17 changed files with 1725 additions and 907 deletions
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148.gitignore
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BINEurope.zip
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BINRest_of_the_World.zip
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1baseline_results.json
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778main.py
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0old/DATA_PREPROCESSING_CODE.ipynb
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0old/MODIFICATIONSANDINSTRUCTIONS.pdf
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1old/baseline_results.json
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0old/data_preprocessing.py
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0old/data_preprocessing_main.py
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0old/final-32-automated-code-new(1).ipynb
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848old/main_old.py
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0old/non_jupyter_version.py
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466old/pipeline_old.py
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3old/requirements.txt
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358pipeline.py
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1preprocessing.py
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# ---> Python |
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# Byte-compiled / optimized / DLL files |
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__pycache__/ |
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*.py[cod] |
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*$py.class |
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# C extensions |
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build/ |
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downloads/ |
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eggs/ |
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share/python-wheels/ |
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*.egg-info/ |
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*.egg |
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MANIFEST |
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*.manifest |
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pip-log.txt |
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htmlcov/ |
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.tox/ |
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.nox/ |
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.coverage |
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.coverage.* |
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.cache |
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nosetests.xml |
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coverage.xml |
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*.cover |
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*.py,cover |
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.hypothesis/ |
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.pytest_cache/ |
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cover/ |
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# Translations |
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*.mo |
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*.pot |
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# Django stuff: |
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*.log |
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local_settings.py |
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db.sqlite3 |
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db.sqlite3-journal |
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# Flask stuff: |
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instance/ |
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docs/_build/ |
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# PyBuilder |
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target/ |
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# Jupyter Notebook |
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.ipynb_checkpoints |
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# IPython |
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profile_default/ |
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ipython_config.py |
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# pyenv |
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# For a library or package, you might want to ignore these files since the code is |
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# intended to run in multiple environments; otherwise, check them in: |
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# .python-version |
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# pipenv |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. |
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# However, in case of collaboration, if having platform-specific dependencies or dependencies |
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# having no cross-platform support, pipenv may install dependencies that don't work, or not |
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# install all needed dependencies. |
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#Pipfile.lock |
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow |
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__pypackages__/ |
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# Celery stuff |
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celerybeat-schedule |
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celerybeat.pid |
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*.sage.py |
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# Environments |
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.env |
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.venv |
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env/ |
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venv/ |
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ENV/ |
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env.bak/ |
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venv.bak/ |
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# Spyder project settings |
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.spyderproject |
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# Rope project settings |
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# mkdocs documentation |
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/site |
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# mypy |
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.mypy_cache/ |
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.dmypy.json |
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dmypy.json |
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# Pyre type checker |
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.pyre/ |
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# pytype static type analyzer |
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# Cython debug symbols |
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cython_debug/ |
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.idea |
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working/tuner |
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working |
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.idea |
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__pycache__ |
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figures |
figures |
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baseline_results.json |
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baseline_results_v3.json |
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results |
results |
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working |
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{"strategy":{"0":"most_frequent","1":"stratified","2":"uniform","3":"most_frequent","4":"stratified","5":"uniform","6":"most_frequent","7":"stratified","8":"uniform","9":"most_frequent","10":"stratified","11":"uniform","12":"most_frequent","13":"stratified","14":"uniform","15":"most_frequent","16":"stratified","17":"uniform"},"time used":{"0":"1HR","1":"1HR","2":"1HR","3":"1HR","4":"1HR","5":"1HR","6":"1HR","7":"1HR","8":"1HR","9":"1HR","10":"1HR","11":"1HR","12":"1HR","13":"1HR","14":"1HR","15":"1HR","16":"1HR","17":"1HR"},"sequence length":{"0":1,"1":1,"2":1,"3":6,"4":6,"5":6,"6":11,"7":11,"8":11,"9":16,"10":16,"11":16,"12":21,"13":21,"14":21,"15":26,"16":26,"17":26},"accuracy":{"0":0.0465710356,"1":0.0387109452,"2":0.0322263706,"3":0.0471161657,"4":0.0337124289,"5":0.0314784728,"6":0.0476990964,"7":0.0355116621,"8":0.0310989704,"9":0.0483239007,"10":0.035263387,"11":0.0343926861,"12":0.0489952585,"13":0.0363513208,"14":0.0325129826,"15":0.0497185741,"16":0.0356472795,"17":0.0300187617},"precision":{"0":0.0021688614,"1":0.038723827,"2":0.035357886,"3":0.0022199331,"4":0.0344564588,"5":0.0368125232,"6":0.0022752038,"7":0.0355258063,"8":0.034249105,"9":0.0023351994,"10":0.0353594615,"11":0.038321258,"12":0.0024005354,"13":0.0363413163,"14":0.0372264035,"15":0.0024719366,"16":0.0354808739,"17":0.0381814576},"recall":{"0":0.0465710356,"1":0.0387109452,"2":0.0322263706,"3":0.0471161657,"4":0.0337124289,"5":0.0314784728,"6":0.0476990964,"7":0.0355116621,"8":0.0310989704,"9":0.0483239007,"10":0.035263387,"11":0.0343926861,"12":0.0489952585,"13":0.0363513208,"14":0.0325129826,"15":0.0497185741,"16":0.0356472795,"17":0.0300187617},"f1 score":{"0":0.0041446997,"1":0.0386459741,"2":0.0329666692,"3":0.0042400894,"4":0.0340329362,"5":0.033074915,"6":0.004343239,"7":0.0354644534,"8":0.0315466073,"9":0.0044551105,"10":0.0352542225,"11":0.0349449461,"12":0.0045768279,"13":0.0362991035,"14":0.0333112368,"15":0.004709713,"16":0.0355062319,"17":0.0327189637}} |
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1
old/baseline_results.json
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import json |
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import os |
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import math |
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import numpy as np |
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import pandas as pd |
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import sklearn |
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from keras.src.regularizers import L1L2 |
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from matplotlib import pyplot as plt |
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from pandas import DataFrame |
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from sklearn.calibration import CalibratedClassifierCV |
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from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis, LinearDiscriminantAnalysis |
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from sklearn.dummy import DummyClassifier |
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from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, VotingClassifier, \ |
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GradientBoostingClassifier, AdaBoostClassifier |
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from sklearn.gaussian_process import GaussianProcessClassifier |
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from sklearn.linear_model import PassiveAggressiveClassifier, RidgeClassifier, RidgeClassifierCV, SGDClassifier, \ |
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LogisticRegression, LogisticRegressionCV, Perceptron |
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from sklearn.metrics import confusion_matrix |
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from sklearn.mixture import GaussianMixture |
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from sklearn.model_selection import GridSearchCV |
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from sklearn.naive_bayes import GaussianNB, BernoulliNB, MultinomialNB |
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from sklearn.neighbors import KNeighborsClassifier, NearestCentroid |
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from sklearn.neural_network import MLPClassifier |
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from sklearn.preprocessing import MinMaxScaler |
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from sklearn.semi_supervised import LabelSpreading, LabelPropagation |
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from sklearn.svm import LinearSVC, SVC, OneClassSVM |
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from sklearn.tree import ExtraTreeClassifier, DecisionTreeClassifier |
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from pipeline_old import ( |
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load_dataset, |
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filter_data, |
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filter_test_data, |
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prepare_user_data, |
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train_models, |
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evaluate_models, |
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prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2, train_one_model, |
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eval_metrics, get_save_id, prepare_data_for_basic_algorithm, train_one_model_v2, |
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) |
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year_str = 'Year' |
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month_str = 'Month' |
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day_str = 'Day' |
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date_str = 'Date' |
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time_str = 'Time' |
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day_of_week_str = 'DayOfWeek' |
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user_str = 'user' |
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split_str = 'split type' |
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data_split_str = 'data percentages' |
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month_split_str = 'month percentages' |
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threshold_str = 'threshold used' |
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with_threshold_str = 'WITH' |
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without_threshold_str = 'WITHOUT' |
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timespan_str = 'time used' |
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hour_timespan_str = '1HR' |
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min_timespan_str = '15MIN' |
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sequence_length_str = 'sequence length' |
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accuracy_str = 'accuracy' |
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precision_str = 'precision' |
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recall_str = 'recall' |
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f1_string = 'f1 score' |
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model_type_str = 'model type' |
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week_column_names = ['DayOfWeek_' + day for day in |
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['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]] |
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figure_path = 'figures/' |
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predicitons_path = 'preds/' |
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# === Configurable Parameters === |
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dataset_path = './Datasets/' |
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dataset_hrs_path = './Datasets/hours.json' |
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dataset_min_path = './Datasets/minutes.json' |
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DATA_PATH = dataset_path +'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' |
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OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx' |
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result_filename_v1 = './working/evaluation_results.json' |
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result_filename_v2 = './working/evaluation_results_v2.json' |
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SEQUENCE_LENGTHS = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30] |
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TRAINING_SCENARIO = [(2018, list(range(1, 13))), (2019, list(range(1, 10)))] |
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VALIDATION_SCENARIO = [(2019, [10, 11, 12])] |
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TEST_SCENARIO = [(2020, [1, 2])] # Jan–Feb 2020 only |
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# === Optional display only === |
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predefined_training_scenarios = { |
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"Scenario 1": {"years_months": [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]}, |
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"Scenario 2": {"years_months": [(2017, list(range(1, 13))), (2018, list(range(1, 13))), (2019, list(range(1, 10)))]} |
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} |
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predefined_validation_scenarios = { |
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"Scenario A": {"years_months": [(2019, [10, 11, 12])]} |
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} |
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def create_dir(path): |
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""" |
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Creates a directory if it doesn't exist yet. |
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:param path: The path to the directory |
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""" |
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if not os.path.exists(path): |
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os.makedirs(path) |
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def remove_covid_data(df): |
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df = df[~(df[year_str]>=2020)] |
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return df |
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def split_data_by_month_percentage(df, percentages): |
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train_p, valid_p, test_p = percentages |
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ids = df[[year_str, month_str]].drop_duplicates().sort_values([year_str, month_str]) |
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tr, va, te = np.split(ids, [int((train_p/100) * len(ids)), int(((train_p + valid_p)/100) * len(ids))]) |
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return df.merge(tr, on=[year_str, month_str], how='inner'), df.merge(va, on=[year_str, month_str], how='inner'), df.merge(te, on=[year_str, month_str], how='inner') |
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def split_data_by_userdata_percentage(df, percentages, sample=100): |
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train_p, valid_p, test_p = percentages |
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tr, va, te = pd.DataFrame(), pd.DataFrame(), pd.DataFrame() |
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for user_id in df[user_str].unique(): |
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# !! following sample creates gaps in data if sample smaller 100 |
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user_data = df[df[user_str]==user_id].sample(frac=sample/ 100).sort_values([date_str]) # have to sort for time shift |
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u_tr, u_va, u_te = np.split(user_data, [int((train_p/100)*len(user_data)), int(((train_p+valid_p)/100)*len(user_data))]) |
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tr = pd.concat([tr, u_tr], ignore_index=True) |
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va = pd.concat([va, u_va], ignore_index=True) |
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te = pd.concat([te, u_te], ignore_index=True) |
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return tr, va, te |
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def main(): |
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# print("=== Training Scenario Setup ===") |
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# display_warning_about_2020_data() |
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# display_warnings_for_scenarios("training", predefined_training_scenarios, predefined_validation_scenarios) |
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# print("\n=== Validation Scenario Setup ===") |
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# display_warning_about_2020_data() |
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# display_warnings_for_scenarios("validation", predefined_training_scenarios, predefined_validation_scenarios) |
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# === Load and preprocess === |
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df = load_dataset(DATA_PATH) |
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = False |
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if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH): |
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = True |
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training_data = filter_data(df, TRAINING_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD) |
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validation_data = filter_data(df, VALIDATION_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD) |
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user_data_train = prepare_user_data(training_data) |
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user_data_val = prepare_user_data(validation_data) |
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# === Train models === |
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best_models = train_models(user_data_train, user_data_val, sequence_lengths=SEQUENCE_LENGTHS) |
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# === Load and evaluate test === |
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test_df = filter_test_data(df, TEST_SCENARIO) |
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evaluate_models(best_models, test_df, SEQUENCE_LENGTHS, OUTPUT_EXCEL_PATH, ALLUSERS32_15MIN_WITHOUTTHREHOLD) |
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print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}") |
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def reduce_columns(df, filename): |
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if min_timespan_str in filename: |
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'] + week_column_names, errors='ignore') |
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else: |
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'], errors='ignore') |
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def reduce_columns_v3(df): |
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return df.drop(columns=[month_str, year_str, date_str]) |
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def load_previous_results(filename): |
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results = pd.DataFrame() |
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if os.path.exists(filename): |
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results = pd.DataFrame(json.load(open(filename))) |
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return results |
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def main_two_v2(model_type): |
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seq_length = range(10,31, 5) |
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for sequence_length in seq_length: |
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for data_filename in os.listdir(dataset_path): |
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timespan_id = hour_timespan_str |
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threshold_id = with_threshold_str |
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if min_timespan_str in data_filename: |
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timespan_id = min_timespan_str |
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if without_threshold_str in data_filename: |
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threshold_id = without_threshold_str |
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results = load_previous_results(result_filename_v2) |
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if len(results) > 0: |
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if len(results[(results[timespan_str]==timespan_id) & |
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(results[threshold_str]==threshold_id) & |
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(results[sequence_length_str]==sequence_length) & |
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(results[model_type_str]==model_type)]) > 0: |
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continue |
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file_path = os.path.join(dataset_path, data_filename) |
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df = load_dataset(file_path) |
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df = remove_covid_data(df) |
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tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10)) |
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tr = reduce_columns(tr, data_filename) |
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val = reduce_columns(val, data_filename) |
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te = reduce_columns(te, data_filename) |
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user_data_train = prepare_user_data(tr) |
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user_data_val = prepare_user_data(val) |
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best_model = train_models_v2(user_data_train, user_data_val, |
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sequence_length=sequence_length, |
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model_type=model_type) |
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results = load_previous_results(result_filename_v2) |
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results = pd.concat([results, |
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evaluate_model_on_test_data(model=best_model, |
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test_df=te, |
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sequence_length=sequence_length, |
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time_span_id=timespan_id, |
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threshold_id=threshold_id, |
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model_type=model_type, |
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split_id=data_split_str)], |
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ignore_index=True) |
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results.to_json(result_filename_v2) |
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def main_two_v1(): |
||||
|
seq_length = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30] |
||||
|
results = pd.DataFrame() |
||||
|
if os.path.exists(result_filename_v1): |
||||
|
results = pd.DataFrame(json.load(open(result_filename_v1))) |
||||
|
for sequence_length in seq_length: |
||||
|
for data_filename in os.listdir(dataset_path): |
||||
|
for split_id, split_method in [(data_split_str, split_data_by_userdata_percentage),(month_split_str, split_data_by_month_percentage)]: |
||||
|
for model_type in [model_type_lstm, model_type_bilstm, model_type_gru]: |
||||
|
timespan_id = hour_timespan_str |
||||
|
threshold_id = with_threshold_str |
||||
|
if min_timespan_str in data_filename: |
||||
|
timespan_id = min_timespan_str |
||||
|
if without_threshold_str in data_filename: |
||||
|
threshold_id = without_threshold_str |
||||
|
if len(results) > 0: |
||||
|
if len(results[(results[split_str]==split_id) & |
||||
|
(results[timespan_str]==timespan_id) & |
||||
|
(results[threshold_str]==threshold_id) & |
||||
|
(results[sequence_length_str]==sequence_length) & |
||||
|
(results[model_type_str]==model_type)]) > 0: |
||||
|
continue |
||||
|
|
||||
|
file_path = os.path.join(dataset_path, data_filename) |
||||
|
df = load_dataset(file_path) |
||||
|
df = remove_covid_data(df) |
||||
|
tr,val,te = split_method(df, percentages=(80,10,10)) |
||||
|
tr = reduce_columns(tr, data_filename) |
||||
|
val = reduce_columns(val, data_filename) |
||||
|
te = reduce_columns(te, data_filename) |
||||
|
|
||||
|
user_data_train = prepare_user_data(tr) |
||||
|
user_data_val = prepare_user_data(val) |
||||
|
|
||||
|
best_models = train_models(user_data_train, user_data_val, sequence_lengths=[sequence_length], model_type=model_type) |
||||
|
|
||||
|
results = pd.concat([results, |
||||
|
evaluate_model_on_test_data(model=best_models[sequence_length]['model'], |
||||
|
test_df=te, split_id=split_id, |
||||
|
sequence_length=sequence_length, |
||||
|
time_span_id=timespan_id, |
||||
|
threshold_id=threshold_id, |
||||
|
model_type=model_type)], ignore_index=True) |
||||
|
results.to_json(result_filename_v1) |
||||
|
|
||||
|
# === Evaluation === |
||||
|
def evaluate_model_on_test_data(model, test_df,sequence_length, split_id, threshold_id, time_span_id, model_type): |
||||
|
user_data = prepare_user_data(test_df) |
||||
|
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length) |
||||
|
|
||||
|
y_pred = model.predict(x, verbose=0) |
||||
|
y_pred_classes = np.argmax(y_pred, axis=1) |
||||
|
|
||||
|
recall = sklearn.metrics.recall_score(y, y_pred_classes, average='weighted') |
||||
|
precision = sklearn.metrics.precision_score(y, y_pred_classes, average='weighted') |
||||
|
f1_score = sklearn.metrics.f1_score(y, y_pred_classes, average='weighted') |
||||
|
return pd.DataFrame({split_str:[split_id], threshold_str:[threshold_id], timespan_str:[time_span_id], |
||||
|
sequence_length_str:[sequence_length], |
||||
|
model_type_str:[model_type], recall_str:[recall], |
||||
|
precision_str:[precision], f1_string:[f1_score]}) |
||||
|
|
||||
|
def visualise_results_v1(): |
||||
|
results = pd.DataFrame(json.load(open(result_filename_v1))) |
||||
|
# Month split ist immer schlechter |
||||
|
results = results[results[split_str] == data_split_str] |
||||
|
with_threshold = results[results[threshold_str] == with_threshold_str] |
||||
|
without_threshold = results[results[threshold_str] == without_threshold_str] |
||||
|
fig, axes = plt.subplots(2, 3) |
||||
|
ax_col_id = 0 |
||||
|
ax_row_id = -1 |
||||
|
for timespan in [hour_timespan_str,min_timespan_str]: |
||||
|
ax_row_id +=1 |
||||
|
for model in [model_type_lstm, model_type_bilstm, model_type_gru]: |
||||
|
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)] |
||||
|
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)] |
||||
|
ax = axes[ax_row_id, ax_col_id] |
||||
|
ax.set_title(model+' '+timespan) |
||||
|
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str) |
||||
|
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str) |
||||
|
ax.legend() |
||||
|
ax_col_id +=1 |
||||
|
ax_col_id %= 3 |
||||
|
fig.tight_layout() |
||||
|
fig.savefig(figure_path+'v1_results.svg') |
||||
|
# Fazit: keine eindeutig besseren Versionen erkennbar |
||||
|
|
||||
|
|
||||
|
def visualise_results_v2(): |
||||
|
results = pd.DataFrame(json.load(open(result_filename_v2))) |
||||
|
with_threshold = results[results[threshold_str] == with_threshold_str] |
||||
|
without_threshold = results[results[threshold_str] == without_threshold_str] |
||||
|
fig, axes = plt.subplots(2, 3) |
||||
|
ax_col_id = 0 |
||||
|
ax_row_id = -1 |
||||
|
for timespan in [hour_timespan_str,min_timespan_str]: |
||||
|
ax_row_id +=1 |
||||
|
for model in [model_type_lstm, model_type_bilstm, model_type_gru]: |
||||
|
with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)] |
||||
|
without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)] |
||||
|
with_sub = with_sub.sort_values(sequence_length_str) |
||||
|
without_sub = without_sub.sort_values(sequence_length_str) |
||||
|
ax = axes[ax_row_id, ax_col_id] |
||||
|
ax.set_title(model+' '+timespan) |
||||
|
ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str) |
||||
|
ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str) |
||||
|
ax.legend() |
||||
|
ax_col_id +=1 |
||||
|
ax_col_id %= 3 |
||||
|
fig.tight_layout() |
||||
|
fig.savefig(figure_path+'v2_results.svg') |
||||
|
# Fazit: keine eindeutig besseren Versionen erkennbar |
||||
|
|
||||
|
|
||||
|
def test(model_type): |
||||
|
sequence_length = 20 |
||||
|
data_filename = os.listdir(dataset_path)[0] |
||||
|
timespan_id = hour_timespan_str |
||||
|
threshold_id = with_threshold_str |
||||
|
|
||||
|
file_path = os.path.join(dataset_path, data_filename) |
||||
|
df = load_dataset(file_path) |
||||
|
df = remove_covid_data(df) |
||||
|
results = pd.DataFrame() |
||||
|
|
||||
|
for percentage in [33,66,100]: |
||||
|
print('Percentage:', percentage) |
||||
|
tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10),sample=percentage) |
||||
|
tr = reduce_columns(tr, data_filename) |
||||
|
val = reduce_columns(val, data_filename) |
||||
|
te = reduce_columns(te, data_filename) |
||||
|
|
||||
|
user_data_train = prepare_user_data(tr) |
||||
|
user_data_val = prepare_user_data(val) |
||||
|
|
||||
|
best_model = train_models_v2(user_data_train, user_data_val, |
||||
|
sequence_length=sequence_length, |
||||
|
model_type=model_type) |
||||
|
|
||||
|
results = pd.concat([results, |
||||
|
evaluate_model_on_test_data(model=best_model, |
||||
|
test_df=te, |
||||
|
sequence_length=sequence_length, |
||||
|
time_span_id=timespan_id, |
||||
|
threshold_id=threshold_id, |
||||
|
model_type=model_type, |
||||
|
split_id=data_split_str)], |
||||
|
ignore_index=True) |
||||
|
print(results) |
||||
|
|
||||
|
def manual_tuning(model_type): |
||||
|
# load dataset |
||||
|
sequence_length = 20 |
||||
|
data_filename = 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx' |
||||
|
timespan_id = min_timespan_str |
||||
|
threshold_id = with_threshold_str |
||||
|
|
||||
|
file_path = os.path.join(dataset_path, data_filename) |
||||
|
df = load_dataset(file_path) |
||||
|
df = remove_covid_data(df) |
||||
|
|
||||
|
tr, val, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=100) |
||||
|
tr = reduce_columns(tr, data_filename) |
||||
|
val = reduce_columns(val, data_filename) |
||||
|
te = reduce_columns(te, data_filename) |
||||
|
|
||||
|
user_data_train = prepare_user_data(tr) |
||||
|
user_data_val = prepare_user_data(val) |
||||
|
|
||||
|
# fit and evaluate model |
||||
|
# config |
||||
|
repeats = 3 |
||||
|
n_batch = 1024 |
||||
|
n_epochs = 500 |
||||
|
n_neurons = 16 |
||||
|
l_rate = 1e-4 |
||||
|
reg = L1L2(l1=0.0, l2=0.0) |
||||
|
|
||||
|
history_list = list() |
||||
|
# run diagnostic tests |
||||
|
for i in range(repeats): |
||||
|
history = train_one_model(user_data_train, user_data_val, n_batch, n_epochs, |
||||
|
n_neurons, l_rate, reg, |
||||
|
sequence_length=sequence_length, |
||||
|
model_type=model_type) |
||||
|
history_list.append(history) |
||||
|
for metric in ['p', 'r', 'f1']: |
||||
|
for history in history_list: |
||||
|
plt.plot(history['train_'+metric], color='blue') |
||||
|
plt.plot(history['test_'+metric], color='orange') |
||||
|
plt.savefig(figure_path+metric+'_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+ |
||||
|
str(n_batch)+'_l'+str(l_rate)+'_diagnostic.png') |
||||
|
plt.clf() |
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
def upsampling(df): |
||||
|
max_user_data = df[user_str].value_counts().max() |
||||
|
for user in df[user_str].unique(): |
||||
|
user_data = df[df[user_str]==user] |
||||
|
user_count = user_data.shape[0] |
||||
|
times = max_user_data / user_count |
||||
|
before_comma = math.floor(times) |
||||
|
after_comma = times % 1 |
||||
|
after_comma_data = user_data.sample(frac=after_comma) |
||||
|
for i in range(1, before_comma): |
||||
|
df = pd.concat([df, user_data], ignore_index=True) |
||||
|
df = pd.concat([df, after_comma_data], ignore_index=True) |
||||
|
return df |
||||
|
|
||||
|
|
||||
|
def manual_tuning_v3(model_type): |
||||
|
# TODO: hrs/min |
||||
|
sequence_length = 1 |
||||
|
|
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path) |
||||
|
|
||||
|
# fit and evaluate model |
||||
|
# config |
||||
|
repeats = 3 |
||||
|
n_batch = 1024 |
||||
|
n_epochs = 10 |
||||
|
n_neurons = 256 |
||||
|
n_neurons2 = 512 |
||||
|
n_neurons3 = 512 |
||||
|
n_neurons4 = 128 |
||||
|
l_rate = 1e-2 |
||||
|
d1 = 256 |
||||
|
reg1 = L1L2(l1=0.0, l2=0.001) |
||||
|
r1 = '0001' |
||||
|
reg2 = L1L2(l1=0.0, l2=0.1) |
||||
|
r2 = '01' |
||||
|
|
||||
|
history_list = list() |
||||
|
# run diagnostic tests |
||||
|
for i in range(repeats): |
||||
|
history = train_one_model(tr, val, n_batch, n_epochs, |
||||
|
n_neurons,n_neurons2, n_neurons3, n_neurons4, l_rate, d1, r1, reg1, r2, reg2, |
||||
|
sequence_length=sequence_length, |
||||
|
model_type=model_type) |
||||
|
history_list.append(history) |
||||
|
for metric in ['acc', 'p', 'r', 'f1']: |
||||
|
for history in history_list: |
||||
|
plt.plot(history['train_'+metric], color='blue') |
||||
|
plt.plot(history['test_'+metric], color='orange') |
||||
|
plt.savefig(figure_path+'v3/'+metric+get_save_id(n_epochs, n_neurons, n_neurons2, n_neurons3,n_neurons4, n_batch, l_rate, d1, r1, r2) |
||||
|
+'.png') |
||||
|
plt.clf() |
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
|
||||
|
def calculate_baselines(): |
||||
|
file_combinations = [(hour_timespan_str, with_threshold_str,'ALL32USERS1HR_WITHTHRESHOLD.xlsx'), |
||||
|
(min_timespan_str, with_threshold_str, 'ALL32USERS15MIN_WITHTHRESHOLD.xlsx'), |
||||
|
(min_timespan_str, without_threshold_str, 'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'), |
||||
|
(hour_timespan_str, without_threshold_str, 'ALLUSERS_32_1HR_WITHOUT_THRESHOLD.xlsx'), |
||||
|
] |
||||
|
baseline_res = pd.DataFrame() |
||||
|
for timespan_id, threshold_id, filename in file_combinations: |
||||
|
file_path = os.path.join(dataset_path, filename) |
||||
|
df = load_dataset(file_path) |
||||
|
df = remove_covid_data(df) |
||||
|
|
||||
|
_, _, te = split_data_by_userdata_percentage(df, percentages=(80, 10, 10), sample=20) |
||||
|
te = reduce_columns(te, filename) |
||||
|
user_data_te = prepare_user_data(te) |
||||
|
for sequence_length in range(5,30, 5): |
||||
|
x, y = prepare_data_for_model(user_data=user_data_te, sequence_length=sequence_length) |
||||
|
|
||||
|
for strategy in ['most_frequent', 'stratified', 'uniform']: |
||||
|
cls = DummyClassifier(strategy=strategy) |
||||
|
cls.fit(x,y) |
||||
|
y_pred = cls.predict(x) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred) |
||||
|
baseline_res = pd.concat([baseline_res, |
||||
|
DataFrame({ 'strategy':[strategy], threshold_str:[threshold_id], |
||||
|
timespan_str:[timespan_id], sequence_length_str:[sequence_length], |
||||
|
accuracy_str:[acc],precision_str:[p],recall_str:[r], |
||||
|
f1_string:f1})], ignore_index=True) |
||||
|
baseline_res.to_json('baseline_results.json') |
||||
|
print('Done') |
||||
|
|
||||
|
def get_prepared_data_v3(filename, sample=100, print_unique=False): |
||||
|
df = pd.read_json(filename) |
||||
|
df = remove_covid_data(df) |
||||
|
|
||||
|
# remove users which are a complete subset of another user (but keep one) |
||||
|
users_to_remove = [] |
||||
|
for user_a in df[user_str].unique(): |
||||
|
for user_b in df[user_str].unique(): |
||||
|
if user_a != user_b: |
||||
|
data = pd.concat([df[df[user_str]==user_a], df[df[user_str]==user_b]]) |
||||
|
columns = data.columns.tolist() |
||||
|
columns.remove(user_str) |
||||
|
|
||||
|
no_dup = data.drop_duplicates(columns, keep=False) |
||||
|
if len(no_dup[no_dup[user_str]==user_a]) == 0: |
||||
|
if print_unique: |
||||
|
print(user_a, 'is subset of',user_b) |
||||
|
if user_b not in users_to_remove: |
||||
|
users_to_remove.append(user_a) |
||||
|
df = df[~df[user_str].isin(users_to_remove)] |
||||
|
|
||||
|
# bin steps per hour TODO: adjust for minutes |
||||
|
for hour in ['Hour_'+str(i) for i in range(24)]: |
||||
|
hour_data = df[hour] |
||||
|
# smaller 1000 - round to 10 |
||||
|
a = ((hour_data[hour_data<1000]/10).round()*10) |
||||
|
# between 1000 and 10000 - round to next 100 |
||||
|
b = ((hour_data[(hour_data>=1000)& (hour_data<10000)]/100).round()*100) |
||||
|
# higher or equal 10000 - one class |
||||
|
c = hour_data[hour_data > 10000] |
||||
|
c = pd.Series(data={ind:10000 for ind in c.index}, index=c.index) |
||||
|
new = pd.concat([a, b, c]).sort_index().astype(int) |
||||
|
df[hour] = new |
||||
|
|
||||
|
# remove users with too little data (optional) |
||||
|
#value_counts = df[user_str].value_counts() |
||||
|
#df = df[df[user_str].isin(value_counts[value_counts>200].index)] |
||||
|
min_datapoints = 500 # 500 leads to at least 75 datapoints in the valid set |
||||
|
users_to_remove = set() |
||||
|
cols = df.columns.tolist() |
||||
|
cols.remove(user_str) |
||||
|
reduced = df.drop_duplicates(subset=cols, keep=False) |
||||
|
for user_id in df[user_str].unique(): |
||||
|
subset = df[df[user_str] == user_id] |
||||
|
reduced_subset = reduced[reduced[user_str] == user_id] |
||||
|
if print_unique: |
||||
|
print(user_id, len(subset), len(reduced_subset)) |
||||
|
if len(reduced_subset) < min_datapoints: |
||||
|
users_to_remove.add(user_id) |
||||
|
if print_unique: |
||||
|
print('removing', user_id) |
||||
|
|
||||
|
df = df[~df[user_str].isin(users_to_remove)] |
||||
|
|
||||
|
tr, val, te = split_data_by_userdata_percentage(df, percentages=(70, 15, 15), sample=sample) |
||||
|
tr = reduce_columns_v3(tr) |
||||
|
val = reduce_columns_v3(val) |
||||
|
te = reduce_columns_v3(te) |
||||
|
|
||||
|
|
||||
|
|
||||
|
print('Train: Users', len(tr[user_str].unique()), 'mean num datapoins:', tr[user_str].value_counts().mean()) |
||||
|
print('Valid: Users', len(val[user_str].unique()), 'mean num datapoins:', val[user_str].value_counts().mean()) |
||||
|
print('Test: Users', len(te[user_str].unique()), 'mean num datapoins:', te[user_str].value_counts().mean()) |
||||
|
|
||||
|
tr, val, te = add_features(tr), add_features(val), add_features(te) |
||||
|
|
||||
|
scaler = MinMaxScaler() |
||||
|
scaler.fit(tr.drop(columns=[user_str])) |
||||
|
|
||||
|
return scale_dataset(scaler, tr), scale_dataset(scaler, val), scale_dataset(scaler, te) |
||||
|
|
||||
|
|
||||
|
def scale_dataset(scaler, df): |
||||
|
y = df[user_str] |
||||
|
x_scaled = scaler.transform(df.drop(columns=[user_str])) |
||||
|
x_scaled = pd.DataFrame(x_scaled) |
||||
|
x_scaled.columns = df.drop(columns=[user_str]).columns |
||||
|
|
||||
|
df_scaled = pd.concat([x_scaled, pd.DataFrame(y.reset_index()[user_str])], axis=1) |
||||
|
# df_scaled.columns = df.columns |
||||
|
return prepare_user_data(df_scaled) |
||||
|
|
||||
|
|
||||
|
def calculate_baselines_v3(): |
||||
|
file_combinations = [(hour_timespan_str, dataset_hrs_path), |
||||
|
# (min_timespan_str, dataset_min_path), # TODO: dataset bining not ready for minutes |
||||
|
] |
||||
|
baseline_res = pd.DataFrame() |
||||
|
for timespan_id, filename in file_combinations: |
||||
|
_, _, te = get_prepared_data_v3(filename) |
||||
|
for sequence_length in range(1,30,5): |
||||
|
x, y = prepare_data_for_model(user_data=te, sequence_length=sequence_length) |
||||
|
|
||||
|
for strategy in ['most_frequent', 'stratified', 'uniform']: |
||||
|
cls = DummyClassifier(strategy=strategy) |
||||
|
cls.fit(x,y) |
||||
|
y_pred = cls.predict(x) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y, y_pred=y_pred) |
||||
|
baseline_res = pd.concat([baseline_res, |
||||
|
DataFrame({ 'strategy':[strategy], |
||||
|
timespan_str:[timespan_id], sequence_length_str:[sequence_length], |
||||
|
accuracy_str:[acc],precision_str:[p],recall_str:[r], |
||||
|
f1_string:f1})], ignore_index=True) |
||||
|
baseline_res.to_json('baseline_results_v3.json') |
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
def hypertune_basic_algorithms(): |
||||
|
# TODO: hrs/min |
||||
|
# iterate over sequence lengths |
||||
|
sequence_length = 7 |
||||
|
|
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path) |
||||
|
|
||||
|
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length) |
||||
|
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length) |
||||
|
|
||||
|
random_state = 17 |
||||
|
results = pd.DataFrame() |
||||
|
for tag, clf, grid in [ |
||||
|
('GradientBoosting', GradientBoostingClassifier(random_state=random_state), |
||||
|
{'loss': ['log_loss', 'exponential'], |
||||
|
'learning_rate': [0.1, 0.5, 1.0,2.0, 5.0], |
||||
|
'n_estimators': [10, 50, 100, 150, 200], |
||||
|
'subsample': [0.1, 0.5, 1.0], |
||||
|
'criterion': ['friedman_mse', 'squared_error'], |
||||
|
'min_samples_split': [2, 10, 100], |
||||
|
'min_samples_leaf': [1, 5, 10], |
||||
|
'min_weight_fraction_leaf': [0.0, 0.1, 0.5], |
||||
|
'max_depth': [None, 2, 10, 100], |
||||
|
'min_impurity_decrease': [0.0, 0.1, 0.5], |
||||
|
'max_features': ['sqrt', 'log2', None, 10, 20], |
||||
|
'max_leaf_nodes': [None, 1, 5, 10], |
||||
|
}), |
||||
|
('Bernoulli', BernoulliNB(), {'fit_prior': [True, False], |
||||
|
'binarize': [0.0, 0.1, 0.25, 0.5, 0.75], |
||||
|
'force_alpha': [True, False], |
||||
|
'alpha':[0.0, 0.25, 0.5, 0.75, 1.0]}), |
||||
|
('extra trees', ExtraTreesClassifier(random_state=random_state, n_jobs=1), |
||||
|
{'n_estimators': [10, 50, 100, 150, 200], |
||||
|
'criterion': ['gini', 'entropy', 'log_loss'], |
||||
|
'max_depth': [None, 2, 10, 100], |
||||
|
'min_samples_split': [2, 10, 100], |
||||
|
'min_samples_leaf': [1, 5, 10], |
||||
|
'min_weight_fraction_leaf': [0.0, 0.1, 0.5], |
||||
|
'max_features': ['sqrt', 'log2', None, 10, 20], |
||||
|
'max_leaf_nodes': [None, 1, 5, 10], |
||||
|
'min_impurity_decrease': [0.0, 0.1, 0.5], |
||||
|
'bootstrap': [True, False], |
||||
|
'class_weight': [None, 'balanced', 'balanced_subsample'], |
||||
|
'max_samples': [None, 0.1, 0.2, 0.3]} |
||||
|
), |
||||
|
('random forest', RandomForestClassifier(random_state=random_state, n_jobs=1), |
||||
|
{'n_estimators':[10, 50, 100, 150, 200], |
||||
|
'criterion':['gini', 'entropy', 'log_loss'], |
||||
|
'max_depth':[None, 2, 10,100], |
||||
|
'min_samples_split': [2,10,100], |
||||
|
'min_samples_leaf':[1,5,10], |
||||
|
'min_weight_fraction_leaf':[0.0,0.1, 0.5], |
||||
|
'max_features':['sqrt', 'log2', None, 10, 20], |
||||
|
'max_leaf_nodes':[None, 1, 5, 10], |
||||
|
'min_impurity_decrease':[0.0, 0.1, 0.5], |
||||
|
'bootstrap':[True, False], |
||||
|
'class_weight':[None, 'balanced', 'balanced_subsample'], |
||||
|
'max_samples':[None,0.1, 0.2, 0.3]}) |
||||
|
]: |
||||
|
grid_search = GridSearchCV( |
||||
|
estimator=clf, param_grid=grid, scoring='f1_weighted', cv=5, n_jobs=1) |
||||
|
grid_search.fit(x_tr, y_tr) |
||||
|
|
||||
|
best_model = grid_search.best_estimator_ |
||||
|
y_pred = best_model.predict(x_val) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred) |
||||
|
results = pd.concat([results, DataFrame({ 'params': str(grid_search.best_params_), |
||||
|
'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True) |
||||
|
results.to_json('basic_ht_results.json') |
||||
|
print('Done') |
||||
|
|
||||
|
def test_basic_algorithms(): |
||||
|
# TODO: hrs/min |
||||
|
# TODO: iterate over sequence lengths |
||||
|
sequence_length = 21 |
||||
|
|
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path) |
||||
|
|
||||
|
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length) |
||||
|
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length) |
||||
|
|
||||
|
random_state = 17 |
||||
|
results = pd.DataFrame() |
||||
|
for tag, clf in [ |
||||
|
# ('Label Propagation', LabelPropagation()), |
||||
|
# ('Label Spreading', LabelSpreading()), |
||||
|
# ('VBGMM', GaussianMixture(random_state=random_state)), |
||||
|
# ('linear discrimenant analysis', LinearDiscriminantAnalysis()), |
||||
|
# ('discriminent analysis', QuadraticDiscriminantAnalysis()), |
||||
|
# ('oneclassSVM', OneClassSVM()), |
||||
|
# ('mlp', MLPClassifier(random_state=random_state)), |
||||
|
# ('Perceptron', Perceptron(random_state=random_state)), |
||||
|
# ('SVC', SVC(random_state=random_state)), |
||||
|
#('logisticRegression', LogisticRegression(random_state=random_state)), |
||||
|
#('logisticRegressionCV', LogisticRegressionCV(random_state=random_state)), |
||||
|
#('multinomialNB', MultinomialNB()), |
||||
|
#('nearestCentroid', NearestCentroid()), |
||||
|
#('linearSVC', LinearSVC(random_state=random_state)), |
||||
|
#('ada boost', AdaBoostClassifier(random_state=random_state)), |
||||
|
#('GradientBoosting', GradientBoostingClassifier(random_state=random_state)), |
||||
|
#('Bernoulli', BernoulliNB()), |
||||
|
#('claibrated', CalibratedClassifierCV()), |
||||
|
#('naive Bayes', GaussianNB()), |
||||
|
#('sgd', SGDClassifier(random_state=random_state)), |
||||
|
#('ridgeCV', RidgeClassifierCV()), |
||||
|
# ('ridge', RidgeClassifier(random_state=random_state)), |
||||
|
# ('passiveAggressive', PassiveAggressiveClassifier(random_state=random_state)), |
||||
|
# ('knn', KNeighborsClassifier()), |
||||
|
# ('bagging', BaggingClassifier(random_state=random_state)), |
||||
|
# ('decision tree', DecisionTreeClassifier(random_state=random_state)), |
||||
|
# ('extra tree', ExtraTreeClassifier(random_state=random_state)), |
||||
|
# ('extra trees', ExtraTreesClassifier(random_state=random_state)), |
||||
|
('random forest', RandomForestClassifier(random_state=random_state)) |
||||
|
]: |
||||
|
clf.fit(x_tr, y_tr) |
||||
|
y_pred = clf.predict(x_val) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred) |
||||
|
results = pd.concat([results, DataFrame({ 'tag':tag,accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True) |
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
def add_features(df): |
||||
|
# indicator weekend |
||||
|
df['weekend'] = df[day_of_week_str + '_Saturday']+df[day_of_week_str + '_Sunday'] |
||||
|
# sum of steps per day |
||||
|
df['day_total'] = sum([df['Hour_'+str(i)] for i in range(23)]) |
||||
|
# sum of steps morning, afternoon, evening, night |
||||
|
df['morning_total'] = sum([df['Hour_' + str(i)] for i in range(6,13)]) |
||||
|
df['afternoon_total'] = sum([df['Hour_' + str(i)] for i in range(13,19)]) |
||||
|
df['evening_total'] = sum([df['Hour_' + str(i)] for i in range(19,23)]) |
||||
|
df['night_total'] = sum([df['Hour_' + str(i)] for i in [23,0,1,2,3,4,5]]) |
||||
|
return df |
||||
|
|
||||
|
|
||||
|
def feature_engineering(): |
||||
|
sequence_length = 1 |
||||
|
|
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path, print_unique=True) |
||||
|
|
||||
|
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length) |
||||
|
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length) |
||||
|
|
||||
|
random_state = 17 |
||||
|
clf=RandomForestClassifier(random_state=random_state) |
||||
|
clf.fit(x_tr, y_tr) |
||||
|
y_pred = clf.predict(x_val) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=y_pred) |
||||
|
cf = confusion_matrix(y_pred=y_pred, y_true=y_val) |
||||
|
# TODO: welche funktionieren schlecht? warum? |
||||
|
# TODO: auf minutes umändern |
||||
|
|
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
def test_sequence_length_on_approach(clf = RandomForestClassifier(random_state=17)): |
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path) |
||||
|
|
||||
|
results_train = pd.DataFrame() |
||||
|
results_valid = pd.DataFrame() |
||||
|
for sequence_length in range(1, 60, 5): |
||||
|
x_tr, y_tr = prepare_data_for_basic_algorithm(user_data=tr, sequence_length=sequence_length) |
||||
|
x_val, y_val = prepare_data_for_basic_algorithm(user_data=val, sequence_length=sequence_length) |
||||
|
clf.fit(x_tr, y_tr) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y_val, y_pred=clf.predict(x_val)) |
||||
|
results_valid = pd.concat([results_valid, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True) |
||||
|
acc, p, r, f1 = eval_metrics(y_true=y_tr, y_pred=clf.predict(x_tr)) |
||||
|
results_train = pd.concat([results_train, DataFrame({sequence_length_str:[sequence_length], accuracy_str:[acc],precision_str:[p],recall_str:[r],f1_string:f1})], ignore_index=True) |
||||
|
|
||||
|
fig = plt.figure() |
||||
|
|
||||
|
for frame in [results_train, results_valid]: |
||||
|
plt.plot(frame[sequence_length_str], frame[f1_string]) |
||||
|
|
||||
|
plt.show() |
||||
|
print('') |
||||
|
|
||||
|
def manual_tuning_v4(model_type): |
||||
|
# TODO: hrs/min |
||||
|
tr, val, te = get_prepared_data_v3(dataset_hrs_path) |
||||
|
n_epochs= 20 |
||||
|
n_neurons = 1024 |
||||
|
results_train = pd.DataFrame() |
||||
|
results_valid = pd.DataFrame() |
||||
|
for sequence_length in range(1, 50, 5): |
||||
|
train_data = prepare_data_for_model(user_data=tr, sequence_length=sequence_length) |
||||
|
val_data = prepare_data_for_model(user_data=val, sequence_length=sequence_length) |
||||
|
|
||||
|
# fit and evaluate model |
||||
|
history_list = list() |
||||
|
repeats = 3 |
||||
|
# run diagnostic tests |
||||
|
for i in range(repeats): |
||||
|
history = train_one_model_v2(train_data, val_data, 1024, n_epochs, |
||||
|
n_neurons, sequence_length=sequence_length, |
||||
|
model_type=model_type) |
||||
|
history_list.append(history) |
||||
|
results = pd.concat([history.tail(1) for history in history_list]).mean() |
||||
|
results_train = pd.concat([results_train, |
||||
|
DataFrame({sequence_length_str:[sequence_length], |
||||
|
accuracy_str:[results['train_acc']], |
||||
|
precision_str:[results['train_p']], |
||||
|
recall_str:[results['train_r']], |
||||
|
f1_string:[results['train_f1']]})], ignore_index=True) |
||||
|
results_valid = pd.concat([results_valid, |
||||
|
DataFrame({sequence_length_str:[sequence_length], |
||||
|
accuracy_str:[results['test_acc']], |
||||
|
precision_str:[results['test_p']], |
||||
|
recall_str:[results['test_r']], |
||||
|
f1_string:[results['test_f1']]})], ignore_index=True) |
||||
|
|
||||
|
fig = plt.figure() |
||||
|
for frame in [results_train, results_valid]: |
||||
|
plt.plot(frame[sequence_length_str], frame[f1_string]) |
||||
|
|
||||
|
plt.show() |
||||
|
print('Done') |
||||
|
|
||||
|
|
||||
|
if __name__ == "__main__": |
||||
|
# Ordner erstellen, die benötigt werden |
||||
|
create_dir('results/') |
||||
|
create_dir(figure_path) |
||||
|
pd.options.mode.copy_on_write = True |
||||
|
|
||||
|
main_two_v1() |
||||
|
visualise_results_v1() |
||||
|
#test(model_type=model_type_gru) |
||||
|
# main_two_v2(model_type=model_type_gru) |
||||
|
#visualise_results_v2() |
||||
|
#manual_tuning(model_type=model_type_lstm) |
||||
|
#calculate_baselines() |
||||
|
|
||||
|
#### Ab hier aktuell (21.01.2026) |
||||
|
#calculate_baselines() |
||||
|
# manual_tuning_v3(model_type=model_type_lstm) |
||||
|
#test_basic_algorithms() |
||||
|
# test_basic_algorithm_on_sequence_lengths() |
||||
|
manual_tuning_v4(model_type=model_type_lstm) |
||||
|
#feature_engineering() |
||||
|
#hypertune_basic_algorithms() |
||||
|
print('Done') |
||||
@ -0,0 +1,466 @@ |
|||||
|
import random |
||||
|
|
||||
|
import keras_tuner |
||||
|
import numpy as np |
||||
|
import pandas as pd |
||||
|
import shutil |
||||
|
|
||||
|
from keras import Input |
||||
|
from keras.src.losses import SparseCategoricalCrossentropy |
||||
|
from keras.src.metrics import F1Score, Precision, Recall, Accuracy, SparseCategoricalAccuracy |
||||
|
from pandas import ExcelWriter, DataFrame |
||||
|
from tensorflow.keras.models import Sequential |
||||
|
from tensorflow.keras.layers import LSTM, Dense, Dropout, Bidirectional,GRU |
||||
|
from tensorflow.keras.optimizers import Adam |
||||
|
from tensorflow.keras.callbacks import ReduceLROnPlateau, EarlyStopping |
||||
|
from keras_tuner import RandomSearch |
||||
|
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, confusion_matrix |
||||
|
|
||||
|
epochs = 5#50 |
||||
|
model_type_gru = 'GRU' |
||||
|
model_type_lstm = 'LSTM' |
||||
|
model_type_bilstm = 'BiLSTM' |
||||
|
|
||||
|
|
||||
|
# === Data functions === |
||||
|
def load_dataset(file_path): |
||||
|
return pd.read_excel(file_path) |
||||
|
|
||||
|
def filter_data(df, scenario, ALLUSERS32_15MIN_WITHOUTREHOLD): |
||||
|
filtered = pd.DataFrame() |
||||
|
for year, months in scenario: |
||||
|
filtered = pd.concat([filtered, df[(df['Year'] == year) & (df['Month'].isin(months))]]) |
||||
|
|
||||
|
if ALLUSERS32_15MIN_WITHOUTREHOLD: |
||||
|
return filtered.drop(columns=['Month', 'Year', 'date', 'DayOfWeek']) |
||||
|
else: |
||||
|
return filtered.drop(columns=['Month', 'Year', 'date']) |
||||
|
|
||||
|
def filter_test_data(df, scenario): |
||||
|
data_parts = [] |
||||
|
for year, months in scenario: |
||||
|
part = df[(df['Year'] == year) & (df['Month'].isin(months))] |
||||
|
data_parts.append(part) |
||||
|
return pd.concat(data_parts, ignore_index=True) |
||||
|
|
||||
|
def prepare_user_data(df): |
||||
|
#df_sorted = df.sort_values(by='user').reset_index(drop=True) |
||||
|
users = df['user'].unique() |
||||
|
return {user: df[df['user'] == user] for user in users} |
||||
|
|
||||
|
def make_sequences(data, sequence_length): |
||||
|
x, y = [], [] |
||||
|
features = data.drop('user', axis=1).values |
||||
|
labels = data['user'].values |
||||
|
for i in range(len(features) - sequence_length+1): # with overlap on days |
||||
|
# for i in range(0, len(features) - sequence_length + 1, sequence_length): # without overlap on days |
||||
|
x.append(features[i:i + sequence_length]) |
||||
|
y.append(labels[i + sequence_length-1]) |
||||
|
return x, y |
||||
|
|
||||
|
def prepare_data_for_basic_algorithm(user_data, sequence_length): |
||||
|
combined = pd.DataFrame() |
||||
|
for user, data in user_data.items(): |
||||
|
x_new, y_new = make_sequences(data, sequence_length) |
||||
|
if len(x_new)>0: |
||||
|
var = [[pd.DataFrame(a[s]) for s in range(sequence_length)] for a in x_new] |
||||
|
df_var = pd.concat([pd.concat(seq_list).T for seq_list in var]) |
||||
|
df_var['user'] = user |
||||
|
combined = pd.concat([combined, df_var], ignore_index=True) |
||||
|
return combined.drop(columns=['user']), combined['user'] |
||||
|
|
||||
|
def prepare_data_for_model(user_data, sequence_length, print_counts=False): |
||||
|
x, y = [], [] |
||||
|
combined = pd.DataFrame() |
||||
|
for user, data in user_data.items(): |
||||
|
x_new, y_new = make_sequences(data, sequence_length) |
||||
|
x = x + x_new |
||||
|
y = y + y_new |
||||
|
if print_counts and len(x_new)>0: |
||||
|
var = [[pd.DataFrame(a[s])for s in range(sequence_length)] for a in x_new ] |
||||
|
df_var = pd.concat([pd.concat(seq_list).T for seq_list in var]) |
||||
|
df_var['user'] = user |
||||
|
combined = pd.concat([combined, df_var], ignore_index=True) |
||||
|
if print_counts: |
||||
|
combined_ohne = combined.drop('user', axis=1) |
||||
|
print('Alle', len(combined)) |
||||
|
print('Unique mit user', len(combined.drop_duplicates())) |
||||
|
print('Unique ohne user', len(combined_ohne.drop_duplicates())) |
||||
|
print('Unique') |
||||
|
print(combined.drop_duplicates()['user'].value_counts()) |
||||
|
print('Alle') |
||||
|
print(combined['user'].value_counts()) |
||||
|
random.Random(17).shuffle(x) |
||||
|
random.Random(17).shuffle(y) |
||||
|
x = np.array(x) |
||||
|
y = np.array(y) |
||||
|
return x,y |
||||
|
|
||||
|
# === Training & Validation === |
||||
|
def train_models(user_data, user_data_val, sequence_lengths, tuner_dir="./working/tuner", model_type=model_type_lstm): |
||||
|
best_models = {} |
||||
|
early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True) |
||||
|
lr_scheduler = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=5, verbose=1) |
||||
|
users = list(user_data.keys()) |
||||
|
|
||||
|
shutil.rmtree(tuner_dir, ignore_errors=True) |
||||
|
|
||||
|
for sequence_length in sequence_lengths: |
||||
|
print(f"\n=== Training for Sequence Length: {sequence_length} ===") |
||||
|
X, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length) |
||||
|
X_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length) |
||||
|
|
||||
|
if X.shape[0] == 0 or X_val.shape[0] == 0: |
||||
|
print(f"⚠️ Skipped sequence length {sequence_length} due to insufficient data.") |
||||
|
continue |
||||
|
|
||||
|
n_features = X.shape[2] |
||||
|
|
||||
|
def build_model(hp): |
||||
|
model = Sequential() |
||||
|
if model_type==model_type_bilstm: |
||||
|
model.add(Bidirectional(LSTM(units=hp.Int('units', 32, 256, step=2), |
||||
|
input_shape=(sequence_length, n_features)))) |
||||
|
if model_type==model_type_lstm: |
||||
|
model.add(LSTM(units=hp.Int('units', 32, 256, step=2), |
||||
|
input_shape=(sequence_length, n_features))) |
||||
|
if model_type==model_type_gru: |
||||
|
model.add(GRU(units=hp.Int('units', 32, 256, step=2), |
||||
|
input_shape=(sequence_length, n_features))) |
||||
|
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.5, step=0.1))) |
||||
|
model.add(Dense(len(users), activation='softmax')) |
||||
|
model.compile( |
||||
|
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-2, 1e-3, 1e-4])), |
||||
|
loss='sparse_categorical_crossentropy', |
||||
|
metrics=['accuracy'] |
||||
|
) |
||||
|
return model |
||||
|
|
||||
|
tuner = RandomSearch( |
||||
|
build_model, |
||||
|
objective='val_loss', |
||||
|
max_trials=30, |
||||
|
executions_per_trial=2, |
||||
|
directory=tuner_dir, |
||||
|
project_name=f'lstm_seq_{sequence_length}' |
||||
|
) |
||||
|
|
||||
|
tuner.search(X, y, epochs=epochs, validation_data=(X_val, y_val), |
||||
|
callbacks=[early_stopping, lr_scheduler], verbose=0) |
||||
|
|
||||
|
best_hps = tuner.get_best_hyperparameters(1)[0] |
||||
|
best_model = tuner.hypermodel.build(best_hps) |
||||
|
best_model.fit(X, y, epochs=epochs, validation_data=(X_val, y_val), |
||||
|
callbacks=[early_stopping, lr_scheduler], verbose=0) |
||||
|
|
||||
|
best_models[sequence_length] = { |
||||
|
'model': best_model, |
||||
|
'best_hyperparameters': { |
||||
|
'units': best_hps.get('units'), |
||||
|
'dropout_rate': best_hps.get('dropout_rate'), |
||||
|
'learning_rate': best_hps.get('learning_rate') |
||||
|
} |
||||
|
} |
||||
|
|
||||
|
return best_models |
||||
|
|
||||
|
# === Training & Validation === |
||||
|
def train_models_v2(user_data, user_data_val, sequence_length, model_type): |
||||
|
tuner_dir = "./working/tuner/"+model_type |
||||
|
#val_metric = 'val_f1' |
||||
|
val_metric = 'val_precision' |
||||
|
|
||||
|
early_stopping = EarlyStopping(monitor=val_metric, patience=3, restore_best_weights=True) |
||||
|
lr_scheduler = ReduceLROnPlateau(monitor=val_metric, factor=0.5, patience=2) |
||||
|
|
||||
|
shutil.rmtree(tuner_dir, ignore_errors=True) |
||||
|
|
||||
|
x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length) |
||||
|
x_val, y_val = prepare_data_for_model(user_data=user_data_val, sequence_length=sequence_length) |
||||
|
|
||||
|
n_features = x.shape[2] |
||||
|
users = list(user_data.keys()) |
||||
|
|
||||
|
#y_val = np.array(y_val).reshape(-1, 1) |
||||
|
#y = np.array(y).reshape(-1, 1) |
||||
|
|
||||
|
def build_model(hp): |
||||
|
units_hp = hp.Int('units', 2, 8, step=2, sampling="log") |
||||
|
# units_hp = hp.Int('units', 2, 256, step=2, sampling="log") |
||||
|
|
||||
|
model = Sequential() |
||||
|
model.add(Input((sequence_length, n_features))) |
||||
|
if model_type==model_type_bilstm: |
||||
|
model.add(Bidirectional(LSTM(units=units_hp))) |
||||
|
if model_type==model_type_lstm: |
||||
|
model.add(LSTM(units=units_hp)) |
||||
|
if model_type==model_type_gru: |
||||
|
model.add(GRU(units=units_hp)) |
||||
|
model.add(Dropout(hp.Float('dropout_rate', 0.1, 0.2, step=0.1))) |
||||
|
model.add(Dense(len(users), activation='softmax')) |
||||
|
model.compile( |
||||
|
optimizer=Adam(learning_rate=hp.Choice('learning_rate', [1e-5])), |
||||
|
loss='sparse_categorical_crossentropy', |
||||
|
metrics=[#F1Score(name='f1', average='weighted'), |
||||
|
Precision(), #Recall(), Accuracy() |
||||
|
] |
||||
|
) |
||||
|
return model |
||||
|
|
||||
|
tuner = RandomSearch( |
||||
|
build_model, |
||||
|
objective=keras_tuner.Objective(val_metric, direction="max"), |
||||
|
max_trials=120, |
||||
|
directory=tuner_dir, |
||||
|
) |
||||
|
|
||||
|
tuner.search(x, y, epochs=epochs, validation_data=(x_val, y_val), |
||||
|
callbacks=[early_stopping, lr_scheduler]) |
||||
|
return tuner.get_best_models(num_models=1)[0] |
||||
|
|
||||
|
|
||||
|
def train_one_model(train_data, val_data, n_batch, n_epochs, n_neurons,n_neurons2,n_neurons3,n_neurons4, l_rate, d1, r1, reg1, r2, reg2, sequence_length, model_type): |
||||
|
x, y = prepare_data_for_model(user_data=train_data, sequence_length=sequence_length) |
||||
|
n_features = x.shape[2] |
||||
|
users = list(train_data.keys()) |
||||
|
|
||||
|
# prepare model |
||||
|
def build_model(): |
||||
|
model = Sequential() |
||||
|
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch)) |
||||
|
if model_type == model_type_bilstm: |
||||
|
model.add(Bidirectional(LSTM(n_neurons))) |
||||
|
if model_type == model_type_lstm: |
||||
|
# model.add(LSTM(n_neurons, kernel_regularizer=reg1, return_sequences=True)) |
||||
|
model.add(LSTM(n_neurons)) |
||||
|
# model.add(LSTM(n_neurons2)) |
||||
|
if model_type == model_type_gru: |
||||
|
model.add(GRU(n_neurons)) |
||||
|
#model.add(Dense(n_neurons, activation='relu')) |
||||
|
#model.add(Dropout(d1)) |
||||
|
model.add(Dense(len(users), activation='softmax')) |
||||
|
model.compile( |
||||
|
optimizer=Adam(learning_rate=l_rate), |
||||
|
loss=SparseCategoricalCrossentropy(), |
||||
|
metrics=[SparseCategoricalAccuracy()], |
||||
|
) |
||||
|
return model |
||||
|
|
||||
|
model = build_model() |
||||
|
|
||||
|
# fit model |
||||
|
train_acc, test_acc, train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list(),list(), list() |
||||
|
for i in range(n_epochs): |
||||
|
model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False) |
||||
|
# evaluate model on train data |
||||
|
acc, p, r, f1 = evaluate(model, train_data, sequence_length, n_batch) |
||||
|
train_acc.append(acc) |
||||
|
train_p.append(p) |
||||
|
train_r.append(r) |
||||
|
train_f1.append(f1) |
||||
|
# evaluate model on test data |
||||
|
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3, n_neurons4, n_batch, l_rate,d1,r1, r2)+'.json' |
||||
|
acc, p, r, f1 = evaluate(model, val_data, sequence_length, n_batch, save_name=savename) |
||||
|
test_acc.append(acc) |
||||
|
test_p.append(p) |
||||
|
test_r.append(r) |
||||
|
test_f1.append(f1) |
||||
|
|
||||
|
history = DataFrame() |
||||
|
history['train_acc'], history['test_acc'] = train_acc, test_acc |
||||
|
history['train_p'], history['test_p'] = train_p, test_p |
||||
|
history['train_r'], history['test_r'] = train_r, test_r |
||||
|
history['train_f1'], history['test_f1'] = train_f1, test_f1 |
||||
|
return history |
||||
|
|
||||
|
|
||||
|
def train_one_model_v2(train_data, val_data, n_batch, n_epochs, n_neurons, sequence_length, model_type): |
||||
|
x, y = train_data |
||||
|
x_v, y_v = val_data |
||||
|
users = list(set(y)) |
||||
|
|
||||
|
# renumber users |
||||
|
user_map = {users[i]:i for i in range(len(users))} |
||||
|
y = np.array([user_map[x] for x in y]) |
||||
|
y_v = np.array([user_map[x] for x in y_v]) |
||||
|
n_features = x.shape[2] |
||||
|
user_num = len(users) |
||||
|
|
||||
|
# prepare model |
||||
|
def build_model(): |
||||
|
model = Sequential() |
||||
|
model.add(Input(shape=(sequence_length, n_features), batch_size=n_batch)) |
||||
|
if model_type == model_type_bilstm: |
||||
|
model.add(Bidirectional(LSTM(n_neurons))) |
||||
|
if model_type == model_type_lstm: |
||||
|
model.add(LSTM(n_neurons)) |
||||
|
if model_type == model_type_gru: |
||||
|
model.add(GRU(n_neurons)) |
||||
|
#model.add(Dense(n_neurons, activation='relu')) |
||||
|
#model.add(Dropout(d1)) |
||||
|
model.add(Dense(user_num, activation='softmax')) |
||||
|
model.compile( |
||||
|
optimizer=Adam(), |
||||
|
loss=SparseCategoricalCrossentropy(), |
||||
|
metrics=[SparseCategoricalAccuracy()], |
||||
|
) |
||||
|
return model |
||||
|
|
||||
|
model = build_model() |
||||
|
|
||||
|
# fit model |
||||
|
train_acc, test_acc, train_p, test_p, train_r, test_r, train_f1, test_f1 = list(), list(),list(), list(),list(), list(),list(), list() |
||||
|
for i in range(n_epochs): |
||||
|
model.fit(x, y, batch_size=n_batch, epochs=1, verbose=0, shuffle=False) |
||||
|
# evaluate model on train data |
||||
|
acc, p, r, f1 = evaluate_v2(model, (x,y), sequence_length, n_batch) |
||||
|
train_acc.append(acc) |
||||
|
train_p.append(p) |
||||
|
train_r.append(r) |
||||
|
train_f1.append(f1) |
||||
|
# evaluate model on test data |
||||
|
savename = 'cf_matrix_'+get_save_id(n_epochs, n_neurons, '', '', '', n_batch, '','','','')+'.json' |
||||
|
acc, p, r, f1 = evaluate_v2(model, (x_v, y_v), sequence_length, n_batch, save_name=savename) |
||||
|
test_acc.append(acc) |
||||
|
test_p.append(p) |
||||
|
test_r.append(r) |
||||
|
test_f1.append(f1) |
||||
|
|
||||
|
history = DataFrame() |
||||
|
history['train_acc'], history['test_acc'] = train_acc, test_acc |
||||
|
history['train_p'], history['test_p'] = train_p, test_p |
||||
|
history['train_r'], history['test_r'] = train_r, test_r |
||||
|
history['train_f1'], history['test_f1'] = train_f1, test_f1 |
||||
|
return history |
||||
|
|
||||
|
def get_save_id(n_epochs, n_neurons, n_neurons2,n_neurons3,n_neurons4, n_batch, l_rate, d1,r1, r2): |
||||
|
return '_e'+str(n_epochs)+'_n'+str(n_neurons)+'_b'+ str(n_batch) |
||||
|
#'x'+str(n_neurons3)+'x'+str(n_neurons4) |
||||
|
#+'_l'+str(l_rate)+'_r'+str(r1)+'xx'+str(r2) |
||||
|
|
||||
|
|
||||
|
def evaluate(model, df, sequence_length, batch_size, save_name=None): |
||||
|
x, y = prepare_data_for_model(user_data=df, sequence_length=sequence_length) |
||||
|
x = np.array(x) |
||||
|
y_true = np.array(y) |
||||
|
|
||||
|
y_pred = model.predict(x, verbose=0, batch_size=batch_size) |
||||
|
y_pred_classes = np.argmax(y_pred, axis=1) |
||||
|
cf_matrix = pd.DataFrame(confusion_matrix(y_true, y_pred_classes)) |
||||
|
if save_name is not None: |
||||
|
cf_matrix.to_json('results/'+save_name) |
||||
|
true_counts = pd.DataFrame(y).value_counts() |
||||
|
print('Top true occurrences', true_counts[:6]) |
||||
|
predicted_counts = pd.DataFrame(y_pred_classes).value_counts() |
||||
|
print('Top predicted occurrences', predicted_counts[:6]) |
||||
|
|
||||
|
return eval_metrics(y_true=y_true, y_pred=y_pred_classes) |
||||
|
|
||||
|
def evaluate_v2(model, data, sequence_length, batch_size, save_name=None): |
||||
|
x, y_true = data |
||||
|
y_pred = model.predict(x, verbose=0, batch_size=batch_size) |
||||
|
y_pred_classes = np.argmax(y_pred, axis=1) |
||||
|
cf_matrix = pd.DataFrame(confusion_matrix(y_true, y_pred_classes)) |
||||
|
if save_name is not None: |
||||
|
cf_matrix.to_json('results/'+save_name) |
||||
|
true_counts = pd.DataFrame(y_true).value_counts() |
||||
|
print('Top true occurrences', true_counts[:6]) |
||||
|
predicted_counts = pd.DataFrame(y_pred_classes).value_counts() |
||||
|
print('Top predicted occurrences', predicted_counts[:6]) |
||||
|
|
||||
|
return eval_metrics(y_true=y_true, y_pred=y_pred_classes) |
||||
|
|
||||
|
|
||||
|
def eval_metrics(y_true, y_pred): |
||||
|
f1 = f1_score(y_true=y_true, y_pred=y_pred, average='weighted') |
||||
|
p = precision_score(y_true=y_true, y_pred=y_pred, average='weighted') |
||||
|
r = recall_score(y_true=y_true, y_pred=y_pred, average='weighted') |
||||
|
acc = accuracy_score(y_true=y_true, y_pred=y_pred) |
||||
|
return acc, p, r, f1 |
||||
|
|
||||
|
# === Evaluation === |
||||
|
def evaluate_models(best_models, df_test, sequence_lengths, output_excel_path, ALLUSERS32_15MIN_WITHOUTTHREHOLD): |
||||
|
print("\n🧪 Evaluating on Test Data...") |
||||
|
with ExcelWriter(output_excel_path) as writer: |
||||
|
for sequence_length in sequence_lengths: |
||||
|
if sequence_length not in best_models: |
||||
|
continue |
||||
|
evaluate_model_on_test_data(best_models[sequence_length]['model'], df_test.copy(), |
||||
|
sequence_length, writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD) |
||||
|
|
||||
|
def evaluate_model_on_test_data(model, test_df, sequence_length, excel_writer, ALLUSERS32_15MIN_WITHOUTTHREHOLD): |
||||
|
if(ALLUSERS32_15MIN_WITHOUTTHREHOLD): |
||||
|
test_df = test_df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek']) |
||||
|
else: |
||||
|
test_df = test_df.drop(columns=['Month', 'Year', 'date']) |
||||
|
|
||||
|
test_df = test_df.sort_values(by='user').reset_index(drop=True) |
||||
|
|
||||
|
users = test_df['user'].unique() |
||||
|
results = [] |
||||
|
accuracy_above_50 = 0 |
||||
|
|
||||
|
for user in users: |
||||
|
user_df = test_df[test_df['user'] == user] |
||||
|
X, y_true = [], [] |
||||
|
user_features = user_df.drop(columns=['user']).values |
||||
|
user_labels = user_df['user'].values |
||||
|
|
||||
|
if len(user_df) <= sequence_length: |
||||
|
print(f"Skipping User {user} (not enough data for sequence length {sequence_length})") |
||||
|
continue |
||||
|
|
||||
|
for i in range(len(user_df) - sequence_length): |
||||
|
seq_x = user_features[i:i + sequence_length] |
||||
|
seq_y = user_labels[i + sequence_length] |
||||
|
X.append(seq_x) |
||||
|
y_true.append(seq_y) |
||||
|
|
||||
|
X = np.array(X) |
||||
|
y_true = np.array(y_true) |
||||
|
|
||||
|
if len(X) == 0: |
||||
|
continue |
||||
|
|
||||
|
y_pred = model.predict(X, verbose=0) |
||||
|
y_pred_classes = np.argmax(y_pred, axis=1) |
||||
|
|
||||
|
# counts which class was predicted how often |
||||
|
unique_pred, counts_pred = np.unique(y_pred_classes, return_counts=True) |
||||
|
label_counts_pred = dict(zip(unique_pred, counts_pred)) |
||||
|
|
||||
|
# counts which class should have been predicted how often (only one class for the user) |
||||
|
unique_true, counts_true = np.unique(y_true, return_counts=True) |
||||
|
label_counts_true = dict(zip(unique_true, counts_true)) |
||||
|
|
||||
|
# the fraction of correctly classified samples |
||||
|
acc = accuracy_score(y_true, y_pred_classes) |
||||
|
if acc > 0.5: |
||||
|
accuracy_above_50 += 1 |
||||
|
|
||||
|
results.append({ |
||||
|
'User': user, |
||||
|
'Accuracy (%)': acc * 100, |
||||
|
'Predicted Class Distribution': str(label_counts_pred), |
||||
|
'Actual Class Distribution': str(label_counts_true) |
||||
|
}) |
||||
|
|
||||
|
print(f"\n=== User {user} ===") |
||||
|
print(f"✅ Accuracy: {acc * 100:.2f}%") |
||||
|
print("📊 Predicted Class Distribution:", label_counts_pred) |
||||
|
print("📌 Actual Class Distribution: ", label_counts_true) |
||||
|
|
||||
|
final_accuracy_percent = (accuracy_above_50 / 32) * 100 |
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print(f"\n🟩 Final Evaluation Summary for Sequence Length {sequence_length}:") |
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print(f"Users with >50% Accuracy: {accuracy_above_50} / 32") |
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print(f"✅ Final Success Rate: {final_accuracy_percent:.2f}%") |
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|
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results.append({ |
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'User': 'TOTAL', |
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'Accuracy (%)': '', |
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'Predicted Class Distribution': f'Users >50% Acc: {accuracy_above_50}/32', |
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'Actual Class Distribution': f'Success Rate: {final_accuracy_percent:.2f}%' |
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}) |
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|
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df_results = pd.DataFrame(results) |
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df_results.to_excel(excel_writer, sheet_name=f"SeqLen_{sequence_length}", index=False) |
||||
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