Refactor AbstractImporter constructor to accept concatenated_samples dataframe/matrix

PyCTBN/classes/abstract_importer.py

@@ -2,39 +2,43 @@
 import typing
 from abc import ABC, abstractmethod
 
+import numpy as np
 import pandas as pd
 
 
 class AbstractImporter(ABC):
     """Abstract class that exposes all the necessary methods to process the trajectories and the net structure.
 
-    :param file_path: the file path
+    :param file_path: the file path, or dataset name if you import already processed data
     :type file_path: str
-    :_concatenated_samples: Dataframe containing the concatenation of all the processed trajectories
+    :param concatenated_samples: Dataframe or numpy array containing the concatenation of all the processed trajectories
-    :_df_structure: Dataframe containing the structure of the network (edges)
+    :type concatenated_samples: typing.Union[pandas.DataFrame, numpy.ndarray]
-    :_df_variables: Dataframe containing the nodes cardinalities
+    :param variables: Dataframe containing the nodes labels and cardinalities
-    :_sorter: A list containing the columns header (excluding the time column) of the ``_concatenated_samples``
+    :type variables: pandas.DataFrame
+    :prior_net_structure: Dataframe containing the structure of the network (edges)
+    :type prior_net_structure: pandas.DataFrame
+    :_sorter: A list containing the variables labels in the SAME order as the columns in ``concatenated_samples``
 
     .. warning::
-        The class members ``_df_variables`` and ``_df_structure`` HAVE to be properly constructed
+        The parameters ``variables`` and ``prior_net_structure`` HAVE to be properly constructed
         as Pandas Dataframes with the following structure:
         Header of _df_structure = [From_Node | To_Node]
         Header of _df_variables = [Variable_Label | Variable_Cardinality]
+        See the tutorial on how to construct a correct ``concatenated_samples`` Dataframe/ndarray.
 
-    .. note::
-        If you don't have prior network structure just leave ``_df_structure`` set to None.
     .. note::
         See :class:``JsonImporter`` for an example implementation
 
     """
 
-    def __init__(self, file_path: str):
+    def __init__(self, file_path: str = None, concatenated_samples: typing.Union[pd.DataFrame, np.ndarray] = None,
+                 variables: pd.DataFrame = None, prior_net_structure: pd.DataFrame = None):
         """Constructor
         """
         self._file_path = file_path
-        self._df_variables = None
+        self._concatenated_samples = concatenated_samples
-        self._df_structure = None
+        self._df_variables = variables
-        self._concatenated_samples = None
+        self._df_structure = prior_net_structure
         self._sorter = None
         super().__init__()
 
@@ -104,21 +108,27 @@ class AbstractImporter(ABC):
         complete_header.extend(shifted_cols_header)
         self._concatenated_samples = self._concatenated_samples[complete_header]
 
-    def build_list_of_samples_array(self, data_frame: pd.DataFrame) -> typing.List:
+    def build_list_of_samples_array(self, concatenated_sample: typing.Union[pd.DataFrame, np.ndarray]) -> typing.List:
-        """Builds a List containing the columns of data_frame and converts them to a numpy array.
+        """Builds a List containing the the delta times numpy array, and the complete transitions matrix
 
-        :param data_frame: the dataframe from which the columns have to be extracted and converted
+        :param concatenated_sample: the dataframe/array from which the time, and transitions matrix have to be extracted
-        :type data_frame: pandas.Dataframe
+            and converted
+        :type concatenated_sample: typing.Union[pandas.Dataframe, numpy.ndarray]
         :return: the resulting list of numpy arrays
         :rtype: List
         """
-        columns_list = [data_frame[column].to_numpy() for column in data_frame]
+        if isinstance(concatenated_sample, pd.DataFrame):
+            concatenated_array = concatenated_sample.to_numpy()
+            columns_list = [concatenated_array[:, 0], concatenated_array[:, 1:].astype(int)]
+        else:
+            columns_list = [concatenated_sample[:, 0], concatenated_sample[:, 1:].astype(int)]
         return columns_list
 
     def clear_concatenated_frame(self) -> None:
         """Removes all values in the dataframe concatenated_samples.
          """
-        self._concatenated_samples = self._concatenated_samples.iloc[0:0]
+        if isinstance(self._concatenated_samples, pd.DataFrame):
+            self._concatenated_samples = self._concatenated_samples.iloc[0:0]
 
     @abstractmethod
     def dataset_id(self) -> object:

PyCTBN/classes/sample_path.py

@@ -1,4 +1,7 @@
 
+import numpy as np
+import pandas as pd
+
 from .abstract_importer import AbstractImporter
 from .structure import Structure
 from .trajectory import Trajectory
@@ -19,10 +22,16 @@ class SamplePath(object):
         """Constructor Method
         """
         self._importer = importer
-        if (self._importer._df_variables is None or self._importer._concatenated_samples is None):
+        if self._importer._df_variables is None or self._importer._concatenated_samples is None:
             raise RuntimeError('The importer object has to contain the all processed data!')
-        if(self._importer._df_variables.empty or self._importer._concatenated_samples.empty):
+        if self._importer._df_variables.empty:
             raise RuntimeError('The importer object has to contain the all processed data!')
+        if isinstance(self._importer._concatenated_samples, pd.DataFrame):
+            if self._importer._concatenated_samples.empty:
+                raise RuntimeError('The importer object has to contain the all processed data!')
+        if isinstance(self._importer._concatenated_samples, np.ndarray):
+            if self._importer._concatenated_samples.size == 0:
+                raise RuntimeError('The importer object has to contain the all processed data!')
         self._trajectories = None
         self._structure = None
         self._total_variables_count = None

PyCTBN/classes/structure_estimator.py

@@ -259,6 +259,7 @@ class StructureEstimator(object):
         graph_to_draw = nx.DiGraph()
         spurious_edges = self.spurious_edges()
         non_spurious_edges = list(set(self._complete_graph.edges) - set(spurious_edges))
+        print(non_spurious_edges)
         edges_colors = ['red' if edge in spurious_edges else 'black' for edge in self._complete_graph.edges]
         graph_to_draw.add_edges_from(spurious_edges)
         graph_to_draw.add_edges_from(non_spurious_edges)
@@ -270,7 +271,7 @@ class StructureEstimator(object):
             'linewidths':2,
             "with_labels":True,
             "font_size":13,
-            'connectionstyle': 'arc3, rad = 0.',
+            'connectionstyle': 'arc3, rad = 0.1',
             "arrowsize": 15,
             "arrowstyle": '<|-',
             "width": 1,

PyCTBN/classes/trajectory.py

@@ -19,11 +19,9 @@ class Trajectory(object):
     def __init__(self, list_of_columns: typing.List, original_cols_number: int):
         """Constructor Method
         """
-        if type(list_of_columns[0][0]) != np.float64:
+        self._times = list_of_columns[0]
-            raise TypeError('The first array in the list has to be Times')
+        self._actual_trajectory = list_of_columns[1]
         self._original_cols_number = original_cols_number
-        self._actual_trajectory = np.array(list_of_columns[1:], dtype=np.int).T
-        self._times = np.array(list_of_columns[0], dtype=np.float)
 
     @property
     def trajectory(self) -> np.ndarray:

docs/classes.html

@@ -134,36 +134,31 @@
 <span id="classes-abstract-importer-module"></span><h2>classes.abstract_importer module<a class="headerlink" href="#module-classes.abstract_importer" title="Permalink to this headline">¶</a></h2>
 <dl class="py class">
 <dt id="classes.abstract_importer.AbstractImporter">
-<em class="property">class </em><code class="sig-prename descclassname">classes.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="sig-prename descclassname">classes.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">concatenated_samples</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
 <p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>file_path</strong> (<em>str</em>) – the file path</p>
+<dd class="field-odd"><ul class="simple">
-</dd>
+<li><p><strong>file_path</strong> (<em>str</em>) – the file path, or dataset name if you import already processed data</p></li>
-<dt class="field-even">_concatenated_samples</dt>
+<li><p><strong>concatenated_samples</strong> (<em>typing.Union</em><em>[</em><em>pandas.DataFrame</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – Dataframe or numpy array containing the concatenation of all the processed trajectories</p></li>
-<dd class="field-even"><p>Dataframe containing the concatenation of all the processed trajectories</p>
+<li><p><strong>variables</strong> (<em>pandas.DataFrame</em>) – Dataframe containing the nodes labels and cardinalities</p></li>
-</dd>
+</ul>
-<dt class="field-odd">_df_structure</dt>
-<dd class="field-odd"><p>Dataframe containing the structure of the network (edges)</p>
 </dd>
-<dt class="field-even">_df_variables</dt>
+<dt class="field-even">Prior_net_structure</dt>
-<dd class="field-even"><p>Dataframe containing the nodes cardinalities</p>
+<dd class="field-even"><p>Dataframe containing the structure of the network (edges)</p>
 </dd>
 <dt class="field-odd">_sorter</dt>
-<dd class="field-odd"><p>A list containing the columns header (excluding the time column) of the <code class="docutils literal notranslate"><span class="pre">_concatenated_samples</span></code></p>
+<dd class="field-odd"><p>A list containing the variables labels in the SAME order as the columns in <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code></p>
 </dd>
 </dl>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
-<p>The class members <code class="docutils literal notranslate"><span class="pre">_df_variables</span></code> and <code class="docutils literal notranslate"><span class="pre">_df_structure</span></code> HAVE to be properly constructed
+<p>The parameters <code class="docutils literal notranslate"><span class="pre">variables</span></code> and <code class="docutils literal notranslate"><span class="pre">prior_net_structure</span></code> HAVE to be properly constructed
 as Pandas Dataframes with the following structure:
 Header of _df_structure = [From_Node | To_Node]
-Header of _df_variables = [Variable_Label | Variable_Cardinality]</p>
+Header of _df_variables = [Variable_Label | Variable_Cardinality]
-</div>
+See the tutorial on how to construct a correct <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code> Dataframe/ndarray.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you don’t have prior network structure just leave <code class="docutils literal notranslate"><span class="pre">_df_structure</span></code> set to None.</p>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -171,11 +166,12 @@ Header of _df_variables = [Variable_Label | Variable_Cardinality]</p>
 </div>
 <dl class="py method">
 <dt id="classes.abstract_importer.AbstractImporter.build_list_of_samples_array">
-<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">concatenated_sample</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Builds a List containing the columns of data_frame and converts them to a numpy array.</p>
+<dd><p>Builds a List containing the the delta times numpy array, and the complete transitions matrix</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>data_frame</strong> (<em>pandas.Dataframe</em>) – the dataframe from which the columns have to be extracted and converted</p>
+<dd class="field-odd"><p><strong>concatenated_sample</strong> (<em>typing.Union</em><em>[</em><em>pandas.Dataframe</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – the dataframe/array from which the time, and transitions matrix have to be extracted
+and converted</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>the resulting list of numpy arrays</p>
@@ -981,6 +977,11 @@ contain the mentioned data.</p>
 Clears all the unused dataframes in <code class="docutils literal notranslate"><span class="pre">_importer</span></code> Object</p>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.sample_path.SamplePath.has_prior_net_structure">
+<em class="property">property </em><code class="sig-name descname">has_prior_net_structure</code><a class="headerlink" href="#classes.sample_path.SamplePath.has_prior_net_structure" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="py method">
 <dt id="classes.sample_path.SamplePath.structure">
 <em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#classes.sample_path.SamplePath.structure" title="Permalink to this definition">¶</a></dt>
@@ -1228,7 +1229,7 @@ The class member <code class="docutils literal notranslate"><span class="pre">_t
 <dl class="py method">
 <dt id="classes.structure_estimator.StructureEstimator.adjacency_matrix">
 <code class="sig-name descname">adjacency_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.adjacency_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Converts the estimated structrure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
+<dd><p>Converts the estimated structure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
 <dd class="field-odd"><p>The adjacency matrix of the graph <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code></p>
@@ -1343,6 +1344,13 @@ it is performed also the chi_test.</p>
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">
+<code class="sig-name descname">save_plot_estimated_structure_graph</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot the estimated structure in a graphical model style.
+Spurious edges are colored in red.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt id="classes.structure_estimator.StructureEstimator.save_results">
 <code class="sig-name descname">save_results</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.save_results" title="Permalink to this definition">¶</a></dt>
@@ -1350,6 +1358,23 @@ it is performed also the chi_test.</p>
 The file is named as the input dataset but the <cite>results_</cite> word is appended to the results file.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.structure_estimator.StructureEstimator.spurious_edges">
+<code class="sig-name descname">spurious_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.spurious_edges" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Return the spurious edges present in the estimated structure, if a prior net structure is present in</dt><dd><p><code class="docutils literal notranslate"><span class="pre">_sample_path.structure</span></code>.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A list containing the spurious edges</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>List</p>
+</dd>
+</dl>
+</dd></dl>
+
 </dd></dl>
 
 </div>

docs/genindex.html

@@ -134,6 +134,7 @@
  | <a href="#E"><strong>E</strong></a>
  | <a href="#F"><strong>F</strong></a>
  | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
  | <a href="#I"><strong>I</strong></a>
  | <a href="#J"><strong>J</strong></a>
  | <a href="#M"><strong>M</strong></a>
@@ -406,6 +407,14 @@
   </ul></td>
 </tr></table>
 
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="classes.html#classes.sample_path.SamplePath.has_prior_net_structure">has_prior_net_structure() (classes.sample_path.SamplePath property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -547,6 +556,8 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="classes.html#classes.sample_path.SamplePath">SamplePath (class in classes.sample_path)</a>
+</li>
+      <li><a href="classes.html#classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">save_plot_estimated_structure_graph() (classes.structure_estimator.StructureEstimator method)</a>
 </li>
       <li><a href="classes.html#classes.structure_estimator.StructureEstimator.save_results">save_results() (classes.structure_estimator.StructureEstimator method)</a>
 </li>
@@ -558,6 +569,8 @@
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="classes.html#classes.structure_estimator.StructureEstimator.spurious_edges">spurious_edges() (classes.structure_estimator.StructureEstimator method)</a>
+</li>
       <li><a href="classes.html#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times">state_residence_times() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>
 </li>
       <li><a href="classes.html#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix">state_transition_matrix() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>

examples/simple_cvs_importer.py

@@ -44,7 +44,7 @@ class CSVImporter(AbstractImporter):
 
 
 def main():
-    read_files = glob.glob(os.path.join('../../data', "*.csv"))
+    read_files = glob.glob(os.path.join('../data', "*.csv"))
     print(read_files[0])
     csvimp = CSVImporter(read_files[0])
     csvimp.import_data()

setup.py

@@ -1,6 +1,5 @@
 from setuptools import setup, find_packages
 
-print(find_packages('.', exclude=['PyCTBN.tests']))
 
 setup(name='PyCTBN',
       version='1.0',
@@ -12,7 +11,7 @@ setup(name='PyCTBN',
       packages=find_packages('.', exclude=['PyCTBN.tests']),
       #packages=['PyCTBN.classes'],
       install_requires=[
-          'numpy', 'pandas', 'networkx', 'scipy', 'tqdm'],
+          'numpy', 'pandas', 'networkx', 'scipy', 'matplotlib', 'tqdm'],
       dependency_links=['https://github.com/numpy/numpy', 'https://github.com/pandas-dev/pandas',
                         'https://github.com/networkx/networkx', 'https://github.com/scipy/scipy',
                         'https://github.com/tqdm/tqdm'],

sphinx_output/_build/html/classes.html

@@ -134,36 +134,31 @@
 <span id="classes-abstract-importer-module"></span><h2>classes.abstract_importer module<a class="headerlink" href="#module-classes.abstract_importer" title="Permalink to this headline">¶</a></h2>
 <dl class="py class">
 <dt id="classes.abstract_importer.AbstractImporter">
-<em class="property">class </em><code class="sig-prename descclassname">classes.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt>
+<em class="property">class </em><code class="sig-prename descclassname">classes.abstract_importer.</code><code class="sig-name descname">AbstractImporter</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">file_path</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">concatenated_samples</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">variables</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">prior_net_structure</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="headerlink" href="#classes.abstract_importer.AbstractImporter" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">abc.ABC</span></code></p>
 <p>Abstract class that exposes all the necessary methods to process the trajectories and the net structure.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>file_path</strong> (<em>str</em>) – the file path</p>
+<dd class="field-odd"><ul class="simple">
-</dd>
+<li><p><strong>file_path</strong> (<em>str</em>) – the file path, or dataset name if you import already processed data</p></li>
-<dt class="field-even">_concatenated_samples</dt>
+<li><p><strong>concatenated_samples</strong> (<em>typing.Union</em><em>[</em><em>pandas.DataFrame</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – Dataframe or numpy array containing the concatenation of all the processed trajectories</p></li>
-<dd class="field-even"><p>Dataframe containing the concatenation of all the processed trajectories</p>
+<li><p><strong>variables</strong> (<em>pandas.DataFrame</em>) – Dataframe containing the nodes labels and cardinalities</p></li>
-</dd>
+</ul>
-<dt class="field-odd">_df_structure</dt>
-<dd class="field-odd"><p>Dataframe containing the structure of the network (edges)</p>
 </dd>
-<dt class="field-even">_df_variables</dt>
+<dt class="field-even">Prior_net_structure</dt>
-<dd class="field-even"><p>Dataframe containing the nodes cardinalities</p>
+<dd class="field-even"><p>Dataframe containing the structure of the network (edges)</p>
 </dd>
 <dt class="field-odd">_sorter</dt>
-<dd class="field-odd"><p>A list containing the columns header (excluding the time column) of the <code class="docutils literal notranslate"><span class="pre">_concatenated_samples</span></code></p>
+<dd class="field-odd"><p>A list containing the variables labels in the SAME order as the columns in <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code></p>
 </dd>
 </dl>
 <div class="admonition warning">
 <p class="admonition-title">Warning</p>
-<p>The class members <code class="docutils literal notranslate"><span class="pre">_df_variables</span></code> and <code class="docutils literal notranslate"><span class="pre">_df_structure</span></code> HAVE to be properly constructed
+<p>The parameters <code class="docutils literal notranslate"><span class="pre">variables</span></code> and <code class="docutils literal notranslate"><span class="pre">prior_net_structure</span></code> HAVE to be properly constructed
 as Pandas Dataframes with the following structure:
 Header of _df_structure = [From_Node | To_Node]
-Header of _df_variables = [Variable_Label | Variable_Cardinality]</p>
+Header of _df_variables = [Variable_Label | Variable_Cardinality]
-</div>
+See the tutorial on how to construct a correct <code class="docutils literal notranslate"><span class="pre">concatenated_samples</span></code> Dataframe/ndarray.</p>
-<div class="admonition note">
-<p class="admonition-title">Note</p>
-<p>If you don’t have prior network structure just leave <code class="docutils literal notranslate"><span class="pre">_df_structure</span></code> set to None.</p>
 </div>
 <div class="admonition note">
 <p class="admonition-title">Note</p>
@@ -171,11 +166,12 @@ Header of _df_variables = [Variable_Label | Variable_Cardinality]</p>
 </div>
 <dl class="py method">
 <dt id="classes.abstract_importer.AbstractImporter.build_list_of_samples_array">
-<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data_frame</span><span class="p">:</span> <span class="n">pandas.core.frame.DataFrame</span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt>
+<code class="sig-name descname">build_list_of_samples_array</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">concatenated_sample</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>pandas.core.frame.DataFrame<span class="p">, </span>numpy.ndarray<span class="p">]</span></span></em><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.abstract_importer.AbstractImporter.build_list_of_samples_array" title="Permalink to this definition">¶</a></dt>
-<dd><p>Builds a List containing the columns of data_frame and converts them to a numpy array.</p>
+<dd><p>Builds a List containing the the delta times numpy array, and the complete transitions matrix</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>data_frame</strong> (<em>pandas.Dataframe</em>) – the dataframe from which the columns have to be extracted and converted</p>
+<dd class="field-odd"><p><strong>concatenated_sample</strong> (<em>typing.Union</em><em>[</em><em>pandas.Dataframe</em><em>, </em><em>numpy.ndarray</em><em>]</em>) – the dataframe/array from which the time, and transitions matrix have to be extracted
+and converted</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>the resulting list of numpy arrays</p>
@@ -981,6 +977,11 @@ contain the mentioned data.</p>
 Clears all the unused dataframes in <code class="docutils literal notranslate"><span class="pre">_importer</span></code> Object</p>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.sample_path.SamplePath.has_prior_net_structure">
+<em class="property">property </em><code class="sig-name descname">has_prior_net_structure</code><a class="headerlink" href="#classes.sample_path.SamplePath.has_prior_net_structure" title="Permalink to this definition">¶</a></dt>
+<dd></dd></dl>
+
 <dl class="py method">
 <dt id="classes.sample_path.SamplePath.structure">
 <em class="property">property </em><code class="sig-name descname">structure</code><a class="headerlink" href="#classes.sample_path.SamplePath.structure" title="Permalink to this definition">¶</a></dt>
@@ -1228,7 +1229,7 @@ The class member <code class="docutils literal notranslate"><span class="pre">_t
 <dl class="py method">
 <dt id="classes.structure_estimator.StructureEstimator.adjacency_matrix">
 <code class="sig-name descname">adjacency_matrix</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; numpy.ndarray<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.adjacency_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Converts the estimated structrure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
+<dd><p>Converts the estimated structure <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code> to a boolean adjacency matrix representation.</p>
 <dl class="field-list simple">
 <dt class="field-odd">Returns</dt>
 <dd class="field-odd"><p>The adjacency matrix of the graph <code class="docutils literal notranslate"><span class="pre">_complete_graph</span></code></p>
@@ -1343,6 +1344,13 @@ it is performed also the chi_test.</p>
 </dl>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">
+<code class="sig-name descname">save_plot_estimated_structure_graph</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph" title="Permalink to this definition">¶</a></dt>
+<dd><p>Plot the estimated structure in a graphical model style.
+Spurious edges are colored in red.</p>
+</dd></dl>
+
 <dl class="py method">
 <dt id="classes.structure_estimator.StructureEstimator.save_results">
 <code class="sig-name descname">save_results</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; None<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.save_results" title="Permalink to this definition">¶</a></dt>
@@ -1350,6 +1358,23 @@ it is performed also the chi_test.</p>
 The file is named as the input dataset but the <cite>results_</cite> word is appended to the results file.</p>
 </dd></dl>
 
+<dl class="py method">
+<dt id="classes.structure_estimator.StructureEstimator.spurious_edges">
+<code class="sig-name descname">spurious_edges</code><span class="sig-paren">(</span><span class="sig-paren">)</span> &#x2192; List<a class="headerlink" href="#classes.structure_estimator.StructureEstimator.spurious_edges" title="Permalink to this definition">¶</a></dt>
+<dd><dl class="simple">
+<dt>Return the spurious edges present in the estimated structure, if a prior net structure is present in</dt><dd><p><code class="docutils literal notranslate"><span class="pre">_sample_path.structure</span></code>.</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Returns</dt>
+<dd class="field-odd"><p>A list containing the spurious edges</p>
+</dd>
+<dt class="field-even">Return type</dt>
+<dd class="field-even"><p>List</p>
+</dd>
+</dl>
+</dd></dl>
+
 </dd></dl>
 
 </div>

sphinx_output/_build/html/genindex.html

@@ -134,6 +134,7 @@
  | <a href="#E"><strong>E</strong></a>
  | <a href="#F"><strong>F</strong></a>
  | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
  | <a href="#I"><strong>I</strong></a>
  | <a href="#J"><strong>J</strong></a>
  | <a href="#M"><strong>M</strong></a>
@@ -406,6 +407,14 @@
   </ul></td>
 </tr></table>
 
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="classes.html#classes.sample_path.SamplePath.has_prior_net_structure">has_prior_net_structure() (classes.sample_path.SamplePath property)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="I">I</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -547,6 +556,8 @@
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="classes.html#classes.sample_path.SamplePath">SamplePath (class in classes.sample_path)</a>
+</li>
+      <li><a href="classes.html#classes.structure_estimator.StructureEstimator.save_plot_estimated_structure_graph">save_plot_estimated_structure_graph() (classes.structure_estimator.StructureEstimator method)</a>
 </li>
       <li><a href="classes.html#classes.structure_estimator.StructureEstimator.save_results">save_results() (classes.structure_estimator.StructureEstimator method)</a>
 </li>
@@ -558,6 +569,8 @@
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="classes.html#classes.structure_estimator.StructureEstimator.spurious_edges">spurious_edges() (classes.structure_estimator.StructureEstimator method)</a>
+</li>
       <li><a href="classes.html#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_residence_times">state_residence_times() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>
 </li>
       <li><a href="classes.html#classes.conditional_intensity_matrix.ConditionalIntensityMatrix.state_transition_matrix">state_transition_matrix() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)</a>