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process_targets.py
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process_targets.py
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import pandas as pd
import os
import numpy as np
def load_from_rds(names):
data = pd.read_csv('../raw/nda_rds_201.csv',
usecols=['src_subject_id', 'eventname'] + names,
na_values=['777', 999, '999', 777])
data = data.loc[data[data['eventname'] == 'baseline_year_1_arm_1'].index]
data = data.set_index('src_subject_id')
data = data.drop('eventname', axis=1)
return data
def main():
# Load the variables which can be loaded directly
data = load_from_rds([
'sex_at_birth',
'interview_age',
'neurocog_pc1.bl',
'neurocog_pc2.bl',
'neurocog_pc3.bl',
'anthro_height_calc',
'anthro_weight_calc',
'anthro_waist_cm',
'devhx_2_birth_wt_lbs_p',
'devhx_5_twin_p',
'devhx_12a_born_premature_p',
'devhx_6_pregnancy_planned_p',
'devhx_20_motor_dev_p',
'demo_prnt_age_p',
'cbcl_scr_syn_rulebreak_r',
'lmt_scr_perc_correct',
'macvs_ss_r_p',
'nihtbx_cardsort_uncorrected',
'nihtbx_list_uncorrected',
'nihtbx_pattern_uncorrected',
'nihtbx_picvocab_uncorrected',
'nihtbx_reading_uncorrected',
'pea_wiscv_trs',
'accult_phenx_q2_p',
'ksads_back_c_det_susp_p',
'ksads_back_c_mh_sa_p',
'married.bl',
'weight_phenx_ss_mean_p',
'rel_family_id'
])
# Fix weird categories
data['ksads_back_c_mh_sa_p'] =\
data['ksads_back_c_mh_sa_p'].replace({'Yes': 1, 'Not sure': np.nan}).astype('float')
# Recode dev motor skill to be an oridinal / regression type problem
recode = {'Much later': 0,
'Somewhat later': 1,
'About average': 2,
'Somewhat earlier': 3,
'Much earlier': 4,
"Don't know": np.nan}
data['devhx_20_motor_dev_p'] = data['devhx_20_motor_dev_p'].replace(recode).astype('float')
# Add some composites for sports activity
sp = 'sports_activity_activities_p___'
# Add composite sum of team sport activites
team_sport = load_from_rds([sp + str(i) for i in [1,2,4,5,7,11,12,15,21]])
data['sports_activity_activities_p_team_sport'] = (team_sport != 'not endorsed').sum(axis=1)
# Add compositive of performance, i.e., Ballet, Music, Drawing ...
performance = load_from_rds([sp + str(i) for i in [0,23,24,25]])
data['sports_activity_activities_p_performance'] = (performance != 'not endorsed').sum(axis=1)
# Load base variables to binarize
to_binary = load_from_rds(['devhx_15_days_incubator_p',
'asr_scr_thought_r',
'cbcl_scr_syn_aggressive_r',
'devhx_18_mnths_breast_fed_p',
'prodrom_psych_ss_severity_score',
'sleep_ss_total_p'])
# Add the different composites to binarize
# Add distress at birth
rep_dic = {"No": 0, "Don't know": 0, "Yes": 1}
devhx_vars = ['devhx_14a_blue_birth_p', 'devhx_14b_slow_heart_beat_p', 'devhx_14c_did_not_breathe_p',
'devhx_14d_convulsions_p', 'devhx_14e_jaundice_p', 'devhx_14f_oxygen_p',
'devhx_14g_blood_transfuse_p', 'devhx_14h_rh_incompatible_p']
d_at_birth = load_from_rds(devhx_vars)
d_at_birth.replace(rep_dic, inplace=True)
to_binary['devhx_distress_at_birth'] = d_at_birth.sum(axis=1)
# Add mother problems
devhx_vars = ['devhx_10a_severe_nausea_p', 'devhx_10b_heavy_bleeding_p',
'devhx_10c_eclampsia_p', 'devhx_10e_persist_proteinuria_p', 'devhx_10d_gall_bladder_p',
'devhx_10f_rubella_p', 'devhx_10g_severe_anemia_p', 'devhx_10h_urinary_infections_p',
'devhx_10i_diabetes_p', 'devhx_10j_high_blood_press_p', 'devhx_10k_problems_placenta_p',
'devhx_10l_accident_injury_p', 'devhx_10m_other_p']
m_probs = load_from_rds(devhx_vars)
m_probs.replace(rep_dic, inplace=True)
to_binary['devhx_mother_probs'] = m_probs.sum(axis=1)
# Add composite alc
avg_alc = load_from_rds(['devhx_ss_8_alcohol_avg_p', 'devhx_ss_9_alcohol_avg_p'])
to_binary['devhx_ss_alcohol_avg_p'] = avg_alc.sum(axis=1)
# Add composite marijuana
m_sum = load_from_rds(['devhx_ss_8_marijuana_amt_p', 'devhx_ss_9_marijuana_amt_p'])
to_binary['devhx_ss_marijuana_amt_p'] = m_sum.sum(axis=1)
# Add screentime composites
st = load_from_rds(['screentime_1_hours_p', 'screentime_1_minutes_p',
'screentime_2_hours_p', 'screentime_2_minutes_p'])
to_binary['screentime_week_p'] = st['screentime_1_hours_p'] + (st['screentime_1_minutes_p']/60)
to_binary['screentime_weekend_p'] = st['screentime_2_hours_p'] + (st['screentime_2_minutes_p']/60)
# Add ADHD composite
adhd = load_from_rds(['ksads_14_853_p', 'ksads_14_854_p', 'ksads_14_855_p', 'ksads_14_856_p'])
to_binary['ksads_adhd_composite'] = adhd.sum(axis=1)
# Add bipolar composite
bipolar = load_from_rds(['ksads_2_830_p', 'ksads_2_830_t', 'ksads_2_831_p',
'ksads_2_831_t', 'ksads_2_832_p',
'ksads_2_832_t', 'ksads_2_833_p',
'ksads_2_833_t', 'ksads_2_834_p',
'ksads_2_834_t', 'ksads_2_835_p',
'ksads_2_835_t', 'ksads_2_836_p',
'ksads_2_836_t', 'ksads_2_837_p',
'ksads_2_837_t', 'ksads_2_838_p',
'ksads_2_838_t', 'ksads_2_839_p',
'ksads_2_839_t'])
to_binary['ksads_bipolar_composite'] = bipolar.sum(axis=1)
# Add OCD
ocd = load_from_rds(['ksads_11_917_p', 'ksads_11_918_p', 'ksads_11_919_p', 'ksads_11_920_p'])
to_binary['ksads_OCD_composite'] = ocd.sum(axis=1)
# Using the following thresholds to
# convert to_binary, to binary versions of each column
o_dict = {
'asr_scr_thought_r': 2,
'cbcl_scr_syn_aggressive_r': 4,
'prodrom_psych_ss_severity_score': 10,
'sleep_ss_total_p': 35,
'devhx_15_days_incubator_p': 0.5,
'devhx_18_mnths_breast_fed_p': 10,
'devhx_distress_at_birth': 0.5,
'devhx_mother_probs': 0.5,
'devhx_ss_alcohol_avg_p': 0.5,
'devhx_ss_marijuana_amt_p': 0.5,
'screentime_week_p': 4,
'screentime_weekend_p': 5,
'ksads_adhd_composite': .5,
'ksads_bipolar_composite': .5,
'ksads_OCD_composite': .5,
}
# Convert, deleting column after done
cols = list(to_binary)
for col in cols:
to_binary[col + '_binary'] = (to_binary[col] > o_dict[col])
to_binary = to_binary.drop(col, axis=1)
# Merge to_binary with data
data = data.merge(to_binary, on='src_subject_id', how='outer')
# Save as csv for future use
os.makedirs('../data/', exist_ok=True)
data.to_csv('../data/targets.csv')
if __name__ == "__main__":
main()