diff --git a/code/vizualisation/csv_to_figure.py b/code/vizualisation/csv_to_figure.py
index 879cbdcbf767aab3f1e55db8c17c67cc461b8af1..73b990f363b8e65a97e7c6a91c6b304a199e3034 100644
--- a/code/vizualisation/csv_to_figure.py
+++ b/code/vizualisation/csv_to_figure.py
@@ -8,16 +8,16 @@ import plotly.io as pio
lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"]
-lst_skip_task = ["correlation", "coherence"]
# lst_skip_subset = ["train/dev"]
lst_skip_subset = []
tasks = [
- "train_score",
- "dev_score",
- "test_score",
- "coherence",
- "correlation"
+ # "train_score",
+ # "dev_score",
+ # "test_score",
+ # "coherence",
+ # "correlation",
+ "negative-percentage"
]
dct_score_metric_fancy = {
@@ -94,8 +94,6 @@ if __name__ == "__main__":
subsets = set(df_results["subset"].values)
for task in tasks:
- if task in lst_skip_task:
- continue
for data_name in datasets:
df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0]
@@ -142,13 +140,14 @@ if __name__ == "__main__":
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,
+ title=title,
xaxis_title="# Selected Trees",
- yaxis_title=dct_score_metric_fancy[score_metric_name],
+ yaxis_title=yaxis_title,
font=dict(
# family="Courier New, monospace",
- size=18,
+ size=24,
color="black"
),
showlegend = False,
@@ -163,7 +162,7 @@ if __name__ == "__main__":
traceorder="normal",
font=dict(
family="sans-serif",
- size=18,
+ size=24,
color="black"
),
# bgcolor="LightSteelBlue",
diff --git a/code/vizualisation/csv_to_table.py b/code/vizualisation/csv_to_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..9547361f311ea1f0c9bb61b9f2454e26a2f8650a
--- /dev/null
+++ b/code/vizualisation/csv_to_table.py
@@ -0,0 +1,308 @@
+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()
diff --git a/code/vizualisation/results_to_csv.py b/code/vizualisation/results_to_csv.py
index ba54f35ee7d045b9fc3826ab0ad9db4198ba93a3..1def6e62935debd9405900c721a1b29b45a2b8cb 100644
--- a/code/vizualisation/results_to_csv.py
+++ b/code/vizualisation/results_to_csv.py
@@ -4,6 +4,7 @@ import pandas as pd
from pprint import pprint
import pickle
from collections import defaultdict
+import numpy as np
from dotenv import load_dotenv, find_dotenv
@@ -56,7 +57,7 @@ dct_dataset_fancy = {
"lfw_pairs": "LFW Pairs"
}
-skip_attributes = ["datetime", "model_weights"]
+skip_attributes = ["datetime"]
set_no_coherence = set()
set_no_corr = set()
@@ -104,6 +105,18 @@ if __name__ == "__main__":
for key_result, val_result in obj_results.items():
if key_result in skip_attributes:
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 == "":
val_result = None
if key_result == "coherence" and val_result is None: