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POP_functions.py
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POP_functions.py
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import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns
import os
def load_predicted_data(group1,group2,exp,robust=0):
if robust==1:
predicted=pd.read_csv('robust_matrix_predicted.csv')
else:
predicted=pd.read_csv('matrix_predicted.csv')
ind_diffs=pd.read_csv('../../../../data_for_analysis/ind_diffs_'+exp+'.csv')
group=ind_diffs.loc[:,['Participant ID',group1,group2]]
group.rename(columns={'Participant ID':'Participant'},inplace=True)
predicted.rename(columns={'Participant.f':'Participant'},inplace=True)
group.set_index('Participant',inplace=True)
predicted.set_index('Participant',inplace=True)
correctp=pd.merge(group,predicted,how='right',on='Participant')
correctp=correctp[((correctp.loc[:,group1]==1) & (correctp.loc[:,'group'] == group1))|
((correctp.loc[:,group2]==1) & (correctp.loc[:,'group'] == group2))]
correctp=correctp.replace('Credit','Voucher')
correctp.loc[:,'group']=correctp.loc[:,'group']+(' predicted')
return(correctp)
def load_predicted_data_ind_diffs(robust=0):
if robust==1:
predicted=pd.read_csv('robust_matrix_predicted.csv')
else:
predicted=pd.read_csv('matrix_predicted.csv')
predicted.rename(columns={'Participant.f':'Participant'},inplace=True)
correctp=predicted
return(correctp)
def load_behav():
data=pd.read_csv('data_with_diagnostics.csv')
data_crop=data.loc[:,['Participant.f','group','Final.balance',
'Trial.no',
'Next.bet.binary','predicted','Result','logResult',
'logLoss.streak','Trial.type.binary','ISIms','logISI','Loss.streak.outcome...zero','sqrtTrial.no']]
data_crop=data_crop.replace('Credit','Voucher')
data_crop.loc[:,'group']=data_crop.loc[:,'group']+(' data')
data_crop.rename(columns={'Participant.f':'Participant'},inplace=True)
return(data_crop)
# +
def plot_for_pop(exp,correctp,data_crop,IV,DV,bins,xticklabels,xlabel,ylabel,robust=0):
"""Draw actual data and model prediction
correctp=predicted data
data_crop=raw data
IV = String - Column of data that is IV
DV = String - Column of data that is DV
bins = define bins you want for raw data plot - int array
xticklabels = how you want to label bins - array
xlabel/ylabel - strings - for plot axes
"""
if not os.path.exists('plots'):
os.makedirs('plots')
if exp == 'POP1':
logLossmedian=1.386294
colors = ["orangered", "silver"]
else:
logLossmedian=1.098612
colors = ["steelblue", "silver"]
customPalette = sns.set_palette(sns.color_palette(colors))
plt.rc('font', size=15)
if IV=='Final.balance':
correctp.loc[correctp['logLoss.streak'] == 0]
if IV=='logLoss.streak':
correctp.loc[correctp['Final.balance'] == 0]
if IV == 'logResult':
correctp.loc[correctp['Final.balance'] == 0]
# Predicted
IV_p=correctp.groupby(['group',IV]).mean().loc[:,'predicted']
IV_p=IV_p.unstack(level=0)
IV_p=pd.DataFrame(IV_p)
if exp == 'POP1':
#IV_p.reset_index(inplace=True)
IV_p=IV_p.loc[:,['Voucher predicted','Cash predicted']]
print(IV_p)
# Raw Data
# Create arbitrary bins and check N in each
data_crop.loc[:,'IV.binned']=pd.cut(data_crop.loc[:,IV],
bins=bins)
IV_nbins=pd.cut(data_crop.loc[:,IV],
bins=bins).value_counts()
IV_data=data_crop.groupby(['Participant','IV.binned','group']).mean().loc[:,DV]
IV_data=pd.DataFrame(IV_data)
IV_data = IV_data.stack(level=0).reset_index(level=0) \
.reset_index().drop('level_2',axis=1) \
.rename(columns={0:'DV'})
print(IV_nbins)
IV_nbins.to_csv('plots/'+IV+'binN.csv',header=False )
# Build figure
fig, (ax1, ax2) = plt.subplots(1,2,figsize=(8,4),sharey=True)
#boxplot ax1
if exp=='POP1':
sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',
ax=ax1,palette=customPalette,linewidth=1,fliersize=5, hue_order=['Voucher data','Cash data'])
else:
sns.boxplot(y='DV', x='IV.binned',data=IV_data,hue='group',
ax=ax1,palette=customPalette,linewidth=1,fliersize=5)
if IV == 'Trial.type.binary':
IV_p.plot.bar(ax=ax2)
ax1.xaxis.set_ticklabels(['Win','Loss'])
ax2.xaxis.set_ticklabels(['Win','Loss'],rotation=0)
else:
IV_p.plot(ax=ax2)
ax1.set_ylabel(ylabel)
ax2.set_ylabel(ylabel)
ax1.set_xlabel(xlabel)
ax2.set_xlabel(xlabel)
fig.legend(title=None,loc=1,frameon=False)
ax1.legend().set_visible(False)
ax2.legend().set_visible(False)
if DV == 'logISI':
ax1.set_ylim(bottom=5,top=9.5)
# Change yaxis labels for boxplot - use minor ticks to define edges and turn off major
xbins=[]
for i in np.arange(-0.5, 100,1):
xbins.append(i)
if len(xbins)==len(xticklabels):
break
if IV != 'Trial.type.binary':
ax1.xaxis.set_ticks(xbins,minor=True)
ax1.xaxis.set_ticklabels(xticklabels,minor=True)
ax1.xaxis.set_ticks([])
ax1.xaxis.set_tick_params(which='minor',length=4)
sns.despine(top=True,right=True)
plt.tight_layout()
if robust==1:
fig.savefig(fname='plots/robust_'+IV +'.png',format='png',transparent=True)
fig.savefig(fname='plots/robust_'+IV + '.eps',format='eps',transparent=True)
else:
fig.savefig(fname='plots/'+IV +'.png',format='png',transparent=True)
fig.savefig(fname='plots/'+IV + '.eps',format='eps',transparent=True)
# -
def plot_hist_for_pop(exp,data_crop,IV):
"""Draw actual data and transformed data histogrmas
"""
if not os.path.exists('plots'):
os.makedirs('plots')
DVdata=data_crop.loc[:,IV]
if exp == 'POP1':
colors = ["orangered", "silver"]
else:
colors = ["steelblue", "silver"]
plt.figure()
customPalette = sns.set_palette(sns.color_palette(colors))
plt.rc('font', size=15)
sns.distplot(DVdata,kde=False)
sns.despine(top=True,right=True)
ax = plt.gca()
if IV == 'Loss.streak.outcome...zero':
ax.set_xlim(right=75)
plt.tight_layout()
plt.savefig(fname='plots/hist_'+IV + '.eps',format='eps',transparent=True)
# +
def plot_for_pop_ind_diffs(exp,correctp,ind_diffs,IV,DV,xlabel,ylabel,robust=0):
if not os.path.exists('plots'):
os.makedirs('plots')
"""Draw model prediction for ind diffs
IV = String - Column of data that is IV
DV = String - Column of data that is DV
ind_diffs = series of binary ind difs to plot
xticklabels = how you want to label bins - array
xlabel/ylabel - strings - for plot axes
"""
if exp == 'POP1':
logLossmedian=1
PGSImean=1
STTWmean=13
GEQ_Flowmean=1
else:
logLossmedian=1
PGSImean=2
STTWmean=14
GEQ_Flowmean=2
# remove centering
if IV=='Final.balance':
correctp.loc[:,'Final.balance']=correctp.loc[:,'Final.balance']+40
if IV=='logLoss.streak':
correctp.loc[:,'logLoss.streak']=correctp.loc[:,'logLoss.streak']+logLossmedian
correctp.loc[:,'PGSI']=correctp.loc[:,'PGSI']+PGSImean
correctp.loc[:,'GEQ_Flow']=correctp.loc[:,'GEQ_Flow']+GEQ_Flowmean
if DV=='Next.bet.binary':
correctp.loc[:,'ST.TW']=correctp.loc[:,'ST.TW']+STTWmean
for binaryIV in ind_diffs:
IV_p=correctp.groupby([binaryIV,IV]).mean().loc[:,'predicted']
IV_p=IV_p.unstack(level=0)
# Build figure
fig=plt.figure()
ax1=fig.add_subplot(111)
if IV == 'Trial.type.binary':
IV_p.plot.bar(ax=ax1)
ax1.xaxis.set_ticklabels(['Win','Loss'],rotation=0)
else:
IV_p.plot(ax=ax1)
ax1.set_ylabel(ylabel)
ax1.set_xlabel(xlabel)
if DV=='Next.bet.binary':
ax1.set_ylim(bottom=0,top=1)
else:
ax1.set_ylim(bottom=6,top=9)
sns.despine(top=True,right=True)
plt.tight_layout()
if robust==1:
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + '.eps'),format='eps',transparent=True)
else:
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '.eps'),format='eps',transparent=True)
def plot_for_pop_ind_diffs_group(exp,correctp,ind_diffs,IV,DV,xlabel,ylabel,robust,group1,group2):
if not os.path.exists('plots'):
os.makedirs('plots')
"""Draw model prediction for ind diffs
IV = String - Column of data that is IV
DV = String - Column of data that is DV
ind_diffs = series of binary ind difs to plot
xticklabels = how you want to label bins - array
xlabel/ylabel - strings - for plot axes
"""
if exp == 'POP1':
logLossmedian=1
PGSImean=1
STTWmean=13
GEQ_Flowmean=1
else:
logLossmedian=1
PGSImean=2
STTWmean=14
GEQ_Flowmean=2
# remove centering
if IV=='Final.balance':
correctp.loc[:,'Final.balance']=correctp.loc[:,'Final.balance']+40
if IV=='logLoss.streak':
correctp.loc[:,'logLoss.streak']=correctp.loc[:,'logLoss.streak']+logLossmedian
correctp.loc[:,'PGSI']=correctp.loc[:,'PGSI']+PGSImean
correctp.loc[:,'GEQ_Flow']=correctp.loc[:,'GEQ_Flow']+GEQ_Flowmean
if DV=='Next.bet.binary':
correctp.loc[:,'ST.TW']=correctp.loc[:,'ST.TW']+STTWmean
group1_data=correctp[correctp.loc[:,group1]==1]
group2_data=correctp[correctp.loc[:,group2]==1]
for binaryIV in ind_diffs:
IV_p=group1_data.groupby([binaryIV,IV]).mean().loc[:,'predicted']
IV_p=IV_p.unstack(level=0)
keep=IV_p
# Build figure
fig=plt.figure()
ax1=fig.add_subplot(111)
if IV == 'Trial.type.binary':
IV_p.plot.bar(ax=ax1)
ax1.xaxis.set_ticklabels(['Win','Loss'],rotation=0)
else:
IV_p.plot(ax=ax1)
ax1.set_ylabel(ylabel)
ax1.set_xlabel(xlabel)
if DV == 'Next.bet.binary':
ax1.set_ylim(bottom=0,top=1)
else:
ax1.set_ylim(bottom=6,top=9)
sns.despine(top=True,right=True)
plt.tight_layout()
if robust==1:
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + group1 + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + group1 + '.eps'),format='eps',transparent=True)
else:
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '_' + group1 + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '_' + group1 + '.eps'),format='eps',transparent=True)
IV_p=group2_data.groupby([binaryIV,IV]).mean().loc[:,'predicted']
IV_p=IV_p.unstack(level=0)
# Build figure
fig=plt.figure()
ax1=fig.add_subplot(111)
if IV == 'Trial.type.binary':
IV_p.plot.bar(ax=ax1)
ax1.xaxis.set_ticklabels(['Win','Loss'],rotation=0)
else:
IV_p.plot(ax=ax1)
ax1.set_ylabel(ylabel)
ax1.set_xlabel(xlabel)
if DV == 'Next.bet.binary':
ax1.set_ylim(bottom=0,top=1)
sns.despine(top=True,right=True)
plt.tight_layout()
if robust==1:
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + group2 + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/robust_'+binaryIV + '_' + IV + group2 + '.eps'),format='eps',transparent=True)
else:
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '_' + group2 + '.png'),format='png',transparent=True)
fig.savefig(fname=('plots/'+binaryIV + '_' + IV + '_' + group2 + '.eps'),format='eps',transparent=True)
return(keep)