import os import sys import pandas as pd import numpy as np from datetime import datetime, timedelta from lib.experiment_specs import study_config from lib.data_helpers import test from lib.data_helpers import data_utils from data.source.clean_master.outcome_variable_cleaners import outcome_cleaner from data.source.exporters.master_contact_generator import MasterContactGenerator from data.source.clean_master.management.baseline_prep import BaselinePrep from data.source.clean_master.management.midline_prep import MidlinePrep from data.source.clean_master.management.endline1_prep import Endline1Prep from data.source.clean_master.management.endline2_prep import Endline2Prep from data.source.clean_master.management.earnings import Earnings from lib.utilities import serialize np.random.seed(12423534) """ cleans the aggregated raw master user level data file by: - adding treatment/payment variables - creates outcome variables and indices - """ class Cleaner(): used_contact_list_directory = os.path.join("data","external","dropbox_confidential","ContactLists","Used") master_file = os.path.join("data","external","intermediate", "MasterCleanUser") master_test_file = os.path.join("data","external","intermediate_test", "MasterCleanUser") qual_path = os.path.join("data", "external", "dropbox_confidential", "QualitativeFeedback") def __init__(self): self.treatment_cl = pd.DataFrame() self.used_contact_lists = self._import_used_contact_lists() self.config_user_dict = serialize.open_yaml("config_user.yaml") self.survey_prep_functions = {"Baseline":BaselinePrep.main, "Midline":MidlinePrep.main, "Endline1":Endline1Prep.main, "Endline2":Endline2Prep.main} #add filler surveys for survey in study_config.surveys.keys(): if "Phase" in survey: self.survey_prep_functions[survey] = Endline2Prep.filler def clean_master(self,raw_master_df): df = self._prepare_proper_sample(raw_master_df) """Prepare Outcome Variables""" df = self.ingest_qual_data("PDBug", df) df = outcome_cleaner.clean_outcome_vars(df) """Prepare Embedded Data for Upcoming Surveys or Ingest Embedded Data from Used CLs""" for phase_name, chars in study_config.phases.items(): start_survey = chars["StartSurvey"]["Name"] end_survey = chars["EndSurvey"]["Name"] if (datetime.now() < study_config.surveys[start_survey]["Start"]+ timedelta(3)): print(f"\n No action for {end_survey} Randomization, {phase_name} isn't 3 days in") continue if (datetime.now() > study_config.surveys[start_survey]["Start"] + timedelta(3)) & (datetime.now() < study_config.surveys[end_survey]["Start"]): print(f"\n Prepping {end_survey} Randomization") df = self.survey_prep_functions[end_survey](df) else: if end_survey in study_config.filler_surveys: continue elif end_survey not in self.used_contact_lists: print(f"{end_survey} CL needs to be in used CL!! Need used treatment assignments") sys.exit() else: print(f"\n Adding embedded data on {end_survey} using CL, since {phase_name} is over") df = self._add_cl_data(df,end_survey) """Calculate Earnings""" df = Earnings().create_payment_vars(df) self.sanity_checks(df) if self.config_user_dict['local']['test']: test.save_test_df(df,self.master_test_file) else: test.select_test_appcodes(df) serialize.save_pickle(df, self.master_file) df_str = df.copy().astype(str).applymap(lambda x: x.strip().replace("\n", "").replace('"', '')) df_str.to_csv(self.master_file+".csv", index = False) #seed_file = os.path.join("data","external","intermediate","Scratch", # "BalanceChecks",f"MasterCleanUser{study_config.seed}.csv") #df_str.to_csv(seed_file, index=False) return df """import used contact lists""" def _import_used_contact_lists(self): contact_lists = {} for survey, cl_name in study_config.used_contact_lists.items(): contact_lists[survey] = MasterContactGenerator.read_in_used_cl(cl_name,survey) return contact_lists def _prepare_proper_sample(self, df): """ This method crops the raw_master_user df to folks that attempted to complete registration The method also asserts that each row is identified by a unique appcode # We want to keep people that never downloaded the app but ATTEMPTED TO COMPLETE registration for attrition analysis # Attempted to keep registration means, they saw the consent form, and clicked continue, though they may not # have downloaded the app. """ initial_code = study_config.surveys[study_config.initial_master_survey]["Code"] df = df.loc[df[f"{initial_code}_Complete"] != "nan"].dropna( subset=[f"{initial_code}_Complete"]) # Reverse Order of DF so complete appear at top df = df.iloc[::-1].reset_index(drop=True) if study_config.surveys[study_config.initial_master_survey]["End"] < datetime.now(): try: assert len(df) == study_config.sample_size except: print(f"length of df ( {len(df)}) not same size as study_config.sample_size: {study_config.sample_size}") sys.exit() appcode_series = df.loc[df["AppCode"].notnull(), 'AppCode'] assert (len(appcode_series) == len(appcode_series.unique())) return df def ingest_qual_data(self, survey, df): file = study_config.qualitative_feedback_files[survey] code = study_config.surveys[survey]["Code"] q = pd.read_csv(os.path.join(self.qual_path, file)) q = data_utils.add_A_to_appcode(q, "AppCode") pii_cols = sum([x for x in study_config.id_cols.values()], []) for col in q.columns: if col in pii_cols + ["RecipientEmail"]: q = q.drop(columns=[col]) elif col in study_config.main_cols+study_config.embedded_main_cols: continue else: q = q.rename(columns={col: code + "_" + col}) q = q.loc[(~q.duplicated(subset=["AppCode"], keep='last'))] new_cols = ["AppCode"] + list(set(q.columns) - set(df.columns)) print(new_cols) df = df.merge(q[new_cols], how='left', on='AppCode') return df def _add_cl_data(self,df,survey): """Override all the treatment columns created, and insert those created in the used contact list Also Add Used CL avg daily use data""" old_phase = study_config.surveys[survey]["OldPhase"] prev_code = study_config.phases[old_phase]["StartSurvey"]["Code"] cl = self.used_contact_lists[survey] cl = cl.rename(columns={"PastActual": f"{prev_code}_Cl{study_config.use_var}"}) cl[f"{prev_code}_Cl{study_config.use_var}"] = pd.to_numeric(cl[f"{prev_code}_Cl{study_config.use_var}"], errors = 'coerce') # only keep prefixed columns (i.e. have a "_") that are not in the main df or main cols not in df cl_vars_to_merge = ["AppCode"] + [x for x in cl.columns.values if ((x not in df.columns) & ("_" in x)) | ((x not in df.columns) & (x in study_config.embedded_main_cols))] print(f"\t {cl_vars_to_merge}") df = df.merge(cl[cl_vars_to_merge], how ='left',on = "AppCode") return df """check that used contact list column values match the recreation the clean_master master""" def sanity_checks(self,df): # Assert no obs were dropped in cleaning if study_config.surveys["Baseline"]["End"] < datetime.now(): if len(df) != study_config.sample_size: print(f"CleanMaster (len = {len(df)}) is not same size a hard coded sample size ({study_config.sample_size})") sys.exit() appcode_series = df.loc[df["AppCode"].notnull(), 'AppCode'] assert (len(appcode_series) == len(appcode_series.unique()))