shortest_augmenting_path

See method documentation: implementation and assumptions vary.

{
  "variable": "shipments",
  "domain": "Retail distribution and logistics planning",
  "feature_names": [
    "origin",
    "destination",
    "capacity_units",
    "cost_per_unit"
  ],
  "routes": [
    [
      "warehouse",
      "hub_a",
      3,
      2
    ],
    [
      "warehouse",
      "hub_b",
      2,
      1
    ],
    [
      "hub_a",
      "store",
      3,
      1
    ],
    [
      "hub_b",
      "store",
      2,
      3
    ],
    [
      "hub_a",
      "hub_b",
      1,
      1
    ]
  ]
}

# DataLens.ai example — distribution network capacity and flow.
# Copy input + code + test includes the named dataset shown above.
import networkx as nx

# Build a directed network of available shipments and capacities.
G = nx.DiGraph()
for origin, destination, capacity, cost in shipments["routes"]:
    G.add_edge(origin, destination, capacity=capacity, weight=cost)

# Verify the result on the example network.
residual = nx.algorithms.flow.shortest_augmenting_path(G, "warehouse", "store")
assert residual.graph["flow_value"] == 5
print("Expected output verified")

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median_abs_deviation mode percentileofscore pmean rankdata sem skew trim_mean variation anderson ansari bartlett binomtest bootstrap brunnermunzel chi2_contingency chisquare cramervonmises epps_singleton_2samp f_oneway fisher_exact fligner friedmanchisquare jarque_bera kendalltau kruskal ks_2samp kstest levene mannwhitneyu median_test mood normaltest page_trend_test pearsonr permutation_test ranksums shapiro sobol_indices spearmanr ttest_1samp ttest_ind ttest_rel wilcoxon bessel butter cheby1 cheby2 chirp coherence convolve correlate correlate2d csd deconvolve detrend ellip fftconvolve filtfilt find_peaks firls firwin firwin2 freqs freqz hilbert istft lfilter lombscargle max_len_seq peak_prominences peak_widths periodogram remez resample resample_poly savgol_filter sosfilt sosfiltfilt spectrogram stft welch wiener all_shortest_paths astar_path astar_path_length average_shortest_path_length bellman_ford_path bellman_ford_path_length bidirectional_dijkstra cycle_basis dag_longest_path dag_longest_path_length dijkstra_path dijkstra_path_length find_cycle floyd_warshall floyd_warshall_numpy goldberg_radzik johnson lexicographical_topological_sort maximum_spanning_edges maximum_spanning_tree minimum_cycle_basis minimum_spanning_edges minimum_spanning_tree multi_source_dijkstra negative_edge_cycle random_spanning_tree shortest_path shortest_path_length simple_cycles single_source_dijkstra topological_sort transitive_closure transitive_reduction adamic_adar_index attribute_assortativity_coefficient betweenness_centrality closeness_centrality cn_soundarajan_hopcroft communicability_betweenness_centrality current_flow_betweenness_centrality degree_assortativity_coefficient degree_centrality edge_betweenness_centrality eigenvector_centrality group_betweenness_centrality harmonic_centrality in_degree_centrality information_centrality jaccard_coefficient katz_centrality load_centrality numeric_assortativity_coefficient out_degree_centrality preferential_attachment ra_index_soundarajan_hopcroft resource_allocation_index second_order_centrality subgraph_centrality trophic_levels within_inter_cluster asyn_fluidc asyn_lpa_communities average_clustering clustering edge_betweenness_partition girvan_newman greedy_modularity_communities kernighan_lin_bisection label_propagation_communities louvain_communities modularity partition_quality square_clustering transitivity triangles all_node_cuts average_node_connectivity boykov_kolmogorov capacity_scaling dinitz edge_connectivity edge_disjoint_paths edmonds_karp k_edge_components k_edge_subgraphs local_edge_connectivity local_node_connectivity max_flow_min_cost maximum_flow min_cost_flow minimum_cut minimum_edge_cut minimum_node_cut network_simplex node_connectivity node_disjoint_paths preflow_push shortest_augmenting_path max_clique max_weight_matching maximal_matching maximum_independent_set min_maximal_matching min_weight_matching min_weighted_vertex_cover braycurtis canberra cdist chebyshev cityblock 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