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exp_model_list.py
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exp_model_list.py
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from sklearn.ensemble import ExtraTreesClassifier, RandomForestClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.tree import ExtraTreeClassifier
from skmultilearn.adapt import MLkNN
from skmultilearn.ensemble import RakelD
from datasources.paths_manager import SAVED_MODEL, PATH_SIGNAL2VEC
from nilmlab.factories import TransformerFactory
from nilmlab.lab import TransformerType
SAX = 'SAX'
SAX1D = 'SAX1D'
SFA = 'SFA'
DFT = 'DFT'
PAA = 'PAA'
WEASEL = 'WEASEL'
SIGNAL2VEC = 'SIGNAL2VEC'
TRANSFORMER_MODELS = 'TRANSFORMER_MODELS'
CLF_MODELS = 'CLF_MODELS'
BOSS = 'BOSS'
TIME_DELAY_EMBEDDING = 'TIME_DELAY_EMBEDDING'
WAVELETS = 'WAVELETS'
selected_models_10mins = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=4, n_bins=20, window_size=10, norm_mean=False,
norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1)
]
},
PAA : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=500)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=500)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True)
]
},
SFA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
RandomForestClassifier(n_jobs=-1, n_estimators=100)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True)
]
}
}
selected_models_4h = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=2, n_bins=26, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=25, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=26, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=26, window_size=10, norm_mean=False,
norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=5),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=10),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=10),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=10)
]
},
WEASEL : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False)
]
},
PAA : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=200),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=500)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=200),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=500)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True)
]
},
SFA : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=9, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=9, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=9, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=9, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam'),
ExtraTreeClassifier(),
ExtraTreeClassifier(),
ExtraTreesClassifier(n_jobs=-1, n_estimators=100)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=50)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=10, supports_approximation=True)
]
}
}
selected_models_8h = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=4, n_bins=10, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=4, n_bins=10, window_size=10, norm_mean=False,
norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=50),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=5)
]
},
WEASEL : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False)
]
},
PAA : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=200)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
RandomForestClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True)
]
},
SFA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=100, supports_approximation=False),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=10, supports_approximation=False),
TransformerFactory.build_tslearn_sax(n_paa_segments=10, n_sax_symbols=20, supports_approximation=False),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=20, supports_approximation=True)
]
}
}
selected_models_1h = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=4, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=2),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1)
]
},
WEASEL : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False)
]
},
PAA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam'),
ExtraTreesClassifier(n_jobs=-1, n_estimators=100),
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=100),
RandomForestClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
]
},
SFA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=200),
RandomForestClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=200)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=50),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=50)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam'),
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True)
]
}
}
selected_models_2h = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=2, n_bins=20, window_size=10, norm_mean=False,
norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=5)
]
},
WEASEL : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_weasel(word_size=2, n_bins=4, norm_mean=False, norm_std=False)
]
},
PAA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 50, 100, 50), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True)
]
},
SFA : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
RandomForestClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
RandomForestClassifier(n_jobs=-1, n_estimators=100)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=50, supports_approximation=True)
]
}
}
selected_models_24h = {
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_boss(word_size=4, n_bins=5, window_size=10, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_boss(word_size=4, n_bins=5, window_size=10, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_boss(word_size=4, n_bins=10, window_size=10, norm_mean=False,
norm_std=False),
TransformerFactory.build_pyts_boss(word_size=4, n_bins=5, window_size=10, norm_mean=False, norm_std=False)
]
},
SIGNAL2VEC: {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=4),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=5),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=2)
]
},
WEASEL : {
CLF_MODELS : [
],
TRANSFORMER_MODELS: [
]
},
PAA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam',
activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True),
TransformerFactory.build_tslearn_paa(n_paa_segments=10, supports_approximation=True)
]
},
DFT : {
CLF_MODELS : [
ExtraTreesClassifier(n_jobs=-1, n_estimators=100),
ExtraTreesClassifier(n_jobs=-1, n_estimators=2000),
ExtraTreesClassifier(n_jobs=-1, n_estimators=500),
ExtraTreesClassifier(n_jobs=-1, n_estimators=200)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True),
TransformerFactory.build_pyts_dft(n_coefs=10, norm_mean=False, norm_std=False,
supports_approximation=True)
]
},
SFA : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100,), learning_rate='adaptive', solver='adam'),
ExtraTreesClassifier(n_jobs=-1, n_estimators=1000),
RandomForestClassifier(n_jobs=-1, n_estimators=200),
ExtraTreesClassifier(n_jobs=-1, n_estimators=100)
],
TRANSFORMER_MODELS: [
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False),
TransformerFactory.build_pyts_sfa(n_coefs=10, n_bins=5, norm_mean=False, norm_std=False)
]
},
SAX1D : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam', activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam', activation='logistic')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=10),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=20),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=50),
TransformerFactory.build_tslearn_one_d_sax(n_paa_segments=50, n_sax_symbols=100)
]
},
SAX : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')
],
TRANSFORMER_MODELS: [
TransformerFactory.build_tslearn_sax(n_paa_segments=50, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=50, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=10, supports_approximation=True),
TransformerFactory.build_tslearn_sax(n_paa_segments=20, n_sax_symbols=50, supports_approximation=True)
]
}
}
model_selection_clf_list = [
MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(1000, 100), learning_rate='adaptive', solver='adam',
activation='logistic'),
MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam',
activation='logistic')
]
model_selection_transformers = [
TransformerFactory.build_pyts_boss(word_size=2, n_bins=5, window_size=10, norm_mean=False, norm_std=False),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=2),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1),
TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, transformer_type=TransformerType.transform)
]
model_selection_mlknn = [MLkNN(k=1, s=1.0, ignore_first_neighbours=0),
MLkNN(k=3, s=1.0, ignore_first_neighbours=0),
MLkNN(k=10, s=1.0, ignore_first_neighbours=0),
MLkNN(k=20, s=1.0, ignore_first_neighbours=0),
MLkNN(k=1, s=0.5, ignore_first_neighbours=0),
MLkNN(k=3, s=0.5, ignore_first_neighbours=0),
MLkNN(k=10, s=0.5, ignore_first_neighbours=0),
MLkNN(k=20, s=0.5, ignore_first_neighbours=0),
MLkNN(k=1, s=0.7, ignore_first_neighbours=0),
MLkNN(k=3, s=0.7, ignore_first_neighbours=0),
MLkNN(k=10, s=0.7, ignore_first_neighbours=0),
MLkNN(k=20, s=0.7, ignore_first_neighbours=0)
]
model_selection_rakel = [
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam')),
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive', solver='adam'), labelset_size=5),
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam')),
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100), learning_rate='adaptive', solver='adam'), labelset_size=5),
RakelD(MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam')),
RakelD(MLPClassifier(hidden_layer_sizes=(2000, 100), learning_rate='adaptive', solver='adam'), labelset_size=5),
RakelD(MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam')),
RakelD(MLPClassifier(hidden_layer_sizes=(2000), learning_rate='adaptive', solver='adam'), labelset_size=5),
RakelD(base_classifier=GaussianNB(), base_classifier_require_dense=[True, True], labelset_size=3),
RakelD(base_classifier=GaussianNB(), base_classifier_require_dense=[True, True], labelset_size=5),
RakelD(base_classifier=GaussianNB(), base_classifier_require_dense=[True, True], labelset_size=7)
]
model_selection_wavelets = [
TransformerFactory.build_wavelet(),
TransformerFactory.build_wavelet(drop_cA=True)
]
model_selection_delay_embeddings = [
TransformerFactory.build_delay_embedding(delay_in_seconds=30, dimension=6),
TransformerFactory.build_delay_embedding(delay_in_seconds=32, dimension=8),
TransformerFactory.build_delay_embedding(delay_in_seconds=6, dimension=8),
TransformerFactory.build_delay_embedding(delay_in_seconds=12, dimension=8)
]
cv_signal2vec = [TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1)]
cv_signal2vec_clf = [MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive',
solver='adam', activation='logistic')]
cv_boss_clf = [MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam')]
cv_boss = [TransformerFactory.build_pyts_boss(word_size=2, n_bins=2, window_size=10,
norm_mean=False, norm_std=False)]
state_of_the_art = {
SIGNAL2VEC : {
CLF_MODELS : [MLPClassifier(hidden_layer_sizes=(1000,), learning_rate='adaptive',
solver='adam', activation='logistic')],
TRANSFORMER_MODELS: [TransformerFactory.build_signal2vec(SAVED_MODEL, PATH_SIGNAL2VEC, num_of_vectors=1)]
},
WAVELETS : {
CLF_MODELS : [MLkNN(ignore_first_neighbours=0, k=3, s=1.0),
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive',
solver='adam'), labelset_size=5)],
TRANSFORMER_MODELS: [TransformerFactory.build_wavelet(), TransformerFactory.build_wavelet()]
},
TIME_DELAY_EMBEDDING: {
CLF_MODELS : [
MLkNN(ignore_first_neighbours=0, k=3, s=1.0),
RakelD(MLPClassifier(hidden_layer_sizes=(100, 100, 100), learning_rate='adaptive',
solver='adam'), labelset_size=5)
],
TRANSFORMER_MODELS: [TransformerFactory.build_delay_embedding(delay_in_seconds=30, dimension=6),
TransformerFactory.build_delay_embedding(delay_in_seconds=30, dimension=6)
]
},
BOSS : {
CLF_MODELS : [
MLPClassifier(hidden_layer_sizes=(2000, 100, 100), learning_rate='adaptive', solver='adam')],
TRANSFORMER_MODELS: [TransformerFactory.build_pyts_boss(word_size=2, n_bins=4, window_size=10,
norm_mean=False, norm_std=False)]
}
}