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cpt_anno.py
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import numpy as np
import pandas as pd
import os
from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from support.acc_funs import auc_decomp
from sklearn.model_selection import GridSearchCV
# DESCRIPTION: THIS SCRIPT GENERATES AUC SCORES FOR THE AGGREGATE AND SUB MODELS.
# THE SUBMODELS ARE DEFINED BY THEIR ORGAN GROUP, NOT INDIVIDUAL CPT CODE
# SAVES TO OUTPUT:
# --- logit_agg_title.csv
# --- logit_sub_title.csv
###############################
# ---- STEP 1: LOAD DATA ---- #
dir_base = os.getcwd()
dir_output = os.path.join(dir_base, '..', 'output')
dir_figures = os.path.join(dir_base, '..', 'figures')
fn_X = 'X_imputed.csv'
fn_Y = 'y_agg.csv'
dat_X = pd.read_csv(os.path.join(dir_output, fn_X))
dat_Y = pd.read_csv(os.path.join(dir_output, fn_Y))
# CREATE DUMMY VARIABLES FOR NON NUMERIC
dat_X = pd.get_dummies(dat_X)
# !! ENCODE CPT AS CATEGORICAL !! #
dat_X['cpt'] = 'c' + dat_X.cpt.astype(str)
# GET CPT ANNOTATIONS
file_name = 'cpt_anno.csv'
cpt_anno = pd.read_csv(os.path.join(dir_output, file_name))
cpt_anno['cpt'] = 'c' + cpt_anno.cpt.astype(str)
# GROUP BY TITLE AND GET COUNTS - REMOVE GROUPS WITH ONLY 1 COUNT
cpt_groups = cpt_anno.groupby('title').size().sort_values(ascending=False)
cpt_groups = pd.DataFrame({'cpt_title': cpt_groups.index, 'count': cpt_groups.values})
# KEEP CPT TITLES THAT HAVE MORE THAN ONE CPT CODE ASSOCIATED WITH THEM
top_groups = cpt_groups[cpt_groups['count'] > 1]
top_groups = top_groups.cpt_title.unique()
# SUBSET CPT GROUPS TO KEEP ONLY CPTS THAT HAVE A CORRESPONDING TITLE
cpt_anno = cpt_anno[cpt_anno.title.isin(top_groups)].reset_index(drop=True)
top_cpts = cpt_anno.cpt.unique()
# SUBSET CPT_GROUPS TO GET
# HERE NEED TO FIND A WAY TO GET THE CPT NAMES ASSOCIATED WITH EACH TITLE (MAYBE REFORMAT DATA IN R)
# SUBET BY DATA FRAMES BY CPT CODES
dat_X = dat_X[dat_X.cpt.isin(top_cpts)].reset_index(drop=True)
dat_Y = dat_Y[dat_Y.caseid.isin(dat_X.caseid)].reset_index(drop=True)
# GET COLUMNS
cn_X = list(dat_X.columns[2:])
cn_Y = list(dat_Y.columns[25:37])
# DELETE NON AGG LABELS
dat_Y.drop(dat_Y.columns[[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]],
axis=1, inplace=True)
###############################################
# ---- STEP 2: LEAVE-ONE-YEAR - ALL VARIABLES ---- #
# START LOOP
holder_y_all = []
for ii, vv in enumerate(cn_Y):
print('##### ------- Outcome %s (%i of %i) -------- #####' % (vv, ii + 1, len(cn_Y)))
tmp_ii = pd.concat([dat_Y.operyr, dat_Y[vv] == -1], axis=1)
tmp_ii = tmp_ii.groupby('operyr')[vv].apply(np.sum).reset_index().rename(columns={vv: 'n'})
tmp_years = tmp_ii[tmp_ii.n == 0].operyr.values
tmp_years = tmp_years.astype(int)
tmp_train_years = tmp_years[tmp_years > (tmp_years.min())]
holder_y = []
for yy in tmp_train_years:
print('Train Year %i' % (yy))
idx_train = dat_X.operyr.isin(tmp_years) & (dat_X.operyr < yy)
idx_test = dat_X.operyr.isin(tmp_years) & (dat_X.operyr == yy)
Xtrain, Xtest = dat_X.loc[idx_train, cn_X].reset_index(drop=True), \
dat_X.loc[idx_test, cn_X].reset_index(drop=True)
ytrain, ytest = dat_Y.loc[idx_train, [vv]].reset_index(drop=True), \
dat_Y.loc[idx_test, [vv]].reset_index(drop=True)
# STORE CPT CODES
tmp_cpt = Xtest.cpt
# REMOVE CPT CODES
del Xtrain['cpt']
del Xtest['cpt']
# grid search
param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
clf = GridSearchCV(LogisticRegression(penalty='l2', solver='liblinear', max_iter=200), param_grid, n_jobs=6, cv=2)
logisiticreg = clf.fit(Xtrain, ytrain.values.ravel())
logit_preds = logisiticreg.predict_proba(Xtest)[:, 1]
# STORE RESULTS FROM AGGREGATE MODEL
tmp_holder = pd.DataFrame({'y_preds': list(logit_preds), 'y_values': list(ytest.values), 'cpt': list(tmp_cpt)})
within_holder = []
# GET TOP TITLES
top_titles = cpt_anno.title.unique()
# LOOP THROUGH EACH CPT TITLE
for cc in top_titles:
title_cpts = cpt_anno[cpt_anno['title']==cc].reset_index(drop=True)
title_cpts = title_cpts.cpt.unique()
sub_tmp_holder = tmp_holder[tmp_holder['cpt'].isin(title_cpts)].reset_index(drop=True)
if all(sub_tmp_holder.y_values.values == 0) or len(sub_tmp_holder.y_values.values) <= 1:
within_holder.append(pd.DataFrame({'auc': 'NA',
'cpt': cc}, index=[0]))
else:
within_holder.append(pd.DataFrame({'auc': metrics.roc_auc_score(list(sub_tmp_holder.y_values.values),
list(sub_tmp_holder.y_preds.values)),
'cpt': cc}, index=[0]))
holder_y.append(pd.concat(within_holder).assign(test_year=yy))
holder_y_all.append(pd.concat(holder_y).assign(outcome=vv))
res_y_all = pd.concat(holder_y_all).reset_index(drop=True)
res_y_all.to_csv(os.path.join(dir_output, 'agg_cpt_title.csv'), index=False)
####################################################
# ---- STEP 3: LEAVE-ONE-YEAR - ALL VARIABLES, FOR EACH CPT CODE, SUB MODELS---- #
holder_y_all = []
for ii, vv in enumerate(cn_Y):
print('##### ------- Outcome %s (%i of %i) -------- #####' % (vv, ii + 1, len(cn_Y)))
tmp_ii = pd.concat([dat_Y.operyr, dat_Y[vv] == -1], axis=1)
tmp_ii = tmp_ii.groupby('operyr')[vv].apply(np.sum).reset_index().rename(columns={vv: 'n'})
tmp_years = tmp_ii[tmp_ii.n == 0].operyr.values
tmp_years = tmp_years.astype(int)
tmp_train_years = tmp_years[tmp_years > (tmp_years.min())]
holder_y = []
for yy in tmp_train_years:
print('Train Year %i' % (yy))
idx_train = dat_X.operyr.isin(tmp_years) & (dat_X.operyr < yy)
idx_test = dat_X.operyr.isin(tmp_years) & (dat_X.operyr == yy)
# get dummies
Xtrain, Xtest = dat_X.loc[idx_train, cn_X].reset_index(drop=True), \
dat_X.loc[idx_test, cn_X].reset_index(drop=True)
ytrain, ytest = dat_Y.loc[idx_train, [vv]].reset_index(drop=True), \
dat_Y.loc[idx_test, [vv]].reset_index(drop=True)
# GET TOP TITLES TO LOOP THROUGH
top_titles = cpt_anno.title.unique()
within_holder = []
for cc in top_titles:
# GET LIST OF TITLES
title_cpts = cpt_anno[cpt_anno['title'] == cc].reset_index(drop=True)
title_cpts = list(title_cpts.cpt.unique())
# SUBSET XTRAIN AND XTEST BY CPT CODE
sub_xtrain = Xtrain[Xtrain['cpt'].isin(title_cpts)]
sub_xtest = Xtest[Xtest['cpt'].isin(title_cpts)]
# SUBSET YTRAIN AND YTEST BY THE CORRESPONDING INDICES IN SUBSETTED XDATA
sub_ytrain = ytrain[ytrain.index.isin(sub_xtrain.index)]
sub_ytest = ytest[ytest.index.isin(sub_xtest.index)]
# REVMOVE CPT COLUMN
del sub_xtrain['cpt']
del sub_xtest['cpt']
# FILL RESULTS WITH NA IF TRAIN OR TEST OUTCOMES ARE ALL ONE VALUE
if all(np.unique(sub_ytrain.values) == 0) or all(np.unique(sub_ytest.values) == 0) or all(sub_ytest.values == 1) or len(sub_ytest.values) <= 1:
within_holder.append(pd.DataFrame({'auc': 'NA',
'cpt': cc}, index=[0]))
else:
# grid search
param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]}
clf = GridSearchCV(LogisticRegression(penalty='l2', solver='liblinear', max_iter=200), param_grid,
n_jobs=6, cv=2)
logisiticreg = clf.fit(sub_xtrain, sub_ytrain.values.ravel())
logit_preds = logisiticreg.predict_proba(sub_xtest)[:, 1]
within_holder.append(
pd.DataFrame({'auc': metrics.roc_auc_score(sub_ytest.values, logit_preds), 'cpt': cc}, index=[0]))
holder_y.append(pd.concat(within_holder).assign(test_year=yy))
holder_y_all.append(pd.concat(holder_y).assign(outcome=vv))
res_y_all = pd.concat(holder_y_all).reset_index(drop=True)
res_y_all.to_csv(os.path.join(dir_output, 'sub_cpt_title.csv'), index=False)