"""
Contains basic hyperparameter optimizations.
"""
import numpy as np
import os
import itertools
import tempfile
import collections
import logging
from functools import reduce
from operator import mul
from typing import Dict, List, Optional, Tuple

from deepchem.data import Dataset
from deepchem.trans import Transformer
from deepchem.models import Model
from deepchem.metrics import Metric
from deepchem.hyper.base_classes import HyperparamOpt
from deepchem.hyper.base_classes import _convert_hyperparam_dict_to_filename

logger = logging.getLogger(__name__)


class GridHyperparamOpt(HyperparamOpt):
    """
    Provides simple grid hyperparameter search capabilities.

    This class performs a grid hyperparameter search over the specified
    hyperparameter space. This implementation is simple and simply does
    a direct iteration over all possible hyperparameters and doesn't use
    parallelization to speed up the search.

    Examples
    --------
    This example shows the type of constructor function expected.

    >>> import sklearn
    >>> import deepchem as dc
    >>> optimizer = dc.hyper.GridHyperparamOpt(lambda **p: dc.models.GraphConvModel(**p))

    Here's a more sophisticated example that shows how to optimize only
    some parameters of a model. In this case, we have some parameters we
    want to optimize, and others which we don't. To handle this type of
    search, we create a `model_builder` which hard codes some arguments
    (in this case, `max_iter` is a hyperparameter which we don't want
    to search over)

    >>> import deepchem as dc
    >>> import numpy as np
    >>> from sklearn.linear_model import LogisticRegression as LR
    >>> # generating data
    >>> X = np.arange(1, 11, 1).reshape(-1, 1)
    >>> y = np.hstack((np.zeros(5), np.ones(5)))
    >>> dataset = dc.data.NumpyDataset(X, y)
    >>> # splitting dataset into train and test
    >>> splitter = dc.splits.RandomSplitter()
    >>> train_dataset, test_dataset = splitter.train_test_split(dataset)
    >>> # metric to evaluate result of a set of parameters
    >>> metric = dc.metrics.Metric(dc.metrics.accuracy_score)
    >>> # defining `model_builder`
    >>> def model_builder(**model_params):
    ...   penalty = model_params['penalty']
    ...   solver = model_params['solver']
    ...   lr = LR(penalty=penalty, solver=solver, max_iter=100)
    ...   return dc.models.SklearnModel(lr)
    >>> # the parameters which are to be optimized
    >>> params = {
    ...   'penalty': ['l1', 'l2'],
    ...   'solver': ['liblinear', 'saga']
    ...   }
    >>> # Creating optimizer and searching over hyperparameters
    >>> optimizer = dc.hyper.GridHyperparamOpt(model_builder)
    >>> best_model, best_hyperparams, all_results = \
    optimizer.hyperparam_search(params, train_dataset, test_dataset, metric)
    >>> best_hyperparams  # the best hyperparameters
    {'penalty': 'l2', 'solver': 'saga'}

    """

    def hyperparam_search(
        self,
        params_dict: Dict,
        train_dataset: Dataset,
        valid_dataset: Dataset,
        metric: Metric,
        output_transformers: List[Transformer] = [],
        nb_epoch: int = 10,
        use_max: bool = True,
        logfile: str = 'results.txt',
        logdir: Optional[str] = None,
        **kwargs,
    ) -> Tuple[Model, Dict, Dict]:
        """Perform hyperparams search according to params_dict.

        Each key to hyperparams_dict is a model_param. The values should
        be a list of potential values for that hyperparam.

        Parameters
        ----------
        params_dict: Dict
            Maps hyperparameter names (strings) to lists of possible
            parameter values.
        train_dataset: Dataset
            dataset used for training
        valid_dataset: Dataset
            dataset used for validation(optimization on valid scores)
        metric: Metric
            metric used for evaluation
        output_transformers: list[Transformer]
            Transformers for evaluation. This argument is needed since
            `train_dataset` and `valid_dataset` may have been transformed
            for learning and need the transform to be inverted before
            the metric can be evaluated on a model.
        nb_epoch: int, (default 10)
            Specifies the number of training epochs during each iteration of optimization.
            Not used by all model types.
        use_max: bool, optional
            If True, return the model with the highest score. Else return
            model with the minimum score.
        logdir: str, optional
            The directory in which to store created models. If not set, will
            use a temporary directory.
        logfile: str, optional (default `results.txt`)
            Name of logfile to write results to. If specified, this is must
            be a valid file name. If not specified, results of hyperparameter
            search will be written to `logdir/results.txt`.

        Returns
        -------
        Tuple[`best_model`, `best_hyperparams`, `all_scores`]
        `(best_model, best_hyperparams, all_scores)` where `best_model` is
        an instance of `dc.model.Model`, `best_hyperparams` is a
        dictionary of parameters, and `all_scores` is a dictionary mapping
        string representations of hyperparameter sets to validation
        scores.

        Notes
        -----
        From DeepChem 2.6, the return type of `best_hyperparams` is a dictionary of
        parameters rather than a tuple of parameters as it was previously. The new
        changes have been made to standardize the behaviour across different
        hyperparameter optimization techniques available in DeepChem.
        """
        hyperparams = params_dict.keys()
        hyperparam_vals = params_dict.values()
        for hyperparam_list in params_dict.values():
            assert isinstance(hyperparam_list, collections.abc.Iterable)

        number_combinations = reduce(mul,
                                     [len(vals) for vals in hyperparam_vals])

        if use_max:
            best_validation_score = -np.inf
        else:
            best_validation_score = np.inf

        best_model = None
        all_scores = {}

        if logdir is not None:
            if not os.path.exists(logdir):
                os.makedirs(logdir, exist_ok=True)
            log_file = os.path.join(logdir, logfile)

        for ind, hyperparameter_tuple in enumerate(
                itertools.product(*hyperparam_vals)):
            model_params = {}
            logger.info("Fitting model %d/%d" % (ind + 1, number_combinations))
            # Construction dictionary mapping hyperparameter names to values
            hyper_params = dict(zip(hyperparams, hyperparameter_tuple))
            for hyperparam, hyperparam_val in zip(hyperparams,
                                                  hyperparameter_tuple):
                model_params[hyperparam] = hyperparam_val
            logger.info("hyperparameters: %s" % str(model_params))

            hp_str = _convert_hyperparam_dict_to_filename(hyper_params)
            if logdir is not None:
                model_dir = os.path.join(logdir, hp_str)
                logger.info("model_dir is %s" % model_dir)
                try:
                    os.makedirs(model_dir)
                except OSError:
                    if not os.path.isdir(model_dir):
                        logger.info(
                            "Error creating model_dir, using tempfile directory"
                        )
                        model_dir = tempfile.mkdtemp()
            else:
                model_dir = tempfile.mkdtemp()
            model_params['model_dir'] = model_dir
            model = self.model_builder(**model_params)
            # mypy test throws error, so ignoring it in try
            try:
                model.fit(train_dataset, nb_epoch=nb_epoch)  # type: ignore
            # Not all models have nb_epoch
            except TypeError:
                model.fit(train_dataset)
            try:
                model.save()
            # Some models autosave
            except NotImplementedError:
                pass

            multitask_scores = model.evaluate(valid_dataset, [metric],
                                              output_transformers)
            valid_score = multitask_scores[metric.name]
            all_scores[hp_str] = valid_score

            if (use_max and valid_score >= best_validation_score) or (
                    not use_max and valid_score <= best_validation_score):
                best_validation_score = valid_score
                best_hyperparams = hyper_params
                best_model = model

            logger.info(
                "Model %d/%d, Metric %s, Validation set %s: %f" %
                (ind + 1, number_combinations, metric.name, ind, valid_score))
            logger.info("\tbest_validation_score so far: %f" %
                        best_validation_score)
        if best_model is None:
            logger.info("No models trained correctly.")
            # arbitrarily return last model
            if logdir is not None:
                with open(log_file, 'w+') as f:
                    f.write(
                        "No model trained correctly. Arbitary models returned")
            best_model, best_hyperparams = model, hyperparameter_tuple
            return best_model, best_hyperparams, all_scores
        multitask_scores = best_model.evaluate(train_dataset, [metric],
                                               output_transformers)
        train_score = multitask_scores[metric.name]
        logger.info("Best hyperparameters: %s" % str(best_hyperparams))
        logger.info("best train score: %f" % train_score)
        logger.info("best validation score: %f" % best_validation_score)
        if logdir is not None:
            with open(log_file, 'w+') as f:
                f.write("Best Hyperparameters dictionary %s\n" %
                        str(best_hyperparams))
                f.write("Best validation score %f\n" % best_validation_score)
                f.write("Best train_score: %f\n" % train_score)
        return best_model, best_hyperparams, all_scores
