| 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} |
| |
| 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) |
|
|
| |
| |
| |
|
|
| 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"]) |
|
|
| |
| 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') |
|
|
| |
| 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): |
| |
| 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())) |
|
|
|
|
|
|
