<reponame>christofid/gt4sd-core
#
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
"""SMILES LSTM training pipeline from GuacaMol."""
import logging
import os
from dataclasses import dataclass, field
from typing import Any, Dict

from guacamol_baselines.smiles_lstm_hc.smiles_rnn_distribution_learner import (
    SmilesRnnDistributionLearner,
)

from ...core import TrainingPipelineArguments
from ..core import GuacaMolBaselinesTrainingPipeline

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class GuacaMolLSTMTrainingPipeline(GuacaMolBaselinesTrainingPipeline):
    """GuacaMol SMILES LSTM training pipeline."""

    def train(  # type: ignore
        self,
        training_args: Dict[str, Any],
        model_args: Dict[str, Any],
        dataset_args: Dict[str, Any],
    ) -> None:
        params = {**training_args, **model_args, **dataset_args}

        os.makedirs(params["output_dir"], exist_ok=True)

        with open(params.pop("train_smiles_filepath")) as f:
            train_list = f.readlines()

        with open(params.pop("test_smiles_filepath")) as f:
            valid_list = f.readlines()
        trainer = SmilesRnnDistributionLearner(**params)

        trainer.train(training_set=train_list, validation_set=valid_list)


@dataclass
class GuacaMolLSTMTrainingArguments(TrainingPipelineArguments):
    """Training Arguments related to SMILES LSTM trainer."""

    __name__ = "training_args"

    output_dir: str = field(metadata={"help": "Output directory."})
    batch_size: int = field(
        default=512, metadata={"help": "Size of a mini-batch for gradient descent."}
    )
    valid_every: int = field(
        default=1000, metadata={"help": "Validate every so many batches."}
    )
    n_epochs: int = field(default=10, metadata={"help": "Number of training epochs."})
    lr: float = field(default=1e-3, metadata={"help": "RNN learning rate."})


@dataclass
class GuacaMolLSTMModelArguments(TrainingPipelineArguments):
    """Arguments related to SMILES LSTM trainer."""

    __name__ = "model_args"

    hidden_size: int = field(default=512, metadata={"help": "Size of hidden layer."})
    n_layers: int = field(
        default=3, metadata={"help": "Number of layers for training."}
    )
    rnn_dropout: float = field(default=0.2, metadata={"help": "Dropout value for RNN."})
    max_len: int = field(
        default=100, metadata={"help": "Max length of a SMILES string."}
    )

#
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
"""SMILES LSTM training pipeline from GuacaMol."""
import logging
import os
from dataclasses import dataclass, field
from typing import Any, Dict

from guacamol_baselines.smiles_lstm_hc.smiles_rnn_distribution_learner import (
    SmilesRnnDistributionLearner,
)

from ...core import TrainingPipelineArguments
from ..core import GuacaMolBaselinesTrainingPipeline

logger = logging.getLogger(__name__)
logger.addHandler(logging.NullHandler())


class GuacaMolLSTMTrainingPipeline(GuacaMolBaselinesTrainingPipeline):
    """GuacaMol SMILES LSTM training pipeline."""

    def train(  # type: ignore
        self,
        training_args: Dict[str, Any],
        model_args: Dict[str, Any],
        dataset_args: Dict[str, Any],
    ) -> None:
        params = {**training_args, **model_args, **dataset_args}

        os.makedirs(params["output_dir"], exist_ok=True)

        with open(params.pop("train_smiles_filepath")) as f:
            train_list = f.readlines()

        with open(params.pop("test_smiles_filepath")) as f:
            valid_list = f.readlines()
        trainer = SmilesRnnDistributionLearner(**params)

        trainer.train(training_set=train_list, validation_set=valid_list)


@dataclass
class GuacaMolLSTMTrainingArguments(TrainingPipelineArguments):
    """Training Arguments related to SMILES LSTM trainer."""

    __name__ = "training_args"

    output_dir: str = field(metadata={"help": "Output directory."})
    batch_size: int = field(
        default=512, metadata={"help": "Size of a mini-batch for gradient descent."}
    )
    valid_every: int = field(
        default=1000, metadata={"help": "Validate every so many batches."}
    )
    n_epochs: int = field(default=10, metadata={"help": "Number of training epochs."})
    lr: float = field(default=1e-3, metadata={"help": "RNN learning rate."})


@dataclass
class GuacaMolLSTMModelArguments(TrainingPipelineArguments):
    """Arguments related to SMILES LSTM trainer."""

    __name__ = "model_args"

    hidden_size: int = field(default=512, metadata={"help": "Size of hidden layer."})
    n_layers: int = field(
        default=3, metadata={"help": "Number of layers for training."}
    )
    rnn_dropout: float = field(default=0.2, metadata={"help": "Dropout value for RNN."})
    max_len: int = field(
        default=100, metadata={"help": "Max length of a SMILES string."}
    )
# # MIT License # # Copyright (c) 2022 GT4SD team # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. # SMILES LSTM training pipeline from GuacaMol. GuacaMol SMILES LSTM training pipeline. # type: ignore Training Arguments related to SMILES LSTM trainer. Arguments related to SMILES LSTM trainer.