Commit 86f7f9c4 authored by Alexander Henkel's avatar Alexander Henkel
Browse files

bugfixing

parent 0af01b36
%% Cell type:code id:18d33b57 tags: %% Cell type:code id:18d33b57 tags:
   
``` python ``` python
%load_ext autoreload %load_ext autoreload
%autoreload 2 %autoreload 2
   
%matplotlib notebook %matplotlib notebook
``` ```
   
%% Cell type:code id:5b007717 tags: %% Cell type:code id:5b007717 tags:
   
``` python ``` python
import sys import sys
import os import os
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from IPython.display import display, Markdown from IPython.display import display, Markdown
import copy import copy
import pandas as pd import pandas as pd
``` ```
   
%% Cell type:code id:c32112e2 tags: %% Cell type:code id:c32112e2 tags:
   
``` python ``` python
module_path = os.path.abspath(os.path.join('..')) module_path = os.path.abspath(os.path.join('..'))
os.chdir(module_path) os.chdir(module_path)
if module_path not in sys.path: if module_path not in sys.path:
sys.path.append(module_path) sys.path.append(module_path)
``` ```
   
%% Cell type:code id:b0e20ed4 tags: %% Cell type:code id:b0e20ed4 tags:
   
``` python ``` python
from personalization_tools.load_data_sets import * from personalization_tools.load_data_sets import *
from personalization_tools.helpers import * from personalization_tools.helpers import *
from personalization_tools.learner_pipeline import LearnerPipeline, Evaluation from personalization_tools.learner_pipeline import LearnerPipeline, Evaluation
from personalization_tools.dataset_builder import * from personalization_tools.dataset_builder import *
from personalization_tools.model_evaluation import ModelEvaluation from personalization_tools.model_evaluation import ModelEvaluation
from personalization_tools.sensor_recorder_data_reader import SensorRecorderDataReader from personalization_tools.sensor_recorder_data_reader import SensorRecorderDataReader
from personalization_tools.dataset_manager import DatasetManager from personalization_tools.dataset_manager import DatasetManager
from personalization_tools.trainings_manager import TrainingsManager from personalization_tools.trainings_manager import TrainingsManager
from personalization_tools.pseudo_model_settings import pseudo_model_settings, thesis_filters, translate_setting from personalization_tools.pseudo_model_settings import pseudo_model_settings, thesis_filters, translate_setting
from personalization_tools.pseudo_label_evaluation import * from personalization_tools.pseudo_label_evaluation import *
from personalization_tools.globals import Indicators from personalization_tools.globals import Indicators
from personalization_tools.evaluation_manager import EvaluationManager from personalization_tools.evaluation_manager import EvaluationManager
``` ```
   
%% Cell type:code id:db3ce6a9 tags: %% Cell type:code id:db3ce6a9 tags:
   
``` python ``` python
pd.set_option('display.max_rows', 500) pd.set_option('display.max_rows', 500)
``` ```
   
%% Cell type:code id:2bdc9c75 tags: %% Cell type:code id:2bdc9c75 tags:
   
``` python ``` python
evaluation_config_file = './data/cluster/pseudo_collections/evaluation_config.yaml' evaluation_config_file = './data/cluster/pseudo_collections/evaluation_config.yaml'
``` ```
   
%% Cell type:code id:c977159b tags: %% Cell type:code id:c977159b tags:
   
``` python ``` python
evaluation_manager = EvaluationManager() evaluation_manager = EvaluationManager()
evaluation_manager.load_config(evaluation_config_file) evaluation_manager.load_config(evaluation_config_file)
``` ```
   
%% Output %% Output
   
load config load config
   
%% Cell type:code id:2f20d006 tags: %% Cell type:code id:5f34a36f tags:
   
``` python ``` python
for model in evaluation_manager.get_run_of_collection('random_synthetic_01'): for model in evaluation_manager.get_run_of_collection('random_synthetic_01'):
evaluation_manager.model_evaluation.clear_evaluations_of_model(model) evaluation_manager.model_evaluation.clear_evaluations_of_model(model)
``` ```
   
%% Cell type:code id:6decca8f tags: %% Cell type:code id:6decca8f tags:
   
``` python ``` python
evaluation_manager.do_predictions() evaluation_manager.do_predictions()
``` ```
   
%% Output %% Output
   
Do Predictions for: Do Predictions for:
Use device: cuda Use device: cuda
run: ('random_01_run1_all_rn2ry0.pt', 01_generated_0) ... 0.8635909557342529 seconds run: ('random_01_run1_all_rn2ry0.pt', 01_generated_0) ... 0.8635909557342529 seconds
run: ('random_01_run1_all_rn2ry0.pt', 01_generated_1) ... 0.7559754848480225 seconds run: ('random_01_run1_all_rn2ry0.pt', 01_generated_1) ... 0.7559754848480225 seconds
run: ('random_01_run1_noneut_rn2ry0.pt', 01_generated_0) ... 0.769207239151001 seconds run: ('random_01_run1_noneut_rn2ry0.pt', 01_generated_0) ... 0.769207239151001 seconds
run: ('random_01_run1_noneut_rn2ry0.pt', 01_generated_1) ... 0.7538094520568848 seconds run: ('random_01_run1_noneut_rn2ry0.pt', 01_generated_1) ... 0.7538094520568848 seconds
run: ('random_01_run1_high_rn2ry0.pt', 01_generated_0) ... 0.7616837024688721 seconds run: ('random_01_run1_high_rn2ry0.pt', 01_generated_0) ... 0.7616837024688721 seconds
run: ('random_01_run1_high_rn2ry0.pt', 01_generated_1) ... 0.7572121620178223 seconds run: ('random_01_run1_high_rn2ry0.pt', 01_generated_1) ... 0.7572121620178223 seconds
run: ('random_01_run1_all_corrected_rn2ry0.pt', 01_generated_0) ... 0.7925519943237305 seconds run: ('random_01_run1_all_corrected_rn2ry0.pt', 01_generated_0) ... 0.7925519943237305 seconds
run: ('random_01_run1_all_corrected_rn2ry0.pt', 01_generated_1) ... 0.7952256202697754 seconds run: ('random_01_run1_all_corrected_rn2ry0.pt', 01_generated_1) ... 0.7952256202697754 seconds
run: ('random_01_run1_all_corrected_noise_rn2ry0.pt', 01_generated_0) ... 0.7728948593139648 seconds run: ('random_01_run1_all_corrected_noise_rn2ry0.pt', 01_generated_0) ... 0.7728948593139648 seconds
run: ('random_01_run1_all_corrected_noise_rn2ry0.pt', 01_generated_1) ... 0.7352817058563232 seconds run: ('random_01_run1_all_corrected_noise_rn2ry0.pt', 01_generated_1) ... 0.7352817058563232 seconds
run: ('random_01_run1_scope_corrected_noise_rn2ry0.pt', 01_generated_0) ... 0.7921650409698486 seconds run: ('random_01_run1_scope_corrected_noise_rn2ry0.pt', 01_generated_0) ... 0.7921650409698486 seconds
run: ('random_01_run1_scope_corrected_noise_rn2ry0.pt', 01_generated_1) ... 0.7624926567077637 seconds run: ('random_01_run1_scope_corrected_noise_rn2ry0.pt', 01_generated_1) ... 0.7624926567077637 seconds
run: ('random_01_run1_noneut_corrected_rn2ry0.pt', 01_generated_0) ... 0.7853000164031982 seconds run: ('random_01_run1_noneut_corrected_rn2ry0.pt', 01_generated_0) ... 0.7853000164031982 seconds
run: ('random_01_run1_noneut_corrected_rn2ry0.pt', 01_generated_1) ... 0.7344820499420166 seconds run: ('random_01_run1_noneut_corrected_rn2ry0.pt', 01_generated_1) ... 0.7344820499420166 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn2ry0.pt', 01_generated_0) ... 0.8096168041229248 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn2ry0.pt', 01_generated_0) ... 0.8096168041229248 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn2ry0.pt', 01_generated_1) ... 0.7759416103363037 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn2ry0.pt', 01_generated_1) ... 0.7759416103363037 seconds
run: ('random_01_run1_allnoise_correctedscore_rn2ry0.pt', 01_generated_0) ... 0.7606852054595947 seconds run: ('random_01_run1_allnoise_correctedscore_rn2ry0.pt', 01_generated_0) ... 0.7606852054595947 seconds
run: ('random_01_run1_allnoise_correctedscore_rn2ry0.pt', 01_generated_1) ... 0.7505133152008057 seconds run: ('random_01_run1_allnoise_correctedscore_rn2ry0.pt', 01_generated_1) ... 0.7505133152008057 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 01_generated_0) ... 0.7556631565093994 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 01_generated_0) ... 0.7556631565093994 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 01_generated_1) ... 0.7928900718688965 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 01_generated_1) ... 0.7928900718688965 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 01_generated_0) ... 0.7585153579711914 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 01_generated_0) ... 0.7585153579711914 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 01_generated_1) ... 0.7369773387908936 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 01_generated_1) ... 0.7369773387908936 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 01_generated_0) ... 0.755394697189331 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 01_generated_0) ... 0.755394697189331 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 01_generated_1) ... 0.7497127056121826 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 01_generated_1) ... 0.7497127056121826 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 01_generated_0) ... 0.7556638717651367 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 01_generated_0) ... 0.7556638717651367 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 01_generated_1) ... 0.7547783851623535 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 01_generated_1) ... 0.7547783851623535 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_0) ... 0.7777938842773438 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_0) ... 0.7777938842773438 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_1) ... 0.7589237689971924 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_1) ... 0.7589237689971924 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_0) ... 0.7642219066619873 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_0) ... 0.7642219066619873 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_1) ... 0.7259056568145752 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 01_generated_1) ... 0.7259056568145752 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 01_generated_0) ... 0.7517473697662354 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 01_generated_0) ... 0.7517473697662354 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 01_generated_1) ... 0.7441573143005371 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 01_generated_1) ... 0.7441573143005371 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 01_generated_0) ... 0.8003270626068115 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 01_generated_0) ... 0.8003270626068115 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 01_generated_1) ... 0.7511992454528809 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 01_generated_1) ... 0.7511992454528809 seconds
run: ('random_01_run1_all_rn3ry0.pt', 01_generated_0) ... 0.7873153686523438 seconds run: ('random_01_run1_all_rn3ry0.pt', 01_generated_0) ... 0.7873153686523438 seconds
run: ('random_01_run1_all_rn3ry0.pt', 01_generated_1) ... 0.7366888523101807 seconds run: ('random_01_run1_all_rn3ry0.pt', 01_generated_1) ... 0.7366888523101807 seconds
run: ('random_01_run1_noneut_rn3ry0.pt', 01_generated_0) ... 0.7884097099304199 seconds run: ('random_01_run1_noneut_rn3ry0.pt', 01_generated_0) ... 0.7884097099304199 seconds
run: ('random_01_run1_noneut_rn3ry0.pt', 01_generated_1) ... 0.7301690578460693 seconds run: ('random_01_run1_noneut_rn3ry0.pt', 01_generated_1) ... 0.7301690578460693 seconds
run: ('random_01_run1_high_rn3ry0.pt', 01_generated_0) ... 0.754291296005249 seconds run: ('random_01_run1_high_rn3ry0.pt', 01_generated_0) ... 0.754291296005249 seconds
run: ('random_01_run1_high_rn3ry0.pt', 01_generated_1) ... 0.7309882640838623 seconds run: ('random_01_run1_high_rn3ry0.pt', 01_generated_1) ... 0.7309882640838623 seconds
run: ('random_01_run1_all_corrected_rn3ry0.pt', 01_generated_0) ... 0.7821619510650635 seconds run: ('random_01_run1_all_corrected_rn3ry0.pt', 01_generated_0) ... 0.7821619510650635 seconds
run: ('random_01_run1_all_corrected_rn3ry0.pt', 01_generated_1) ... 0.7578375339508057 seconds run: ('random_01_run1_all_corrected_rn3ry0.pt', 01_generated_1) ... 0.7578375339508057 seconds
run: ('random_01_run1_all_corrected_noise_rn3ry0.pt', 01_generated_0) ... 0.7645065784454346 seconds run: ('random_01_run1_all_corrected_noise_rn3ry0.pt', 01_generated_0) ... 0.7645065784454346 seconds
run: ('random_01_run1_all_corrected_noise_rn3ry0.pt', 01_generated_1) ... 0.7596299648284912 seconds run: ('random_01_run1_all_corrected_noise_rn3ry0.pt', 01_generated_1) ... 0.7596299648284912 seconds
run: ('random_01_run1_scope_corrected_noise_rn3ry0.pt', 01_generated_0) ... 0.7941877841949463 seconds run: ('random_01_run1_scope_corrected_noise_rn3ry0.pt', 01_generated_0) ... 0.7941877841949463 seconds
run: ('random_01_run1_scope_corrected_noise_rn3ry0.pt', 01_generated_1) ... 0.7285556793212891 seconds run: ('random_01_run1_scope_corrected_noise_rn3ry0.pt', 01_generated_1) ... 0.7285556793212891 seconds
run: ('random_01_run1_noneut_corrected_rn3ry0.pt', 01_generated_0) ... 0.780742883682251 seconds run: ('random_01_run1_noneut_corrected_rn3ry0.pt', 01_generated_0) ... 0.780742883682251 seconds
run: ('random_01_run1_noneut_corrected_rn3ry0.pt', 01_generated_1) ... 0.7276768684387207 seconds run: ('random_01_run1_noneut_corrected_rn3ry0.pt', 01_generated_1) ... 0.7276768684387207 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn3ry0.pt', 01_generated_0) ... 0.7688539028167725 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn3ry0.pt', 01_generated_0) ... 0.7688539028167725 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn3ry0.pt', 01_generated_1) ... 0.732712984085083 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn3ry0.pt', 01_generated_1) ... 0.732712984085083 seconds
run: ('random_01_run1_allnoise_correctedscore_rn3ry0.pt', 01_generated_0) ... 0.7582430839538574 seconds run: ('random_01_run1_allnoise_correctedscore_rn3ry0.pt', 01_generated_0) ... 0.7582430839538574 seconds
run: ('random_01_run1_allnoise_correctedscore_rn3ry0.pt', 01_generated_1) ... 0.7498233318328857 seconds run: ('random_01_run1_allnoise_correctedscore_rn3ry0.pt', 01_generated_1) ... 0.7498233318328857 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 01_generated_0) ... 0.8055596351623535 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 01_generated_0) ... 0.8055596351623535 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 01_generated_1) ... 0.8094184398651123 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 01_generated_1) ... 0.8094184398651123 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 01_generated_0) ... 0.8268589973449707 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 01_generated_0) ... 0.8268589973449707 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 01_generated_1) ... 0.7235498428344727 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 01_generated_1) ... 0.7235498428344727 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 01_generated_0) ... 0.8530466556549072 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 01_generated_0) ... 0.8530466556549072 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 01_generated_1) ... 0.8100605010986328 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 01_generated_1) ... 0.8100605010986328 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 01_generated_0) ... 0.8334698677062988 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 01_generated_0) ... 0.8334698677062988 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 01_generated_1) ... 0.7387728691101074 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 01_generated_1) ... 0.7387728691101074 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_0) ... 0.8335132598876953 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_0) ... 0.8335132598876953 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_1) ... 0.8061039447784424 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_1) ... 0.8061039447784424 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_0) ... 0.8185348510742188 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_0) ... 0.8185348510742188 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_1) ... 0.7325232028961182 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 01_generated_1) ... 0.7325232028961182 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 01_generated_0) ... 0.8085942268371582 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 01_generated_0) ... 0.8085942268371582 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 01_generated_1) ... 0.7617881298065186 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 01_generated_1) ... 0.7617881298065186 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 01_generated_0) ... 0.8054249286651611 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 01_generated_0) ... 0.8054249286651611 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 01_generated_1) ... 0.7698974609375 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 01_generated_1) ... 0.7698974609375 seconds
run: ('random_01_run1_all_rn4ry0.pt', 01_generated_0) ... 0.8151412010192871 seconds run: ('random_01_run1_all_rn4ry0.pt', 01_generated_0) ... 0.8151412010192871 seconds
run: ('random_01_run1_all_rn4ry0.pt', 01_generated_1) ... 0.7857952117919922 seconds run: ('random_01_run1_all_rn4ry0.pt', 01_generated_1) ... 0.7857952117919922 seconds
run: ('random_01_run1_noneut_rn4ry0.pt', 01_generated_0) ... 0.7914161682128906 seconds run: ('random_01_run1_noneut_rn4ry0.pt', 01_generated_0) ... 0.7914161682128906 seconds
run: ('random_01_run1_noneut_rn4ry0.pt', 01_generated_1) ... 0.7642643451690674 seconds run: ('random_01_run1_noneut_rn4ry0.pt', 01_generated_1) ... 0.7642643451690674 seconds
run: ('random_01_run1_high_rn4ry0.pt', 01_generated_0) ... 0.8170721530914307 seconds run: ('random_01_run1_high_rn4ry0.pt', 01_generated_0) ... 0.8170721530914307 seconds
run: ('random_01_run1_high_rn4ry0.pt', 01_generated_1) ... 0.8080852031707764 seconds run: ('random_01_run1_high_rn4ry0.pt', 01_generated_1) ... 0.8080852031707764 seconds
run: ('random_01_run1_all_corrected_rn4ry0.pt', 01_generated_0) ... 0.8258817195892334 seconds run: ('random_01_run1_all_corrected_rn4ry0.pt', 01_generated_0) ... 0.8258817195892334 seconds
run: ('random_01_run1_all_corrected_rn4ry0.pt', 01_generated_1) ... 0.7757227420806885 seconds run: ('random_01_run1_all_corrected_rn4ry0.pt', 01_generated_1) ... 0.7757227420806885 seconds
run: ('random_01_run1_all_corrected_noise_rn4ry0.pt', 01_generated_0) ... 0.8063132762908936 seconds run: ('random_01_run1_all_corrected_noise_rn4ry0.pt', 01_generated_0) ... 0.8063132762908936 seconds
run: ('random_01_run1_all_corrected_noise_rn4ry0.pt', 01_generated_1) ... 0.7625775337219238 seconds run: ('random_01_run1_all_corrected_noise_rn4ry0.pt', 01_generated_1) ... 0.7625775337219238 seconds
   
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run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry8.pt', 01_generated_1) ... 0.7285308837890625 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry8.pt', 01_generated_1) ... 0.7285308837890625 seconds
run: ('random_01_run1_all_rn0ry9.pt', 01_generated_0) ... 0.7921938896179199 seconds run: ('random_01_run1_all_rn0ry9.pt', 01_generated_0) ... 0.7921938896179199 seconds
run: ('random_01_run1_all_rn0ry9.pt', 01_generated_1) ... 0.7515230178833008 seconds run: ('random_01_run1_all_rn0ry9.pt', 01_generated_1) ... 0.7515230178833008 seconds
run: ('random_01_run1_noneut_rn0ry9.pt', 01_generated_0) ... 0.8025062084197998 seconds run: ('random_01_run1_noneut_rn0ry9.pt', 01_generated_0) ... 0.8025062084197998 seconds
run: ('random_01_run1_noneut_rn0ry9.pt', 01_generated_1) ... 0.7435953617095947 seconds run: ('random_01_run1_noneut_rn0ry9.pt', 01_generated_1) ... 0.7435953617095947 seconds
run: ('random_01_run1_high_rn0ry9.pt', 01_generated_0) ... 0.7583441734313965 seconds run: ('random_01_run1_high_rn0ry9.pt', 01_generated_0) ... 0.7583441734313965 seconds
run: ('random_01_run1_high_rn0ry9.pt', 01_generated_1) ... 0.7263915538787842 seconds run: ('random_01_run1_high_rn0ry9.pt', 01_generated_1) ... 0.7263915538787842 seconds
run: ('random_01_run1_all_corrected_rn0ry9.pt', 01_generated_0) ... 0.7556836605072021 seconds run: ('random_01_run1_all_corrected_rn0ry9.pt', 01_generated_0) ... 0.7556836605072021 seconds
run: ('random_01_run1_all_corrected_rn0ry9.pt', 01_generated_1) ... 0.7479240894317627 seconds run: ('random_01_run1_all_corrected_rn0ry9.pt', 01_generated_1) ... 0.7479240894317627 seconds
run: ('random_01_run1_all_corrected_noise_rn0ry9.pt', 01_generated_0) ... 0.7595236301422119 seconds run: ('random_01_run1_all_corrected_noise_rn0ry9.pt', 01_generated_0) ... 0.7595236301422119 seconds
run: ('random_01_run1_all_corrected_noise_rn0ry9.pt', 01_generated_1) ... 0.7346436977386475 seconds run: ('random_01_run1_all_corrected_noise_rn0ry9.pt', 01_generated_1) ... 0.7346436977386475 seconds
run: ('random_01_run1_scope_corrected_noise_rn0ry9.pt', 01_generated_0) ... 0.7576563358306885 seconds run: ('random_01_run1_scope_corrected_noise_rn0ry9.pt', 01_generated_0) ... 0.7576563358306885 seconds
run: ('random_01_run1_scope_corrected_noise_rn0ry9.pt', 01_generated_1) ... 0.7307660579681396 seconds run: ('random_01_run1_scope_corrected_noise_rn0ry9.pt', 01_generated_1) ... 0.7307660579681396 seconds
run: ('random_01_run1_noneut_corrected_rn0ry9.pt', 01_generated_0) ... 0.7544121742248535 seconds run: ('random_01_run1_noneut_corrected_rn0ry9.pt', 01_generated_0) ... 0.7544121742248535 seconds
run: ('random_01_run1_noneut_corrected_rn0ry9.pt', 01_generated_1) ... 0.7278764247894287 seconds run: ('random_01_run1_noneut_corrected_rn0ry9.pt', 01_generated_1) ... 0.7278764247894287 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn0ry9.pt', 01_generated_0) ... 0.755192756652832 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn0ry9.pt', 01_generated_0) ... 0.755192756652832 seconds
run: ('random_01_run1_allnoise_correctedhwgt_rn0ry9.pt', 01_generated_1) ... 0.7274973392486572 seconds run: ('random_01_run1_allnoise_correctedhwgt_rn0ry9.pt', 01_generated_1) ... 0.7274973392486572 seconds
run: ('random_01_run1_allnoise_correctedscore_rn0ry9.pt', 01_generated_0) ... 0.7551116943359375 seconds run: ('random_01_run1_allnoise_correctedscore_rn0ry9.pt', 01_generated_0) ... 0.7551116943359375 seconds
run: ('random_01_run1_allnoise_correctedscore_rn0ry9.pt', 01_generated_1) ... 0.7750239372253418 seconds run: ('random_01_run1_allnoise_correctedscore_rn0ry9.pt', 01_generated_1) ... 0.7750239372253418 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn0ry9.pt', 01_generated_0) ... 0.7833237648010254 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn0ry9.pt', 01_generated_0) ... 0.7833237648010254 seconds
run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn0ry9.pt', 01_generated_1) ... 0.727121114730835 seconds run: ('random_01_run1_allnoise_correctbydeepconvfilter_rn0ry9.pt', 01_generated_1) ... 0.727121114730835 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn0ry9.pt', 01_generated_0) ... 0.7878110408782959 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn0ry9.pt', 01_generated_0) ... 0.7878110408782959 seconds
run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn0ry9.pt', 01_generated_1) ... 0.7507467269897461 seconds run: ('random_01_run1_allnoise_correctbyfcndaefilter_rn0ry9.pt', 01_generated_1) ... 0.7507467269897461 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn0ry9.pt', 01_generated_0) ... 0.7537708282470703 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn0ry9.pt', 01_generated_0) ... 0.7537708282470703 seconds
run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn0ry9.pt', 01_generated_1) ... 0.745764970779419 seconds run: ('random_01_run1_allnoise_correctbyconvlstmfilter_rn0ry9.pt', 01_generated_1) ... 0.745764970779419 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn0ry9.pt', 01_generated_0) ... 0.7598302364349365 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn0ry9.pt', 01_generated_0) ... 0.7598302364349365 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn0ry9.pt', 01_generated_1) ... 0.7436184883117676 seconds run: ('random_01_run1_allnoise_correctbyconvlstm2filter_rn0ry9.pt', 01_generated_1) ... 0.7436184883117676 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_0) ... 0.7500631809234619 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_0) ... 0.7500631809234619 seconds
run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_1) ... 0.7381124496459961 seconds run: ('random_01_run1_allnoise_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_1) ... 0.7381124496459961 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_0) ... 0.7494466304779053 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_0) ... 0.7494466304779053 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_1) ... 0.7250678539276123 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry9.pt', 01_generated_1) ... 0.7250678539276123 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry9.pt', 01_generated_0) ... 0.7515983581542969 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry9.pt', 01_generated_0) ... 0.7515983581542969 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry9.pt', 01_generated_1) ... 0.72646164894104 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry9.pt', 01_generated_1) ... 0.72646164894104 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry9.pt', 01_generated_0) ... 0.7531909942626953 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry9.pt', 01_generated_0) ... 0.7531909942626953 seconds
run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry9.pt', 01_generated_1) ... 0.738947868347168 seconds run: ('random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry9.pt', 01_generated_1) ... 0.738947868347168 seconds
saved predictions saved predictions
   
%% Cell type:code id:94cc572d tags: %% Cell type:code id:94cc572d tags:
   
``` python ``` python
evaluation_manager.model_evaluation.models.keys() evaluation_manager.model_evaluation.models.keys()
``` ```
   
%% Output %% Output
   
dict_keys(['HandWashingDeepConvLSTMA_trunc_01.pt', 'synthetic_inc_01.pt', '01_run1_all.pt', '01_run1_scope.pt', '01_run1_noneut.pt', '01_run1_high.pt', '01_run1_inchc.pt', '01_run1_all_corrected.pt', '01_run1_all_corrected_noise.pt', '01_run1_scope_corrected_noise.pt', '01_run1_scope_corrected.pt', '01_run1_noneut_corrected.pt', '01_run1_correctedhwgt.pt', '01_run1_noneut_correctedhwgt.pt', '01_run1_inchc_correctedhwgt.pt', '01_run1_synthetic.pt', '01_run1_allnoise_correctedhwgt.pt', '01_run1_allnoise_correctedhwgt_scope.pt', '01_run1_allnoise_corrected.pt', '01_run1_allnoise_correctedscore.pt', '01_run1_allnoise_correctedscore_flatten.pt', '01_run1_allnoise_correctbydeepconvfilter.pt', '01_run1_allnoise_correctbyfcndaefilter.pt', '01_run1_allnoise_correctbyfcndaefiltertest.pt', '01_run1_allnoise_correctbyconvlstmfilter.pt', '01_run1_allnoise_correctbyconvlstm2filter.pt', '01_run1_allnoise_correctbyconvlstm3filter.pt', '01_run1_alldeepconv_correctbyconvlstm3filter.pt', '01_run1_alldeepconv_correctbyconvlstm3filter2.pt', '01_run1_alldeepconv_correctbyconvlstm3filter3.pt', '01_run1_alldeepconv_correctbyconvlstm3filter4.pt', '01_run1_alldeepconv_correctbyconvlstm3filter5.pt', '01_run1_alldeepconv_correctbyconvlstm3filter6.pt', '01_run1_alldeepconv_correctbyconvlstm2filter6.pt', 'HandWashingDeepConvLSTMA_trunc_02.pt', 'synthetic_inc_02.pt', '02_run1_all.pt', '02_run1_scope.pt', '02_run1_noneut.pt', '02_run1_high.pt', '02_run1_inchc.pt', '02_run1_all_corrected.pt', '02_run1_all_corrected_noise.pt', '02_run1_scope_corrected_noise.pt', '02_run1_scope_corrected.pt', '02_run1_noneut_corrected.pt', '02_run1_correctedhwgt.pt', '02_run1_noneut_correctedhwgt.pt', '02_run1_inchc_correctedhwgt.pt', '02_run1_synthetic.pt', '02_run1_allnoise_correctedhwgt.pt', '02_run1_allnoise_correctedhwgt_scope.pt', '02_run1_allnoise_corrected.pt', '02_run1_allnoise_correctedscore.pt', '02_run1_allnoise_correctedscore_flatten.pt', '02_run1_allnoise_correctbydeepconvfilter.pt', '02_run1_allnoise_correctbyfcndaefilter.pt', '02_run1_allnoise_correctbyfcndaefiltertest.pt', '02_run1_allnoise_correctbyconvlstmfilter.pt', '02_run1_allnoise_correctbyconvlstm2filter.pt', '02_run1_allnoise_correctbyconvlstm3filter.pt', '02_run1_alldeepconv_correctbyconvlstm3filter.pt', '02_run1_alldeepconv_correctbyconvlstm3filter2.pt', '02_run1_alldeepconv_correctbyconvlstm3filter3.pt', '02_run1_alldeepconv_correctbyconvlstm3filter4.pt', '02_run1_alldeepconv_correctbyconvlstm3filter5.pt', '02_run1_alldeepconv_correctbyconvlstm3filter6.pt', '02_run1_alldeepconv_correctbyconvlstm2filter6.pt', 'HandWashingDeepConvLSTMA_trunc_10.pt', 'synthetic_inc_10.pt', '10_run1_all.pt', '10_run1_scope.pt', '10_run1_noneut.pt', '10_run1_high.pt', '10_run1_inchc.pt', '10_run1_all_corrected.pt', '10_run1_all_corrected_noise.pt', '10_run1_scope_corrected_noise.pt', '10_run1_scope_corrected.pt', '10_run1_noneut_corrected.pt', '10_run1_correctedhwgt.pt', '10_run1_noneut_correctedhwgt.pt', '10_run1_inchc_correctedhwgt.pt', '10_run1_synthetic.pt', '10_run1_allnoise_correctedhwgt.pt', '10_run1_allnoise_correctedhwgt_scope.pt', '10_run1_allnoise_corrected.pt', '10_run1_allnoise_correctedscore.pt', '10_run1_allnoise_correctedscore_flatten.pt', '10_run1_allnoise_correctbydeepconvfilter.pt', '10_run1_allnoise_correctbyfcndaefilter.pt', '10_run1_allnoise_correctbyfcndaefiltertest.pt', '10_run1_allnoise_correctbyconvlstmfilter.pt', '10_run1_allnoise_correctbyconvlstm2filter.pt', '10_run1_allnoise_correctbyconvlstm3filter.pt', '10_run1_alldeepconv_correctbyconvlstm3filter.pt', '10_run1_alldeepconv_correctbyconvlstm3filter2.pt', '10_run1_alldeepconv_correctbyconvlstm3filter3.pt', '10_run1_alldeepconv_correctbyconvlstm3filter4.pt', '10_run1_alldeepconv_correctbyconvlstm3filter5.pt', '10_run1_alldeepconv_correctbyconvlstm3filter6.pt', '10_run1_alldeepconv_correctbyconvlstm2filter6.pt', 'random_01_run1_all_rn2ry0.pt', 'random_01_run1_noneut_rn2ry0.pt', 'random_01_run1_high_rn2ry0.pt', 'random_01_run1_all_corrected_rn2ry0.pt', 'random_01_run1_all_corrected_noise_rn2ry0.pt', 'random_01_run1_scope_corrected_noise_rn2ry0.pt', 'random_01_run1_noneut_corrected_rn2ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn2ry0.pt', 'random_01_run1_allnoise_correctedscore_rn2ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 'random_01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 'random_01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 'random_01_run1_all_rn3ry0.pt', 'random_01_run1_noneut_rn3ry0.pt', 'random_01_run1_high_rn3ry0.pt', 'random_01_run1_all_corrected_rn3ry0.pt', 'random_01_run1_all_corrected_noise_rn3ry0.pt', 'random_01_run1_scope_corrected_noise_rn3ry0.pt', 'random_01_run1_noneut_corrected_rn3ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn3ry0.pt', 'random_01_run1_allnoise_correctedscore_rn3ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 'random_01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 'random_01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 'random_01_run1_all_rn4ry0.pt', 'random_01_run1_noneut_rn4ry0.pt', 'random_01_run1_high_rn4ry0.pt', 'random_01_run1_all_corrected_rn4ry0.pt', 'random_01_run1_all_corrected_noise_rn4ry0.pt', 'random_01_run1_scope_corrected_noise_rn4ry0.pt', 'random_01_run1_noneut_corrected_rn4ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn4ry0.pt', 'random_01_run1_allnoise_correctedscore_rn4ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn4ry0.pt', 'random_01_run1_allnoise_correctbyfcndaefilter_rn4ry0.pt', 'random_01_run1_allnoise_correctbyconvlstmfilter_rn4ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm2filter_rn4ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm3filter_rn4ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter_rn4ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn4ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn4ry0.pt', 'random_01_run1_all_rn5ry0.pt', 'random_01_run1_noneut_rn5ry0.pt', 'random_01_run1_high_rn5ry0.pt', 'random_01_run1_all_corrected_rn5ry0.pt', 'random_01_run1_all_corrected_noise_rn5ry0.pt', 'random_01_run1_scope_corrected_noise_rn5ry0.pt', 'random_01_run1_noneut_corrected_rn5ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn5ry0.pt', 'random_01_run1_allnoise_correctedscore_rn5ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn5ry0.pt', 'random_01_run1_allnoise_correctbyfcndaefilter_rn5ry0.pt', 'random_01_run1_allnoise_correctbyconvlstmfilter_rn5ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm2filter_rn5ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm3filter_rn5ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter_rn5ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn5ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn5ry0.pt', 'random_01_run1_all_rn6ry0.pt', 'random_01_run1_noneut_rn6ry0.pt', 'random_01_run1_high_rn6ry0.pt', 'random_01_run1_all_corrected_rn6ry0.pt', 'random_01_run1_all_corrected_noise_rn6ry0.pt', 'random_01_run1_scope_corrected_noise_rn6ry0.pt', 'random_01_run1_noneut_corrected_rn6ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn6ry0.pt', 'random_01_run1_allnoise_correctedscore_rn6ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn6ry0.pt', 'random_01_run1_allnoise_correctbyfcndaefilter_rn6ry0.pt', 'random_01_run1_allnoise_correctbyconvlstmfilter_rn6ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm2filter_rn6ry0.pt', 'random_01_run1_allnoise_correctbyconvlstm3filter_rn6ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter_rn6ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm3filter6_rn6ry0.pt', 'random_01_run1_alldeepconv_correctbyconvlstm2filter6_rn6ry0.pt', 'random_01_run1_all_rn7ry0.pt', 'random_01_run1_noneut_rn7ry0.pt', 'random_01_run1_high_rn7ry0.pt', 'random_01_run1_all_corrected_rn7ry0.pt', 'random_01_run1_all_corrected_noise_rn7ry0.pt', 'random_01_run1_scope_corrected_noise_rn7ry0.pt', 'random_01_run1_noneut_corrected_rn7ry0.pt', 'random_01_run1_allnoise_correctedhwgt_rn7ry0.pt', 'random_01_run1_allnoise_correctedscore_rn7ry0.pt', 'random_01_run1_allnoise_correctbydeepconvfilter_rn7ry0.pt', 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'random_rec01_run1_allnoise_correctedscore_rn2ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn2ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn2ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn2ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn2ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn2ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn2ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn2ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn2ry0.pt', 'random_rec01_run1_all_rn3ry0.pt', 'random_rec01_run1_noneut_rn3ry0.pt', 'random_rec01_run1_high_rn3ry0.pt', 'random_rec01_run1_all_corrected_rn3ry0.pt', 'random_rec01_run1_all_corrected_noise_rn3ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn3ry0.pt', 'random_rec01_run1_noneut_corrected_rn3ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn3ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn3ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn3ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn3ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn3ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn3ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn3ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn3ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn3ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn3ry0.pt', 'random_rec01_run1_all_rn4ry0.pt', 'random_rec01_run1_noneut_rn4ry0.pt', 'random_rec01_run1_high_rn4ry0.pt', 'random_rec01_run1_all_corrected_rn4ry0.pt', 'random_rec01_run1_all_corrected_noise_rn4ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn4ry0.pt', 'random_rec01_run1_noneut_corrected_rn4ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn4ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn4ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn4ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn4ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn4ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn4ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn4ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn4ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn4ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn4ry0.pt', 'random_rec01_run1_all_rn5ry0.pt', 'random_rec01_run1_noneut_rn5ry0.pt', 'random_rec01_run1_high_rn5ry0.pt', 'random_rec01_run1_all_corrected_rn5ry0.pt', 'random_rec01_run1_all_corrected_noise_rn5ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn5ry0.pt', 'random_rec01_run1_noneut_corrected_rn5ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn5ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn5ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn5ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn5ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn5ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn5ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn5ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn5ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn5ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn5ry0.pt', 'random_rec01_run1_all_rn6ry0.pt', 'random_rec01_run1_noneut_rn6ry0.pt', 'random_rec01_run1_high_rn6ry0.pt', 'random_rec01_run1_all_corrected_rn6ry0.pt', 'random_rec01_run1_all_corrected_noise_rn6ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn6ry0.pt', 'random_rec01_run1_noneut_corrected_rn6ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn6ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn6ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn6ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn6ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn6ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn6ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn6ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn6ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn6ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn6ry0.pt', 'random_rec01_run1_all_rn7ry0.pt', 'random_rec01_run1_noneut_rn7ry0.pt', 'random_rec01_run1_high_rn7ry0.pt', 'random_rec01_run1_all_corrected_rn7ry0.pt', 'random_rec01_run1_all_corrected_noise_rn7ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn7ry0.pt', 'random_rec01_run1_noneut_corrected_rn7ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn7ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn7ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn7ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn7ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn7ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn7ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn7ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn7ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn7ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn7ry0.pt', 'random_rec01_run1_all_rn8ry0.pt', 'random_rec01_run1_noneut_rn8ry0.pt', 'random_rec01_run1_high_rn8ry0.pt', 'random_rec01_run1_all_corrected_rn8ry0.pt', 'random_rec01_run1_all_corrected_noise_rn8ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn8ry0.pt', 'random_rec01_run1_noneut_corrected_rn8ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn8ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn8ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn8ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn8ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn8ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn8ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn8ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn8ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn8ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn8ry0.pt', 'random_rec01_run1_all_rn9ry0.pt', 'random_rec01_run1_noneut_rn9ry0.pt', 'random_rec01_run1_high_rn9ry0.pt', 'random_rec01_run1_all_corrected_rn9ry0.pt', 'random_rec01_run1_all_corrected_noise_rn9ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn9ry0.pt', 'random_rec01_run1_noneut_corrected_rn9ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn9ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn9ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn9ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn9ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn9ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn9ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn9ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn9ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn9ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn9ry0.pt', 'random_rec01_run1_all_rn0ry0.pt', 'random_rec01_run1_noneut_rn0ry0.pt', 'random_rec01_run1_high_rn0ry0.pt', 'random_rec01_run1_all_corrected_rn0ry0.pt', 'random_rec01_run1_all_corrected_noise_rn0ry0.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry0.pt', 'random_rec01_run1_noneut_corrected_rn0ry0.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry0.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry0.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry0.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry0.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry0.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry0.pt', 'random_rec01_run1_all_rn0ry2.pt', 'random_rec01_run1_noneut_rn0ry2.pt', 'random_rec01_run1_high_rn0ry2.pt', 'random_rec01_run1_all_corrected_rn0ry2.pt', 'random_rec01_run1_all_corrected_noise_rn0ry2.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry2.pt', 'random_rec01_run1_noneut_corrected_rn0ry2.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry2.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry2.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry2.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry2.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry2.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry2.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry2.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry2.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry2.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry2.pt', 'random_rec01_run1_all_rn0ry3.pt', 'random_rec01_run1_noneut_rn0ry3.pt', 'random_rec01_run1_high_rn0ry3.pt', 'random_rec01_run1_all_corrected_rn0ry3.pt', 'random_rec01_run1_all_corrected_noise_rn0ry3.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry3.pt', 'random_rec01_run1_noneut_corrected_rn0ry3.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry3.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry3.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry3.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry3.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry3.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry3.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry3.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry3.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry3.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry3.pt', 'random_rec01_run1_all_rn0ry4.pt', 'random_rec01_run1_noneut_rn0ry4.pt', 'random_rec01_run1_high_rn0ry4.pt', 'random_rec01_run1_all_corrected_rn0ry4.pt', 'random_rec01_run1_all_corrected_noise_rn0ry4.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry4.pt', 'random_rec01_run1_noneut_corrected_rn0ry4.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry4.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry4.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry4.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry4.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry4.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry4.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry4.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry4.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry4.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry4.pt', 'random_rec01_run1_all_rn0ry5.pt', 'random_rec01_run1_noneut_rn0ry5.pt', 'random_rec01_run1_high_rn0ry5.pt', 'random_rec01_run1_all_corrected_rn0ry5.pt', 'random_rec01_run1_all_corrected_noise_rn0ry5.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry5.pt', 'random_rec01_run1_noneut_corrected_rn0ry5.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry5.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry5.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry5.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry5.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry5.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry5.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry5.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry5.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry5.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry5.pt', 'random_rec01_run1_all_rn0ry6.pt', 'random_rec01_run1_noneut_rn0ry6.pt', 'random_rec01_run1_high_rn0ry6.pt', 'random_rec01_run1_all_corrected_rn0ry6.pt', 'random_rec01_run1_all_corrected_noise_rn0ry6.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry6.pt', 'random_rec01_run1_noneut_corrected_rn0ry6.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry6.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry6.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry6.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry6.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry6.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry6.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry6.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry6.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry6.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry6.pt', 'random_rec01_run1_all_rn0ry7.pt', 'random_rec01_run1_noneut_rn0ry7.pt', 'random_rec01_run1_high_rn0ry7.pt', 'random_rec01_run1_all_corrected_rn0ry7.pt', 'random_rec01_run1_all_corrected_noise_rn0ry7.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry7.pt', 'random_rec01_run1_noneut_corrected_rn0ry7.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry7.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry7.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry7.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry7.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry7.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry7.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry7.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry7.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry7.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry7.pt', 'random_rec01_run1_all_rn0ry8.pt', 'random_rec01_run1_noneut_rn0ry8.pt', 'random_rec01_run1_high_rn0ry8.pt', 'random_rec01_run1_all_corrected_rn0ry8.pt', 'random_rec01_run1_all_corrected_noise_rn0ry8.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry8.pt', 'random_rec01_run1_noneut_corrected_rn0ry8.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry8.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry8.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry8.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry8.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry8.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry8.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry8.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry8.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry8.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry8.pt', 'random_rec01_run1_all_rn0ry9.pt', 'random_rec01_run1_noneut_rn0ry9.pt', 'random_rec01_run1_high_rn0ry9.pt', 'random_rec01_run1_all_corrected_rn0ry9.pt', 'random_rec01_run1_all_corrected_noise_rn0ry9.pt', 'random_rec01_run1_scope_corrected_noise_rn0ry9.pt', 'random_rec01_run1_noneut_corrected_rn0ry9.pt', 'random_rec01_run1_allnoise_correctedhwgt_rn0ry9.pt', 'random_rec01_run1_allnoise_correctedscore_rn0ry9.pt', 'random_rec01_run1_allnoise_correctbydeepconvfilter_rn0ry9.pt', 'random_rec01_run1_allnoise_correctbyfcndaefilter_rn0ry9.pt', 'random_rec01_run1_allnoise_correctbyconvlstmfilter_rn0ry9.pt', 'random_rec01_run1_allnoise_correctbyconvlstm2filter_rn0ry9.pt', 'random_rec01_run1_allnoise_correctbyconvlstm3filter_rn0ry9.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter_rn0ry9.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm3filter6_rn0ry9.pt', 'random_rec01_run1_alldeepconv_correctbyconvlstm2filter6_rn0ry9.pt'])
   
%% Cell type:code id:c384beaf tags: %% Cell type:code id:c384beaf tags:
   
``` python ``` python
relevant_models = list(pseudo_model_settings.keys()) relevant_models = list(pseudo_model_settings.keys())
relevant_models = thesis_filters relevant_models = thesis_filters
#relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3', 'alldeepconv_correctbyconvlstm3filter4','alldeepconv_correctbyconvlstm3filter5', 'alldeepconv_correctbyconvlstm3filter6'] #relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3', 'alldeepconv_correctbyconvlstm3filter4','alldeepconv_correctbyconvlstm3filter5', 'alldeepconv_correctbyconvlstm3filter6']
# relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3'] # relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3']
# relevant_models = ['allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyfcndaefilter', 'alldeepconv_correctbyconvlstm3filter3'] # relevant_models = ['allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyfcndaefilter', 'alldeepconv_correctbyconvlstm3filter3']
#relevant_models = ['alldeepconv_correctbyconvlstm3filter6', ] #relevant_models = ['alldeepconv_correctbyconvlstm3filter6', ]
``` ```
   
%% Cell type:code id:2c516d6b tags: %% Cell type:code id:2c516d6b tags:
   
``` python ``` python
def get_target_pseudo_models(training_runs, training_manager): def get_target_pseudo_models(training_runs, training_manager):
target_models = [] target_models = []
collection = training_runs collection = training_runs
for pseudo_model in relevant_models: for pseudo_model in relevant_models:
possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection) possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection)
for model in possible_models[:1]: for model in possible_models[:1]:
if model in training_runs and model not in target_models: if model in training_runs and model not in target_models:
target_models.append(model) target_models.append(model)
return target_models return target_models
   
   
def calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score=False): def calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score=False):
mean_y_vals = [] mean_y_vals = []
mean_x_vals = [] mean_x_vals = []
mean_base = [] mean_base = []
mean_base_inc = [] mean_base_inc = []
for target_collection in target_collections: for target_collection in target_collections:
collection_config = evaluation_manager.get_collection_config(target_collection) collection_config = evaluation_manager.get_collection_config(target_collection)
test_collection = evaluation_manager.get_test_sets_of_collection(target_collection) test_collection = evaluation_manager.get_test_sets_of_collection(target_collection)
target_models = evaluation_manager.get_run_of_collection(target_collection) target_models = evaluation_manager.get_run_of_collection(target_collection)
#print(target_models) #print(target_models)
target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection)) target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection))
# print(target_models) # print(target_models)
model_evaluation = evaluation_manager.model_evaluation model_evaluation = evaluation_manager.model_evaluation
base_model = collection_config['base_model'] base_model = collection_config['base_model']
base_inc_model = collection_config['base_inc_model'] base_inc_model = collection_config['base_inc_model']
pseudo_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, target_models, test_collection) pseudo_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, target_models, test_collection)
base_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, [base_model, base_inc_model], test_collection) base_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, [base_model, base_inc_model], test_collection)
pseudo_average_values = [] pseudo_average_values = []
for model_name in target_models: for model_name in target_models:
pseudo_average_values.append(pseudo_averages[model_name]) pseudo_average_values.append(pseudo_averages[model_name])
   
if relative_score: if relative_score:
score_divider = base_averages[base_model] score_divider = base_averages[base_model]
base_averages[base_model] /= score_divider base_averages[base_model] /= score_divider
base_averages[base_inc_model] /= score_divider base_averages[base_inc_model] /= score_divider
for pseudo_average in pseudo_averages: for pseudo_average in pseudo_averages:
pseudo_averages[pseudo_average] /= score_divider pseudo_averages[pseudo_average] /= score_divider
   
# y_vals = list(pseudo_averages.values()) # y_vals = list(pseudo_averages.values())
y_vals = [pseudo_averages[key] for key in target_models] y_vals = [pseudo_averages[key] for key in target_models]
x_vals = remove_model_name_suffix(relevant_models) x_vals = remove_model_name_suffix(relevant_models)
if len(y_vals) == 1: if len(y_vals) == 1:
y_vals = np.repeat(y_vals, 2, axis=0) y_vals = np.repeat(y_vals, 2, axis=0)
x_vals = np.repeat(x_vals, 2, axis=0) x_vals = np.repeat(x_vals, 2, axis=0)
x_vals[-1] = '_' x_vals[-1] = '_'
mean_y_vals.append(y_vals) mean_y_vals.append(y_vals)
mean_x_vals.append(x_vals) mean_x_vals.append(x_vals)
mean_base.append(base_averages[base_model]) mean_base.append(base_averages[base_model])
mean_base_inc.append(base_averages[base_inc_model]) mean_base_inc.append(base_averages[base_inc_model])
   
mean_y_vals = np.array(mean_y_vals) mean_y_vals = np.array(mean_y_vals)
mean_y_vals = np.mean(mean_y_vals, axis=0) mean_y_vals = np.mean(mean_y_vals, axis=0)
mean_base = np.mean(np.array(mean_base)) mean_base = np.mean(np.array(mean_base))
mean_base_inc = np.mean(np.array(mean_base_inc)) mean_base_inc = np.mean(np.array(mean_base_inc))
x_vals = mean_x_vals[0] x_vals = mean_x_vals[0]
x_vals_translated = [translate_setting(x_val) for x_val in x_vals] x_vals_translated = [translate_setting(x_val) for x_val in x_vals]
   
return x_vals_translated, mean_y_vals, mean_base, mean_base_inc return x_vals_translated, mean_y_vals, mean_base, mean_base_inc
   
def calc_mean_training_values_of_pseudo_models(target_collections, target_value, relative_score=False): def calc_mean_training_values_of_pseudo_models(target_collections, target_value, relative_score=False):
mean_y_vals = [] mean_y_vals = []
mean_x_vals = [] mean_x_vals = []
for target_collection in target_collections: for target_collection in target_collections:
collection_config = evaluation_manager.get_collection_config(target_collection) collection_config = evaluation_manager.get_collection_config(target_collection)
test_collection = evaluation_manager.get_test_sets_of_collection(target_collection) test_collection = evaluation_manager.get_test_sets_of_collection(target_collection)
target_models = evaluation_manager.get_run_of_collection(target_collection) target_models = evaluation_manager.get_run_of_collection(target_collection)
#print(target_models) #print(target_models)
target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection)) target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection))
# print(target_models) # print(target_models)
training_manager = evaluation_manager.get_training_manager_of_collection(target_collection) training_manager = evaluation_manager.get_training_manager_of_collection(target_collection)
pseudo_average_values = [] pseudo_average_values = []
training_sized = [] training_sized = []
training_sizes_null = [] training_sizes_null = []
training_sizes_hw = [] training_sizes_hw = []
for model_name in target_models: for model_name in target_models:
model_info = training_manager.get_all_information()[model_name] model_info = training_manager.get_all_information()[model_name]
# print(model_info) # print(model_info)
pseudo_average_values.append(model_info['pseudo_evaluation'][target_value]) pseudo_average_values.append(model_info['pseudo_evaluation'][target_value])
training_sized.append(model_info['training_size']['overall_size']) training_sized.append(model_info['training_size']['overall_size'])
training_sizes_null.append(model_info['training_size']['null_size']) training_sizes_null.append(model_info['training_size']['null_size'])
training_sizes_hw.append(model_info['training_size']['hw_size']) training_sizes_hw.append(model_info['training_size']['hw_size'])
#pseudo_average_values.append(pseudo_averages[model_name]) #pseudo_average_values.append(pseudo_averages[model_name])
   
if relative_score: if relative_score:
score_divider = base_averages[base_model] score_divider = base_averages[base_model]
for pseudo_average in pseudo_averages: for pseudo_average in pseudo_averages:
pseudo_averages[pseudo_average] /= score_divider pseudo_averages[pseudo_average] /= score_divider
   
# y_vals = list(pseudo_averages.values()) # y_vals = list(pseudo_averages.values())
y_vals = pseudo_average_values y_vals = pseudo_average_values
x_vals = remove_model_name_suffix(relevant_models) x_vals = remove_model_name_suffix(relevant_models)
if len(y_vals) == 1: if len(y_vals) == 1:
y_vals = np.repeat(y_vals, 2, axis=0) y_vals = np.repeat(y_vals, 2, axis=0)
x_vals = np.repeat(x_vals, 2, axis=0) x_vals = np.repeat(x_vals, 2, axis=0)
x_vals[-1] = '_' x_vals[-1] = '_'
mean_y_vals.append(y_vals) mean_y_vals.append(y_vals)
mean_x_vals.append(x_vals) mean_x_vals.append(x_vals)
   
   
mean_y_vals = np.array(mean_y_vals) mean_y_vals = np.array(mean_y_vals)
mean_y_vals = np.mean(mean_y_vals, axis=0) mean_y_vals = np.mean(mean_y_vals, axis=0)
mean_training_sized = np.array(training_sized) mean_training_sized = np.array(training_sized)
mean_training_sizes_null = np.array(training_sizes_null) mean_training_sizes_null = np.array(training_sizes_null)
mean_training_sizes_hw = np.array(training_sizes_hw) mean_training_sizes_hw = np.array(training_sizes_hw)
   
x_vals = mean_x_vals[0] x_vals = mean_x_vals[0]
x_vals_translated = [translate_setting(x_val) for x_val in x_vals] x_vals_translated = [translate_setting(x_val) for x_val in x_vals]
   
return x_vals_translated, mean_y_vals, mean_training_sized, mean_training_sizes_null, mean_training_sizes_hw return x_vals_translated, mean_y_vals, mean_training_sized, mean_training_sizes_null, mean_training_sizes_hw
   
   
def calc_mean_confusion_matrix(target_collections): def calc_mean_confusion_matrix(target_collections):
mean_ppv = [] mean_ppv = []
mean_npv = [] mean_npv = []
   
mean_tp = [] mean_tp = []
mean_fp = [] mean_fp = []
mean_tn = [] mean_tn = []
mean_fn = [] mean_fn = []
   
mean_training_sizes = [] mean_training_sizes = []
mean_training_sizes_null = [] mean_training_sizes_null = []
mean_training_sizes_hw = [] mean_training_sizes_hw = []
   
for target_collection in target_collections: for target_collection in target_collections:
collection_config = evaluation_manager.get_collection_config(target_collection) collection_config = evaluation_manager.get_collection_config(target_collection)
test_collection = evaluation_manager.get_test_sets_of_collection(target_collection) test_collection = evaluation_manager.get_test_sets_of_collection(target_collection)
target_models = evaluation_manager.get_run_of_collection(target_collection) target_models = evaluation_manager.get_run_of_collection(target_collection)
#print(target_models) #print(target_models)
target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection)) target_models = get_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection))
# print(target_models) # print(target_models)
training_manager = evaluation_manager.get_training_manager_of_collection(target_collection) training_manager = evaluation_manager.get_training_manager_of_collection(target_collection)
pseudo_average_values = [] pseudo_average_values = []
ppv = [] ppv = []
npv = [] npv = []
   
tp = [] tp = []
fp = [] fp = []
tn = [] tn = []
fn = [] fn = []
   
training_sizes = [] training_sizes = []
training_sizes_null = [] training_sizes_null = []
training_sizes_hw = [] training_sizes_hw = []
for model_name in target_models: for model_name in target_models:
model_info = training_manager.get_all_information()[model_name] model_info = training_manager.get_all_information()[model_name]
training_sizes.append(model_info['training_size']['overall_size']) training_sizes.append(model_info['training_size']['overall_size'])
training_sizes_null.append(model_info['training_size']['null_size']) training_sizes_null.append(model_info['training_size']['null_size'])
training_sizes_hw.append(model_info['training_size']['hw_size']) training_sizes_hw.append(model_info['training_size']['hw_size'])
ppv.append(model_info['confusion_matrix']['ppv']) ppv.append(model_info['confusion_matrix']['ppv'])
npv.append(model_info['confusion_matrix']['npv']) npv.append(model_info['confusion_matrix']['npv'])
   
tp.append(model_info['confusion_matrix']['tp']) tp.append(model_info['confusion_matrix']['tp'])
fp.append(model_info['confusion_matrix']['fp']) fp.append(model_info['confusion_matrix']['fp'])
tn.append(model_info['confusion_matrix']['tn']) tn.append(model_info['confusion_matrix']['tn'])
fn.append(model_info['confusion_matrix']['fn']) fn.append(model_info['confusion_matrix']['fn'])
   
mean_ppv.append(ppv) mean_ppv.append(ppv)
mean_npv.append(npv) mean_npv.append(npv)
mean_training_sizes.append(training_sizes) mean_training_sizes.append(training_sizes)
mean_training_sizes_null.append(training_sizes_null) mean_training_sizes_null.append(training_sizes_null)
mean_training_sizes_hw.append(training_sizes_hw) mean_training_sizes_hw.append(training_sizes_hw)
mean_tp.append(tp) mean_tp.append(tp)
mean_fp.append(fp) mean_fp.append(fp)
mean_tn.append(tn) mean_tn.append(tn)
mean_fn.append(fn) mean_fn.append(fn)
   
x_vals = remove_model_name_suffix(relevant_models) x_vals = remove_model_name_suffix(relevant_models)
x_vals_translated = [translate_setting(x_val) for x_val in x_vals] x_vals_translated = [translate_setting(x_val) for x_val in x_vals]
   
mean_ppv = np.mean(np.array(mean_ppv), axis=0) mean_ppv = np.mean(np.array(mean_ppv), axis=0)
mean_npv = np.mean(np.array(mean_npv), axis=0) mean_npv = np.mean(np.array(mean_npv), axis=0)
   
mean_tp = np.mean(np.array(mean_tp), axis=0) mean_tp = np.mean(np.array(mean_tp), axis=0)
mean_fp = np.mean(np.array(mean_fp), axis=0) mean_fp = np.mean(np.array(mean_fp), axis=0)
mean_tn = np.mean(np.array(mean_tn), axis=0) mean_tn = np.mean(np.array(mean_tn), axis=0)
mean_fn = np.mean(np.array(mean_fn), axis=0) mean_fn = np.mean(np.array(mean_fn), axis=0)
   
   
mean_training_sizes = np.mean(np.array(mean_training_sizes), axis=0) mean_training_sizes = np.mean(np.array(mean_training_sizes), axis=0)
mean_training_sizes_null = np.mean(np.array(mean_training_sizes_null), axis=0) mean_training_sizes_null = np.mean(np.array(mean_training_sizes_null), axis=0)
mean_training_sizes_hw = np.mean(np.array(mean_training_sizes_hw), axis=0) mean_training_sizes_hw = np.mean(np.array(mean_training_sizes_hw), axis=0)
   
return x_vals_translated, mean_ppv, mean_npv, mean_tp, mean_fp, mean_tn, mean_fn, mean_training_sizes, mean_training_sizes_null, mean_training_sizes_hw return x_vals_translated, mean_ppv, mean_npv, mean_tp, mean_fp, mean_tn, mean_fn, mean_training_sizes, mean_training_sizes_null, mean_training_sizes_hw
   
def plot_mean_average_of_pseudo_models(target_collections, target_value, relative_score=False): def plot_mean_average_of_pseudo_models(target_collections, target_value, relative_score=False):
x_vals_translated, mean_y_vals, mean_base, mean_base_inc = calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score) x_vals_translated, mean_y_vals, mean_base, mean_base_inc = calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score)
fig, ax = plt.subplots(figsize=(9,7)) fig, ax = plt.subplots(figsize=(9,7))
ax.set_title(target_value) ax.set_title(target_value)
ax.axhline(mean_base, linestyle=':', label='base', color='red') ax.axhline(mean_base, linestyle=':', label='base', color='red')
ax.axhline(mean_base_inc, linestyle=':', label='inc', color='green') ax.axhline(mean_base_inc, linestyle=':', label='inc', color='green')
ax.plot(x_vals_translated, mean_y_vals) ax.plot(x_vals_translated, mean_y_vals)
   
ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor') ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor')
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
ax.set_xlabel('modelname') ax.set_xlabel('modelname')
plt.tight_layout() plt.tight_layout()
fig.show() fig.show()
   
def barplot_mean_average_of_pseudo_models(target_collections, target_value, pseudo_target_value, relative_score=False, plot_pseudo=False, base_ax=None): def barplot_mean_average_of_pseudo_models(target_collections, target_value, pseudo_target_value, relative_score=False, plot_pseudo=False, base_ax=None):
x_vals_translated, mean_y_vals, mean_base, mean_base_inc = calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score) x_vals_translated, mean_y_vals, mean_base, mean_base_inc = calc_mean_values_of_pseudo_models(target_collections, target_value, relative_score)
pseudo_x_vals, pseudo_y_vals, mean_training_sized, mean_training_sizes_null, mean_training_sizes_hw = calc_mean_training_values_of_pseudo_models(target_collections, pseudo_target_value, relative_score) pseudo_x_vals, pseudo_y_vals, mean_training_sized, mean_training_sizes_null, mean_training_sizes_hw = calc_mean_training_values_of_pseudo_models(target_collections, pseudo_target_value, relative_score)
# print(mean_training_sized) # print(mean_training_sized)
   
if base_ax is None: if base_ax is None:
fig, ax = plt.subplots(figsize=(9,7)) fig, ax = plt.subplots(figsize=(9,7))
# ax.set_title(target_value) # ax.set_title(target_value)
else: else:
ax = base_ax ax = base_ax
   
barWidth = 0.55 barWidth = 0.55
if plot_pseudo: if plot_pseudo:
barWidth = 0.35 barWidth = 0.35
r1 = np.arange(len(x_vals_translated)) r1 = np.arange(len(x_vals_translated))
r2 = [x + barWidth for x in r1] r2 = [x + barWidth for x in r1]
r3 = [x + barWidth for x in r2] r3 = [x + barWidth for x in r2]
   
bar_vals = [] bar_vals = []
for i in range(len(pseudo_y_vals)): for i in range(len(pseudo_y_vals)):
#bar_vals.append(pseudo_y_vals[i] * mean_training_sized[i]) #bar_vals.append(pseudo_y_vals[i] * mean_training_sized[i])
bar_vals.append(pseudo_y_vals[i] * mean_training_sizes_null[i]) bar_vals.append(pseudo_y_vals[i] * mean_training_sizes_null[i])
#bar_vals.append(pseudo_y_vals[i] * min(1.0, mean_training_sizes_hw[i])) #bar_vals.append(pseudo_y_vals[i] * min(1.0, mean_training_sizes_hw[i]))
#bar_vals.append(pseudo_y_vals[i]) #bar_vals.append(pseudo_y_vals[i])
   
p1 = ax.bar(r1, mean_y_vals, width=barWidth, edgecolor='white', label='model evaluation') p1 = ax.bar(r1, mean_y_vals, width=barWidth, edgecolor='white', label='model evaluation')
# ax.bar(r1, mean_y_vals*mean_training_sized, width=barWidth,edgecolor='black', color='cornflowerblue', alpha=0.5, label='scaled model evaluation') # ax.bar(r1, mean_y_vals*mean_training_sized, width=barWidth,edgecolor='black', color='cornflowerblue', alpha=0.5, label='scaled model evaluation')
if plot_pseudo: if plot_pseudo:
p2 = ax.bar(r2, bar_vals, width=barWidth, edgecolor='white', label='train set evaluation') p2 = ax.bar(r2, bar_vals, width=barWidth, edgecolor='white', label='train set evaluation')
#bars = ax.bar(r3, mean_training_sized, width=barWidth, edgecolor='white') #bars = ax.bar(r3, mean_training_sized, width=barWidth, edgecolor='white')
   
   
#for bars in ax.containers: #for bars in ax.containers:
# #ax.bar_label(bars) # #ax.bar_label(bars)
# texts = ax.bar_label(bars, rotation=90, label_type='center', backgroundcolor=(1, 1, 1, 0.3), fmt='%.3f') # texts = ax.bar_label(bars, rotation=90, label_type='center', backgroundcolor=(1, 1, 1, 0.3), fmt='%.3f')
# #print(texts) # #print(texts)
# for text in texts: # for text in texts:
# bb = text.get_bbox_patch() # bb = text.get_bbox_patch()
# bb.set_boxstyle("square", pad=0) # bb.set_boxstyle("square", pad=0)
# text.set(y=15) # text.set(y=15)
   
ax.bar_label(p1,rotation=90, label_type='center', backgroundcolor=(1, 1, 1, 0.3), fmt='%.3f') ax.bar_label(p1,rotation=90, label_type='center', backgroundcolor=(1, 1, 1, 0.3), fmt='%.3f')
   
ax.axhline(mean_base, linestyle=':', label='base model', color='red') ax.axhline(mean_base, linestyle=':', label='base model', color='red')
ax.axhline(mean_base_inc, linestyle=':', label='base inc model', color='green') ax.axhline(mean_base_inc, linestyle=':', label='base inc model', color='green')
ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor') ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor')
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
ax.set_xlabel('configuration', fontweight='bold') ax.set_xlabel('configuration', fontweight='bold')
   
# plt.xlabel('configuration', fontweight='bold') # plt.xlabel('configuration', fontweight='bold')
tick_shift = barWidth tick_shift = barWidth
if plot_pseudo: if plot_pseudo:
tick_shift = (barWidth/2) tick_shift = (barWidth/2)
ax.set_xticks([r + barWidth - tick_shift for r in range(len(x_vals_translated))], x_vals_translated, rotation=75, ha='right', rotation_mode='anchor') ax.set_xticks([r + barWidth - tick_shift for r in range(len(x_vals_translated))], x_vals_translated, rotation=75, ha='right', rotation_mode='anchor')
# plt.xticks(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor') # plt.xticks(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor')
translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'} translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'}
ax.set_ylabel(translate_plot_value[target_value]) ax.set_ylabel(translate_plot_value[target_value])
# plt.tight_layout() # plt.tight_layout()
#plt.subplots_adjust(top=0.9, bottom=0.28) #plt.subplots_adjust(top=0.9, bottom=0.28)
ax.legend(loc='lower right' ) ax.legend(loc='lower right' )
if base_ax is None: if base_ax is None:
fig.legend() fig.legend()
fig.show() fig.show()
   
   
def barplot_mean_training_data(target_collections, target_values, base_ax=None): def barplot_mean_training_data(target_collections, target_values, base_ax=None):
mean_training_evaluations = [] mean_training_evaluations = []
for target_value in target_values: for target_value in target_values:
mean_training_evaluations.append(calc_mean_training_values_of_pseudo_models(target_collections, target_value, False)) mean_training_evaluations.append(calc_mean_training_values_of_pseudo_models(target_collections, target_value, False))
if base_ax is None: if base_ax is None:
fig, ax = plt.subplots(figsize=(9,5)) fig, ax = plt.subplots(figsize=(9,5))
# ax.set_title(target_value) # ax.set_title(target_value)
else: else:
ax = base_ax ax = base_ax
   
barWidth = 0.8 / len(target_values) barWidth = 0.8 / len(target_values)
r = [np.arange(len(mean_training_evaluations[0][0]))] r = [np.arange(len(mean_training_evaluations[0][0]))]
for i in range(1, len(target_values)): for i in range(1, len(target_values)):
r.append([x + barWidth for x in r[i-1]]) r.append([x + barWidth for x in r[i-1]])
   
translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'} translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'}
color_pairs = (('tab:blue', 'blue'), ('tab:orange', 'orange'), ('tab:green', 'green'), ('tab:red', 'red')) color_pairs = (('tab:blue', 'blue'), ('tab:orange', 'orange'), ('tab:green', 'green'), ('tab:red', 'red'))
for i in range(len(target_values)): for i in range(len(target_values)):
p1 = ax.bar(r[i], mean_training_evaluations[i][1], width=barWidth, color=color_pairs[i][0], edgecolor=None, label=translate_plot_value[target_values[i]]) p1 = ax.bar(r[i], mean_training_evaluations[i][1], width=barWidth, color=color_pairs[i][0], edgecolor=None, label=translate_plot_value[target_values[i]])
   
scaled_vals = mean_training_evaluations[i][1]*mean_training_evaluations[i][2] scaled_vals = mean_training_evaluations[i][1]*mean_training_evaluations[i][2]
dividers = scaled_vals dividers = scaled_vals
dividers[scaled_vals >= mean_training_evaluations[i][1] - 0.01] = -1 dividers[scaled_vals >= mean_training_evaluations[i][1] - 0.01] = -1
ax.bar(r[i], dividers, width=barWidth, color='white', alpha=0.4, edgecolor=None) ax.bar(r[i], dividers, width=barWidth, color='white', alpha=0.4, edgecolor=None)
ax.bar(r[i], 0.005, bottom=scaled_vals, color='black', width=barWidth) ax.bar(r[i], 0.005, bottom=scaled_vals, color='black', width=barWidth)
   
tick_shift = barWidth/2 tick_shift = barWidth/2
ax.set_xticks([r + barWidth + tick_shift for r in range(len(mean_training_evaluations[0][0]))], mean_training_evaluations[0][0], rotation=55, ha='right', rotation_mode='anchor') ax.set_xticks([r + barWidth + tick_shift for r in range(len(mean_training_evaluations[0][0]))], mean_training_evaluations[0][0], rotation=55, ha='right', rotation_mode='anchor')
   
   
ax.set_xlabel('configuration', fontweight='bold') ax.set_xlabel('configuration', fontweight='bold')
ax.legend(loc='lower right' ) ax.legend(loc='lower right' )
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
ax.set_ylim(0,1.1) ax.set_ylim(0,1.1)
if base_ax is None: if base_ax is None:
fig.tight_layout() fig.tight_layout()
fig.show() fig.show()
   
   
def barplot_confusion_matrix_training_data(target_collections, base_ax=None): def barplot_confusion_matrix_training_data(target_collections, base_ax=None):
x_vals_translated, mean_ppv, mean_npv, mean_tp, mean_fp, mean_tn, mean_fn, mean_training_sizes, mean_training_sizes_null, mean_training_sizes_hw = calc_mean_confusion_matrix(target_collections) x_vals_translated, mean_ppv, mean_npv, mean_tp, mean_fp, mean_tn, mean_fn, mean_training_sizes, mean_training_sizes_null, mean_training_sizes_hw = calc_mean_confusion_matrix(target_collections)
if base_ax is None: if base_ax is None:
fig, ax = plt.subplots(figsize=(9,5)) fig, ax = plt.subplots(figsize=(9,5))
# ax.set_title(target_value) # ax.set_title(target_value)
else: else:
ax = base_ax ax = base_ax
   
display_bars = 4 display_bars = 4
   
   
barWidth = 0.8 / display_bars barWidth = 0.8 / display_bars
r = [np.arange(len(x_vals_translated))] r = [np.arange(len(x_vals_translated))]
for i in range(1, display_bars): for i in range(1, display_bars):
r.append([x + barWidth for x in r[i-1]]) r.append([x + barWidth for x in r[i-1]])
   
translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'} translate_plot_value = {'specificity': 'specificity', 'sensitivity': 'sensitivity', 'f1': 'F1 score', 's1': 'S score', 'mcc': 'MCC score'}
   
#p1 = ax.bar(r[0], mean_ppv[:, 1], width=barWidth, edgecolor='white', label='mean_ppv') #p1 = ax.bar(r[0], mean_ppv[:, 1], width=barWidth, edgecolor='white', label='mean_ppv')
#p2 = ax.bar(r[1], mean_npv[:, 1], width=barWidth, edgecolor='white', label='mean_npv') #p2 = ax.bar(r[1], mean_npv[:, 1], width=barWidth, edgecolor='white', label='mean_npv')
p3 = ax.bar(r[0], mean_tp, width=barWidth, color='tab:green', label='tp') p3 = ax.bar(r[0], mean_tp, width=barWidth, color='tab:green', label='tp')
p4 = ax.bar(r[1], mean_fp, width=barWidth, color='tab:red', label='fp') p4 = ax.bar(r[1], mean_fp, width=barWidth, color='tab:red', label='fp')
p5 = ax.bar(r[2], mean_tn, width=barWidth, color='tab:blue', label='tn') p5 = ax.bar(r[2], mean_tn, width=barWidth, color='tab:blue', label='tn')
p5 = ax.bar(r[3], mean_fn, width=barWidth, color='tab:orange', label='fn') p5 = ax.bar(r[3], mean_fn, width=barWidth, color='tab:orange', label='fn')
#p6 = ax.bar(r[4], mean_training_sizes, width=barWidth, color='tab:olive', label='training_size') #p6 = ax.bar(r[4], mean_training_sizes, width=barWidth, color='tab:olive', label='training_size')
   
   
tick_shift = barWidth/2 tick_shift = barWidth/2
ax.set_xticks([r + barWidth + tick_shift for r in range(len(x_vals_translated))], x_vals_translated, rotation=55, ha='right', rotation_mode='anchor') ax.set_xticks([r + barWidth + tick_shift for r in range(len(x_vals_translated))], x_vals_translated, rotation=55, ha='right', rotation_mode='anchor')
ax.set_xlabel('configuration', fontweight='bold') ax.set_xlabel('configuration', fontweight='bold')
#ax.set_ylim(0, 1.4) #ax.set_ylim(0, 1.4)
ax.legend(loc='lower right' ) ax.legend(loc='lower right' )
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
if base_ax is None: if base_ax is None:
#fig.legend() #fig.legend()
fig.tight_layout() fig.tight_layout()
fig.show() fig.show()
   
table_dict = dict() table_dict = dict()
for i in range(len(x_vals_translated)): for i in range(len(x_vals_translated)):
table_dict[x_vals_translated[i]] = {'tp': mean_tp[i], 'fp': mean_fp[i], 'tn': mean_tn[i], 'fn': mean_fn[i]} table_dict[x_vals_translated[i]] = {'tp': mean_tp[i], 'fp': mean_fp[i], 'tn': mean_tn[i], 'fn': mean_fn[i]}
   
return table_dict return table_dict
   
``` ```
   
%% Cell type:code id:6ca0d24a tags: %% Cell type:code id:6ca0d24a tags:
   
``` python ``` python
include_collections = [] include_collections = []
include_collections += ['synthetic_01', 'synthetic_02', 'synthetic_10'] include_collections += ['synthetic_01', 'synthetic_02', 'synthetic_10']
include_collections += ['recorded_01', 'recorded_02'] include_collections += ['recorded_01', 'recorded_02']
#include_collections = ['recorded_01', 'recorded_02'] #include_collections = ['recorded_01', 'recorded_02']
   
include_collections = ['recorded_01'] include_collections = ['recorded_01']
   
fig, axes = plt.subplots(1,2, figsize=(15, 6)) fig, axes = plt.subplots(1,2, figsize=(15, 6))
barplot_mean_average_of_pseudo_models(include_collections, 'specificity', 'specificity', plot_pseudo=False, base_ax=axes[0]) barplot_mean_average_of_pseudo_models(include_collections, 'specificity', 'specificity', plot_pseudo=False, base_ax=axes[0])
barplot_mean_average_of_pseudo_models(include_collections, 'sensitivity', 'sensitivity', plot_pseudo=False, base_ax=axes[1]) barplot_mean_average_of_pseudo_models(include_collections, 'sensitivity', 'sensitivity', plot_pseudo=False, base_ax=axes[1])
fig.tight_layout() fig.tight_layout()
#fig.legend() #fig.legend()
fig.show() fig.show()
   
fig, axes = plt.subplots(1,2, figsize=(15, 6)) fig, axes = plt.subplots(1,2, figsize=(15, 6))
barplot_mean_average_of_pseudo_models(include_collections, 'f1', 'f1', plot_pseudo=False, base_ax=axes[0]) barplot_mean_average_of_pseudo_models(include_collections, 'f1', 'f1', plot_pseudo=False, base_ax=axes[0])
barplot_mean_average_of_pseudo_models(include_collections, 's1', 's1', plot_pseudo=False, base_ax=axes[1]) barplot_mean_average_of_pseudo_models(include_collections, 's1', 's1', plot_pseudo=False, base_ax=axes[1])
fig.tight_layout() fig.tight_layout()
#fig.legend() #fig.legend()
fig.show() fig.show()
``` ```
   
%% Output %% Output
   
   
   
<ipython-input-27-30713955afc6>:246: UserWarning: FixedFormatter should only be used together with FixedLocator <ipython-input-27-30713955afc6>:246: UserWarning: FixedFormatter should only be used together with FixedLocator
ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor') ax.set_xticklabels(x_vals_translated, rotation=75, ha='right', rotation_mode='anchor')
   
   
   
%% Cell type:code id:246d8e98 tags: %% Cell type:code id:246d8e98 tags:
   
``` python ``` python
include_collections = [] include_collections = []
include_collections += ['synthetic_01', 'synthetic_02', 'synthetic_10'] include_collections += ['synthetic_01', 'synthetic_02', 'synthetic_10']
include_collections += ['recorded_01', 'recorded_02'] include_collections += ['recorded_01', 'recorded_02']
#include_collections = ['recorded_01', 'recorded_02'] #include_collections = ['recorded_01', 'recorded_02']
   
   
barplot_mean_training_data(include_collections, ['specificity', 'sensitivity', 'f1', 's1']) barplot_mean_training_data(include_collections, ['specificity', 'sensitivity', 'f1', 's1'])
table_dict = barplot_confusion_matrix_training_data(include_collections) table_dict = barplot_confusion_matrix_training_data(include_collections)
data_frame = pd.DataFrame.from_dict(table_dict, orient='index') data_frame = pd.DataFrame.from_dict(table_dict, orient='index')
data_frame data_frame
``` ```
   
%% Output %% Output
   
   
   
   
   
tp fp tn fn tp fp tn fn
all 0.782806 0.109959 0.890041 0.217194 all 0.782806 0.109959 0.890041 0.217194
high_conf 0.335298 0.004089 0.623328 0.009102 high_conf 0.335298 0.004089 0.623328 0.009102
scope 0.754095 0.031934 0.047074 0.208741 scope 0.754095 0.031934 0.047074 0.208741
all_corrected_null 0.782806 0.081051 0.918949 0.217194 all_corrected_null 0.782806 0.081051 0.918949 0.217194
scope_corrected_null 0.754095 0.003026 0.075982 0.208741 scope_corrected_null 0.754095 0.003026 0.075982 0.208741
all_corrected_null_hwgt 0.782806 0.081063 0.918937 0.217194 all_corrected_null_hwgt 0.782806 0.081063 0.918937 0.217194
scope_corrected_null_hwgt 0.754095 0.003038 0.075969 0.208741 scope_corrected_null_hwgt 0.754095 0.003038 0.075969 0.208741
all_null_hwgt 0.994162 0.000000 1.000000 0.005838 all_null_hwgt 0.994162 0.000000 1.000000 0.005838
all_null_score 0.768328 0.000409 0.999591 0.231672 all_null_score 0.768328 0.000409 0.999591 0.231672
all_null_deepconv 0.767056 0.000684 0.999316 0.232944 all_null_deepconv 0.767056 0.000684 0.999316 0.232944
all_null_fcndae 0.868311 0.001502 0.998498 0.131689 all_null_fcndae 0.868311 0.001502 0.998498 0.131689
all_null_convlstm1 0.872169 0.001494 0.998506 0.127831 all_null_convlstm1 0.872169 0.001494 0.998506 0.127831
all_null_convlstm2 0.870221 0.001429 0.998571 0.129779 all_null_convlstm2 0.870221 0.001429 0.998571 0.129779
all_null_convlstm3 0.874198 0.001565 0.998435 0.125802 all_null_convlstm3 0.874198 0.001565 0.998435 0.125802
all_cnn_convlstm3 0.878977 0.004642 0.917894 0.120518 all_cnn_convlstm3 0.878977 0.004642 0.917894 0.120518
all_cnn_convlstm2_hard 0.838896 0.001429 0.917125 0.129274 all_cnn_convlstm2_hard 0.838896 0.001429 0.917125 0.129274
all_cnn_convlstm3_hard 0.853719 0.001505 0.917048 0.114451 all_cnn_convlstm3_hard 0.853719 0.001505 0.917048 0.114451
   
%% Cell type:markdown id:028749d9 tags: %% Cell type:markdown id:028749d9 tags:
   
# Randomized # Randomized
   
%% Cell type:code id:d3d3e7a0 tags: %% Cell type:code id:d3d3e7a0 tags:
   
``` python ``` python
def get_multiple_target_pseudo_models(training_runs, training_manager): def get_multiple_target_pseudo_models(training_runs, training_manager):
target_models = dict() target_models = dict()
collection = training_runs collection = training_runs
for pseudo_model in relevant_models: for pseudo_model in relevant_models:
possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection) possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection)
for model in possible_models: for model in possible_models:
if model in training_runs and model not in target_models: if model in training_runs and model not in target_models:
if pseudo_model not in target_models: if pseudo_model not in target_models:
target_models[pseudo_model] = [] target_models[pseudo_model] = []
target_models[pseudo_model].append(model) target_models[pseudo_model].append(model)
return target_models return target_models
   
def get_models_by_filter(target_filter, relevant_model_info): def get_models_by_filter(target_filter, relevant_model_info):
models = [] models = []
for model, values in relevant_model_info.items(): for model, values in relevant_model_info.items():
add_model = True add_model = True
#print(model, values) #print(model, values)
for target_key, target_value in target_filter.items(): for target_key, target_value in target_filter.items():
#print(target_key, target_value, values) #print(target_key, target_value, values)
if values[target_key] != target_value: if values[target_key] != target_value:
add_model = False add_model = False
break break
if add_model: if add_model:
models.append(model) models.append(model)
return models return models
   
def get_model_infos(models, all_info): def get_model_infos(models, all_info):
infos = dict() infos = dict()
for model in models: for model in models:
model_info = all_info[model] model_info = all_info[model]
evaluation = model_info['pseudo_evaluation'] evaluation = model_info['pseudo_evaluation']
training_size = model_info['training_size'] training_size = model_info['training_size']
confusion_matrix = model_info['confusion_matrix'] confusion_matrix = model_info['confusion_matrix']
info_entry = {'s1': evaluation['s1'], 'overall_size': training_size['overall_size'], 'null_size': training_size['null_size'], 'hw_size': training_size['hw_size'], info_entry = {'s1': evaluation['s1'], 'overall_size': training_size['overall_size'], 'null_size': training_size['null_size'], 'hw_size': training_size['hw_size'],
'tp': confusion_matrix['tp'], 'fp': confusion_matrix['fp'], 'tn': confusion_matrix['tn'], 'fn': confusion_matrix['fn'], 'tp': confusion_matrix['tp'], 'fp': confusion_matrix['fp'], 'tn': confusion_matrix['tn'], 'fn': confusion_matrix['fn'],
'random_no': model_info['random_no'], 'random_yes': model_info['random_yes'], 'hw_regions': training_size['hw_regions']} 'random_no': model_info['random_no'], 'random_yes': model_info['random_yes'], 'hw_regions': training_size['hw_regions']}
infos[model] = info_entry infos[model] = info_entry
return infos return infos
   
# target_collections, target_value, pseudo_target_value, relative_score=False, plot_pseudo=False, base_ax=None # target_collections, target_value, pseudo_target_value, relative_score=False, plot_pseudo=False, base_ax=None
def plot_randomized_average(target_collections, target_score, target_random_value, base_ax=None): def plot_randomized_average(target_collections, target_score, target_random_value, base_ax=None):
fig, ax = plt.subplots(figsize=(10,5)) fig, ax = plt.subplots(figsize=(10,5))
   
ax.set_title(target_score) ax.set_title(target_score)
ax.set_ylabel(target_score) ax.set_ylabel(target_score)
ax.set_xlabel('evaluation reliability') ax.set_xlabel('evaluation reliability')
target_randoms = [] target_randoms = []
for i in np.arange(0.2, 1.05, 0.1): for i in np.arange(0.2, 1.05, 0.1):
i = np.around(i, decimals=3) i = np.around(i, decimals=3)
target_randoms.append(i) target_randoms.append(i)
target_steady_value = 'random_no' if target_random_value == 'random_yes' else 'random_yes' target_steady_value = 'random_no' if target_random_value == 'random_yes' else 'random_yes'
   
overall_info = dict() overall_info = dict()
relevant_models = ['alldeepconv_correctbyconvlstm3filter6'] relevant_models = ['alldeepconv_correctbyconvlstm3filter6']
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
mean_y = [] mean_y = []
for target_collection in target_collections: for target_collection in target_collections:
# print(target_collection) # print(target_collection)
collection_config = evaluation_manager.get_collection_config(target_collection) collection_config = evaluation_manager.get_collection_config(target_collection)
#print(collection_config) #print(collection_config)
test_collection = evaluation_manager.get_test_sets_of_collection(target_collection) test_collection = evaluation_manager.get_test_sets_of_collection(target_collection)
target_models = evaluation_manager.get_run_of_collection(target_collection) target_models = evaluation_manager.get_run_of_collection(target_collection)
#print(target_models) #print(target_models)
target_models = get_multiple_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection)) target_models = get_multiple_target_pseudo_models(target_models, evaluation_manager.get_training_manager_of_collection(target_collection))
#print(target_models) #print(target_models)
training_manager = evaluation_manager.get_training_manager_of_collection(target_collection) training_manager = evaluation_manager.get_training_manager_of_collection(target_collection)
   
filtered_target_models = [] filtered_target_models = []
for random_value in target_randoms: for random_value in target_randoms:
model_filter = {target_random_value: random_value, target_steady_value: 1.00} model_filter = {target_random_value: random_value, target_steady_value: 1.00}
possible_models = get_models_by_filter(model_filter, training_manager.get_information_of_models(target_models[pseudo_model_name])) possible_models = get_models_by_filter(model_filter, training_manager.get_information_of_models(target_models[pseudo_model_name]))
target_model = None target_model = None
for pseudo_model_list in target_models[pseudo_model_name]: for pseudo_model_list in target_models[pseudo_model_name]:
for possible_model in possible_models: for possible_model in possible_models:
if possible_model in pseudo_model_list: if possible_model in pseudo_model_list:
# print(possible_model, pseudo_model_list) # print(possible_model, pseudo_model_list)
target_model = possible_model target_model = possible_model
break break
#print(target_model) #print(target_model)
filtered_target_models.append(target_model) filtered_target_models.append(target_model)
   
pseudo_averages = calc_average_pseudo_model_evaluation(evaluation_manager.model_evaluation.predictions, target_score, filtered_target_models, test_collection) pseudo_averages = calc_average_pseudo_model_evaluation(evaluation_manager.model_evaluation.predictions, target_score, filtered_target_models, test_collection)
pseudo_average_values = [] pseudo_average_values = []
for model_name in filtered_target_models: for model_name in filtered_target_models:
pseudo_average_values.append(pseudo_averages[model_name]) pseudo_average_values.append(pseudo_averages[model_name])
infos = get_model_infos(filtered_target_models, training_manager.get_all_information()) infos = get_model_infos(filtered_target_models, training_manager.get_all_information())
overall_info.update(infos) overall_info.update(infos)
y_vals = [pseudo_averages[key] for key in filtered_target_models] y_vals = [pseudo_averages[key] for key in filtered_target_models]
mean_y.append(y_vals) mean_y.append(y_vals)
mean_y = np.mean(np.array(mean_y), axis=0) mean_y = np.mean(np.array(mean_y), axis=0)
ax.plot(target_randoms, mean_y, label=translate_setting(pseudo_model_name)) ax.plot(target_randoms, mean_y, label=translate_setting(pseudo_model_name))
   
   
# plt.xticks(rotation=45, ha='right') # plt.xticks(rotation=45, ha='right')
plt.subplots_adjust(left=0.1, right=0.7, top=0.9, bottom=0.15) plt.subplots_adjust(left=0.1, right=0.7, top=0.9, bottom=0.15)
fig.legend() fig.legend()
   
return ax, overall_info return ax, overall_info
   
   
include_collections = ['random_synthetic_01'] include_collections = ['random_synthetic_01']
_, info = plot_randomized_average(include_collections, 's1', 'random_no') _, info = plot_randomized_average(include_collections, 's1', 'random_no')
data_frame = pd.DataFrame.from_dict(info, orient='index') data_frame = pd.DataFrame.from_dict(info, orient='index')
data_frame.filter(like='alldeepconv', axis=0) data_frame.filter(like='alldeepconv', axis=0)
``` ```
   
%% Output %% Output
   
   
   
s1 overall_size \ s1 overall_size \
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954194 0.859584 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954194 0.859584
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.957823 0.850537 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.957823 0.850537
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.955018 0.848911 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.955018 0.848911
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954794 0.847177 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954794 0.847177
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.956105 0.847177 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.956105 0.847177
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.955401 0.847177 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.955401 0.847177
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954685 0.847177 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954685 0.847177
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954470 0.847177 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.954470 0.847177
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.831699 0.836112 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.831699 0.836112
null_size hw_size \ null_size hw_size \
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.858235 0.963931 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.858235 0.963931
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.848973 0.972416 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.848973 0.972416
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.847281 0.976446 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.847281 0.976446
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845640 0.965578 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845640 0.965578
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845526 0.975093 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845526 0.975093
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845649 0.965739 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845649 0.965739
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845623 0.966901 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845623 0.966901
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845540 0.976769 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845540 0.976769
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.841473 0.328709 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.841473 0.328709
tp fp \ tp fp \
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.923913 0.000442 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.923913 0.000442
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.930751 0.000462 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.930751 0.000462
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.927913 0.000541 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.927913 0.000541
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.927913 0.000423 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.927913 0.000423
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.930751 0.000504 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.930751 0.000504
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.925236 0.000456 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.925236 0.000456
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.928913 0.000425 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.928913 0.000425
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.925913 0.000553 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.925913 0.000553
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.327709 0.000015 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.327709 0.000015
tn fn \ tn fn \
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.857549 0.076087 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.857549 0.076087
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.848379 0.069249 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.848379 0.069249
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.846654 0.072087 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.846654 0.072087
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845013 0.072087 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845013 0.072087
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844932 0.069249 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844932 0.069249
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844980 0.074764 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844980 0.074764
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845011 0.071087 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.845011 0.071087
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844883 0.074087 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.844883 0.074087
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.840298 0.116056 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.840298 0.116056
random_no random_yes \ random_no random_yes \
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.2 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.2 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.3 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.3 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.4 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.4 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.5 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.5 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.6 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.6 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.7 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.7 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.8 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.8 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.9 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 0.9 1.0
random_01_run1_alldeepconv_correctbyconvlstm3fi... 1.0 1.0 random_01_run1_alldeepconv_correctbyconvlstm3fi... 1.0 1.0
hw_regions hw_regions
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 26 random_01_run1_alldeepconv_correctbyconvlstm3fi... 26
random_01_run1_alldeepconv_correctbyconvlstm3fi... 6 random_01_run1_alldeepconv_correctbyconvlstm3fi... 6
   
%% Cell type:markdown id:07cd0892 tags: %% Cell type:markdown id:07cd0892 tags:
   
# Old stuff # Old stuff
   
%% Cell type:code id:e562d499 tags: %% Cell type:code id:e562d499 tags:
   
``` python ``` python
# Synthetic # Synthetic
predictions_db = './data/cluster/pseudo_collections/synthetic_predictions_db' predictions_db = './data/cluster/pseudo_collections/synthetic_predictions_db'
dataset_db = './data/synthetic_dataset_db' dataset_db = './data/synthetic_dataset_db'
training_db = './data/cluster/pseudo_collections/synthetic_pseudo_training_db' training_db = './data/cluster/pseudo_collections/synthetic_pseudo_training_db'
models_directory = './data/cluster/pseudo_collections/synthetic_models/' models_directory = './data/cluster/pseudo_collections/synthetic_models/'
base_models = ['HandWashingDeepConvLSTMA_trunc_01.pt', 'HandWashingDeepConvLSTMA_trunc_02.pt','HandWashingDeepConvLSTMA_trunc_10.pt', 'HandWashingDeepConvLSTMA_trunc_01.pt'] base_models = ['HandWashingDeepConvLSTMA_trunc_01.pt', 'HandWashingDeepConvLSTMA_trunc_02.pt','HandWashingDeepConvLSTMA_trunc_10.pt', 'HandWashingDeepConvLSTMA_trunc_01.pt']
base_inc_models = ['synthetic_inc_01.pt', 'synthetic_inc_02.pt', 'synthetic_inc_10.pt'] base_inc_models = ['synthetic_inc_01.pt', 'synthetic_inc_02.pt', 'synthetic_inc_10.pt']
test_collection_names = ['01_test', '02_test', '10_test'] test_collection_names = ['01_test', '02_test', '10_test']
training_collection_names = ['01_training', '02_training', '10_training'] training_collection_names = ['01_training', '02_training', '10_training']
collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ('02_test', 'pseudo_collection_02_run1'), ('10_test', 'pseudo_collection_10_run1')] collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ('02_test', 'pseudo_collection_02_run1'), ('10_test', 'pseudo_collection_10_run1')]
``` ```
   
%% Cell type:code id:f44c7fa8 tags: %% Cell type:code id:f44c7fa8 tags:
   
``` python ``` python
# Synthetic randomized # Synthetic randomized
predictions_db = './data/cluster/pseudo_randomized/synthetic_predictions_db' predictions_db = './data/cluster/pseudo_randomized/synthetic_predictions_db'
dataset_db = './data/synthetic_dataset_db' dataset_db = './data/synthetic_dataset_db'
training_db = './data/cluster/pseudo_randomized/pseudo_training_db' training_db = './data/cluster/pseudo_randomized/pseudo_training_db'
models_directory = './data/cluster/pseudo_randomized/pseudo_models/' models_directory = './data/cluster/pseudo_randomized/pseudo_models/'
base_models = ['HandWashingDeepConvLSTMA_trunc_01.pt'] base_models = ['HandWashingDeepConvLSTMA_trunc_01.pt']
base_inc_models = ['synthetic_inc_01.pt'] base_inc_models = ['synthetic_inc_01.pt']
test_collection_names = ['01_test'] test_collection_names = ['01_test']
training_collection_names = ['01_training'] training_collection_names = ['01_training']
collection_training_run_assignments = [('01_test', 'pseudo_randomeval_01_run1')] collection_training_run_assignments = [('01_test', 'pseudo_randomeval_01_run1')]
``` ```
   
%% Cell type:code id:1cba0e77 tags: %% Cell type:code id:1cba0e77 tags:
   
``` python ``` python
# Recordings # Recordings
predictions_db = './data/cluster/pseudo_collections/recorded_predictions_db' predictions_db = './data/cluster/pseudo_collections/recorded_predictions_db'
dataset_db = './data/recorded_dataset_db' dataset_db = './data/recorded_dataset_db'
training_db = './data/cluster/pseudo_collections/recorded_pseudo_training_db' training_db = './data/cluster/pseudo_collections/recorded_pseudo_training_db'
models_directory = './data/cluster/pseudo_collections/recorded_models/' models_directory = './data/cluster/pseudo_collections/recorded_models/'
base_models = ['DeepConvLSTMA_statedict.pt', 'DeepConvLSTMA_statedict.pt'] base_models = ['DeepConvLSTMA_statedict.pt', 'DeepConvLSTMA_statedict.pt']
base_inc_models = ['recorded_inc_01.pt', 'recorded_inc_02.pt'] base_inc_models = ['recorded_inc_01.pt', 'recorded_inc_02.pt']
test_collection_names = ['01_test', '02_test'] test_collection_names = ['01_test', '02_test']
training_collection_names = ['01_training', '02_training'] training_collection_names = ['01_training', '02_training']
collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ('02_test', 'pseudo_collection_02_run1')] collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ('02_test', 'pseudo_collection_02_run1')]
``` ```
   
%% Cell type:code id:59d9a9a7 tags: %% Cell type:code id:59d9a9a7 tags:
   
``` python ``` python
# Recordings # Recordings
predictions_db = './data/cluster/recorded_pseudo_predictions_db_2' predictions_db = './data/cluster/recorded_pseudo_predictions_db_2'
dataset_db = './data/recorded_dataset_db_2' dataset_db = './data/recorded_dataset_db_2'
training_db = './data/cluster/recorded_pseudo_training_db_2' training_db = './data/cluster/recorded_pseudo_training_db_2'
models_directory = './data/cluster/recorded_pseudo_models_2/' models_directory = './data/cluster/recorded_pseudo_models_2/'
base_models = ['DeepConvLSTMA_statedict.pt',] base_models = ['DeepConvLSTMA_statedict.pt',]
base_inc_models = ['recorded_inc_01.pt',] base_inc_models = ['recorded_inc_01.pt',]
test_collection_names = ['01_test', ] test_collection_names = ['01_test', ]
training_collection_names = ['01_training',] training_collection_names = ['01_training',]
collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ] collection_training_run_assignments = [('01_test', 'pseudo_collection_01_run1'), ]
``` ```
   
%% Cell type:code id:5ee876c5 tags: %% Cell type:code id:5ee876c5 tags:
   
``` python ``` python
model_evaluation = ModelEvaluation() model_evaluation = ModelEvaluation()
model_evaluation.load_predictions(predictions_db) model_evaluation.load_predictions(predictions_db)
   
dataset_manager = DatasetManager(dataset_db) dataset_manager = DatasetManager(dataset_db)
   
unseen_datasets = [] unseen_datasets = []
test_collections = dict() test_collections = dict()
for test_collection_name in test_collection_names: for test_collection_name in test_collection_names:
unseen_datasets.append(list(dataset_manager.filter_by_category(test_collection_name).keys())) unseen_datasets.append(list(dataset_manager.filter_by_category(test_collection_name).keys()))
test_collections[test_collection_name] = list(dataset_manager.filter_by_category(test_collection_name).values()) test_collections[test_collection_name] = list(dataset_manager.filter_by_category(test_collection_name).values())
   
training_collections = [] training_collections = []
for training_collection_name in training_collection_names: for training_collection_name in training_collection_names:
training_collections.append(dataset_manager.filter_by_category(training_collection_name)) training_collections.append(dataset_manager.filter_by_category(training_collection_name))
   
model_evaluation.clear_datasets() model_evaluation.clear_datasets()
for test_collection in test_collections.values(): for test_collection in test_collections.values():
model_evaluation.add_collection(test_collection) model_evaluation.add_collection(test_collection)
   
training_manager = TrainingsManager(training_db) training_manager = TrainingsManager(training_db)
training_runs = training_manager.get_all_training_runs() training_runs = training_manager.get_all_training_runs()
print(training_runs.keys()) print(training_runs.keys())
   
for run in training_runs.keys(): for run in training_runs.keys():
print('add run:', run) print('add run:', run)
for model in training_runs[run]: for model in training_runs[run]:
model_evaluation.add_model(models_directory + model) model_evaluation.add_model(models_directory + model)
   
for base_model in base_models: for base_model in base_models:
model_evaluation.add_model('./data/' + base_model) model_evaluation.add_model('./data/' + base_model)
for inc_model in base_inc_models: for inc_model in base_inc_models:
model_evaluation.add_model('./data/' + inc_model) model_evaluation.add_model('./data/' + inc_model)
   
#for trained_collection in training_collections: #for trained_collection in training_collections:
# model_evaluation.predict_models_on_collection(base_models, trained_collection.values()) # model_evaluation.predict_models_on_collection(base_models, trained_collection.values())
   
for collection_training_run_assignment in collection_training_run_assignments: for collection_training_run_assignment in collection_training_run_assignments:
test_collection = test_collections[collection_training_run_assignment[0]] test_collection = test_collections[collection_training_run_assignment[0]]
models_list = training_runs[collection_training_run_assignment[1]] models_list = training_runs[collection_training_run_assignment[1]]
#print('assign', test_collection, 'to', models_list) #print('assign', test_collection, 'to', models_list)
model_evaluation.assign_collection_to_model_list(test_collection, models_list) model_evaluation.assign_collection_to_model_list(test_collection, models_list)
``` ```
   
%% Cell type:code id:cb82065e tags: %% Cell type:code id:cb82065e tags:
   
``` python ``` python
# model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstmfilter.pt') # model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstmfilter.pt')
# model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm3filter.pt') # model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm3filter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter2.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter2.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter3.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_alldeepconv_correctbyconvlstm3filter3.pt')
   
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm3filter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm3filter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm2filter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstm2filter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstmfilter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyconvlstmfilter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyfcndaefiltertest.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyfcndaefiltertest.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyfcndaefilter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbyfcndaefilter.pt')
#model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbydeepconvfilter.pt') #model_evaluation.clear_evaluations_of_model('pseudo_collection_01_run1_allnoise_correctbydeepconvfilter.pt')
model_evaluation.clear_evaluations_of_model('pseudo_collection_10_run1_allnoise_correctedscore.pt') model_evaluation.clear_evaluations_of_model('pseudo_collection_10_run1_allnoise_correctedscore.pt')
``` ```
   
%% Cell type:code id:ab8b166e tags: %% Cell type:code id:ab8b166e tags:
   
``` python ``` python
model_evaluation.clear_evaluations() model_evaluation.clear_evaluations()
``` ```
   
%% Cell type:code id:92ee8a8a tags: %% Cell type:code id:92ee8a8a tags:
   
``` python ``` python
model_evaluation.do_predictions(use_soft=False) model_evaluation.do_predictions(use_soft=False)
``` ```
   
%% Cell type:code id:3398d867 tags: %% Cell type:code id:3398d867 tags:
   
``` python ``` python
model_evaluation.save_predictions(predictions_db) model_evaluation.save_predictions(predictions_db)
``` ```
   
%% Cell type:code id:8bac064e tags: %% Cell type:code id:8bac064e tags:
   
``` python ``` python
evaluations = model_evaluation.get_evaluations(calc_averages=True, sort_by='model', include_datasets=[dataset.name for dataset in test_collections['01_test']]) evaluations = model_evaluation.get_evaluations(calc_averages=True, sort_by='model', include_datasets=[dataset.name for dataset in test_collections['01_test']])
   
evaluations evaluations
``` ```
   
%% Cell type:code id:85417e82 tags: %% Cell type:code id:85417e82 tags:
   
``` python ``` python
pseudo_randomeval_01_run1_allnoise_correctedhwgt_rn0ry2.pt pseudo_randomeval_01_run1_allnoise_correctedhwgt_rn0ry2.pt
#evaluations = model_evaluation.get_evaluations(include_models=training_runs['pseudo_collection_01_run1'], calc_averages=True, sort_by='S1') #evaluations = model_evaluation.get_evaluations(include_models=training_runs['pseudo_collection_01_run1'], calc_averages=True, sort_by='S1')
#evaluations = model_evaluation.get_evaluations(include_models=training_runs['pseudo_collection_01_run1']) #evaluations = model_evaluation.get_evaluations(include_models=training_runs['pseudo_collection_01_run1'])
#evaluations = model_evaluation.get_evaluations(include_datasets=[dataset.name for dataset in test_collections['10_test']], calc_averages=False, sort_by='f1') #evaluations = model_evaluation.get_evaluations(include_datasets=[dataset.name for dataset in test_collections['10_test']], calc_averages=False, sort_by='f1')
evaluations = model_evaluation.get_evaluations(calc_averages=True, sort_by='f1', include_datasets=[dataset.name for dataset in test_collections['01_test']]) evaluations = model_evaluation.get_evaluations(calc_averages=True, sort_by='f1', include_datasets=[dataset.name for dataset in test_collections['01_test']])
   
evaluations evaluations
``` ```
   
%% Cell type:code id:544f21ef tags: %% Cell type:code id:544f21ef tags:
   
``` python ``` python
requests = 0 requests = 0
participant = 1 participant = 1
   
observed_collection = list(dataset_manager.filter_by_category(training_collection_names[participant]).values()) observed_collection = list(dataset_manager.filter_by_category(training_collection_names[participant]).values())
for dataset in observed_collection: for dataset in observed_collection:
evaluations = dataset.get_indicators()[1] evaluations = dataset.get_indicators()[1]
for evaluation in evaluations: for evaluation in evaluations:
if evaluation[1] != Indicators.NEUTRAL: if evaluation[1] != Indicators.NEUTRAL:
requests += 1 requests += 1
print('Num requests:', requests) print('Num requests:', requests)
print('requests per dataset:', requests/len(observed_collection)) print('requests per dataset:', requests/len(observed_collection))
``` ```
   
%% Cell type:code id:12e7b48a tags: %% Cell type:code id:12e7b48a tags:
   
``` python ``` python
relevant_models = list(pseudo_model_settings.keys()) relevant_models = list(pseudo_model_settings.keys())
relevant_models = thesis_filters relevant_models = thesis_filters
#relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3', 'alldeepconv_correctbyconvlstm3filter4','alldeepconv_correctbyconvlstm3filter5', 'alldeepconv_correctbyconvlstm3filter6'] #relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3', 'alldeepconv_correctbyconvlstm3filter4','alldeepconv_correctbyconvlstm3filter5', 'alldeepconv_correctbyconvlstm3filter6']
# relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3'] # relevant_models = ['allnoise_correctedscore', 'allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyconvlstmfilter', 'allnoise_correctbyconvlstm2filter', 'allnoise_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter', 'alldeepconv_correctbyconvlstm3filter2', 'alldeepconv_correctbyconvlstm3filter3']
# relevant_models = ['allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyfcndaefilter', 'alldeepconv_correctbyconvlstm3filter3'] # relevant_models = ['allnoise_correctbydeepconvfilter', 'allnoise_correctbyfcndaefiltertest', 'allnoise_correctbyfcndaefilter', 'alldeepconv_correctbyconvlstm3filter3']
#relevant_models = ['alldeepconv_correctbyconvlstm3filter6', ] #relevant_models = ['alldeepconv_correctbyconvlstm3filter6', ]
``` ```
   
%% Cell type:code id:4376c450 tags: %% Cell type:code id:4376c450 tags:
   
``` python ``` python
def plot_collection(target_value, collection_name, test_collection, base_model, base_inc_model, ax=None, plot_baelines=True): def plot_collection(target_value, collection_name, test_collection, base_model, base_inc_model, ax=None, plot_baelines=True):
if ax is None: if ax is None:
fig, ax = plt.subplots() fig, ax = plt.subplots()
ax.set_title(target_value) ax.set_title(target_value)
collection = training_runs[collection_name] collection = training_runs[collection_name]
for i, target_dataset in enumerate(test_collection): for i, target_dataset in enumerate(test_collection):
y_vals = [] y_vals = []
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
generators = pseudo_model_settings[pseudo_model_name] generators = pseudo_model_settings[pseudo_model_name]
models = get_models_with_generators(generators, training_manager.get_all_information(), collection) models = get_models_with_generators(generators, training_manager.get_all_information(), collection)
for model in models[:1]: for model in models[:1]:
if model in training_runs[collection_name]: if model in training_runs[collection_name]:
evaluation = (model, target_dataset) evaluation = (model, target_dataset)
val = getattr(model_evaluation.predictions[evaluation].evaluation, target_value) val = getattr(model_evaluation.predictions[evaluation].evaluation, target_value)
y_vals.append(val) y_vals.append(val)
   
y_vals = np.array(y_vals) y_vals = np.array(y_vals)
x_vals = np.array(relevant_models) x_vals = np.array(relevant_models)
if y_vals.shape[0] == 1: if y_vals.shape[0] == 1:
y_vals = np.repeat(y_vals, 2, axis=0) y_vals = np.repeat(y_vals, 2, axis=0)
x_vals = np.repeat(x_vals, 2, axis=0) x_vals = np.repeat(x_vals, 2, axis=0)
x_vals[-1] = '_' x_vals[-1] = '_'
label = None label = None
if plot_baelines: if plot_baelines:
label = target_dataset label = target_dataset
print(x_vals, y_vals.shape[0]) print(x_vals, y_vals.shape[0])
base_line, = ax.plot(x_vals, y_vals, label=label) base_line, = ax.plot(x_vals, y_vals, label=label)
   
label = None label = None
if plot_baelines and i == 0: if plot_baelines and i == 0:
label = 'genereal model' label = 'genereal model'
val = getattr(model_evaluation.predictions[(base_model, target_dataset)].evaluation, target_value) val = getattr(model_evaluation.predictions[(base_model, target_dataset)].evaluation, target_value)
ax.axhline(val, linestyle='--', label=label, color=base_line.get_color()) ax.axhline(val, linestyle='--', label=label, color=base_line.get_color())
if plot_baelines and i == 0: if plot_baelines and i == 0:
label = 'gt inc model' label = 'gt inc model'
val = getattr(model_evaluation.predictions[(base_inc_model, target_dataset)].evaluation, target_value) val = getattr(model_evaluation.predictions[(base_inc_model, target_dataset)].evaluation, target_value)
ax.axhline(val, linestyle=':', label=label, color=base_line.get_color()) ax.axhline(val, linestyle=':', label=label, color=base_line.get_color())
   
   
#y_vals = [] #y_vals = []
#for average in pseudo_evaluations_avg: #for average in pseudo_evaluations_avg:
# y_vals.append(average[1][target_value]) # y_vals.append(average[1][target_value])
#ax.plot(list(pseudo_model_settings.keys()), y_vals, label='Pseudo average hard') #ax.plot(list(pseudo_model_settings.keys()), y_vals, label='Pseudo average hard')
   
#y_vals = [] #y_vals = []
#for average in pseudo_evaluations_avg: #for average in pseudo_evaluations_avg:
# y_vals.append(average[2][target_value]) # y_vals.append(average[2][target_value])
#ax.plot(list(pseudo_model_settings.keys()), y_vals, label='Pseudo average soft') #ax.plot(list(pseudo_model_settings.keys()), y_vals, label='Pseudo average soft')
   
   
ax.tick_params(labelrotation=75) ax.tick_params(labelrotation=75)
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
   
#val = training_manager.get_information(model)['pseudo_evaluation'][target_value] #val = training_manager.get_information(model)['pseudo_evaluation'][target_value]
   
def plot_pseudo_evaluation(target_value, collection_name, ax): def plot_pseudo_evaluation(target_value, collection_name, ax):
y_vals = [] y_vals = []
collection = training_runs[collection_name] collection = training_runs[collection_name]
for pseudo_model_name, generators in pseudo_model_settings.items(): for pseudo_model_name, generators in pseudo_model_settings.items():
if relevant_models is not None and pseudo_model_name not in relevant_models: if relevant_models is not None and pseudo_model_name not in relevant_models:
continue continue
models = get_models_with_generators(generators, training_manager.get_all_information(), collection) models = get_models_with_generators(generators, training_manager.get_all_information(), collection)
for model in models[:1]: for model in models[:1]:
if model in training_runs[collection_name]: if model in training_runs[collection_name]:
val = training_manager.get_information(model)['pseudo_evaluation'][target_value] val = training_manager.get_information(model)['pseudo_evaluation'][target_value]
y_vals.append(val) y_vals.append(val)
y_vals = np.array(y_vals) y_vals = np.array(y_vals)
if relevant_models is None: if relevant_models is None:
ax.plot(list(pseudo_model_settings.keys()), y_vals, label='label evaluation', linestyle='-.') ax.plot(list(pseudo_model_settings.keys()), y_vals, label='label evaluation', linestyle='-.')
else: else:
ax.plot(relevant_models, y_vals, label='label evaluation', linestyle='-.') ax.plot(relevant_models, y_vals, label='label evaluation', linestyle='-.')
   
def plot_all_for_collection(collection_name, test_collection, base_model, base_inc_model): def plot_all_for_collection(collection_name, test_collection, base_model, base_inc_model):
fig, axes = plt.subplots(4, figsize=(10,32)) fig, axes = plt.subplots(4, figsize=(10,32))
#fig.tight_layout() #fig.tight_layout()
plot_collection('specificity', collection_name, test_collection, base_model, base_inc_model, axes[0], True) plot_collection('specificity', collection_name, test_collection, base_model, base_inc_model, axes[0], True)
plot_pseudo_evaluation('specificity', collection_name, axes[0]) plot_pseudo_evaluation('specificity', collection_name, axes[0])
plot_collection('sensitivity', collection_name, test_collection, base_model, base_inc_model, axes[1], False) plot_collection('sensitivity', collection_name, test_collection, base_model, base_inc_model, axes[1], False)
plot_pseudo_evaluation('sensitivity', collection_name, axes[1]) plot_pseudo_evaluation('sensitivity', collection_name, axes[1])
plot_collection('f1', collection_name, test_collection, base_model, base_inc_model, axes[2], False) plot_collection('f1', collection_name, test_collection, base_model, base_inc_model, axes[2], False)
plot_pseudo_evaluation('f1', collection_name, axes[2]) plot_pseudo_evaluation('f1', collection_name, axes[2])
plot_collection('s1', collection_name, test_collection, base_model, base_inc_model, axes[3], False) plot_collection('s1', collection_name, test_collection, base_model, base_inc_model, axes[3], False)
plot_pseudo_evaluation('s1', collection_name, axes[3]) plot_pseudo_evaluation('s1', collection_name, axes[3])
   
plt.subplots_adjust(hspace=0.8) plt.subplots_adjust(hspace=0.8)
plt.subplots_adjust(bottom=0.0) plt.subplots_adjust(bottom=0.0)
fig.legend() fig.legend()
fig.show() fig.show()
   
``` ```
   
%% Cell type:code id:54139635 tags: %% Cell type:code id:54139635 tags:
   
``` python ``` python
# print(pseudo_model_settings.keys()) # print(pseudo_model_settings.keys())
target_participant = 0 target_participant = 0
model_collection = collection_training_run_assignments[target_participant][1] model_collection = collection_training_run_assignments[target_participant][1]
test_datasets = [dataset.name for dataset in test_collections[test_collection_names[target_participant]]] test_datasets = [dataset.name for dataset in test_collections[test_collection_names[target_participant]]]
base_model = base_models[target_participant] base_model = base_models[target_participant]
base_inc_model = base_inc_models[target_participant] base_inc_model = base_inc_models[target_participant]
plot_all_for_collection(model_collection, test_datasets, base_model, base_inc_model) plot_all_for_collection(model_collection, test_datasets, base_model, base_inc_model)
``` ```
   
%% Cell type:code id:a2230ff7 tags: %% Cell type:code id:a2230ff7 tags:
   
``` python ``` python
def get_target_pseudo_models(collection_name): def get_target_pseudo_models(collection_name):
target_models = [] target_models = []
collection = training_runs[collection_name] collection = training_runs[collection_name]
for pseudo_model in relevant_models: for pseudo_model in relevant_models:
possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection) possible_models = get_models_with_generators(pseudo_model_settings[pseudo_model], training_manager.get_all_information(), collection)
for model in possible_models[:1]: for model in possible_models[:1]:
if model in training_runs[collection_name] and model not in target_models: if model in training_runs[collection_name] and model not in target_models:
target_models.append(model) target_models.append(model)
return target_models return target_models
   
def plot_average_of_pseudo_models(collection_name, target_value, test_dataset, base_model, base_inc_model, relative_score=False): def plot_average_of_pseudo_models(collection_name, target_value, test_dataset, base_model, base_inc_model, relative_score=False):
target_models = get_target_pseudo_models(collection_name) target_models = get_target_pseudo_models(collection_name)
pseudo_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, target_models, test_dataset) pseudo_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, target_models, test_dataset)
base_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, [base_model, base_inc_model], test_dataset) base_averages = calc_average_pseudo_model_evaluation(model_evaluation.predictions, target_value, [base_model, base_inc_model], test_dataset)
pseudo_average_values = [] pseudo_average_values = []
for model_name in target_models: for model_name in target_models:
pseudo_average_values.append(pseudo_averages[model_name]) pseudo_average_values.append(pseudo_averages[model_name])
   
   
   
if relative_score: if relative_score:
score_divider = base_averages[base_model] score_divider = base_averages[base_model]
base_averages[base_model] /= score_divider base_averages[base_model] /= score_divider
base_averages[base_inc_model] /= score_divider base_averages[base_inc_model] /= score_divider
for pseudo_average in pseudo_averages: for pseudo_average in pseudo_averages:
pseudo_averages[pseudo_average] /= score_divider pseudo_averages[pseudo_average] /= score_divider
   
y_vals = list(pseudo_averages.values()) y_vals = list(pseudo_averages.values())
x_vals = remove_model_name_suffix(relevant_models) x_vals = remove_model_name_suffix(relevant_models)
if len(y_vals) == 1: if len(y_vals) == 1:
y_vals = np.repeat(y_vals, 2, axis=0) y_vals = np.repeat(y_vals, 2, axis=0)
x_vals = np.repeat(x_vals, 2, axis=0) x_vals = np.repeat(x_vals, 2, axis=0)
x_vals[-1] = '_' x_vals[-1] = '_'
   
   
fig, ax = plt.subplots(figsize=(9,7)) fig, ax = plt.subplots(figsize=(9,7))
ax.set_title(target_value) ax.set_title(target_value)
ax.axhline(base_averages[base_model], linestyle=':', label='base', color='red') ax.axhline(base_averages[base_model], linestyle=':', label='base', color='red')
ax.axhline(base_averages[base_inc_model], linestyle=':', label='inc', color='green') ax.axhline(base_averages[base_inc_model], linestyle=':', label='inc', color='green')
ax.plot(x_vals, y_vals) ax.plot(x_vals, y_vals)
   
ax.set_xticklabels(x_vals, rotation=75, ha='right', rotation_mode='anchor') ax.set_xticklabels(x_vals, rotation=75, ha='right', rotation_mode='anchor')
ax.grid(linestyle='-', linewidth=0.1) ax.grid(linestyle='-', linewidth=0.1)
ax.set_xlabel('modelname') ax.set_xlabel('modelname')
plt.tight_layout() plt.tight_layout()
fig.show() fig.show()
   
   
target_participant = 0 target_participant = 0
test_datasets = [dataset.name for dataset in test_collections[test_collection_names[target_participant]]] test_datasets = [dataset.name for dataset in test_collections[test_collection_names[target_participant]]]
base_model = base_models[target_participant] base_model = base_models[target_participant]
base_inc_model = base_inc_models[target_participant] base_inc_model = base_inc_models[target_participant]
   
run_name = collection_training_run_assignments[target_participant][1] run_name = collection_training_run_assignments[target_participant][1]
plot_average_of_pseudo_models(run_name, 'specificity', test_datasets, base_model, base_inc_model) plot_average_of_pseudo_models(run_name, 'specificity', test_datasets, base_model, base_inc_model)
plot_average_of_pseudo_models(run_name, 'sensitivity', test_datasets, base_model, base_inc_model) plot_average_of_pseudo_models(run_name, 'sensitivity', test_datasets, base_model, base_inc_model)
plot_average_of_pseudo_models(run_name, 'f1', test_datasets, base_model, base_inc_model, relative_score=False) plot_average_of_pseudo_models(run_name, 'f1', test_datasets, base_model, base_inc_model, relative_score=False)
plot_average_of_pseudo_models(run_name, 's1', test_datasets, base_model, base_inc_model) plot_average_of_pseudo_models(run_name, 's1', test_datasets, base_model, base_inc_model)
plot_average_of_pseudo_models(run_name, 'mcc', test_datasets, base_model, base_inc_model) plot_average_of_pseudo_models(run_name, 'mcc', test_datasets, base_model, base_inc_model)
``` ```
   
%% Cell type:code id:26b45e5e tags: %% Cell type:code id:26b45e5e tags:
   
``` python ``` python
def get_models_by_filter(target_filter, relevant_model_info): def get_models_by_filter(target_filter, relevant_model_info):
models = [] models = []
for model, values in relevant_model_info.items(): for model, values in relevant_model_info.items():
add_model = True add_model = True
for target_key, target_value in target_filter.items(): for target_key, target_value in target_filter.items():
#print(target_key, target_value, values) #print(target_key, target_value, values)
if values[target_key] != target_value: if values[target_key] != target_value:
add_model = False add_model = False
break break
if add_model: if add_model:
models.append(model) models.append(model)
return models return models
   
def get_model_by_filter(target_filter, relevant_model_info): def get_model_by_filter(target_filter, relevant_model_info):
for model, values in relevant_model_info.items(): for model, values in relevant_model_info.items():
add_model = True add_model = True
for target_key, target_value in target_filter.items(): for target_key, target_value in target_filter.items():
if values[target_key] != target_value: if values[target_key] != target_value:
add_model = False add_model = False
break break
   
if add_model: if add_model:
return model return model
   
   
def plot_randomized(target_dataset, target_score, target_values, model_infos, collection_name): def plot_randomized(target_dataset, target_score, target_values, model_infos, collection_name):
fig, ax = plt.subplots(figsize=(9,5)) fig, ax = plt.subplots(figsize=(9,5))
   
ax.set_title(target_score) ax.set_title(target_score)
ax.set_ylabel(target_score) ax.set_ylabel(target_score)
ax.set_xlabel('evaluation reliability') ax.set_xlabel('evaluation reliability')
   
   
   
collection = training_runs[collection_name] collection = training_runs[collection_name]
pseudo_models = dict() pseudo_models = dict()
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
if pseudo_model_name not in pseudo_models: if pseudo_model_name not in pseudo_models:
pseudo_models[pseudo_model_name] = [] pseudo_models[pseudo_model_name] = []
pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection)) pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection))
   
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
y_vals = [] y_vals = []
x_vals = [] x_vals = []
for target_value in target_values: for target_value in target_values:
model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]} model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]}
possible_models = get_models_by_filter(model_filter, model_infos) possible_models = get_models_by_filter(model_filter, model_infos)
target_model = None target_model = None
for pseudo_model_list in pseudo_models[pseudo_model_name]: for pseudo_model_list in pseudo_models[pseudo_model_name]:
for possible_model in possible_models: for possible_model in possible_models:
if possible_model in pseudo_model_list: if possible_model in pseudo_model_list:
#print(possible_model, pseudo_model_list) #print(possible_model, pseudo_model_list)
target_model = possible_model target_model = possible_model
break break
#print(target_model) #print(target_model)
if target_model is not None: if target_model is not None:
evaluation = (target_model, target_dataset) evaluation = (target_model, target_dataset)
val = getattr(model_evaluation.predictions[evaluation].evaluation, target_score) val = getattr(model_evaluation.predictions[evaluation].evaluation, target_score)
y_vals.append(val) y_vals.append(val)
nonzero_val = target_value[0] nonzero_val = target_value[0]
if nonzero_val == 0 or nonzero_val == 1: if nonzero_val == 0 or nonzero_val == 1:
nonzero_val = target_value[1] nonzero_val = target_value[1]
x_vals.append(nonzero_val) x_vals.append(nonzero_val)
   
ax.plot(x_vals, y_vals, label=pseudo_model_name) ax.plot(x_vals, y_vals, label=pseudo_model_name)
   
#val = getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset)].evaluation, target_score) #val = getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset)].evaluation, target_score)
#ax.axhline(val, color='red', linestyle='--', label='genereal model') #ax.axhline(val, color='red', linestyle='--', label='genereal model')
#val = getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset)].evaluation, target_score) #val = getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset)].evaluation, target_score)
#ax.axhline(val, color='green', linestyle='--', label='gt inc model') #ax.axhline(val, color='green', linestyle='--', label='gt inc model')
   
   
   
# plt.xticks(rotation=45, ha='right') # plt.xticks(rotation=45, ha='right')
try: try:
fig.legend(loc='lower right') fig.legend(loc='lower right')
except NameError: except NameError:
pass pass
   
return ax return ax
   
   
def plot_randomized_average(test_collection_name, target_score, target_values, model_infos, collection_name): def plot_randomized_average(test_collection_name, target_score, target_values, model_infos, collection_name):
fig, ax = plt.subplots(figsize=(9,5)) fig, ax = plt.subplots(figsize=(9,5))
   
ax.set_title(target_score) ax.set_title(target_score)
ax.set_ylabel(target_score) ax.set_ylabel(target_score)
ax.set_xlabel('evaluation reliability') ax.set_xlabel('evaluation reliability')
   
   
   
collection = training_runs[collection_name] collection = training_runs[collection_name]
pseudo_models = dict() pseudo_models = dict()
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
if pseudo_model_name not in pseudo_models: if pseudo_model_name not in pseudo_models:
pseudo_models[pseudo_model_name] = [] pseudo_models[pseudo_model_name] = []
pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection)) pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection))
   
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
y_vals = [] y_vals = []
x_vals = [] x_vals = []
for target_value in target_values: for target_value in target_values:
model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]} model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]}
possible_models = get_models_by_filter(model_filter, model_infos) possible_models = get_models_by_filter(model_filter, model_infos)
target_model = None target_model = None
for pseudo_model_list in pseudo_models[pseudo_model_name]: for pseudo_model_list in pseudo_models[pseudo_model_name]:
for possible_model in possible_models: for possible_model in possible_models:
if possible_model in pseudo_model_list: if possible_model in pseudo_model_list:
#print(possible_model, pseudo_model_list) #print(possible_model, pseudo_model_list)
target_model = possible_model target_model = possible_model
break break
#print(target_model) #print(target_model)
if target_model is not None: if target_model is not None:
val = 0 val = 0
for target_dataset in test_collections[test_collection_name]: for target_dataset in test_collections[test_collection_name]:
evaluation = (target_model, target_dataset.name) evaluation = (target_model, target_dataset.name)
val += getattr(model_evaluation.predictions[evaluation].evaluation, target_score) val += getattr(model_evaluation.predictions[evaluation].evaluation, target_score)
y_vals.append(val/len(test_collections[test_collection_name])) y_vals.append(val/len(test_collections[test_collection_name]))
nonzero_val = target_value[0] nonzero_val = target_value[0]
if nonzero_val == 0 or nonzero_val == 1: if nonzero_val == 0 or nonzero_val == 1:
nonzero_val = target_value[1] nonzero_val = target_value[1]
x_vals.append(nonzero_val) x_vals.append(nonzero_val)
   
ax.plot(x_vals, y_vals, label=pseudo_model_name) ax.plot(x_vals, y_vals, label=pseudo_model_name)
   
val = 0 val = 0
for target_dataset in test_collections[test_collection_name]: for target_dataset in test_collections[test_collection_name]:
val += getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset.name)].evaluation, target_score) val += getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset.name)].evaluation, target_score)
ax.axhline(val/len(test_collections[test_collection_name]), color='red', linestyle='--', label='genereal model') ax.axhline(val/len(test_collections[test_collection_name]), color='red', linestyle='--', label='genereal model')
   
val = 0 val = 0
for target_dataset in test_collections[test_collection_name]: for target_dataset in test_collections[test_collection_name]:
val += getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset.name)].evaluation, target_score) val += getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset.name)].evaluation, target_score)
ax.axhline(val/len(test_collections[test_collection_name]), color='green', linestyle='--', label='gt inc model') ax.axhline(val/len(test_collections[test_collection_name]), color='green', linestyle='--', label='gt inc model')
   
   
   
# plt.xticks(rotation=45, ha='right') # plt.xticks(rotation=45, ha='right')
try: try:
fig.legend(loc='lower right') fig.legend(loc='lower right')
except NameError: except NameError:
pass pass
   
return ax return ax
   
   
def plot_randomized_pseudo_label_data(target_dataset, target_score, target_values, model_infos, collection_name): def plot_randomized_pseudo_label_data(target_dataset, target_score, target_values, model_infos, collection_name):
fig, ax = plt.subplots(figsize=(9,5)) fig, ax = plt.subplots(figsize=(9,5))
   
ax.set_title(target_score) ax.set_title(target_score)
ax.set_ylabel(target_score) ax.set_ylabel(target_score)
ax.set_xlabel('evaluation reliability') ax.set_xlabel('evaluation reliability')
   
   
   
collection = training_runs[collection_name] collection = training_runs[collection_name]
pseudo_models = dict() pseudo_models = dict()
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
if pseudo_model_name not in pseudo_models: if pseudo_model_name not in pseudo_models:
pseudo_models[pseudo_model_name] = [] pseudo_models[pseudo_model_name] = []
pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection)) pseudo_models[pseudo_model_name].append(get_models_with_generators(pseudo_model_settings[pseudo_model_name], training_manager.get_all_information(), collection))
   
for pseudo_model_name in relevant_models: for pseudo_model_name in relevant_models:
y_vals = [] y_vals = []
x_vals = [] x_vals = []
for target_value in target_values: for target_value in target_values:
model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]} model_filter = {'random_no': target_value[0], 'random_yes': target_value[1]}
possible_models = get_models_by_filter(model_filter, model_infos) possible_models = get_models_by_filter(model_filter, model_infos)
target_model = None target_model = None
for pseudo_model_list in pseudo_models[pseudo_model_name]: for pseudo_model_list in pseudo_models[pseudo_model_name]:
for possible_model in possible_models: for possible_model in possible_models:
if possible_model in pseudo_model_list: if possible_model in pseudo_model_list:
#print(possible_model, pseudo_model_list) #print(possible_model, pseudo_model_list)
target_model = possible_model target_model = possible_model
break break
#print(target_model) #print(target_model)
if target_model is not None: if target_model is not None:
# evaluation = (target_model, target_dataset) # evaluation = (target_model, target_dataset)
# print(training_manager.get_all_information()[target_model]) # print(training_manager.get_all_information()[target_model])
val = training_manager.get_all_information()[target_model]['pseudo_evaluation'][target_score] val = training_manager.get_all_information()[target_model]['pseudo_evaluation'][target_score]
   
#val = getattr(model_evaluation.predictions[evaluation].evaluation, target_score) #val = getattr(model_evaluation.predictions[evaluation].evaluation, target_score)
y_vals.append(val) y_vals.append(val)
if np.isnan(val): if np.isnan(val):
print(target_model) print(target_model)
nonzero_val = target_value[0] nonzero_val = target_value[0]
if nonzero_val == 0 or nonzero_val == 1: if nonzero_val == 0 or nonzero_val == 1:
nonzero_val = target_value[1] nonzero_val = target_value[1]
x_vals.append(nonzero_val) x_vals.append(nonzero_val)
   
ax.plot(x_vals, y_vals, label=pseudo_model_name) ax.plot(x_vals, y_vals, label=pseudo_model_name)
   
val = getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset)].evaluation, target_score) val = getattr(model_evaluation.predictions[('HandWashingDeepConvLSTMA_trunc_01.pt', target_dataset)].evaluation, target_score)
ax.axhline(val, color='red', linestyle='--', label='genereal model') ax.axhline(val, color='red', linestyle='--', label='genereal model')
val = getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset)].evaluation, target_score) val = getattr(model_evaluation.predictions[('synthetic_inc_01.pt', target_dataset)].evaluation, target_score)
ax.axhline(val, color='green', linestyle='--', label='gt inc model') ax.axhline(val, color='green', linestyle='--', label='gt inc model')
   
   
   
# plt.xticks(rotation=45, ha='right') # plt.xticks(rotation=45, ha='right')
try: try:
fig.legend(loc='lower right')