datasets

main_ray_tune.py

@@ -57,12 +57,12 @@ def train_model(config, args):
             optimizer.load_state_dict(optimizer_state)
 
     if args.forward == 'both':
-        data_train, data_val, data_test = common_dataset.load_data(path_train=args.dataset_train,
+        data_train, data_val= common_dataset.load_data(path_train=args.dataset_train,
                                                                    path_val=args.dataset_val,
                                                                    path_test=args.dataset_test,
                                                                    batch_size=int(config["batch_size"]), length=25)
     else:
-        data_train, data_val, data_test = dataloader.load_data(data_source=args.dataset_train,
+        data_train, data_val = dataloader.load_data(data_source=args.dataset_train,
                                                                batch_size=int(config["batch_size"]), length=25)
 
     for epoch in range(100):  # loop over the dataset multiple times

prosit_rt_ori.py

+import os
+import sys
+
+import pandas as pd
+import tensorflow as tf
+
+from dlomix.data import RetentionTimeDataset
+from dlomix.eval import TimeDeltaMetric
+from dlomix.models import PrositRetentionTimePredictor
+from dlomix.reports import RetentionTimeReport
+
+# sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
+
+# consider the use-case for starting from a saved model
+
+model = PrositRetentionTimePredictor(seq_length=30)
+
+optimizer = tf.keras.optimizers.Adam(learning_rate=0.0001)
+
+
+d = RetentionTimeDataset(
+    data_format="csv",
+    data_source='database/data_train.csv',
+    test_data_source='database/data_holdout.csv',
+    sequence_column="sequence",
+    label_column="irt",
+    max_seq_len=30,
+    batch_size=512,
+    val_ratio=0.2,
+)
+
+print(d)
+
+test_targets = d["test"]["irt"]
+test_sequences = d["test"]["sequence"]
+
+model.compile(
+    optimizer=optimizer, loss="mse", metrics=["mean_absolute_error", TimeDeltaMetric()]
+)
+
+weights_file = ".weights.h5"
+checkpoint = tf.keras.callbacks.ModelCheckpoint(
+    weights_file, save_best_only=True, save_weights_only=True
+)
+decay = tf.keras.callbacks.ReduceLROnPlateau(
+    monitor="val_loss", factor=0.1, patience=10, verbose=1, min_lr=0
+)
+early_stop = tf.keras.callbacks.EarlyStopping(patience=20)
+callbacks = [checkpoint, early_stop, decay]
+
+
+history = model.fit(
+    d.tensor_train_data,
+    epochs=25,
+    validation_data=d.tensor_val_data,
+    callbacks=callbacks,
+)
+
+predictions = model.predict(test_sequences)
+predictions = predictions.ravel()
+
+print(test_sequences[:5])
+print(test_targets[:5])
+print(predictions[:5])
+
+
+report = RetentionTimeReport(output_path="./output", history=history)
+
+print("R2: ", report.calculate_r2(test_targets, predictions))
+
+pd.DataFrame(
+    {
+        "sequence": d["test"]["_parsed_sequence"],
+        "irt": test_targets,
+        "predicted_irt": predictions,
+    }
+).to_csv("./predictions_prosit_fullrun.csv", index=False)
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