stress_at_work_analysis/machine_learning/labels.py

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import datetime
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import warnings
from pathlib import Path
from typing import Collection
import pandas as pd
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from pyprojroot import here
import participants.query_db
from features import esm
from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
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from machine_learning.helper import read_csv_with_settings, to_csv_with_settings
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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,
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) -> None:
self.grouping_variable_name = grouping_variable
self.grouping_variable = [grouping_variable]
self.questionnaires = labels.keys()
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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()
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self.folder: Path = Path()
self.filename_prefix = ""
self.construct_export_path()
print("Labels initialized.")
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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.")
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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),
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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),
)
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def get_aggregated_labels(self) -> pd.DataFrame:
return self.df_esm_means
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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()