From e35b35bf24c10982c1bcc906d5bd7cc1cff2e9f1 Mon Sep 17 00:00:00 2001
From: Luc Giffon <luc.giffon@lis-lab.fr>
Date: Thu, 27 Aug 2020 16:09:36 +0200
Subject: [PATCH] code figures (these luc)
---
code/vizualisation/csv_to_figure.py | 166 +++++++-----
code/vizualisation/csv_to_table.py | 65 ++---
code/vizualisation/csv_to_table_these.py | 323 +++++++++++++++++++++++
code/vizualisation/results_to_csv.py | 4 +-
4 files changed, 455 insertions(+), 103 deletions(-)
create mode 100644 code/vizualisation/csv_to_table_these.py
diff --git a/code/vizualisation/csv_to_figure.py b/code/vizualisation/csv_to_figure.py
index 25f5976..a4d0042 100644
--- a/code/vizualisation/csv_to_figure.py
+++ b/code/vizualisation/csv_to_figure.py
@@ -15,7 +15,7 @@ lst_task_train_dev = ["coherence", "correlation"]
tasks = [
# "train_score",
- # "dev_score",
+ "dev_score",
"test_score",
# "coherence",
# "correlation",
@@ -109,8 +109,8 @@ def add_trace_from_df(df, fig, task, strat, stop_on_flat=False):
global GLOBAL_TRACE_TO_ADD_LAST
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)
+ df_groupby_forest_size = df.groupby(['pruning_percent'])
+ forest_sizes = list(df_groupby_forest_size["pruning_percent"].mean().values)
mean_value = df_groupby_forest_size[task].mean().values
std_value = df_groupby_forest_size[task].std().values
@@ -132,7 +132,8 @@ def add_trace_from_df(df, fig, task, strat, stop_on_flat=False):
width=2
)
),
- showlegend=False
+ name="Final NN-OMP",
+ showlegend=True
)
forest_sizes = forest_sizes[:index_flat]
@@ -169,7 +170,21 @@ dct_metric_figure = {
"mean_squared_error": go.Figure()
}
-def base_figures():
+dct_gamma_by_dataset = {
+ "Boston": 5,
+ "Breast Cancer": 5,
+ "California Housing": 5,
+ "Diabetes": 5,
+ "Diamonds": 5,
+ "Kin8nm": 5,
+ "KR-VS-KP": 5,
+ "Spambase": 5,
+ "Steel Plates": 5,
+ "Gamma": 5,
+ "LFW Pairs": 5,
+}
+
+def base_figures(skip_NN=False):
for task in tasks:
for data_name in datasets:
@@ -183,40 +198,40 @@ def base_figures():
# all techniques #
##################
for strat in strategies:
- if strat in lst_skip_strategy:
+ if strat in lst_skip_strategy or (skip_NN and "NN-OMP" in strat):
continue
- if task == "negative-percentage-test-score":
- if strat == "OMP":
- df_strat = df_data[df_data["strategy"] == strat]
- df_strat = df_strat[df_strat["subset"] == "train+dev/train+dev"]
- df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
-
- df_groupby_forest_size = df_strat_wo_weights.groupby(['forest_size'])
-
-
- forest_sizes = df_groupby_forest_size["forest_size"].mean().values
- x_values = df_groupby_forest_size["negative-percentage"].mean().values
- y_values = df_groupby_forest_size["test_score"].mean().values
- # print(df_strat)
- fig.add_trace(go.Scatter(x=x_values, y=y_values,
- mode='markers',
- name=strat,
- # color=forest_sizes,
- marker=dict(
- # size=16,
- # cmax=39,
- # cmin=0,
- color=forest_sizes,
- colorbar=dict(
- title="Forest Size"
- ),
- # colorscale="Viridis"
- ),
- # marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
- ))
-
- continue
+ # if task == "negative-percentage-test-score":
+ # if strat == "OMP":
+ # df_strat = df_data[df_data["strategy"] == strat]
+ # df_strat = df_strat[df_strat["subset"] == "train+dev/train+dev"]
+ # df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
+ #
+ # df_groupby_forest_size = df_strat_wo_weights.groupby(['forest_size'])
+ #
+ #
+ # forest_sizes = df_groupby_forest_size["forest_size"].mean().values
+ # x_values = df_groupby_forest_size["negative-percentage"].mean().values
+ # y_values = df_groupby_forest_size["test_score"].mean().values
+ # # print(df_strat)
+ # fig.add_trace(go.Scatter(x=x_values, y=y_values,
+ # mode='markers',
+ # name=strat,
+ # # color=forest_sizes,
+ # marker=dict(
+ # # size=16,
+ # # cmax=39,
+ # # cmin=0,
+ # color=forest_sizes,
+ # colorbar=dict(
+ # title="Forest Size"
+ # ),
+ # # colorscale="Viridis"
+ # ),
+ # # marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
+ # ))
+ #
+ # continue
df_strat = df_data[df_data["strategy"] == strat]
@@ -252,9 +267,10 @@ def base_figures():
title = "{} {}".format(task, data_name)
yaxis_title = "% negative weights" if task == "negative-percentage" else dct_score_metric_fancy[score_metric_name]
- xaxis_title = "% negative weights" if task == "negative-percentage-test-score" else "# Selected Trees"
+ xaxis_title = "% negative weights" if task == "negative-percentage-test-score" else "% Selected Trees"
- fig.add_trace(GLOBAL_TRACE_TO_ADD_LAST)
+ if not skip_nn:
+ fig.add_trace(GLOBAL_TRACE_TO_ADD_LAST)
fig.update_layout(barmode='group',
# title=title,
xaxis_title=xaxis_title,
@@ -264,7 +280,7 @@ def base_figures():
size=24,
color="black"
),
- # showlegend = False,
+ showlegend = False,
margin = dict(
l=1,
r=1,
@@ -285,6 +301,9 @@ def base_figures():
)
)
# fig.show()
+ if skip_NN:
+ str_no_nn = " no nn"
+ title += str_no_nn
sanitize = lambda x: x.replace(" ", "_").replace("/", "_").replace("+", "_")
filename = sanitize(title)
output_dir = out_dir / sanitize(task)
@@ -375,14 +394,14 @@ def global_figure():
# fig.show()
def weights_wrt_size():
- lst_skip_data_weight_effect = ["Gamma", "KR-VS-KP", "Steel Plates"]
-
+ # lst_skip_data_weight_effect = ["Gamma", "KR-VS-KP", "Steel Plates"]
+ lst_skip_data_weight_effect = ["Gamma"]
fig = go.Figure()
for data_name in datasets:
- # if data_name in lst_skip_data_weight_effect:
- # continue
+ if data_name in lst_skip_data_weight_effect:
+ continue
df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0]
@@ -401,7 +420,7 @@ def weights_wrt_size():
y_values = df_groupby_forest_size["negative-percentage"].mean().values
y_values = (y_values - np.min(y_values)) / (np.max(y_values) - np.min(y_values))
- x_values = np.around(df_groupby_forest_size["pruning_percent"].mean().values, decimals=1)
+ x_values = df_groupby_forest_size["pruning_percent"].mean().values
# x_values = (x_values - np.min(x_values)) / (np.max(x_values) - np.min(x_values))
# if score_metric_name == "mean_squared_error":
@@ -410,8 +429,8 @@ def weights_wrt_size():
lin_reg = svm.SVR(gamma=10)
lin_reg.fit(x_values[:, np.newaxis], y_values)
- xx = np.linspace(0, 1)
- yy = lin_reg.predict(xx[:, np.newaxis])
+ # xx = np.linspace(0, 1)
+ yy = lin_reg.predict(x_values[:, np.newaxis])
# print(df_strat)
fig.add_trace(go.Scatter(x=x_values, y=y_values,
@@ -430,7 +449,7 @@ def weights_wrt_size():
),
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
))
- fig.add_trace(go.Scatter(x=xx, y=yy,
+ fig.add_trace(go.Scatter(x=x_values, y=yy,
mode='lines',
name=strat,
# color=forest_sizes,
@@ -452,8 +471,8 @@ def weights_wrt_size():
title = "{}".format("weight wrt size")
fig.update_layout(barmode='group',
- title=title,
- xaxis_title="Pruning percentage",
+ # title=title,
+ xaxis_title="% Selected Trees",
yaxis_title="Standardized % negative weights",
font=dict(
# family="Courier New, monospace",
@@ -464,8 +483,8 @@ def weights_wrt_size():
margin=dict(
l=1,
r=1,
- b=1,
- t=1,
+ b=3,
+ t=10,
# pad=4
),
legend=dict(
@@ -488,12 +507,13 @@ def weights_wrt_size():
fig.write_image(str((output_dir / filename).absolute()) + ".png")
def effect_of_weights_figure():
- lst_skip_data_weight_effect = ["Gamma", "KR-VS-KP", "Steel Plates"]
+ lst_skip_data_weight_effect = ["Gamma"]
+ # lst_skip_data_weight_effect = ["Gamma", "KR-VS-KP", "Steel Plates"]
fig = go.Figure()
for data_name in datasets:
-
+ #
# if data_name in lst_skip_data_weight_effect:
# continue
df_data = df_results[df_results["dataset"] == data_name]
@@ -506,29 +526,31 @@ def effect_of_weights_figure():
df_strat = df_data[df_data["strategy"] == strat]
df_strat = df_strat[df_strat["subset"] == "train+dev/train+dev"]
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
-
df_strat_wo_weights.sort_values(by="pruning_percent", inplace=True)
df_groupby_forest_size = df_strat_wo_weights.groupby(['forest_size'])
x_values = df_groupby_forest_size["negative-percentage"].mean().values
- x_values = (x_values - np.min(x_values)) / (np.max(x_values) - np.min(x_values))
-
y_values = df_groupby_forest_size["test_score"].mean().values
-
if score_metric_name == "mean_squared_error":
y_values = 1/y_values
+
+ x_values = x_values[3:]
+ y_values = y_values[3:]
+
+ x_values = (x_values - np.min(x_values)) / (np.max(x_values) - np.min(x_values))
y_values = (y_values - np.min(y_values)) / (np.max(y_values) - np.min(y_values))
- bins = np.histogram(x_values)[1]
- indices_x_values = np.digitize(x_values, bins)-1
- mean_val = np.empty(len(bins)-1)
- for idx_group in range(len(bins) - 1):
- mean_val[idx_group] = np.mean(y_values[indices_x_values == idx_group])
+ # bins = np.histogram(x_values)[1]
+ # indices_x_values = np.digitize(x_values, bins)-1
+ # mean_val = np.empty(len(bins)-1)
+ # for idx_group in range(len(bins) - 1):
+ # mean_val[idx_group] = np.mean(y_values[indices_x_values == idx_group])
# lin_reg = LinearRegression()
- lin_reg = svm.SVR(gamma=5)
+ # lin_reg = svm.SVR(gamma=dct_gamma_by_dataset[data_name])
+ lin_reg = svm.SVR(gamma=1.)
lin_reg.fit(x_values[:, np.newaxis], y_values)
xx = np.linspace(0, 1)
@@ -540,6 +562,7 @@ def effect_of_weights_figure():
fig.add_trace(go.Scatter(x=x_values, y=y_values,
mode='markers',
name=strat,
+ showlegend=False,
# color=forest_sizes,
marker=dict(
# size=16,
@@ -576,15 +599,15 @@ def effect_of_weights_figure():
title = "{}".format("negative weights effect")
fig.update_layout(barmode='group',
- title=title,
- xaxis_title="Standardized % negative weights",
- yaxis_title="Normalized Performance",
+ # title=title,
+ xaxis_title="Standardized % Negative Weights",
+ yaxis_title="Standardized Performance",
font=dict(
# family="Courier New, monospace",
size=24,
color="black"
),
- showlegend = False,
+ # showlegend = False,
margin=dict(
l=1,
r=1,
@@ -626,7 +649,8 @@ if __name__ == "__main__":
strategies = set(df_results["strategy"].values)
subsets = set(df_results["subset"].values)
- # base_figures()
- effect_of_weights_figure()
- weights_wrt_size()
+ for skip_nn in [True, False]:
+ base_figures(skip_nn)
+ # effect_of_weights_figure()
+ # weights_wrt_size()
# global_figure()
diff --git a/code/vizualisation/csv_to_table.py b/code/vizualisation/csv_to_table.py
index 440e5fc..0e05e33 100644
--- a/code/vizualisation/csv_to_table.py
+++ b/code/vizualisation/csv_to_table.py
@@ -33,18 +33,32 @@ dct_score_metric_best_fct = {
"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_short = {
- "Spambase": "Spambase",
- "Diamonds": "Diamonds",
- "Diabetes": "Diabetes",
- "Steel Plates": "Steel P.",
- "KR-VS-KP": "KR-VS-KP",
- "Breast Cancer": "Breast C.",
- "Kin8nm": "Kin8nm",
+ "Spambase": "Sp. B.",
+ "Diamonds": "Diam.",
+ "Diabetes": "Diab.",
+ "Steel Plates": "St. P.",
+ "KR-VS-KP": "KR-KP",
+ "Breast Cancer": "B. C.",
+ "Kin8nm": "Kin.",
"LFW Pairs": "LFW P.",
- "Gamma": "Gamma",
- "California Housing": "California H.",
- "Boston": "Boston",
+ "Gamma": "Gam.",
+ "California Housing": "C. H.",
+ "Boston": "Bos.",
}
dct_data_best = {
@@ -101,7 +115,7 @@ def get_max_from_df(df, best_fct):
if __name__ == "__main__":
load_dotenv(find_dotenv('.env'))
- dir_name = "bolsonaro_models_25-03-20"
+ dir_name = "bolsonaro_models_29-03-20_v3_2"
dir_path = Path(os.environ["project_dir"]) / "results" / dir_name
out_dir = Path(os.environ["project_dir"]) / "reports/figures" / dir_name
@@ -155,29 +169,19 @@ if __name__ == "__main__":
if "OMP" in strat:
###########################
- # traitement avec weights #
+ # traitement without 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)
+ df_strat_wo_weights = df_strat[df_strat["wo_weights"] == True]
- if "OMP" in strat and subset_name == "train/dev":
- continue
- elif "Random" not in strat and subset_name == "train/dev":
- continue
+ strat_woweights = "{} 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_woweights not in lst_strats: lst_strats.append(strat_woweights)
#################################
# 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]
+ df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
- 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)
@@ -219,7 +223,8 @@ if __name__ == "__main__":
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])
+ # add metric in parenthesis
+ # 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))
@@ -292,8 +297,8 @@ if __name__ == "__main__":
print("\\midrule")
if idx_lin == 6:
print("\\midrule")
- if lst_data_ordered[idx_lin-1] == "Diamonds":
- print("%", end="")
+ # if lst_data_ordered[idx_lin-1] == "Diamonds":
+ # print("%", end="")
line_print = " ".join(list(lin))
line_print = line_print.rstrip(" &") + "\\\\"
print(line_print)
diff --git a/code/vizualisation/csv_to_table_these.py b/code/vizualisation/csv_to_table_these.py
new file mode 100644
index 0000000..8d4dbee
--- /dev/null
+++ b/code/vizualisation/csv_to_table_these.py
@@ -0,0 +1,323 @@
+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_short = {
+ "Spambase": "Sp. B.",
+ "Diamonds": "Diam.",
+ "Diabetes": "Diab.",
+ "Steel Plates": "St. P.",
+ "KR-VS-KP": "KR-KP",
+ "Breast Cancer": "B. C.",
+ "Kin8nm": "Kin.",
+ "LFW Pairs": "LFW P.",
+ "Gamma": "Gam.",
+ "California Housing": "C. H.",
+ "Boston": "Bos.",
+}
+
+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 = 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]
+ 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_29-03-20_v3_2"
+ 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 without weights #
+ ###########################
+ df_strat_wo_weights = df_strat[df_strat["wo_weights"] == True]
+
+ strat_woweights = "{} 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_woweights not in lst_strats: lst_strats.append(strat_woweights)
+
+ #################################
+ # traitement general wo_weights #
+ #################################
+ df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
+
+
+ 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)
+ # add metric in parenthesis
+ # 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(s_data_tmp))
+ 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
+
+ arr_results_str_classif = arr_results_str[:, 6:]
+ arr_results_str_classif = np.hstack([arr_results_str[:, 0:1], arr_results_str_classif])
+ arr_results_str_reg = arr_results_str[:, :6]
+
+ for arr_results_str in [arr_results_str_classif, arr_results_str_reg]:
+ print(r"\toprule")
+ for idx_lin, lin in enumerate(arr_results_str):
+ if idx_lin == 1:
+ 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)
+ print(r"\bottomrule")
+ # 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 db43618..53c7785 100644
--- a/code/vizualisation/results_to_csv.py
+++ b/code/vizualisation/results_to_csv.py
@@ -60,7 +60,7 @@ dct_dataset_fancy = {
}
dct_dataset_base_forest_size = {
- "boston": 1000,
+ "boston": 100,
"breast_cancer": 1000,
"california_housing": 1000,
"diabetes": 108,
@@ -132,7 +132,7 @@ if __name__ == "__main__":
dct_results["wo_weights"].append(bool_wo_weights)
dct_results["base_forest_size"].append(dct_dataset_base_forest_size[dataset])
pruning_percent = forest_size / dct_dataset_base_forest_size[dataset]
- dct_results["pruning_percent"].append(np.round(pruning_percent, decimals=1))
+ dct_results["pruning_percent"].append(np.round(pruning_percent, decimals=2))
dct_nb_val_scores = {}
--
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