From ac1c6aecfa4be7d187128af5f2b6d1f4f4d1a32c Mon Sep 17 00:00:00 2001 From: Israel Figueroa Date: Tue, 18 Mar 2025 21:10:50 -0300 Subject: [PATCH] fit --- TrainerClass.py | 18 +++++++++--------- train_sequence.py | 4 ++-- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/TrainerClass.py b/TrainerClass.py index b44d738..1b85c2b 100644 --- a/TrainerClass.py +++ b/TrainerClass.py @@ -449,25 +449,25 @@ class eNoseTrainer: if Y_discrete.ndim == 2: Y_discrete = np.sum(Y_discrete, axis=1) - dataset = f'Conv1d-base-w{window}' - for i, (train_index, test_index) in enumerate(gss.split(X_conv1d, Y_discrete, G_conv1d)): + dataset = f'Conv1D-w{window}' + for i, (train_index, test_index) in enumerate(gss.split(X_Conv1d, Y_discrete, G_conv1d)): self.logger.info("{:=^60}".format(f'CV {i+1}/{int(1/self.ratio)} {dataset}')) - os.makedirs('{}/{}/{}-w{}'.format(self.name, self.loader.target, dataset, window), exist_ok=True) + os.makedirs('{}/{}/{}'.format(self.name, self.loader.target, dataset), exist_ok=True) X_train, X_test = X_conv1d[train_index], X_conv1d[test_index] Y_train, Y_test = Y_conv1d[train_index], Y_conv1d[test_index] G_train, G_test = G_conv1d[train_index], G_conv1d[test_index] # self.logger.debug(f"X_train: {X_train.shape}") # self.logger.debug(f"X_test: {X_test.shape}") - model_id = "Conv1d-base_{}".format(i) + model_id = "Conv1D_v1_{}".format(i) self.trained += 1 if self.row_exists(dataset, model_id): self.bar.update() continue - model_file = '{}/{}/{}-w{}/{}'.format(self.name, self.loader.target, dataset, window, model_id ) + model_file = '{}/{}/{}/{}'.format(self.name, self.loader.target, dataset, model_id ) X_train_sample, _, Y_train_sample, _ = train_test_split(X_train, Y_train, stratify=G_train, train_size=0.8*sample_size / len(X_train), random_state=get_seed()) X_test_sample, _, Y_test_sample, _ = train_test_split(X_test, Y_test, stratify=G_test, train_size=0.2*sample_size / len(X_test), random_state=get_seed()) @@ -536,11 +536,11 @@ class eNoseTrainer: if Y_discrete.ndim == 2: Y_discrete = np.sum(Y_discrete, axis=1) - dataset = f'Conv1d-base-w{window}-{filter}' + dataset = f'Conv1d-w{window}-{self.loader.smooth}' for i, (train_index, test_index) in enumerate(gss.split(X_conv1d, Y_discrete, G_conv1d)): self.logger.info("{:=^60}".format(f'CV {i+1}/{int(1/self.ratio)} {dataset}')) - os.makedirs('{}/{}/{}-w{}'.format(self.name, self.loader.target, dataset, window), exist_ok=True) + os.makedirs('{}/{}/{}'.format(self.name, self.loader.target, dataset), exist_ok=True) X_train, X_test = X_conv1d[train_index], X_conv1d[test_index] Y_train, Y_test = Y_conv1d[train_index], Y_conv1d[test_index] G_train, G_test = G_conv1d[train_index], G_conv1d[test_index] @@ -550,14 +550,14 @@ class eNoseTrainer: self.logger.debug(f"Y_test: {Y_test.shape}") - model_id = "Conv1d-base_{}".format(i) + model_id = "Conv1d_v1_{}".format(i) self.trained += 1 if self.row_exists(dataset, model_id): self.bar.update() continue - model_file = '{}/{}/{}-w{}/{}'.format(self.name, self.loader.target, dataset, window, model_id ) + model_file = '{}/{}/{}/{}'.format(self.name, self.loader.target, dataset, model_id ) X_train_sample, _, Y_train_sample, _ = train_test_split(X_train, Y_train, stratify=G_train, train_size=0.8*sample_size / len(X_train), random_state=get_seed()) X_test_sample, _, Y_test_sample, _ = train_test_split(X_test, Y_test, stratify=G_test, train_size=0.2*sample_size / len(X_test), random_state=get_seed()) diff --git a/train_sequence.py b/train_sequence.py index b89a74b..f1feb7d 100644 --- a/train_sequence.py +++ b/train_sequence.py @@ -10,10 +10,10 @@ target_variables=['C2H2', 'CH4', 'C3H6', 'CO', 'C2H6', 'C3H8', 'C2H4', 'H2', 'O2 eNoseLoader = GasSensorDataLoader("enose_dataset", threshold=0.85, source_channels=source_channels, target_list=target_variables, debug=False) eNose = eNoseTrainer(eNoseLoader, test_size=0.2, debug=False) -eNoseLoader.target_list=['H2',] -eNose.fit() eNoseLoader.target_list=['H2', 'C2H2', 'CH4', 'C2H4', 'C2H6',] eNose.fit() +eNoseLoader.target_list=['H2',] +eNose.fit() eNoseLoader.target_list=['C2H2',] eNose.fit() eNoseLoader.target_list=['CH4',]