1057 data generation for GNNs

cpp/memilio/mobility/graph_simulation.h

@@ -22,6 +22,7 @@
 
 #include "memilio/mobility/graph.h"
 #include "memilio/utils/random_number_generator.h"
+#include <chrono>
 
 namespace mio
 {
@@ -60,7 +61,9 @@ class GraphSimulationBase
 
     void advance(double t_max = 1.0)
     {
-        auto dt = m_dt;
+        auto dt         = m_dt;
+        auto start_time = std::chrono::high_resolution_clock::now(); // Startzeit erfassen
+
         while (m_t < t_max) {
             if (m_t + dt > t_max) {
                 dt = t_max - m_t;
@@ -77,6 +80,12 @@ class GraphSimulationBase
                             m_graph.nodes()[e.end_node_idx].property);
             }
         }
+
+        auto end_time                                = std::chrono::high_resolution_clock::now(); // Endzeit erfassen
+        std::chrono::duration<double> execution_time = end_time - start_time; // Ausführungszeit berechnen
+
+        std::cout << "t = " << m_t << " execution time (Graph Simulation): " << execution_time.count() << "sec"
+                  << std::endl;
     }
 
     double get_t() const

pycode/memilio-surrogatemodel/memilio/surrogatemodel/GNN/GNN_utils.py

@@ -0,0 +1,166 @@
+import numpy as np
+import pandas as pd
+import os
+from sklearn.preprocessing import FunctionTransformer
+
+from memilio.epidata import transformMobilityData as tmd
+from memilio.epidata import getDataIntoPandasDataFrame as gd
+from memilio.simulation.osecir import (ModelGraph, set_edges)
+from memilio.epidata import modifyDataframeSeries as mdfs
+
+
+def remove_confirmed_compartments(dataset_entries, num_groups):
+    """! The compartments which contain confirmed cases are not needed and are 
+        therefore omitted by summarizing the confirmed compartment with the 
+        original compartment. 
+    @param dataset_entries Array that contains the compartmental data with 
+            confirmed compartments. 
+    @param num_groups Number of age groups.
+    @return Array that contains the compartmental data without confirmed compartments. 
+   """
+
+    new_dataset_entries = []
+    for i in dataset_entries:
+        dataset_entries_reshaped = i.reshape(
+            [num_groups, int(np.asarray(dataset_entries).shape[1]/num_groups)]
+        )
+        sum_inf_no_symp = np.sum(dataset_entries_reshaped[:, [2, 3]], axis=1)
+        sum_inf_symp = np.sum(dataset_entries_reshaped[:, [4, 5]], axis=1)
+        dataset_entries_reshaped[:, 2] = sum_inf_no_symp
+        dataset_entries_reshaped[:, 4] = sum_inf_symp
+        new_dataset_entries.append(
+            np.delete(dataset_entries_reshaped, [3, 5], axis=1).flatten()
+        )
+    return new_dataset_entries
+
+
+def getBaselineMatrix():
+    """! loads the baselinematrix
+    """
+
+    baseline_contact_matrix0 = os.path.join(
+        "./data/contacts/baseline_home.txt")
+    baseline_contact_matrix1 = os.path.join(
+        "./data/contacts/baseline_school_pf_eig.txt")
+    baseline_contact_matrix2 = os.path.join(
+        "./data/contacts/baseline_work.txt")
+    baseline_contact_matrix3 = os.path.join(
+        "./data/contacts/baseline_other.txt")
+
+    baseline = np.loadtxt(baseline_contact_matrix0) \
+        + np.loadtxt(baseline_contact_matrix1) + \
+        np.loadtxt(baseline_contact_matrix2) + \
+        np.loadtxt(baseline_contact_matrix3)
+
+    return baseline
+
+
+def getMinimumMatrix():
+    """! loads the minimum matrix
+    """
+
+    minimum_contact_matrix0 = os.path.join(
+        "./data/contacts/minimum_home.txt")
+    minimum_contact_matrix1 = os.path.join(
+        "./data/contacts/minimum_school_pf_eig.txt")
+    minimum_contact_matrix2 = os.path.join(
+        "./data/contacts/minimum_work.txt")
+    minimum_contact_matrix3 = os.path.join(
+        "./data/contacts/minimum_other.txt")
+
+    minimum = np.loadtxt(minimum_contact_matrix0) \
+        + np.loadtxt(minimum_contact_matrix1) + \
+        np.loadtxt(minimum_contact_matrix2) + \
+        np.loadtxt(minimum_contact_matrix3)
+
+    return minimum
+
+
+def make_graph(directory, num_regions, countykey_list, models):
+    """! 
+    @param directory Directory with mobility data. 
+    @param num_regions Number (int) of counties that should be added to the 
+            grap-ODE model. Equals 400 for whole Germany. 
+    @param countykey_list List of keys/IDs for each county. 
+    @models models List of osecir Model with one model per population. 
+    @return graph Graph-ODE model. 
+   """
+    graph = ModelGraph()
+    for i in range(num_regions):
+        graph.add_node(int(countykey_list[i]), models[i])
+
+    num_locations = 4
+
+    set_edges(os.path.abspath(os.path.join(directory, os.pardir)),
+              graph, num_locations)
+    return graph
+
+
+def transform_mobility_directory():
+    """! Transforms the mobility data by merging Eisenach and Wartburgkreis
+    """
+    # get mobility data directory
+    arg_dict = gd.cli("commuter_official")
+
+    directory = arg_dict['out_folder'].split('/pydata')[0]
+    directory = os.path.join(directory, 'mobility/')
+
+    # Merge Eisenach and Wartbugkreis in Input Data
+    tmd.updateMobility2022(directory, mobility_file='twitter_scaled_1252')
+    tmd.updateMobility2022(
+        directory, mobility_file='commuter_migration_scaled')
+    return directory
+
+
+def get_population():
+    df_population = pd.read_json(
+        'data/pydata/Germany/county_population.json')
+    age_groups = ['0-4', '5-14', '15-34', '35-59', '60-79', '80-130']
+
+    df_population_agegroups = pd.DataFrame(
+        columns=[df_population.columns[0]] + age_groups)
+    for region_id in df_population.iloc[:, 0]:
+        df_population_agegroups.loc[len(df_population_agegroups.index), :] = [int(region_id)] + list(
+            mdfs.fit_age_group_intervals(df_population[df_population.iloc[:, 0] == int(region_id)].iloc[:, 2:], age_groups))
+
+    population = df_population_agegroups.values.tolist()
+    return population
+
+
+def scale_data(data):
+    num_groups = int(np.asarray(data['inputs']).shape[2] / 8)
+    transformer = FunctionTransformer(np.log1p, validate=True)
+
+    # Scale inputs
+    inputs = np.asarray(
+        data['inputs']).transpose(2, 0, 1, 3).reshape(num_groups * 8, -1)
+    scaled_inputs = transformer.transform(inputs)
+    original_shape_input = np.asarray(data['inputs']).shape
+
+    # Reverse the reshape
+    reshaped_back = scaled_inputs.reshape(original_shape_input[2],
+                                          original_shape_input[0],
+                                          original_shape_input[1],
+                                          original_shape_input[3])
+
+    # Reverse the transpose
+    original_inputs = reshaped_back.transpose(1, 2, 0, 3)
+    scaled_inputs = original_inputs.transpose(0, 3, 1, 2)
+
+    # Scale labels
+    labels = np.asarray(
+        data['labels']).transpose(2, 0, 1, 3).reshape(num_groups * 8, -1)
+    scaled_labels = transformer.transform(labels)
+    original_shape_labels = np.asarray(data['labels']).shape
+
+    # Reverse the reshape
+    reshaped_back = scaled_labels.reshape(original_shape_labels[2],
+                                          original_shape_labels[0],
+                                          original_shape_labels[1],
+                                          original_shape_labels[3])
+
+    # Reverse the transpose
+    original_labels = reshaped_back.transpose(1, 2, 0, 3)
+    scaled_labels = original_labels.transpose(0, 3, 1, 2)
+
+    return scaled_inputs, scaled_labels