stress_at_work_analysis/machine_learning/features_sensor.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 proximity, helper, communication

WARNING_PARTICIPANTS_LABEL = (
    "Before calculating features, 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 SensorFeatures:
    def __init__(
        self,
        grouping_variable: str,
        features: dict,
        participants_usernames: Collection = None,
    ):

        self.grouping_variable_name = grouping_variable
        self.grouping_variable = [grouping_variable]

        self.data_types = features.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_features_all = pd.DataFrame()

        self.df_proximity = pd.DataFrame()
        self.df_proximity_counts = pd.DataFrame()

        self.df_calls = pd.DataFrame()
        self.df_sms = pd.DataFrame()
        self.df_calls_sms = pd.DataFrame()

        self.folder = None
        self.filename_prefix = ""
        self.construct_export_path()
        print("SensorFeatures initialized.")

    def set_sensor_data(self):
        print("Querying database ...")
        if "proximity" in self.data_types:
            self.df_proximity = proximity.get_proximity_data(
                self.participants_usernames
            )
            print("Got proximity data from the DB.")
            self.df_proximity = helper.get_date_from_timestamp(self.df_proximity)
            self.df_proximity = proximity.recode_proximity(self.df_proximity)
        if "communication" in self.data_types:
            self.df_calls = communication.get_call_data(self.participants_usernames)
            self.df_calls = helper.get_date_from_timestamp(self.df_calls)
            print("Got calls data from the DB.")

            self.df_sms = communication.get_sms_data(self.participants_usernames)
            self.df_sms = helper.get_date_from_timestamp(self.df_sms)
            print("Got sms data from the DB.")

    def get_sensor_data(self, data_type) -> pd.DataFrame:
        if data_type == "proximity":
            return self.df_proximity
        elif data_type == "communication":
            return self.df_calls_sms
        else:
            raise KeyError("This data type has not been implemented.")

    def calculate_features(self):
        print("Calculating features ...")
        if not self.participants_label:
            raise ValueError(WARNING_PARTICIPANTS_LABEL)
        if "proximity" in self.data_types:
            self.df_proximity_counts = proximity.count_proximity(
                self.df_proximity, self.grouping_variable
            )
            self.df_features_all = safe_outer_merge_on_index(
                self.df_features_all, self.df_proximity_counts
            )
            print("Calculated proximity features.")
        to_csv_with_settings(
            self.df_proximity, self.folder, self.filename_prefix, data_type="prox"
        )

        if "communication" in self.data_types:
            self.df_calls_sms = communication.calls_sms_features(
                df_calls=self.df_calls,
                df_sms=self.df_sms,
                group_by=self.grouping_variable,
            )
            self.df_features_all = safe_outer_merge_on_index(
                self.df_features_all, self.df_calls_sms
            )
            print("Calculated communication features.")
        to_csv_with_settings(
            self.df_calls_sms, self.folder, self.filename_prefix, data_type="comm"
        )

        self.df_features_all.fillna(
            value=proximity.FILL_NA_PROXIMITY, inplace=True, downcast="infer",
        )
        self.df_features_all.fillna(
            value=communication.FILL_NA_CALLS_SMS_ALL, inplace=True, downcast="infer",
        )

    def get_features(self, data_type, feature_names) -> pd.DataFrame:
        if data_type == "proximity":
            if feature_names == "all":
                feature_names = proximity.FEATURES_PROXIMITY
            return self.df_proximity_counts[feature_names]
        elif data_type == "communication":
            if feature_names == "all":
                feature_names = communication.FEATURES_CALLS_SMS_ALL
            return self.df_calls_sms[feature_names]
        elif data_type == "all":
            return self.df_features_all
        else:
            raise KeyError("This data type has not been implemented.")

    def construct_export_path(self):
        if not self.participants_label:
            warnings.warn(WARNING_PARTICIPANTS_LABEL, UserWarning)
        self.folder = here("machine_learning/intermediate_results/features", warn=True)
        self.filename_prefix = (
            self.participants_label + "_" + self.grouping_variable_name
        )

    def set_participants_label(self, label: str):
        self.participants_label = label
        self.construct_export_path()


def safe_outer_merge_on_index(left, right):
    if left.empty:
        return right
    elif right.empty:
        return left
    else:
        return pd.merge(
            left,
            right,
            how="outer",
            left_index=True,
            right_index=True,
            validate="one_to_one",
        )


def to_csv_with_settings(
    df: pd.DataFrame, folder: Path, filename_prefix: str, data_type: str
) -> None:
    export_filename = filename_prefix + "_" + data_type + ".csv"
    full_path = folder / export_filename
    df.to_csv(
        path_or_buf=full_path,
        sep=",",
        na_rep="NA",
        header=True,
        index=False,
        encoding="utf-8",
    )
    print("Exported the dataframe to " + str(full_path))