def main(argv):
dirname = argv[0]
print "Parsing files in ", dirname
agg_results_filename = os.path.join(dirname, "agg_results")
for filename in os.listdir(argv[0]):
full_name = os.path.join(dirname, filename)
if os.path.isfile(full_name) and filename.endswith("job_log"):
print "Parsing file ", full_name
parse_logs.parse(full_name, agg_results_filename)
def main(argv):
dirname = argv[0]
print "Parsing files in ", dirname
agg_results_filename = os.path.join(dirname, "agg_results")
for filename in os.listdir(argv[0]):
full_name = os.path.join(dirname, filename)
if os.path.isfile(full_name) and filename.endswith("job_log"):
print "Parsing file ", full_name
parse_logs.parse(full_name, agg_results_filename)
def run_tracker(t_id, lock, tools, parsed_data):
to_continue = True
exe = os.path.join("..", "build", "3D_object_tracking")
lock.acquire()
print(f"Thread {t_id} starts")
lock.release()
while to_continue:
try:
case = tools.get(block=False)
os.makedirs(f"./log/img/{case[0]}_{case[1]}")
command_line = [exe] + f"-det {case[0]} -des {case[1]} -dir ./log/img/{case[0]}_{case[1]} -sel SEL_NN".split()
proc = Popen(command_line, stderr=PIPE, stdout=PIPE)
out_bin, err_bin = proc.communicate()
if proc.returncode != 0:
lock.acquire()
print(case)
lock.release()
if len(err_bin):
parsed_data.put((f"{case[0]}_{case[1]}", None))
else:
parsed_data.put((f"{case[0]}_{case[1]}", parse(out_bin.decode())))
with open(os.path.join(output_log_dir, f"{case[0]}_{case[1]}.log"), "wb") as fid:
fid.write(out_bin)
tools.task_done()
except queue.Empty:
to_continue = False
def load_scores(result_table, metric, domain):
for corpus in result_table:
for run in result_table[corpus]:
log_file = parse(run["path"])
scores = calculate_perfect_validation(log_file, corpus, 0)
run["scores"] = scores
#sort the entries by validation score in reverese order
result_table[corpus] = sorted(result_table[corpus],
key=lambda run: run["scores"][domain].
get(metric, {"score": 0})["score"])[::-1]
return result_table
def show_runs(corpora, log_files=LOG_FILES, metric="valid", domain="mixed"):
"""
Prints valid and missing runs from corpora selection
"""
if isinstance(corpora, str):
corpora = corpora.split()
logs = parse(
# Load only files from current selection
[
log for corpus, files in log_files.items() for log in files
if corpus in corpora
])
scores = load_scores(
{
corpus: [run["params"] for run in logs[corpus]["train"]]
for corpus in logs
}, metric, domain)
params = []
for corpus in corpora:
for run in scores[corpus]:
for param in run:
if param not in params:
params.append(param)
trainings_table = PrettyTable(["corpus"] + [
param for param in params
if param not in ["corpus", "scores", "path", "size"]
] + ["score", "step", "path", "size"])
trainings_table.align["score"] = "r"
trainings_table.align["size"] = "r"
trainings_table.align["path"] = "l"
trainings_table.float_format = "0.3"
for corpus in corpora:
for run in scores[corpus]:
trainings_table.add_row([corpus] + [
run.get(k) for k in params
if k not in ["corpus", "scores", "path", "size"]
] + [
run['scores'].get(domain, {}).get(metric, {"score": 0.0})
["score"], run['scores'].get(domain, {}).get(
metric, {
"score": 0.0
}).get("step"),
basename(run['path']),
sizeof_fmt(getsize(run["path"]))
])
print(trainings_table.get_string(sortby="score", reversesort=True))
def build_trainings_config(corpora,
log_files=LOG_FILES,
schedule=HYPER_CONFIGS,
default_params=TRAININGS_PARAMS,
metric="valid",
domain="mixed"):
"""
Builds dictonary, containng all the configs that are missing in the traingings data.
Works with mulitple corpora as well!
"""
if isinstance(corpora, str):
corpora = corpora.split()
logs = parse(
# Load only files from current selection
[
log for corpus, files in log_files.items() for log in files
if corpus in corpora
])
results = build_result_table(
{
corpus: configs
for corpus, configs in schedule.items() if corpus in corpora
}, logs)
scores = load_scores(results, metric, domain)
missing_configs = {}
for corpus in corpora:
for config in schedule[corpus]:
runs = [
run for run in scores[corpus]
if config == {k: v
for k, v in run.items() if k in config}
and isinstance(run["scores"]["valid"][1], float)
]
if not runs:
if corpus not in missing_configs:
missing_configs[corpus] = []
missing_configs[corpus].append(config)
for corpus, configs in missing_configs.items():
for i, config in enumerate(configs):
with open(f"train.{corpus}.{i}.config", "w") as config_file:
dump({**config, **default_params.get(corpus, {})}, config_file)
def plot_trainings_curve(files=EXAMPLE_RUNS["training"]):
"""Plots a trainings curve for the given runs.
!!!Currently broken and output disabled!!!
"""
for name, path in files.items():
train_stats = parse(path)
for corpus in train_stats:
fig = plt.figure()
fig.suptitle(f"Trainings Curve: {corpus}")
plot_index = 1
for run in train_stats[corpus]["train"]:
axis = plt.subplot(len(train_stats[corpus]["train"]), 1,
plot_index)
axis.set_xlabel("Trainings Steps")
axis.set_ylabel("Accuracy")
plot_index += 1
axis.set_ylim(0, 100)
data = {
"step": [point.get("step", 0) for point in run["steps"]],
"train":
[point.get("train_accuracy", 0) for point in run["steps"]],
"valid":
[point.get("valid_accuracy", 0) for point in run["steps"]],
}
plt.scatter(data["step"],
data["train"],
label="Trainings Accuracy")
plt.scatter(data["step"],
data["valid"],
label="Validation Accuracy")
axis.legend()
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
#plt.savefig(f"{IMAGE_DIR}/trainings_curve-{name}.png", bbox_inches="tight", dpi=200)
plt.clf()
def show_schedule(corpora,
log_files=LOG_FILES,
schedule=HYPER_CONFIGS,
metric="valid",
domain="mixed"):
"""
Prints valid and missing runs from corpora selection
"""
if isinstance(corpora, str):
corpora = corpora.split()
logs = parse(
# Load only files from current selection
[
log for corpus, files in log_files.items() for log in files
if corpus in corpora
])
results = build_result_table(
{
corpus: configs
for corpus, configs in schedule.items() if corpus in corpora
}, logs)
scores = load_scores(results, metric, domain)
for corpus in corpora:
for config in schedule[corpus]:
if config == schedule[corpus][0]:
trainings_table = PrettyTable(
["corpus"] + list(config.keys()) +
["runs", "score", "step", "path", "size"])
trainings_table.align["score"] = "r"
trainings_table.align["size"] = "r"
trainings_table.align["path"] = "l"
trainings_table.float_format = "0.3"
runs = [
run for run in scores[corpus]
if config == {k: v
for k, v in run.items() if k in config}
#and isinstance(run["scores"]["valid"][1], float)
]
if runs:
best_run = reduce(
lambda x, y: x
if x["scores"].get(domain, {}).get(metric, {"score": 0})[
"score"] > y["scores"].get(domain, {}).get(
metric, {"score": 0})["score"] else y, runs)
trainings_table.add_row(
[corpus] + [str(v) for v in config.values()] + [
len(runs), best_run['scores'].get(domain, {}).get(
metric, {"score": 0.0})["score"],
best_run['scores'].get(domain, {}).get(
metric, {
"score": 0.0
}).get("step"),
basename(best_run["path"]),
sizeof_fmt(getsize(best_run["path"]))
])
else:
trainings_table.add_row([corpus] +
[str(v) for v in config.values()] +
[len(runs), 0, 0.0, "Not found", 0.0])
print(trainings_table.get_string(sortby="score", reversesort=True))
def plot_language_comparison(files=EXAMPLE_RUNS["side_constraint"],
metrics=["train", "valid", "bleu"],
name=None):
results = {}
for pair, data_sets in files.items():
results[pair] = {}
for metric in metrics:
results[pair][metric] = {"labels": [], "Tagged": [], "Clean": []}
for domain, groups in data_sets.items():
logs = parse([f for f in groups.values()])
scores = load_scores(
{
corpus:
[run["params"] for run in logs[corpus]["train"]]
for corpus in logs
}, metric, domain)
if scores:
results[pair][metric]["labels"].append(domain)
for corpus in scores:
group = corpus.split("-")[0]
best_run = reduce(
lambda x, y: x if x["scores"].get(domain, {}).get(
metric, {"score": 0})["score"] > y["scores"].get(
domain, {}).get(metric, {"score": 0})["score"]
else y, scores[corpus])
results[pair][metric][group].append(best_run["scores"].get(
domain, {}).get(metric, {"score": 0})["score"])
results[pair][metric]["Delta"] = [
((results[pair][metric]["Tagged"][i] -
results[pair][metric]["Clean"][i]) /
results[pair][metric]["Clean"][i]) * 100
for i in range(len(results[pair][metric]["labels"]))
]
# Settings for the actual bars
# stolen from https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/barchart.html
fig = plt.figure()
fig.suptitle(f"Score Change between Language Pairs")
width = 0.35
for pair in files:
plot_index = 1
for metric in metrics:
axis = plt.subplot(len(metrics), 1, plot_index)
axis.set_ylabel(f"$\\Delta$ {metric} in %")
if pair == "de-en":
x_positions = range(len(results[pair][metric]["labels"]))
axis.set_xticklabels(results[pair][metric]["labels"])
axis.set_xticks(x_positions)
axis.spines['bottom'].set_position('zero')
axis.spines['right'].set_visible(False)
axis.spines['top'].set_visible(False)
axis.xaxis.tick_top()
# build bars
axis.bar(
[
x + (width / 2 * (-1 if pair == "de-en" else 1))
for x in x_positions
],
results[pair][metric]["Delta"],
width,
edgecolor="black",
# hatch="" if pair == "de-en" else "o",
label=pair)
for i, _ in enumerate(results[pair][metric]["labels"]):
axis.annotate(
"{0:.2f}".format(results[pair][metric]["Delta"][i]),
xy=(x_positions[i] + (width / 2 *
(-1 if pair == "de-en" else 1)),
results[pair][metric]["Delta"][i]),
xytext=(0, -1
if results[pair][metric]["Delta"][i] < 0 else 1),
textcoords="offset points",
ha="center",
va="top"
if results[pair][metric]["Delta"][i] < 0 else "bottom")
plot_index += 1
# axis.legend(loc="upper right")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
fig.legend(files.keys(), loc="lower right")
plt.savefig(f"{IMAGE_DIR}/{name}"
if name else f"{IMAGE_DIR}/language-comparison.png",
bbox_inches="tight",
dpi=200)
fig.clf()
def plot_side_constraint_comparison(files=EXAMPLE_RUNS["side_constraint"],
metric="bleu",
name=None):
fig = plt.figure()
fig.suptitle(f"Performance grouped by Domain")
plot_index = 1
for pair, data_sets in files.items():
results = {"labels": [], "Tagged": [], "Clean": []}
for domain, groups in data_sets.items():
logs = parse([f for f in groups.values()])
scores = load_scores(
{
corpus: [run["params"] for run in logs[corpus]["train"]]
for corpus in logs
}, metric, domain)
if scores:
results["labels"].append(domain)
for corpus in scores:
group = corpus.split("-")[0]
best_run = reduce(
lambda x, y: x
if x["scores"].get(domain, {}).get(metric, {"score": 0})
["score"] > y["scores"].get(domain, {}).get(
metric, {"score": 0})["score"] else y, scores[corpus])
results[group].append(
best_run["scores"].get(domain, {}).get(
metric, {"score": 0})["score"] *
(1 if metric in ["valid", "train", "bleu", "bleu_lc"] else
100))
# Settings for the actual bars
# stolen from https://matplotlib.org/3.1.1/gallery/lines_bars_and_markers/barchart.html
axis = plt.subplot(len(files), 1, plot_index)
x_positions = range(len(results["labels"]))
axis.spines['top'].set_visible(False)
axis.spines['right'].set_visible(False)
axis.spines['bottom'].set_position('zero')
axis.set_ylabel(f"{metric}-Score")
axis.set_xlabel("Domains")
axis.set_title(f"Performance for {pair}")
axis.set_ylim(0, 100)
width = 0.35
axis.set_xticks(x_positions)
axis.set_xticklabels(results["labels"])
# build bars
axis.bar([x - width / 2 for x in x_positions],
results["Clean"],
width,
label="Clean",
edgecolor="black")
axis.bar([x + width / 2 for x in x_positions],
results["Tagged"],
width,
label="Tagged",
edgecolor="black")
for i, _ in enumerate(results["labels"]):
axis.annotate("{0:.2f}".format(results["Clean"][i]),
xy=(x_positions[i] - width / 2,
results["Clean"][i] + 1),
xytext=(0, 3),
textcoords="offset points",
ha='center')
axis.annotate("{0:.2f}".format(results["Tagged"][i]),
xy=(x_positions[i] + width / 2,
results["Tagged"][i] + 1),
xytext=(0, 3),
textcoords="offset points",
ha='center')
plot_index += 1
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
fig.legend(["Clean", "Tagged"])
plt.savefig(f"{IMAGE_DIR}/{name}"
if name else f"{IMAGE_DIR}/absolute-performace-comparison.png",
bbox_inches="tight",
dpi=200)
fig.clf()
def plot_hyperparameter_optim(files=EXAMPLE_RUNS["hyper_opt"], metric="bleu"):
"""Plots a comparison of the valid score and another metric (bleu by default)
!!!Currently broken and output disabled!!!
"""
for group in files:
fig = plt.figure()
fig.suptitle(f"Model Fittness according to {metric}-Score")
train_stats = parse(files[group])
for corpus in train_stats:
plot_index = 1
axis = plt.subplot(len(train_stats) * 2, 1, plot_index)
for i, run in enumerate(train_stats[corpus]["train"]):
axis.set_ylim(0, 100)
axis.set_xlabel("Trainings Steps")
axis.set_ylabel("Score")
data = {
"train-steps":
[int(point.get("step", 0)) for point in run["steps"][::5]],
"valid": [
float(point.get("valid_accuracy", 0))
for point in run["steps"][::5]
],
}
if i == 0:
plt.scatter(data["train-steps"],
data["valid"],
marker="x",
label=f"Validation Accuracy")
else:
plt.scatter(data["train-steps"],
data["valid"],
0.1,
marker="x")
axis.legend()
plot_index += 1
axis = plt.subplot(len(train_stats) * 2, 1, plot_index)
for i, run in enumerate(train_stats[corpus]["train"]):
axis.set_ylim(0, 100)
axis.set_xlabel("Trainings Steps")
axis.set_ylabel("Score")
data = {
"score-steps":
[int(point.get("step", 0)) for point in run["scores"]],
"score": [
float(point.get(metric, 0)) *
(1 if metric in ["bleu", "bleu_lc"] else 100)
for point in run["scores"]
],
}
if i == 0:
plt.scatter(data["score-steps"],
data["score"],
marker="^",
label=f"{metric}-Score")
else:
plt.scatter(data["score-steps"],
data["score"],
5,
marker="o")
axis.legend()
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
# plt.savefig(f"{IMAGE_DIR}/optim_comparison-{group.replace(' ','_')}.png", bbox_inches="tight", dpi=200)
plt.clf()
