from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Conv1D
from keras.layers import Dropout
from keras.layers import Bidirectional

from keras.preprocessing import sequence as seqq
from keras.layers import Input

from keras.models import Model

from keras.optimizers import Adam

from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint

import numpy as np
import sys
import argparse

def main(args):
    """
        Program's main function.

        Script to train a model for diffusion exponent regression for trajectories of length between 400 and 500 under the scope of the ANDI Challenge.

        Params:
         - train_data: path to the dataset used to train the model.
         - truth_file: path to the truth file
         - path_to_svae_model: path where the models are to be saved.

        Output:
         - models saved to the location previously indicated.

    """


    train_file = args.train_data
    truth_file = args.truth_file
    path_to_model = args.path_to_save_model

    with open(train_file, "r") as f:
        task = [np.array(x.split(";"), dtype="float64") for x in f.readlines()]
        task = np.array([x[1:] for x in task]) 
        task = task[:,]

    with open(truth_file, "r") as f:
        ref = [x.split(";") for x in f.readlines()]
        ref_cat = np.array([float(x[1].replace("\n", "")) for x in ref])

    max_length = 500
    task_HomLen = seqq.pad_sequences(task, maxlen=max_length, dtype="float64")
    task_HomLenRes = task_HomLen.reshape((-1, max_length, 1))    

    n_train = 1800000
    n_trainval = 2000000
    trainX, testX = task_HomLenRes[:n_train, :], task_HomLenRes[n_train:n_trainval, :]
    trainY, testY = ref_cat[:n_train], ref_cat[n_train:n_trainval]

    # define model
    optimizer = Adam(lr=0.001, decay=1e-6)

    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu', input_shape=(max_length,1)))
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu'))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=False)))    
    model.add(Dense(1, activation='linear'))

    model.compile(loss="mean_absolute_error", optimizer=optimizer, metrics=["mean_absolute_error"])
    
    es = EarlyStopping(monitor='val_mean_absolute_error', mode='min', verbose=1, patience=20)
    mc = ModelCheckpoint(path_to_model + 'task1_len_400_500-{epoch:03d}-{val_mean_absolute_error:.4f}.h5', monitor='val_mean_absolute_error', mode='min', verbose=0, save_best_only=True)

    print('------------------------------------------------------------------------')
    print(f'Training ...')
    
    history = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=200, verbose=1, callbacks=[es, mc])

    print("Program finished.")
    sys.stdout.flush()

    

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument("--train_data", help='Path to the training text file', default=".")
    parser.add_argument("--truth_file", help="Path to the truth txt file", default=".")
    parser.add_argument("--path_to_save_model", help="Path to save the best model", default=".")
    parser.add_argument("-v", "--verbose", action="store_true")
    args = parser.parse_args()
    main(args)



03a935483f9157135bf2db0ee7352e304955ae0c
<mask token>

<mask token>


def main(args):
    """
        Program's main function.

        Script to train a model for diffusion exponent regression for trajectories of length between 400 and 500 under the scope of the ANDI Challenge.

        Params:
         - train_data: path to the dataset used to train the model.
         - truth_file: path to the truth file
         - path_to_svae_model: path where the models are to be saved.

        Output:
         - models saved to the location previously indicated.

    """
    train_file = args.train_data
    truth_file = args.truth_file
    path_to_model = args.path_to_save_model
    with open(train_file, 'r') as f:
        task = [np.array(x.split(';'), dtype='float64') for x in f.readlines()]
        task = np.array([x[1:] for x in task])
        task = task[:,]
    with open(truth_file, 'r') as f:
        ref = [x.split(';') for x in f.readlines()]
        ref_cat = np.array([float(x[1].replace('\n', '')) for x in ref])
    max_length = 500
    task_HomLen = seqq.pad_sequences(task, maxlen=max_length, dtype='float64')
    task_HomLenRes = task_HomLen.reshape((-1, max_length, 1))
    n_train = 1800000
    n_trainval = 2000000
    trainX, testX = task_HomLenRes[:n_train, :], task_HomLenRes[n_train:
        n_trainval, :]
    trainY, testY = ref_cat[:n_train], ref_cat[n_train:n_trainval]
    optimizer = Adam(lr=0.001, decay=1e-06)
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu',
        input_shape=(max_length, 1)))
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu'))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=False)))
    model.add(Dense(1, activation='linear'))
    model.compile(loss='mean_absolute_error', optimizer=optimizer, metrics=
        ['mean_absolute_error'])
    es = EarlyStopping(monitor='val_mean_absolute_error', mode='min',
        verbose=1, patience=20)
    mc = ModelCheckpoint(path_to_model +
        'task1_len_400_500-{epoch:03d}-{val_mean_absolute_error:.4f}.h5',
        monitor='val_mean_absolute_error', mode='min', verbose=0,
        save_best_only=True)
    print(
        '------------------------------------------------------------------------'
        )
    print(f'Training ...')
    history = model.fit(trainX, trainY, validation_data=(testX, testY),
        epochs=200, verbose=1, callbacks=[es, mc])
    print('Program finished.')
    sys.stdout.flush()


<mask token>

<mask token>


def main(args):
    """
        Program's main function.

        Script to train a model for diffusion exponent regression for trajectories of length between 400 and 500 under the scope of the ANDI Challenge.

        Params:
         - train_data: path to the dataset used to train the model.
         - truth_file: path to the truth file
         - path_to_svae_model: path where the models are to be saved.

        Output:
         - models saved to the location previously indicated.

    """
    train_file = args.train_data
    truth_file = args.truth_file
    path_to_model = args.path_to_save_model
    with open(train_file, 'r') as f:
        task = [np.array(x.split(';'), dtype='float64') for x in f.readlines()]
        task = np.array([x[1:] for x in task])
        task = task[:,]
    with open(truth_file, 'r') as f:
        ref = [x.split(';') for x in f.readlines()]
        ref_cat = np.array([float(x[1].replace('\n', '')) for x in ref])
    max_length = 500
    task_HomLen = seqq.pad_sequences(task, maxlen=max_length, dtype='float64')
    task_HomLenRes = task_HomLen.reshape((-1, max_length, 1))
    n_train = 1800000
    n_trainval = 2000000
    trainX, testX = task_HomLenRes[:n_train, :], task_HomLenRes[n_train:
        n_trainval, :]
    trainY, testY = ref_cat[:n_train], ref_cat[n_train:n_trainval]
    optimizer = Adam(lr=0.001, decay=1e-06)
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu',
        input_shape=(max_length, 1)))
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu'))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=False)))
    model.add(Dense(1, activation='linear'))
    model.compile(loss='mean_absolute_error', optimizer=optimizer, metrics=
        ['mean_absolute_error'])
    es = EarlyStopping(monitor='val_mean_absolute_error', mode='min',
        verbose=1, patience=20)
    mc = ModelCheckpoint(path_to_model +
        'task1_len_400_500-{epoch:03d}-{val_mean_absolute_error:.4f}.h5',
        monitor='val_mean_absolute_error', mode='min', verbose=0,
        save_best_only=True)
    print(
        '------------------------------------------------------------------------'
        )
    print(f'Training ...')
    history = model.fit(trainX, trainY, validation_data=(testX, testY),
        epochs=200, verbose=1, callbacks=[es, mc])
    print('Program finished.')
    sys.stdout.flush()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_data', help=
        'Path to the training text file', default='.')
    parser.add_argument('--truth_file', help='Path to the truth txt file',
        default='.')
    parser.add_argument('--path_to_save_model', help=
        'Path to save the best model', default='.')
    parser.add_argument('-v', '--verbose', action='store_true')
    args = parser.parse_args()
    main(args)

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Conv1D
from keras.layers import Dropout
from keras.layers import Bidirectional
from keras.preprocessing import sequence as seqq
from keras.layers import Input
from keras.models import Model
from keras.optimizers import Adam
from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint
import numpy as np
import sys
import argparse


def main(args):
    """
        Program's main function.

        Script to train a model for diffusion exponent regression for trajectories of length between 400 and 500 under the scope of the ANDI Challenge.

        Params:
         - train_data: path to the dataset used to train the model.
         - truth_file: path to the truth file
         - path_to_svae_model: path where the models are to be saved.

        Output:
         - models saved to the location previously indicated.

    """
    train_file = args.train_data
    truth_file = args.truth_file
    path_to_model = args.path_to_save_model
    with open(train_file, 'r') as f:
        task = [np.array(x.split(';'), dtype='float64') for x in f.readlines()]
        task = np.array([x[1:] for x in task])
        task = task[:,]
    with open(truth_file, 'r') as f:
        ref = [x.split(';') for x in f.readlines()]
        ref_cat = np.array([float(x[1].replace('\n', '')) for x in ref])
    max_length = 500
    task_HomLen = seqq.pad_sequences(task, maxlen=max_length, dtype='float64')
    task_HomLenRes = task_HomLen.reshape((-1, max_length, 1))
    n_train = 1800000
    n_trainval = 2000000
    trainX, testX = task_HomLenRes[:n_train, :], task_HomLenRes[n_train:
        n_trainval, :]
    trainY, testY = ref_cat[:n_train], ref_cat[n_train:n_trainval]
    optimizer = Adam(lr=0.001, decay=1e-06)
    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu',
        input_shape=(max_length, 1)))
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu'))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32, return_sequences=False)))
    model.add(Dense(1, activation='linear'))
    model.compile(loss='mean_absolute_error', optimizer=optimizer, metrics=
        ['mean_absolute_error'])
    es = EarlyStopping(monitor='val_mean_absolute_error', mode='min',
        verbose=1, patience=20)
    mc = ModelCheckpoint(path_to_model +
        'task1_len_400_500-{epoch:03d}-{val_mean_absolute_error:.4f}.h5',
        monitor='val_mean_absolute_error', mode='min', verbose=0,
        save_best_only=True)
    print(
        '------------------------------------------------------------------------'
        )
    print(f'Training ...')
    history = model.fit(trainX, trainY, validation_data=(testX, testY),
        epochs=200, verbose=1, callbacks=[es, mc])
    print('Program finished.')
    sys.stdout.flush()


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--train_data', help=
        'Path to the training text file', default='.')
    parser.add_argument('--truth_file', help='Path to the truth txt file',
        default='.')
    parser.add_argument('--path_to_save_model', help=
        'Path to save the best model', default='.')
    parser.add_argument('-v', '--verbose', action='store_true')
    args = parser.parse_args()
    main(args)

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import Conv1D
from keras.layers import Dropout
from keras.layers import Bidirectional

from keras.preprocessing import sequence as seqq
from keras.layers import Input

from keras.models import Model

from keras.optimizers import Adam

from keras.callbacks import EarlyStopping
from keras.callbacks import ModelCheckpoint

import numpy as np
import sys
import argparse

def main(args):
    """
        Program's main function.

        Script to train a model for diffusion exponent regression for trajectories of length between 400 and 500 under the scope of the ANDI Challenge.

        Params:
         - train_data: path to the dataset used to train the model.
         - truth_file: path to the truth file
         - path_to_svae_model: path where the models are to be saved.

        Output:
         - models saved to the location previously indicated.

    """


    train_file = args.train_data
    truth_file = args.truth_file
    path_to_model = args.path_to_save_model

    with open(train_file, "r") as f:
        task = [np.array(x.split(";"), dtype="float64") for x in f.readlines()]
        task = np.array([x[1:] for x in task]) 
        task = task[:,]

    with open(truth_file, "r") as f:
        ref = [x.split(";") for x in f.readlines()]
        ref_cat = np.array([float(x[1].replace("\n", "")) for x in ref])

    max_length = 500
    task_HomLen = seqq.pad_sequences(task, maxlen=max_length, dtype="float64")
    task_HomLenRes = task_HomLen.reshape((-1, max_length, 1))    

    n_train = 1800000
    n_trainval = 2000000
    trainX, testX = task_HomLenRes[:n_train, :], task_HomLenRes[n_train:n_trainval, :]
    trainY, testY = ref_cat[:n_train], ref_cat[n_train:n_trainval]

    # define model
    optimizer = Adam(lr=0.001, decay=1e-6)

    model = Sequential()
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu', input_shape=(max_length,1)))
    model.add(Conv1D(filters=64, kernel_size=8, activation='relu'))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=True)))
    model.add(Dropout(0.1))
    model.add(Bidirectional(LSTM(32,return_sequences=False)))    
    model.add(Dense(1, activation='linear'))

    model.compile(loss="mean_absolute_error", optimizer=optimizer, metrics=["mean_absolute_error"])
    
    es = EarlyStopping(monitor='val_mean_absolute_error', mode='min', verbose=1, patience=20)
    mc = ModelCheckpoint(path_to_model + 'task1_len_400_500-{epoch:03d}-{val_mean_absolute_error:.4f}.h5', monitor='val_mean_absolute_error', mode='min', verbose=0, save_best_only=True)

    print('------------------------------------------------------------------------')
    print(f'Training ...')
    
    history = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=200, verbose=1, callbacks=[es, mc])

    print("Program finished.")
    sys.stdout.flush()

    

if __name__ == '__main__':

    parser = argparse.ArgumentParser()
    parser.add_argument("--train_data", help='Path to the training text file', default=".")
    parser.add_argument("--truth_file", help="Path to the truth txt file", default=".")
    parser.add_argument("--path_to_save_model", help="Path to save the best model", default=".")
    parser.add_argument("-v", "--verbose", action="store_true")
    args = parser.parse_args()
    main(args)


