Skip to content
Snippets Groups Projects
Commit 6d0be473 authored by Luc Giffon's avatar Luc Giffon
Browse files

last commit before these luc

parent e35b35bf
No related branches found
No related tags found
1 merge request!24Resolve "non negative omp"
Show whitespace changes
Inline Side-by-side
code/vizualisation/csv_to_figure.py
+ 11
− 2
View file @ 6d0be473
......@@ -308,6 +308,9 @@ def base_figures(skip_NN=False):
filename = sanitize(title)
output_dir = out_dir / sanitize(task)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
def global_figure():
......@@ -504,6 +507,9 @@ def weights_wrt_size():
filename = sanitize(title)
output_dir = out_dir / sanitize(title)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
def effect_of_weights_figure():
......@@ -632,6 +638,9 @@ def effect_of_weights_figure():
filename = sanitize(title)
output_dir = out_dir / sanitize(title)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
if __name__ == "__main__":
......@@ -651,6 +660,6 @@ if __name__ == "__main__":
for skip_nn in [True, False]:
base_figures(skip_nn)
# effect_of_weights_figure()
# weights_wrt_size()
effect_of_weights_figure()
weights_wrt_size()
# global_figure()
code/vizualisation/csv_to_figure_these.py 0 → 100644
+ 665
− 0
View file @ 6d0be473
from dotenv import load_dotenv, find_dotenv
from pathlib import Path
import os
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.io as pio
from scipy.special import softmax
from sklearn import svm
from sklearn.linear_model import LinearRegression
lst_skip_strategy = ["None", "OMP Distillation", "OMP Distillation w/o weights"]
# lst_skip_subset = ["train/dev"]
lst_task_train_dev = ["coherence", "correlation"]
tasks = [
# "train_score",
"dev_score",
"test_score",
# "coherence",
# "correlation",
# "negative-percentage",
# "dev_strength",
# "test_strength",
# "dev_correlation",
# "test_correlation",
# "dev_coherence",
# "test_coherence",
# "negative-percentage-test-score"
]
dct_score_metric_fancy = {
"accuracy_score": "% de Précision",
"mean_squared_error": "MSE"
}
pio.templates.default = "plotly_white"
dct_color_by_strategy = {
"OMP": (255, 117, 26), # orange
"NN-OMP": (255, 0, 0), # red
"OMP Distillation": (255, 0, 0), # red
"OMP Distillation w/o weights": (255, 0, 0), # red
"OMP w/o weights": (255, 117, 26), # orange
"NN-OMP w/o weights": (255, 0, 0), # grey
"Random": (128, 128, 128), # black
"Zhang Similarities": (255,105,180), # rose
'Zhang Predictions': (128, 0, 128), # turquoise
'Ensemble': (0, 0, 255), # blue
"Kmeans": (0, 255, 0) # red
}
dct_data_color = {
"Boston": (255, 117, 26),
"Breast Cancer": (255, 0, 0),
"California Housing": (255,105,180),
"Diabetes": (128, 0, 128),
"Diamonds": (0, 0, 255),
"Kin8nm": (128, 128, 128),
"KR-VS-KP": (0, 255, 0),
"Spambase": (0, 128, 0),
"Steel Plates": (128, 0, 0),
"Gamma": (0, 0, 128),
"LFW Pairs": (64, 64, 64),
}
dct_dash_by_strategy = {
"OMP": "solid",
"NN-OMP": "solid",
"OMP Distillation": "dash",
"OMP Distillation w/o weights": "dash",
"OMP w/o weights": "dot",
"NN-OMP w/o weights": "dot",
"Random": "longdash",
"Zhang Similarities": "dash",
'Zhang Predictions': "dash",
'Ensemble': "dash",
"Kmeans": "dash"
}
dct_symbol_by_strategy = {
"OMP": "x",
"NN-OMP": "star",
"OMP Distillation": "x",
"OMP Distillation w/o weights": "x",
"OMP w/o weights": "x",
"NN-OMP w/o weights": "star",
"Random": "x",
"Zhang Similarities": "hexagon",
'Zhang Predictions': "hexagon2",
'Ensemble': "pentagon",
"Kmeans": "octagon",
}
def get_index_of_first_last_repeted_elemen(iterabl):
last_elem = iterabl[-1]
reversed_idx = 0
for idx, elm in enumerate(iterabl[::-1]):
if elm != last_elem:
break
reversed_idx = -(idx+1)
index_flat = len(iterabl) + reversed_idx
return index_flat
GLOBAL_TRACE_TO_ADD_LAST = None
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(['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
index_flat = len(forest_sizes)
if stop_on_flat:
actual_forest_sizes = list(df_groupby_forest_size["actual-forest-size"].mean().values)
index_flat = get_index_of_first_last_repeted_elemen(actual_forest_sizes)
# for this trace to appear on top of all others
GLOBAL_TRACE_TO_ADD_LAST = go.Scatter(
mode='markers',
x=[forest_sizes[index_flat-1]],
y=[mean_value[index_flat-1]],
marker_symbol="star",
marker=dict(
color="rgb{}".format(dct_color_by_strategy[strat]),
size=15,
line=dict(
color='Black',
width=2
)
),
name="Final NN-OMP",
showlegend=True
)
forest_sizes = forest_sizes[:index_flat]
mean_value = mean_value[:index_flat]
std_value = std_value[:index_flat]
std_value_upper = list(mean_value + std_value)
std_value_lower = list(mean_value - std_value)
# print(df_strat)
fig.add_trace(go.Scatter(x=forest_sizes, y=mean_value,
mode='lines',
name=strat,
line=dict(dash=dct_dash_by_strategy[strat], color="rgb{}".format(dct_color_by_strategy[strat]))
))
fig.add_trace(go.Scatter(
x=forest_sizes + forest_sizes[::-1],
y=std_value_upper + std_value_lower[::-1],
fill='toself',
showlegend=False,
fillcolor='rgba{}'.format(dct_color_by_strategy[strat] + tpl_transparency),
line_color='rgba(255,255,255,0)',
name=strat
))
tpl_transparency = (0.1,)
dct_metric_lambda_prop_amelioration = {
"accuracy_score": (lambda mean_value_acc, mean_value_random_acc: (mean_value_acc - mean_value_random_acc) / mean_value_random_acc),
"mean_squared_error": (lambda mean_value_mse, mean_value_random_mse: (mean_value_random_mse - mean_value_mse) / mean_value_random_mse)
}
dct_metric_figure = {
"accuracy_score":go.Figure(),
"mean_squared_error": go.Figure()
}
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:
df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0]
# This figure is for basic representation: task metric wrt the number of pruned tree
fig = go.Figure()
##################
# all techniques #
##################
for strat in strategies:
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
df_strat = df_data[df_data["strategy"] == strat]
df_strat = df_strat[df_strat["subset"] == "train+dev/train+dev"]
# df_strat = df_strat[df_strat["subset"] == "train/dev"]
if "OMP" in strat:
###########################
# traitement avec weights #
###########################
df_strat_wo_weights = df_strat[df_strat["wo_weights"] == False]
if strat == "NN-OMP":
add_trace_from_df(df_strat_wo_weights, fig, task, strat, stop_on_flat=True)
else:
add_trace_from_df(df_strat_wo_weights, fig, task, strat)
#################################
# 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)
if strat == "NN-OMP":
add_trace_from_df(df_strat_wo_weights, fig, task, strat, stop_on_flat=True)
else:
add_trace_from_df(df_strat_wo_weights, fig, task, strat)
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 "% d'Arbres sélectionnés"
if not skip_nn:
fig.add_trace(GLOBAL_TRACE_TO_ADD_LAST)
fig.update_layout(barmode='group',
# title=title,
xaxis_title=xaxis_title,
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="sans-serif",
size=24,
color="black"
),
# bgcolor="LightSteelBlue",
# bordercolor="Black",
borderwidth=1,
)
)
# 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)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
def global_figure():
for task in tasks:
for metric in ["accuracy_score", "mean_squared_error"]:
# fig = go.Figure()
df_data = df_results
df_strat_random = df_data[df_data["strategy"] == "Random"]
df_strat_random = df_strat_random[df_strat_random["subset"] == "train+dev/train+dev"]
df_strat_random_wo_weights = df_strat_random[df_strat_random["wo_weights"] == False]
df_strat_random_wo_weights.sort_values(by="pruning_percent", inplace=True)
# df_strat_random_wo_weights_acc = df_strat_random_wo_weights[df_strat_random_wo_weights["score_metric"] == "accuracy_score"]
# df_groupby_random_forest_size_acc = df_strat_random_wo_weights_acc.groupby(['pruning_percent'])
# forest_sizes_random_acc = df_groupby_random_forest_size_acc["pruning_percent"].mean().values
# mean_value_random_acc = df_groupby_random_forest_size_acc[task].mean().values
df_strat_random_wo_weights_mse = df_strat_random_wo_weights[df_strat_random_wo_weights["score_metric"] == metric]
# df_strat_random_wo_weights_mse = df_strat_random_wo_weights[df_strat_random_wo_weights["score_metric"] == "mean_squared_error"]
df_groupby_random_forest_size_mse = df_strat_random_wo_weights_mse.groupby(['pruning_percent'])
forest_sizes_random_mse = df_groupby_random_forest_size_mse["pruning_percent"].mean().values
# assert np.allclose(forest_sizes_random_acc, forest_sizes_random_mse)
mean_value_random_mse = df_groupby_random_forest_size_mse[task].mean().values
for strat in strategies:
if strat in lst_skip_strategy or strat == "Random":
continue
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)
# "accuracy_score"
# "mean_squared_error"
# df_accuracy = df_strat_wo_weights[df_strat_wo_weights["score_metric"] == "accuracy_score"]
# df_groupby_forest_size = df_accuracy.groupby(['pruning_percent'])
# forest_sizes_acc = df_groupby_forest_size["pruning_percent"].mean().values
# mean_value_acc = df_groupby_forest_size[task].mean().values
# propo_ameliration_mean_value_acc = (mean_value_acc - mean_value_random_acc)/mean_value_random_acc
df_mse = df_strat_wo_weights[df_strat_wo_weights["score_metric"] == metric]
# df_mse = df_strat_wo_weights[df_strat_wo_weights["score_metric"] == "mean_squared_error"]
df_groupby_forest_size_mse = df_mse.groupby(['pruning_percent'])
forest_sizes_mse = df_groupby_forest_size_mse["pruning_percent"].mean().values
# assert np.allclose(forest_sizes_mse, forest_sizes_acc)
# assert np.allclose(forest_sizes_random_acc, forest_sizes_acc)
mean_value_mse = df_groupby_forest_size_mse[task].mean().values
# propo_ameliration_mean_value_mse = (mean_value_random_mse - mean_value_mse) / mean_value_random_mse
propo_ameliration_mean_value_mse = dct_metric_lambda_prop_amelioration[metric](mean_value_mse, mean_value_random_mse)
# mean_value = np.mean([propo_ameliration_mean_value_acc, propo_ameliration_mean_value_mse], axis=0)
mean_value = np.mean([propo_ameliration_mean_value_mse], axis=0)
# std_value = df_groupby_forest_size[task].std().values
# print(df_strat)
dct_metric_figure[metric].add_trace(go.Scatter(x=forest_sizes_mse, y=mean_value,
mode='markers',
name=strat,
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat])),
marker_symbol = dct_symbol_by_strategy[strat],
marker = dict(
color="rgb{}".format(dct_color_by_strategy[strat]),
size=20,
# line=dict(
# color='Black',
# width=2
# )
),
))
title_global_figure = "Global {} {}".format(task, metric)
sanitize = lambda x: x.replace(" ", "_").replace("/", "_").replace("+", "_")
filename = sanitize(title_global_figure)
dct_metric_figure[metric].update_layout(title=filename)
dct_metric_figure[metric].write_image(str((out_dir / filename).absolute()) + ".png")
# fig.show()
def weights_wrt_size():
# 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
df_data = df_results[df_results["dataset"] == data_name]
score_metric_name = df_data["score_metric"].values[0]
##################
# all techniques #
##################
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_strat_wo_weights.sort_values(by="pruning_percent", inplace=True)
df_groupby_forest_size = df_strat_wo_weights.groupby(['forest_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 = 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":
# y_values = 1/y_values
lin_reg = svm.SVR(gamma=10)
lin_reg.fit(x_values[:, np.newaxis], y_values)
# 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,
mode='markers',
name=strat,
# color=forest_sizes,
marker=dict(
# size=16,
# cmax=39,
# cmin=0,
color="rgb{}".format(dct_data_color[data_name]),
# colorbar=dict(
# title="Forest Size"
# ),
# colorscale="Viridis"
),
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
))
fig.add_trace(go.Scatter(x=x_values, y=yy,
mode='lines',
name=strat,
# color=forest_sizes,
marker=dict(
# size=16,
# cmax=39,
# cmin=0,
color="rgba{}".format(tuple(list(dct_data_color[data_name]) + [0.5])),
# colorbar=dict(
# title="Forest Size"
# ),
# colorscale="Viridis"
),
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
))
title = "{}".format("weight wrt size")
fig.update_layout(barmode='group',
# title=title,
xaxis_title="% d'Arbres selectionnés",
yaxis_title="% de poids négatifs standardisé",
font=dict(
# family="Courier New, monospace",
size=24,
color="black"
),
showlegend = False,
margin=dict(
l=1,
r=1,
b=3,
t=10,
# 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(title)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
def effect_of_weights_figure():
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]
score_metric_name = df_data["score_metric"].values[0]
##################
# all techniques #
##################
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_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
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])
# lin_reg = LinearRegression()
# 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)
yy = lin_reg.predict(xx[:, np.newaxis])
# print(df_strat)
fig.add_trace(go.Scatter(x=x_values, y=y_values,
mode='markers',
name=strat,
showlegend=False,
# color=forest_sizes,
marker=dict(
# size=16,
# cmax=39,
# cmin=0,
color="rgb{}".format(dct_data_color[data_name]),
# colorbar=dict(
# title="Forest Size"
# ),
# colorscale="Viridis"
),
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
))
fig.add_trace(go.Scatter(x=xx, y=yy,
mode='lines',
name=data_name,
# color=forest_sizes,
marker=dict(
# size=16,
# cmax=39,
# cmin=0,
color="rgba{}".format(tuple(list(dct_data_color[data_name]) + [0.5])),
# colorbar=dict(
# title="Forest Size"
# ),
# colorscale="Viridis"
),
# marker=dict(color="rgb{}".format(dct_color_by_strategy[strat]))
))
title = "{}".format("negative weights effect")
fig.update_layout(barmode='group',
# title=title,
xaxis_title="% de poids négatifs standardisé",
yaxis_title="Performance standardisée",
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="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(title)
output_dir.mkdir(parents=True, exist_ok=True)
fig.update_xaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, ticks="outside", linewidth=2, linecolor='black', mirror=True)
fig.write_image(str((output_dir / filename).absolute()) + ".png")
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 = set(df_results["strategy"].values)
subsets = set(df_results["subset"].values)
for skip_nn in [True, False]:
base_figures(skip_nn)
effect_of_weights_figure()
weights_wrt_size()
# global_figure()
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment