Python graphviz.Source方法代碼示例

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def visualize_tree(data,clf,clf_name):
	features = data.columns
	features = features[:-1]
	class_names = list(set(data.iloc[:,-1]))
	dot_data = tree.export_graphviz(clf, out_file=None,  \
		feature_names=features,class_names=class_names,  \
		filled=True, rounded=True, special_characters=True)
	graph = graphviz.Source(dot_data)
	graph.render('dtree_render_'+clf_name,view=True)

# Function to perform training with giniIndex. 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def dump_graph(dot_string,
               show_svg=True,
               dot_file_path='',
               output_dot_string=False):
    """Output dot_string in various formats."""
    if dot_file_path:
        try:
            dot_file = open(dot_file_path, "w+")
            dot_file.write(dot_string)
            dot_file.close()
        except IOError:
            print('Cannot open file: ' + dot_file_path)
    if show_svg:
        from IPython.display import display
        from IPython.display import SVG
        src = Source(dot_string)
        display(SVG(src.pipe(format='svg')))
    if output_dot_string:
        return dot_string
    return None 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def graphviz(expr):
    '''Plot sympy expression tree using graphviz
    Args:
        expr (sympy expression)
    '''

    return Source(dotprint(expr)) 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def plot_tree(dot_data):
    return graphviz.Source(dot_data) 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def visualize(self, graph_attrs=None, node_attrs=None, **kw):
        from graphviz import Source

        g = self.build_graph(**kw)
        dot = g.to_dot(graph_attrs=graph_attrs, node_attrs=node_attrs,
                       result_chunk_keys={c.key for c in self.chunks})

        return Source(dot) 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def get_svg(self, dot_str):
        import graphviz as gv

        return gv.Source(dot_str, format='svg', engine=self.engine).pipe().decode('utf-8') 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def visualize_tree(data,clf,clf_name):
	features = data.columns
	features = features[:-1]
	class_names = list(set(data.iloc[:,-1]))
	dot_data = tree.export_graphviz(clf, out_file=None,  feature_names=features,class_names=class_names,  filled=True, rounded=True, special_characters=True)
	graph = graphviz.Source(dot_data)
	graph.render('dtree_render_'+clf_name,view=True)

# Function to perform training with giniIndex. 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def load(self):
        """Load TensorFlow model into MIL program."""

        logging.info("Loading TensorFlow model '{}'".format(self.model))
        outputs = self.kwargs.get("outputs", None)
        self._graph_def = self._graph_def_from_model(outputs)

        if self._graph_def is not None and len(self._graph_def.node) == 0:
            msg = "tf.Graph should have at least 1 node, Got empty graph."
            raise ValueError(msg)

        self._tf_ssa = self._tf_ssa_from_graph_def()

        del self._graph_def
        gc.collect()

        if self.debug:
            import graphviz

            dot_string = self._tf_ssa.get_dot_string(
                annotation=True, name_and_op_style=True, highlight_debug_nodes=[]
            )
            graphviz.Source(dot_string).view(
                filename="/tmp/ssa_before_tf_passes", cleanup=True
            )

        program = self._program_from_tf_ssa()
        logging.debug("program:\n{}".format(program))
        return program

    # @abstractmethod 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def _program_from_tf_ssa(self):
        """Overwrites TFLoader._mil_program_from_tf_ssa()"""
        # Applying frontend passes on TFSSA. Note that these are different from
        # passes applied to MIL in TF frontend.
        tf_passes = [
            delete_asserts,
            functionalize_loops,
            constant_propagation,
            cond_to_where,
            remove_variable_nodes,
            fuse_dilation_conv,
        ]

        if self.debug:
            for tf_pass in _tqdm(
                tf_passes, desc="Running TensorFlow Graph Passes", unit=" passes"
            ):
                try:
                    tf_pass(self._tf_ssa)
                except Exception as e:
                    logging.exception('Exception in pass "{}": {}'.format(tf_pass, e))
                    logging.info("Ignoring exception and continuing to next pass")
        else:
            for tf_pass in _tqdm(
                tf_passes, desc="Running TensorFlow Graph Passes", unit=" passes"
            ):
                tf_pass(self._tf_ssa)

        if self.debug:
            import graphviz

            dot_string = self._tf_ssa.get_dot_string(
                annotation=True, name_and_op_style=True, highlight_debug_nodes=[]
            )
            graphviz.Source(dot_string).view(
                filename="/tmp/ssa_after_tf_passes", cleanup=True
            )

        converter = TFConverter(self._tf_ssa, **self.kwargs)
        return converter.convert() 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def draw_text_graph(
        words_to_machines, out_dir, fn='text', orig_machines=[]):
    graph = MachineGraph.create_from_machines(
        words_to_machines.values(), orig_machines=orig_machines)
    src_str = graph.to_dot().encode('utf-8')
    src = graphviz.Source(src_str, format='png')
    pic_path = src.render(filename=fn, directory=out_dir)
    return pic_path 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def draw_dep_graph(deps, out_dir, fn):
    dot_str = dep_to_dot(deps)
    src = graphviz.Source(dot_str, format='png')
    pic_path = src.render(filename=fn, directory=out_dir)
    return pic_path 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def dot_to_img(dot, file = 'report.png'):
    import os

    name, ext = os.path.splitext(file)

    graph = graphviz.Source(dot)
    graph.format = ext[1:]
    graph.view(name, cleanup = True) 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def graph(self, format='svg'):
        try:
            from graphviz import Source
        except ImportError:
            raise ImportError("You don't have graphviz package installed.\n"
                              "Please install graphviz or use write_file function to save dot file and generate the "
                              "graph manually.")
        dot = self.generate()
        graph = Source(dot)
        graph.format = format
        return graph 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def plot_model(model, output, fig_name, fig_format):
    """
    Plot neural network model architecture
    :param model: network model
    :param output: network output variable
    :param fig_name: figure output name
    :param format: figure format, such as "png", "pdf"
    :return: dot file string
    """
    var = make_dot(output, params=dict(model.named_parameters()))
    s = Source(var, filename=fig_name, format=fig_format)
    s.view()
    return var 

# 需要導入模塊: import graphviz [as 別名]
# 或者: from graphviz import Source [as 別名]
def view_dot_graph(graph, filename=None, view=False):
    """
    View the given DOT source.  If view is True, the image is rendered
    and viewed by the default application in the system.  The file path of
    the output is returned.  If view is False, a graphviz.Source object is
    returned.  If view is False and the environment is in a IPython session,
    an IPython image object is returned and can be displayed inline in the
    notebook.

    This function requires the graphviz package.

    Args
    ----
    - graph [str]: a DOT source code
    - filename [str]: optional.  if given and view is True, this specifies
                      the file path for the rendered output to write to.
    - view [bool]: if True, opens the rendered output file.

    """
    # Optionally depends on graphviz package
    import graphviz as gv

    src = gv.Source(graph)
    if view:
        # Returns the output file path
        return src.render(filename, view=view)
    else:
        # Attempts to show the graph in IPython notebook
        try:
            __IPYTHON__
        except NameError:
            return src
        else:
            import IPython.display as display
            format = 'svg'
            return display.SVG(data=src.pipe(format))


# Ctypes binding