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          -
          -property cims
          +
          +property cims
          -
          -property dyn_str
          +
          +property dyn_str
          -
          -generate_cims(min_val, max_val)
          +
          +generate_cims(min_val, max_val)
          For each node, generate the corresponding SetOfCims. The objective is to group the CIMs

          (actually generated by private method __generate_cim) according to parents possibles states of every node. This method must obviously be executed after the graph has been generated.

          @@ -212,8 +212,8 @@ This method must obviously be executed after the graph has been generated.

          -
          -generate_graph(density, fixed: bool = False)
          +
          +generate_graph(density, fixed: bool = False)
          Generate the edges according to specified density, and then instantiate the NetworkGraph object

          to represent the network

          @@ -229,29 +229,29 @@ This method must obviously be executed after the graph has been generated.

          -
          -property graph
          +
          +property graph
          -
          -property variables
          +
          +property variables
          -
          -

          PyCTBN.PyCTBN.structure_graph.network_graph module

          +
          +

          pyctbn.legacy.structure_graph.network_graph module

          -
          -class PyCTBN.PyCTBN.structure_graph.network_graph.NetworkGraph(graph_struct: PyCTBN.PyCTBN.structure_graph.structure.Structure)
          +
          +class pyctbn.legacy.structure_graph.network_graph.NetworkGraph(graph_struct: pyctbn.legacy.structure_graph.structure.Structure)

          Bases: object

          Abstracts the infos contained in the Structure class in the form of a directed graph. Has the task of creating all the necessary filtering and indexing structures for parameters estimation

          Parameters
          -

          graph_struct (Structure) – the Structure object from which infos about the net will be extracted

          +

          graph_struct (Structure) – the Structure object from which infos about the net will be extracted

          _graph

          directed graph

          @@ -278,8 +278,8 @@ from one state to another

          -
          -add_edges(list_of_edges: List) → None
          +
          +add_edges(list_of_edges: List) → None

          Add the edges to the _graph contained in the list list_of_edges.

          Parameters
          @@ -289,8 +289,8 @@ from one state to another

          -
          -add_nodes(list_of_nodes: List) → None
          +
          +add_nodes(list_of_nodes: List) → None

          Adds the nodes to the _graph contained in the list of nodes list_of_nodes. Sets all the properties that identify a nodes (index, positional index, cardinality)

          @@ -301,8 +301,8 @@ Sets all the properties that identify a nodes (index, positional index, cardinal
          -
          -static build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray
          +
          +static build_p_comb_structure_for_a_node(parents_values: List) → numpy.ndarray

          Builds the combinatorial structure that contains the combinations of all the values contained in parents_values.

          @@ -319,8 +319,8 @@ Sets all the properties that identify a nodes (index, positional index, cardinal
          -
          -static build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.ndarray
          +
          +static build_time_columns_filtering_for_a_node(node_indx: int, p_indxs: List) → numpy.ndarray

          Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset. This structute will be used in the computation of the state res times. @@ -333,8 +333,8 @@ This structute will be used in the computation of the state res times.

          -
          -static build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray
          +
          +static build_time_scalar_indexing_structure_for_a_node(node_states: int, parents_vals: List) → numpy.ndarray

          Builds an indexing structure for the computation of state residence times values.

          Parameters
          @@ -353,8 +353,8 @@ This structute will be used in the computation of the state res times.
          -
          -static build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.ndarray
          +
          +static build_transition_filtering_for_a_node(node_indx: int, p_indxs: List, nodes_number: int) → numpy.ndarray

          Builds the necessary structure to filter the desired columns indicated by node_indx and p_indxs in the dataset. This structure will be used in the computation of the state transitions values. @@ -369,8 +369,8 @@ This structure will be used in the computation of the state transitions values.

          -
          -static build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray
          +
          +static build_transition_scalar_indexing_structure_for_a_node(node_states_number: int, parents_vals: List) → numpy.ndarray

          Builds an indexing structure for the computation of state transitions values.

          Parameters
          @@ -389,19 +389,19 @@ This structure will be used in the computation of the state transitions values.
          -
          -clear_indexing_filtering_structures() → None
          +
          +clear_indexing_filtering_structures() → None

          Initialize all the filtering/indexing structures.

          -
          -property edges
          +
          +property edges
          -
          -fast_init(node_id: str) → None
          +
          +fast_init(node_id: str) → None

          Initializes all the necessary structures for parameters estimation of the node identified by the label node_id

          @@ -412,13 +412,13 @@ node_id

          -
          -get_node_indx(node_id) → int
          +
          +get_node_indx(node_id) → int
          -
          -get_ordered_by_indx_set_of_parents(node: str) → Tuple
          +
          +get_ordered_by_indx_set_of_parents(node: str) → Tuple

          Builds the aggregated structure that holds all the infos relative to the parent set of the node, namely (parents_labels, parents_indexes, parents_cardinalities).

          @@ -435,8 +435,8 @@ node_id

          -
          -get_parents_by_id(node_id) → List
          +
          +get_parents_by_id(node_id) → List

          Returns a list of labels of the parents of the node node_id

          Parameters
          @@ -452,18 +452,18 @@ node_id

          -
          -get_positional_node_indx(node_id) → int
          +
          +get_positional_node_indx(node_id) → int
          -
          -get_states_number(node_id) → int
          +
          +get_states_number(node_id) → int
          -
          -has_edge(edge: tuple) → bool
          +
          +has_edge(edge: tuple) → bool

          Check if the graph contains a specific edge

          Parameters:

          edge: a tuple that rappresents the edge

          @@ -474,28 +474,28 @@ node_id

          -
          -property nodes
          +
          +property nodes
          -
          -property nodes_indexes
          +
          +property nodes_indexes
          -
          -property nodes_values
          +
          +property nodes_values
          -
          -property p_combs
          +
          +property p_combs
          -
          -remove_edges(list_of_edges: List) → None
          +
          +remove_edges(list_of_edges: List) → None

          Remove the edges to the graph contained in the list list_of_edges.

          Parameters
          @@ -505,47 +505,47 @@ node_id

          -
          -remove_node(node_id: str) → None
          +
          +remove_node(node_id: str) → None

          Remove the node node_id from all the class members. Initialize all the filtering/indexing structures.

          -
          -property time_filtering
          +
          +property time_filtering
          -
          -property time_scalar_indexing_strucure
          +
          +property time_scalar_indexing_strucure
          -
          -property transition_filtering
          +
          +property transition_filtering
          -
          -property transition_scalar_indexing_structure
          +
          +property transition_scalar_indexing_structure
          -
          -

          PyCTBN.PyCTBN.structure_graph.sample_path module

          +
          +

          pyctbn.legacy.structure_graph.sample_path module

          -
          -class PyCTBN.PyCTBN.structure_graph.sample_path.SamplePath(importer: PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter)
          +
          +class pyctbn.legacy.structure_graph.sample_path.SamplePath(importer: pyctbn.legacy.utility.abstract_importer.AbstractImporter)

          Bases: object

          Aggregates all the informations about the trajectories, the real structure of the sampled net and variables cardinalites. Has the task of creating the objects Trajectory and Structure that will contain the mentioned data.

          Parameters
          -

          importer (AbstractImporter) – the Importer object which contains the imported and processed data

          +

          importer (AbstractImporter) – the Importer object which contains the imported and processed data

          _trajectories

          the Trajectory object that will contain all the concatenated trajectories

          @@ -558,51 +558,51 @@ contain the mentioned data.

          -
          -build_structure() → None
          +
          +build_structure() → None

          Builds the Structure object that aggregates all the infos about the net.

          -
          -build_trajectories() → None
          +
          +build_trajectories() → None

          Builds the Trajectory object that will contain all the trajectories. Clears all the unused dataframes in _importer Object

          -
          -clear_memory()
          +
          +clear_memory()
          -
          -property has_prior_net_structure
          +
          +property has_prior_net_structure
          -
          -property structure
          +
          +property structure
          -
          -property total_variables_count
          +
          +property total_variables_count
          -
          -property trajectories
          +
          +property trajectories
          -
          -

          PyCTBN.PyCTBN.structure_graph.set_of_cims module

          +
          +

          pyctbn.legacy.structure_graph.set_of_cims module

          -
          -class PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray, cims: numpy.ndarray = None)
          +
          +class pyctbn.legacy.structure_graph.set_of_cims.SetOfCims(node_id: str, parents_states_number: List, node_states_number: int, p_combs: numpy.ndarray, cims: numpy.ndarray = None)

          Bases: object

          Aggregates all the CIMS of the node identified by the label _node_id.

          @@ -625,13 +625,13 @@ Clears all the unused dataframes in <
          -
          -property actual_cims
          +
          +property actual_cims
          -
          -build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.ndarray) → None
          +
          +build_cims(state_res_times: numpy.ndarray, transition_matrices: numpy.ndarray) → None

          Build the ConditionalIntensityMatrix objects given the state residence times and transitions matrices. Compute the cim coefficients.The class member _actual_cims will contain the computed cims.

          @@ -645,14 +645,14 @@ Compute the cim coefficients.The class member -
          -build_times_and_transitions_structures() → None
          +
          +build_times_and_transitions_structures() → None

          Initializes at the correct dimensions the state residence times matrix and the state transition matrices.

          -
          -filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray
          +
          +filter_cims_with_mask(mask_arr: numpy.ndarray, comb: List) → numpy.ndarray

          Filter the cims contained in the array _actual_cims given the boolean mask mask_arr and the index comb.

          @@ -672,23 +672,23 @@ Compute the cim coefficients.The class member -
          -get_cims_number()
          +
          +get_cims_number()
          -
          -property p_combs
          +
          +property p_combs
          -
          -

          PyCTBN.PyCTBN.structure_graph.structure module

          +
          +

          pyctbn.legacy.structure_graph.structure module

          -
          -class PyCTBN.PyCTBN.structure_graph.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)
          +
          +class pyctbn.legacy.structure_graph.structure.Structure(nodes_labels_list: List, nodes_indexes_arr: numpy.ndarray, nodes_vals_arr: numpy.ndarray, edges_list: List, total_variables_number: int)

          Bases: object

          Contains all the infos about the network structure(nodes labels, nodes caridinalites, edges, indexes)

          @@ -703,28 +703,28 @@ Compute the cim coefficients.The class member -
          -add_edge(edge: tuple)
          +
          +add_edge(edge: tuple)
          -
          -clean_structure_edges()
          +
          +clean_structure_edges()
          -
          -contains_edge(edge: tuple) → bool
          +
          +contains_edge(edge: tuple) → bool
          -
          -property edges
          +
          +property edges
          -
          -get_node_id(node_indx: int) → str
          +
          +get_node_id(node_indx: int) → str

          Given the node_index returns the node label.

          Parameters
          @@ -740,8 +740,8 @@ Compute the cim coefficients.The class member -
          -get_node_indx(node_id: str) → int
          +
          +get_node_indx(node_id: str) → int

          Given the node_index returns the node label.

          Parameters
          @@ -757,13 +757,13 @@ Compute the cim coefficients.The class member -
          -get_positional_node_indx(node_id: str) → int
          +
          +get_positional_node_indx(node_id: str) → int
          -
          -get_states_number(node: str) → int
          +
          +get_states_number(node: str) → int

          Given the node label node returns the cardinality of the node.

          Parameters
          @@ -779,45 +779,45 @@ Compute the cim coefficients.The class member -
          -property nodes_indexes
          +
          +property nodes_indexes
          -
          -property nodes_labels
          +
          +property nodes_labels
          -
          -property nodes_values
          +
          +property nodes_values
          -
          -remove_edge(edge: tuple)
          +
          +remove_edge(edge: tuple)
          -
          -remove_node(node_id: str) → None
          +
          +remove_node(node_id: str) → None

          Remove the node node_id from all the class members. The class member _total_variables_number since it refers to the total number of variables in the dataset.

          -
          -property total_variables_number
          +
          +property total_variables_number
          -
          -

          PyCTBN.PyCTBN.structure_graph.trajectory module

          +
          +

          pyctbn.legacy.structure_graph.trajectory module

          -
          -class PyCTBN.PyCTBN.structure_graph.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)
          +
          +class pyctbn.legacy.structure_graph.trajectory.Trajectory(list_of_columns: List, original_cols_number: int)

          Bases: object

          Abstracts the infos about a complete set of trajectories, represented as a numpy array of doubles (the time deltas) and a numpy matrix of ints (the changes of states).

          @@ -836,40 +836,40 @@ The class member _t
          -
          -property complete_trajectory
          +
          +property complete_trajectory
          -
          -size()
          +
          +size()
          -
          -property times
          +
          +property times
          -
          -property trajectory
          +
          +property trajectory

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        -
        -

        PyCTBN.PyCTBN.structure_graph.trajectory_generator module

        +
        +

        pyctbn.legacy.structure_graph.trajectory_generator module

        -
        -class PyCTBN.PyCTBN.structure_graph.trajectory_generator.TrajectoryGenerator(importer: PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter = None, variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)
        +
        +class pyctbn.legacy.structure_graph.trajectory_generator.TrajectoryGenerator(importer: pyctbn.legacy.utility.abstract_importer.AbstractImporter = None, variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)

        Bases: object

        Provides the methods to generate a trajectory basing on the network defined in the importer.

        Parameters
          -
        • _importer (AbstractImporter) – the Importer object which contains the imported and processed data

        • +
        • _importer (AbstractImporter) – the Importer object which contains the imported and processed data

        • _vnames (List) – List of the variables labels that belong to the network

        • _parents (Dict) – It contains, for each variable label (the key), the list of related parents labels

        • _cims (Dict) – It contains, for each variable label (the key), the SetOfCims object related to it

        • @@ -878,8 +878,8 @@ in the importer.

        -
        -CTBN_Sample(t_end=- 1, max_tr=- 1)
        +
        +CTBN_Sample(t_end=- 1, max_tr=- 1)
        This method implements the generation of a trajectory, basing on the network structure and

        on the coefficients defined in the CIMs. The variables are initialized with value 0, and the method takes care of adding the @@ -897,8 +897,8 @@ conventional last row made up of -1.

        -
        -multi_trajectory(t_ends: list = None, max_trs: list = None)
        +
        +multi_trajectory(t_ends: list = None, max_trs: list = None)

        Generate n trajectories in parallel, where n is the number of items in t_ends, if defined, or the number of items in max_trs otherwise

        @@ -912,8 +912,8 @@ t_ends, if defined, or the number of items in max_trs otherwise

        -
        -worker(t_end, max_tr, trajectories)
        +
        +worker(t_end, max_tr, trajectories)

        Single process that will be executed in parallel in order to generate one trajectory.

        Parameters
        @@ -929,8 +929,8 @@ t_ends, if defined, or the number of items in max_trs otherwise

        -
        -

        Module contents

        +
        +

        Module contents

        @@ -940,10 +940,10 @@ t_ends, if defined, or the number of items in max_trs otherwise

        diff --git a/docs-out/_build/html/PyCTBN.PyCTBN.utility.html b/docs/_build/html/PyCTBN.PyCTBN.utility.html similarity index 82% rename from docs-out/_build/html/PyCTBN.PyCTBN.utility.html rename to docs/_build/html/PyCTBN.PyCTBN.utility.html index fe87d56..5ce433a 100644 --- a/docs-out/_build/html/PyCTBN.PyCTBN.utility.html +++ b/docs/_build/html/PyCTBN.PyCTBN.utility.html @@ -8,7 +8,7 @@ - PyCTBN.PyCTBN.utility package — PyCTBN 2.0 documentation + pyctbn.legacy.utility package — PyCTBN 2.0 documentation @@ -27,9 +27,9 @@ href="genindex.html"/> - + - + @@ -63,9 +63,9 @@

        Contents:

          -
        • PyCTBN.PyCTBN package
            -
          • Subpackages
          • -
          • Module contents
          • +
          • pyctbn.legacy package
          • Examples
              @@ -116,15 +116,15 @@
              -

              PyCTBN.PyCTBN.utility package

              +

              pyctbn.legacy.utility package

              Submodules

              -
              -

              PyCTBN.PyCTBN.utility.abstract_exporter module

              +
              +

              pyctbn.legacy.utility.abstract_exporter module

              -
              -class PyCTBN.PyCTBN.utility.abstract_exporter.AbstractExporter(variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)
              +
              +class pyctbn.legacy.utility.abstract_exporter.AbstractExporter(variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)

              Bases: abc.ABC

              Abstract class that exposes the methods to save in json format a network information along with one or more trajectories generated basing on it

              @@ -139,8 +139,8 @@ along with one or more trajectories generated basing on it

              -
              -add_trajectory(trajectory: list)
              +
              +add_trajectory(trajectory: list)

              Add a new trajectory to the current list

              Parameters
              @@ -150,8 +150,8 @@ along with one or more trajectories generated basing on it

              -
              -abstract out_file(filename)
              +
              +abstract out_file(filename)
              Create a file in current directory and write on it the previously added data

              (variables, dyn_str, dyn_cims and trajectories)

              @@ -166,11 +166,11 @@ along with one or more trajectories generated basing on it

              -
              -

              PyCTBN.PyCTBN.utility.abstract_importer module

              +
              +

              pyctbn.legacy.utility.abstract_importer module

              -
              -class PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter(file_path: str = None, trajectory_list: Union[pandas.core.frame.DataFrame, numpy.ndarray] = None, variables: pandas.core.frame.DataFrame = None, prior_net_structure: pandas.core.frame.DataFrame = None)
              +
              +class pyctbn.legacy.utility.abstract_importer.AbstractImporter(file_path: str = None, trajectory_list: 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.

              @@ -201,8 +201,8 @@ See the tutorial on how to construct a correct JsonImporter for an example implementation

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

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

              Parameters
              @@ -219,8 +219,8 @@ and converted

              -
              -abstract build_sorter(trajecory_header: object) → List
              +
              +abstract build_sorter(trajecory_header: object) → List

              Initializes the _sorter class member from a trajectory dataframe, exctracting the header of the frame and keeping ONLY the variables symbolic labels, cutting out the time label in the header.

              @@ -237,14 +237,14 @@ and keeping ONLY the variables symbolic labels, cutting out the time label in th
              -
              -clear_concatenated_frame() → None
              +
              +clear_concatenated_frame() → None

              Removes all values in the dataframe concatenated_samples.

              -
              -compute_row_delta_in_all_samples_frames(df_samples_list: List) → None
              +
              +compute_row_delta_in_all_samples_frames(df_samples_list: List) → None

              Calls the method compute_row_delta_sigle_samples_frame on every dataframe present in the list df_samples_list. Concatenates the result in the dataframe concatanated_samples

              @@ -267,8 +267,8 @@ and merged trajectories

              -
              -compute_row_delta_sigle_samples_frame(sample_frame: pandas.core.frame.DataFrame, columns_header: List, shifted_cols_header: List) → pandas.core.frame.DataFrame
              +
              +compute_row_delta_sigle_samples_frame(sample_frame: pandas.core.frame.DataFrame, columns_header: List, shifted_cols_header: List) → pandas.core.frame.DataFrame

              Computes the difference between each value present in th time column. Copies and shift by one position up all the values present in the remaining columns.

              @@ -294,45 +294,45 @@ Header of sample_frame = [Time | Variable values]

              -
              -property concatenated_samples
              +
              +property concatenated_samples
              -
              -abstract dataset_id() → object
              +
              +abstract dataset_id() → object

              If the original dataset contains multiple dataset, this method returns a unique id to identify the current dataset

              -
              -property file_path
              +
              +property file_path
              -
              -property sorter
              +
              +property sorter
              -
              -property structure
              +
              +property structure
              -
              -property variables
              +
              +property variables
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    -
    -

    PyCTBN.PyCTBN.utility.cache module

    +
    +

    pyctbn.legacy.utility.cache module

    -
    -class PyCTBN.PyCTBN.utility.cache.Cache
    +
    +class pyctbn.legacy.utility.cache.Cache

    Bases: object

    This class acts as a cache of SetOfCims objects for a node.

    @@ -345,14 +345,14 @@ index is related

    -
    -clear()
    +
    +clear()

    Clear the contents both of __actual_cache and __list_of_sets_of_parents.

    -
    -find(parents_comb: Set)
    +
    +find(parents_comb: Set)

    Tries to find in cache given the symbolic parents combination parents_comb the SetOfCims related to that parents_comb.

    @@ -364,21 +364,21 @@ related to that par None otherwise.

    Return type
    -

    SetOfCims

    +

    SetOfCims

    -
    -put(parents_comb: Set, socim: PyCTBN.PyCTBN.structure_graph.set_of_cims.SetOfCims)
    +
    +put(parents_comb: Set, socim: pyctbn.legacy.structure_graph.set_of_cims.SetOfCims)

    Place in cache the SetOfCims object, and the related symbolic index parents_comb in __list_of_sets_of_parents.

    Parameters
    • parents_comb (Set) – the symbolic set index

    • -
    • socim (SetOfCims) – the related SetOfCims object

    • +
    • socim (SetOfCims) – the related SetOfCims object

    @@ -387,12 +387,12 @@ None otherwise.

    -
    -

    PyCTBN.PyCTBN.utility.json_exporter module

    +
    +

    pyctbn.legacy.utility.json_exporter module

    -
    -class PyCTBN.PyCTBN.utility.json_exporter.JsonExporter(variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)
    -

    Bases: PyCTBN.PyCTBN.utility.abstract_exporter.AbstractExporter

    +
    +class pyctbn.legacy.utility.json_exporter.JsonExporter(variables: pandas.core.frame.DataFrame = None, dyn_str: pandas.core.frame.DataFrame = None, dyn_cims: dict = None)
    +

    Bases: pyctbn.legacy.utility.abstract_exporter.AbstractExporter

    Provides the methods to save in json format a network information along with one or more trajectories generated basing on it

    @@ -406,13 +406,13 @@ along with one or more trajectories generated basing on it

    -
    -cims_to_json() → dict
    +
    +cims_to_json() → dict
    -
    -out_file(filename)
    +
    +out_file(filename)
    Create a file in current directory and write on it the previously added data

    (variables, dyn_str, dyn_cims and trajectories)

    @@ -427,12 +427,12 @@ along with one or more trajectories generated basing on it

    -
    -

    PyCTBN.PyCTBN.utility.json_importer module

    +
    +

    pyctbn.legacy.utility.json_importer module

    -
    -class PyCTBN.PyCTBN.utility.json_importer.JsonImporter(file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, variables_key: str, cims_label: str = None)
    -

    Bases: PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter

    +
    +class pyctbn.legacy.utility.json_importer.JsonImporter(file_path: str, samples_label: str, structure_label: str, variables_label: str, time_key: str, variables_key: str, cims_label: str = None)
    +

    Bases: pyctbn.legacy.utility.abstract_importer.AbstractImporter

    Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare the data in json extension.

    @@ -457,27 +457,27 @@ the data in json extension.

    -
    -build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
    +
    +build_sorter(sample_frame: pandas.core.frame.DataFrame) → List

    Implements the abstract method build_sorter of the AbstractImporter for this dataset.

    -
    -clear_data_frame_list() → None
    +
    +clear_data_frame_list() → None

    Removes all values present in the dataframes in the list _df_samples_list.

    -
    -dataset_id() → object
    +
    +dataset_id() → object

    If the original dataset contains multiple dataset, this method returns a unique id to identify the current dataset

    -
    -import_data(indx: int = 0) → None
    +
    +import_data(indx: int = 0) → None

    Implements the abstract method of AbstractImporter.

    Parameters
    @@ -487,8 +487,8 @@ dataset

    -
    -import_sampled_cims(raw_data: List, indx: int, cims_key: str) → Dict
    +
    +import_sampled_cims(raw_data: List, indx: int, cims_key: str) → Dict

    Imports the synthetic CIMS in the dataset in a dictionary, using variables labels as keys for the set of CIMS of a particular node.

    @@ -509,8 +509,8 @@ as keys for the set of CIMS of a particular node.

    -
    -import_structure(raw_data: List) → pandas.core.frame.DataFrame
    +
    +import_structure(raw_data: List) → pandas.core.frame.DataFrame

    Imports in a dataframe the data in the list raw_data at the key _structure_label

    Parameters
    @@ -526,8 +526,8 @@ as keys for the set of CIMS of a particular node.

    -
    -import_trajectories(raw_data: List) → List
    +
    +import_trajectories(raw_data: List) → List

    Imports the trajectories from the list of dicts raw_data.

    Parameters
    @@ -543,8 +543,8 @@ as keys for the set of CIMS of a particular node.

    -
    -import_variables(raw_data: List) → pandas.core.frame.DataFrame
    +
    +import_variables(raw_data: List) → pandas.core.frame.DataFrame

    Imports the data in raw_data at the key _variables_label.

    Parameters
    @@ -560,8 +560,8 @@ as keys for the set of CIMS of a particular node.

    -
    -normalize_trajectories(raw_data: List, indx: int, trajectories_key: str) → List
    +
    +normalize_trajectories(raw_data: List, indx: int, trajectories_key: str) → List

    Extracts the trajectories in raw_data at the index index at the key trajectories key.

    Parameters
    @@ -581,8 +581,8 @@ as keys for the set of CIMS of a particular node.

    -
    -one_level_normalizing(raw_data: List, indx: int, key: str) → pandas.core.frame.DataFrame
    +
    +one_level_normalizing(raw_data: List, indx: int, key: str) → pandas.core.frame.DataFrame

    Extracts the one-level nested data in the list raw_data at the index indx at the key key.

    Parameters
    @@ -602,8 +602,8 @@ as keys for the set of CIMS of a particular node.

    -
    -read_json_file() → List
    +
    +read_json_file() → List

    Reads the JSON file in the path self.filePath.

    Returns
    @@ -618,12 +618,12 @@ as keys for the set of CIMS of a particular node.

    -
    -

    PyCTBN.PyCTBN.utility.sample_importer module

    +
    +

    pyctbn.legacy.utility.sample_importer module

    -
    -class PyCTBN.PyCTBN.utility.sample_importer.SampleImporter(trajectory_list: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, variables: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, prior_net_structure: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None)
    -

    Bases: PyCTBN.PyCTBN.utility.abstract_importer.AbstractImporter

    +
    +class pyctbn.legacy.utility.sample_importer.SampleImporter(trajectory_list: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, variables: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None, prior_net_structure: Union[pandas.core.frame.DataFrame, numpy.ndarray, List] = None)
    +

    Bases: pyctbn.legacy.utility.abstract_importer.AbstractImporter

    Implements the abstracts methods of AbstractImporter and adds all the necessary methods to process and prepare the data loaded directly by using DataFrame

    @@ -642,28 +642,28 @@ the data loaded directly by using DataFrame

    -
    -build_sorter(sample_frame: pandas.core.frame.DataFrame) → List
    +
    +build_sorter(sample_frame: pandas.core.frame.DataFrame) → List

    Implements the abstract method build_sorter of the AbstractImporter in order to get the ordered variables list.

    -
    -dataset_id() → str
    +
    +dataset_id() → str

    If the original dataset contains multiple dataset, this method returns a unique id to identify the current dataset

    -
    -import_data(header_column=None)
    +
    +import_data(header_column=None)
    -
    -

    Module contents

    +
    +

    Module contents

    @@ -676,7 +676,7 @@ dataset

    Next - Previous + Previous diff --git a/docs-out/_build/html/PyCTBN.html b/docs/_build/html/PyCTBN.html similarity index 64% rename from docs-out/_build/html/PyCTBN.html rename to docs/_build/html/PyCTBN.html index c4d5c15..8fbf841 100644 --- a/docs-out/_build/html/PyCTBN.html +++ b/docs/_build/html/PyCTBN.html @@ -60,9 +60,9 @@

    Contents:

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