From d6b252a83e06f3470305dd1e9b7b4ee0974bccf3 Mon Sep 17 00:00:00 2001 From: jajupmochi Date: Thu, 27 Feb 2020 17:56:17 +0100 Subject: [PATCH] rename library. --- .gitignore | 12 +- README.md | 20 +- docs/Makefile | 4 +- docs/_build/doctrees/environment.pickle | Bin 133618 -> 0 bytes docs/_build/doctrees/index.doctree | Bin 4775 -> 0 bytes docs/_build/doctrees/modules.doctree | Bin 2562 -> 0 bytes docs/_build/doctrees/pygraph.doctree | Bin 5784 -> 0 bytes docs/_build/doctrees/pygraph.utils.doctree | Bin 187554 -> 0 bytes docs/_build/html/.buildinfo | 4 - docs/_build/html/_modules/index.html | 196 - .../html/_modules/pygraph/utils/graphdataset.html | 502 - .../html/_modules/pygraph/utils/graphfiles.html | 811 -- .../html/_modules/pygraph/utils/isNotebook.html | 205 - .../html/_modules/pygraph/utils/kernels.html | 347 - .../html/_modules/pygraph/utils/logger2file.html | 216 - .../pygraph/utils/model_selection_precomputed.html | 1148 --- .../html/_modules/pygraph/utils/parallel.html | 252 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rename {preimage => gklearn/preimage}/pathfrequency.py (100%) rename {preimage => gklearn/preimage}/preimage_iam.py (100%) rename {preimage => gklearn/preimage}/preimage_random.py (99%) rename {preimage => gklearn/preimage}/test.py (98%) rename {preimage => gklearn/preimage}/test_fitDistance.py (95%) create mode 100644 gklearn/preimage/test_ged.py rename {preimage => gklearn/preimage}/test_iam.py (99%) rename {preimage => gklearn/preimage}/test_k_closest_graphs.py (62%) rename {preimage => gklearn/preimage}/test_others.py (99%) rename {preimage => gklearn/preimage}/test_preimage_iam.py (99%) rename {preimage => gklearn/preimage}/test_preimage_mix.py (99%) rename {preimage => gklearn/preimage}/test_preimage_random.py (99%) rename {preimage => gklearn/preimage}/utils.py (81%) create mode 100644 gklearn/preimage/visualization.py create mode 100644 gklearn/preimage/xp_fit_method.py rename {preimage => gklearn/preimage}/xp_letter_h.py (98%) create mode 100644 gklearn/preimage/xp_monoterpenoides.py rename {pygraph => gklearn}/utils/__init__.py (92%) rename {pygraph => gklearn}/utils/graphdataset.py (97%) rename {pygraph => gklearn}/utils/graphfiles.py (64%) create mode 100644 gklearn/utils/ipython_log.py rename {pygraph => gklearn}/utils/isNotebook.py (100%) rename {pygraph => gklearn}/utils/kernels.py (94%) create mode 100644 gklearn/utils/logger2file.py rename {pygraph => gklearn}/utils/model_selection_precomputed.py (99%) rename {pygraph => gklearn}/utils/parallel.py (100%) rename {pygraph => gklearn}/utils/trie.py (93%) rename {pygraph => gklearn}/utils/unfinished/openblassettings.py (95%) rename {pygraph => gklearn}/utils/unused/suffix_tree.py (98%) rename {pygraph => gklearn}/utils/utils.py (99%) create mode 100644 notebooks/else/compute_spkernel_for_syntheticnew.py create mode 100644 notebooks/else/compute_sspkernel_for_syntheticnew.py create mode 100644 notebooks/tests/test_parallel_chunksize_2.py create mode 100644 notebooks/utils/plot_all_graphs.py create mode 100644 setup.py diff --git a/.gitignore b/.gitignore index b38480d..4e41d57 100644 --- a/.gitignore +++ b/.gitignore @@ -18,15 +18,19 @@ notebooks/results/* notebooks/check_gm/* notebooks/test_parallel/* requirements/* -pygraph/model.py -pygraph/kernels/*_sym.py +gklearn/model.py +gklearn/kernels/*_sym.py *.npy *.eps *.dat *.pyc -preimage/* -!preimage/*.py +gklearn/preimage/* +!gklearn/preimage/*.py __pycache__ ##*# + +docs/build/* +!docs/build/latex/*.pdf +docs/log* diff --git a/README.md b/README.md index 0503a35..2d467d9 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ -# py-graph -[![Build Status](https://travis-ci.org/jajupmochi/py-graph.svg?branch=ljia)](https://travis-ci.org/jajupmochi/py-graph) -[![codecov](https://codecov.io/gh/jajupmochi/py-graph/branch/ljia/graph/badge.svg)](https://codecov.io/gh/jajupmochi/py-graph) -[![Documentation Status](https://readthedocs.org/projects/py-graph/badge/?version=ljia)](https://py-graph.readthedocs.io/en/ljia/?badge=ljia) +# graphkit-learn +[![Build Status](https://travis-ci.org/jajupmochi/graphkit-learn.svg?branch=ljia)](https://travis-ci.org/jajupmochi/graphkit-learn) +[![codecov](https://codecov.io/gh/jajupmochi/graphkit-learn/branch/ljia/graph/badge.svg)](https://codecov.io/gh/jajupmochi/graphkit-learn) +[![Documentation Status](https://readthedocs.org/projects/graphkit-learn/badge/?version=ljia)](https://graphkit-learn.readthedocs.io/en/ljia/?badge=ljia) A python package for graph kernels. @@ -20,11 +20,11 @@ A python package for graph kernels. ## How to use? -Simply clone this repository and voilà! Then check [`notebooks`](https://github.com/jajupmochi/py-graph/tree/ljia/notebooks) directory for demos: -* [`notebooks`](https://github.com/jajupmochi/py-graph/tree/ljia/notebooks) directory includes test codes of graph kernels based on linear patterns; -* [`notebooks/tests`](https://github.com/jajupmochi/py-graph/tree/ljia/notebooks/tests) directory includes codes that test some libraries and functions; -* [`notebooks/utils`](https://github.com/jajupmochi/py-graph/tree/ljia/notebooks/utils) directory includes some useful tools, such as a Gram matrix checker and a function to get properties of datasets; -* [`notebooks/else`](https://github.com/jajupmochi/py-graph/tree/ljia/notebooks/else) directory includes other codes that we used for experiments. +Simply clone this repository and voilà! Then check [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/ljia/notebooks) directory for demos: +* [`notebooks`](https://github.com/jajupmochi/graphkit-learn/tree/ljia/notebooks) directory includes test codes of graph kernels based on linear patterns; +* [`notebooks/tests`](https://github.com/jajupmochi/graphkit-learn/tree/ljia/notebooks/tests) directory includes codes that test some libraries and functions; +* [`notebooks/utils`](https://github.com/jajupmochi/graphkit-learn/tree/ljia/notebooks/utils) directory includes some useful tools, such as a Gram matrix checker and a function to get properties of datasets; +* [`notebooks/else`](https://github.com/jajupmochi/graphkit-learn/tree/ljia/notebooks/else) directory includes other codes that we used for experiments. ## List of graph kernels @@ -77,6 +77,8 @@ Check this paper for detailed description of graph kernels and experimental resu Linlin Jia, Benoit Gaüzère, and Paul Honeine. Graph Kernels Based on Linear Patterns: Theoretical and Experimental Comparisons. working paper or preprint, March 2019. URL https://hal-normandie-univ.archives-ouvertes.fr/hal-02053946. +A comparison of performances of graph kernels on benchmark datasets can be found [here](https://graphkit-learn.readthedocs.io/en/ljia/index.html#experiments). + ## References [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003. diff --git a/docs/Makefile b/docs/Makefile index 298ea9e..69fe55e 100644 --- a/docs/Makefile +++ b/docs/Makefile @@ -4,8 +4,8 @@ # You can set these variables from the command line. 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7f6bd34..0000000 --- a/docs/_build/html/.buildinfo +++ /dev/null @@ -1,4 +0,0 @@ -# Sphinx build info version 1 -# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: b72b368ba10131aed8c3edbb863096bd -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/_build/html/_modules/index.html b/docs/_build/html/_modules/index.html deleted file mode 100644 index a0e2ef3..0000000 --- a/docs/_build/html/_modules/index.html +++ /dev/null @@ -1,196 +0,0 @@ - - - - - - - - - - - Overview: module code — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/graphdataset.html b/docs/_build/html/_modules/pygraph/utils/graphdataset.html deleted file mode 100644 index f7515d7..0000000 --- a/docs/_build/html/_modules/pygraph/utils/graphdataset.html +++ /dev/null @@ -1,502 +0,0 @@ - - - - - - - - - - - pygraph.utils.graphdataset — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    • - - -
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Source code for pygraph.utils.graphdataset

-""" Obtain all kinds of attributes of a graph dataset.
-"""
-
-
-
[docs]def get_dataset_attributes(Gn,
-                           target=None,
-                           attr_names=[],
-                           node_label=None,
-                           edge_label=None):
-    """Returns the structure and property information of the graph dataset Gn.
-
-    Parameters
-    ----------
-    Gn : List of NetworkX graph
-        List of graphs whose information will be returned.
-    target : list
-        The list of classification targets corresponding to Gn. Only works for 
-        classification problems.
-    attr_names : list
-        List of strings which indicate which informations will be returned. The
-        possible choices includes:
-        'substructures': sub-structures Gn contains, including 'linear', 'non 
-            linear' and 'cyclic'.
-        'node_labeled': whether vertices have symbolic labels.
-        'edge_labeled': whether egdes have symbolic labels.
-        'is_directed': whether graphs in Gn are directed.
-        'dataset_size': number of graphs in Gn.
-        'ave_node_num': average number of vertices of graphs in Gn.
-        'min_node_num': minimum number of vertices of graphs in Gn.
-        'max_node_num': maximum number of vertices of graphs in Gn.
-        'ave_edge_num': average number of edges of graphs in Gn.
-        'min_edge_num': minimum number of edges of graphs in Gn.
-        'max_edge_num': maximum number of edges of graphs in Gn.
-        'ave_node_degree': average vertex degree of graphs in Gn.
-        'min_node_degree': minimum vertex degree of graphs in Gn.
-        'max_node_degree': maximum vertex degree of graphs in Gn.
-        'ave_fill_factor': average fill factor (number_of_edges / 
-            (number_of_nodes ** 2)) of graphs in Gn.
-        'min_fill_factor': minimum fill factor of graphs in Gn.
-        'max_fill_factor': maximum fill factor of graphs in Gn.
-        'node_label_num': number of symbolic vertex labels.
-        'edge_label_num': number of symbolic edge labels.
-        'node_attr_dim': number of dimensions of non-symbolic vertex labels. 
-            Extracted from the 'attributes' attribute of graph nodes.
-        'edge_attr_dim': number of dimensions of non-symbolic edge labels.
-            Extracted from the 'attributes' attribute of graph edges.
-        'class_number': number of classes. Only available for classification 
-            problems.
-    node_label : string
-        Node attribute used as label. The default node label is atom. Mandatory
-        when 'node_labeled' or 'node_label_num' is required.
-    edge_label : string
-        Edge attribute used as label. The default edge label is bond_type. 
-        Mandatory when 'edge_labeled' or 'edge_label_num' is required.
-
-    Return
-    ------
-    attrs : dict
-        Value for each property.
-    """
-    import networkx as nx
-    import numpy as np
-
-    attrs = {}
-
-    def get_dataset_size(Gn):
-        return len(Gn)
-
-    def get_all_node_num(Gn):
-        return [nx.number_of_nodes(G) for G in Gn]
-
-    def get_ave_node_num(all_node_num):
-        return np.mean(all_node_num)
-
-    def get_min_node_num(all_node_num):
-        return np.amin(all_node_num)
-
-    def get_max_node_num(all_node_num):
-        return np.amax(all_node_num)
-
-    def get_all_edge_num(Gn):
-        return [nx.number_of_edges(G) for G in Gn]
-
-    def get_ave_edge_num(all_edge_num):
-        return np.mean(all_edge_num)
-
-    def get_min_edge_num(all_edge_num):
-        return np.amin(all_edge_num)
-
-    def get_max_edge_num(all_edge_num):
-        return np.amax(all_edge_num)
-
-    def is_node_labeled(Gn):
-        return False if node_label is None else True
-
-    def get_node_label_num(Gn):
-        nl = set()
-        for G in Gn:
-            nl = nl | set(nx.get_node_attributes(G, node_label).values())
-        return len(nl)
-
-    def is_edge_labeled(Gn):
-        return False if edge_label is None else True
-
-    def get_edge_label_num(Gn):
-        el = set()
-        for G in Gn:
-            el = el | set(nx.get_edge_attributes(G, edge_label).values())
-        return len(el)
-
-    def is_directed(Gn):
-        return nx.is_directed(Gn[0])
-
-    def get_ave_node_degree(Gn):
-        return np.mean([np.mean(list(dict(G.degree()).values())) for G in Gn])
-
-    def get_max_node_degree(Gn):
-        return np.amax([np.mean(list(dict(G.degree()).values())) for G in Gn])
-
-    def get_min_node_degree(Gn):
-        return np.amin([np.mean(list(dict(G.degree()).values())) for G in Gn])
-    
-    # get fill factor, the number of non-zero entries in the adjacency matrix.
-    def get_ave_fill_factor(Gn):
-        return np.mean([nx.number_of_edges(G) / (nx.number_of_nodes(G) 
-                                           * nx.number_of_nodes(G)) for G in Gn])
-
-    def get_max_fill_factor(Gn):
-        return np.amax([nx.number_of_edges(G) / (nx.number_of_nodes(G) 
-                                           * nx.number_of_nodes(G)) for G in Gn])
-
-    def get_min_fill_factor(Gn):
-        return np.amin([nx.number_of_edges(G) / (nx.number_of_nodes(G) 
-                                           * nx.number_of_nodes(G)) for G in Gn])
-
-    def get_substructures(Gn):
-        subs = set()
-        for G in Gn:
-            degrees = list(dict(G.degree()).values())
-            if any(i == 2 for i in degrees):
-                subs.add('linear')
-            if np.amax(degrees) >= 3:
-                subs.add('non linear')
-            if 'linear' in subs and 'non linear' in subs:
-                break
-
-        if is_directed(Gn):
-            for G in Gn:
-                if len(list(nx.find_cycle(G))) > 0:
-                    subs.add('cyclic')
-                    break
-        # else:
-        #     # @todo: this method does not work for big graph with large amount of edges like D&D, try a better way.
-        #     upper = np.amin([nx.number_of_edges(G) for G in Gn]) * 2 + 10
-        #     for G in Gn:
-        #         if (nx.number_of_edges(G) < upper):
-        #             cyc = list(nx.simple_cycles(G.to_directed()))
-        #             if any(len(i) > 2 for i in cyc):
-        #                 subs.add('cyclic')
-        #                 break
-        #     if 'cyclic' not in subs:
-        #         for G in Gn:
-        #             cyc = list(nx.simple_cycles(G.to_directed()))
-        #             if any(len(i) > 2 for i in cyc):
-        #                 subs.add('cyclic')
-        #                 break
-
-        return subs
-
-    def get_class_num(target):
-        return len(set(target))
-
-    def get_node_attr_dim(Gn):
-        for G in Gn:
-            for n in G.nodes(data=True):
-                if 'attributes' in n[1]:
-                    return len(n[1]['attributes'])
-        return 0
-
-    def get_edge_attr_dim(Gn):
-        for G in Gn:
-            if nx.number_of_edges(G) > 0:
-                for e in G.edges(data=True):
-                    if 'attributes' in e[2]:
-                        return len(e[2]['attributes'])
-        return 0
-
-    if attr_names == []:
-        attr_names = [
-            'substructures',
-            'node_labeled',
-            'edge_labeled',
-            'is_directed',
-            'dataset_size',
-            'ave_node_num',
-            'min_node_num',
-            'max_node_num',
-            'ave_edge_num',
-            'min_edge_num',
-            'max_edge_num',
-            'ave_node_degree',
-            'min_node_degree',
-            'max_node_degree',
-            'ave_fill_factor',
-            'min_fill_factor',
-            'max_fill_factor',
-            'node_label_num',
-            'edge_label_num',
-            'node_attr_dim',
-            'edge_attr_dim',
-            'class_number',
-        ]
-
-    # dataset size
-    if 'dataset_size' in attr_names:
-
-        attrs.update({'dataset_size': get_dataset_size(Gn)})
-
-    # graph node number
-    if any(i in attr_names
-           for i in ['ave_node_num', 'min_node_num', 'max_node_num']):
-
-        all_node_num = get_all_node_num(Gn)
-
-    if 'ave_node_num' in attr_names:
-
-        attrs.update({'ave_node_num': get_ave_node_num(all_node_num)})
-
-    if 'min_node_num' in attr_names:
-
-        attrs.update({'min_node_num': get_min_node_num(all_node_num)})
-
-    if 'max_node_num' in attr_names:
-
-        attrs.update({'max_node_num': get_max_node_num(all_node_num)})
-
-    # graph edge number
-    if any(i in attr_names for i in
-           ['ave_edge_num', 'min_edge_num', 'max_edge_num']):
-
-        all_edge_num = get_all_edge_num(Gn)
-
-    if 'ave_edge_num' in attr_names:
-
-        attrs.update({'ave_edge_num': get_ave_edge_num(all_edge_num)})
-
-    if 'max_edge_num' in attr_names:
-
-        attrs.update({'max_edge_num': get_max_edge_num(all_edge_num)})
-
-    if 'min_edge_num' in attr_names:
-
-        attrs.update({'min_edge_num': get_min_edge_num(all_edge_num)})
-
-    # label number
-    if any(i in attr_names for i in ['node_labeled', 'node_label_num']):
-        is_nl = is_node_labeled(Gn)
-        node_label_num = get_node_label_num(Gn)
-
-    if 'node_labeled' in attr_names:
-        # graphs are considered node unlabeled if all nodes have the same label.
-        attrs.update({'node_labeled': is_nl if node_label_num > 1 else False})
-
-    if 'node_label_num' in attr_names:
-        attrs.update({'node_label_num': node_label_num})
-
-    if any(i in attr_names for i in ['edge_labeled', 'edge_label_num']):
-        is_el = is_edge_labeled(Gn)
-        edge_label_num = get_edge_label_num(Gn)
-
-    if 'edge_labeled' in attr_names:
-        # graphs are considered edge unlabeled if all edges have the same label.
-        attrs.update({'edge_labeled': is_el if edge_label_num > 1 else False})
-
-    if 'edge_label_num' in attr_names:
-        attrs.update({'edge_label_num': edge_label_num})
-
-    if 'is_directed' in attr_names:
-        attrs.update({'is_directed': is_directed(Gn)})
-
-    if 'ave_node_degree' in attr_names:
-        attrs.update({'ave_node_degree': get_ave_node_degree(Gn)})
-
-    if 'max_node_degree' in attr_names:
-        attrs.update({'max_node_degree': get_max_node_degree(Gn)})
-
-    if 'min_node_degree' in attr_names:
-        attrs.update({'min_node_degree': get_min_node_degree(Gn)})
-        
-    if 'ave_fill_factor' in attr_names:
-        attrs.update({'ave_fill_factor': get_ave_fill_factor(Gn)})
-
-    if 'max_fill_factor' in attr_names:
-        attrs.update({'max_fill_factor': get_max_fill_factor(Gn)})
-
-    if 'min_fill_factor' in attr_names:
-        attrs.update({'min_fill_factor': get_min_fill_factor(Gn)})
-
-    if 'substructures' in attr_names:
-        attrs.update({'substructures': get_substructures(Gn)})
-
-    if 'class_number' in attr_names:
-        attrs.update({'class_number': get_class_num(target)})
-
-    if 'node_attr_dim' in attr_names:
-        attrs['node_attr_dim'] = get_node_attr_dim(Gn)
-
-    if 'edge_attr_dim' in attr_names:
-        attrs['edge_attr_dim'] = get_edge_attr_dim(Gn)
-
-    from collections import OrderedDict
-    return OrderedDict(
-        sorted(attrs.items(), key=lambda i: attr_names.index(i[0])))

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/graphfiles.html b/docs/_build/html/_modules/pygraph/utils/graphfiles.html deleted file mode 100644 index 08c1fb7..0000000 --- a/docs/_build/html/_modules/pygraph/utils/graphfiles.html +++ /dev/null @@ -1,811 +0,0 @@ - - - - - - - - - - - pygraph.utils.graphfiles — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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- -
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    • - -
  • pygraph.utils.graphfiles
    • - - -
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- -

Source code for pygraph.utils.graphfiles

-""" Utilities function to manage graph files
-"""
-from os.path import dirname, splitext
-
-
[docs]def loadCT(filename):
-    """load data from a Chemical Table (.ct) file.
-
-    Notes
-    ------
-    a typical example of data in .ct is like this:
-
-     3 2  <- number of nodes and edges
-        0.0000    0.0000    0.0000 C <- each line describes a node (x,y,z + label)
-        0.0000    0.0000    0.0000 C
-        0.0000    0.0000    0.0000 O
-      1  3  1  1 <- each line describes an edge : to, from, bond type, bond stereo
-      2  3  1  1
-      
-    Check https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=10&ved=2ahUKEwivhaSdjsTlAhVhx4UKHczHA8gQFjAJegQIARAC&url=https%3A%2F%2Fwww.daylight.com%2Fmeetings%2Fmug05%2FKappler%2Fctfile.pdf&usg=AOvVaw1cDNrrmMClkFPqodlF2inS
-    for detailed format discription.
-    """
-    import networkx as nx
-    from os.path import basename
-    g = nx.Graph()
-    with open(filename) as f:
-        content = f.read().splitlines()
-        g = nx.Graph(
-            name = str(content[0]),
-            filename = basename(filename))  # set name of the graph
-        tmp = content[1].split(" ")
-        if tmp[0] == '':
-            nb_nodes = int(tmp[1])  # number of the nodes
-            nb_edges = int(tmp[2])  # number of the edges
-        else:
-            nb_nodes = int(tmp[0])
-            nb_edges = int(tmp[1])
-            # patch for compatibility : label will be removed later
-        for i in range(0, nb_nodes):
-            tmp = content[i + 2].split(" ")
-            tmp = [x for x in tmp if x != '']
-            g.add_node(i, atom=tmp[3].strip(), 
-                       label=[item.strip() for item in tmp[3:]], 
-                       attributes=[item.strip() for item in tmp[0:3]])
-        for i in range(0, nb_edges):
-            tmp = content[i + g.number_of_nodes() + 2].split(" ")
-            tmp = [x for x in tmp if x != '']
-            g.add_edge(int(tmp[0]) - 1, int(tmp[1]) - 1,
-                       bond_type=tmp[2].strip(),
-                       label=[item.strip() for item in tmp[2:]])
-    return g

-
-
-
[docs]def loadGXL(filename):
-    from os.path import basename
-    import networkx as nx
-    import xml.etree.ElementTree as ET
-
-    tree = ET.parse(filename)
-    root = tree.getroot()
-    index = 0
-    g = nx.Graph(filename=basename(filename), name=root[0].attrib['id'])
-    dic = {}  # used to retrieve incident nodes of edges
-    for node in root.iter('node'):
-        dic[node.attrib['id']] = index
-        labels = {}
-        for attr in node.iter('attr'):
-            labels[attr.attrib['name']] = attr[0].text
-        if 'chem' in labels:
-            labels['label'] = labels['chem']
-            labels['atom'] = labels['chem']
-        g.add_node(index, **labels)
-        index += 1
-
-    for edge in root.iter('edge'):
-        labels = {}
-        for attr in edge.iter('attr'):
-            labels[attr.attrib['name']] = attr[0].text
-        if 'valence' in labels:
-            labels['label'] = labels['valence']
-            labels['bond_type'] = labels['valence']
-        g.add_edge(dic[edge.attrib['from']], dic[edge.attrib['to']], **labels)
-    return g

-
-
-
[docs]def saveGXL(graph, filename, method='benoit'):
-    if method == 'benoit':
-        import xml.etree.ElementTree as ET
-        root_node = ET.Element('gxl')
-        attr = dict()
-        attr['id'] = str(graph.graph['name'])
-        attr['edgeids'] = 'true'
-        attr['edgemode'] = 'undirected'
-        graph_node = ET.SubElement(root_node, 'graph', attrib=attr)
-    
-        for v in graph:
-            current_node = ET.SubElement(graph_node, 'node', attrib={'id': str(v)})
-            for attr in graph.nodes[v].keys():
-                cur_attr = ET.SubElement(
-                    current_node, 'attr', attrib={'name': attr})
-                cur_value = ET.SubElement(cur_attr,
-                                          graph.nodes[v][attr].__class__.__name__)
-                cur_value.text = graph.nodes[v][attr]
-    
-        for v1 in graph:
-            for v2 in graph[v1]:
-                if (v1 < v2):  # Non oriented graphs
-                    cur_edge = ET.SubElement(
-                        graph_node,
-                        'edge',
-                        attrib={
-                            'from': str(v1),
-                            'to': str(v2)
-                        })
-                    for attr in graph[v1][v2].keys():
-                        cur_attr = ET.SubElement(
-                            cur_edge, 'attr', attrib={'name': attr})
-                        cur_value = ET.SubElement(
-                            cur_attr, graph[v1][v2][attr].__class__.__name__)
-                        cur_value.text = str(graph[v1][v2][attr])
-    
-        tree = ET.ElementTree(root_node)
-        tree.write(filename)
-    elif method == 'gedlib':
-        # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
-#        pass
-        gxl_file = open(filename, 'w')
-        gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
-        gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
-        gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
-        gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"true\" edgemode=\"undirected\">\n")
-        for v, attrs in graph.nodes(data=True):
-            gxl_file.write("<node id=\"_" + str(v) + "\">")
-            gxl_file.write("<attr name=\"" + "chem" + "\"><int>" + str(attrs['chem']) + "</int></attr>")
-            gxl_file.write("</node>\n")
-        for v1, v2, attrs in graph.edges(data=True):
-            gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\">")
-            gxl_file.write("<attr name=\"valence\"><int>" + str(attrs['valence']) + "</int></attr>")
-#            gxl_file.write("<attr name=\"valence\"><int>" + "1" + "</int></attr>")
-            gxl_file.write("</edge>\n")
-        gxl_file.write("</graph>\n")
-        gxl_file.write("</gxl>")
-        gxl_file.close()
-    elif method == 'gedlib-letter':
-        # reference: https://github.com/dbblumenthal/gedlib/blob/master/data/generate_molecules.py#L22
-        # and https://github.com/dbblumenthal/gedlib/blob/master/data/datasets/Letter/HIGH/AP1_0000.gxl
-        gxl_file = open(filename, 'w')
-        gxl_file.write("<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n")
-        gxl_file.write("<!DOCTYPE gxl SYSTEM \"http://www.gupro.de/GXL/gxl-1.0.dtd\">\n")
-        gxl_file.write("<gxl xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n")
-        gxl_file.write("<graph id=\"" + str(graph.graph['name']) + "\" edgeids=\"false\" edgemode=\"undirected\">\n")
-        for v, attrs in graph.nodes(data=True):
-            gxl_file.write("<node id=\"_" + str(v) + "\">")
-            gxl_file.write("<attr name=\"x\"><float>" + str(attrs['attributes'][0]) + "</float></attr>")
-            gxl_file.write("<attr name=\"y\"><float>" + str(attrs['attributes'][1]) + "</float></attr>")
-            gxl_file.write("</node>\n")
-        for v1, v2, attrs in graph.edges(data=True):
-            gxl_file.write("<edge from=\"_" + str(v1) + "\" to=\"_" + str(v2) + "\"/>\n")
-        gxl_file.write("</graph>\n")
-        gxl_file.write("</gxl>")
-        gxl_file.close()

-
-
-
[docs]def loadSDF(filename):
-    """load data from structured data file (.sdf file).
-
-    Notes
-    ------
-    A SDF file contains a group of molecules, represented in the similar way as in MOL format.
-    Check http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx, 2018 for detailed structure.
-    """
-    import networkx as nx
-    from os.path import basename
-    from tqdm import tqdm
-    import sys
-    data = []
-    with open(filename) as f:
-        content = f.read().splitlines()
-        index = 0
-        pbar = tqdm(total=len(content) + 1, desc='load SDF', file=sys.stdout)
-        while index < len(content):
-            index_old = index
-
-            g = nx.Graph(name=content[index].strip())  # set name of the graph
-
-            tmp = content[index + 3]
-            nb_nodes = int(tmp[:3])  # number of the nodes
-            nb_edges = int(tmp[3:6])  # number of the edges
-
-            for i in range(0, nb_nodes):
-                tmp = content[i + index + 4]
-                g.add_node(i, atom=tmp[31:34].strip())
-
-            for i in range(0, nb_edges):
-                tmp = content[i + index + g.number_of_nodes() + 4]
-                tmp = [tmp[i:i + 3] for i in range(0, len(tmp), 3)]
-                g.add_edge(
-                    int(tmp[0]) - 1, int(tmp[1]) - 1, bond_type=tmp[2].strip())
-
-            data.append(g)
-
-            index += 4 + g.number_of_nodes() + g.number_of_edges()
-            while content[index].strip() != '$$$$':  # seperator
-                index += 1
-            index += 1
-
-            pbar.update(index - index_old)
-        pbar.update(1)
-        pbar.close()
-
-    return data

-
-
-
[docs]def loadMAT(filename, extra_params):
-    """Load graph data from a MATLAB (up to version 7.1) .mat file.
-
-    Notes
-    ------
-    A MAT file contains a struct array containing graphs, and a column vector lx containing a class label for each graph.
-    Check README in downloadable file in http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/, 2018 for detailed structure.
-    """
-    from scipy.io import loadmat
-    import numpy as np
-    import networkx as nx
-    data = []
-    content = loadmat(filename)
-    order = extra_params['am_sp_al_nl_el']
-    # print(content)
-    # print('----')
-    for key, value in content.items():
-        if key[0] == 'l':  # class label
-            y = np.transpose(value)[0].tolist()
-            # print(y)
-        elif key[0] != '_':
-            # print(value[0][0][0])
-            # print()
-            # print(value[0][0][1])
-            # print()
-            # print(value[0][0][2])
-            # print()
-            # if len(value[0][0]) > 3:
-            #     print(value[0][0][3])
-            # print('----')
-            # if adjacency matrix is not compressed / edge label exists
-            if order[1] == 0:
-                for i, item in enumerate(value[0]):
-                    # print(item)
-                    # print('------')
-                    g = nx.Graph(name=i)  # set name of the graph
-                    nl = np.transpose(item[order[3]][0][0][0])  # node label
-                    # print(item[order[3]])
-                    # print()
-                    for index, label in enumerate(nl[0]):
-                        g.add_node(index, atom=str(label))
-                    el = item[order[4]][0][0][0]  # edge label
-                    for edge in el:
-                        g.add_edge(
-                            edge[0] - 1, edge[1] - 1, bond_type=str(edge[2]))
-                    data.append(g)
-            else:
-                from scipy.sparse import csc_matrix
-                for i, item in enumerate(value[0]):
-                    # print(item)
-                    # print('------')
-                    g = nx.Graph(name=i)  # set name of the graph
-                    nl = np.transpose(item[order[3]][0][0][0])  # node label
-                    # print(nl)
-                    # print()
-                    for index, label in enumerate(nl[0]):
-                        g.add_node(index, atom=str(label))
-                    sam = item[order[0]]  # sparse adjacency matrix
-                    index_no0 = sam.nonzero()
-                    for col, row in zip(index_no0[0], index_no0[1]):
-                        # print(col)
-                        # print(row)
-                        g.add_edge(col, row)
-                    data.append(g)
-                    # print(g.edges(data=True))
-    return data, y

-
-
-
[docs]def loadTXT(dirname_dataset):
-    """Load graph data from a .txt file.
-
-    Notes
-    ------
-    The graph data is loaded from separate files.
-    Check README in downloadable file http://tiny.cc/PK_MLJ_data, 2018 for detailed structure.
-    """
-    import numpy as np
-    import networkx as nx
-    from os import listdir
-    from os.path import dirname
-
-    # load data file names
-    for name in listdir(dirname_dataset):
-        if '_A' in name:
-            fam = dirname_dataset + '/' + name
-        elif '_graph_indicator' in name:
-            fgi = dirname_dataset + '/' + name
-        elif '_graph_labels' in name:
-            fgl = dirname_dataset + '/' + name
-        elif '_node_labels' in name:
-            fnl = dirname_dataset + '/' + name
-        elif '_edge_labels' in name:
-            fel = dirname_dataset + '/' + name
-        elif '_edge_attributes' in name:
-            fea = dirname_dataset + '/' + name
-        elif '_node_attributes' in name:
-            fna = dirname_dataset + '/' + name
-        elif '_graph_attributes' in name:
-            fga = dirname_dataset + '/' + name
-        # this is supposed to be the node attrs, make sure to put this as the last 'elif'
-        elif '_attributes' in name:
-            fna = dirname_dataset + '/' + name
-
-    content_gi = open(fgi).read().splitlines()  # graph indicator
-    content_am = open(fam).read().splitlines()  # adjacency matrix
-    content_gl = open(fgl).read().splitlines()  # lass labels
-
-    # create graphs and add nodes
-    data = [nx.Graph(name=i) for i in range(0, len(content_gl))]
-    if 'fnl' in locals():
-        content_nl = open(fnl).read().splitlines()  # node labels
-        for i, line in enumerate(content_gi):
-            # transfer to int first in case of unexpected blanks
-            data[int(line) - 1].add_node(i, atom=str(int(content_nl[i])))
-    else:
-        for i, line in enumerate(content_gi):
-            data[int(line) - 1].add_node(i)
-            
-    # add edges
-    for line in content_am:
-        tmp = line.split(',')
-        n1 = int(tmp[0]) - 1
-        n2 = int(tmp[1]) - 1
-        # ignore edge weight here.
-        g = int(content_gi[n1]) - 1
-        data[g].add_edge(n1, n2)
-
-    # add edge labels
-    if 'fel' in locals():
-        content_el = open(fel).read().splitlines()
-        for index, line in enumerate(content_el):
-            label = line.strip()
-            n = [int(i) - 1 for i in content_am[index].split(',')]
-            g = int(content_gi[n[0]]) - 1
-            data[g].edges[n[0], n[1]]['bond_type'] = label
-
-    # add node attributes
-    if 'fna' in locals():
-        content_na = open(fna).read().splitlines()
-        for i, line in enumerate(content_na):
-            attrs = [i.strip() for i in line.split(',')]
-            g = int(content_gi[i]) - 1
-            data[g].nodes[i]['attributes'] = attrs
-
-    # add edge attributes
-    if 'fea' in locals():
-        content_ea = open(fea).read().splitlines()
-        for index, line in enumerate(content_ea):
-            attrs = [i.strip() for i in line.split(',')]
-            n = [int(i) - 1 for i in content_am[index].split(',')]
-            g = int(content_gi[n[0]]) - 1
-            data[g].edges[n[0], n[1]]['attributes'] = attrs
-
-    # load y
-    y = [int(i) for i in content_gl]
-
-    return data, y

-
-
-
[docs]def loadDataset(filename, filename_y=None, extra_params=None):
-    """Read graph data from filename and load them as NetworkX graphs.
-
-    Parameters
-    ----------
-    filename : string
-        The name of the file from where the dataset is read.
-    filename_y : string
-        The name of file of the targets corresponding to graphs.
-    extra_params : dict
-        Extra parameters only designated to '.mat' format.
-
-    Return
-    ------
-    data : List of NetworkX graph.
-    y : List
-        Targets corresponding to graphs.
-        
-    Notes
-    -----
-    This function supports following graph dataset formats:
-    'ds': load data from .ds file. See comments of function loadFromDS for a example.
-    'cxl': load data from Graph eXchange Language file (.cxl file). See 
-        http://www.gupro.de/GXL/Introduction/background.html, 2019 for detail.
-    'sdf': load data from structured data file (.sdf file). See 
-        http://www.nonlinear.com/progenesis/sdf-studio/v0.9/faq/sdf-file-format-guidance.aspx, 
-        2018 for details.
-    'mat': Load graph data from a MATLAB (up to version 7.1) .mat file. See
-        README in downloadable file in http://mlcb.is.tuebingen.mpg.de/Mitarbeiter/Nino/WL/, 
-        2018 for details.
-    'txt': Load graph data from a special .txt file. See
-        https://ls11-www.cs.tu-dortmund.de/staff/morris/graphkerneldatasets,
-        2019 for details. Note here filename is the name of either .txt file in
-        the dataset directory.
-    """
-    extension = splitext(filename)[1][1:]
-    if extension == "ds":
-        data, y = loadFromDS(filename, filename_y)
-    elif extension == "cxl":
-        import xml.etree.ElementTree as ET
-        
-        dirname_dataset = dirname(filename)
-        tree = ET.parse(filename)
-        root = tree.getroot()
-        data = []
-        y = []
-        for graph in root.iter('graph'):
-            mol_filename = graph.attrib['file']
-            mol_class = graph.attrib['class']
-            data.append(loadGXL(dirname_dataset + '/' + mol_filename))
-            y.append(mol_class)
-    elif extension == 'xml':
-        data, y = loadFromXML(filename, extra_params)
-    elif extension == "sdf":
-        import numpy as np
-        from tqdm import tqdm
-        import sys
-
-        data = loadSDF(filename)
-
-        y_raw = open(filename_y).read().splitlines()
-        y_raw.pop(0)
-        tmp0 = []
-        tmp1 = []
-        for i in range(0, len(y_raw)):
-            tmp = y_raw[i].split(',')
-            tmp0.append(tmp[0])
-            tmp1.append(tmp[1].strip())
-
-        y = []
-        for i in tqdm(range(0, len(data)), desc='ajust data', file=sys.stdout):
-            try:
-                y.append(tmp1[tmp0.index(data[i].name)].strip())
-            except ValueError:  # if data[i].name not in tmp0
-                data[i] = []
-        data = list(filter(lambda a: a != [], data))
-    elif extension == "mat":
-        data, y = loadMAT(filename, extra_params)
-    elif extension == 'txt':
-        dirname_dataset = dirname(filename)
-        data, y = loadTXT(dirname_dataset)
-        # print(len(y))
-        # print(y)
-        # print(data[0].nodes(data=True))
-        # print('----')
-        # print(data[0].edges(data=True))
-        # for g in data:
-        #     print(g.nodes(data=True))
-        #     print('----')
-        #     print(g.edges(data=True))
-
-    return data, y

-
-
-
[docs]def loadFromXML(filename, extra_params):
-    import xml.etree.ElementTree as ET
-    
-    if extra_params:
-        dirname_dataset = extra_params
-    else:
-        dirname_dataset = dirname(filename)
-    tree = ET.parse(filename)
-    root = tree.getroot()
-    data = []
-    y = []
-    for graph in root.iter('graph'):
-        mol_filename = graph.attrib['file']
-        mol_class = graph.attrib['class']
-        data.append(loadGXL(dirname_dataset + '/' + mol_filename))
-        y.append(mol_class)
-        
-    return data, y

-            
-
-
[docs]def loadFromDS(filename, filename_y):
-    """Load data from .ds file.
-    Possible graph formats include:
-        '.ct': see function loadCT for detail.
-        '.gxl': see dunction loadGXL for detail.
-    Note these graph formats are checked automatically by the extensions of 
-    graph files.
-    """
-    dirname_dataset = dirname(filename)
-    data = []
-    y = []
-    content = open(filename).read().splitlines()
-    extension = splitext(content[0].split(' ')[0])[1][1:]
-    if filename_y is None or filename_y == '':
-        if extension == 'ct': 
-            for i in range(0, len(content)):
-                tmp = content[i].split(' ')
-                # remove the '#'s in file names
-                data.append(
-                    loadCT(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))
-                y.append(float(tmp[1]))
-        elif extension == 'gxl':
-            for i in range(0, len(content)):
-                tmp = content[i].split(' ')
-                # remove the '#'s in file names
-                data.append(
-                    loadGXL(dirname_dataset + '/' + tmp[0].replace('#', '', 1)))
-                y.append(float(tmp[1]))
-    else:  # y in a seperate file
-        if extension == 'ct':
-            for i in range(0, len(content)):
-                tmp = content[i]
-                # remove the '#'s in file names
-                data.append(
-                    loadCT(dirname_dataset + '/' + tmp.replace('#', '', 1)))
-        elif extension == 'gxl':
-            for i in range(0, len(content)):
-                tmp = content[i]
-                # remove the '#'s in file names
-                data.append(
-                    loadGXL(dirname_dataset + '/' + tmp.replace('#', '', 1)))                                                       
-                                                               
-        content_y = open(filename_y).read().splitlines()
-        # assume entries in filename and filename_y have the same order.
-        for item in content_y:
-            tmp = item.split(' ')
-            # assume the 3rd entry in a line is y (for Alkane dataset)
-            y.append(float(tmp[2]))
-            
-    return data, y

-                
-
-
[docs]def saveDataset(Gn, y, gformat='gxl', group=None, filename='gfile', xparams=None):
-    """Save list of graphs.
-    """
-    import os
-    dirname_ds = os.path.dirname(filename)
-    if dirname_ds != '':
-        dirname_ds += '/'
-        if not os.path.exists(dirname_ds) :
-            os.makedirs(dirname_ds)
-                
-    if 'graph_dir' in xparams:
-        graph_dir = xparams['graph_dir'] + '/'
-        if not os.path.exists(graph_dir):
-            os.makedirs(graph_dir)
-    else:
-        graph_dir = dirname_ds 
-            
-    if group == 'xml' and gformat == 'gxl':
-        with open(filename + '.xml', 'w') as fgroup:
-            fgroup.write("<?xml version=\"1.0\"?>")
-            fgroup.write("\n<!DOCTYPE GraphCollection SYSTEM \"http://www.inf.unibz.it/~blumenthal/dtd/GraphCollection.dtd\">")
-            fgroup.write("\n<GraphCollection>")
-            for idx, g in enumerate(Gn):
-                fname_tmp = "graph" + str(idx) + ".gxl"
-                saveGXL(g, graph_dir + fname_tmp, method=xparams['method'])
-                fgroup.write("\n\t<graph file=\"" + fname_tmp + "\" class=\"" + str(y[idx]) + "\"/>")
-            fgroup.write("\n</GraphCollection>")
-            fgroup.close()

-            
-            
-if __name__ == '__main__':    
-#    ### Load dataset from .ds file.
-#    # .ct files.
-#    ds = {'name': 'Alkane', 'dataset': '../../datasets/Alkane/dataset.ds',
-#        'dataset_y': '../../datasets/Alkane/dataset_boiling_point_names.txt'}
-#    Gn, y = loadDataset(ds['dataset'], filename_y=ds['dataset_y'])
-##    ds = {'name': 'Acyclic', 'dataset': '../../datasets/acyclic/dataset_bps.ds'}  # node symb
-##    Gn, y = loadDataset(ds['dataset'])
-##    ds = {'name': 'MAO', 'dataset': '../../datasets/MAO/dataset.ds'} # node/edge symb
-##    Gn, y = loadDataset(ds['dataset'])
-##    ds = {'name': 'PAH', 'dataset': '../../datasets/PAH/dataset.ds'} # unlabeled
-##    Gn, y = loadDataset(ds['dataset'])
-#    print(Gn[1].nodes(data=True))
-#    print(Gn[1].edges(data=True))
-#    print(y[1])
-    
-#    # .gxl file.
-#    ds = {'name': 'monoterpenoides', 
-#          'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'}  # node/edge symb
-#    Gn, y = loadDataset(ds['dataset'])
-#    print(Gn[1].nodes(data=True))
-#    print(Gn[1].edges(data=True))
-#    print(y[1])
-    
-    ### Convert graph from one format to another.
-    # .gxl file.
-    import networkx as nx
-    ds = {'name': 'monoterpenoides', 
-          'dataset': '../../datasets/monoterpenoides/dataset_10+.ds'}  # node/edge symb
-    Gn, y = loadDataset(ds['dataset'])
-    y = [int(i) for i in y]
-    print(Gn[1].nodes(data=True))
-    print(Gn[1].edges(data=True))
-    print(y[1])
-    # Convert a graph to the proper NetworkX format that can be recognized by library gedlib.
-    Gn_new = []
-    for G in Gn:
-        G_new = nx.Graph()
-        for nd, attrs in G.nodes(data=True):
-            G_new.add_node(str(nd), chem=attrs['atom'])
-        for nd1, nd2, attrs in G.edges(data=True):
-            G_new.add_edge(str(nd1), str(nd2), valence=attrs['bond_type'])
-#            G_new.add_edge(str(nd1), str(nd2))
-        Gn_new.append(G_new)
-    print(Gn_new[1].nodes(data=True))
-    print(Gn_new[1].edges(data=True))
-    print(Gn_new[1])
-    filename = '/media/ljia/DATA/research-repo/codes/others/gedlib/tests_linlin/generated_datsets/monoterpenoides/gxl/monoterpenoides'
-    xparams = {'method': 'gedlib'}
-    saveDataset(Gn, y, gformat='gxl', group='xml', filename=filename, xparams=xparams)
-    
-#    ds = {'name': 'MUTAG', 'dataset': '../../datasets/MUTAG/MUTAG.mat',
-#          'extra_params': {'am_sp_al_nl_el': [0, 0, 3, 1, 2]}}  # node/edge symb
-#    Gn, y = loadDataset(ds['dataset'], extra_params=ds['extra_params'])
-#    saveDataset(Gn, y, group='xml', filename='temp/temp')
-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/isNotebook.html b/docs/_build/html/_modules/pygraph/utils/isNotebook.html deleted file mode 100644 index ebc4a18..0000000 --- a/docs/_build/html/_modules/pygraph/utils/isNotebook.html +++ /dev/null @@ -1,205 +0,0 @@ - - - - - - - - - - - pygraph.utils.isNotebook — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
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Source code for pygraph.utils.isNotebook

-""" Functions for python system.
-"""
-
-
[docs]def isNotebook():
-    """check if code is executed in the IPython notebook.
-    """
-    try:
-        shell = get_ipython().__class__.__name__
-        if shell == 'ZMQInteractiveShell':
-            return True   # Jupyter notebook or qtconsole
-        elif shell == 'TerminalInteractiveShell':
-            return False  # Terminal running IPython
-        else:
-            return False  # Other type (?)
-    except NameError:
-        return False      # Probably standard Python interpreter

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/kernels.html b/docs/_build/html/_modules/pygraph/utils/kernels.html deleted file mode 100644 index 487f6bf..0000000 --- a/docs/_build/html/_modules/pygraph/utils/kernels.html +++ /dev/null @@ -1,347 +0,0 @@ - - - - - - - - - - - pygraph.utils.kernels — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
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- -

Source code for pygraph.utils.kernels

-"""Those who are not graph kernels. We can be kernels for nodes or edges!
-These kernels are defined between pairs of vectors.
-"""
-import numpy as np
-
-
[docs]def deltakernel(x, y):
-    """Delta kernel. Return 1 if x == y, 0 otherwise.
-
-    Parameters
-    ----------
-    x, y : any
-        Two parts to compare.
-
-    Return
-    ------
-    kernel : integer
-        Delta kernel.
-
-    References
-    ----------
-    [1] H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized kernels between 
-    labeled graphs. In Proceedings of the 20th International Conference on 
-    Machine Learning, Washington, DC, United States, 2003.
-    """
-    return x == y  #(1 if condition else 0)

-
-
-
[docs]def gaussiankernel(x, y, gamma=None):
-    """Gaussian kernel.
-    Compute the rbf (gaussian) kernel between x and y:
-
-        K(x, y) = exp(-gamma ||x-y||^2).
-
-    Read more in the :ref:`User Guide <rbf_kernel>`.
-
-    Parameters
-    ----------
-    x, y : array
-
-    gamma : float, default None
-        If None, defaults to 1.0 / n_features
-
-    Returns
-    -------
-    kernel : float
-    """
-    if gamma is None:
-        gamma = 1.0 / len(x)
-
-    xt = np.array([float(itm) for itm in x])
-    yt = np.array([float(itm) for itm in y])
-    kernel = xt - yt
-    kernel = kernel ** 2
-    kernel = np.sum(kernel)
-    kernel *= -gamma
-    kernel = np.exp(kernel)
-    return kernel

-
-
-
[docs]def polynomialkernel(x, y, d=1, c=0):
-    """Polynomial kernel.
-    Compute the polynomial kernel between x and y:
-
-        K(x, y) = <x, y> ^d + c.
-
-    Parameters
-    ----------
-    x, y : array
-
-    d : integer, default 1
-    
-    c : float, default 0
-
-    Returns
-    -------
-    kernel : float
-    """
-    return np.dot(x, y) ** d + c

-
-
-
[docs]def linearkernel(x, y):
-    """Polynomial kernel.
-    Compute the polynomial kernel between x and y:
-
-        K(x, y) = <x, y>.
-
-    Parameters
-    ----------
-    x, y : array
-
-    d : integer, default 1
-    
-    c : float, default 0
-
-    Returns
-    -------
-    kernel : float
-    """
-    return np.dot(x, y)

-
-
-
[docs]def kernelsum(k1, k2, d11, d12, d21=None, d22=None, lamda1=1, lamda2=1):
-    """Sum of a pair of kernels.
-
-    k = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
-
-    Parameters
-    ----------
-    k1, k2 : function
-        A pair of kernel functions.
-    d11, d12:
-        Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
-    d21, d22:
-        Inputs of k2.
-    lamda1, lamda2: float
-        Coefficients of the product.
-
-    Return
-    ------
-    kernel : integer
-
-    """
-    if d21 == None or d22 == None:
-        kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d11, d12)
-    else:
-        kernel = lamda1 * k1(d11, d12) + lamda2 * k2(d21, d22)
-    return kernel

-
-
-
[docs]def kernelproduct(k1, k2, d11, d12, d21=None, d22=None, lamda=1):
-    """Product of a pair of kernels.
-
-    k = lamda * k1(d11, d12) * k2(d21, d22)
-
-    Parameters
-    ----------
-    k1, k2 : function
-        A pair of kernel functions.
-    d11, d12:
-        Inputs of k1. If d21 or d22 is None, apply d11, d12 to both k1 and k2.
-    d21, d22:
-        Inputs of k2.
-    lamda: float
-        Coefficient of the product.
-
-    Return
-    ------
-    kernel : integer
-    """
-    if d21 == None or d22 == None:
-        kernel = lamda * k1(d11, d12) * k2(d11, d12)
-    else:
-        kernel = lamda * k1(d11, d12) * k2(d21, d22)
-    return kernel

-
-
-if __name__ == '__main__':
-    o = polynomialkernel([1, 2], [3, 4], 2, 3)
-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/logger2file.html b/docs/_build/html/_modules/pygraph/utils/logger2file.html deleted file mode 100644 index ce45a0d..0000000 --- a/docs/_build/html/_modules/pygraph/utils/logger2file.html +++ /dev/null @@ -1,216 +0,0 @@ - - - - - - - - - - - pygraph.utils.logger2file — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for pygraph.utils.logger2file

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Fri Nov  8 14:21:25 2019
-
-@author: ljia
-"""
-
-import sys
-import time
-
-
[docs]class Logger(object):
-    def __init__(self):
-        self.terminal = sys.stdout
-        self.log = open("log." + str(time.time()) + ".log", "a")
-
-
[docs]    def write(self, message):
-        self.terminal.write(message)
-        self.log.write(message)  

-
-
[docs]    def flush(self):
-        #this flush method is needed for python 3 compatibility.
-        #this handles the flush command by doing nothing.
-        #you might want to specify some extra behavior here.
-        pass    

-
-sys.stdout = Logger()
-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/model_selection_precomputed.html b/docs/_build/html/_modules/pygraph/utils/model_selection_precomputed.html deleted file mode 100644 index b467f7f..0000000 --- a/docs/_build/html/_modules/pygraph/utils/model_selection_precomputed.html +++ /dev/null @@ -1,1148 +0,0 @@ - - - - - - - - - - - pygraph.utils.model_selection_precomputed — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Source code for pygraph.utils.model_selection_precomputed

-import numpy as np
-import matplotlib
-matplotlib.use('Agg')
-from matplotlib import pyplot as plt
-from sklearn.kernel_ridge import KernelRidge
-from sklearn.svm import SVC
-from sklearn.metrics import accuracy_score, mean_squared_error
-from sklearn.model_selection import KFold, train_test_split, ParameterGrid
-
-#from joblib import Parallel, delayed
-from multiprocessing import Pool, Array
-from functools import partial
-import sys
-sys.path.insert(0, "../")
-import os
-import time
-import datetime
-#from os.path import basename, splitext
-from pygraph.utils.graphfiles import loadDataset
-from tqdm import tqdm
-
-#from memory_profiler import profile
-
-#@profile
-
[docs]def model_selection_for_precomputed_kernel(datafile,
-                                           estimator,
-                                           param_grid_precomputed,
-                                           param_grid,
-                                           model_type,
-                                           NUM_TRIALS=30,
-                                           datafile_y=None,
-                                           extra_params=None,
-                                           ds_name='ds-unknown',
-                                           n_jobs=1,
-                                           read_gm_from_file=False,
-                                           verbose=True):
-    """Perform model selection, fitting and testing for precomputed kernels 
-    using nested CV. Print out neccessary data during the process then finally 
-    the results.
-
-    Parameters
-    ----------
-    datafile : string
-        Path of dataset file.
-    estimator : function
-        kernel function used to estimate. This function needs to return a gram matrix.
-    param_grid_precomputed : dictionary
-        Dictionary with names (string) of parameters used to calculate gram 
-        matrices as keys and lists of parameter settings to try as values. This 
-        enables searching over any sequence of parameter settings. Params with 
-        length 1 will be omitted.
-    param_grid : dictionary
-        Dictionary with names (string) of parameters used as penelties as keys 
-        and lists of parameter settings to try as values. This enables 
-        searching over any sequence of parameter settings. Params with length 1
-        will be omitted.
-    model_type : string
-        Type of the problem, can be 'regression' or 'classification'.
-    NUM_TRIALS : integer
-        Number of random trials of outer cv loop. The default is 30.
-    datafile_y : string
-        Path of file storing y data. This parameter is optional depending on 
-        the given dataset file.
-    extra_params : dict
-        Extra parameters for loading dataset. See function pygraph.utils.
-        graphfiles.loadDataset for detail.
-    ds_name : string
-        Name of the dataset.
-    n_jobs : int
-        Number of jobs for parallelization.
-    read_gm_from_file : boolean
-        Whether gram matrices are loaded from a file.
-
-    Examples
-    --------
-    >>> import numpy as np
-    >>> import sys
-    >>> sys.path.insert(0, "../")
-    >>> from pygraph.utils.model_selection_precomputed import model_selection_for_precomputed_kernel
-    >>> from pygraph.kernels.untilHPathKernel import untilhpathkernel
-    >>>
-    >>> datafile = '../datasets/MUTAG/MUTAG_A.txt'
-    >>> estimator = untilhpathkernel
-    >>> param_grid_precomputed = {’depth’:  np.linspace(1, 10, 10), ’k_func’:
-            [’MinMax’, ’tanimoto’], ’compute_method’:  [’trie’]}
-    >>> # ’C’ for classification problems and ’alpha’ for regression problems.
-    >>> param_grid = [{’C’: np.logspace(-10, 10, num=41, base=10)}, {’alpha’:
-            np.logspace(-10, 10, num=41, base=10)}]
-    >>>
-    >>> model_selection_for_precomputed_kernel(datafile, estimator, 
-            param_grid_precomputed, param_grid[0], 'classification', ds_name=’MUTAG’)
-    """
-    tqdm.monitor_interval = 0
-
-    results_dir = '../notebooks/results/' + estimator.__name__
-    if not os.path.exists(results_dir):
-        os.makedirs(results_dir)
-    # a string to save all the results.
-    str_fw = '###################### log time: ' + datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S") + '. ######################\n\n'
-    str_fw += '# This file contains results of ' + estimator.__name__ + ' on dataset ' + ds_name + ',\n# including gram matrices, serial numbers for gram matrix figures and performance.\n\n'
-
-    # setup the model type
-    model_type = model_type.lower()
-    if model_type != 'regression' and model_type != 'classification':
-        raise Exception(
-            'The model type is incorrect! Please choose from regression or classification.'
-        )
-    if verbose:
-        print()
-        print('--- This is a %s problem ---' % model_type)
-    str_fw += 'This is a %s problem.\n' % model_type
-    
-    # calculate gram matrices rather than read them from file.
-    if read_gm_from_file == False:
-        # Load the dataset
-        if verbose:
-            print()
-            print('\n1. Loading dataset from file...')
-        if isinstance(datafile, str):
-            dataset, y_all = loadDataset(
-                    datafile, filename_y=datafile_y, extra_params=extra_params)
-        else: # load data directly from variable.
-            dataset = datafile
-            y_all = datafile_y                
-
-        #     import matplotlib.pyplot as plt
-        #     import networkx as nx
-        #     nx.draw_networkx(dataset[30])
-        #     plt.show()
-    
-        # Grid of parameters with a discrete number of values for each.
-        param_list_precomputed = list(ParameterGrid(param_grid_precomputed))
-        param_list = list(ParameterGrid(param_grid))
-    
-        gram_matrices = [
-        ]  # a list to store gram matrices for all param_grid_precomputed
-        gram_matrix_time = [
-        ]  # a list to store time to calculate gram matrices
-        param_list_pre_revised = [
-        ]  # list to store param grids precomputed ignoring the useless ones
-    
-        # calculate all gram matrices
-        if verbose:
-            print()
-            print('2. Calculating gram matrices. This could take a while...')
-        str_fw += '\nII. Gram matrices.\n\n'
-        tts = time.time()  # start training time
-        nb_gm_ignore = 0  # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
-        for idx, params_out in enumerate(param_list_precomputed):
-            y = y_all[:]
-            params_out['n_jobs'] = n_jobs
-            params_out['verbose'] = verbose
-#            print(dataset)
-#            import networkx as nx
-#            nx.draw_networkx(dataset[1])
-#            plt.show()
-            rtn_data = estimator(dataset[:], **params_out)
-            Kmatrix = rtn_data[0]
-            current_run_time = rtn_data[1]
-            # for some kernels, some graphs in datasets may not meet the 
-            # kernels' requirements for graph structure. These graphs are trimmed. 
-            if len(rtn_data) == 3:
-                idx_trim = rtn_data[2]  # the index of trimmed graph list
-                y = [y[idxt] for idxt in idx_trim] # trim y accordingly
-#            Kmatrix = np.random.rand(2250, 2250)
-#            current_run_time = 0.1
-            
-            # remove graphs whose kernels with themselves are zeros 
-            # @todo: y not changed accordingly?
-            Kmatrix_diag = Kmatrix.diagonal().copy()
-            nb_g_ignore = 0
-            for idxk, diag in enumerate(Kmatrix_diag):
-                if diag == 0:
-                    Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0)
-                    Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1)
-                    nb_g_ignore += 1
-            # normalization
-            # @todo: works only for undirected graph?
-            Kmatrix_diag = Kmatrix.diagonal().copy()
-            for i in range(len(Kmatrix)):
-                for j in range(i, len(Kmatrix)):
-                    Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
-                    Kmatrix[j][i] = Kmatrix[i][j]
-            if verbose:
-                print()
-            if params_out == {}:
-                if verbose:
-                    print('the gram matrix is: ')
-                str_fw += 'the gram matrix is:\n\n'
-            else:
-                if verbose:
-                    print('the gram matrix with parameters', params_out, 'is: \n\n')
-                str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out
-            if len(Kmatrix) < 2:
-                nb_gm_ignore += 1
-                if verbose:
-                    print('ignored, as at most only one of all its diagonal value is non-zero.')
-                str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n'
-            else:                
-                if np.isnan(Kmatrix).any(
-                ):  # if the matrix contains elements that are not numbers
-                    nb_gm_ignore += 1
-                    if verbose:
-                        print('ignored, as it contains elements that are not numbers.')
-                    str_fw += 'ignored, as it contains elements that are not numbers.\n\n'
-                else:
-#                    print(Kmatrix)
-                    str_fw += np.array2string(
-                            Kmatrix,
-                            separator=',') + '\n\n'
-#                            separator=',',
-#                            threshold=np.inf,
-#                            floatmode='unique') + '\n\n'
-
-                    fig_file_name = results_dir + '/GM[ds]' + ds_name
-                    if params_out != {}:
-                        fig_file_name += '[params]' + str(idx)
-                    plt.imshow(Kmatrix)
-                    plt.colorbar()
-                    plt.savefig(fig_file_name + '.eps', format='eps', dpi=300)
-#                    plt.show()
-                    plt.clf()
-                    gram_matrices.append(Kmatrix)
-                    gram_matrix_time.append(current_run_time)
-                    param_list_pre_revised.append(params_out)
-                    if nb_g_ignore > 0:
-                        if verbose:
-                            print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
-                        str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore
-        if verbose:
-            print()
-            print(
-            '{} gram matrices are calculated, {} of which are ignored.'.format(
-                len(param_list_precomputed), nb_gm_ignore))
-        str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore)
-        str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n'
-        str_fw += ''.join([
-            '{}: {}\n'.format(idx, params_out)
-            for idx, params_out in enumerate(param_list_precomputed)
-        ])
-
-        if verbose:
-            print()
-        if len(gram_matrices) == 0:
-            if verbose:
-                print('all gram matrices are ignored, no results obtained.')
-            str_fw += '\nall gram matrices are ignored, no results obtained.\n\n'
-        else:
-            # save gram matrices to file.
-#            np.savez(results_dir + '/' + ds_name + '.gm', 
-#                     gms=gram_matrices, params=param_list_pre_revised, y=y, 
-#                     gmtime=gram_matrix_time)
-            if verbose:
-                print(
-                '3. Fitting and predicting using nested cross validation. This could really take a while...'
-                )
-            
-            # ---- use pool.imap_unordered to parallel and track progress. ----
-#            train_pref = []
-#            val_pref = []
-#            test_pref = []
-#            def func_assign(result, var_to_assign):
-#                for idx, itm in enumerate(var_to_assign):
-#                    itm.append(result[idx])                
-#            trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, y, model_type)
-#                      
-#            parallel_me(trial_do_partial, range(NUM_TRIALS), func_assign, 
-#                        [train_pref, val_pref, test_pref], glbv=gram_matrices,
-#                        method='imap_unordered', n_jobs=n_jobs, chunksize=1,
-#                        itr_desc='cross validation')
-            
-            def init_worker(gms_toshare):
-                global G_gms
-                G_gms = gms_toshare
-            
-#            gram_matrices = np.array(gram_matrices)
-#            gms_shape = gram_matrices.shape
-#            gms_array = Array('d', np.reshape(gram_matrices.copy(), -1, order='C'))
-#            pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gms_array, gms_shape))
-            pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))
-            trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)
-            train_pref = []
-            val_pref = []
-            test_pref = []
-#            if NUM_TRIALS < 1000 * n_jobs:
-#                chunksize = int(NUM_TRIALS / n_jobs) + 1
-#            else:
-#                chunksize = 1000
-            chunksize = 1
-            if verbose:
-                iterator = tqdm(pool.imap_unordered(trial_do_partial, 
-                        range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
-            else:
-                iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
-            for o1, o2, o3 in iterator:
-                train_pref.append(o1)
-                val_pref.append(o2)
-                test_pref.append(o3)
-            pool.close()
-            pool.join()
-    
-#            # ---- use pool.map to parallel. ----
-#            pool =  Pool(n_jobs)
-#            trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y[0:250], model_type)
-#            result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))
-#            train_pref = [item[0] for item in result_perf]
-#            val_pref = [item[1] for item in result_perf]
-#            test_pref = [item[2] for item in result_perf]
-    
-#            # ---- direct running, normally use a single CPU core. ----
-#            train_pref = []
-#            val_pref = []
-#            test_pref = []
-#            for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):
-#                o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)
-#                train_pref.append(o1)
-#                val_pref.append(o2)
-#                test_pref.append(o3)
-#            print()
-    
-            if verbose:
-                print()
-                print('4. Getting final performance...')
-            str_fw += '\nIII. Performance.\n\n'
-            # averages and confidences of performances on outer trials for each combination of parameters
-            average_train_scores = np.mean(train_pref, axis=0)
-#            print('val_pref: ', val_pref[0][0])
-            average_val_scores = np.mean(val_pref, axis=0)
-#            print('test_pref: ', test_pref[0][0])
-            average_perf_scores = np.mean(test_pref, axis=0)
-            # sample std is used here
-            std_train_scores = np.std(train_pref, axis=0, ddof=1)
-            std_val_scores = np.std(val_pref, axis=0, ddof=1)
-            std_perf_scores = np.std(test_pref, axis=0, ddof=1)
-    
-            if model_type == 'regression':
-                best_val_perf = np.amin(average_val_scores)
-            else:
-                best_val_perf = np.amax(average_val_scores)
-#            print('average_val_scores: ', average_val_scores)
-#            print('best_val_perf: ', best_val_perf)
-#            print()
-            best_params_index = np.where(average_val_scores == best_val_perf)
-            # find smallest val std with best val perf.
-            best_val_stds = [
-                std_val_scores[value][best_params_index[1][idx]]
-                for idx, value in enumerate(best_params_index[0])
-            ]
-            min_val_std = np.amin(best_val_stds)
-            best_params_index = np.where(std_val_scores == min_val_std)
-            best_params_out = [
-                param_list_pre_revised[i] for i in best_params_index[0]
-            ]
-            best_params_in = [param_list[i] for i in best_params_index[1]]
-            if verbose:
-                print('best_params_out: ', best_params_out)
-                print('best_params_in: ', best_params_in)
-                print()
-                print('best_val_perf: ', best_val_perf)
-                print('best_val_std: ', min_val_std)
-            str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
-            str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
-            str_fw += 'best_val_perf: %s\n' % best_val_perf
-            str_fw += 'best_val_std: %s\n' % min_val_std
-    
-#            print(best_params_index)
-#            print(best_params_index[0])
-#            print(average_perf_scores)
-            final_performance = [
-                average_perf_scores[value][best_params_index[1][idx]]
-                for idx, value in enumerate(best_params_index[0])
-            ]
-            final_confidence = [
-                std_perf_scores[value][best_params_index[1][idx]]
-                for idx, value in enumerate(best_params_index[0])
-            ]
-            if verbose:
-                print('final_performance: ', final_performance)
-                print('final_confidence: ', final_confidence)
-            str_fw += 'final_performance: %s\n' % final_performance
-            str_fw += 'final_confidence: %s\n' % final_confidence
-            train_performance = [
-                average_train_scores[value][best_params_index[1][idx]]
-                for idx, value in enumerate(best_params_index[0])
-            ]
-            train_std = [
-                std_train_scores[value][best_params_index[1][idx]]
-                for idx, value in enumerate(best_params_index[0])
-            ]
-            if verbose:
-                print('train_performance: %s' % train_performance)
-                print('train_std: ', train_std)
-            str_fw += 'train_performance: %s\n' % train_performance
-            str_fw += 'train_std: %s\n\n' % train_std
-
-            if verbose:
-                print()
-            tt_total = time.time() - tts  # training time for all hyper-parameters
-            average_gram_matrix_time = np.mean(gram_matrix_time)
-            std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
-            best_gram_matrix_time = [
-                gram_matrix_time[i] for i in best_params_index[0]
-            ]
-            ave_bgmt = np.mean(best_gram_matrix_time)
-            std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
-            if verbose:
-                print('time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
-                      .format(average_gram_matrix_time, std_gram_matrix_time))
-                print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
-                        ave_bgmt, std_bgmt))
-                print('total training time with all hyper-param choices: {:.2f}s'.format(
-                        tt_total))
-            str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
-            str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
-            str_fw += 'total training time with all hyper-param choices: {:.2f}s\n\n'.format(tt_total)
-    
-            # # save results to file
-            # np.savetxt(results_name_pre + 'average_train_scores.dt',
-            #            average_train_scores)
-            # np.savetxt(results_name_pre + 'average_val_scores', average_val_scores)
-            # np.savetxt(results_name_pre + 'average_perf_scores.dt',
-            #            average_perf_scores)
-            # np.savetxt(results_name_pre + 'std_train_scores.dt', std_train_scores)
-            # np.savetxt(results_name_pre + 'std_val_scores.dt', std_val_scores)
-            # np.savetxt(results_name_pre + 'std_perf_scores.dt', std_perf_scores)
-    
-            # np.save(results_name_pre + 'best_params_index', best_params_index)
-            # np.save(results_name_pre + 'best_params_pre.dt', best_params_out)
-            # np.save(results_name_pre + 'best_params_in.dt', best_params_in)
-            # np.save(results_name_pre + 'best_val_perf.dt', best_val_perf)
-            # np.save(results_name_pre + 'best_val_std.dt', best_val_std)
-            # np.save(results_name_pre + 'final_performance.dt', final_performance)
-            # np.save(results_name_pre + 'final_confidence.dt', final_confidence)
-            # np.save(results_name_pre + 'train_performance.dt', train_performance)
-            # np.save(results_name_pre + 'train_std.dt', train_std)
-    
-            # np.save(results_name_pre + 'gram_matrix_time.dt', gram_matrix_time)
-            # np.save(results_name_pre + 'average_gram_matrix_time.dt',
-            #         average_gram_matrix_time)
-            # np.save(results_name_pre + 'std_gram_matrix_time.dt',
-            #         std_gram_matrix_time)
-            # np.save(results_name_pre + 'best_gram_matrix_time.dt',
-            #         best_gram_matrix_time)
-    
-    # read gram matrices from file.
-    else:    
-        # Grid of parameters with a discrete number of values for each.
-#        param_list_precomputed = list(ParameterGrid(param_grid_precomputed))
-        param_list = list(ParameterGrid(param_grid))
-    
-        # read gram matrices from file.
-        if verbose:
-            print()
-            print('2. Reading gram matrices from file...')
-        str_fw += '\nII. Gram matrices.\n\nGram matrices are read from file, see last log for detail.\n'
-        gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')
-        gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed
-        gram_matrix_time = gmfile['gmtime'] # time used to compute the gram matrices
-        param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones
-        y = gmfile['y'].tolist()
-        
-        tts = time.time()  # start training time
-#        nb_gm_ignore = 0  # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)            
-        if verbose:
-            print(
-                    '3. Fitting and predicting using nested cross validation. This could really take a while...'
-                    )
- 
-        # ---- use pool.imap_unordered to parallel and track progress. ----
-        def init_worker(gms_toshare):
-            global G_gms
-            G_gms = gms_toshare
-
-        pool = Pool(processes=n_jobs, initializer=init_worker, initargs=(gram_matrices,))
-        trial_do_partial = partial(parallel_trial_do, param_list_pre_revised, param_list, y, model_type)
-        train_pref = []
-        val_pref = []
-        test_pref = []
-        chunksize = 1
-        if verbose:
-            iterator = tqdm(pool.imap_unordered(trial_do_partial, 
-                    range(NUM_TRIALS), chunksize), desc='cross validation', file=sys.stdout)
-        else:
-            iterator = pool.imap_unordered(trial_do_partial, range(NUM_TRIALS), chunksize)
-        for o1, o2, o3 in iterator:
-            train_pref.append(o1)
-            val_pref.append(o2)
-            test_pref.append(o3)
-        pool.close()
-        pool.join()
-        
-        # # ---- use pool.map to parallel. ----
-        # result_perf = pool.map(trial_do_partial, range(NUM_TRIALS))
-        # train_pref = [item[0] for item in result_perf]
-        # val_pref = [item[1] for item in result_perf]
-        # test_pref = [item[2] for item in result_perf]
-
-        # # ---- use joblib.Parallel to parallel and track progress. ----
-        # trial_do_partial = partial(trial_do, param_list_pre_revised, param_list, gram_matrices, y, model_type)
-        # result_perf = Parallel(n_jobs=n_jobs, verbose=10)(delayed(trial_do_partial)(trial) for trial in range(NUM_TRIALS))
-        # train_pref = [item[0] for item in result_perf]
-        # val_pref = [item[1] for item in result_perf]
-        # test_pref = [item[2] for item in result_perf]
-
-#        # ---- direct running, normally use a single CPU core. ----
-#        train_pref = []
-#        val_pref = []
-#        test_pref = []
-#        for i in tqdm(range(NUM_TRIALS), desc='cross validation', file=sys.stdout):
-#            o1, o2, o3 = trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, i)
-#            train_pref.append(o1)
-#            val_pref.append(o2)
-#            test_pref.append(o3)
-
-        if verbose:
-            print()
-            print('4. Getting final performance...')
-        str_fw += '\nIII. Performance.\n\n'
-        # averages and confidences of performances on outer trials for each combination of parameters
-        average_train_scores = np.mean(train_pref, axis=0)
-        average_val_scores = np.mean(val_pref, axis=0)
-        average_perf_scores = np.mean(test_pref, axis=0)
-        # sample std is used here
-        std_train_scores = np.std(train_pref, axis=0, ddof=1)
-        std_val_scores = np.std(val_pref, axis=0, ddof=1)
-        std_perf_scores = np.std(test_pref, axis=0, ddof=1)
-
-        if model_type == 'regression':
-            best_val_perf = np.amin(average_val_scores)
-        else:
-            best_val_perf = np.amax(average_val_scores)
-        best_params_index = np.where(average_val_scores == best_val_perf)
-        # find smallest val std with best val perf.
-        best_val_stds = [
-            std_val_scores[value][best_params_index[1][idx]]
-            for idx, value in enumerate(best_params_index[0])
-        ]
-        min_val_std = np.amin(best_val_stds)
-        best_params_index = np.where(std_val_scores == min_val_std)
-        best_params_out = [
-            param_list_pre_revised[i] for i in best_params_index[0]
-        ]
-        best_params_in = [param_list[i] for i in best_params_index[1]]
-        if verbose:
-            print('best_params_out: ', best_params_out)
-            print('best_params_in: ', best_params_in)
-            print()
-            print('best_val_perf: ', best_val_perf)
-            print('best_val_std: ', min_val_std)
-        str_fw += 'best settings of hyper-params to build gram matrix: %s\n' % best_params_out
-        str_fw += 'best settings of other hyper-params: %s\n\n' % best_params_in
-        str_fw += 'best_val_perf: %s\n' % best_val_perf
-        str_fw += 'best_val_std: %s\n' % min_val_std
-
-        final_performance = [
-            average_perf_scores[value][best_params_index[1][idx]]
-            for idx, value in enumerate(best_params_index[0])
-        ]
-        final_confidence = [
-            std_perf_scores[value][best_params_index[1][idx]]
-            for idx, value in enumerate(best_params_index[0])
-        ]
-        if verbose:
-            print('final_performance: ', final_performance)
-            print('final_confidence: ', final_confidence)
-        str_fw += 'final_performance: %s\n' % final_performance
-        str_fw += 'final_confidence: %s\n' % final_confidence
-        train_performance = [
-            average_train_scores[value][best_params_index[1][idx]]
-            for idx, value in enumerate(best_params_index[0])
-        ]
-        train_std = [
-            std_train_scores[value][best_params_index[1][idx]]
-            for idx, value in enumerate(best_params_index[0])
-        ]
-        if verbose:
-            print('train_performance: %s' % train_performance)
-            print('train_std: ', train_std)
-        str_fw += 'train_performance: %s\n' % train_performance
-        str_fw += 'train_std: %s\n\n' % train_std
-
-        if verbose:
-            print()
-        average_gram_matrix_time = np.mean(gram_matrix_time)
-        std_gram_matrix_time = np.std(gram_matrix_time, ddof=1) if len(gram_matrix_time) > 1 else 0
-        best_gram_matrix_time = [
-            gram_matrix_time[i] for i in best_params_index[0]
-        ]
-        ave_bgmt = np.mean(best_gram_matrix_time)
-        std_bgmt = np.std(best_gram_matrix_time, ddof=1) if len(best_gram_matrix_time) > 1 else 0
-        if verbose:        
-            print(
-                    'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s'
-                    .format(average_gram_matrix_time, std_gram_matrix_time))
-            print('time to calculate best gram matrix: {:.2f}±{:.2f}s'.format(
-                    ave_bgmt, std_bgmt))
-        tt_poster = time.time() - tts  # training time with hyper-param choices who did not participate in calculation of gram matrices
-        if verbose:
-            print(
-                    'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s'.format(
-                            tt_poster))
-            print('total training time with all hyper-param choices: {:.2f}s'.format(
-                    tt_poster + np.sum(gram_matrix_time)))
-#        str_fw += 'time to calculate gram matrix with different hyper-params: {:.2f}±{:.2f}s\n'.format(average_gram_matrix_time, std_gram_matrix_time)
-#        str_fw += 'time to calculate best gram matrix: {:.2f}±{:.2f}s\n'.format(ave_bgmt, std_bgmt)
-        str_fw += 'training time with hyper-param choices who did not participate in calculation of gram matrices: {:.2f}s\n\n'.format(tt_poster)
-
-        # open file to save all results for this dataset.
-        if not os.path.exists(results_dir):
-            os.makedirs(results_dir)
-            
-    # print out results as table.
-    str_fw += printResultsInTable(param_list, param_list_pre_revised, average_val_scores,
-              std_val_scores, average_perf_scores, std_perf_scores,
-              average_train_scores, std_train_scores, gram_matrix_time,
-              model_type, verbose)
-            
-    # open file to save all results for this dataset.
-    if not os.path.exists(results_dir + '/' + ds_name + '.output.txt'):
-        with open(results_dir + '/' + ds_name + '.output.txt', 'w') as f:
-            f.write(str_fw)
-    else:
-        with open(results_dir + '/' + ds_name + '.output.txt', 'r+') as f:
-            content = f.read()
-            f.seek(0, 0)
-            f.write(str_fw + '\n\n\n' + content)

-
-
-
[docs]def trial_do(param_list_pre_revised, param_list, gram_matrices, y, model_type, trial): # Test set level
-
-#    # get gram matrices from global variables.
-#    gram_matrices = np.reshape(G_gms.copy(), G_gms_shape, order='C')
-    
-    # Arrays to store scores
-    train_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
-    val_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
-    test_pref = np.zeros((len(param_list_pre_revised), len(param_list)))
-
-    # randomness added to seeds of split function below. "high" is "size" times
-    # 10 so that at least 10 different random output will be yielded. Remove
-    # these lines if identical outputs is required.
-    rdm_out = np.random.RandomState(seed=None)
-    rdm_seed_out_l = rdm_out.uniform(high=len(param_list_pre_revised) * 10, 
-                                   size=len(param_list_pre_revised))
-#    print(trial, rdm_seed_out_l)
-#    print()
-    # loop for each outer param tuple
-    for index_out, params_out in enumerate(param_list_pre_revised):
-        # get gram matrices from global variables.
-#        gm_now = G_gms[index_out * G_gms_shape[1] * G_gms_shape[2]:(index_out + 1) * G_gms_shape[1] * G_gms_shape[2]]
-#        gm_now = np.reshape(gm_now.copy(), (G_gms_shape[1], G_gms_shape[2]), order='C')
-        gm_now = gram_matrices[index_out].copy()
-    
-        # split gram matrix and y to app and test sets.
-        indices = range(len(y))
-        # The argument "random_state" in function "train_test_split" can not be
-        # set to None, because it will use RandomState instance used by 
-        # np.random, which is possible for multiple subprocesses to inherit the
-        # same seed if they forked at the same time, leading to identical 
-        # random variates for different subprocesses. Instead, we use "trial" 
-        # and "index_out" parameters to generate different seeds for different 
-        # trials/subprocesses and outer loops. "rdm_seed_out_l" is used to add 
-        # randomness into seeds, so that it yields a different output every 
-        # time the program is run. To yield identical outputs every time,
-        # remove the second line below. Same method is used to the "KFold"
-        # function in the inner loop.
-        rdm_seed_out = (trial + 1) * (index_out + 1)
-        rdm_seed_out = (rdm_seed_out + int(rdm_seed_out_l[index_out])) % (2 ** 32 - 1)
-#        print(trial, rdm_seed_out)
-        X_app, X_test, y_app, y_test, idx_app, idx_test = train_test_split(
-            gm_now, y, indices, test_size=0.1, 
-            random_state=rdm_seed_out, shuffle=True)
-#        print(trial, idx_app, idx_test)
-#        print()
-        X_app = X_app[:, idx_app]
-        X_test = X_test[:, idx_app]
-        y_app = np.array(y_app)
-        y_test = np.array(y_test)
-
-        rdm_seed_in_l = rdm_out.uniform(high=len(param_list) * 10, 
-                                   size=len(param_list))
-        # loop for each inner param tuple
-        for index_in, params_in in enumerate(param_list):
-#            if trial == 0:
-#                print(index_out, index_in)
-#                print('params_in: ', params_in)
-#            st = time.time()
-            rdm_seed_in = (trial + 1) * (index_out + 1) * (index_in + 1)
-#            print("rdm_seed_in1: ", trial, index_in, rdm_seed_in)
-            rdm_seed_in = (rdm_seed_in + int(rdm_seed_in_l[index_in])) % (2 ** 32 - 1)
-#            print("rdm_seed_in2: ", trial, index_in, rdm_seed_in)
-            inner_cv = KFold(n_splits=10, shuffle=True, random_state=rdm_seed_in)
-            current_train_perf = []
-            current_valid_perf = []
-            current_test_perf = [] 
-
-            # For regression use the Kernel Ridge method
-#            try:
-            if model_type == 'regression':
-                kr = KernelRidge(kernel='precomputed', **params_in)
-                # loop for each split on validation set level
-                # validation set level
-                for train_index, valid_index in inner_cv.split(X_app):
-#                    print("train_index, valid_index: ", trial, index_in, train_index, valid_index)
-#                    if trial == 0:
-#                        print('train_index: ', train_index)
-#                        print('valid_index: ', valid_index)
-#                        print('idx_test: ', idx_test)
-#                        print('y_app[train_index]: ', y_app[train_index])
-#                        print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index])
-#                        print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index])
-                    kr.fit(X_app[train_index, :][:, train_index],
-                           y_app[train_index])
-
-                    # predict on the train, validation and test set
-                    y_pred_train = kr.predict(
-                        X_app[train_index, :][:, train_index])
-                    y_pred_valid = kr.predict(
-                        X_app[valid_index, :][:, train_index])
-#                    if trial == 0:     
-#                        print('y_pred_valid: ', y_pred_valid)
-#                        print()
-                    y_pred_test = kr.predict(
-                        X_test[:, train_index])
-
-                    # root mean squared errors
-                    current_train_perf.append(
-                        np.sqrt(
-                            mean_squared_error(
-                                y_app[train_index], y_pred_train)))
-                    current_valid_perf.append(
-                        np.sqrt(
-                            mean_squared_error(
-                                y_app[valid_index], y_pred_valid)))
-#                    if trial == 0:
-#                        print(mean_squared_error(
-#                                y_app[valid_index], y_pred_valid))
-                    current_test_perf.append(
-                        np.sqrt(
-                            mean_squared_error(
-                                y_test, y_pred_test)))
-            # For clcassification use SVM
-            else:
-                svc = SVC(kernel='precomputed', cache_size=200, 
-                          verbose=False, **params_in)
-                # loop for each split on validation set level
-                # validation set level
-                for train_index, valid_index in inner_cv.split(X_app):
-#                        np.savez("bug.npy",X_app[train_index, :][:, train_index],y_app[train_index])
-#                    if trial == 0:
-#                        print('train_index: ', train_index)
-#                        print('valid_index: ', valid_index)
-#                        print('idx_test: ', idx_test)
-#                        print('y_app[train_index]: ', y_app[train_index])
-#                        print('X_app[train_index, :][:, train_index]: ', X_app[train_index, :][:, train_index])
-#                        print('X_app[valid_index, :][:, train_index]: ', X_app[valid_index, :][:, train_index])
-                    svc.fit(X_app[train_index, :][:, train_index],
-                           y_app[train_index])
-                    
-                    # predict on the train, validation and test set
-                    y_pred_train = svc.predict(
-                        X_app[train_index, :][:, train_index])
-                    y_pred_valid = svc.predict(
-                        X_app[valid_index, :][:, train_index])
-                    y_pred_test = svc.predict(
-                        X_test[:, train_index])
-
-                    # root mean squared errors
-                    current_train_perf.append(
-                        accuracy_score(y_app[train_index],
-                                       y_pred_train))
-                    current_valid_perf.append(
-                        accuracy_score(y_app[valid_index],
-                                       y_pred_valid))
-                    current_test_perf.append(
-                        accuracy_score(y_test, y_pred_test))
-#            except ValueError:
-#                print(sys.exc_info()[0])
-#                print(params_out, params_in)
-
-            # average performance on inner splits
-            train_pref[index_out][index_in] = np.mean(
-                current_train_perf)
-            val_pref[index_out][index_in] = np.mean(
-                current_valid_perf)
-            test_pref[index_out][index_in] = np.mean(
-                current_test_perf)
-#            print(time.time() - st)
-#    if trial == 0:
-#        print('val_pref: ', val_pref)
-#        print('test_pref: ', test_pref)
-
-    return train_pref, val_pref, test_pref

-
-
[docs]def parallel_trial_do(param_list_pre_revised, param_list, y, model_type, trial):
-    train_pref, val_pref, test_pref = trial_do(param_list_pre_revised, 
-                                               param_list, G_gms, y, 
-                                               model_type, trial)
-    return train_pref, val_pref, test_pref

-
-
-
[docs]def compute_gram_matrices(dataset, y, estimator, param_list_precomputed, 
-                          results_dir, ds_name,
-                          n_jobs=1, str_fw='', verbose=True):
-    gram_matrices = [
-        ]  # a list to store gram matrices for all param_grid_precomputed
-    gram_matrix_time = [
-        ]  # a list to store time to calculate gram matrices
-    param_list_pre_revised = [
-        ]  # list to store param grids precomputed ignoring the useless ones
-    
-    nb_gm_ignore = 0  # the number of gram matrices those should not be considered, as they may contain elements that are not numbers (NaN)
-    for idx, params_out in enumerate(param_list_precomputed):
-        params_out['n_jobs'] = n_jobs
-#            print(dataset)
-#            import networkx as nx
-#            nx.draw_networkx(dataset[1])
-#            plt.show()
-        rtn_data = estimator(dataset[:], **params_out)
-        Kmatrix = rtn_data[0]
-        current_run_time = rtn_data[1]
-        # for some kernels, some graphs in datasets may not meet the 
-        # kernels' requirements for graph structure. These graphs are trimmed. 
-        if len(rtn_data) == 3:
-            idx_trim = rtn_data[2]  # the index of trimmed graph list
-            y = [y[idxt] for idxt in idx_trim] # trim y accordingly
-
-        Kmatrix_diag = Kmatrix.diagonal().copy()
-        # remove graphs whose kernels with themselves are zeros
-        nb_g_ignore = 0
-        for idxk, diag in enumerate(Kmatrix_diag):
-            if diag == 0:
-                Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=0)
-                Kmatrix = np.delete(Kmatrix, (idxk - nb_g_ignore), axis=1)
-                nb_g_ignore += 1
-        # normalization
-        for i in range(len(Kmatrix)):
-            for j in range(i, len(Kmatrix)):
-                Kmatrix[i][j] /= np.sqrt(Kmatrix_diag[i] * Kmatrix_diag[j])
-                Kmatrix[j][i] = Kmatrix[i][j]
-
-        if verbose:
-            print()
-        if params_out == {}:
-            if verbose:
-                print('the gram matrix is: ')
-            str_fw += 'the gram matrix is:\n\n'
-        else:
-            if verbose:
-                print('the gram matrix with parameters', params_out, 'is: ')
-            str_fw += 'the gram matrix with parameters %s is:\n\n' % params_out
-        if len(Kmatrix) < 2:
-            nb_gm_ignore += 1
-            if verbose:
-                print('ignored, as at most only one of all its diagonal value is non-zero.')
-            str_fw += 'ignored, as at most only one of all its diagonal value is non-zero.\n\n'
-        else:                
-            if np.isnan(Kmatrix).any(
-            ):  # if the matrix contains elements that are not numbers
-                nb_gm_ignore += 1
-                if verbose:
-                    print('ignored, as it contains elements that are not numbers.')
-                str_fw += 'ignored, as it contains elements that are not numbers.\n\n'
-            else:
-#                    print(Kmatrix)
-                str_fw += np.array2string(
-                        Kmatrix,
-                        separator=',') + '\n\n'
-#                            separator=',',
-#                            threshold=np.inf,
-#                            floatmode='unique') + '\n\n'
-
-                fig_file_name = results_dir + '/GM[ds]' + ds_name
-                if params_out != {}:
-                    fig_file_name += '[params]' + str(idx)
-                plt.imshow(Kmatrix)
-                plt.colorbar()
-                plt.savefig(fig_file_name + '.eps', format='eps', dpi=300)
-#                    plt.show()
-                plt.clf()
-                gram_matrices.append(Kmatrix)
-                gram_matrix_time.append(current_run_time)
-                param_list_pre_revised.append(params_out)
-                if nb_g_ignore > 0:
-                    if verbose:
-                        print(', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore)
-                    str_fw += ', where %d graphs are ignored as their graph kernels with themselves are zeros.' % nb_g_ignore
-    if verbose:
-        print()
-        print(
-            '{} gram matrices are calculated, {} of which are ignored.'.format(
-                len(param_list_precomputed), nb_gm_ignore))
-    str_fw += '{} gram matrices are calculated, {} of which are ignored.\n\n'.format(len(param_list_precomputed), nb_gm_ignore)
-    str_fw += 'serial numbers of gram matrix figures and their corresponding parameters settings:\n\n'
-    str_fw += ''.join([
-        '{}: {}\n'.format(idx, params_out)
-        for idx, params_out in enumerate(param_list_precomputed)
-    ])
-            
-    return gram_matrices, gram_matrix_time, param_list_pre_revised, y, str_fw

-
-
-
[docs]def read_gram_matrices_from_file(results_dir, ds_name):
-    gmfile = np.load(results_dir + '/' + ds_name + '.gm.npz')
-    gram_matrices = gmfile['gms'] # a list to store gram matrices for all param_grid_precomputed
-    param_list_pre_revised = gmfile['params'] # list to store param grids precomputed ignoring the useless ones
-    y = gmfile['y'].tolist()
-    return gram_matrices, param_list_pre_revised, y

-
-
-
[docs]def printResultsInTable(param_list, param_list_pre_revised, average_val_scores,
-                        std_val_scores, average_perf_scores, std_perf_scores,
-                        average_train_scores, std_train_scores, gram_matrix_time,
-                        model_type, verbose):
-    from collections import OrderedDict
-    from tabulate import tabulate
-    table_dict = {}
-    if model_type == 'regression':
-        for param_in in param_list:
-            param_in['alpha'] = '{:.2e}'.format(param_in['alpha'])
-    else:
-        for param_in in param_list:
-            param_in['C'] = '{:.2e}'.format(param_in['C'])
-    table_dict['params'] = [{**param_out, **param_in}
-                            for param_in in param_list for param_out in param_list_pre_revised]
-    table_dict['gram_matrix_time'] = [
-        '{:.2f}'.format(gram_matrix_time[index_out])
-        for param_in in param_list
-        for index_out, _ in enumerate(param_list_pre_revised)
-    ]
-    table_dict['valid_perf'] = [
-        '{:.2f}±{:.2f}'.format(average_val_scores[index_out][index_in],
-                               std_val_scores[index_out][index_in])
-        for index_in, _ in enumerate(param_list)
-        for index_out, _ in enumerate(param_list_pre_revised)
-    ]
-    table_dict['test_perf'] = [
-        '{:.2f}±{:.2f}'.format(average_perf_scores[index_out][index_in],
-                               std_perf_scores[index_out][index_in])
-        for index_in, _ in enumerate(param_list)
-        for index_out, _ in enumerate(param_list_pre_revised)
-    ]
-    table_dict['train_perf'] = [
-        '{:.2f}±{:.2f}'.format(average_train_scores[index_out][index_in],
-                               std_train_scores[index_out][index_in])
-        for index_in, _ in enumerate(param_list)
-        for index_out, _ in enumerate(param_list_pre_revised)
-    ]
-    
-    keyorder = [
-        'params', 'train_perf', 'valid_perf', 'test_perf',
-        'gram_matrix_time'
-    ]
-    if verbose:
-        print()
-    tb_print = tabulate(OrderedDict(sorted(table_dict.items(), 
-                        key=lambda i: keyorder.index(i[0]))), headers='keys')
-#            print(tb_print)
-    return 'table of performance v.s. hyper-params:\n\n%s\n\n' % tb_print

-
- -
- -
- - -
-
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- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/parallel.html b/docs/_build/html/_modules/pygraph/utils/parallel.html deleted file mode 100644 index b707fea..0000000 --- a/docs/_build/html/_modules/pygraph/utils/parallel.html +++ /dev/null @@ -1,252 +0,0 @@ - - - - - - - - - - - pygraph.utils.parallel — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
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Source code for pygraph.utils.parallel

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Tue Dec 11 11:39:46 2018
-Parallel aid functions.
-@author: ljia
-"""
-import multiprocessing
-from multiprocessing import Pool
-from tqdm import tqdm
-import sys
-
-
[docs]def parallel_me(func, func_assign, var_to_assign, itr, len_itr=None, init_worker=None, 
-                glbv=None, method=None, n_jobs=None, chunksize=None, itr_desc='',
-                verbose=True):
-    '''
-    '''
-    if method == 'imap_unordered':
-        if glbv: # global varibles required.
-#            def init_worker(v_share):
-#                global G_var
-#                G_var = v_share
-            if n_jobs == None:
-                n_jobs = multiprocessing.cpu_count()
-            with Pool(processes=n_jobs, initializer=init_worker, 
-                      initargs=glbv) as pool:                
-                if chunksize == None:
-                    if len_itr < 100 * n_jobs:
-                        chunksize = int(len_itr / n_jobs) + 1
-                    else:
-                        chunksize = 100
-                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
-                    desc=itr_desc, file=sys.stdout) if verbose else 
-                    pool.imap_unordered(func, itr, chunksize)):
-                    func_assign(result, var_to_assign)
-        else:
-            if n_jobs == None:
-                n_jobs = multiprocessing.cpu_count()
-            with Pool(processes=n_jobs) as pool:
-                if chunksize == None:
-                    if len_itr < 100 * n_jobs:
-                        chunksize = int(len_itr / n_jobs) + 1
-                    else:
-                        chunksize = 100
-                for result in (tqdm(pool.imap_unordered(func, itr, chunksize),
-                    desc=itr_desc, file=sys.stdout) if verbose else 
-                    pool.imap_unordered(func, itr, chunksize)):
-                    func_assign(result, var_to_assign)

-            
-                    
-
-
[docs]def parallel_gm(func, Kmatrix, Gn, init_worker=None, glbv=None, 
-                method='imap_unordered', n_jobs=None, chunksize=None, 
-                verbose=True):
-    from itertools import combinations_with_replacement
-    def func_assign(result, var_to_assign):
-        var_to_assign[result[0]][result[1]] = result[2]
-        var_to_assign[result[1]][result[0]] = result[2]
-    itr = combinations_with_replacement(range(0, len(Gn)), 2)
-    len_itr = int(len(Gn) * (len(Gn) + 1) / 2)
-    parallel_me(func, func_assign, Kmatrix, itr, len_itr=len_itr,
-        init_worker=init_worker, glbv=glbv, method=method, n_jobs=n_jobs, 
-        chunksize=chunksize, itr_desc='calculating kernels', verbose=verbose)

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Source code for pygraph.utils.trie

-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-"""
-Created on Wed Jan 30 10:48:49 2019
-
-Trie (prefix tree)
-@author: ljia
-@references:
-        https://viblo.asia/p/nlp-build-a-trie-data-structure-from-scratch-with-python-3P0lPzroKox, 2019.1
-"""
-
-import pickle
-import json
-
-""" Trie class
-"""
-
[docs]class Trie:
-    # init Trie class
-    def __init__(self):
-        self.root = self.getNode()
-
-
[docs]    def getNode(self):
-        return {"isEndOfWord": False, "children": {}}

-
-
[docs]    def insertWord(self, word):
-        current = self.root
-        for ch in word:
-
-            if ch in current["children"]:
-                node = current["children"][ch]
-            else:
-                node = self.getNode()
-                current["children"][ch] = node
-
-            current = node
-        current["isEndOfWord"] = True
-        if 'count' in current:
-            current['count'] += 1
-        else:
-            current['count'] = 1

-
-
[docs]    def searchWord(self, word):
-        current = self.root
-        for ch in word:
-            if ch not in current["children"]:
-                return 0
-            node = current["children"][ch]
-
-            current = node
-        if 'count' in current:
-            return current["count"]
-        else:
-            return 0

-
-
[docs]    def searchWordPrefix(self, word):
-        current = self.root
-        for ch in word:
-            if not current["children"].has_key(ch):
-                return False
-            node = current["children"][ch]
-
-            current = node
-        # return True if children contain keys and values
-        return bool(current["children"])

-
-
[docs]    def deleteWord(self, word):
-        self._delete(self.root, word, 0)

-
-    def _delete(self, current, word, index):
-        if(index == len(word)):
-            if not current["isEndOfWord"]:
-                return False
-            current["isEndOfWord"] = False
-            return len(current["children"].keys()) == 0
-
-        ch = word[index]
-        if not current["children"].has_key(ch):
-            return False
-        node = current["children"][ch]
-
-        should_delete_current_node = self._delete(node, word, index + 1)
-
-        if should_delete_current_node:
-            current["children"].pop(ch)
-            return len(current["children"].keys()) == 0
-
-        return False
-
-
[docs]    def save_to_pickle(self, file_name):
-        f = open(file_name + ".pkl", "wb")
-        pickle.dump(self.root, f)
-        f.close()

-
-
[docs]    def load_from_pickle(self, file_name):
-        f = open(file_name + ".pkl", "rb")
-        self.root = pickle.load(f)
-        f.close()

-        
-
[docs]    def to_json(self):
-        return json.dump(self.root)

-
-
[docs]    def save_to_json(self, file_name):
-        json_data = json.dumps(self.root)
-        f = open(file_name + ".json", "w")
-        f.write(json_data)
-        f.close()

-
-
[docs]    def load_from_json(self, file_name):
-        json_file = open(file_name + ".json", "r")
-        self.root = json.load(json_file)
-        json_file.close()

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_modules/pygraph/utils/utils.html b/docs/_build/html/_modules/pygraph/utils/utils.html deleted file mode 100644 index 5679427..0000000 --- a/docs/_build/html/_modules/pygraph/utils/utils.html +++ /dev/null @@ -1,451 +0,0 @@ - - - - - - - - - - - pygraph.utils.utils — py-graph documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - - -
- - - - - -
- -
- - - - - - - - - - - - - - - - - -
- - - - -
-
-
-
- -

Source code for pygraph.utils.utils

-import networkx as nx
-import numpy as np
-from copy import deepcopy
-#from itertools import product
-
-# from tqdm import tqdm
-
-
-
[docs]def getSPLengths(G1):
-    sp = nx.shortest_path(G1)
-    distances = np.zeros((G1.number_of_nodes(), G1.number_of_nodes()))
-    for i in sp.keys():
-        for j in sp[i].keys():
-            distances[i, j] = len(sp[i][j]) - 1
-    return distances

-
-
-
[docs]def getSPGraph(G, edge_weight=None):
-    """Transform graph G to its corresponding shortest-paths graph.
-
-    Parameters
-    ----------
-    G : NetworkX graph
-        The graph to be tramsformed.
-    edge_weight : string
-        edge attribute corresponding to the edge weight.
-
-    Return
-    ------
-    S : NetworkX graph
-        The shortest-paths graph corresponding to G.
-
-    Notes
-    ------
-    For an input graph G, its corresponding shortest-paths graph S contains the same set of nodes as G, while there exists an edge between all nodes in S which are connected by a walk in G. Every edge in S between two nodes is labeled by the shortest distance between these two nodes.
-
-    References
-    ----------
-    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
-    """
-    return floydTransformation(G, edge_weight=edge_weight)

-
-
-
[docs]def floydTransformation(G, edge_weight=None):
-    """Transform graph G to its corresponding shortest-paths graph using Floyd-transformation.
-
-    Parameters
-    ----------
-    G : NetworkX graph
-        The graph to be tramsformed.
-    edge_weight : string
-        edge attribute corresponding to the edge weight. The default edge weight is bond_type.
-
-    Return
-    ------
-    S : NetworkX graph
-        The shortest-paths graph corresponding to G.
-
-    References
-    ----------
-    [1] Borgwardt KM, Kriegel HP. Shortest-path kernels on graphs. InData Mining, Fifth IEEE International Conference on 2005 Nov 27 (pp. 8-pp). IEEE.
-    """
-    spMatrix = nx.floyd_warshall_numpy(G, weight=edge_weight)
-    S = nx.Graph()
-    S.add_nodes_from(G.nodes(data=True))
-    ns = list(G.nodes())
-    for i in range(0, G.number_of_nodes()):
-        for j in range(i + 1, G.number_of_nodes()):
-            if spMatrix[i, j] != np.inf:
-                S.add_edge(ns[i], ns[j], cost=spMatrix[i, j])
-    return S

-
-
-
[docs]def untotterTransformation(G, node_label, edge_label):
-    """Transform graph G according to Mahé et al.'s work to filter out tottering patterns of marginalized kernel and tree pattern kernel.
-
-    Parameters
-    ----------
-    G : NetworkX graph
-        The graph to be tramsformed.
-    node_label : string
-        node attribute used as label. The default node label is 'atom'.
-    edge_label : string
-        edge attribute used as label. The default edge label is 'bond_type'.
-
-    Return
-    ------
-    gt : NetworkX graph
-        The transformed graph corresponding to G.
-
-    References
-    ----------
-    [1] Pierre Mahé, Nobuhisa Ueda, Tatsuya Akutsu, Jean-Luc Perret, and Jean-Philippe Vert. Extensions of marginalized graph kernels. In Proceedings of the twenty-first international conference on Machine learning, page 70. ACM, 2004.
-    """
-    # arrange all graphs in a list
-    G = G.to_directed()
-    gt = nx.Graph()
-    gt.graph = G.graph
-    gt.add_nodes_from(G.nodes(data=True))
-    for edge in G.edges():
-        gt.add_node(edge)
-        gt.node[edge].update({node_label: G.node[edge[1]][node_label]})
-        gt.add_edge(edge[0], edge)
-        gt.edges[edge[0], edge].update({
-            edge_label:
-            G[edge[0]][edge[1]][edge_label]
-        })
-        for neighbor in G[edge[1]]:
-            if neighbor != edge[0]:
-                gt.add_edge(edge, (edge[1], neighbor))
-                gt.edges[edge, (edge[1], neighbor)].update({
-                    edge_label:
-                    G[edge[1]][neighbor][edge_label]
-                })
-    # nx.draw_networkx(gt)
-    # plt.show()
-
-    # relabel nodes using consecutive integers for convenience of kernel calculation.
-    gt = nx.convert_node_labels_to_integers(
-        gt, first_label=0, label_attribute='label_orignal')
-    return gt

-
-
-
[docs]def direct_product(G1, G2, node_label, edge_label):
-    """Return the direct/tensor product of directed graphs G1 and G2.
-
-    Parameters
-    ----------
-    G1, G2 : NetworkX graph
-        The original graphs.
-    node_label : string
-        node attribute used as label. The default node label is 'atom'.
-    edge_label : string
-        edge attribute used as label. The default edge label is 'bond_type'.
-
-    Return
-    ------
-    gt : NetworkX graph
-        The direct product graph of G1 and G2.
-
-    Notes
-    -----
-    This method differs from networkx.tensor_product in that this method only adds nodes and edges in G1 and G2 that have the same labels to the direct product graph.
-
-    References
-    ----------
-    [1] Thomas Gärtner, Peter Flach, and Stefan Wrobel. On graph kernels: Hardness results and efficient alternatives. Learning Theory and Kernel Machines, pages 129–143, 2003.
-    """
-    # arrange all graphs in a list
-    from itertools import product
-    # G = G.to_directed()
-    gt = nx.DiGraph()
-    # add nodes
-    for u, v in product(G1, G2):
-        if G1.nodes[u][node_label] == G2.nodes[v][node_label]:
-            gt.add_node((u, v))
-            gt.nodes[(u, v)].update({node_label: G1.nodes[u][node_label]})
-    # add edges, faster for sparse graphs (no so many edges), which is the most case for now.
-    for (u1, v1), (u2, v2) in product(G1.edges, G2.edges):
-        if (u1, u2) in gt and (
-                v1, v2
-        ) in gt and G1.edges[u1, v1][edge_label] == G2.edges[u2,
-                                                             v2][edge_label]:
-            gt.add_edge((u1, u2), (v1, v2))
-            gt.edges[(u1, u2), (v1, v2)].update({
-                edge_label:
-                G1.edges[u1, v1][edge_label]
-            })
-
-    # # add edges, faster for dense graphs (a lot of edges, complete graph would be super).
-    # for u, v in product(gt, gt):
-    #     if (u[0], v[0]) in G1.edges and (
-    #             u[1], v[1]
-    #     ) in G2.edges and G1.edges[u[0],
-    #                                v[0]][edge_label] == G2.edges[u[1],
-    #                                                              v[1]][edge_label]:
-    #         gt.add_edge((u[0], u[1]), (v[0], v[1]))
-    #         gt.edges[(u[0], u[1]), (v[0], v[1])].update({
-    #             edge_label:
-    #             G1.edges[u[0], v[0]][edge_label]
-    #         })
-
-    # relabel nodes using consecutive integers for convenience of kernel calculation.
-    # gt = nx.convert_node_labels_to_integers(
-    #     gt, first_label=0, label_attribute='label_orignal')
-    return gt

-
-
-
[docs]def graph_deepcopy(G):
-    """Deep copy a graph, including deep copy of all nodes, edges and 
-    attributes of the graph, nodes and edges.
-    
-    Note
-    ----
-    It is the same as the NetworkX function graph.copy(), as far as I know.
-    """
-    # add graph attributes.
-    labels = {}
-    for k, v in G.graph.items():
-        labels[k] = deepcopy(v)
-    if G.is_directed():
-        G_copy = nx.DiGraph(**labels)
-    else:
-        G_copy = nx.Graph(**labels)
-        
-    # add nodes    
-    for nd, attrs in G.nodes(data=True):
-        labels = {}
-        for k, v in attrs.items():
-            labels[k] = deepcopy(v)
-        G_copy.add_node(nd, **labels)
-        
-    # add edges.
-    for nd1, nd2, attrs in G.edges(data=True):
-        labels = {}
-        for k, v in attrs.items():
-            labels[k] = deepcopy(v)
-        G_copy.add_edge(nd1, nd2, **labels)
-    
-    return G_copy

-
-
-
[docs]def graph_isIdentical(G1, G2):
-    """Check if two graphs are identical, including: same nodes, edges, node
-    labels/attributes, edge labels/attributes.
-    
-    Notes
-    ----
-    1. The type of graphs has to be the same.
-    2. Global/Graph attributes are neglected as they may contain names for graphs.
-    """
-    # check nodes.
-    nlist1 = [n for n in G1.nodes(data=True)]
-    nlist2 = [n for n in G2.nodes(data=True)]
-    if not nlist1 == nlist2:
-        return False
-    # check edges.
-    elist1 = [n for n in G1.edges(data=True)]
-    elist2 = [n for n in G2.edges(data=True)]
-    if not elist1 == elist2:
-        return False
-    # check graph attributes.
-    
-    return True

-
-
-
[docs]def get_node_labels(Gn, node_label):
-    """Get node labels of dataset Gn.
-    """
-    nl = set()
-    for G in Gn:
-        nl = nl | set(nx.get_node_attributes(G, node_label).values())
-    return nl

-
-
-
[docs]def get_edge_labels(Gn, edge_label):
-    """Get edge labels of dataset Gn.
-    """
-    el = set()
-    for G in Gn:
-        el = el | set(nx.get_edge_attributes(G, edge_label).values())
-    return el

-
- -
- -
- - -
-
- -
- -
- - - - - - - - - - - - \ No newline at end of file diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt deleted file mode 100644 index d2d2ed9..0000000 --- a/docs/_build/html/_sources/index.rst.txt +++ /dev/null @@ -1,20 +0,0 @@ -.. py-graph documentation master file, created by - sphinx-quickstart on Tue Jan 28 17:13:42 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to py-graph's documentation! -==================================== - -.. toctree:: - :maxdepth: 2 - :caption: Contents: - - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/_build/html/_sources/modules.rst.txt b/docs/_build/html/_sources/modules.rst.txt deleted file mode 100644 index a563717..0000000 --- a/docs/_build/html/_sources/modules.rst.txt +++ /dev/null @@ -1,7 +0,0 @@ -py-graph -======== - -.. toctree:: - :maxdepth: 4 - - pygraph diff --git a/docs/_build/html/_sources/pygraph.rst.txt b/docs/_build/html/_sources/pygraph.rst.txt deleted file mode 100644 index 592eb54..0000000 --- a/docs/_build/html/_sources/pygraph.rst.txt +++ /dev/null @@ -1,17 +0,0 @@ -pygraph package -=============== - -Subpackages ------------ - -.. toctree:: - - pygraph.utils - -Module contents ---------------- - -.. automodule:: pygraph - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_sources/pygraph.utils.rst.txt b/docs/_build/html/_sources/pygraph.utils.rst.txt deleted file mode 100644 index 07ec351..0000000 --- a/docs/_build/html/_sources/pygraph.utils.rst.txt +++ /dev/null @@ -1,94 +0,0 @@ -pygraph.utils package -===================== - -Submodules ----------- - -pygraph.utils.graphdataset module ---------------------------------- - -.. automodule:: pygraph.utils.graphdataset - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.graphfiles module -------------------------------- - -.. automodule:: pygraph.utils.graphfiles - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.ipython\_log module ---------------------------------- - -.. automodule:: pygraph.utils.ipython_log - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.isNotebook module -------------------------------- - -.. automodule:: pygraph.utils.isNotebook - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.kernels module ----------------------------- - -.. automodule:: pygraph.utils.kernels - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.logger2file module --------------------------------- - -.. automodule:: pygraph.utils.logger2file - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.model\_selection\_precomputed module --------------------------------------------------- - -.. automodule:: pygraph.utils.model_selection_precomputed - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.parallel module ------------------------------ - -.. automodule:: pygraph.utils.parallel - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.trie module -------------------------- - -.. automodule:: pygraph.utils.trie - :members: - :undoc-members: - :show-inheritance: - -pygraph.utils.utils module --------------------------- - -.. automodule:: pygraph.utils.utils - :members: - :undoc-members: - :show-inheritance: - - -Module contents ---------------- - -.. automodule:: pygraph.utils - :members: - :undoc-members: - :show-inheritance: diff --git a/docs/_build/html/_static/ajax-loader.gif b/docs/_build/html/_static/ajax-loader.gif deleted file mode 100644 index 61faf8cab23993bd3e1560bff0668bd628642330..0000000000000000000000000000000000000000 GIT binary patch literal 0 KcmV+b0RR6000031 literal 673 zcmV;S0$%+`Nk%w1VGsZi0P+9;|Ns9HReZ+Q-%fy?5mkI_madAczL~bhA^sXea%Ew3 zWn>_CX>@2HVQOJ`Y;R#?E@^INZvY|r1ON&E001HX3rITNLCBEIlvXbWg<~8J^cha^NkUGA_8lS%e z03rDV015yD04x9i000mG5C8xJGYGn3B`eA@hLP-?R$qmSrU`{13sA22v98jr}Nayd)@03rDV015yE04x9i000mG5C8xJG6=eICo4+lWEAii z^GtyZCU(PMTw+v|g@m~tXjwuj&&og$10Lx-AHyar8jr}NQj!1wA^8LV3IGKFEC2ui z01yBW0009s2)ZD`kU1CQbmzR;F zG?s-yM#V4`kYJm(0SyGgKoFZTfWp?!kpKW8`2+w800#gp000005C9MW00S}zy4?OS z%1NtbLe!io*B%H6!yt~waSjAAFsaJ0u22}lVHgKi+NA;@bz&HTo?8)+0027x00000 H00000^&r*D diff --git a/docs/_build/html/_static/alabaster.css b/docs/_build/html/_static/alabaster.css deleted file mode 100644 index 25e7738..0000000 --- a/docs/_build/html/_static/alabaster.css +++ /dev/null @@ -1,688 +0,0 @@ -@import url("basic.css"); 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team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - */ -jQuery.urldecode = function(x) { - return decodeURIComponent(x).replace(/\+/g, ' '); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var bbox = span.getBBox(); - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - var parentOfText = node.parentNode.parentNode; - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) { - this.initOnKeyListeners(); - } - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('

' + _('Hide Search Matches') + '

') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - $(document).keyup(function(event) { - var activeElementType = document.activeElement.tagName; - // don't navigate when in search box or textarea - if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT') { - switch (event.keyCode) { - case 37: // left - var prevHref = $('link[rel="prev"]').prop('href'); - if (prevHref) { - window.location.href = prevHref; - return false; - } - case 39: // right - var nextHref = $('link[rel="next"]').prop('href'); - if (nextHref) { - window.location.href = nextHref; - return false; - } - } - } - }); - } -}; - -// quick alias for translations -_ = Documentation.gettext; - -$(document).ready(function() { - Documentation.init(); -}); diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js deleted file mode 100644 index 168d437..0000000 --- a/docs/_build/html/_static/documentation_options.js +++ /dev/null @@ -1,296 +0,0 @@ -var DOCUMENTATION_OPTIONS = { - URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'), - VERSION: '', - LANGUAGE: 'None', - COLLAPSE_INDEX: false, - FILE_SUFFIX: '.html', - HAS_SOURCE: true, - SOURCELINK_SUFFIX: '.txt', - NAVIGATION_WITH_KEYS: false, - SEARCH_LANGUAGE_STOP_WORDS: ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"] -}; - - - -/* Non-minified version JS is _stemmer.js if file is provided */ -/** - * Porter Stemmer - */ -var Stemmer = function() { - - var step2list = { - ational: 'ate', - tional: 'tion', - enci: 'ence', - anci: 'ance', - izer: 'ize', - bli: 'ble', - alli: 'al', - entli: 'ent', - eli: 'e', - ousli: 'ous', - ization: 'ize', - ation: 'ate', - ator: 'ate', - alism: 'al', - iveness: 'ive', - fulness: 'ful', - ousness: 'ous', - aliti: 'al', - iviti: 'ive', - biliti: 'ble', - logi: 'log' - }; - - var step3list = { - icate: 'ic', - ative: '', - alize: 'al', - iciti: 'ic', - ical: 'ic', - ful: '', - ness: '' - }; - - var c = "[^aeiou]"; // consonant - var v = "[aeiouy]"; // vowel - var C = c + "[^aeiouy]*"; // consonant sequence - var V = v + "[aeiou]*"; // vowel sequence - - var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0 - var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1 - var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1 - var s_v = "^(" + C + ")?" + v; // vowel in stem - - this.stemWord = function (w) { - var stem; - var suffix; - var firstch; - var origword = w; - - if (w.length < 3) - return w; - - var re; - var re2; - var re3; - var re4; - - firstch = w.substr(0,1); - if (firstch == "y") - w = firstch.toUpperCase() + w.substr(1); - - // Step 1a - re = /^(.+?)(ss|i)es$/; - re2 = /^(.+?)([^s])s$/; - - if (re.test(w)) - w = w.replace(re,"$1$2"); - else if (re2.test(w)) - w = w.replace(re2,"$1$2"); - - // Step 1b - re = /^(.+?)eed$/; - re2 = /^(.+?)(ed|ing)$/; - if (re.test(w)) { - var fp = re.exec(w); - re = new RegExp(mgr0); - if (re.test(fp[1])) { - re = /.$/; - w = w.replace(re,""); - } - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1]; - re2 = new RegExp(s_v); - if (re2.test(stem)) { - w = stem; - re2 = /(at|bl|iz)$/; - re3 = new RegExp("([^aeiouylsz])\\1$"); - re4 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re2.test(w)) - w = w + "e"; - else if (re3.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - else if (re4.test(w)) - w = w + "e"; - } - } - - // Step 1c - re = /^(.+?)y$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(s_v); - if (re.test(stem)) - w = stem + "i"; - } - - // Step 2 - re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step2list[suffix]; - } - - // Step 3 - re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - suffix = fp[2]; - re = new RegExp(mgr0); - if (re.test(stem)) - w = stem + step3list[suffix]; - } - - // Step 4 - re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/; - re2 = /^(.+?)(s|t)(ion)$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - if (re.test(stem)) - w = stem; - } - else if (re2.test(w)) { - var fp = re2.exec(w); - stem = fp[1] + fp[2]; - re2 = new RegExp(mgr1); - if (re2.test(stem)) - w = stem; - } - - // Step 5 - re = /^(.+?)e$/; - if (re.test(w)) { - var fp = re.exec(w); - stem = fp[1]; - re = new RegExp(mgr1); - re2 = new RegExp(meq1); - re3 = new RegExp("^" + C + v + "[^aeiouwxy]$"); - if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) - w = stem; - } - re = /ll$/; - re2 = new RegExp(mgr1); - if (re.test(w) && re2.test(w)) { - re = /.$/; - w = w.replace(re,""); - } - - // and turn initial Y back to y - if (firstch == "y") - w = firstch.toLowerCase() + w.substr(1); - return w; - } -} - - - - - -var splitChars = (function() { - var result = {}; - var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648, - 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702, - 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971, - 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345, - 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761, - 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823, - 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125, - 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695, - 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587, - 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141]; - var i, j, start, end; - for (i = 0; i < singles.length; i++) { - result[singles[i]] = true; - } - var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709], - [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161], - [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568], - [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807], - [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047], - [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383], - [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450], - [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547], - [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673], - [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820], - [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946], - [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023], - [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173], - [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332], - [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481], - [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718], - [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791], - [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095], - [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205], - [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687], - [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968], - [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869], - [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102], - [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271], - [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592], - [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822], - [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167], - [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959], - [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143], - [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318], - [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483], - [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101], - [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567], - [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292], - [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444], - [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783], - [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311], - [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511], - [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774], - [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071], - [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263], - [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519], - [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647], - [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967], - [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295], - [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274], - [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007], - [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381], - [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]]; - for (i = 0; i < ranges.length; i++) { - start = ranges[i][0]; - end = ranges[i][1]; - for (j = start; j <= end; j++) { - result[j] = true; - } - } - return result; -})(); - -function splitQuery(query) { - var result = []; - var start = -1; - for (var i = 0; i < query.length; i++) { - if (splitChars[query.charCodeAt(i)]) { - if (start !== -1) { - result.push(query.slice(start, i)); - start = -1; - } - } else if (start === -1) { - start = i; - } - } - if 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