stress_at_work_analysis/machine_learning/labels.py

import datetime
import warnings
from pathlib import Path
from typing import Collection

import pandas as pd
from pyprojroot import here

import participants.query_db
from features import esm
from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
from machine_learning.helper import read_csv_with_settings, to_csv_with_settings

WARNING_PARTICIPANTS_LABEL = (
    "Before aggregating labels, please set participants label using self.set_participants_label() "
    "to be used as a filename prefix when exporting data. "
    "The filename will be of the form: %participants_label_%grouping_variable_%data_type.csv"
)


class Labels:
    def __init__(
        self,
        grouping_variable: str,
        labels: dict,
        participants_usernames: Collection = None,
    ) -> None:
        self.grouping_variable_name = grouping_variable
        self.grouping_variable = [grouping_variable]

        self.questionnaires = labels.keys()

        self.participants_label: str = ""
        if participants_usernames is None:
            participants_usernames = participants.query_db.get_usernames(
                collection_start=datetime.date.fromisoformat("2020-08-01")
            )
            self.participants_label = "all"
        self.participants_usernames = participants_usernames

        self.df_esm = pd.DataFrame()
        self.df_esm_preprocessed = pd.DataFrame()
        self.df_esm_interest = pd.DataFrame()
        self.df_esm_clean = pd.DataFrame()

        self.df_esm_means = pd.DataFrame()

        self.folder: Path = Path()
        self.filename_prefix = ""
        self.construct_export_path()
        print("Labels initialized.")

    def set_labels(self) -> None:
        print("Querying database ...")
        self.df_esm = esm.get_esm_data(self.participants_usernames)
        print("Got ESM data from the DB.")
        self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
        print("ESM data preprocessed.")
        if "PANAS" in self.questionnaires:
            self.df_esm_interest = self.df_esm_preprocessed[
                (
                    self.df_esm_preprocessed["questionnaire_id"]
                    == QUESTIONNAIRE_IDS.get("PANAS").get("PA")
                )
                | (
                    self.df_esm_preprocessed["questionnaire_id"]
                    == QUESTIONNAIRE_IDS.get("PANAS").get("NA")
                )
            ]
        self.df_esm_clean = esm.clean_up_esm(self.df_esm_interest)
        print("ESM data cleaned.")

    def get_labels(self, questionnaire: str) -> pd.DataFrame:
        if questionnaire == "PANAS":
            return self.df_esm_clean
        else:
            raise KeyError("This questionnaire has not been implemented as a label.")

    def aggregate_labels(self, cached=True) -> None:
        print("Aggregating labels ...")
        if not self.participants_label:
            raise ValueError(WARNING_PARTICIPANTS_LABEL)

        try:
            if not cached:  # Do not use the file, even if it exists.
                raise FileNotFoundError
            self.df_esm_means = read_csv_with_settings(
                self.folder,
                self.filename_prefix,
                data_type="_".join(self.questionnaires),
                grouping_variable=self.grouping_variable,
            )
            print("Read labels from the file.")
        except FileNotFoundError:
            # We need to recalculate the features in this case.
            self.df_esm_means = (
                self.df_esm_clean.groupby(
                    ["participant_id", "questionnaire_id"] + self.grouping_variable
                )
                .esm_user_answer_numeric.agg("mean")
                .reset_index()
                .rename(columns={"esm_user_answer_numeric": "esm_numeric_mean"})
            )
            self.df_esm_means = (
                self.df_esm_means.pivot(
                    index=["participant_id"] + self.grouping_variable,
                    columns="questionnaire_id",
                    values="esm_numeric_mean",
                )
                .reset_index(col_level=1)
                .rename(columns=QUESTIONNAIRE_IDS_RENAME)
                .set_index(["participant_id"] + self.grouping_variable)
            )
            print("Labels aggregated.")
            to_csv_with_settings(
                self.df_esm_means,
                self.folder,
                self.filename_prefix,
                data_type="_".join(self.questionnaires),
            )

    def get_aggregated_labels(self) -> pd.DataFrame:
        return self.df_esm_means

    def construct_export_path(self) -> None:
        if not self.participants_label:
            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
        self.folder = here("machine_learning/intermediate_results/labels", warn=True)
        self.filename_prefix = (
            self.participants_label + "_" + self.grouping_variable_name
        )

    def set_participants_label(self, label: str) -> None:
        self.participants_label = label
        self.construct_export_path()