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Data_Preprocessing_res.py
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#!/usr/bin/env python
# coding: utf-8
# # Data Exploration and Preprocessing
# TCGA Reannotated Ovarian Cancer Clinical Data
# In[131]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.font_manager
import seaborn as sns
from scipy import stats
from openpyxl import load_workbook
import sidetable
from functools import reduce
import itertools
import math
import random
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from collections import namedtuple
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.preprocessing import MinMaxScaler
import time
import statsmodels.api as sm
from scipy import stats
from lifelines import CoxPHFitter
device = torch.device('cpu')
# Randomness
random.seed(2021)
np.random.seed(2021)
torch.manual_seed(2021)
# Import Data
# In[132]:
# Villalobos 2018 reannotated TCGA data (https://ascopubs.org/doi/suppl/10.1200/CCI.17.00096)
tcga_ov_1 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-1.xlsx',
sheet_name='Master clinical dataset'
)
tcga_ov_2 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-2.xlsx',
sheet_name='Months'
)
tcga_ov_3 = pd.read_excel('./Data/Villalobos_TCGA/ds_CCI.17.00096-3.xlsx',
sheet_name='clinical_drug_all_OV.txt'
)
# TCGA Drug standardization (https://gdisc.bme.gatech.edu/cgi-bin/gdisc/tap5.cgi#)
drugs = pd.read_csv('./Data/DrugCorrection1.csv')
drugs['Correction'] = drugs['Correction'].str.strip()
# ## Clean and Reorganize
# TCGA 3: Clean
# In[133]:
# Drop columns with all missing values
print(tcga_ov_3.shape)
print(tcga_ov_3.isnull().sum(), '\n')
tcga_ov_3_drop = tcga_ov_3.dropna(axis=1, how='all')
# Drop rows where drug is missing
tcga_ov_3_drop = tcga_ov_3_drop[tcga_ov_3_drop['drug_name'].notna()]
# Drop rows where start AND end are missing (right now it's actually OR, see below)
tcga_ov_3_drop = tcga_ov_3_drop.dropna(how='any', subset=['days_to_drug_therapy_end', 'days_to_drug_therapy_start']) #change to how='all' when figure out what to do with start OR end missing
# Standardize drug names
# Merge using values from TCGA drug standardization (https://gdisc.bme.gatech.edu/cgi-bin/gdisc/tap5.cgi)
tcga_ov_3_clean = tcga_ov_3_drop.merge(drugs, how='left', left_on='drug_name', right_on='OldName').drop(['OldName'], axis=1)
# Additional replacement rules
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Doxoribicin', 'Correction'] = 'Doxorubicin'
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'gemcitabin', 'Correction'] = 'Gemcitabine'
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Hexlalen', 'Correction'] = 'Altretamine'
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Cisplatin/Gemzar', 'Correction'] = 'Cisplatin' # This applies to only one line, which has another sample just for Gemzar (Gemcitabine)
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Ilex', 'Correction'] = 'Ilex'
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'ILIZ', 'Correction'] = 'ILIZ'
tcga_ov_3_clean.loc[tcga_ov_3_clean['drug_name'] == 'Lily', 'Correction'] = 'Lily'
# Print replacement rules and replace 'drug_name'
drug_name_old = tcga_ov_3_clean['drug_name']
drug_name_new = tcga_ov_3_clean['Correction']
rules = pd.DataFrame({'drug_name_old': drug_name_old, 'drug_name_new': drug_name_new}).drop_duplicates().sort_values(by=['drug_name_old']).reset_index().drop('index', axis=1)
pd.set_option('display.max_rows', None)
print('Replacement Rules:\n', rules.head(), '\n')
pd.reset_option('max_rows')
tcga_ov_3_clean['drug_name'] = tcga_ov_3_clean['Correction']
tcga_ov_3_clean = tcga_ov_3_clean.drop('Correction', axis=1)
# List of drugs in dataset
drug_list = [x for x in list(tcga_ov_3_clean['drug_name'].drop_duplicates()) if str(x) != 'nan']
drug_list.sort()
print('Unique Drugs:', len(drug_list), '\n', drug_list, '\n')
# Drop where therapy start = therapy end
tcga_ov_3_clean = tcga_ov_3_clean[tcga_ov_3_clean['days_to_drug_therapy_end'] != tcga_ov_3_clean['days_to_drug_therapy_start']]
# TCGA 3: Fix/standardize time variables and fix order of therapy lines
# In[134]:
# Fix values where start and end are switched
tcga_ov_3_clean.loc[tcga_ov_3_clean['days_to_drug_therapy_start'] > tcga_ov_3_clean['days_to_drug_therapy_end'], ['days_to_drug_therapy_start', 'days_to_drug_therapy_end']] = tcga_ov_3_clean.loc[tcga_ov_3_clean['days_to_drug_therapy_start'] > tcga_ov_3_clean['days_to_drug_therapy_end'], ['days_to_drug_therapy_end', 'days_to_drug_therapy_start']].values
# Set earliest drug therapy start to zero and subtract everything else by min days
ther_start = tcga_ov_3_clean.groupby('bcr_patient_barcode')['days_to_drug_therapy_start']
tcga_timefix = tcga_ov_3_clean.assign(start_day=ther_start.transform(min))
# keep start days for later use with tcga_ov_1
tcga_start_days = tcga_timefix[['bcr_patient_barcode', 'start_day']].drop_duplicates()
tcga_timefix['therapy_start'] = tcga_timefix['days_to_drug_therapy_start'] - tcga_timefix['start_day']
tcga_timefix['therapy_end'] = tcga_timefix['days_to_drug_therapy_end'] - tcga_timefix['start_day']
tcga_timefix = tcga_timefix.drop(['days_to_drug_therapy_end', 'days_to_drug_therapy_start', 'start_day'], axis=1)
tcga_timefix = tcga_timefix.sort_values(by=['bcr_patient_barcode', 'therapy_start', 'therapy_end'])
# Set up state list for each patient: barcode, timing, drug combo
tcga_drug_lines = []
for barcode in tcga_timefix['bcr_patient_barcode'].unique():
tcga_time = tcga_timefix[tcga_timefix['bcr_patient_barcode'] == barcode]
tcga_time = tcga_time[['therapy_start', 'therapy_end', 'drug_name']].drop_duplicates(keep='first').values.tolist() # Drop duplicate drugs that have different dosages or administration but same timing
points = [] # list of (offset, plus/minus, drug) tuples
for start,stop,drug in tcga_time:
points.append((start,'+',drug))
points.append((stop,'-',drug))
points.sort()
ranges = [] # output list of (start, stop, drug_set) tuples
current_set = []
last_start = None
for offset,pm,drug in points:
if pm == '+':
if last_start is not None:
ranges.append([last_start,offset,list(set(current_set.copy()))])
current_set.append(drug)
last_start = offset
elif pm == '-':
ranges.append([last_start,offset,list(set(current_set.copy()))])
current_set.remove(drug)
last_start = offset
# Finish off
if last_start is not None:
ranges.append([last_start,offset,list(set(current_set.copy()))])
# Remove the ranges where start = stop
range_drug = []
for i in range(len(ranges)):
if ranges[i][0] != ranges[i][1]: # add condition: <& (ranges[i][2] != [])> to drop no-drug periods
range_drug.append(ranges[i])
# Sort drugs in each drug combo
for i in range(len(range_drug)):
range_drug[i][2].sort()
# Remove overlapping/back-to-back duplicate lines. Drop this section if decide to do something with dosages
ranges_final = []
for line in range(0, len(range_drug)-1):
if (range_drug[line+1][2] == range_drug[line][2]) & (range_drug[line+1][0] <= range_drug[line][1]):
range_drug[line][1] = range_drug[line+1][1]
range_drug[line+1][0] = range_drug[line][0]
if (range_drug[line][2] != range_drug[line+1][2]) | (range_drug[line][0] != range_drug[line+1][0]):
ranges_final.append(range_drug[line])
ranges_final.append(range_drug[len(range_drug)-1])
# Add the number of previous lines of therapy
for line in range(len(ranges_final)):
if line == 0:
ranges_final[line].append(0)
elif ranges_final[line-1][2] == []:
ranges_final[line].append(ranges_final[line-1][3])
else:
ranges_final[line].append(ranges_final[line-1][3] + 1)
# Add treat transition var
for line in range(len(ranges_final)):
try:
if (ranges_final[line][2] != []) & (ranges_final[line+1][2] != []):
ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30))
elif (ranges_final[line][2] != []) & (ranges_final[line+1][2] == []):
ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30) - 1)
elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] != []):
ranges_final[line].append(1)
elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] == []):
ranges_final[line].append(0)
except IndexError:
ranges_final[line].append(0)
# Add no treat transition var
for line in range(len(ranges_final)):
try:
if (ranges_final[line][2] != []) & (ranges_final[line+1][2] != []):
ranges_final[line].append(0)
elif (ranges_final[line][2] != []) & (ranges_final[line+1][2] == []):
ranges_final[line].append(1)
elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] != []):
ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30) - 1)
elif (ranges_final[line][2] == []) & (ranges_final[line+1][2] == []):
ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30))
except IndexError:
ranges_final[line].append(math.ceil((ranges_final[line][1] - ranges_final[line][0]) / 30)-1)
# Add patient barcodes
for line in range(len(ranges_final)):
ranges_final[line].insert(0, barcode)
tcga_drug_lines.extend(ranges_final)
tcga_drug_lines[3] = ['TCGA-04-1332', 0.0, 151.0, ['Carboplatin', 'Paclitaxel', 'Topotecan'], 0, 5, 1] # Special case to fix. Make sure to check this if make changes above
# Back to df
lines_df = pd.DataFrame(tcga_drug_lines, columns=['bcr_patient_barcode', 'start', 'end', 'therapy', 'previous_lines', 'treat', 'no_treat'])
# List of patient barcodes
tcga_barcodes = list(lines_df['bcr_patient_barcode'].unique())
# Notes
# One thing to be aware of is that this code drops all values where therapy start and therapy end are equal
# TCGA 1: Clean
# In[135]:
# Keep subset of variables
print('NaNs: ', tcga_ov_1.isnull().sum())
tcga_ov_1_keep = tcga_ov_1[['bcr_patient_barcode',
'total_days_overall_survival',
'outcome_overall_survival_censoring',
# 'vital_status',
# 'days_to_tumor_progression',
# 'days_to_death',
# 'days_to_last_followup',
# 'days_to_tumor_recurrence',
# 'time_to_failure',
# 'Cycles_of_adjuvant_therapy',
# 'Adjuvant_chemotherapy_dose_intensity',
'age_at_initial_pathologic_diagnosis',
# 'anatomic_organ_subdivision',
# 'days_to_birth',
# 'initial_pathologic_diagnosis_method',
# 'person_neoplasm_cancer_status',
# 'pretreatment_history',
# 'primary_therapy_outcome_success', # ask EKO about this
'race',
# 'residual_tumor',
# 'site_of_tumor_first_recurrence',
# 'tissue_source_site',
'tumor_grade',
# 'tumor_residual_disease',
'tumor_stage',
# 'tumor_tissue_site'
# 'year_of_initial_pathologic_diagnosis',
# 'Days off platinum prior to recurrence 1st line',
# 'Last day of platinum 1st line',
# 'Chemotherapy number of lines of therapy'
]]
# Drop cases that don't have a survival metric
tcga_ov_1_keep = tcga_ov_1_keep.dropna(subset=['total_days_overall_survival'])
tcga_ov_1_keep.drop(tcga_ov_1_keep[tcga_ov_1_keep['total_days_overall_survival'] == 'cannot assess'].index, inplace=True)
# Only keep samples that are in the cleaned 'lines' data
tcga_ov_1_keep = tcga_ov_1_keep[tcga_ov_1_keep['bcr_patient_barcode'].isin(tcga_barcodes)].sort_values(by=['bcr_patient_barcode']).reset_index().drop('index', axis=1)
# Adjust final survival by start of therapy day
tcga_start_days = tcga_start_days[tcga_start_days['bcr_patient_barcode'].isin(tcga_barcodes)].sort_values(by=['bcr_patient_barcode']).reset_index().drop('index', axis=1)
tcga_ov_1_keep['total_days_overall_survival'] = tcga_ov_1_keep['total_days_overall_survival'] - tcga_start_days['start_day'] # this needs work, getting some negative values
# Fill blanks
tcga_ov_1_keep = tcga_ov_1_keep.replace(r'^\s*$', np.nan, regex=True)
tcga_ov_1_keep = tcga_ov_1_keep.replace(np.nan, 'NOT SPECIFIED', regex=True)
# Deceased only
tcga_ov_1_keep_d = tcga_ov_1_keep.loc[tcga_ov_1_keep['outcome_overall_survival_censoring'] == 1].reset_index(drop=True)
# Save CSVs
# tcga_ov_1_keep.to_csv('tcga_ov_1_keep.csv', index=False)
# tcga_ov_1_keep_d.to_csv('tcga_ov_1_keep_d.csv', index=False)
# Show data
tcga_ov_1_keep
# Data summary table(s)
# In[136]:
############## Fix so that if a wb doesn't already exist it creates one
# book = load_workbook('Table_1.xlsx')
# book.template = False
# writer = pd.ExcelWriter('Table_1.xlsx', engine='openpyxl')
# writer.book = book
# ##################### All Patients
# # Race
# try:
# del book['race']
# except:
# pass
# tcga_ov_1_keep.stb.freq(['race']).to_excel(writer, sheet_name='race')
# # Tumor grade
# try:
# del book['tumor grade']
# except:
# pass
# tcga_ov_1_keep.stb.freq(['tumor_grade']).to_excel(writer, sheet_name='tumor grade')
# # Tumor stage
# try:
# del book['tumor stage']
# except:
# pass
# tcga_ov_1_keep.stb.freq(['tumor_stage']).to_excel(writer, sheet_name='tumor stage')
# # Death
# try:
# del book['death']
# except:
# pass
# tcga_ov_1_keep.stb.freq(['outcome_overall_survival_censoring']).to_excel(writer, sheet_name='death')
# # Age
# try:
# del book['age']
# except:
# pass
# pd.DataFrame(tcga_ov_1_keep['age_at_initial_pathologic_diagnosis'].describe()).to_excel(writer, sheet_name='age')
# # Overall survival
# try:
# del book['days survival']
# except:
# pass
# pd.DataFrame(tcga_ov_1_keep['total_days_overall_survival'].describe()).to_excel(writer, sheet_name='days survival')
# ##################### Deceased Patients
# # Race
# try:
# del book['race_d']
# except:
# pass
# tcga_ov_1_keep_d.stb.freq(['race']).to_excel(writer, sheet_name='race_d')
# # Tumor grade
# try:
# del book['tumor grade_d']
# except:
# pass
# tcga_ov_1_keep_d.stb.freq(['tumor_grade']).to_excel(writer, sheet_name='tumor grade_d')
# # Tumor stage
# try:
# del book['tumor stage_d']
# except:
# pass
# tcga_ov_1_keep_d.stb.freq(['tumor_stage']).to_excel(writer, sheet_name='tumor stage_d')
# # Age
# try:
# del book['age_d']
# except:
# pass
# pd.DataFrame(tcga_ov_1_keep_d['age_at_initial_pathologic_diagnosis'].describe()).to_excel(writer, sheet_name='age_d')
# # Overall survival
# try:
# del book['days survival_d']
# except:
# pass
# pd.DataFrame(tcga_ov_1_keep_d['total_days_overall_survival'].describe()).to_excel(writer, sheet_name='days survival_d')
# writer.save()
# writer.close()
# Add death dummy to drug lines data
# In[137]:
# Merge in final death event for each patient
lines_df_2 = lines_df.merge(tcga_ov_1_keep[['bcr_patient_barcode', 'outcome_overall_survival_censoring', 'race',
'age_at_initial_pathologic_diagnosis', 'tumor_stage', 'tumor_grade']],
on='bcr_patient_barcode').rename(columns={'bcr_patient_barcode': 'patient', 'outcome_overall_survival_censoring': 'death', 'age_at_initial_pathologic_diagnosis': 'age'})
# Only keep death event on last line
lines_df_2.loc[lines_df_2['patient'] == lines_df_2['patient'].shift(-1), 'death'] = 0
# Fix no treat on last line where death = 0
def fix_notreat(row):
if (row['treat'] == 0) & (row['no_treat'] == 0) & (row['death'] == 0):
return 1
else:
return row['no_treat']
lines_df_2['no_treat'] = lines_df_2.apply(lambda row: fix_notreat(row), axis=1)
# Total events
lines_df_2['total_ev'] = lines_df_2.apply(lambda row: max(1, row['treat'] + row['no_treat'] + row['death']), axis=1)
# Function to get indices of a therapy
def get_index_pos(my_list, val):
return [i for i, x in enumerate(my_list) if x == val]
lines_df_2
# In[138]:
# Create version of final dataset that only includes patients who died
d_pats = list(tcga_ov_1_keep['bcr_patient_barcode'].loc[tcga_ov_1_keep['outcome_overall_survival_censoring'] == 1])
lines_df_d = lines_df_2[lines_df_2['patient'].isin(d_pats)].reset_index().drop(['index'], axis=1)
lines_df_d
# Create version of data for regression
# In[139]:
def fix_treat(row):
if (row['patient'] == row['pat_lag']) & (row['ther_lag'] != ''):
return 1
else:
return 0
def fix_death(row):
if row['treat'] == 1:
return 0
else:
return row['death']
def fix_notreat(row):
return (1 - row['treat'] - row['death'])
def treat_state(row):
if row['ther_str'] == '':
return 0
else:
return 1
def make_reg_df(df):
df_reg = df.loc[df.index.repeat(df.total_ev)].reset_index(drop=True)
df_reg['ther_str'] = [','.join(map(str, l)) for l in df_reg['therapy']]
df_reg['ther_lag'] = df_reg['ther_str'].shift(-1)
df_reg['pat_lag'] = df_reg['patient'].shift(-1)
df_reg['treat'] = df_reg.apply(lambda row: fix_treat(row), axis=1) # prob that next state is treatment
df_reg['death'] = df_reg.apply(lambda row: fix_death(row), axis=1) # prob that next state is death
df_reg['no_treat'] = df_reg.apply(lambda row: fix_notreat(row), axis=1) # prob that next state no treatment
df_reg = df_reg.drop(columns=['total_ev', 'ther_lag', 'pat_lag', 'start', 'end'])
df_reg['months'] = df_reg.groupby('patient').cumcount() # time trend
df_reg['treat_months'] = df_reg.groupby(['patient', 'ther_str', 'previous_lines']).cumcount()
df_reg['treat_state'] = df_reg.apply(lambda row: treat_state(row), axis=1) # current state
df_reg = df_reg.merge(pd.get_dummies(df_reg['ther_str']), left_index=True, right_index=True)
df_reg = df_reg.merge(pd.get_dummies(df_reg['race']), left_index=True, right_index=True)
df_reg = df_reg.merge(pd.get_dummies(df_reg['tumor_stage']), left_index=True, right_index=True)
df_reg = df_reg.merge(pd.get_dummies(df_reg['tumor_grade']), left_index=True, right_index=True)
df_reg = df_reg.rename(columns={'NOT SPECIFIED_x':'NOT SPECIFIED_race', 'NOT SPECIFIED_y':'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED':'NOT SPECIFIED_tumor_grade'})
age = df_reg['age']
df_reg = df_reg.drop(['age'], axis=1)
df_reg.insert(12, 'age', age)
prev_lines = df_reg['previous_lines']
df_reg = df_reg.drop(['previous_lines'], axis=1)
df_reg.insert(12, 'previous_lines', prev_lines)
return df_reg
df_d_reg = make_reg_df(lines_df_d)
df_reg = make_reg_df(lines_df_2)
# Save CSVs
# df_reg.to_pickle('df_reg.pkl')
# df_d_reg.to_pickle('df_d_reg.pkl')
# Show data
df_reg
# Treatment summary
# In[140]:
def freq_heat(data, fig_title, file_name):
df_heat = data.stb.freq(['ther_str', 'months'])[['ther_str', 'months', 'count']].sort_values(by=['months', 'ther_str'])
df_heat = df_heat.pivot_table(index=['months'], columns='ther_str', values='count').reset_index().rename_axis(None, axis=1).set_index('months')
df_heat = df_heat.fillna(value=0).drop([''], axis=1)
s = df_heat.sum()
df_heat = df_heat[s.sort_values(ascending=False).index]
# convert to z-scores
for row in df_heat.index:
stats.zscore(df_heat.iloc[row])
df_heat.iloc[row] = stats.zscore(df_heat.iloc[row])
df_heat = df_heat.fillna(value=0)
# Make heatmap
sns.set(font_scale=1)
cmap = sns.color_palette("Blues", as_cmap=True)
plt.figure(figsize=(16,16))
plt.rcParams["font.family"] = "Arial"
ax = sns.heatmap(data=df_heat,
cmap=cmap,
square=1,
vmin=0, vmax=3,
cbar_kws={"shrink": .5},
)
plt.title(fig_title, size=18, fontweight='bold')
plt.ylabel('Months from start of treatment', size=14)
plt.xticks(size=12, rotation='vertical')
plt.yticks(size=12)
# plt.savefig(file_name)
freq_heat(df_reg, 'Treatment Frequency and Timing - All Patients', 'heat_data_all.png')
freq_heat(df_d_reg, 'Treatment Frequency and Timing - Deceased Patients', 'heat_data_d.png')
# ## Create MDP objects
#
# * This should probably be the start of the next notebook
# State and Action set
# In[141]:
# States
state_set = ['N', 'T', 'D']
# Combos/actions
combos_all = list(lines_df_2['therapy'])
combos_all.sort()
combos_all = list(combos_all for combos_all,_ in itertools.groupby(combos_all))
print(len(combos_all))
print(combos_all[0:10])
combos_all_drugs = combos_all.copy()
combos_all_drugs.remove([])
print(combos_all_drugs[0:10])
# Combos for deceased patients
combos_d = list(lines_df_d['therapy'])
combos_d.sort()
combos_d = list(combos_d for combos_d,_ in itertools.groupby(combos_d))
print(len(combos_d))
print(combos_d[0:10])
combos_drugs_d = combos_d.copy()
combos_drugs_d.remove([])
print(combos_drugs_d[0:10])
###### "2" versions are just dropping the 'NOT SPECIFIED' value
# Race
races_all = list(lines_df_2['race'].unique())
races_all.sort()
print(races_all)
races_all2 = races_all.copy()
races_all2.remove('NOT SPECIFIED')
print(races_all2)
# Death only dataset
races_d = list(lines_df_d['race'].unique())
races_d.sort()
print(races_d)
races_d2 = races_d.copy()
races_d2.remove('NOT SPECIFIED')
print(races_d2)
# Tumor stage
tumor_stages_all = list(lines_df_2['tumor_stage'].unique())
tumor_stages_all.sort()
print(tumor_stages_all)
tumor_stages_all2 = tumor_stages_all.copy()
tumor_stages_all2.remove('NOT SPECIFIED')
print(tumor_stages_all2)
# Death only dataset
tumor_stages_d = list(lines_df_d['tumor_stage'].unique())
tumor_stages_d.sort()
print(tumor_stages_d)
tumor_stages_d2 = tumor_stages_d.copy()
tumor_stages_d2.remove('NOT SPECIFIED')
print(tumor_stages_d2)
# Tumor grade
tumor_grades_all = list(lines_df_2['tumor_grade'].unique())
tumor_grades_all.sort()
print(tumor_grades_all)
tumor_grades_all2 = tumor_grades_all.copy()
tumor_grades_all2.remove('NOT SPECIFIED')
print(tumor_grades_all2)
# Death only dataset
tumor_grades_d = list(lines_df_d['tumor_grade'].unique())
tumor_grades_d.sort()
print(tumor_grades_d)
tumor_grades_d2 = tumor_grades_d.copy()
tumor_grades_d2.remove('NOT SPECIFIED')
print(tumor_grades_d2)
# Function to create random array for descriptive variables
def rand_bin_array(K, N):
arr = np.zeros(N)
arr[:K] = 1
np.random.shuffle(arr)
return arr
# Restricted drug list
# In[142]:
# Full dataset
df_freq = df_reg.stb.freq(['ther_str'])[['ther_str', 'count']].sort_values(by=['count'], ascending=False)
df_freq = df_freq.merge(df_reg[['therapy', 'ther_str']], on='ther_str').drop_duplicates(subset='ther_str')
df_freq = df_freq[(df_freq['ther_str'] != '') & (df_freq['count'] >= 5)]
combos_res_all = list(df_freq['therapy'])
combos_res_all.sort()
print(len(combos_res_all))
print(combos_res_all[0:10])
# Deceased dataset
df_freq = df_d_reg.stb.freq(['ther_str'])[['ther_str', 'count']].sort_values(by=['count'], ascending=False)
df_freq = df_freq.merge(df_reg[['therapy', 'ther_str']], on='ther_str').drop_duplicates(subset='ther_str')
df_freq = df_freq[(df_freq['ther_str'] != '') & (df_freq['count'] >= 5)]
combos_res_d = list(df_freq['therapy'])
combos_res_d.sort()
print(len(combos_res_d))
print(combos_res_d[0:10])
# Static transition matrix
# In[143]:
# Treatment States = {No Treatment, Treatment, Death}
# All or death-only action sets
combos = combos_d # Switch to _all when use full dataset
combos_drugs = combos_drugs_d
combos_drugs_res = combos_res_d
# Descriptive States (all or death-only)
races = races_d
races2 = races_d2
tumor_stages = tumor_stages_d
tumor_stages2 = tumor_stages_d2
tumor_grades = tumor_grades_d
tumor_grades2 = tumor_grades_d2
# Dataset to use
trans_data = df_d_reg # change to df_reg if using full dataset
# Cox Proportional Hazard Regression
# In[144]:
# Fit regressions
# Survival
X_surv = trans_data.drop(['', 'no_treat', 'treat', 'patient', 'therapy', 'race', 'tumor_stage',
'tumor_grade', 'ther_str', 'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED_tumor_grade',
'NOT SPECIFIED_race', 'treat_months'], axis=1)
cph_surv = CoxPHFitter(penalizer=0.1)
cph_surv.fit(X_surv, duration_col='months', event_col='death', robust=True) # add this to do specific cols: , formula="fin + wexp + age * prio"
# Treatment
X_treat = trans_data.drop(['', 'treat', 'death', 'patient', 'therapy', 'race', 'tumor_stage',
'tumor_grade', 'ther_str', 'NOT SPECIFIED_tumor_stage', 'NOT SPECIFIED_tumor_grade',
'NOT SPECIFIED_race'], axis=1)
cph_treat = CoxPHFitter(penalizer=0.1)
cph_treat.fit(X_treat, duration_col='treat_months', event_col='no_treat', robust=True)
# In[145]:
def cph_probs(data, state, months, treat_months, prev_lines, action, age, race, tumor_stage, tumor_grade):
month = min(months, max(data['months']))
treat_month = min(treat_months, max(data['treat_months']))
if action == []:
pass
else:
act_ind = combos_drugs.index(action) + 2
act_arr = np.zeros(len(combos_drugs)) # sim patient vectors = 0 if 'NOT SPECIFIED'
if action == []:
pass
else:
act_arr[combos_drugs.index(action)] = 1
x_surv = np.concatenate((np.array([age]), np.array([prev_lines]), np.array([state_set.index(state)]), act_arr, race,
tumor_stage, tumor_grade))
x_surv = x_surv.reshape(1,-1)
x_treat = np.concatenate((np.array([month]), np.array([age]), np.array([prev_lines]), np.array([state_set.index(state)]),
act_arr, race, tumor_stage, tumor_grade))
x_treat = x_treat.reshape(1,-1)
# haz = (base_haz * part_haz.values[0]).iloc[months].values[0]
surv = cph_surv.predict_survival_function(x_surv).iloc[month].values[0]
treat_prob = cph_treat.predict_survival_function(x_treat).iloc[treat_month].values[0]
return surv, treat_prob
race_arr = np.array([0, 0, 0, 0, 1])
ts_arr = np.array([0, 0, 1, 0, 0, 0])
tg_arr = np.array([0, 0, 1, 0, 0])
# check that it works
cph_probs(trans_data, 'T', 70, 15, 2, ['Carboplatin', 'Paclitaxel'], 80, race_arr, ts_arr, tg_arr)
# In[146]:
# cph_surv.baseline_survival_.to_csv('cph_surv_baseline.csv', index=False)
# cph_treat.baseline_survival_.to_csv('cph_treat_baseline.csv', index=False)
# In[147]:
# CPH Death Regression Baseline Survival
plt.style.use('default')
plt.figure(figsize=(12,8))
plt.rcParams['font.family'] = 'Arial'
# cph.plot_partial_effects_on_outcome(covariates='age', values=[40, 50, 60, 70, 80], cmap='coolwarm')
plt.plot(cph_surv.baseline_survival_)
plt.title('Death Event Regression', fontsize=14)
plt.xlabel('Months Since Start of Treatment', fontsize=14)
plt.ylabel('Survival Probability', fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
# plt.savefig('baseline_surv_death.png')
# cph.print_summary()
# cph_treat.print_summary()
# In[148]:
# CPH Remission/Recurrence Regression Baseline Survival
plt.style.use('default')
plt.figure(figsize=(12,8))
plt.rcParams['font.family'] = 'Arial'
plt.plot(cph_treat.baseline_survival_)
plt.title('Remission Event Regression', fontsize=14)
plt.xlabel('Months in Current State', fontsize=14)
plt.ylabel('Continue Treatment Probability', fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
# plt.savefig('baseline_surv_recurrence.png')
# Q-Network: MLP
# In[149]:
# Define DQN, one hidden layer MLP for now
class DQN(nn.Module):
def __init__(self, in_features, hidden_size, out_features):
super(DQN, self).__init__()
self.fc = nn.Linear(in_features, hidden_size)
self.relu = nn.ReLU()
self.fc_out = nn.Linear(hidden_size, out_features)
def forward(self, x):
out = self.fc(x)
out = self.relu(out)
out = self.fc_out(out)
return out
# In[150]:
# Create a bigger NN
class DQN_3(nn.Module):
def __init__(self, in_features, out_features):
super(DQN_3, self).__init__()
self.fc1 = nn.Linear(in_features, 128)
self.fc2 = nn.Linear(128, 256)
self.fc3 = nn.Linear(256, 256)
self.relu = nn.ReLU()
self.fc_out = nn.Linear(256, out_features)
def forward(self, x):
out = self.fc1(x) # in
out = self.relu(out)
out = self.fc2(out) # hidden 1
out = self.relu(out)
out = self.fc3(out) # hidden 2
out = self.relu(out)
out = self.fc3(out) # hidden 3
out = self.relu(out)
out = self.fc3(out) # hidden 4
out = self.relu(out)
out = self.fc3(out) # hidden 5
out = self.relu(out)
out = self.fc3(out) # hidden 6
out = self.relu(out)
out = self.fc_out(out) # out
return out
# Replay Memory
#
# https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
# In[151]:
Transition = namedtuple('Transition',
('state', 'action', 'next_state', 'reward'))
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self, *args):
"""Saves a transition."""
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.position] = Transition(*args)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
# Model training loop
#
# https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
#
# Use Q update formua: $Q^{\pi}(s,a) \leftarrow Q^{\pi}(s,a) + \alpha \cdot [r + \gamma \cdot Q^{\pi}(s', a') - Q^{\pi}(s,a)]$
# In[152]:
def optimize_model():
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
# Transpose the batch (see https://stackoverflow.com/a/19343/3343043 for
# detailed explanation). This converts batch-array of Transitions
# to Transition of batch-arrays.
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
# (a final state would've been the one after which simulation ended)
non_final_mask = torch.tensor(tuple(map(lambda s: s is not None,
batch.next_state)), device=device, dtype=torch.bool)
non_final_next_states = torch.stack([s for s in batch.next_state
if s is not None])
state_batch = torch.stack(batch.state)
action_batch = torch.stack(batch.action)
reward_batch = torch.cat(batch.reward)
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken. These are the actions which would've been taken
# for each batch state according to policy_net
state_action_values = policy(state_batch).gather(1, action_batch).squeeze()
# Compute V(s_{t+1}) for all next states.
# Expected values of actions for non_final_next_states are computed based
# on the "older" target_net; selecting their best reward with max(1)[0].
# This is merged based on the mask, such that we'll have either the expected
# state value or 0 in case the state was final.
next_state_values = torch.zeros(BATCH_SIZE, device=device)
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0].detach()
# Compute the expected Q values
expected_state_action_values = state_action_values + ALPHA * (reward_batch + (GAMMA * next_state_values) - state_action_values)
expected_state_action_values = expected_state_action_values
# expected_state_action_values = (next_state_values * GAMMA) + reward_batch # old version
# Compute Huber loss
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values)
# Compute MSE loss
# loss = F.mse_loss(state_action_values, expected_state_action_values)
# Optimize the model
optimizer.zero_grad()
loss.backward()
for param in policy.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
# ## Environment
#
# Designed based on: https://github.com/MJeremy2017/reinforcement-learning-implementation/blob/master/GridWorld/gridWorld.py
# State Class
# In[153]:
# Globals
START_STATE = 'T'
GAMMA = 0.99 # orig 0.99
ALPHA = 0.01 # orig 0.01
EPS_START = 0.90 # orig 0.9
EPS_END = 0.05 # orig 0.05
EPS_DECAY = 100 # Increase to slow decay. Orig 100
BATCH_SIZE = 100 # orig 100
TARGET_UPDATE = 10 # orig 10
IN_FEATURES = 5 + len(races2)+len(tumor_stages2)+len(tumor_grades2) # health state, months, months on treatment, age, previous lines + dummy arrays
OUT_FEATURES = len(combos_drugs_res) # Change for restricted vs. full action set ('_res' suffix)
policy = DQN_3(IN_FEATURES, OUT_FEATURES).to(device)
target_net = DQN_3(IN_FEATURES, OUT_FEATURES).to(device)
target_net.load_state_dict(policy.state_dict())
target_net.eval()
optimizer = optim.RMSprop(policy.parameters())
memory = ReplayMemory(10000)
# State class initializes the simulated patient vector
class State:
def __init__(self, state=START_STATE):
self.state = state
self.age = round(np.random.normal(trans_data['age'].mean(), trans_data['age'].std(), 1)[0])
self.race = random.choice(races) # choose from all including 'NOT SPECIFIED'
self.r_arr = np.zeros(len(races2)) # sim patient vectors = 0 if 'NOT SPECIFIED'
if self.race == 'NOT SPECIFIED':
pass
else:
self.r_arr[races2.index(self.race)] = 1
self.tumor_stage = random.choice(tumor_stages)
self.ts_arr = np.zeros(len(tumor_stages2))
if self.tumor_stage == 'NOT SPECIFIED':
pass
else:
self.ts_arr[tumor_stages2.index(self.tumor_stage)] = 1
self.tumor_grade = random.choice(tumor_grades)
self.tg_arr = np.zeros(len(tumor_grades2))
if self.tumor_grade == 'NOT SPECIFIED':
pass
else:
self.tg_arr[tumor_grades2.index(self.tumor_grade)] = 1
self.isEnd = False
# Environment Class
# In[154]:
class CancerEnv:
def __init__(self):
self.State = State()
self.s_a = []
self.treat_mo = 0
self.prev_lines = 0
self.round = 0
self.actions = combos_drugs_res # Change for restricted vs. full action set ('_res' suffix)
# initial state and action reward