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