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| import gradio as gr | |
| import numpy as np | |
| import plotly.graph_objects as go | |
| import plotly.express as px | |
| from sklearn.metrics import pairwise_distances | |
| import torch | |
| from facility_location import multi_eval | |
| import pickle | |
| def solver_plot(data_npy, boost=False): | |
| multi_eval.main(data_npy, boost) | |
| all_solutions = pickle.loads(open('./facility_location/solutions.pkl', 'rb').read()) | |
| data = data_npy.split('\n') | |
| n = len(data) | |
| p = int((len(data[0].split(' '))-2) / 2) | |
| positions = [] | |
| demands = [] | |
| actual_facilities = [] | |
| for row in data: | |
| row = row.split(' ') | |
| row = [x for x in row if len(x)] | |
| positions.append([float(row[0]), float(row[1])]) | |
| demand = [] | |
| for i in range(2, 2+p): | |
| demand.append(float(row[i])) | |
| demands.append(demand) | |
| actual_facility = [] | |
| for i in range(2+p, 2+2*p): | |
| actual_facility.append(bool(int(float(row[i])))) | |
| actual_facilities.append(actual_facility) | |
| positions = np.array(positions) | |
| demands = np.array(demands) | |
| actual_facilities = np.array(actual_facilities) | |
| solution_facilities = np.array(all_solutions).T | |
| # print(solution_facilities) | |
| # print(actual_facilities) | |
| actual_fig = go.Figure() | |
| solution_fig = go.Figure() | |
| for i in range(p): | |
| actual_fig.add_trace(go.Scattermapbox( | |
| lat=positions[actual_facilities[:, i]][:, 0], | |
| lon=positions[actual_facilities[:, i]][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=f'Facility {i+1}' | |
| )) | |
| solution_fig.add_trace(go.Scattermapbox( | |
| lat=positions[solution_facilities[:, i]][:, 0], | |
| lon=positions[solution_facilities[:, i]][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=f'Facility {i+1}' | |
| )) | |
| actual_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[actual_facilities[:, i]][:, 0]), \ | |
| lon=np.mean(positions[actual_facilities[:, i]][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| solution_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[solution_facilities[:, i]][:, 0]), \ | |
| lon=np.mean(positions[solution_facilities[:, i]][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| # show legend | |
| actual_fig.update_layout(showlegend=True) | |
| solution_fig.update_layout(showlegend=True) | |
| positions = np.deg2rad(positions) | |
| dist = pairwise_distances(positions, metric='haversine') * 6371 | |
| actual_ac = 0 | |
| solution_ac = 0 | |
| for i in range(p): | |
| ac_matrix = dist * demands[:, i][:, None] | |
| actual_ac += ac_matrix[:, actual_facilities[:, i]].min(axis=-1).sum() | |
| solution_ac += ac_matrix[:, solution_facilities[:, i]].min(axis=-1).sum() | |
| return actual_fig, solution_fig, actual_ac, solution_ac | |
| def demo_plot(city, facility): | |
| facility_name = ["π« School", "π₯ Hospital", "π³ Park"] | |
| all_facility = ["π« School", "π₯ Hospital", "π³ Park"] | |
| for i in range(len(all_facility)): | |
| if all_facility[i] in facility: | |
| all_facility[i] = True | |
| else: | |
| all_facility[i] = False | |
| city_name = city.replace(' ', '_') | |
| data = np.loadtxt(f'demo/{city_name}.npy') | |
| positions = data[:, :2] | |
| demands = data[:, 2:5] | |
| actual_facility = data[:, 5:8] | |
| solution_facility = data[:, 8:11] | |
| actual_fig = go.Figure() | |
| solution_fig = go.Figure() | |
| for i in range(len(all_facility)): | |
| if not all_facility[i]: | |
| continue | |
| actual_fig.add_trace(go.Scattermapbox( | |
| lat=positions[actual_facility[:, i] == 1][:, 0], | |
| lon=positions[actual_facility[:, i] == 1][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=facility_name[i], | |
| )) | |
| solution_fig.add_trace(go.Scattermapbox( | |
| lat=positions[solution_facility[:, i] == 1][:, 0], | |
| lon=positions[solution_facility[:, i] == 1][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=facility_name[i], | |
| )) | |
| actual_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[actual_facility[:, i] == 1][:, 0]), \ | |
| lon=np.mean(positions[actual_facility[:, i] == 1][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| solution_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[solution_facility[:, i] == 1][:, 0]), \ | |
| lon=np.mean(positions[solution_facility[:, i] == 1][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| # show legend | |
| actual_fig.update_layout(showlegend=True) | |
| solution_fig.update_layout(showlegend=True) | |
| positions = np.deg2rad(positions) | |
| dist = pairwise_distances(positions, metric='haversine') * 6371 | |
| actual_ac = 0 | |
| solution_ac = 0 | |
| for i in range(len(all_facility)): | |
| if not all_facility[i]: | |
| continue | |
| ac_matrix = dist * demands[:, i][:, None] | |
| actual_ac += ac_matrix[:, actual_facility[:, i] == 1].min(axis=-1).sum() | |
| solution_ac += ac_matrix[:, solution_facility[:, i] == 1].min(axis=-1).sum() | |
| return actual_fig, solution_fig, actual_ac, solution_ac | |
| def solver_plot1(data_npy, boost=False): | |
| data = data_npy.split('\n') | |
| n = len(data) | |
| p = int((len(data[0].split(' '))-2) / 2) | |
| positions = [] | |
| demands = [] | |
| actual_facilities = [] | |
| for row in data: | |
| row = row.split(' ') | |
| row = [x for x in row if len(x)] | |
| positions.append([float(row[0]), float(row[1])]) | |
| demand = [] | |
| for i in range(2, 2+p): | |
| demand.append(float(row[i])) | |
| demands.append(demand) | |
| actual_facility = [] | |
| for i in range(2+p, 2+2*p): | |
| actual_facility.append(bool(int(float(row[i])))) | |
| actual_facilities.append(actual_facility) | |
| positions = np.array(positions) | |
| demands = np.array(demands) | |
| actual_facilities = np.array(actual_facilities) | |
| solution_facilities = ~actual_facilities | |
| actual_fig = go.Figure() | |
| solution_fig = go.Figure() | |
| for i in range(p): | |
| actual_fig.add_trace(go.Scattermapbox( | |
| lat=positions[actual_facilities[:, i]][:, 0], | |
| lon=positions[actual_facilities[:, i]][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=f'Facility {i+1}' | |
| )) | |
| solution_fig.add_trace(go.Scattermapbox( | |
| lat=positions[solution_facilities[:, i]][:, 0], | |
| lon=positions[solution_facilities[:, i]][:, 1], | |
| mode='markers', | |
| marker=go.scattermapbox.Marker( | |
| size=10, | |
| color=px.colors.qualitative.Plotly[i] | |
| ), | |
| name=f'Facility {i+1}' | |
| )) | |
| actual_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[actual_facilities[:, i]][:, 0]), \ | |
| lon=np.mean(positions[actual_facilities[:, i]][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| solution_fig.update_layout( | |
| mapbox=dict( | |
| style='carto-positron', | |
| center=dict(lat=np.mean(positions[solution_facilities[:, i]][:, 0]), \ | |
| lon=np.mean(positions[solution_facilities[:, i]][:, 1])), | |
| zoom=11.0 | |
| ), | |
| margin=dict(l=0, r=0, b=0, t=0),) | |
| # show legend | |
| actual_fig.update_layout(showlegend=True) | |
| solution_fig.update_layout(showlegend=True) | |
| positions = np.deg2rad(positions) | |
| dist = pairwise_distances(positions, metric='haversine') * 6371 | |
| actual_ac = 0 | |
| solution_ac = 0 | |
| for i in range(p): | |
| ac_matrix = dist * demands[:, i][:, None] | |
| actual_ac += ac_matrix[:, actual_facilities[:, i]].min(axis=-1).sum() | |
| solution_ac += ac_matrix[:, solution_facilities[:, i]].min(axis=-1).sum() | |
| return actual_fig, solution_fig, actual_ac, solution_ac | |
| def get_example(): | |
| return [ | |
| ('40.71 -73.93 213 1\n40.72 -73.99 15 1\n40.65 -73.88 365 1\n40.57 -73.96 629 0\n40.70 -73.97 106 0\n40.61 -73.95 189 1'), | |
| ("40.71 -73.93 213 124 0 1\n40.72 -73.99 15 43 1 0\n40.65 -73.88 365 214 1 0\n40.57 -73.96 629 431 0 1\n40.70 -73.97 106 241 0 1\n40.60 -73.92 129 214 1 0\n40.61 -73.95 189 264 0 1\n40.63 -73.94 124 164 1 0"), | |
| ] | |
| def load_npy_file(file_obj): | |
| data = np.loadtxt(file_obj.name) | |
| string_array = '\n'.join([' '.join(map(str, row)) for row in data]) | |
| return string_array | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## Demo") | |
| with gr.Column(): | |
| city = gr.Radio(choices=["New York", "Boston", "Los Angeles", "Chicago"], value="New York", label="Select City:") | |
| facility = gr.CheckboxGroup(choices=["π« School", "π₯ Hospital", "π³ Park"], value=["π₯ Hospital"], label="Select Facility:") | |
| btn = gr.Button(value="π Generate") | |
| with gr.Row(): | |
| actual_map = gr.Plot(label='Actual Facility Distribution') | |
| solution_map = gr.Plot(label='Relocated Facility Distribution') | |
| with gr.Row(): | |
| actual_ac = gr.Textbox(label='Real-world Access Cost') | |
| solution_ac = gr.Textbox(label='Relocated Access Cost') | |
| demo.load(fn=demo_plot, inputs=[city, facility], outputs=[actual_map, solution_map, actual_ac, solution_ac]) | |
| btn.click(fn=demo_plot, inputs=[city, facility], outputs=[actual_map, solution_map, actual_ac, solution_ac]) | |
| gr.Markdown("## FLP & IUMFLP Solver") | |
| with gr.Column(): | |
| with gr.Row(): | |
| data_npy = gr.Textbox(label="Input") | |
| data_file = gr.UploadButton( | |
| label="π Upload a npy file", | |
| file_count="single", | |
| file_types=[".npy"]) | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=get_example(), | |
| inputs=[data_npy], | |
| fn=solver_plot1, | |
| outputs=[actual_map, solution_map, actual_ac, solution_ac], | |
| ) | |
| with gr.Row(): | |
| boost = gr.Checkbox(label="Turbo Boost (accelerate solution generation with fewer SWAP steps)", value=False) | |
| btn2 = gr.Button(value="π Generate") | |
| with gr.Row(): | |
| actual_map = gr.Plot(label='Initial Solution') | |
| solution_map = gr.Plot(label='Final Solution') | |
| with gr.Row(): | |
| actual_ac = gr.Textbox(label='Initial Access Cost') | |
| solution_ac = gr.Textbox(label='Final Access Cost') | |
| data_file.upload(fn=load_npy_file, inputs=[data_file], outputs=[data_npy]) | |
| btn2.click(fn=solver_plot, inputs=[data_npy, boost], outputs=[actual_map, solution_map, actual_ac, solution_ac]) | |
| if __name__ == "__main__": | |
| demo.launch() |