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results_summary.py
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import glob
import pandas as pd
import os
from map_utils import ct_nia_mapping,get_nias
from graph_utils import pednet_NIA
import matplotlib.pyplot as plt
import numpy as np
import json
import geopandas as gpd
from model_latest import opt_single, cur_assignment_single, opt_multiple, opt_single_depth, cur_assignment_single_depth, weights_array, dist_to_score, L_a, L_f_a, weights_array_multi, choice_weights
def shifted_geo_mean(L, s=1):
a = np.array(L)
shifted = a+s
return (shifted.prod())**(1.0/len(a)) - s
def get_results_df(results_folder, model_name,amenity_name=None):
L = []
if amenity_name:
all_files = glob.glob(os.path.join(results_folder,"summary",model_name) + "/*" + amenity_name + ".csv")
else:
all_files = glob.glob(os.path.join(results_folder, "summary", model_name) + "/*" + ".csv")
for filename in all_files:
df = pd.read_csv(filename, index_col=None, header=0)
L.append(df)
final_df = pd.concat(L, axis=0, ignore_index=True)
return final_df
def plot_time_vs_size_multiple(results_folder, plot_folder, models, display_names, save_name):
data_root = "/Users/weimin/Documents/MASC/walkability_data"
D_NIA = ct_nia_mapping(
os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
plt.clf()
for i in range(len(models)):
model_name = models[i]
display_name = display_names[i]
results_df = get_results_df(results_folder, model_name)
results_df = results_df[(results_df["k_L_grocery"]>0) & (results_df["k_L_restaurant"]>0) & (results_df["k_L_school"]>0)]
for nia in list(D_NIA.keys()):
nia_df = results_df[results_df["nia_id"] == nia]
if len(nia_df) < 9:
print("missing for",nia)
size = results_df.groupby("nia_id").mean()["num_res"] + results_df.groupby("nia_id").mean()["num_parking"]
avg_time = results_df.groupby("nia_id")["solving_time"].apply(shifted_geo_mean)
new_x, new_y = zip(*sorted(zip(size, avg_time)))
plt.plot(new_x, new_y, '--o', label=display_name)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Shifted geo mean")
plt.title(save_name)
plt.savefig(os.path.join(plot_folder, save_name))
return
def plot_time_vs_size_single(results_folder, plot_folder, models, display_names, save_name):
plt.clf()
for i in range(len(models)):
model_name = models[i]
display_name = display_names[i]
if "Depth" in model_name:
amenities = ["restaurant"]
else:
amenities = ["grocery","restaurant","school"]
for amenity in amenities:
results_df = get_results_df(results_folder, model_name, amenity)
results_df=results_df[results_df["k"]>0]
size = results_df.groupby("nia_id").mean()["num_res"] + results_df.groupby("nia_id").mean()["num_parking"]
avg_time = results_df.groupby("nia_id")["solving_time"].apply(shifted_geo_mean)
new_x, new_y = zip(*sorted(zip(size, avg_time)))
plt.plot(new_x, new_y, '--o', label=display_name + "-"+ amenity)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Shifted geo mean")
plt.title(save_name)
plt.savefig(os.path.join(plot_folder, save_name))
return
def plot_obj_vs_k(results_df, plot_folder, amenity_name, model_name, display_name):
data_root = "/Users/weimin/Documents/MASC/walkability_data"
D_NIA = ct_nia_mapping(os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
if 'Depth' in model_name:
for ind in [0,1]:
plt.clf()
plt.figure(figsize=(15, 10))
for nia in list(D_NIA.keys()):
nia_df = results_df[results_df["nia_id"] == nia]
nia_df = nia_df.sort_values(by=['k'])
k_list = nia_df["k"]
dist_list = [L[ind] for L in [json.loads(x) for x in nia_df["dist_obj"]]]
dist_list = [None if item == 0 else item for item in dist_list]
plt.plot(k_list, dist_list, '--o', label=D_NIA[nia]['name'])
plt.legend(prop={'size': 6})
plt.xlabel("k")
plt.ylabel("dist (m)")
plt.savefig(os.path.join(plot_folder, amenity_name + "_" + display_name + ("_k_vs_dist_%s.png" % (ind+1))))
else:
plt.clf()
plt.figure(figsize=(15, 10))
for nia in list(D_NIA.keys()):
nia_df=results_df[results_df["nia_id"]==nia]
nia_df=nia_df.sort_values(by=['k'])
k_list = nia_df["k"]
dist_list = nia_df["dist_obj"]
plt.plot(k_list, dist_list, '--o', label=D_NIA[nia]['name'])
plt.legend(prop={'size': 6})
plt.xlabel("k")
plt.ylabel("dist (m)")
plt.savefig(os.path.join(plot_folder, amenity_name + "_"+ display_name + "_k_vs_dist.png"))
plt.clf()
plt.figure(figsize=(15, 10))
for nia in list(D_NIA.keys()):
nia_df = results_df[results_df["nia_id"] == nia]
if len(nia_df)<10:
if len(nia_df[nia_df['k']==0])>0:
print("missing values?")
print(model_name,amenity_name,nia,len(nia_df))
nia_df = nia_df.sort_values(by=['k'])
k_list = nia_df["k"]
score_list = nia_df["obj"]
plt.plot(k_list, score_list, '--o', label=D_NIA[nia]['name'])
plt.legend(prop={'size': 6})
plt.xlabel("k")
plt.ylabel("score")
plt.savefig(os.path.join(plot_folder, amenity_name + "_"+ display_name + "_k_vs_score.png"))
return
def all_instances_obj(data_root, results_folder,processed_folder):
INCLUDE_BP=False
D_NIA = ct_nia_mapping(os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
# opt single
print("single case")
amenities = ["grocery", "restaurant", "school"]
for amenity in amenities:
L_nia = []
L_mip_obj = []
L_cp_obj = []
L_greedy_obj = []
if INCLUDE_BP:
L_mip_bp_obj = []
L_cp_bp_obj = []
L_k = []
L_mip_status = []
L_cp_status = []
for nia in list(D_NIA.keys()):
print("nia:",nia)
for k in range(1,10):
filename = "assignment_NIA_%s_%s_%s.csv" % (nia,k,amenity)
L_nia.append(nia)
L_k.append(k)
# MIP
if os.path.exists(os.path.join(results_folder,"sol","OptSingle_False_0",filename)):
mip_df = pd.read_csv(os.path.join(results_folder,"sol","OptSingle_False_0",filename), index_col=None, header=0)
scores = dist_to_score(np.array(mip_df["dist"]), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_obj.append(score_obj)
#L_mip_status.append(mip_df["model_status"])
else:
L_mip_obj.append(None)
#L_mip_status.append("null")
# CP
if os.path.exists(os.path.join(results_folder, "sol", "OptSingleCP_False_0", filename)):
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptSingleCP_False_0", filename),index_col=None, header=0)
scores = dist_to_score(np.array(cp_df["dist"]), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_obj.append(score_obj)
#L_cp_status.append(cp_df["model_status"])
else:
L_cp_obj.append(None)
#L_cp_status.append("null")
# Greedy
if os.path.exists(os.path.join(results_folder, "summary", "GreedySingle_False_0", "NIA_%s_%s_%s.csv" % (nia,k,amenity))):
greedy_df = pd.read_csv(os.path.join(results_folder, "summary", "GreedySingle_False_0", "NIA_%s_%s_%s.csv" % (nia,k,amenity)), index_col=None,header=0)
L_greedy_obj.append(greedy_df["obj"].values[0])
else:
L_greedy_obj.append(None)
results_D = { "nia":L_nia, "k": L_k ,"mip":L_mip_obj,"cp":L_cp_obj,"greedy":L_greedy_obj,}
df_filename = os.path.join(processed_folder, "single_%s.csv" % (amenity))
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy"]].max(axis=1)
summary_df.to_csv(df_filename,index=False)
# opt single depth
print("single depth case")
amenities = ["restaurant"]
for amenity in amenities:
L_nia = []
L_mip_obj = []
L_cp_obj = []
L_greedy_obj = []
if INCLUDE_BP:
L_mip_bp_obj = []
L_cp_bp_obj = []
L_k = []
L_mip_status = []
L_cp_status = []
for nia in list(D_NIA.keys()):
print("nia:", nia)
for k in range(1,10):
filename = "assignment_NIA_%s_%s_%s.csv" % (nia, k, amenity)
L_nia.append(nia)
L_k.append(k)
# MIP
if os.path.exists(os.path.join(results_folder, "sol", "OptSingleDepth_False_0", filename)):
mip_df = pd.read_csv(os.path.join(results_folder, "sol", "OptSingleDepth_False_0", filename),index_col=None, header=0)
choices_dist = [mip_df[str(c)+"_dist"] if (str(c)+"_dist") in mip_df.columns else L_a[-2] for c in range(10)]
choices_dist = np.array(choices_dist)
weighted_choices = np.dot(np.array(choice_weights), choices_dist)
scores = dist_to_score(np.array(weighted_choices), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_obj.append(score_obj)
#L_mip_status.append(mip_df["model_status"])
else:
L_mip_obj.append(None)
#L_mip_status.append("null")
# CP
if os.path.exists(os.path.join(results_folder, "sol", "OptSingleDepthCP_False_0", filename)):
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptSingleDepthCP_False_0", filename),index_col=None, header=0)
choices_dist = [cp_df[str(c) + "_dist"] if (str(c) + "_dist") in cp_df.columns else L_a[-2] for c in range(10)]
choices_dist = np.array(choices_dist)
weighted_choices = np.dot(np.array(choice_weights), choices_dist)
scores = dist_to_score(np.array(weighted_choices), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_obj.append(score_obj)
#L_cp_status.append(cp_df["model_status"])
else:
L_cp_obj.append(None)
#L_cp_status.append("null")
# Greedy
if os.path.exists(os.path.join(results_folder, "summary", "GreedySingleDepth_False_0", "NIA_%s_%s_%s.csv" % (nia,k,amenity))):
greedy_df = pd.read_csv(os.path.join(results_folder, "summary", "GreedySingleDepth_False_0", "NIA_%s_%s_%s.csv" % (nia,k,amenity)),index_col=None, header=0)
L_greedy_obj.append(greedy_df["obj"].values[0])
else:
L_greedy_obj.append(None)
results_D = {"nia": L_nia, "k": L_k, "mip": L_mip_obj, "cp": L_cp_obj, "greedy": L_greedy_obj }
df_filename = os.path.join(processed_folder, "single_depth_%s.csv" % (amenity))
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy"]].max(axis=1)
summary_df.to_csv(df_filename, index=False)
# opt multiple
print("multiple case")
L_nia = []
L_mip_obj = []
L_cp_obj = []
L_greedy_obj = []
if INCLUDE_BP:
L_mip_bp_obj = []
L_k = []
L_mip_status = []
L_cp_status = []
for nia in list(D_NIA.keys()):
print("nia:", nia)
for k in range(1,10):
filename = "assignment_NIA_%s_%s,%s,%s.csv" % (nia,k,k,k)
L_nia.append(nia)
L_k.append(k)
# MIP
if os.path.exists(os.path.join(results_folder, "sol", "OptMultiple_False_0", filename)):
mip_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultiple_False_0", filename),index_col=None, header=0)
dist_grocery = mip_df["dist_grocery"]
dist_restaurant = mip_df["dist_restaurant"]
dist_school = mip_df["dist_school"]
multiple_dist = np.array([dist_grocery, dist_restaurant, dist_school])
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_obj.append(score_obj)
#L_mip_status.append(mip_df["model_status"])
else:
L_mip_obj.append(None)
#L_mip_status.append("null")
if INCLUDE_BP:
# MIP - branch priority
if os.path.exists(os.path.join(results_folder, "sol", "OptMultiple_True_0", filename)):
mip_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultiple_True_0", filename),
index_col=None, header=0)
dist_grocery = mip_df["dist_grocery"]
dist_restaurant = mip_df["dist_restaurant"]
dist_school = mip_df["dist_school"]
multiple_dist = np.array([dist_grocery, dist_restaurant, dist_school])
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_bp_obj.append(score_obj)
# L_mip_status.append(mip_df["model_status"])
else:
L_mip_bp_obj.append(None)
# L_mip_status.append("null")
# CP
if os.path.exists(os.path.join(results_folder, "sol", "OptMultipleCP_False_0", filename)):
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultipleCP_False_0", filename),index_col=None, header=0)
dist_grocery = cp_df["dist_grocery"]
dist_restaurant = cp_df["dist_restaurant"]
dist_school = cp_df["dist_school"]
multiple_dist = np.array([dist_grocery, dist_restaurant, dist_school])
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_obj.append(score_obj)
#L_cp_status.append(cp_df["model_status"])
else:
L_cp_obj.append(None)
#L_cp_status.append("null")
# Greedy
if os.path.exists(os.path.join(results_folder, "summary", "GreedyMultiple_False_0", "NIA_%s_%s,%s,%s.csv" % (nia,k,k,k))):
greedy_df = pd.read_csv(os.path.join(results_folder, "summary", "GreedyMultiple_False_0", "NIA_%s_%s,%s,%s.csv" % (nia,k,k,k)),index_col=None, header=0)
L_greedy_obj.append(greedy_df["obj"].values[0])
else:
L_greedy_obj.append(None)
if INCLUDE_BP:
results_D = {"nia": L_nia, "k": L_k, "mip": L_mip_obj, "cp": L_cp_obj, "greedy": L_greedy_obj,"mip-bp": L_mip_bp_obj}
df_filename = os.path.join(processed_folder, "multiple.csv")
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy","mip-bp"]].max(axis=1)
summary_df.to_csv(df_filename, index=False)
else:
results_D = {"nia": L_nia, "k": L_k, "mip": L_mip_obj, "cp": L_cp_obj, "greedy": L_greedy_obj}
df_filename = os.path.join(processed_folder, "multiple.csv" )
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy"]].max(axis=1)
summary_df.to_csv(df_filename, index=False)
# opt multiple depth
print("multiple depth case")
L_nia = []
L_mip_obj = []
L_cp_obj = []
L_greedy_obj = []
if INCLUDE_BP:
L_mip_bp_obj = []
L_cp_bp_obj = []
L_k = []
L_mip_status = []
L_cp_status = []
for nia in list(D_NIA.keys()):
print("nia:", nia)
for k in range(1,10):
filename = "assignment_NIA_%s_%s,%s,%s.csv" % (nia, k, k, k)
L_nia.append(nia)
L_k.append(k)
# MIP
if os.path.exists(os.path.join(results_folder, "sol", "OptMultipleDepth_False_0", filename)):
mip_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultipleDepth_False_0", filename),index_col=None, header=0)
dist_grocery = mip_df["dist_grocery"]
choices_dist = [mip_df[str(c) + "_dist_restaurant"] if (str(c) + "_dist_restaurant") in mip_df.columns else L_a[-2] for c in range(10)]
dist_school = mip_df["dist_school"]
multiple_dist = np.array([dist_grocery] + choices_dist + [dist_school])
weighted_dist = np.dot(np.array(weights_array_multi), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_obj.append(score_obj)
#L_mip_status.append(mip_df["model_status"])
else:
L_mip_obj.append(None)
#L_mip_status.append("null")
if INCLUDE_BP:
# MIP - branch priority
if os.path.exists(os.path.join(results_folder, "sol", "OptMultipleDepth_True_0", filename)):
mip_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultipleDepth_True_0", filename),
index_col=None, header=0)
dist_grocery = mip_df["dist_grocery"]
choices_dist = [
mip_df[str(c) + "_dist_restaurant"] if (str(c) + "_dist_restaurant") in mip_df.columns else L_a[
-2] for c in range(10)]
dist_school = mip_df["dist_school"]
multiple_dist = np.array([dist_grocery] + choices_dist + [dist_school])
weighted_dist = np.dot(np.array(weights_array_multi), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_bp_obj.append(score_obj)
# L_mip_status.append(mip_df["model_status"])
else:
L_mip_bp_obj.append(None)
# L_mip_status.append("null")
# CP
if os.path.exists(os.path.join(results_folder, "sol", "OptMultipleDepthCP_False_0", filename)):
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultipleDepthCP_False_0", filename), index_col=None, header=0)
dist_grocery = cp_df["dist_grocery"]
choices_dist = [cp_df[str(c) + "_dist_restaurant"] if (str(c) + "_dist_restaurant") in mip_df.columns else L_a[-2] for c in range(10)]
dist_school = cp_df["dist_school"]
multiple_dist = np.array([dist_grocery] + choices_dist + [dist_school])
weighted_dist = np.dot(np.array(weights_array_multi), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_obj.append(score_obj)
#L_cp_status.append(cp_df["model_status"])
else:
L_cp_obj.append(None)
#L_cp_status.append("null")
if INCLUDE_BP:
# CP - branch priority
if os.path.exists(os.path.join(results_folder, "sol", "OptMultipleDepthCP_True_0", filename)):
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptMultipleDepthCP_True_0", filename),
index_col=None, header=0)
dist_grocery = cp_df["dist_grocery"]
choices_dist = [
cp_df[str(c) + "_dist_restaurant"] if (str(c) + "_dist_restaurant") in mip_df.columns else L_a[
-2] for c in range(10)]
dist_school = cp_df["dist_school"]
multiple_dist = np.array([dist_grocery] + choices_dist + [dist_school])
weighted_dist = np.dot(np.array(weights_array_multi), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_bp_obj.append(score_obj)
# L_cp_status.append(cp_df["model_status"])
else:
L_cp_bp_obj.append(None)
# L_cp_status.append("null")
# Greedy
if os.path.exists(os.path.join(results_folder, "summary", "GreedyMultipleDepth_False_0", "NIA_%s_%s,%s,%s.csv" % (nia,k,k,k))):
greedy_df = pd.read_csv(os.path.join(results_folder, "summary", "GreedyMultipleDepth_False_0", "NIA_%s_%s,%s,%s.csv" % (nia,k,k,k)),index_col=None, header=0)
L_greedy_obj.append(greedy_df["obj"].values[0])
else:
L_greedy_obj.append(None)
if INCLUDE_BP:
results_D = {"nia": L_nia, "k": L_k, "mip": L_mip_obj, "cp": L_cp_obj, "greedy": L_greedy_obj,"mip-bp": L_mip_bp_obj, "cp-bp": L_cp_bp_obj}
df_filename = os.path.join(processed_folder, "multiple_depth.csv")
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy","mip-bp","cp-bp"]].max(axis=1)
summary_df.to_csv(df_filename, index=False)
else:
results_D = {"nia": L_nia, "k": L_k, "mip": L_mip_obj, "cp": L_cp_obj, "greedy": L_greedy_obj}
df_filename = os.path.join(processed_folder, "multiple_depth.csv")
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp","greedy"]].max(axis=1)
summary_df.to_csv(df_filename, index=False)
return
def single_aggregate_obj(data_root, results_folder,processed_folder):
# NOTE: THIS IS A RELATXATION, AS IT ALLOWS COMPETING RESOURCES TO BE TOGETHER AND DISREGARDS THE CAPACITY CONSTRAINTS
D_NIA = ct_nia_mapping(os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
# no depth
print("single aggregate, no depth")
L_nia = []
L_mip_obj = []
L_cp_obj = []
L_greedy_obj = []
L_k = []
for nia in list(D_NIA.keys()):
print("nia:",nia)
for k in range(1,10):
L_nia.append(nia)
L_k.append(k)
amenities = ["grocery", "restaurant", "school"]
# MIP
all_type_dist=[]
for amenity in amenities:
filename = "assignment_NIA_%s_%s_%s.csv" % (nia,k,amenity)
mip_df = pd.read_csv(os.path.join(results_folder,"sol","OptSingle_False_0",filename), index_col=None, header=0)
all_type_dist.append(mip_df["dist"])
multiple_dist = np.array(all_type_dist)
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_mip_obj.append(score_obj)
# CP
all_type_dist = []
for amenity in amenities:
filename = "assignment_NIA_%s_%s_%s.csv" % (nia, k, amenity)
cp_df = pd.read_csv(os.path.join(results_folder, "sol", "OptSingleCP_False_0", filename),index_col=None, header=0)
all_type_dist.append(cp_df["dist"])
multiple_dist = np.array(all_type_dist)
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_cp_obj.append(score_obj)
# Greedy
all_type_dist = []
for amenity in amenities:
filename = "assignment_NIA_%s_%s_%s.csv" % (nia, k, amenity)
greedy_df = pd.read_csv(os.path.join(results_folder, "sol", "GreedySingle_False_0", filename), index_col=None,header=0)
all_type_dist.append(greedy_df["dist"])
multiple_dist = np.array(all_type_dist)
weighted_dist = np.dot(np.array(weights_array), multiple_dist)
scores = dist_to_score(np.array(weighted_dist), L_a, L_f_a)
score_obj = np.mean(scores)
L_greedy_obj.append(score_obj)
results_D = { "nia":L_nia, "k": L_k ,"mip":L_mip_obj,"cp":L_cp_obj,"greedy":L_greedy_obj}
df_filename = os.path.join(processed_folder, "single_aggregate_no_depth.csv")
summary_df = pd.DataFrame(results_D)
summary_df["best"] = summary_df[["mip", "cp", "greedy"]].max(axis=1)
summary_df.to_csv(df_filename,index=False)
return
def plot_quality(processed_folder):
data_root = "/Users/weimin/Documents/MASC/walkability_data"
D_NIA = ct_nia_mapping(os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
plt.clf()
# single case
for amenity in ["grocery","restaurant","school"]:
L_size = []
L_gap_mip = []
L_gap_cp = []
L_gap_greedy = []
all_obj_df = pd.read_csv(os.path.join(processed_folder, "single_" + amenity+".csv"), index_col=None, header=0)
for nia in list(D_NIA.keys()):
results_df = get_results_df(results_folder, "OptSingle_False_0", amenity)
results_df = results_df[results_df["nia_id"] == nia]
obj_df = all_obj_df[all_obj_df["nia"]==nia]
L_size.append(results_df["num_res"].mean() + results_df["num_parking"].mean())
L_gap_mip.append(np.mean(obj_df['best']-obj_df['mip']))
L_gap_cp.append(np.mean(obj_df['best'] - obj_df['cp']))
L_gap_greedy.append(np.mean(obj_df['best'] - obj_df['greedy']))
new_x, new_gap_mip = zip(*sorted(zip(L_size, L_gap_mip)))
new_x, new_gap_cp = zip(*sorted(zip(L_size, L_gap_cp)))
new_x, new_gap_greedy = zip(*sorted(zip(L_size, L_gap_greedy)))
plt.plot(new_x, new_gap_mip, '--o', label="MILP" + "-"+ amenity)
plt.plot(new_x, new_gap_cp, '--o', label="CP" + "-" + amenity)
plt.plot(new_x, new_gap_greedy, '--o', label="Greedy" + "-" + amenity)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Relative error")
plt.title("solution quality - single amenity case")
plt.savefig(os.path.join(plot_folder, "quality", "single"))
plt.clf()
for amenity in ["restaurant"]:
L_size = []
L_gap_mip = []
L_gap_cp = []
L_gap_greedy = []
all_obj_df = pd.read_csv(os.path.join(processed_folder, "single_depth_" + amenity+".csv"), index_col=None, header=0)
for nia in list(D_NIA.keys()):
results_df = get_results_df(results_folder, "OptSingleDepth_False_0", amenity)
results_df = results_df[results_df["nia_id"] == nia]
obj_df = all_obj_df[all_obj_df["nia"]==nia]
L_size.append(results_df["num_res"].mean() + results_df["num_parking"].mean())
L_gap_mip.append(np.mean(obj_df['best']-obj_df['mip']))
L_gap_cp.append(np.mean(obj_df['best'] - obj_df['cp']))
L_gap_greedy.append(np.mean(obj_df['best'] - obj_df['greedy']))
new_x, new_gap_mip = zip(*sorted(zip(L_size, L_gap_mip)))
new_x, new_gap_cp = zip(*sorted(zip(L_size, L_gap_cp)))
new_x, new_gap_greedy = zip(*sorted(zip(L_size, L_gap_greedy)))
plt.plot(new_x, new_gap_mip, '--o', label="MILP" + "-"+ amenity)
plt.plot(new_x, new_gap_cp, '--o', label="CP" + "-" + amenity)
plt.plot(new_x, new_gap_greedy, '--o', label="Greedy" + "-" + amenity)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Relative error")
plt.title("solution quality - single amenity with depth of choice")
plt.savefig(os.path.join(plot_folder, "quality", "single_depth"))
# multiple amenity case
plt.clf()
L_size = []
L_gap_mip = []
L_gap_cp = []
L_gap_greedy = []
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple.csv"), index_col=None, header=0)
for nia in list(D_NIA.keys()):
results_df = get_results_df(results_folder, "OptMultiple_False_0")
results_df = results_df[results_df["nia_id"] == nia]
obj_df = all_obj_df[all_obj_df["nia"] == nia]
L_size.append(results_df["num_res"].mean() + results_df["num_parking"].mean())
L_gap_mip.append(np.mean(obj_df['best'] - obj_df['mip']))
L_gap_cp.append(np.mean(obj_df['best'] - obj_df['cp']))
L_gap_greedy.append(np.mean(obj_df['best'] - obj_df['greedy']))
new_x, new_gap_mip = zip(*sorted(zip(L_size, L_gap_mip)))
new_x, new_gap_cp = zip(*sorted(zip(L_size, L_gap_cp)))
new_x, new_gap_greedy = zip(*sorted(zip(L_size, L_gap_greedy)))
plt.plot(new_x, new_gap_mip, '--o', label="MILP" + "-" + amenity)
plt.plot(new_x, new_gap_cp, '--o', label="CP" + "-" + amenity)
plt.plot(new_x, new_gap_greedy, '--o', label="Greedy" + "-" + amenity)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Relative error")
plt.title("solution quality - multiple amenity case")
plt.savefig(os.path.join(plot_folder, "quality", "multiple"))
# multiple amenity case with depth of choice
plt.clf()
L_size = []
L_gap_mip = []
L_gap_cp = []
L_gap_greedy = []
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple_depth.csv"), index_col=None, header=0)
for nia in list(D_NIA.keys()):
results_df = get_results_df(results_folder, "OptMultipleDepth_False_0")
results_df = results_df[results_df["nia_id"] == nia]
obj_df = all_obj_df[all_obj_df["nia"] == nia]
L_size.append(results_df["num_res"].mean() + results_df["num_parking"].mean())
L_gap_mip.append(np.mean((obj_df['best'] - obj_df['mip'])/obj_df['best']))
L_gap_cp.append(np.mean((obj_df['best'] - obj_df['cp'])/obj_df['best']))
L_gap_greedy.append(np.mean((obj_df['best'] - obj_df['greedy'])/obj_df['best']))
new_x, new_gap_mip = zip(*sorted(zip(L_size, L_gap_mip)))
new_x, new_gap_cp = zip(*sorted(zip(L_size, L_gap_cp)))
new_x, new_gap_greedy = zip(*sorted(zip(L_size, L_gap_greedy)))
plt.plot(new_x, new_gap_mip, '--o', label="MILP" + "-" + amenity)
plt.plot(new_x, new_gap_cp, '--o', label="CP" + "-" + amenity)
plt.plot(new_x, new_gap_greedy, '--o', label="Greedy" + "-" + amenity)
plt.legend(prop={'size': 6})
plt.xlabel("|M|+|N|")
plt.ylabel("Relative error")
plt.title("solution quality - multiple amenity with depth of choice")
plt.savefig(os.path.join(plot_folder, "quality", "multiple_depth"))
return
def network_charac(data_root, results_folder,processed_folder):
D_NIA = ct_nia_mapping(os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
L_nia = []
L_m = []
L_n = []
L_num_grocery = []
L_num_res = []
L_num_school = []
for nia in list(D_NIA.keys()):
print("nia:",nia)
filename = "NIA_%s_%s,%s,%s.csv" % (nia,1,1,1)
L_nia.append(nia)
# MIP
mip_df = pd.read_csv(os.path.join(results_folder,"summary","OptMultiple_False_0",filename), index_col=None, header=0)
L_m.append(mip_df["num_parking"][0])
L_n.append(mip_df["num_res"][0])
L_num_grocery.append(mip_df["num_existing_L_grocery"][0])
L_num_res.append(mip_df["num_existing_L_restaurant"][0])
L_num_school.append(mip_df["num_existing_L_school"][0])
results_D = { "nia":L_nia, "n": L_n ,"m":L_m,"grocery":L_num_grocery,"res":L_num_res,"school":L_num_school}
df_filename = os.path.join(processed_folder, "nia_summary.csv")
summary_df = pd.DataFrame(results_D)
summary_df["m+n"]=summary_df["m"]+summary_df["n"]
summary_df.to_csv(df_filename,index=False)
# m_split = 60
# n_split = 200
# print("m>=80,n>=200",len(summary_df[(summary_df["m"] >= m_split) & (summary_df["n"]>=n_split)]))
# print("m>=80,n<200", len(summary_df[(summary_df["m"] >= m_split) & (summary_df["n"] < n_split)]))
# print("m<80,n>=200", len(summary_df[(summary_df["m"] < m_split) & (summary_df["n"] >= n_split)]))
# print("m<80,n<00", len(summary_df[(summary_df["m"] < m_split) & (summary_df["n"] < n_split)]))
# plt.scatter(L_m, L_n, s=np.array(L_num_grocery)**2, alpha=0.5)
# plt.show()
output_D = {}
model = "OptMultiple_False_0"
group_thres = [0, 200, 400, 600, 2000]
output_D["group"] = []
output_D["num_nia"] = []
output_D["num_ins"] = []
output_D["avg grocery"] = []
output_D["avg res"] = []
output_D["avg school"] = []
results_df = get_results_df(results_folder, model)
results_df = results_df[(results_df["k_L_grocery"] > 0) & (results_df["k_L_restaurant"] > 0) & (results_df["k_L_school"] > 0)]
results_df["m+n"] = results_df["num_res"] + results_df["num_parking"]
for p in range(len(group_thres) - 1):
group_df = results_df[(results_df["m+n"] >= group_thres[p]) & (results_df["m+n"] < group_thres[p + 1])]
output_D["num_ins"].append(len(group_df))
group_df = group_df[group_df["k_L_grocery"]==1]
output_D["num_nia"].append(len(group_df))
output_D["group"].append(str(group_thres[p])+"-"+str(group_thres[p+1]))
output_D["avg grocery"].append(group_df["num_existing_L_grocery"].mean())
output_D["avg res"].append(group_df["num_existing_L_restaurant"].mean())
output_D["avg school"].append(group_df["num_existing_L_school"].mean())
df_filename = os.path.join(processed_folder, "instances_summary.csv")
output_df = pd.DataFrame(output_D)
output_df["avg grocery"] = output_df["avg grocery"].map('{:.2f}'.format)
output_df["avg res"] = output_df["avg res"].map('{:.2f}'.format)
output_df["avg school"] = output_df["avg school"].map('{:.2f}'.format)
output_df.to_csv(df_filename, index=False)
print(output_df.to_latex(index=False))
return
return
def plot_time_by_group_multiple(results_folder, plot_folder, models, display_names, save_name):
plt.figure(dpi=300)
group_gap = 1
model_gap = 0.2
all_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
data_root = "/Users/weimin/Documents/MASC/walkability_data"
D_NIA = ct_nia_mapping(
os.path.join(data_root, "neighbourhood-improvement-areas-wgs84/processed_TSNS 2020 NIA Census Tracts.xlsx"))
plt.clf()
group_thres = [0,200,400,600,1115]
def define_box_properties(plot_name, color_code, label):
for k, v in plot_name.items():
plt.setp(plot_name.get(k), color=color_code)
for element in ['boxes', 'whiskers', 'fliers', 'means', 'medians', 'caps']:
plt.setp(plot_name[element], color=color_code)
plt.setp(plot_name["fliers"], markeredgecolor=color_code)
# for element in ['medians']:
# plt.setp(plot_name[element], color='black')
# for patch in plot_name['boxes']:
# patch.set(facecolor=color_code)
# use plot function to draw a small line to name the legend.
#plt.plot([], c=color_code, label=label)
plt.plot([], label=label)
plt.legend()
for i in range(len(models)):
model_name = models[i]
display_name = display_names[i]
results_df = get_results_df(results_folder, model_name)
results_df = results_df[(results_df["k_L_grocery"]>0) & (results_df["k_L_restaurant"]>0) & (results_df["k_L_school"]>0)]
results_df["m+n"]=results_df["num_res"]+results_df["num_parking"]
# for nia in list(D_NIA.keys()):
# nia_df = results_df[results_df["nia_id"] == nia]
# if len(nia_df) < 9:
# print("missing for",nia)
group_array=[]
avg_time = results_df.groupby("nia_id")["solving_time"].apply(shifted_geo_mean)
for p in range(len(group_thres)-1):
group_df = results_df[(results_df["m+n"] >= group_thres[p]) & (results_df["m+n"] < group_thres[p+1])]
group_array.append(avg_time[group_df['nia_id'].unique()])
#model_plot = plt.boxplot(group_array,positions=np.array(np.arange(len(group_array))) * 2.0 + i*0.35, widths=0.3,patch_artist=True)
model_plot = plt.boxplot(group_array, positions=np.array(np.arange(len(group_array))) * group_gap + i * 0.2,widths=model_gap,medianprops = dict(linewidth=3.5))
define_box_properties(model_plot, all_colors[i], display_name)
# set the x label values
#ticks = ['[0,200)', '[200,400)', '[400,600)]', '[[600,inf)]']
ticks = ['1', '2', '3', '4']
plt.xticks(np.arange(0, len(ticks) * group_gap, group_gap), ticks)
#plt.xlabel("|M|+|N|")
plt.xlabel("Group")
plt.ylabel("Shifted Geo Mean (s)")
#plt.yticks([])
SMALL_SIZE = 8
MEDIUM_SIZE = 13
BIGGER_SIZE = 18
# if L_all_type_names[ind]=="grocery":
# BIGGER_SIZE = 25
# plt.rc('font', size=SMALL_SIZE) # controls default text sizes
plt.rc('axes', titlesize=BIGGER_SIZE) # fontsize of the axes title
plt.rc('axes', labelsize=BIGGER_SIZE) # fontsize of the x and y labels
plt.rc('xtick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('ytick', labelsize=BIGGER_SIZE) # fontsize of the tick labels
plt.rc('legend', fontsize=BIGGER_SIZE) # legend fontsize
plt.rcParams["figure.figsize"] = (4, 4)
#plt.set_size_inches(18.5, 18.5)
#plt.setp(plt.gca(),ylim=(-5,))
#plt.title(save_name)
plt.savefig(os.path.join(plot_folder, save_name+".pdf"),bbox_inches='tight')
return
def quality_table_by_group_multiple(processed_folder):
summary_filename = os.path.join(processed_folder, "nia_summary.csv")
summary_df = pd.read_csv(summary_filename)
num_nia = []
num_ins = []
L_MILP_no_depth = []
L_CP_no_depth = []
L_greedy_no_depth = []
L_MILP_depth = []
L_CP_depth = []
L_greedy_depth = []
group_thres = [0, 200, 400, 600, 2000]
groups_name = ['[0,200)', '[200,400)', '[400,600)]', '[[600,inf)]']
for p in range(len(group_thres) - 1):
group_df = summary_df[(summary_df["m+n"] >= group_thres[p]) & (summary_df["m+n"] < group_thres[p + 1])]
num_nia.append(len(group_df))
num_ins.append(len(group_df)*9)
# no depth
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple.csv"), index_col=None, header=0)
for p in range(len(group_thres) - 1):
group_df = summary_df[(summary_df["m+n"] >= group_thres[p]) & (summary_df["m+n"] < group_thres[p + 1])]
obj_group_df = all_obj_df[all_obj_df["nia"].isin(group_df['nia'])]
L_MILP_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['mip']) / obj_group_df['best']))
L_CP_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['cp']) / obj_group_df['best']))
L_greedy_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['greedy']) / obj_group_df['best']))
# depth of choice
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple_depth.csv"), index_col=None, header=0)
for p in range(len(group_thres) - 1):
group_df = summary_df[(summary_df["m+n"] >= group_thres[p]) & (summary_df["m+n"] < group_thres[p + 1])]
obj_group_df = all_obj_df[all_obj_df["nia"].isin(group_df['nia'])]
L_MILP_depth.append(np.mean((obj_group_df['best'] - obj_group_df['mip']) / obj_group_df['best']))
L_CP_depth.append(np.mean((obj_group_df['best'] - obj_group_df['cp']) / obj_group_df['best']))
L_greedy_depth.append(np.mean((obj_group_df['best'] - obj_group_df['greedy']) / obj_group_df['best']))
output_D = { "|M|+|N|":groups_name,
"MILP":L_MILP_no_depth, "CP": L_CP_no_depth ,"Greedy":L_greedy_no_depth,
"MILP_d":L_MILP_depth, "CP_d": L_CP_depth ,"Greedy_d":L_greedy_depth}
df_filename = os.path.join(processed_folder, "quality_by_group_multiple.csv")
output_df = pd.DataFrame(output_D)
output_df.loc[:, "MILP"] = output_df["MILP"].map('{:.4f}'.format)
output_df.loc[:, "CP"] = output_df["CP"].map('{:.4f}'.format)
output_df.loc[:, "Greedy"] = output_df["Greedy"].map('{:.4f}'.format)
output_df.loc[:, "MILP_d"] = output_df["MILP_d"].map('{:.4f}'.format)
output_df.loc[:, "CP_d"] = output_df["CP_d"].map('{:.4f}'.format)
output_df.loc[:, "Greedy_d"] = output_df["Greedy_d"].map('{:.4f}'.format)
output_df.to_csv(df_filename,index=False)
return
def quality_table_by_k_multiple(processed_folder):
summary_filename = os.path.join(processed_folder, "nia_summary.csv")
summary_df = pd.read_csv(summary_filename)
num_nia = []
num_ins = []
L_MILP_no_depth = []
L_CP_no_depth = []
L_greedy_no_depth = []
L_MILP_depth = []
L_CP_depth = []
L_greedy_depth = []
groups_name = list(range(1,10))
# no depth
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple.csv"), index_col=None, header=0)
for k in range(1,10):
obj_group_df = all_obj_df[all_obj_df['k']==k]
L_MILP_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['mip']) / obj_group_df['best']))
L_CP_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['cp']) / obj_group_df['best']))
L_greedy_no_depth.append(np.mean((obj_group_df['best'] - obj_group_df['greedy']) / obj_group_df['best']))
# depth of choice
all_obj_df = pd.read_csv(os.path.join(processed_folder, "multiple_depth.csv"), index_col=None, header=0)
for k in range(1,10):
obj_group_df = all_obj_df[all_obj_df['k']==k]
L_MILP_depth.append(np.mean((obj_group_df['best'] - obj_group_df['mip']) / obj_group_df['best']))
L_CP_depth.append(np.mean((obj_group_df['best'] - obj_group_df['cp']) / obj_group_df['best']))
L_greedy_depth.append(np.mean((obj_group_df['best'] - obj_group_df['greedy']) / obj_group_df['best']))
output_D = { "k":groups_name,
"MILP":L_MILP_no_depth, "CP": L_CP_no_depth ,"Greedy":L_greedy_no_depth,
"MILP_d":L_MILP_depth, "CP_d": L_CP_depth ,"Greedy_d":L_greedy_depth}
df_filename = os.path.join(processed_folder, "quality_by_k_multiple.csv")
output_df = pd.DataFrame(output_D)
output_df.to_csv(df_filename,index=False)
return
def quality_table_by_group_single(processed_folder):
summary_filename = os.path.join(processed_folder, "nia_summary.csv")
summary_df = pd.read_csv(summary_filename)
num_nia = []
num_ins = []
L_MILP_no_depth = []
L_CP_no_depth = []
L_greedy_no_depth = []
L_MILP_depth = []
L_CP_depth = []
L_greedy_depth = []