_complete, - log_warning, - log_debug) -from .base import DataGraph +from .base import DataGraph, PyGSPGraph class kNNGraph(DataGraph): @@ -127,6 +124,8 @@ def set_params(self, **params): raise ValueError("Cannot update thresh. Please create a new graph") if 'n_jobs' in params: self.n_jobs = params['n_jobs'] + if hasattr(self, "_knn_tree"): + self.knn_tree.set_params(n_jobs=self.n_jobs) if 'random_state' in params: self.random_state = params['random_state'] if 'verbose' in params: @@ -219,13 +218,13 @@ def build_kernel_to_data(self, Y, knn=None): k=knn, n=self.data.shape[0])) Y = self._check_extension_shape(Y) - log_start("KNN search") + tasklogger.log_start("KNN search") if self.decay is None or self.thresh == 1: # binary connectivity matrix K = self.knn_tree.kneighbors_graph( Y, n_neighbors=knn, mode='connectivity') - log_complete("KNN search") + tasklogger.log_complete("KNN search") else: # sparse fast alpha decay knn_tree = self.knn_tree @@ -246,15 +245,15 @@ def build_kernel_to_data(self, Y, knn=None): "Consider removing duplicates to avoid errors in " "downstream processing.".format(duplicate_names), RuntimeWarning) - log_complete("KNN search") - log_start("affinities") + tasklogger.log_complete("KNN search") + tasklogger.log_start("affinities") bandwidth = distances[:, knn - 1] radius = bandwidth * np.power(-1 * np.log(self.thresh), 1 / self.decay) update_idx = np.argwhere( np.max(distances, axis=1) < radius).reshape(-1) - log_debug("search_knn = {}; {} remaining".format(search_knn, - len(update_idx))) + tasklogger.log_debug("search_knn = {}; {} remaining".format( + search_knn, len(update_idx))) if len(update_idx) > 0: distances = [d for d in distances] indices = [i for i in indices] @@ -269,14 +268,16 @@ def build_kernel_to_data(self, Y, knn=None): indices[idx] = ind_new[i] update_idx = [i for i, d in enumerate(distances) if np.max(d) < radius[i]] - log_debug("search_knn = {}; {} remaining".format( + tasklogger.log_debug("search_knn = {}; {} remaining".format( search_knn, len(update_idx))) if search_knn > self.data_nu.shape[0] / 2: - knn_tree = NearestNeighbors(knn, algorithm='brute', - n_jobs=self.n_jobs).fit(self.data_nu) + knn_tree = NearestNeighbors( + knn, algorithm='brute', + n_jobs=self.n_jobs).fit(self.data_nu) if len(update_idx) > 0: - log_debug("radius search on {}".format(len(update_idx))) + tasklogger.log_debug( + "radius search on {}".format(len(update_idx))) # give up - radius search dist_new, ind_new = knn_tree.radius_neighbors( Y[update_idx, :], @@ -292,12 +293,14 @@ def build_kernel_to_data(self, Y, knn=None): K = sparse.csr_matrix((data, indices, indptr), shape=(Y.shape[0], self.data_nu.shape[0])) K.data = np.exp(-1 * np.power(K.data, self.decay)) + # handle nan + K.data = np.where(np.isnan(K.data), 1, K.data) # TODO: should we zero values that are below thresh? K.data[K.data < self.thresh] = 0 K = K.tocoo() K.eliminate_zeros() K = K.tocsr() - log_complete("affinities") + tasklogger.log_complete("affinities") return K @@ -446,6 +449,20 @@ def transitions(self): self.build_landmark_op() return self._transitions + def _landmarks_to_data(self): + landmarks = np.unique(self._clusters) + if sparse.issparse(self.kernel): + pmn = sparse.vstack( + [sparse.csr_matrix(self.kernel[self._clusters == i, :].sum( + axis=0)) for i in landmarks]) + else: + pmn = np.array([np.sum(self.kernel[self._clusters == i, :], axis=0) + for i in landmarks]) + return pmn + + def _data_transitions(self): + return normalize(self._landmarks_to_data(), 'l1', axis=1) + def build_landmark_op(self): """Build the landmark operator @@ -453,15 +470,15 @@ def build_landmark_op(self): probabilities between cluster centers by using transition probabilities between samples assigned to each cluster. """ - log_start("landmark operator") + tasklogger.log_start("landmark operator") is_sparse = sparse.issparse(self.kernel) # spectral clustering - log_start("SVD") - _, _, VT = randomized_svd(self.diff_op, + tasklogger.log_start("SVD") + _, _, VT = randomized_svd(self.diff_aff, n_components=self.n_svd, random_state=self.random_state) - log_complete("SVD") - log_start("KMeans") + tasklogger.log_complete("SVD") + tasklogger.log_start("KMeans") kmeans = MiniBatchKMeans( self.n_landmark, init_size=3 * self.n_landmark, @@ -470,29 +487,23 @@ def build_landmark_op(self): self._clusters = kmeans.fit_predict( self.diff_op.dot(VT.T)) # some clusters are not assigned - landmarks = np.unique(self._clusters) - log_complete("KMeans") + tasklogger.log_complete("KMeans") # transition matrices - if is_sparse: - pmn = sparse.vstack( - [sparse.csr_matrix(self.kernel[self._clusters == i, :].sum( - axis=0)) for i in landmarks]) - else: - pmn = np.array([np.sum(self.kernel[self._clusters == i, :], axis=0) - for i in landmarks]) + pmn = self._landmarks_to_data() + # row normalize pnm = pmn.transpose() pmn = normalize(pmn, norm='l1', axis=1) pnm = normalize(pnm, norm='l1', axis=1) - diff_op = pmn.dot(pnm) # sparsity agnostic matrix multiplication + landmark_op = pmn.dot(pnm) # sparsity agnostic matrix multiplication if is_sparse: # no need to have a sparse landmark operator - diff_op = diff_op.toarray() + landmark_op = landmark_op.toarray() # store output - self._landmark_op = diff_op + self._landmark_op = landmark_op self._transitions = pnm - log_complete("landmark operator") + tasklogger.log_complete("landmark operator") def extend_to_data(self, data, **kwargs): """Build transition matrix from new data to the graph @@ -595,7 +606,8 @@ class TraditionalGraph(DataGraph): All affinities below `thresh` will be set to zero in order to save on time and memory constraints. - precomputed : {'distance', 'affinity', 'adjacency', `None`}, optional (default: `None`) + precomputed : {'distance', 'affinity', 'adjacency', `None`}, + optional (default: `None`) If the graph is precomputed, this variable denotes which graph matrix is provided as `data`. Only one of `precomputed` and `n_pca` can be set. @@ -669,14 +681,14 @@ def set_params(self, **params): raise ValueError("Cannot update precomputed. " "Please create a new graph") if 'distance' in params and params['distance'] != self.distance and \ - self.precomputed is not None: + self.precomputed is None: raise ValueError("Cannot update distance. " "Please create a new graph") if 'knn' in params and params['knn'] != self.knn and \ - self.precomputed is not None: + self.precomputed is None: raise ValueError("Cannot update knn. Please create a new graph") if 'decay' in params and params['decay'] != self.decay and \ - self.precomputed is not None: + self.precomputed is None: raise ValueError("Cannot update decay. Please create a new graph") # update superclass parameters super().set_params(**params) @@ -714,7 +726,7 @@ def build_kernel(self): K = K.tolil() K = set_diagonal(K, 1) else: - log_start("affinities") + tasklogger.log_start("affinities") if sparse.issparse(self.data_nu): self.data_nu = self.data_nu.toarray() if self.precomputed == "distance": @@ -744,7 +756,9 @@ def build_kernel(self): epsilon = np.max(knn_dist, axis=1) pdx = (pdx.T / epsilon).T K = np.exp(-1 * np.power(pdx, self.decay)) - log_complete("affinities") + # handle nan + K = np.where(np.isnan(K), 1, K) + tasklogger.log_complete("affinities") # truncate if sparse.issparse(K): if not (isinstance(K, sparse.csr_matrix) or @@ -794,15 +808,17 @@ def build_kernel_to_data(self, Y, knn=None): if self.precomputed is not None: raise ValueError("Cannot extend kernel on precomputed graph") else: - log_start("affinities") + tasklogger.log_start("affinities") Y = self._check_extension_shape(Y) pdx = cdist(Y, self.data_nu, metric=self.distance) knn_dist = np.partition(pdx, knn, axis=1)[:, :knn] epsilon = np.max(knn_dist, axis=1) pdx = (pdx.T / epsilon).T K = np.exp(-1 * pdx**self.decay) + # handle nan + K = np.where(np.isnan(K), 1, K) K[K < self.thresh] = 0 - log_complete("affinities") + tasklogger.log_complete("affinities") return K @@ -843,6 +859,7 @@ def __init__(self, data, sample_idx, decay=None, distance='euclidean', thresh=1e-4, + n_jobs=1, **kwargs): self.beta = beta self.sample_idx = sample_idx @@ -853,6 +870,7 @@ def __init__(self, data, sample_idx, self.decay = decay self.distance = distance self.thresh = thresh + self.n_jobs = n_jobs self.weighted_knn = self._weight_knn() if sample_idx is None: @@ -860,7 +878,8 @@ def __init__(self, data, sample_idx, " batch correction, use kNNGraph.") elif len(sample_idx) != data.shape[0]: raise ValueError("sample_idx ({}) must be the same length as " - "data ({})".format(len(sample_idx), data.shape[0])) + "data ({})".format(len(sample_idx), + data.shape[0])) elif len(self.samples) == 1: raise ValueError( "sample_idx must contain more than one unique value") @@ -923,7 +942,7 @@ def _weight_knn(self, sample_size=None): elif self.adaptive_k == 'sqrt': # the samples are sqrt'd first, then smallest has k knn_weight = np.sqrt(self.n_cells / np.min(self.n_cells)) - elif self.adaptive_k == 'none': + elif self.adaptive_k is None: knn_weight = np.repeat(1, len(self.n_cells)) weighted_knn = np.round(self.knn * knn_weight).astype(np.int32) if len(weighted_knn) == 1: @@ -935,8 +954,12 @@ def get_params(self): """ params = super().get_params() params.update({'beta': self.beta, - 'adaptive_k': self.adaptive_k}) - params.update(self.knn_args) + 'adaptive_k': self.adaptive_k, + 'knn': self.knn, + 'decay': self.decay, + 'distance': self.distance, + 'thresh': self.thresh, + 'n_jobs': self.n_jobs}) return params def set_params(self, **params): @@ -975,15 +998,14 @@ def set_params(self, **params): knn_kernel_args = ['knn', 'decay', 'distance', 'thresh'] knn_other_args = ['n_jobs', 'random_state', 'verbose'] for arg in knn_kernel_args: - if arg in params and (arg not in self.knn_args or - params[arg] != self.knn_args[arg]): + if arg in params and params[arg] != getattr(self, arg): raise ValueError("Cannot update {}. " "Please create a new graph".format(arg)) for arg in knn_other_args: - self.__setattr__(arg, params[arg]) - - # update subgraph parameters - [g.set_params(**knn_other_args) for g in self.subgraphs] + if arg in params: + self.__setattr__(arg, params[arg]) + for g in self.subgraphs: + g.set_params(**{arg: params[arg]}) # update superclass parameters super().set_params(**params) @@ -1000,13 +1022,15 @@ def build_kernel(self): symmetric matrix with ones down the diagonal with no non-negative entries. """ - log_start("subgraphs") + tasklogger.log_start("subgraphs") self.subgraphs = [] from .api import Graph # iterate through sample ids for i, idx in enumerate(self.samples): - log_debug("subgraph {}: sample {}, n = {}, knn = {}".format( - i, idx, np.sum(self.sample_idx == idx), self.weighted_knn[i])) + tasklogger.log_debug("subgraph {}: sample {}, " + "n = {}, knn = {}".format( + i, idx, np.sum(self.sample_idx == idx), + self.weighted_knn[i])) # select data for sample data = self.data_nu[self.sample_idx == idx] # build a kNN graph for cells within sample @@ -1017,9 +1041,10 @@ def build_kernel(self): thresh=self.thresh, verbose=self.verbose, random_state=self.random_state, + n_jobs=self.n_jobs, initialize=False) self.subgraphs.append(graph) # append to list of subgraphs - log_complete("subgraphs") + tasklogger.log_complete("subgraphs") if self.thresh > 0 or self.decay is None: K = sparse.lil_matrix( @@ -1028,7 +1053,7 @@ def build_kernel(self): K = np.zeros([self.data_nu.shape[0], self.data_nu.shape[0]]) for i, X in enumerate(self.subgraphs): for j, Y in enumerate(self.subgraphs): - log_start( + tasklogger.log_start( "kernel from sample {} to {}".format(self.samples[i], self.samples[j])) Kij = Y.build_kernel_to_data( @@ -1039,7 +1064,7 @@ def build_kernel(self): Kij = Kij * self.beta K = set_submatrix(K, self.sample_idx == self.samples[i], self.sample_idx == self.samples[j], Kij) - log_complete( + tasklogger.log_complete( "kernel from sample {} to {}".format(self.samples[i], self.samples[j])) return K @@ -1051,8 +1076,8 @@ def symmetrize_kernel(self, K): # Gamma can be a matrix with specific values transitions for # each batch. This allows for technical replicates and # experimental samples to be corrected simultaneously - log_debug("Using gamma symmetrization. " - "Gamma:\n{}".format(self.gamma)) + tasklogger.log_debug("Using gamma symmetrization. " + "Gamma:\n{}".format(self.gamma)) for i, sample_i in enumerate(self.samples): for j, sample_j in enumerate(self.samples): if j < i: @@ -1106,7 +1131,7 @@ def build_kernel_to_data(self, Y, gamma=None): Transition matrix from `Y` to `self.data` """ raise NotImplementedError - log_warning("building MNN kernel to gamma is experimental") + tasklogger.log_warning("building MNN kernel to gamma is experimental") if not isinstance(self.gamma, str) and \ not isinstance(self.gamma, numbers.Number): if gamma is None: @@ -1152,3 +1177,39 @@ def build_kernel_to_data(self, Y, gamma=None): K = self.gamma * kernel_xy.minimum(kernel_yx.T) + \ (1 - self.gamma) * kernel_xy.maximum(kernel_yx.T) return K + + +class kNNLandmarkGraph(kNNGraph, LandmarkGraph): + pass + + +class MNNLandmarkGraph(MNNGraph, LandmarkGraph): + pass + + +class TraditionalLandmarkGraph(TraditionalGraph, LandmarkGraph): + pass + + +class kNNPyGSPGraph(kNNGraph, PyGSPGraph): + pass + + +class MNNPyGSPGraph(MNNGraph, PyGSPGraph): + pass + + +class TraditionalPyGSPGraph(TraditionalGraph, PyGSPGraph): + pass + + +class kNNLandmarkPyGSPGraph(kNNGraph, LandmarkGraph, PyGSPGraph): + pass + + +class MNNLandmarkPyGSPGraph(MNNGraph, LandmarkGraph, PyGSPGraph): + pass + + +class TraditionalLandmarkPyGSPGraph(TraditionalGraph, LandmarkGraph, PyGSPGraph): + pass diff --git a/graphtools/logging.py b/graphtools/logging.py deleted file mode 100644 index 43055eb..0000000 --- a/graphtools/logging.py +++ /dev/null @@ -1,165 +0,0 @@ -from __future__ import absolute_import, print_function -from builtins import super, bytes -import os -import logging -import time -import sys - - -__logger_name__ = "graphtools" - - -class RSafeStdErr(object): - """ - R's reticulate package inadvertently captures stderr and stdout - This class writes directly to stderr to avoid this. - """ - - def __init__(self): - try: - __IPYTHON__ - self.write = self.write_ipython - except NameError: - self.write = self.write_r_safe - - def write_ipython(self, msg): - print(msg, end='', file=sys.stdout) - - def write_r_safe(self, msg): - os.write(1, bytes(msg, 'utf8')) - - def flush(self): - sys.stdout.flush() - - -class TaskLogger(object): - """ - Class which deals with timing and logging tasks - """ - - def __init__(self, logger, *args, **kwargs): - self.tasks = {} - self.logger = logger - super().__init__(*args, **kwargs) - - def log(self, msg): - self.logger.info(msg) - - def start_task(self, name): - self.tasks[name] = time.time() - self.log("Calculating {}...".format(name)) - - def complete_task(self, name): - try: - runtime = time.time() - self.tasks[name] - if runtime >= 0.01: - self.log("Calculated {} in {:.2f} seconds.".format( - name, runtime)) - del self.tasks[name] - except KeyError: - self.log("Calculated {}.".format(name)) - - -def set_logging(level=1): - """Set up logging - - Parameters - ---------- - - level : `int` or `bool` (optional, default: 1) - If False or 0, prints WARNING and higher messages. - If True or 1, prints INFO and higher messages. - If 2 or higher, prints all messages. - """ - if level is True or level == 1: - level = logging.INFO - level_name = "INFO" - elif level is False or level <= 0: - level = logging.WARNING - level_name = "WARNING" - elif level >= 2: - level = logging.DEBUG - level_name = "DEBUG" - - logger = get_logger() - logger.setLevel(level) - if not logger.handlers: - logger.task_logger = TaskLogger(logger) - logger.propagate = False - handler = logging.StreamHandler(stream=RSafeStdErr()) - handler.setFormatter(logging.Formatter(fmt='%(message)s')) - logger.addHandler(handler) - log_debug("Set {} logging to {}".format(__logger_name__, level_name)) - - -def get_logger(): - return logging.getLogger(__logger_name__) - - -def get_task_logger(): - return get_logger().task_logger - - -def log_start(name): - """ - Convenience function to log a task in the default - TaskLogger - """ - try: - get_task_logger().start_task(name) - except AttributeError: - if not hasattr(logging.getLogger, "task_logger"): - set_logging(logging.INFO) - log_start(name) - else: - raise - - -def log_complete(name): - """ - Convenience function to log a task in the default - TaskLogger - """ - try: - get_task_logger().complete_task(name) - except AttributeError: - if not hasattr(logging.getLogger, "task_logger"): - set_logging(logging.INFO) - log_complete(name) - else: - raise - - -def log_debug(msg): - """ - Convenience function to log a message to the default Logger - """ - get_logger().debug(msg) - - -def log_info(msg): - """ - Convenience function to log a message to the default Logger - """ - get_logger().info(msg) - - -def log_warning(msg): - """ - Convenience function to log a message to the default Logger - """ - get_logger().warning(msg) - - -def log_error(msg): - """ - Convenience function to log a message to the default Logger - """ - get_logger().error(msg) - - -def log_critical(msg): - """ - Convenience function to log a message to the default Logger - """ - get_logger().critical(msg) diff --git a/graphtools/version.py b/graphtools/version.py index c11f861..569b121 100644 --- a/graphtools/version.py +++ b/graphtools/version.py @@ -1 +1 @@ -__version__ = "0.1.9" +__version__ = "0.1.10" diff --git a/requirements.txt b/requirements.txt index 5025a07..c19f67d 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,4 +2,5 @@ numpy>=1.14.0 scipy>=1.1.0 pygsp>=>=0.5.1 scikit-learn>=0.19.1 -future \ No newline at end of file +future +tasklogger>=0.2 diff --git a/setup.py b/setup.py index 12c1c18..fca28af 100644 --- a/setup.py +++ b/setup.py @@ -8,6 +8,7 @@ 'pygsp>=0.5.1', 'scikit-learn>=0.19.1', 'future', + 'tasklogger>=0.2', ] test_requires = [ @@ -23,6 +24,7 @@ doc_requires = [ 'sphinx', 'sphinxcontrib-napoleon', + 'sphinxcontrib-bibtex' ] if sys.version_info[:2] < (2, 7) or (3, 0) <= sys.version_info[:2] < (3, 5): diff --git a/test/load_tests/__init__.py b/test/load_tests/__init__.py index cbeb8e1..3f62c9d 100644 --- a/test/load_tests/__init__.py +++ b/test/load_tests/__init__.py @@ -10,7 +10,22 @@ import nose2 from nose.tools import raises, assert_raises, make_decorator -warnings.filterwarnings("error") + + +def reset_warnings(): + warnings.resetwarnings() + warnings.simplefilter("error") + ignore_numpy_warning() + + +def ignore_numpy_warning(): + warnings.filterwarnings( + "ignore", category=PendingDeprecationWarning, + message="the matrix subclass is not the recommended way to represent " + "matrices or deal with linear algebra ") + + +reset_warnings() global digits global data @@ -65,9 +80,11 @@ def decorate(func): def newfunc(*arg, **kw): with warnings.catch_warnings(record=True) as w: - warnings.filterwarnings("always") + warnings.resetwarnings() + warnings.simplefilter("always") + ignore_numpy_warning() func(*arg, **kw) - warnings.filterwarnings("error") + reset_warnings() try: for warn in w: raise warn.category diff --git a/test/test_data.py b/test/test_data.py index 6907821..39d7966 100644 --- a/test/test_data.py +++ b/test/test_data.py @@ -183,3 +183,17 @@ def test_inverse_transform_sparse_no_pca(): assert_raises(ValueError, G.inverse_transform, sp.csr_matrix(G.data)[:, 0]) assert_raises(ValueError, G.inverse_transform, sp.csr_matrix(G.data)[:, :15]) + + +############# +# Test API +############# + + +def test_set_params(): + G = graphtools.base.Data(data, n_pca=20) + assert G.get_params() == {'n_pca': 20, 'random_state': None} + G.set_params(random_state=13) + assert G.random_state == 13 + assert_raises(ValueError, G.set_params, n_pca=10) + G.set_params(n_pca=G.n_pca) diff --git a/test/test_exact.py b/test/test_exact.py index f43f859..542e625 100644 --- a/test/test_exact.py +++ b/test/test_exact.py @@ -332,7 +332,32 @@ def test_precomputed_interpolate(): G.build_kernel_to_data(data) +#################### +# Test API +#################### + + def test_verbose(): print() print("Verbose test: Exact") build_graph(data, decay=10, thresh=0, verbose=True) + + +def test_set_params(): + G = build_graph(data, decay=10, thresh=0) + assert G.get_params() == {'n_pca': 20, + 'random_state': 42, + 'kernel_symm': '+', + 'gamma': None, + 'knn': 3, + 'decay': 10, + 'distance': 'euclidean', + 'precomputed': None} + assert_raises(ValueError, G.set_params, knn=15) + assert_raises(ValueError, G.set_params, decay=15) + assert_raises(ValueError, G.set_params, distance='manhattan') + assert_raises(ValueError, G.set_params, precomputed='distance') + G.set_params(knn=G.knn, + decay=G.decay, + distance=G.distance, + precomputed=G.precomputed) diff --git a/test/test_knn.py b/test/test_knn.py index 1ea6e12..b8682c2 100644 --- a/test/test_knn.py +++ b/test/test_knn.py @@ -167,7 +167,48 @@ def test_knn_interpolate(): G.interpolate(pca_data, transitions=transitions))) +#################### +# Test API +#################### + + def test_verbose(): print() print("Verbose test: kNN") build_graph(data, decay=None, verbose=True) + + +def test_set_params(): + G = build_graph(data, decay=None) + assert G.get_params() == { + 'n_pca': 20, + 'random_state': 42, + 'kernel_symm': '+', + 'gamma': None, + 'knn': 3, + 'decay': None, + 'distance': 'euclidean', + 'thresh': 0, + 'n_jobs': -1, + 'verbose': 0 + } + G.set_params(n_jobs=4) + assert G.n_jobs == 4 + assert G.knn_tree.n_jobs == 4 + G.set_params(random_state=13) + assert G.random_state == 13 + G.set_params(verbose=2) + assert G.verbose == 2 + G.set_params(verbose=0) + assert_raises(ValueError, G.set_params, knn=15) + assert_raises(ValueError, G.set_params, decay=10) + assert_raises(ValueError, G.set_params, distance='manhattan') + assert_raises(ValueError, G.set_params, thresh=1e-3) + assert_raises(ValueError, G.set_params, gamma=0.99) + assert_raises(ValueError, G.set_params, kernel_symm='*') + G.set_params(knn=G.knn, + decay=G.decay, + thresh=G.thresh, + distance=G.distance, + gamma=G.gamma, + kernel_symm=G.kernel_symm) diff --git a/test/test_landmark.py b/test/test_landmark.py index 17f386c..42d9025 100644 --- a/test/test_landmark.py +++ b/test/test_landmark.py @@ -5,10 +5,12 @@ data, digits, build_graph, + assert_raises, raises, warns, generate_swiss_roll ) +import pygsp ##################################################### @@ -65,6 +67,50 @@ def test_landmark_mnn_graph(): assert(isinstance(G, graphtools.graphs.LandmarkGraph)) +##################################################### +# Check PyGSP +##################################################### + + +def test_landmark_exact_pygsp_graph(): + n_landmark = 100 + # exact graph + G = build_graph(data, n_landmark=n_landmark, + thresh=0, n_pca=10, + decay=10, knn=3, random_state=42, + use_pygsp=True) + assert(G.landmark_op.shape == (n_landmark, n_landmark)) + assert(isinstance(G, graphtools.graphs.TraditionalGraph)) + assert(isinstance(G, graphtools.graphs.LandmarkGraph)) + assert(isinstance(G, pygsp.graphs.Graph)) + + +def test_landmark_knn_pygsp_graph(): + n_landmark = 500 + # knn graph + G = build_graph(data, n_landmark=n_landmark, n_pca=10, + decay=None, knn=3, random_state=42, + use_pygsp=True) + assert(G.landmark_op.shape == (n_landmark, n_landmark)) + assert(isinstance(G, graphtools.graphs.kNNGraph)) + assert(isinstance(G, graphtools.graphs.LandmarkGraph)) + assert(isinstance(G, pygsp.graphs.Graph)) + + +def test_landmark_mnn_pygsp_graph(): + n_landmark = 150 + X, sample_idx = generate_swiss_roll() + # mnn graph + G = build_graph(X, n_landmark=n_landmark, + thresh=1e-3, n_pca=None, + decay=10, knn=3, random_state=42, + sample_idx=sample_idx, use_pygsp=True) + assert(G.landmark_op.shape == (n_landmark, n_landmark)) + assert(isinstance(G, graphtools.graphs.MNNGraph)) + assert(isinstance(G, graphtools.graphs.LandmarkGraph)) + assert(isinstance(G, pygsp.graphs.Graph)) + + ##################################################### # Check interpolation ##################################################### @@ -73,7 +119,34 @@ def test_landmark_mnn_graph(): # TODO: add interpolation tests +############# +# Test API +############# + def test_verbose(): print() print("Verbose test: Landmark") build_graph(data, decay=None, n_landmark=500, verbose=True).landmark_op + + +def test_set_params(): + G = build_graph(data, n_landmark=500, decay=None) + G.landmark_op + assert G.get_params() == {'n_pca': 20, + 'random_state': 42, + 'kernel_symm': '+', + 'gamma': None, + 'n_landmark': 500, + 'knn': 3, + 'decay': None, + 'distance': + 'euclidean', + 'thresh': 0, + 'n_jobs': -1, + 'verbose': 0} + G.set_params(n_landmark=300) + assert G.landmark_op.shape == (300, 300) + G.set_params(n_landmark=G.n_landmark, n_svd=G.n_svd) + assert hasattr(G, "_landmark_op") + G.set_params(n_svd=50) + assert not hasattr(G, "_landmark_op") diff --git a/test/test_mnn.py b/test/test_mnn.py index 51d58e5..330a4e4 100644 --- a/test/test_mnn.py +++ b/test/test_mnn.py @@ -222,3 +222,50 @@ def test_verbose(): build_graph(X, sample_idx=sample_idx, kernel_symm='gamma', gamma=0.5, n_pca=None, verbose=True) + + +def test_set_params(): + X, sample_idx = generate_swiss_roll() + G = build_graph(X, sample_idx=sample_idx, + kernel_symm='gamma', gamma=0.5, + n_pca=None, + thresh=1e-4) + assert G.get_params() == { + 'n_pca': None, + 'random_state': 42, + 'kernel_symm': 'gamma', + 'gamma': 0.5, + 'beta': 1, + 'adaptive_k': 'sqrt', + 'knn': 3, + 'decay': 10, + 'distance': 'euclidean', + 'thresh': 1e-4, + 'n_jobs': 1 + } + G.set_params(n_jobs=4) + assert G.n_jobs == 4 + for graph in G.subgraphs: + assert graph.n_jobs == 4 + assert graph.knn_tree.n_jobs == 4 + G.set_params(random_state=13) + assert G.random_state == 13 + for graph in G.subgraphs: + assert graph.random_state == 13 + G.set_params(verbose=2) + assert G.verbose == 2 + for graph in G.subgraphs: + assert graph.verbose == 2 + G.set_params(verbose=0) + assert_raises(ValueError, G.set_params, knn=15) + assert_raises(ValueError, G.set_params, decay=15) + assert_raises(ValueError, G.set_params, distance='manhattan') + assert_raises(ValueError, G.set_params, thresh=1e-3) + assert_raises(ValueError, G.set_params, beta=0.2) + assert_raises(ValueError, G.set_params, adaptive_k='min') + G.set_params(knn=G.knn, + decay=G.decay, + thresh=G.thresh, + distance=G.distance, + beta=G.beta, + adaptive_k=G.adaptive_k) diff --git a/unittest.cfg b/unittest.cfg index 7128d00..0f1a4ec 100644 --- a/unittest.cfg +++ b/unittest.cfg @@ -2,4 +2,5 @@ verbose = True [coverage] -always-on = True \ No newline at end of file +always-on = True +coverage = graphtools \ No newline at end of file