r"""
.. _custom_dataset:

Custom Dataset
==============

.. note:: These examples are basically copies from the same examples in tpcp, but using gait algorithms!
          These examples are less often updated than the official tpcp examples.
          Hence, it makes sense to cross-check the official examples.

Gaitmap has the concept of Dataset.
Datasets represent a set of recordings that should all be processed in the same way.
For example the data of multiple participants in a study, multiple days of recording, or multiple gait tests.
The goal of datasets is to provide a consistent interface to access the raw data, meta data and potential reference
information in an object oriented way.
It is up to you to define, what is considered a single "data-point" for your dataset.
Note, that datasets can be arbitrarily nested (e.g. multiple subjects with multiple recordings).

Datasets work best in combination with `Pipelines` and are further compatible with concepts like `GridSearch` and
`cross_validation`.
"""

# %%
# Defining your own dataset
# -------------------------
# Fundamentally you only need to create a subclass of :func:`~tpcp.Dataset` and define the
# `create_index` method.
# This method should return a dataframe describing all the data-points that should be available in the dataset.
#
# In the following we will create an example dataset, that is independent of the concept of gait analysis,
# but can be used to demonstrate most functionality.
# At the end we will discuss, how gait specific data should be integrated.
#
# We will define an index that contains 5 participants, with 3 recordings each. Recording 3 has 2 trials,
# while the others have only one.
# Note, that we implement this as a static index here, but most of the time, you would create the index by e.g. scanning
# and listing the files in your data directory.
# It is important that you don't want to load the entire actual data (e.g. the imu samples) in memory, but just list
# the available data-points in the index.
# Then you can filter the dataset first and load the data once you know which data-points you want to access.
# We will discuss this later in the example.
from itertools import product
from typing import Optional, Union

import pandas as pd

trials = list(product(("rec_1", "rec_2", "rec_3"), ("trial_1",)))
trials.append(("rec_3", "trial_2"))
index = [(p, *t) for p, t in product((f"p{i}" for i in range(1, 6)), trials)]
index = pd.DataFrame(index, columns=["participant", "recording", "trial"])
index

# %%
# Now we use this index as the index of our new dataset.
# To see the dataset in action, we need to create an instance of it.
# Its string representation will show us the most important information.
from tpcp import Dataset


class CustomDataset(Dataset):
    def create_index(self):
        return index


dataset = CustomDataset()
dataset

# %%
# Subsets
# -------
# When working with a dataset, the first thing is usually to select the data you want to use.
# For this, you can primarily use the method `get_subset`.
# Here we want to select only recording 2 and 3 from participant 1 to 4.
# Note that the returned subset is an instance of your dataset class as well.
subset = dataset.get_subset(participant=["p1", "p2", "p3", "p4"], recording=["rec_2", "rec_3"])
subset

# %%
# The subset can then be filtered further.
# For more advanced filter approaches you can also filter the index directly and use a bool-map to index the dataset
example_bool_map = subset.index["participant"].isin(["p1", "p2"])
final_subset = subset.get_subset(bool_map=example_bool_map)
final_subset

# %%
# Iteration and Groups
# --------------------------------
# After selecting the part of the data you want to use, you usually want/need to iterate over the data to apply your
# processing steps.
#
# By default, you can simply iterate over all rows.
# Note, that each row itself is a dataset again, but just with a single entry.
for row in final_subset:
    print(row)
    print(f"This row contains {len(row)} data-point", end="\n\n")

# %%
# However, in many cases, we don't want to iterate over all rows, but rather iterate over groups of the datasets (
# e.g. all participants or all tests) individually.
# We can do that in 2 ways (depending on what is needed).
# For example, if we want to iterate over all recordings, we can do this:
for trial in final_subset.iter_level("recording"):
    print(trial, end="\n\n")

# %%
# You can see that we get two subsets, one for each recording label.
# But what, if we want to iterate over the participants and the recordings together?
# In these cases, we need to group our dataset first.
# Note that the grouped_subset shows the new groupby columns as the index in the representation and the length of the
# dataset is reported to be the number of groups.
grouped_subset = final_subset.groupby(["participant", "recording"])
print(f"The dataset contains {len(grouped_subset)} groups.")
grouped_subset

# %%
# If we now iterate the dataset, it will iterate over the unique groups.
#
# Grouping also changes the meaning of a "single datapoint".
# Each group reports a shape of `(1,)` independent of the number of rows in each group.
for group in grouped_subset:
    print(f"This group has the shape {group.shape}")
    print(group, end="\n\n")

# %%
# At any point, you can view all unique groups/rows in the dataset using the `groups` attribute.
# The order shown here, is the same order used when iterating the dataset.
# When creating a new subset, the order might change!
grouped_subset.groups

# %%
# Note that for an "un-grouped" dataset, this corresponds to all rows.
final_subset.groups

# %%
# If you want, you can also ungroup a dataset again.
# This can be useful for a nested iteration:
for outer, group in enumerate(grouped_subset):
    ungrouped = group.groupby(None)
    for inner, subgroup in enumerate(ungrouped):
        print(outer, inner)
        print(subgroup, end="\n\n")

# %%
# Splitting
# ---------
# If you are evaluating algorithms, it is often important to split your data into a train and a test set, or multiple
# distinct sets for a cross-validation.
#
# The `Dataset` objects directly support the `sklearn` helper functions for this.
# For example, to split our subset into training and testing we can do the following:
from sklearn.model_selection import train_test_split

train, test = train_test_split(final_subset, train_size=0.5)
print("Train:\n", train, end="\n\n")
print("Test:\n", test)

# %%
# Such splitting always occurs on a data-point level and can therefore be influenced by grouping.
# If we want to split our datasets into training and testing, but only based on the participants, we can do this:
train, test = train_test_split(final_subset.groupby("participant"), train_size=0.5)
print("Train:\n", train, end="\n\n")
print("Test:\n", test)

# %%
# In the same way you can use the dataset (grouped or not) with the cross-validation helper functions
# (KFold is just an example, all should work):
from sklearn.model_selection import KFold

cv = KFold(n_splits=2)
grouped_subset = final_subset.groupby("participant")
for train, test in cv.split(grouped_subset):
    # We only print the train set here
    print(grouped_subset[train], end="\n\n")

# %%
# While this works well, it is not always what we want.
# Sometimes, we still want to consider each row a single datapoint, but want to prevent that data of e.g. a single
# participant is partially put into train- and partially into the test-split.
# For this, we can use `GroupKFold` in combination with `dataset.create_group_labels`.
#
# `create_group_labels` generates a unique identifier for each row/group:
group_labels = final_subset.create_group_labels("participant")
group_labels

# %%
# They can then be used as the `group` parameter in `GroupKFold` (and similar methods).
# Now the data of the two participants is never split between train and test set.
from sklearn.model_selection import GroupKFold

cv = GroupKFold(n_splits=2)
for train, test in cv.split(final_subset, groups=group_labels):
    # We only print the train set here
    print(final_subset[train], end="\n\n")

# %%
# Creating labels also works for datasets that are already grouped.
# But, the columns that should be contained in the label must be a subset of the groupby columns in this case.
#
# The number of group labels is 4 in this case, as there are only 4 groups after grouping the datset.
group_labels = final_subset.groupby(["participant", "recording"]).create_group_labels("participant")
group_labels

# %%
# Adding Data
# -----------
# So far we only operated on the index of the dataset.
# But if we want to run algorithms, we need the actual data (i.e. IMU samples, clinical data, ...).
#
# Because the data and the structure of the data can vary widely from dataset to dataset, it is up to you to implement
# data access.
# It comes down to documentation to ensure that users access the data in the correct way.
#
# However, if you want to make sure your dataset "feels" like part of gaitmap, you should follow these recommendations:
#
# - Data access should be provided via `@property` decorator on the dataset objects, loading the data on demand.
# - The names of these properties should follow the naming scheme used in gaitmap (e.g. `data` for IMU-data)
#   and should return values using the established gaitmap datatypes.
# - The names of values that represent gold standard information (i.e. values you would only have in an evaluation
#   dataset), should have a trailing `_`, which marks them as result in the context of gaitmap.
#
# This should look something like this:
from gaitmap.utils.datatype_helper import MultiSensorData, MultiSensorStrideList


class CustomDataset(Dataset):
    @property
    def data(self) -> MultiSensorData:
        # Some logic to load data from disc
        raise NotImplementedError()

    @property
    def sampling_rate_hz(self) -> float:
        return 204.8

    @property
    def segmented_stride_list_(self) -> MultiSensorStrideList:
        # Some custom logic to load the gold-standard stride list of this validation dataset.
        # Note the trailing `_` in the name.
        raise NotImplementedError()

    def create_index(self):
        return index


# %%
# For each of the data-values you need to decide, on which "level" you provide data access.
# Meaning, do you want/can return data, when there are still multiple participants/recordings in the dataset, or can you
# only return the data, when there is only a single trial of a single participant left.
#
# Usually, we recommend to always return the data on the lowest logical level (e.g. if you recorded separate IMU
# sessions per trial, you should provide access only, if there is just a single trail by a single participant left in
# the dataset).
# Otherwise, you should throw an error.
# This pattern can be simplified using the `is_single` or `assert_is_single` helper method.
# These helpers check based on the provided `groupby_cols` if there is really just a single group/row left with the
# given groupby settings.
#
# Let's say `data` can be accessed on either a `recording` or a `trail` level, and `segmented_stride_list` can only
# be accessed on a `trail` level.
# Then we could do something like this:


class CustomDataset(Dataset):
    @property
    def data(self) -> MultiSensorData:
        # Note that we need to make our checks from the least restrictive to the most restrictive (if there is only a
        # single trail, there is only just a single recording).
        if self.is_single(["participant", "recording"]):
            return "This is the data for participant {} and rec {}".format(*self.groups[0])
        # None -> single row
        if self.is_single(None):
            return "This is the data for participant {}, rec {} and trial {}".format(*self.groups[0])
        raise ValueError(
            "Data can only be accessed when their is only a single recording of a single participant in the subset"
        )

    @property
    def sampling_rate_hz(self) -> float:
        return 204.8

    @property
    def segmented_stride_list_(self) -> MultiSensorStrideList:
        # We use assert here, as we don't have multiple options.
        # (We could also used `None` for the `groupby_cols` here)
        self.assert_is_single(["participant", "recording", "trial"], "segmented_stride_list_")
        return "This is the segmented stride list for participant {}, rec {} and trial {}".format(*self.groups[0])

    def create_index(self):
        return index


# %%
# If we select a single trial (row), we can get data and the stride list:
test_dataset = CustomDataset()
single_trial = test_dataset[0]
print(single_trial.data)
print(single_trial.segmented_stride_list_)

# %%
# If we only select a recording, we get an error for the stride list:

# We select only recording 3 here, as it has 2 trials.
single_recording = test_dataset.get_subset(recording="rec_3").groupby(["participant", "recording"])[0]
print(single_recording.data)
try:
    print(single_recording.segmented_stride_list_)
except Exception as e:
    print("ValueError: ", e)

# %%
# Custom parameter
# ----------------
# Often it is required to pass some parameters/configuration to the dataset.
# This could be for example the place where the data is stored or if a specific part of the dataset should be included,
# if some preprocessing should be applied to the data, ... .
#
# Such additional configuration can be provided via a custom `__init__` and is then available for all methods to be
# used.
# Note that you **must** assign the configuration values to attributes with the same name and **must not** forget to
# call `super().__init__`


class CustomDatasetWithConfig(Dataset):
    data_folder: str
    custom_config_para: bool

    def __init__(
        self,
        data_folder: str,
        custom_config_para: bool = False,
        *,
        groupby_cols: Optional[Union[list[str], str]] = None,
        subset_index: Optional[pd.DataFrame] = None,
    ) -> None:
        self.data_folder = data_folder
        self.custom_config_para = custom_config_para
        super().__init__(groupby_cols=groupby_cols, subset_index=subset_index)

    def create_index(self):
        # Use e.g. `self.data_folder` to load the data.
        return index