diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/PyCTBN/classes/abstract_importer.py b/PyCTBN/classes/abstract_importer.py index 513a6e7..c7b7503 100644 --- a/PyCTBN/classes/abstract_importer.py +++ b/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 - :_df_structure: Dataframe containing the structure of the network (edges) - :_df_variables: Dataframe containing the nodes cardinalities - :_sorter: A list containing the columns header (excluding the time column) of the ``_concatenated_samples`` + :param concatenated_samples: Dataframe or numpy array containing the concatenation of all the processed trajectories + :type concatenated_samples: typing.Union[pandas.DataFrame, numpy.ndarray] + :param variables: Dataframe containing the nodes labels and cardinalities + :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._df_structure = None - self._concatenated_samples = None + self._concatenated_samples = concatenated_samples + self._df_variables = variables + 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: - """Builds a List containing the columns of data_frame and converts them to a numpy array. + def build_list_of_samples_array(self, concatenated_sample: typing.Union[pd.DataFrame, np.ndarray]) -> typing.List: + """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 - :type data_frame: pandas.Dataframe + :param concatenated_sample: the dataframe/array from which the time, and transitions matrix have to be extracted + 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: diff --git a/PyCTBN/classes/sample_path.py b/PyCTBN/classes/sample_path.py index 93d1b72..f9b9b6c 100644 --- a/PyCTBN/classes/sample_path.py +++ b/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 diff --git a/PyCTBN/classes/structure_estimator.py b/PyCTBN/classes/structure_estimator.py index ac315f6..b28c95d 100644 --- a/PyCTBN/classes/structure_estimator.py +++ b/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, diff --git a/PyCTBN/classes/trajectory.py b/PyCTBN/classes/trajectory.py index 6cb41f4..0a0a861 100644 --- a/PyCTBN/classes/trajectory.py +++ b/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: - raise TypeError('The first array in the list has to be Times') + self._times = list_of_columns[0] + 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: diff --git a/docs/classes.html b/docs/classes.html index 5f7476b..7b3d260 100644 --- a/docs/classes.html +++ b/docs/classes.html @@ -134,36 +134,31 @@

classes.abstract_importer module

-class classes.abstract_importer.AbstractImporter(file_path: str)
+class classes.abstract_importer.AbstractImporter(file_path: str = None, concatenated_samples: Union[pandas.core.frame.DataFrame, numpy.ndarray] = None, variables: pandas.core.frame.DataFrame = None, prior_net_structure: pandas.core.frame.DataFrame = None)

Bases: abc.ABC

Abstract class that exposes all the necessary methods to process the trajectories and the net structure.

Parameters
-

file_path (str) – the file path

-
-
_concatenated_samples
-

Dataframe containing the concatenation of all the processed trajectories

-
-
_df_structure
-

Dataframe containing the structure of the network (edges)

+
    +

  • file_path (str) – the file path, or dataset name if you import already processed data

    • +

  • concatenated_samples (typing.Union[pandas.DataFrame, numpy.ndarray]) – Dataframe or numpy array containing the concatenation of all the processed trajectories

    • +

  • variables (pandas.DataFrame) – Dataframe containing the nodes labels and cardinalities

    • +
-
_df_variables
-

Dataframe containing the nodes cardinalities

+
Prior_net_structure
+

Dataframe containing the structure of the network (edges)

_sorter
-

A list containing the columns header (excluding the time column) of the _concatenated_samples

+

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]

-
-
-

Note

-

If you don’t have prior network structure just leave _df_structure set to None.

+Header of _df_variables = [Variable_Label | Variable_Cardinality] +See the tutorial on how to construct a correct concatenated_samples Dataframe/ndarray.

Note

@@ -171,11 +166,12 @@ Header of _df_variables = [Variable_Label | Variable_Cardinality]

-build_list_of_samples_array(data_frame: pandas.core.frame.DataFrame) → List
-

Builds a List containing the columns of data_frame and converts them to a numpy array.

+build_list_of_samples_array(concatenated_sample: Union[pandas.core.frame.DataFrame, numpy.ndarray]) → List +

Builds a List containing the the delta times numpy array, and the complete transitions matrix

Parameters
-

data_frame (pandas.Dataframe) – the dataframe from which the columns have to be extracted and converted

+

concatenated_sample (typing.Union[pandas.Dataframe, numpy.ndarray]) – the dataframe/array from which the time, and transitions matrix have to be extracted +and converted

Returns

the resulting list of numpy arrays

@@ -981,6 +977,11 @@ contain the mentioned data.

Clears all the unused dataframes in _importer Object

+
+
+property has_prior_net_structure
+
+
property structure
@@ -1228,7 +1229,7 @@ The class member _t
adjacency_matrix() → numpy.ndarray
-

Converts the estimated structrure _complete_graph to a boolean adjacency matrix representation.

+

Converts the estimated structure _complete_graph to a boolean adjacency matrix representation.

Returns

The adjacency matrix of the graph _complete_graph

@@ -1343,6 +1344,13 @@ it is performed also the chi_test.

+
+
+save_plot_estimated_structure_graph() → None
+

Plot the estimated structure in a graphical model style. +Spurious edges are colored in red.

+
+
save_results() → None
@@ -1350,6 +1358,23 @@ it is performed also the chi_test.

The file is named as the input dataset but the results_ word is appended to the results file.

+
+
+spurious_edges() → List
+
+
Return the spurious edges present in the estimated structure, if a prior net structure is present in

_sample_path.structure.

+
+
+
+
Returns
+

A list containing the spurious edges

+
+
Return type
+

List

+
+
+
+
diff --git a/docs/genindex.html b/docs/genindex.html index e17f709..6c1d048 100644 --- a/docs/genindex.html +++ b/docs/genindex.html @@ -134,6 +134,7 @@ | E | F | G + | H | I | J | M @@ -406,6 +407,14 @@ +

H

+ + +
+

I

    @@ -547,6 +556,8 @@
      +
    • spurious_edges() (classes.structure_estimator.StructureEstimator method) +
    • state_residence_times() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)
    • state_transition_matrix() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property) diff --git a/docs/objects.inv b/docs/objects.inv index 973ae93..d3c3f62 100644 Binary files a/docs/objects.inv and b/docs/objects.inv differ diff --git a/docs/searchindex.js b/docs/searchindex.js index 8aedd55..e113010 100644 --- a/docs/searchindex.js +++ b/docs/searchindex.js @@ -1 +1 @@ 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package","Examples","Welcome to PyCTBN\u2019s documentation!","classes"],titleterms:{"class":[0,3],"import":1,abstract_import:0,cach:0,conditional_intensity_matrix:0,content:0,data:1,document:2,estim:1,exampl:1,implement:1,indic:2,instal:1,json_import:0,modul:0,network_graph:0,own:1,packag:0,paramet:1,parameters_estim:0,pyctbn:2,sample_path:0,set_of_cim:0,structur:[0,1],structure_estim:0,submodul:0,tabl:2,trajectori:0,usag:1,welcom:2,your:1}}) \ No newline at end of file diff --git a/examples/simple_cvs_importer.py b/examples/simple_cvs_importer.py index 7655ff7..35e554c 100644 --- a/examples/simple_cvs_importer.py +++ b/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() diff --git a/setup.py b/setup.py index fbb2182..740b006 100644 --- a/setup.py +++ b/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'], diff --git a/sphinx_output/_build/doctrees/classes.doctree b/sphinx_output/_build/doctrees/classes.doctree index 1d7d804..b49a587 100644 Binary files a/sphinx_output/_build/doctrees/classes.doctree and b/sphinx_output/_build/doctrees/classes.doctree differ diff --git a/sphinx_output/_build/doctrees/environment.pickle b/sphinx_output/_build/doctrees/environment.pickle index 42c0e4b..252e803 100644 Binary files a/sphinx_output/_build/doctrees/environment.pickle and b/sphinx_output/_build/doctrees/environment.pickle differ diff --git a/sphinx_output/_build/html/classes.html b/sphinx_output/_build/html/classes.html index 5f7476b..7b3d260 100644 --- a/sphinx_output/_build/html/classes.html +++ b/sphinx_output/_build/html/classes.html @@ -134,36 +134,31 @@

      classes.abstract_importer module

      -class classes.abstract_importer.AbstractImporter(file_path: str)
      +class classes.abstract_importer.AbstractImporter(file_path: str = None, concatenated_samples: Union[pandas.core.frame.DataFrame, numpy.ndarray] = None, variables: pandas.core.frame.DataFrame = None, prior_net_structure: pandas.core.frame.DataFrame = None)

      Bases: abc.ABC

      Abstract class that exposes all the necessary methods to process the trajectories and the net structure.

      Parameters
      -

      file_path (str) – the file path

      -
      -
      _concatenated_samples
      -

      Dataframe containing the concatenation of all the processed trajectories

      -
      -
      _df_structure
      -

      Dataframe containing the structure of the network (edges)

      +
        +

      • file_path (str) – the file path, or dataset name if you import already processed data

        • +

      • concatenated_samples (typing.Union[pandas.DataFrame, numpy.ndarray]) – Dataframe or numpy array containing the concatenation of all the processed trajectories

        • +

      • variables (pandas.DataFrame) – Dataframe containing the nodes labels and cardinalities

        • +
      -
      _df_variables
      -

      Dataframe containing the nodes cardinalities

      +
      Prior_net_structure
      +

      Dataframe containing the structure of the network (edges)

      _sorter
      -

      A list containing the columns header (excluding the time column) of the _concatenated_samples

      +

      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]

      -
      -
      -

      Note

      -

      If you don’t have prior network structure just leave _df_structure set to None.

      +Header of _df_variables = [Variable_Label | Variable_Cardinality] +See the tutorial on how to construct a correct concatenated_samples Dataframe/ndarray.

      Note

      @@ -171,11 +166,12 @@ Header of _df_variables = [Variable_Label | Variable_Cardinality]

      -build_list_of_samples_array(data_frame: pandas.core.frame.DataFrame) → List
      -

      Builds a List containing the columns of data_frame and converts them to a numpy array.

      +build_list_of_samples_array(concatenated_sample: Union[pandas.core.frame.DataFrame, numpy.ndarray]) → List +

      Builds a List containing the the delta times numpy array, and the complete transitions matrix

      Parameters
      -

      data_frame (pandas.Dataframe) – the dataframe from which the columns have to be extracted and converted

      +

      concatenated_sample (typing.Union[pandas.Dataframe, numpy.ndarray]) – the dataframe/array from which the time, and transitions matrix have to be extracted +and converted

      Returns

      the resulting list of numpy arrays

      @@ -981,6 +977,11 @@ contain the mentioned data.

      Clears all the unused dataframes in _importer Object

      +
      +
      +property has_prior_net_structure
      +
      +
      property structure
      @@ -1228,7 +1229,7 @@ The class member _t
      adjacency_matrix() → numpy.ndarray
      -

      Converts the estimated structrure _complete_graph to a boolean adjacency matrix representation.

      +

      Converts the estimated structure _complete_graph to a boolean adjacency matrix representation.

      Returns

      The adjacency matrix of the graph _complete_graph

      @@ -1343,6 +1344,13 @@ it is performed also the chi_test.

      +
      +
      +save_plot_estimated_structure_graph() → None
      +

      Plot the estimated structure in a graphical model style. +Spurious edges are colored in red.

      +
      +
      save_results() → None
      @@ -1350,6 +1358,23 @@ it is performed also the chi_test.

      The file is named as the input dataset but the results_ word is appended to the results file.

      +
      +
      +spurious_edges() → List
      +
      +
      Return the spurious edges present in the estimated structure, if a prior net structure is present in

      _sample_path.structure.

      +
      +
      +
      +
      Returns
      +

      A list containing the spurious edges

      +
      +
      Return type
      +

      List

      +
      +
      +
      +
      diff --git a/sphinx_output/_build/html/genindex.html b/sphinx_output/_build/html/genindex.html index e17f709..6c1d048 100644 --- a/sphinx_output/_build/html/genindex.html +++ b/sphinx_output/_build/html/genindex.html @@ -134,6 +134,7 @@ | E | F | G + | H | I | J | M @@ -406,6 +407,14 @@
    +

    H

    + + +
    +

    I

      @@ -547,6 +556,8 @@
        +
      • spurious_edges() (classes.structure_estimator.StructureEstimator method) +
      • state_residence_times() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property)
      • state_transition_matrix() (classes.conditional_intensity_matrix.ConditionalIntensityMatrix property) diff --git a/sphinx_output/_build/html/objects.inv b/sphinx_output/_build/html/objects.inv index 973ae93..d3c3f62 100644 Binary files a/sphinx_output/_build/html/objects.inv and b/sphinx_output/_build/html/objects.inv differ diff --git a/sphinx_output/_build/html/searchindex.js b/sphinx_output/_build/html/searchindex.js index 8aedd55..e113010 100644 --- a/sphinx_output/_build/html/searchindex.js +++ b/sphinx_output/_build/html/searchindex.js @@ -1 +1 @@ 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