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Commit f0f48756 authored by Luc Giffon's avatar Luc Giffon
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generate table

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1 merge request!23Resolve "integration-sota"
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code/vizualisation/csv_to_figure.py
+ 11
− 12
View file @ f0f48756
...@@ -8,16 +8,16 @@ import plotly.io as pio ...@@ -8,16 +8,16 @@ import plotly.io as pio
lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"] lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"]
lst_skip_task = ["correlation", "coherence"]
# lst_skip_subset = ["train/dev"] # lst_skip_subset = ["train/dev"]
lst_skip_subset = [] lst_skip_subset = []
tasks = [ tasks = [
"train_score", # "train_score",
"dev_score", # "dev_score",
"test_score", # "test_score",
"coherence", # "coherence",
"correlation" # "correlation",
"negative-percentage"
] ]
dct_score_metric_fancy = { dct_score_metric_fancy = {
...@@ -94,8 +94,6 @@ if __name__ == "__main__": ...@@ -94,8 +94,6 @@ if __name__ == "__main__":
subsets = set(df_results["subset"].values) subsets = set(df_results["subset"].values)
for task in tasks: for task in tasks:
if task in lst_skip_task:
continue
for data_name in datasets: for data_name in datasets:
df_data = df_results[df_results["dataset"] == data_name] df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0] score_metric_name = df_data["score_metric"].values[0]
...@@ -142,13 +140,14 @@ if __name__ == "__main__": ...@@ -142,13 +140,14 @@ if __name__ == "__main__":
add_trace_from_df(df_strat_wo_weights, fig) add_trace_from_df(df_strat_wo_weights, fig)
title = "{} {} {}".format(task, data_name, subset_name) 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', fig.update_layout(barmode='group',
# title=title, title=title,
xaxis_title="# Selected Trees", xaxis_title="# Selected Trees",
yaxis_title=dct_score_metric_fancy[score_metric_name], yaxis_title=yaxis_title,
font=dict( font=dict(
# family="Courier New, monospace", # family="Courier New, monospace",
size=18, size=24,
color="black" color="black"
), ),
showlegend = False, showlegend = False,
...@@ -163,7 +162,7 @@ if __name__ == "__main__": ...@@ -163,7 +162,7 @@ if __name__ == "__main__":
traceorder="normal", traceorder="normal",
font=dict( font=dict(
family="sans-serif", family="sans-serif",
size=18, size=24,
color="black" color="black"
), ),
# bgcolor="LightSteelBlue", # bgcolor="LightSteelBlue",
......
code/vizualisation/csv_to_table.py 0 → 100644
+ 308
− 0
View file @ f0f48756
import copy
from dotenv import load_dotenv, find_dotenv
from pathlib import Path
import os
import pandas as pd
import numpy as np
from pprint import pprint
import plotly.graph_objects as go
import plotly.io as pio
from collections import defaultdict
lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"]
lst_skip_task = ["correlation", "coherence"]
# lst_skip_task = []
lst_skip_subset = ["train/dev"]
# lst_skip_subset = []
tasks = [
# "train_score",
# "dev_score",
"test_score",
# "coherence",
# "correlation"
]
dct_score_metric_fancy = {
"accuracy_score": "% Accuracy",
"mean_squared_error": "MSE"
}
dct_score_metric_best_fct = {
"accuracy_score": np.argmax,
"mean_squared_error": np.argmin
}
dct_data_short = {
"Spambase": "Spambase",
"Diamonds": "Diamonds",
"Diabetes": "Diabetes",
"Steel Plates": "Steel P.",
"KR-VS-KP": "KR-VS-KP",
"Breast Cancer": "Breast C.",
"Kin8nm": "Kin8nm",
"LFW Pairs": "LFW P.",
"Gamma": "Gamma",
"California Housing": "California H.",
"Boston": "Boston",
}
dct_data_best = {
"Spambase": np.max,
"Diamonds": np.min,
"Diabetes": np.min,
"Steel Plates": np.max,
"KR-VS-KP": np.max,
"Breast Cancer": np.max,
"Kin8nm": np.min,
"LFW Pairs": np.max,
"Gamma": np.max,
"California Housing": np.min,
"Boston": np.min,
}
dct_data_metric = {
"Spambase": "Acc.",
"Diamonds": "MSE",
"Diabetes": "MSE",
"Steel Plates": "Acc.",
"KR-VS-KP": "Acc.",
"Breast Cancer": "Acc.",
"Kin8nm": "MSE",
"LFW Pairs": "Acc.",
"Gamma": "Acc.",
"California Housing": "MSE",
"Boston": "MSE",
}
def get_max_from_df(df, best_fct):
nb_to_consider = 30
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]
mean_value = df_groupby_forest_size[task].mean().values[:nb_to_consider]
std_value = df_groupby_forest_size[task].std().values[:nb_to_consider]
try:
argmax = best_fct(mean_value)
except:
print("no results", strat, data_name, task, subset_name)
return -1, -1, -1
max_mean = mean_value[argmax]
max_std = std_value[argmax]
max_forest_size = forest_sizes[argmax]
return max_forest_size, max_mean, max_std
if __name__ == "__main__":
load_dotenv(find_dotenv('.env'))
dir_name = "bolsonaro_models_25-03-20"
dir_path = Path(os.environ["project_dir"]) / "results" / dir_name
out_dir = Path(os.environ["project_dir"]) / "reports/figures" / dir_name
input_dir_file = dir_path / "results.csv"
df_results = pd.read_csv(open(input_dir_file, 'rb'))
datasets = set(df_results["dataset"].values)
strategies = sorted(list(set(df_results["strategy"].values) - set(lst_skip_strategy)))
subsets = set(df_results["subset"].values)
r"""
\begin{table}[!h]
\centering
\begin{tabular}{l{}}
\toprule
\multicolumn{1}{c}{\textbf{Dataset}} & \textbf{Data dim.} $\datadim$ & \textbf{\# classes} & \textbf{Train size} $\nexamples$ & \textbf{Test size} $\nexamples'$ \\ \midrule
\texttt{MNIST}~\cite{lecun-mnisthandwrittendigit-2010} & 784 & 10 & 60 000 & 10 000 \\ %\hline
\texttt{Kddcup99}~\cite{Dua:2019} & 116 & 23 & 4 893 431 & 5 000 \\
\bottomrule
\end{tabular}
\caption{Main features of the datasets. Discrete, unordered attributes for dataset Kddcup99 have been encoded as one-hot attributes.}
\label{table:data}
\end{table}
"""
for task in tasks:
if task in lst_skip_task:
continue
dct_data_lst_tpl_results = defaultdict(lambda: [])
lst_strats = []
for data_name in datasets:
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]
##################
# 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]
dct_data_lst_tpl_results[data_name].append(get_max_from_df(df_strat_wo_weights, dct_score_metric_best_fct[score_metric_name]))
if strat not in lst_strats: lst_strats.append(strat)
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)
dct_data_lst_tpl_results[data_name].append(get_max_from_df(df_strat_wo_weights, dct_score_metric_best_fct[score_metric_name]))
if strat not in lst_strats: lst_strats.append(strat)
title = "{} {} {}".format(task, data_name, subset_name)
# 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")
# pprint(dct_data_lst_tpl_results)
lst_data_ordered = [
"Diamonds",
"Diabetes",
"Kin8nm",
"California Housing",
"Boston",
"Spambase",
"Steel Plates",
"KR-VS-KP",
"Breast Cancer",
"LFW Pairs",
"Gamma"
]
arr_results_str = np.empty((len(lst_strats)+1, len(datasets) + 1 ), dtype="object")
nb_spaces = 25
dct_strat_str = defaultdict(lambda: [])
s_empty = "{}" + " "*(nb_spaces-2) + " & "
arr_results_str[0][0] = s_empty
# arr_results_str[0][1] = s_empty
for idx_data, data_name in enumerate(lst_data_ordered):
lst_tpl_results = dct_data_lst_tpl_results[data_name]
data_name_short = dct_data_short[data_name]
s_data_tmp = "{}".format(data_name_short)
s_data_tmp += "({})".format(dct_data_metric[data_name])
# s_data_tmp = "\\texttt{{ {} }}".format(data_name_short)
# s_data_tmp = "\\multicolumn{{2}}{{c}}{{ \\texttt{{ {} }} }}".format(data_name)
s_data_tmp += " "*(nb_spaces - len(data_name_short))
s_data_tmp += " & "
arr_results_str[0, idx_data + 1] = s_data_tmp
array_results = np.array(lst_tpl_results)
best_result_perf = dct_data_best[data_name](array_results[:, 1])
best_result_perf_indexes = np.argwhere(array_results[:, 1] == best_result_perf)
copye_array_results = copy.deepcopy(array_results)
if dct_data_best[data_name] is np.min:
copye_array_results[best_result_perf_indexes] = np.inf
else:
copye_array_results[best_result_perf_indexes] = -np.inf
best_result_perf_2 = dct_data_best[data_name](copye_array_results[:, 1])
best_result_perf_indexes_2 = np.argwhere(copye_array_results[:, 1] == best_result_perf_2)
best_result_prune = np.min(array_results[:, 0])
best_result_prune_indexes = np.argwhere(array_results[:, 0] == best_result_prune)
for idx_strat, tpl_results in enumerate(array_results):
# str_strat = "\\texttt{{ {} }}".format(lst_strats[idx_strat])
# str_strat = "\\multicolumn{{2}}{{c}}{{ \\texttt{{ {} }} }}".format(lst_strats[idx_strat])
# str_strat = "\\multicolumn{{2}}{{c}}{{ \\thead{{ \\texttt{{ {} }} }} }}".format("}\\\\ \\texttt{".join(lst_strats[idx_strat].split(" ", 1)))
str_strat = "\\multicolumn{{2}}{{c}}{{ \\thead{{ {} }} }} ".format("\\\\".join(lst_strats[idx_strat].split(" ", 1)))
str_strat += " " * (nb_spaces - len(str_strat)) + " & "
arr_results_str[idx_strat+1, 0] = str_strat
# str_header = " {} & #tree &".format(dct_data_metric[data_name])
# arr_results_str[idx_strat + 1, 1] = str_header
best_forest_size = tpl_results[0]
best_mean = tpl_results[1]
best_std = tpl_results[2]
if dct_data_metric[data_name] == "Acc.":
str_perf = "{:.2f}\\%".format(best_mean * 100)
else:
str_perf = "{:.3E}".format(best_mean)
str_prune = "{:d}".format(int(best_forest_size))
if idx_strat in best_result_perf_indexes:
# str_formating = "\\textbf{{ {} }}".format(str_result_loc)
str_formating = "\\textbf[{}]"
# str_formating = "\\textbf{{ {:.3E} }}(\\~{:.3E})".format(best_mean, best_std)
elif idx_strat in best_result_perf_indexes_2:
str_formating = "\\underline[{}]"
# str_formating = "\\underline{{ {:.3E} }}(\\~{:.3E})".format(best_mean, best_std)
else:
str_formating = "{}"
# str_formating = "{:.3E}(~{:.3E})".format(best_mean, best_std)
if idx_strat in best_result_prune_indexes:
str_formating = str_formating.format("\\textit[{}]")
# str_prune = " & \\textit{{ {:d} }}".format(int(best_forest_size))
# else:
# str_prune = " & {:d}".format(int(best_forest_size))
str_result = str_formating.format(str_perf) + " & " + str_formating.format(str_prune)
str_result += " "*(nb_spaces - len(str_result))
str_result = str_result.replace("[", "{").replace("]", "}")
arr_results_str[idx_strat+1, idx_data+1] = str_result + " & "
dct_strat_str[lst_strats[idx_strat]].append(str_result)
arr_results_str = arr_results_str.T
for idx_lin, lin in enumerate(arr_results_str):
if idx_lin == 1:
print("\\midrule")
if idx_lin == 6:
print("\\midrule")
line_print = " ".join(list(lin))
line_print = line_print.rstrip(" &") + "\\\\"
print(line_print)
# s_data = s_data.rstrip(" &") + "\\\\"
# print(s_data)
# for strat, lst_str_results in dct_strat_str.items():
# str_strat = "\\texttt{{ {} }}".format(strat)
# str_strat += " "*(nb_spaces - len(str_strat))
# str_strat += " & " + " & ".join(lst_str_results)
# str_strat += "\\\\"
# print(str_strat)
# exit()
code/vizualisation/results_to_csv.py
+ 14
− 1
View file @ f0f48756
...@@ -4,6 +4,7 @@ import pandas as pd ...@@ -4,6 +4,7 @@ import pandas as pd
from pprint import pprint from pprint import pprint
import pickle import pickle
from collections import defaultdict from collections import defaultdict
import numpy as np
from dotenv import load_dotenv, find_dotenv from dotenv import load_dotenv, find_dotenv
...@@ -56,7 +57,7 @@ dct_dataset_fancy = { ...@@ -56,7 +57,7 @@ dct_dataset_fancy = {
"lfw_pairs": "LFW Pairs" "lfw_pairs": "LFW Pairs"
} }
skip_attributes = ["datetime", "model_weights"] skip_attributes = ["datetime"]
set_no_coherence = set() set_no_coherence = set()
set_no_corr = set() set_no_corr = set()
...@@ -104,6 +105,18 @@ if __name__ == "__main__": ...@@ -104,6 +105,18 @@ if __name__ == "__main__":
for key_result, val_result in obj_results.items(): for key_result, val_result in obj_results.items():
if key_result in skip_attributes: if key_result in skip_attributes:
continue continue
if key_result == "model_weights":
if val_result == "":
dct_results["negative-percentage"].append(None)
else:
lt_zero = val_result < 0
gt_zero = val_result > 0
nb_lt_zero = np.sum(lt_zero)
nb_gt_zero = np.sum(gt_zero)
percentage_lt_zero = nb_lt_zero / (nb_gt_zero + nb_lt_zero)
dct_results["negative-percentage"].append(percentage_lt_zero)
if val_result == "": if val_result == "":
val_result = None val_result = None
if key_result == "coherence" and val_result is None: if key_result == "coherence" and val_result is None:
......
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