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Commit f41d9087 authored by Luc Giffon's avatar Luc Giffon
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change results treatment

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1 merge request!23Resolve "integration-sota"
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code/playground/nn_omp.py 0 → 100644
+ 0
− 0
View file @ f41d9087
code/vizualisation/csv_to_figure.py
+ 69
− 77
View file @ f41d9087
......@@ -9,15 +9,15 @@ import plotly.io as pio
lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"]
# lst_skip_subset = ["train/dev"]
lst_skip_subset = []
lst_task_train_dev = ["coherence", "correlation"]
tasks = [
# "train_score",
# "dev_score",
# "test_score",
# "coherence",
# "correlation",
"negative-percentage"
"coherence",
"correlation",
# "negative-percentage"
]
dct_score_metric_fancy = {
......@@ -98,83 +98,75 @@ if __name__ == "__main__":
df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0]
for subset_name in subsets:
if subset_name in lst_skip_subset:
continue
df_subset = df_data[df_data["subset"] == subset_name]
fig = go.Figure()
##################
# all techniques #
##################
for strat in strategies:
if strat in lst_skip_strategy:
continue
df_strat = df_subset[df_subset["strategy"] == strat]
if "OMP" in strat:
###########################
# traitement avec weights #
###########################
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
if data_name == "Boston" and subset_name == "train+dev/train+dev":
df_strat_wo_weights = df_strat_wo_weights[df_strat_wo_weights["forest_size"] < 400]
add_trace_from_df(df_strat_wo_weights, fig)
if "OMP" in strat and subset_name == "train/dev":
continue
elif "Random" not in strat and subset_name == "train/dev":
continue
#################################
# traitement general wo_weights #
#################################
if "Random" in strat:
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
else:
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == True]
if "OMP" in strat:
strat = "{} w/o weights".format(strat)
fig = go.Figure()
##################
# all techniques #
##################
for strat in strategies:
if strat in lst_skip_strategy:
continue
df_strat = df_data[df_data["strategy"] == strat]
df_strat = df_strat[df_strat["subset"] == "train+dev/train+dev"]
if "OMP" in strat:
###########################
# traitement avec weights #
###########################
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
if data_name == "Boston":
df_strat_wo_weights = df_strat_wo_weights[df_strat_wo_weights["forest_size"] < 400]
add_trace_from_df(df_strat_wo_weights, fig)
title = "{} {} {}".format(task, data_name, subset_name)
yaxis_title = "% negative weights" if task == "negative-percentage" else dct_score_metric_fancy[score_metric_name]
fig.update_layout(barmode='group',
title=title,
xaxis_title="# Selected Trees",
yaxis_title=yaxis_title,
#################################
# traitement general wo_weights #
#################################
if "OMP" in strat:
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == True]
else:
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
if "OMP" in strat:
strat = "{} w/o weights".format(strat)
add_trace_from_df(df_strat_wo_weights, fig)
title = "{} {}".format(task, data_name)
yaxis_title = "% negative weights" if task == "negative-percentage" else dct_score_metric_fancy[score_metric_name]
fig.update_layout(barmode='group',
title=title,
xaxis_title="# Selected Trees",
yaxis_title=yaxis_title,
font=dict(
# family="Courier New, monospace",
size=24,
color="black"
),
showlegend = False,
margin = dict(
l=1,
r=1,
b=1,
t=1,
# pad=4
),
legend=dict(
traceorder="normal",
font=dict(
# family="Courier New, monospace",
family="sans-serif",
size=24,
color="black"
),
showlegend = False,
margin = dict(
l=1,
r=1,
b=1,
t=1,
# pad=4
),
legend=dict(
traceorder="normal",
font=dict(
family="sans-serif",
size=24,
color="black"
),
# bgcolor="LightSteelBlue",
# bordercolor="Black",
borderwidth=1,
)
)
# fig.show()
sanitize = lambda x: x.replace(" ", "_").replace("/", "_").replace("+", "_")
filename = sanitize(title)
output_dir = out_dir / sanitize(subset_name) / sanitize(task)
output_dir.mkdir(parents=True, exist_ok=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
# exit()
# bgcolor="LightSteelBlue",
# bordercolor="Black",
borderwidth=1,
)
)
# fig.show()
sanitize = lambda x: x.replace(" ", "_").replace("/", "_").replace("+", "_")
filename = sanitize(title)
output_dir = out_dir / sanitize(task)
output_dir.mkdir(parents=True, exist_ok=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
# exit()
code/vizualisation/csv_to_table.py
+ 3
− 1
View file @ f41d9087
......@@ -77,7 +77,7 @@ dct_data_metric = {
def get_max_from_df(df, best_fct):
nb_to_consider = 30
nb_to_consider = 10
df.sort_values(by="forest_size", inplace=True)
df_groupby_forest_size = df.groupby(['forest_size'])
forest_sizes = list(df_groupby_forest_size["forest_size"].mean().values)[:nb_to_consider]
......@@ -292,6 +292,8 @@ if __name__ == "__main__":
print("\\midrule")
if idx_lin == 6:
print("\\midrule")
if lst_data_ordered[idx_lin-1] == "Diamonds":
print("%", end="")
line_print = " ".join(list(lin))
line_print = line_print.rstrip(" &") + "\\\\"
print(line_print)
......
code/vizualisation/results_to_csv.py
+ 4
− 1
View file @ f41d9087
......@@ -77,8 +77,10 @@ if __name__ == "__main__":
continue
path_dir = Path(root)
path_file = path_dir / file_str
print(path_file)
try:
obj_results = pickle.load(open(path_file, 'rb'))
with open(path_file, 'rb') as pickle_file:
obj_results = pickle.load(pickle_file)
except:
print("problem loading pickle file {}".format(path_file))
......@@ -118,6 +120,7 @@ if __name__ == "__main__":
percentage_lt_zero = nb_lt_zero / (nb_gt_zero + nb_lt_zero)
dct_results["negative-percentage"].append(percentage_lt_zero)
if val_result == "":
# print(key_result, val_result)
val_result = None
if key_result == "coherence" and val_result is None:
set_no_coherence.add(id_xp)
......
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