docs/graph/preprocessor.html

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<header>
<h1 class="title">Module <code>ktrain.graph.preprocessor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from .. import utils as U
from ..imports import *
from ..preprocessor import Preprocessor


class NodePreprocessor(Preprocessor):
    &#34;&#34;&#34;
    ```
    Node preprocessing base class
    ```
    &#34;&#34;&#34;

    def __init__(self, G_nx, df, sample_size=10, missing_label_value=None):

        self.sampsize = sample_size  # neighbor sample size
        self.df = df  # node attributes and targets
        # TODO: eliminate storage redundancy
        self.G = G_nx  # networkx graph
        self.G_sg = None  # StellarGraph

        # clean df
        df.index = df.index.map(str)
        df = df[df.index.isin(list(self.G.nodes()))]

        # class names
        self.c = list(set([c[0] for c in df[[&#34;target&#34;]].values]))
        if missing_label_value is not None:
            self.c.remove(missing_label_value)
        self.c.sort()

        # feature names + target
        self.colnames = list(df.columns.values)
        if self.colnames[-1] != &#34;target&#34;:
            raise ValueError(&#39;last column of df must be &#34;target&#34;&#39;)

        # set by preprocess_train
        self.y_encoding = None

    def get_preprocessor(self):
        return (self.G, self.df)

    def get_classes(self):
        return self.c

    @property
    def feature_names(self):
        return self.colnames[:-1]

    def preprocess(self, df, G):
        return self.preprocess_test(df, G)

    def ids_exist(self, node_ids):
        &#34;&#34;&#34;
        ```
        check validity of node IDs
        ```
        &#34;&#34;&#34;
        df = self.df[self.df.index.isin(node_ids)]
        return df.shape[0] &gt; 0

    def preprocess_train(self, node_ids):
        &#34;&#34;&#34;
        ```
        preprocess training set
        ```
        &#34;&#34;&#34;
        if not self.ids_exist(node_ids):
            raise ValueError(&#34;node_ids must exist in self.df&#34;)

        # subset df for training nodes
        df_tr = self.df[self.df.index.isin(node_ids)]

        # one-hot-encode target
        self.y_encoding = sklearn.feature_extraction.DictVectorizer(sparse=False)
        train_targets = self.y_encoding.fit_transform(
            df_tr[[&#34;target&#34;]].to_dict(&#34;records&#34;)
        )

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        G_sg = sg.StellarGraph(self.G, node_features=self.df[self.feature_names])
        self.G_sg = G_sg
        generator = GraphSAGENodeGenerator(
            G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        train_gen = generator.flow(df_tr.index, train_targets, shuffle=True)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(train_gen)

    def preprocess_valid(self, node_ids):
        &#34;&#34;&#34;
        ```
        preprocess validation nodes (transductive inference)
        node_ids (list):  list of node IDs that generator will yield
        ```
        &#34;&#34;&#34;
        if not self.ids_exist(node_ids):
            raise ValueError(&#34;node_ids must exist in self.df&#34;)
        if self.y_encoding is None:
            raise Exception(
                &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
            )

        # subset df for validation nodes
        df_val = self.df[self.df.index.isin(node_ids)]

        # one-hot-encode target
        val_targets = self.y_encoding.transform(df_val[[&#34;target&#34;]].to_dict(&#34;records&#34;))

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        if self.G_sg is None:
            self.G_sg = sg.StellarGraph(
                self.G, node_features=self.df[self.feature_names]
            )
        generator = GraphSAGENodeGenerator(
            self.G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        val_gen = generator.flow(df_val.index, val_targets, shuffle=False)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(val_gen)

    def preprocess_test(self, df_te, G_te):
        &#34;&#34;&#34;
        ```
        preprocess for inductive inference
        df_te (DataFrame): pandas dataframe containing new node attributes
        G_te (Graph):  a networkx Graph containing new nodes
        ```
        &#34;&#34;&#34;
        try:
            import networkx as nx
        except ImportError:
            raise ImportError(&#34;Please install networkx:  pip install networkx&#34;)

        if self.y_encoding is None:
            raise Exception(
                &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
            )

        # get aggregrated df
        # df_agg = pd.concat([df_te, self.df]).drop_duplicates(keep=&#39;last&#39;)
        df_agg = pd.concat([df_te, self.df])
        # df_te = pd.concat([self.df, df_agg]).drop_duplicates(keep=False)

        # get aggregrated graph
        is_subset = set(self.G.nodes()) &lt;= set(G_te.nodes())
        if not is_subset:
            raise ValueError(&#34;Nodes in self.G must be subset of G_te&#34;)
        G_agg = nx.compose(self.G, G_te)

        # one-hot-encode target
        if &#34;target&#34; in df_te.columns:
            test_targets = self.y_encoding.transform(
                df_te[[&#34;target&#34;]].to_dict(&#34;records&#34;)
            )
        else:
            test_targets = [-1] * len(df_te.shape[0])

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        G_sg = sg.StellarGraph(G_agg, node_features=df_agg[self.feature_names])
        generator = GraphSAGENodeGenerator(
            G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        test_gen = generator.flow(df_te.index, test_targets, shuffle=False)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(test_gen)


class LinkPreprocessor(Preprocessor):
    &#34;&#34;&#34;
    ```
    Link preprocessing base class
    ```
    &#34;&#34;&#34;

    def __init__(self, G, sample_sizes=[10, 20]):
        self.sample_sizes = sample_sizes
        self.G = G  # original graph under consideration with all original links

        # class names
        self.c = [&#34;negative&#34;, &#34;positive&#34;]

    def get_preprocessor(self):
        return self

    def get_classes(self):
        return self.c

    def preprocess(self, G, edge_ids):
        edge_labels = [1] * len(edge_ids)
        return self.preprocess_valid(G, edge_ids, edge_labels)

    def preprocess_train(self, G, edge_ids, edge_labels, mode=&#34;train&#34;):
        &#34;&#34;&#34;
        ```
        preprocess training set
        Args:
          G (networkx graph): networkx graph
          edge_ids(list): list of tuples representing edge ids
          edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
        ```
        &#34;&#34;&#34;
        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGELinkGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # edge_labels = to_categorical(edge_labels)
        G_sg = sg.StellarGraph(G, node_features=&#34;feature&#34;)
        # print(G_sg.info())
        shuffle = True if mode == &#34;train&#34; else False
        link_seq = GraphSAGELinkGenerator(G_sg, U.DEFAULT_BS, self.sample_sizes).flow(
            edge_ids, edge_labels, shuffle=shuffle
        )
        from .sg_wrappers import LinkSequenceWrapper

        return LinkSequenceWrapper(link_seq)

    def preprocess_valid(self, G, edge_ids, edge_labels):
        &#34;&#34;&#34;
        ```
        preprocess training set
        Args:
          G (networkx graph): networkx graph
          edge_ids(list): list of tuples representing edge ids
          edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
        ```
        &#34;&#34;&#34;
        return self.preprocess_train(G, edge_ids, edge_labels, mode=&#34;valid&#34;)</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor"><code class="flex name class">
<span>class <span class="ident">LinkPreprocessor</span></span>
<span>(</span><span>G, sample_sizes=[10, 20])</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Link preprocessing base class
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class LinkPreprocessor(Preprocessor):
    &#34;&#34;&#34;
    ```
    Link preprocessing base class
    ```
    &#34;&#34;&#34;

    def __init__(self, G, sample_sizes=[10, 20]):
        self.sample_sizes = sample_sizes
        self.G = G  # original graph under consideration with all original links

        # class names
        self.c = [&#34;negative&#34;, &#34;positive&#34;]

    def get_preprocessor(self):
        return self

    def get_classes(self):
        return self.c

    def preprocess(self, G, edge_ids):
        edge_labels = [1] * len(edge_ids)
        return self.preprocess_valid(G, edge_ids, edge_labels)

    def preprocess_train(self, G, edge_ids, edge_labels, mode=&#34;train&#34;):
        &#34;&#34;&#34;
        ```
        preprocess training set
        Args:
          G (networkx graph): networkx graph
          edge_ids(list): list of tuples representing edge ids
          edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
        ```
        &#34;&#34;&#34;
        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGELinkGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # edge_labels = to_categorical(edge_labels)
        G_sg = sg.StellarGraph(G, node_features=&#34;feature&#34;)
        # print(G_sg.info())
        shuffle = True if mode == &#34;train&#34; else False
        link_seq = GraphSAGELinkGenerator(G_sg, U.DEFAULT_BS, self.sample_sizes).flow(
            edge_ids, edge_labels, shuffle=shuffle
        )
        from .sg_wrappers import LinkSequenceWrapper

        return LinkSequenceWrapper(link_seq)

    def preprocess_valid(self, G, edge_ids, edge_labels):
        &#34;&#34;&#34;
        ```
        preprocess training set
        Args:
          G (networkx graph): networkx graph
          edge_ids(list): list of tuples representing edge ids
          edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
        ```
        &#34;&#34;&#34;
        return self.preprocess_train(G, edge_ids, edge_labels, mode=&#34;valid&#34;)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.preprocessor.Preprocessor" href="../preprocessor.html#ktrain.preprocessor.Preprocessor">Preprocessor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_classes(self):
    return self.c</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor.get_preprocessor"><code class="name flex">
<span>def <span class="ident">get_preprocessor</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_preprocessor(self):
    return self</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor.preprocess"><code class="name flex">
<span>def <span class="ident">preprocess</span></span>(<span>self, G, edge_ids)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess(self, G, edge_ids):
    edge_labels = [1] * len(edge_ids)
    return self.preprocess_valid(G, edge_ids, edge_labels)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor.preprocess_train"><code class="name flex">
<span>def <span class="ident">preprocess_train</span></span>(<span>self, G, edge_ids, edge_labels, mode='train')</span>
</code></dt>
<dd>
<div class="desc"><pre><code>preprocess training set
Args:
  G (networkx graph): networkx graph
  edge_ids(list): list of tuples representing edge ids
  edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_train(self, G, edge_ids, edge_labels, mode=&#34;train&#34;):
    &#34;&#34;&#34;
    ```
    preprocess training set
    Args:
      G (networkx graph): networkx graph
      edge_ids(list): list of tuples representing edge ids
      edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
    ```
    &#34;&#34;&#34;
    # import stellargraph
    try:
        import stellargraph as sg
        from stellargraph.mapper import GraphSAGELinkGenerator
    except:
        raise Exception(SG_ERRMSG)
    if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
        raise Exception(SG_ERRMSG)

    # edge_labels = to_categorical(edge_labels)
    G_sg = sg.StellarGraph(G, node_features=&#34;feature&#34;)
    # print(G_sg.info())
    shuffle = True if mode == &#34;train&#34; else False
    link_seq = GraphSAGELinkGenerator(G_sg, U.DEFAULT_BS, self.sample_sizes).flow(
        edge_ids, edge_labels, shuffle=shuffle
    )
    from .sg_wrappers import LinkSequenceWrapper

    return LinkSequenceWrapper(link_seq)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.LinkPreprocessor.preprocess_valid"><code class="name flex">
<span>def <span class="ident">preprocess_valid</span></span>(<span>self, G, edge_ids, edge_labels)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>preprocess training set
Args:
  G (networkx graph): networkx graph
  edge_ids(list): list of tuples representing edge ids
  edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_valid(self, G, edge_ids, edge_labels):
    &#34;&#34;&#34;
    ```
    preprocess training set
    Args:
      G (networkx graph): networkx graph
      edge_ids(list): list of tuples representing edge ids
      edge_labels(list): edge labels (1 or 0 to indicated whether it is a true edge in original graph or not)
    ```
    &#34;&#34;&#34;
    return self.preprocess_train(G, edge_ids, edge_labels, mode=&#34;valid&#34;)</code></pre>
</details>
</dd>
</dl>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor"><code class="flex name class">
<span>class <span class="ident">NodePreprocessor</span></span>
<span>(</span><span>G_nx, df, sample_size=10, missing_label_value=None)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Node preprocessing base class
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class NodePreprocessor(Preprocessor):
    &#34;&#34;&#34;
    ```
    Node preprocessing base class
    ```
    &#34;&#34;&#34;

    def __init__(self, G_nx, df, sample_size=10, missing_label_value=None):

        self.sampsize = sample_size  # neighbor sample size
        self.df = df  # node attributes and targets
        # TODO: eliminate storage redundancy
        self.G = G_nx  # networkx graph
        self.G_sg = None  # StellarGraph

        # clean df
        df.index = df.index.map(str)
        df = df[df.index.isin(list(self.G.nodes()))]

        # class names
        self.c = list(set([c[0] for c in df[[&#34;target&#34;]].values]))
        if missing_label_value is not None:
            self.c.remove(missing_label_value)
        self.c.sort()

        # feature names + target
        self.colnames = list(df.columns.values)
        if self.colnames[-1] != &#34;target&#34;:
            raise ValueError(&#39;last column of df must be &#34;target&#34;&#39;)

        # set by preprocess_train
        self.y_encoding = None

    def get_preprocessor(self):
        return (self.G, self.df)

    def get_classes(self):
        return self.c

    @property
    def feature_names(self):
        return self.colnames[:-1]

    def preprocess(self, df, G):
        return self.preprocess_test(df, G)

    def ids_exist(self, node_ids):
        &#34;&#34;&#34;
        ```
        check validity of node IDs
        ```
        &#34;&#34;&#34;
        df = self.df[self.df.index.isin(node_ids)]
        return df.shape[0] &gt; 0

    def preprocess_train(self, node_ids):
        &#34;&#34;&#34;
        ```
        preprocess training set
        ```
        &#34;&#34;&#34;
        if not self.ids_exist(node_ids):
            raise ValueError(&#34;node_ids must exist in self.df&#34;)

        # subset df for training nodes
        df_tr = self.df[self.df.index.isin(node_ids)]

        # one-hot-encode target
        self.y_encoding = sklearn.feature_extraction.DictVectorizer(sparse=False)
        train_targets = self.y_encoding.fit_transform(
            df_tr[[&#34;target&#34;]].to_dict(&#34;records&#34;)
        )

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        G_sg = sg.StellarGraph(self.G, node_features=self.df[self.feature_names])
        self.G_sg = G_sg
        generator = GraphSAGENodeGenerator(
            G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        train_gen = generator.flow(df_tr.index, train_targets, shuffle=True)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(train_gen)

    def preprocess_valid(self, node_ids):
        &#34;&#34;&#34;
        ```
        preprocess validation nodes (transductive inference)
        node_ids (list):  list of node IDs that generator will yield
        ```
        &#34;&#34;&#34;
        if not self.ids_exist(node_ids):
            raise ValueError(&#34;node_ids must exist in self.df&#34;)
        if self.y_encoding is None:
            raise Exception(
                &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
            )

        # subset df for validation nodes
        df_val = self.df[self.df.index.isin(node_ids)]

        # one-hot-encode target
        val_targets = self.y_encoding.transform(df_val[[&#34;target&#34;]].to_dict(&#34;records&#34;))

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        if self.G_sg is None:
            self.G_sg = sg.StellarGraph(
                self.G, node_features=self.df[self.feature_names]
            )
        generator = GraphSAGENodeGenerator(
            self.G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        val_gen = generator.flow(df_val.index, val_targets, shuffle=False)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(val_gen)

    def preprocess_test(self, df_te, G_te):
        &#34;&#34;&#34;
        ```
        preprocess for inductive inference
        df_te (DataFrame): pandas dataframe containing new node attributes
        G_te (Graph):  a networkx Graph containing new nodes
        ```
        &#34;&#34;&#34;
        try:
            import networkx as nx
        except ImportError:
            raise ImportError(&#34;Please install networkx:  pip install networkx&#34;)

        if self.y_encoding is None:
            raise Exception(
                &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
            )

        # get aggregrated df
        # df_agg = pd.concat([df_te, self.df]).drop_duplicates(keep=&#39;last&#39;)
        df_agg = pd.concat([df_te, self.df])
        # df_te = pd.concat([self.df, df_agg]).drop_duplicates(keep=False)

        # get aggregrated graph
        is_subset = set(self.G.nodes()) &lt;= set(G_te.nodes())
        if not is_subset:
            raise ValueError(&#34;Nodes in self.G must be subset of G_te&#34;)
        G_agg = nx.compose(self.G, G_te)

        # one-hot-encode target
        if &#34;target&#34; in df_te.columns:
            test_targets = self.y_encoding.transform(
                df_te[[&#34;target&#34;]].to_dict(&#34;records&#34;)
            )
        else:
            test_targets = [-1] * len(df_te.shape[0])

        # import stellargraph
        try:
            import stellargraph as sg
            from stellargraph.mapper import GraphSAGENodeGenerator
        except:
            raise Exception(SG_ERRMSG)
        if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
            raise Exception(SG_ERRMSG)

        # return generator
        G_sg = sg.StellarGraph(G_agg, node_features=df_agg[self.feature_names])
        generator = GraphSAGENodeGenerator(
            G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
        )
        test_gen = generator.flow(df_te.index, test_targets, shuffle=False)
        from .sg_wrappers import NodeSequenceWrapper

        return NodeSequenceWrapper(test_gen)</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li><a title="ktrain.preprocessor.Preprocessor" href="../preprocessor.html#ktrain.preprocessor.Preprocessor">Preprocessor</a></li>
<li>abc.ABC</li>
</ul>
<h3>Instance variables</h3>
<dl>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.feature_names"><code class="name">var <span class="ident">feature_names</span></code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@property
def feature_names(self):
    return self.colnames[:-1]</code></pre>
</details>
</dd>
</dl>
<h3>Methods</h3>
<dl>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_classes(self):
    return self.c</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.get_preprocessor"><code class="name flex">
<span>def <span class="ident">get_preprocessor</span></span>(<span>self)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_preprocessor(self):
    return (self.G, self.df)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.ids_exist"><code class="name flex">
<span>def <span class="ident">ids_exist</span></span>(<span>self, node_ids)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>check validity of node IDs
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def ids_exist(self, node_ids):
    &#34;&#34;&#34;
    ```
    check validity of node IDs
    ```
    &#34;&#34;&#34;
    df = self.df[self.df.index.isin(node_ids)]
    return df.shape[0] &gt; 0</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.preprocess"><code class="name flex">
<span>def <span class="ident">preprocess</span></span>(<span>self, df, G)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess(self, df, G):
    return self.preprocess_test(df, G)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.preprocess_test"><code class="name flex">
<span>def <span class="ident">preprocess_test</span></span>(<span>self, df_te, G_te)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>preprocess for inductive inference
df_te (DataFrame): pandas dataframe containing new node attributes
G_te (Graph):  a networkx Graph containing new nodes
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_test(self, df_te, G_te):
    &#34;&#34;&#34;
    ```
    preprocess for inductive inference
    df_te (DataFrame): pandas dataframe containing new node attributes
    G_te (Graph):  a networkx Graph containing new nodes
    ```
    &#34;&#34;&#34;
    try:
        import networkx as nx
    except ImportError:
        raise ImportError(&#34;Please install networkx:  pip install networkx&#34;)

    if self.y_encoding is None:
        raise Exception(
            &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
        )

    # get aggregrated df
    # df_agg = pd.concat([df_te, self.df]).drop_duplicates(keep=&#39;last&#39;)
    df_agg = pd.concat([df_te, self.df])
    # df_te = pd.concat([self.df, df_agg]).drop_duplicates(keep=False)

    # get aggregrated graph
    is_subset = set(self.G.nodes()) &lt;= set(G_te.nodes())
    if not is_subset:
        raise ValueError(&#34;Nodes in self.G must be subset of G_te&#34;)
    G_agg = nx.compose(self.G, G_te)

    # one-hot-encode target
    if &#34;target&#34; in df_te.columns:
        test_targets = self.y_encoding.transform(
            df_te[[&#34;target&#34;]].to_dict(&#34;records&#34;)
        )
    else:
        test_targets = [-1] * len(df_te.shape[0])

    # import stellargraph
    try:
        import stellargraph as sg
        from stellargraph.mapper import GraphSAGENodeGenerator
    except:
        raise Exception(SG_ERRMSG)
    if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
        raise Exception(SG_ERRMSG)

    # return generator
    G_sg = sg.StellarGraph(G_agg, node_features=df_agg[self.feature_names])
    generator = GraphSAGENodeGenerator(
        G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
    )
    test_gen = generator.flow(df_te.index, test_targets, shuffle=False)
    from .sg_wrappers import NodeSequenceWrapper

    return NodeSequenceWrapper(test_gen)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.preprocess_train"><code class="name flex">
<span>def <span class="ident">preprocess_train</span></span>(<span>self, node_ids)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>preprocess training set
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_train(self, node_ids):
    &#34;&#34;&#34;
    ```
    preprocess training set
    ```
    &#34;&#34;&#34;
    if not self.ids_exist(node_ids):
        raise ValueError(&#34;node_ids must exist in self.df&#34;)

    # subset df for training nodes
    df_tr = self.df[self.df.index.isin(node_ids)]

    # one-hot-encode target
    self.y_encoding = sklearn.feature_extraction.DictVectorizer(sparse=False)
    train_targets = self.y_encoding.fit_transform(
        df_tr[[&#34;target&#34;]].to_dict(&#34;records&#34;)
    )

    # import stellargraph
    try:
        import stellargraph as sg
        from stellargraph.mapper import GraphSAGENodeGenerator
    except:
        raise Exception(SG_ERRMSG)
    if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
        raise Exception(SG_ERRMSG)

    # return generator
    G_sg = sg.StellarGraph(self.G, node_features=self.df[self.feature_names])
    self.G_sg = G_sg
    generator = GraphSAGENodeGenerator(
        G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
    )
    train_gen = generator.flow(df_tr.index, train_targets, shuffle=True)
    from .sg_wrappers import NodeSequenceWrapper

    return NodeSequenceWrapper(train_gen)</code></pre>
</details>
</dd>
<dt id="ktrain.graph.preprocessor.NodePreprocessor.preprocess_valid"><code class="name flex">
<span>def <span class="ident">preprocess_valid</span></span>(<span>self, node_ids)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>preprocess validation nodes (transductive inference)
node_ids (list):  list of node IDs that generator will yield
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def preprocess_valid(self, node_ids):
    &#34;&#34;&#34;
    ```
    preprocess validation nodes (transductive inference)
    node_ids (list):  list of node IDs that generator will yield
    ```
    &#34;&#34;&#34;
    if not self.ids_exist(node_ids):
        raise ValueError(&#34;node_ids must exist in self.df&#34;)
    if self.y_encoding is None:
        raise Exception(
            &#34;Unset parameters. Are you sure you called preprocess_train first?&#34;
        )

    # subset df for validation nodes
    df_val = self.df[self.df.index.isin(node_ids)]

    # one-hot-encode target
    val_targets = self.y_encoding.transform(df_val[[&#34;target&#34;]].to_dict(&#34;records&#34;))

    # import stellargraph
    try:
        import stellargraph as sg
        from stellargraph.mapper import GraphSAGENodeGenerator
    except:
        raise Exception(SG_ERRMSG)
    if version.parse(sg.__version__) &lt; version.parse(&#34;0.8&#34;):
        raise Exception(SG_ERRMSG)

    # return generator
    if self.G_sg is None:
        self.G_sg = sg.StellarGraph(
            self.G, node_features=self.df[self.feature_names]
        )
    generator = GraphSAGENodeGenerator(
        self.G_sg, U.DEFAULT_BS, [self.sampsize, self.sampsize]
    )
    val_gen = generator.flow(df_val.index, val_targets, shuffle=False)
    from .sg_wrappers import NodeSequenceWrapper

    return NodeSequenceWrapper(val_gen)</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain.graph" href="index.html">ktrain.graph</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.graph.preprocessor.LinkPreprocessor" href="#ktrain.graph.preprocessor.LinkPreprocessor">LinkPreprocessor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.graph.preprocessor.LinkPreprocessor.get_classes" href="#ktrain.graph.preprocessor.LinkPreprocessor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.LinkPreprocessor.get_preprocessor" href="#ktrain.graph.preprocessor.LinkPreprocessor.get_preprocessor">get_preprocessor</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.LinkPreprocessor.preprocess" href="#ktrain.graph.preprocessor.LinkPreprocessor.preprocess">preprocess</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.LinkPreprocessor.preprocess_train" href="#ktrain.graph.preprocessor.LinkPreprocessor.preprocess_train">preprocess_train</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.LinkPreprocessor.preprocess_valid" href="#ktrain.graph.preprocessor.LinkPreprocessor.preprocess_valid">preprocess_valid</a></code></li>
</ul>
</li>
<li>
<h4><code><a title="ktrain.graph.preprocessor.NodePreprocessor" href="#ktrain.graph.preprocessor.NodePreprocessor">NodePreprocessor</a></code></h4>
<ul class="two-column">
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.feature_names" href="#ktrain.graph.preprocessor.NodePreprocessor.feature_names">feature_names</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.get_classes" href="#ktrain.graph.preprocessor.NodePreprocessor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.get_preprocessor" href="#ktrain.graph.preprocessor.NodePreprocessor.get_preprocessor">get_preprocessor</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.ids_exist" href="#ktrain.graph.preprocessor.NodePreprocessor.ids_exist">ids_exist</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.preprocess" href="#ktrain.graph.preprocessor.NodePreprocessor.preprocess">preprocess</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.preprocess_test" href="#ktrain.graph.preprocessor.NodePreprocessor.preprocess_test">preprocess_test</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.preprocess_train" href="#ktrain.graph.preprocessor.NodePreprocessor.preprocess_train">preprocess_train</a></code></li>
<li><code><a title="ktrain.graph.preprocessor.NodePreprocessor.preprocess_valid" href="#ktrain.graph.preprocessor.NodePreprocessor.preprocess_valid">preprocess_valid</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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