test
parent
48cecaa8cf
commit
5e87e9b8f2
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@ -177,7 +177,7 @@ class eNoseTrainer:
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else:
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return {}
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def train_and_score_conv1D_v1(self, X_train_orig, X_test_orig, Y_train_orig, Y_test_orig, epochs=30, num_samples=25):
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def search_best_conv1D_v1(self, X_train_orig, X_test_orig, Y_train_orig, Y_test_orig, epochs=30, nsamples=0.1):
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ray.init(ignore_reinit_error=True)
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X_train_ref = ray.put(X_train_orig)
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Y_train_ref = ray.put(Y_train_orig)
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@ -187,9 +187,9 @@ class eNoseTrainer:
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def build_model_conv1D(config, input_shape, output_dim):
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model = keras.Sequential([
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layers.Conv1D(filters=config['filters'], kernel_size=config['kernel_size'], activation='relu', input_shape=input_shape),
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layers.Conv1D(filters=config['filters'], kernel_size=config['kernel_size'], stride=config['kernel_size']//2, activation='relu', input_shape=input_shape),
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layers.MaxPooling1D(pool_size=config['pool_size']),
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layers.Conv1D(filters=config['filters'] * 2, kernel_size=config['kernel_size'], activation='relu'),
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layers.Conv1D(filters=config['filters'] * 2, kernel_size=config['kernel_size'], stride=config['kernel_size']//2, activation='relu'),
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layers.MaxPooling1D(pool_size=config['pool_size']),
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layers.Flatten(),
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layers.Dense(config['dense_units'], activation='relu'),
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@ -216,7 +216,7 @@ class eNoseTrainer:
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validation_data=(X_testc1D, Y_testc1D),
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epochs=config['epochs'],
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batch_size=config['batch_size'],
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verbose=1,
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verbose=0,
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callbacks=[early_stopping]
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)
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@ -226,7 +226,7 @@ class eNoseTrainer:
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config_space = {
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'filters': tune.choice([16, 32, 64]),
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'kernel_size': tune.choice([3, 5]),
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'kernel_size': tune.choice([3, 5, 7]),
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'pool_size': tune.choice([2, 3]),
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'dense_units': tune.choice([32, 64, 128]),
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'dropout': tune.choice([0.1, 0.2, 0.3]),
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@ -234,10 +234,11 @@ class eNoseTrainer:
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'batch_size': tune.choice([16, 32, 64]),
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'epochs': epochs
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}
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total_space = (3*3*2*3*3*3*3)
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scheduler = ASHAScheduler(metric='mse', mode='min', max_t=epochs, grace_period=5, reduction_factor=2)
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# analysis = tune.run(train_model, config=config_space, num_samples=num_samples, scheduler=scheduler)
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analysis = tune.run( tune.with_parameters(train_model_conv1D), config=config_space, num_samples=num_samples, scheduler=scheduler, max_concurrent_trials=3 )
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analysis = tune.run( tune.with_parameters(train_model_conv1D), config=config_space, num_samples=int(nsamples*total_space), scheduler=scheduler, max_concurrent_trials=8 )
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best_config = analysis.get_best_config(metric='mse', mode='min')
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best_model = build_model_conv1D(best_config, X_train_orig.shape[1:], Y_train_orig.shape[1])
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@ -483,7 +484,7 @@ class eNoseTrainer:
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self.saveCheckPoint()
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sample_size = 50000
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self.loader.smooth = None
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self.loader.reset()
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for window in windows:
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@ -516,19 +517,19 @@ class eNoseTrainer:
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model_file = '{}/{}/{}-w{}/{}'.format(self.name, self.loader.target, dataset, window, model_id )
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sample_size = 25000
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X_train_sample, _, Y_train_sample, _ = train_test_split(X_train, Y_train, stratify=G_train, train_size=sample_size / len(X_train), random_state=get_seed())
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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())
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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())
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self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
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self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
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optimized_model, model_params = self.train_and_score_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
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optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
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optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=0)
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self.logger.info(f"Training Model {model_id} with {model_params}"))
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optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
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Y_train_pred = best_model.predict(X_train)
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Y_test_pred = best_model.predict(X_test)
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Y_train_pred = optimized_model.predict(X_train)
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Y_test_pred = optimized_model.predict(X_test)
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mse_train = mean_squared_error(Y_train, Y_train_pred)
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mae_test = mean_absolute_error(Y_test, Y_test_pred)
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@ -592,19 +593,18 @@ class eNoseTrainer:
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self.bar.update()
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continue
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sample_size = 25000
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X_train_sample, _, Y_train_sample, _ = train_test_split(X_train, Y_train, stratify=G_train, train_size=sample_size / len(X_train), random_state=get_seed())
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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())
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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())
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self.logger.debug(f"Y_train_sample: {Y_train_sample.shape}")
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self.logger.debug(f"Y_test_sample: {Y_test_sample.shape}")
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optimized_model, model_params = self.train_and_score_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
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optimized_model, model_params = self.search_best_conv1D_v1(X_train_sample, X_test_sample, Y_train_sample, Y_test_sample)
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optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=0)
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optimized_model.fit(X_train, Y_train, epochs=model_params['epochs'], batch_size=model_params['batch_size'], verbose=1)
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Y_train_pred = best_model.predict(X_train)
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Y_test_pred = best_model.predict(X_test)
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Y_train_pred = optimized_model.predict(X_train)
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Y_test_pred = optimized_model.predict(X_test)
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mse_train = mean_squared_error(Y_train, Y_train_pred)
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mae_test = mean_absolute_error(Y_test, Y_test_pred)
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@ -13,7 +13,7 @@ fi
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env_name=$1
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conda create -n "$env_name" python scikit-learn==1.3.1 xgboost tensorflow conda-forge::ray-tune keras pandas numpy matplotlib openpyxl xlsxwriter conda-forge::enlighten
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conda create -n "$env_name" python scikit-learn==1.3.1 xgboost conda-forge::tensorflow-cpu conda-forge::ray-tune keras pandas numpy matplotlib openpyxl xlsxwriter conda-forge::enlighten
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if [ $? -eq 0 ]; then
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echo "Packages installed successfully in environment '$env_name'"
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else
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