ANL-CEEESA
/
UnitCommitment.jl
mirror of https://github.com/ANL-CEEESA/UnitCommitment.jl.git
1
0
Fork 0
Code Issues Packages Projects Releases Wiki Activity

Reformat Python scripts

Browse Source
bugfix/formulations
Alinson S. Xavier 4 years ago
parent 3687d42733
commit a684419f33
2 changed files with 96 additions and 59 deletions
Show Stats Download Patch File Download Diff File

65
benchmark/scripts/compare.py
Unescape View File

@ -8,41 +8,49 @@ import seaborn as sns
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import sys import sys
#easy_cutoff = 120 # easy_cutoff = 120
b1 = pd.read_csv(sys.argv[1], index_col=0) b1 = pd.read_csv(sys.argv[1], index_col=0)
b2 = pd.read_csv(sys.argv[2], index_col=0) b2 = pd.read_csv(sys.argv[2], index_col=0)
c1 = b1.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean() c1 = b1.groupby(["Group", "Instance", "Sample"])[
c2 = b2.groupby(["Group", "Instance", "Sample"])[["Optimization time (s)", "Primal bound"]].mean() ["Optimization time (s)", "Primal bound"]
].mean()
c2 = b2.groupby(["Group", "Instance", "Sample"])[
["Optimization time (s)", "Primal bound"]
].mean()
c1.columns = ["A Time (s)", "A Value"] c1.columns = ["A Time (s)", "A Value"]
c2.columns = ["B Time (s)", "B Value"] c2.columns = ["B Time (s)", "B Value"]
merged = pd.concat([c1, c2], axis=1) merged = pd.concat([c1, c2], axis=1)
merged["Speedup"] = merged["A Time (s)"] / merged["B Time (s)"] merged["Speedup"] = merged["A Time (s)"] / merged["B Time (s)"]
merged["Time diff (s)"] = merged["B Time (s)"] - merged["A Time (s)"] merged["Time diff (s)"] = merged["B Time (s)"] - merged["A Time (s)"]
merged["Value diff (%)"] = np.round((merged["B Value"] - merged["A Value"]) / merged["A Value"] * 100.0, 5) merged["Value diff (%)"] = np.round(
(merged["B Value"] - merged["A Value"]) / merged["A Value"] * 100.0, 5
)
merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Speedup"] = float("nan") merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Speedup"] = float("nan")
merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Time diff (s)"] = float("nan") merged.loc[merged.loc[:, "B Time (s)"] <= 0, "Time diff (s)"] = float("nan")
#merged = merged[(merged["A Time (s)"] >= easy_cutoff) | (merged["B Time (s)"] >= easy_cutoff)] # merged = merged[(merged["A Time (s)"] >= easy_cutoff) | (merged["B Time (s)"] >= easy_cutoff)]
merged.reset_index(inplace=True) merged.reset_index(inplace=True)
merged["Name"] = merged["Group"] + "/" + merged["Instance"] merged["Name"] = merged["Group"] + "/" + merged["Instance"]
#merged = merged.sort_values(by="Speedup", ascending=False) # merged = merged.sort_values(by="Speedup", ascending=False)
k = len(merged.groupby("Name")) k = len(merged.groupby("Name"))
plt.figure(figsize=(12, 0.50 * k)) plt.figure(figsize=(12, 0.50 * k))
plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = True plt.rcParams["xtick.bottom"] = plt.rcParams["xtick.labelbottom"] = True
plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True plt.rcParams["xtick.top"] = plt.rcParams["xtick.labeltop"] = True
sns.set_style("whitegrid") sns.set_style("whitegrid")
sns.set_palette("Set1") sns.set_palette("Set1")
sns.barplot(data=merged, sns.barplot(
x="Speedup", data=merged,
y="Name", x="Speedup",
color="tab:red", y="Name",
capsize=0.15, color="tab:red",
errcolor="k", capsize=0.15,
errwidth=1.25) errcolor="k",
errwidth=1.25,
)
plt.axvline(1.0, linestyle="--", color="k") plt.axvline(1.0, linestyle="--", color="k")
plt.tight_layout() plt.tight_layout()
@ -50,15 +58,18 @@ print("Writing tables/compare.png")
plt.savefig("tables/compare.png", dpi=150) plt.savefig("tables/compare.png", dpi=150)
print("Writing tables/compare.csv") print("Writing tables/compare.csv")
merged.loc[:, ["Group", merged.loc[
"Instance", :,
"Sample", [
"A Time (s)", "Group",
"B Time (s)", "Instance",
"Speedup", "Sample",
"Time diff (s)", "A Time (s)",
"A Value", "B Time (s)",
"B Value", "Speedup",
"Value diff (%)", "Time diff (s)",
] "A Value",
].to_csv("tables/compare.csv", index_label="Index") "B Value",
"Value diff (%)",
],
].to_csv("tables/compare.csv", index_label="Index")

90
benchmark/scripts/table.py
Unescape View File

@ -9,8 +9,8 @@ from tabulate import tabulate
def process_all_log_files(): def process_all_log_files():
pathlist = list(Path(".").glob('results/*/*/*.log')) pathlist = list(Path(".").glob("results/*/*/*.log"))
pathlist += list(Path(".").glob('results/*/*.log')) pathlist += list(Path(".").glob("results/*/*.log"))
rows = [] rows = []
for path in pathlist: for path in pathlist:
if ".ipy" in str(path): if ".ipy" in str(path):
@ -22,8 +22,8 @@ def process_all_log_files():
df.index = range(len(df)) df.index = range(len(df))
print("Writing tables/benchmark.csv") print("Writing tables/benchmark.csv")
df.to_csv("tables/benchmark.csv", index_label="Index") df.to_csv("tables/benchmark.csv", index_label="Index")
def process(filename): def process(filename):
parts = filename.replace(".log", "").split("/") parts = filename.replace(".log", "").split("/")
group_name = "/".join(parts[1:-1]) group_name = "/".join(parts[1:-1])
@ -45,56 +45,74 @@ def process(filename):
read_time, model_time, isf_time, total_time = None, None, None, None read_time, model_time, isf_time, total_time = None, None, None, None
cb_calls, cb_time = 0, 0.0 cb_calls, cb_time = 0, 0.0
transmission_count, transmission_time, transmission_calls = 0, 0.0, 0 transmission_count, transmission_time, transmission_calls = 0, 0.0, 0
# m = re.search("case([0-9]*)", instance_name) # m = re.search("case([0-9]*)", instance_name)
# n_buses = int(m.group(1)) # n_buses = int(m.group(1))
n_buses = 0 n_buses = 0
with open(filename) as file: with open(filename) as file:
for line in file.readlines(): for line in file.readlines():
m = re.search(r"Explored ([0-9.e+]*) nodes \(([0-9.e+]*) simplex iterations\) in ([0-9.e+]*) seconds", line) m = re.search(
r"Explored ([0-9.e+]*) nodes \(([0-9.e+]*) simplex iterations\) in ([0-9.e+]*) seconds",
line,
)
if m is not None: if m is not None:
nodes += int(m.group(1)) nodes += int(m.group(1))
simplex_iterations += int(m.group(2)) simplex_iterations += int(m.group(2))
optimize_time += float(m.group(3)) optimize_time += float(m.group(3))
m = re.search(r"Best objective ([0-9.e+]*), best bound ([0-9.e+]*), gap ([0-9.e+]*)\%", line) m = re.search(
r"Best objective ([0-9.e+]*), best bound ([0-9.e+]*), gap ([0-9.e+]*)\%",
line,
)
if m is not None: if m is not None:
primal_bound = float(m.group(1)) primal_bound = float(m.group(1))
dual_bound = float(m.group(2)) dual_bound = float(m.group(2))
gap = round(float(m.group(3)), 3) gap = round(float(m.group(3)), 3)
m = re.search(r"Root relaxation: objective ([0-9.e+]*), ([0-9.e+]*) iterations, ([0-9.e+]*) seconds", line) m = re.search(
r"Root relaxation: objective ([0-9.e+]*), ([0-9.e+]*) iterations, ([0-9.e+]*) seconds",
line,
)
if m is not None: if m is not None:
root_obj = float(m.group(1)) root_obj = float(m.group(1))
root_iterations += int(m.group(2)) root_iterations += int(m.group(2))
root_time += float(m.group(3)) root_time += float(m.group(3))
m = re.search(r"Presolved: ([0-9.e+]*) rows, ([0-9.e+]*) columns, ([0-9.e+]*) nonzeros", line) m = re.search(
r"Presolved: ([0-9.e+]*) rows, ([0-9.e+]*) columns, ([0-9.e+]*) nonzeros",
line,
)
if m is not None: if m is not None:
n_rows_presolved = int(m.group(1)) n_rows_presolved = int(m.group(1))
n_cols_presolved = int(m.group(2)) n_cols_presolved = int(m.group(2))
n_nz_presolved = int(m.group(3)) n_nz_presolved = int(m.group(3))
m = re.search(r"Optimize a model with ([0-9.e+]*) rows, ([0-9.e+]*) columns and ([0-9.e+]*) nonzeros", line) m = re.search(
r"Optimize a model with ([0-9.e+]*) rows, ([0-9.e+]*) columns and ([0-9.e+]*) nonzeros",
line,
)
if m is not None: if m is not None:
n_rows_orig = int(m.group(1)) n_rows_orig = int(m.group(1))
n_cols_orig = int(m.group(2)) n_cols_orig = int(m.group(2))
n_nz_orig = int(m.group(3)) n_nz_orig = int(m.group(3))
m = re.search(r"Variable types: ([0-9.e+]*) continuous, ([0-9.e+]*) integer \(([0-9.e+]*) binary\)", line) m = re.search(
r"Variable types: ([0-9.e+]*) continuous, ([0-9.e+]*) integer \(([0-9.e+]*) binary\)",
line,
)
if m is not None: if m is not None:
n_cont_vars_presolved = int(m.group(1)) n_cont_vars_presolved = int(m.group(1))
n_bin_vars_presolved = int(m.group(3)) n_bin_vars_presolved = int(m.group(3))
m = re.search(r"Read problem in ([0-9.e+]*) seconds", line) m = re.search(r"Read problem in ([0-9.e+]*) seconds", line)
if m is not None: if m is not None:
read_time = float(m.group(1)) read_time = float(m.group(1))
m = re.search(r"Computed ISF in ([0-9.e+]*) seconds", line) m = re.search(r"Computed ISF in ([0-9.e+]*) seconds", line)
if m is not None: if m is not None:
isf_time = float(m.group(1)) isf_time = float(m.group(1))
m = re.search(r"Built model in ([0-9.e+]*) seconds", line) m = re.search(r"Built model in ([0-9.e+]*) seconds", line)
if m is not None: if m is not None:
model_time = float(m.group(1)) model_time = float(m.group(1))
@ -103,7 +121,10 @@ def process(filename):
if m is not None: if m is not None:
total_time = float(m.group(1)) total_time = float(m.group(1))
m = re.search(r"User-callback calls ([0-9.e+]*), time in user-callback ([0-9.e+]*) sec", line) m = re.search(
r"User-callback calls ([0-9.e+]*), time in user-callback ([0-9.e+]*) sec",
line,
)
if m is not None: if m is not None:
cb_calls = int(m.group(1)) cb_calls = int(m.group(1))
cb_time = float(m.group(2)) cb_time = float(m.group(2))
@ -116,7 +137,7 @@ def process(filename):
m = re.search(r".*MW overflow", line) m = re.search(r".*MW overflow", line)
if m is not None: if m is not None:
transmission_count += 1 transmission_count += 1
return { return {
"Group": group_name, "Group": group_name,
"Instance": instance_name, "Instance": instance_name,
@ -150,6 +171,7 @@ def process(filename):
"Transmission screening calls": transmission_calls, "Transmission screening calls": transmission_calls,
} }
def generate_chart(): def generate_chart():
import pandas as pd import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
@ -159,7 +181,9 @@ def generate_chart():
files = ["tables/benchmark.csv"] files = ["tables/benchmark.csv"]
for f in files: for f in files:
table = pd.read_csv(f, index_col=0) table = pd.read_csv(f, index_col=0)
table.loc[:, "Instance"] = table.loc[:,"Group"] + "/" + table.loc[:,"Instance"] table.loc[:, "Instance"] = (
table.loc[:, "Group"] + "/" + table.loc[:, "Instance"]
)
table.loc[:, "Filename"] = f table.loc[:, "Filename"] = f
tables += [table] tables += [table]
benchmark = pd.concat(tables, sort=True) benchmark = pd.concat(tables, sort=True)
@ -168,16 +192,18 @@ def generate_chart():
plt.figure(figsize=(12, 0.50 * k)) plt.figure(figsize=(12, 0.50 * k))
sns.set_style("whitegrid") sns.set_style("whitegrid")
sns.set_palette("Set1") sns.set_palette("Set1")
sns.barplot(y="Instance", sns.barplot(
x="Total time (s)", y="Instance",
color="tab:red", x="Total time (s)",
capsize=0.15, color="tab:red",
errcolor="k", capsize=0.15,
errwidth=1.25, errcolor="k",
data=benchmark); errwidth=1.25,
data=benchmark,
)
plt.tight_layout() plt.tight_layout()
print("Writing tables/benchmark.png") print("Writing tables/benchmark.png")
plt.savefig("tables/benchmark.png", dpi=150); plt.savefig("tables/benchmark.png", dpi=150)
if __name__ == "__main__": if __name__ == "__main__":

Write Preview
Loading…
Cancel
Save