Analysis_final_mult.py 59.2 KB
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#!/usr/bin/env python
# coding: utf-8

# In[1]:


import pandas as pd
import numpy as np
import ndjson
import jsonlines
import json
import pickle
import os
import sys
import random as rd
import json
import re, regex
from joblib import dump, load
import collections
import math
import statistics
import itertools
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import multiprocessing as mp
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from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import PassiveAggressiveClassifier, SGDClassifier
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

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import matplotlib
matplotlib.use('agg')
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import matplotlib.colors as cl
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix
import seaborn as sns # data visualization library  
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# In[2]:

path = '/draco/ptmp/mschuber/PAN/Data/pan19-celebrity-profiling-training-dataset-2019-01-31/stratified_subsample/'
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savedir = '/draco/ptmp/mschuber/PAN/Data/results/'
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#path = '../Data/pan19-celebrity-profiling-training-dataset-2019-01-31/stratified_subsample/'

subana_l = ['org/', 'min_tweets_1000/', 'complete_balance/']
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#subana_l = ['org/']
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subsets_l = [200, 500, 1000, 2000]
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#subsets_l = [200]
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classifiers = ['SVM']

datafolder = 'split_data/'

ml_results = 'ml/'

filebeg = 'stratified_subsample_'

labels_l = ['age', 'gender', 'author']

phases_l = ['child_21', 'young_adult_35', 'adult_50', 'old_adult_65', 'retiree']


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    #get_ipython().run_line_magic('matplotlib', 'inline')
    #import matplotlib

cmap = cl.LinearSegmentedColormap.from_list("", ["skyblue","cadetblue","darkblue", "steelblue"]) #define colors
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# In[3]:


####tester

#subana = ['org/']

#subsets = [200]

#classifiers = ['SVM']

#datafolder = 'split_data/'

#ml_results = 'ml/'

#filebeg = 'stratified_subsample_'

#labels = ['age', 'gender', 'author']


# In[4]:


def identity_tokenizer(text):
    return text

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def plot_confusion_matrix(cm, classes,normalize=True,title='Confusion matrix',cmap=plt.cm.Blues, ax = None):
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   """
   This function prints and plots the confusion matrix.
   Normalization can be applied by setting `normalize=True`.
   """
   if not ax:
       ax = plt.gca()
       
       
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   if normalize:
       cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
       print("Normalized confusion matrix")
   else:
       print('Confusion matrix, without normalization')
   #print(cm)
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   cm_old = cm
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   im = ax.imshow(cm, interpolation='nearest', cmap=cmap)
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   #plt.title(title)
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   cbar = ax.figure.colorbar(im, ax = ax, ticks=[], label = "Heat per Row (Normalized from 0 to 1)")
   tick_marks = np.arange(len(classes))
   #ax.set_xticks(tick_marks)
   #ax.set_yticks(tick_marks)
   #ax.set_xticklabels(classes, rotation=45)
   #ax.set_yticklabels(classes)
   ax.set(xticks=np.arange(cm.shape[1]),
          yticks=np.arange(cm.shape[0]),
          # ... and label them with the respective list entries
          xticklabels=classes, yticklabels=classes,
          ylabel='True label',
          xlabel='Predicted label')    
   # Rotate the tick labels and set their alignment.
   plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
            rotation_mode="anchor")
   #make it so that text is on minor label and not on edge of boundary (i.e. half cutoff at top and bottom) 
   ax.set_xticks(np.arange(cm.shape[1]+1)-.5, minor=True)
   ax.set_yticks(np.arange(cm.shape[0]+1)-.5, minor=True)
   
   fmt = '.2f' if normalize else 'd'
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   thresh = cm.max() / 1.3
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   for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
       ax.text(j, i, format(cm_old[i, j], fmt),
                ha="center",
                va="center",
                color="white" if cm[i, j] > thresh else "black")
       
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    # Start with analysis of precision and recall as well as heatmaps/confusion matrices:
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def plotter(subsets, subana, phases, labels):
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    df_dic = {}
    res_dic = {}
    author_dic = {}

    for st in subsets:

        res_dic[st] = {}
       
        df_dic[st] = {}
        author_dic[st] = {}
        for ana in subana:
            
            res_dic[st][ana.split('/')[0]] = {}
            df_dic[st][ana.split('/')[0]] = {}
            author_dic[st][ana.split('/')[0]] = {}
            ###make dic with all authors
            with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_author_train.json', 'r', encoding='utf-8') as f:
                authors = json.load(f)
            with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_gender_train.json', 'r', encoding='utf-8') as f:
                gender = json.load(f)            
            with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_age_train.json', 'r', encoding='utf-8') as f:
                year = json.load(f)
            

            for i in range(0, len(year)):
                age = 2019 - year[i]
                
                if age <22:
                    lifePhase = 'child_21'
                elif age <36:
                    lifePhase = 'young_adult_35'
                elif age < 51:
                    lifePhase = 'adult_50'
                elif age <66:
                    lifePhase = 'old_adult_65'
                else:
                    lifePhase = 'retiree'
                
                author_dic[st][ana.split('/')[0]][authors[i]] = {}
                author_dic[st][ana.split('/')[0]][authors[i]]['life_phase'] = lifePhase
                author_dic[st][ana.split('/')[0]][authors[i]]['age'] = year[i]
                author_dic[st][ana.split('/')[0]][authors[i]]['gender'] = gender[i]
                      
            
            df = pd.DataFrame()
            with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_bigram_vocab.json', 'r', encoding='utf-8') as f:
                vocab = json.load(f)
                
                res_dic[st][ana.split('/')[0]]['vocab'] = vocab
                res_dic[st][ana.split('/')[0]]['vocab_inverse'] = {v:k for k,v in vocab.items()}
            ##update  "vocab" to include the tweet length as feature to display
            if len(vocab) not in res_dic[st][ana.split('/')[0]]['vocab_inverse']:
                leng = len(vocab)
                res_dic[st][ana.split('/')[0]]['vocab']['§LENGTH§'] = leng
                res_dic[st][ana.split('/')[0]]['vocab_inverse'][leng] = '§LENGTH§'
            else:
                print('error; key already exists')
                print(res_dic[st][ana.split('/')[0]]['vocab_inverse'][len(vocab)])
                sys.exit(1)
            
            
            for label in labels:
                
                res_dic[st][ana.split('/')[0]][label] = {}
                
                enc = load(path+ana+str(st)+'/'+ml_results+filebeg+label+'_'+str(st)+'_encoder.jlib')
                res_dic[st][ana.split('/')[0]][label]['label_encoder'] = enc          
                
                for clf in classifiers:
                    
                    clf_l = load(path+ana+str(st)+'/'+ml_results+filebeg+clf+'_'+label+'_'+str(st)+'_svm_out_count.jlib')
                    
                    with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_'+label+'_test.json') as f:
                        lab = json.load(f)
                        df[ana.split('/')[0]+'_'+str(st)+'_'+clf+'_'+label] = lab
                        print(str(st)+'_'+ana+'_'+label+'_'+clf)
                        if label == 'age':
                            phase = []
                            for el in lab:
                                age = 2019 - el
                                if age <22:
                                    lifePhase = 'child_21'
                                elif age <36:
                                    lifePhase = 'young_adult_35'
                                elif age < 51:
                                    lifePhase = 'adult_50'
                                elif age <66:
                                    lifePhase = 'old_adult_65'
                                else:
                                    lifePhase = 'retiree'
                                phase.append(lifePhase)
                            df[ana.split('/')[0]+'_'+str(st)+'_life_phase'] = phase
                            
                        
                        
                        #print(len(json.load(f)))
                    
                    res_dic[st][ana.split('/')[0]][label][clf] = clf_l.coef_
       
                    
                    
                    df[ana.split('/')[0]+'_'+str(st)+'_'+clf+'_'+label+'_pred_enc'] = list(load(path+ana+str(st)+'/'+ml_results+filebeg+clf+'_'+label+'_'+str(st)+'_predictions_count.jlib'))
                    rev_enc = list(enc.inverse_transform(load(path+ana+str(st)+'/'+ml_results+filebeg+clf+'_'+label+'_'+str(st)+'_predictions_count.jlib')))
                    df[ana.split('/')[0]+'_'+str(st)+'_'+clf+'_'+label+'_pred'] = rev_enc
                    if label == 'age':
                        phase = []
                        for el in rev_enc:
                            age = 2019 - el
                            if age <22:
                                lifePhase = 'child_21'
                            elif age <36:
                                lifePhase = 'young_adult_35'
                            elif age < 51:
                                lifePhase = 'adult_50'
                            elif age <66:
                                lifePhase = 'old_adult_65'
                            else:
                                lifePhase = 'retiree'
                            phase.append(lifePhase)
                                
                        df[ana.split('/')[0]+'_'+str(st)+'_life_phase_pred'] = phase
                        
                        
                    res_dic[st][ana.split('/')[0]][label]['labels'] = {}
                    for l in lab:
                        res_dic[st][ana.split('/')[0]][label]['labels'][l] = {}
                    
                    
                    
            df_dic[st][ana.split('/')[0]]['df'] = df


    # In[ ]:





    # In[ ]:





    # In[ ]:


    for st in subsets:
        for ana in subana:
            an = ana.split('/')[0]
            for label in labels:
                
                enc = res_dic[st][an][label]['label_encoder']
                coef = res_dic[st][an][label]['SVM']
                key_len = len(res_dic[st][an][label]['labels'].keys())
                df = df_dic[st][an]['df']
                res_dic[st][an][label]['acc'] = accuracy_score(df[an+'_'+str(st)+'_SVM_'+label], df[an+'_'+str(st)+'_SVM_'+label+'_pred']).round(3)
                res_dic[st][an][label]['prec'] = precision_score(df[an+'_'+str(st)+'_SVM_'+label], df[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)
                res_dic[st][an][label]['rec'] = recall_score(df[an+'_'+str(st)+'_SVM_'+label], df[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)
                res_dic[st][an][label]['f1'] = f1_score(df[an+'_'+str(st)+'_SVM_'+label], df[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)
                
                for key in res_dic[st][an][label]['labels'].keys():
                    key_enc = enc.transform([key])[0]
        
                    subDf = df.loc[df[an+'_'+str(st)+'_SVM_'+label] == key]
                    if label == 'author':
                        row = df.loc[df[an+'_'+str(st)+'_'+'SVM'+'_'+'author'] == key].iloc[0]
                        res_dic[st][an][label]['labels'][key]['gender'] = row[an+'_'+str(st)+'_'+'SVM'+'_'+'gender']
                        res_dic[st][an][label]['labels'][key]['age'] = row[an+'_'+str(st)+'_'+'SVM'+'_'+'age']
                        res_dic[st][an][label]['labels'][key]['life_phase'] = row[an+'_'+str(st)+'_'+'life_phase']
                    elif label == 'age':
                        age = 2019 -key
                        if age <22:
                            lifePhase = 'child_21'
                        elif age <36:
                            lifePhase = 'young_adult_35'
                        elif age < 51:
                            lifePhase = 'adult_50'
                        elif age <66:
                            lifePhase = 'old_adult_65'
                        else:
                            lifePhase = 'retiree'

                            
                        res_dic[st][an][label]['labels'][key]['life_phase'] = lifePhase
                        
                        
                    res_dic[st][an][label]['labels'][key]['acc'] = accuracy_score(subDf[an+'_'+str(st)+'_SVM_'+label], subDf[an+'_'+str(st)+'_SVM_'+label+'_pred']).round(3)
                    ##no second category for subanalysis: prec = 1
                    #res_dic[st][an][label]['labels'][key]['prec'] = precision_score(subDf[an+'_'+str(st)+'_SVM_'+label], subDf[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)
                    ## precision always equals recall in subanalyis
                    #res_dic[st][an][label]['labels'][key]['rec'] = recall_score(subDf[an+'_'+str(st)+'_SVM_'+label], subDf[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)
                    ##f1 score is ill defined
                    #res_dic[st][an][label]['labels'][key]['f1'] = f1_score(subDf[an+'_'+str(st)+'_SVM_'+label], subDf[an+'_'+str(st)+'_SVM_'+label+'_pred'], average='weighted').round(3)

                    if key_len > 2:
                        res_dic[st][an][label]['labels'][key]['feature_vec'] = coef[key_enc]
                    
                    elif key_enc > 0:
                        res_dic[st][an][label]['labels'][key]['feature_vec'] = coef[0]
                        



    ###save top 25 most predictive labels for each set and each subset in groups


    most_pred = {}


    for st in subsets:
        most_pred[st] = {}
        for ana in subana:
            an = ana.split('/')[0]
            most_pred[st][an] = {}
            for label in labels:
                most_pred[st][an][label] = {'feature_vecs':[]}
                for ph in phases:
                    most_pred[st][an][label][ph] = {'feature_vecs_pos': [], 'feature_vecs_neg': [], 'val_array': []}
                    if label in ['author']:
                        for sex in ['male', 'female']:
                            most_pred[st][an][label][ph][sex] = {'feature_vecs_pos':[], 'feature_vecs_neg':[],
                                                                    'val_array': []}
                            most_pred[st][an][label][sex] = {'feature_vecs_pos':[], 'feature_vecs_neg':[],
                                                             'val_array': []}

                with open(path+ana+str(st)+'/'+datafolder+filebeg+str(st)+'_bigram_vocab.json' , 'r', encoding = 'utf-8') as f:
                    most_pred[st][an]['vocab'] = json.load(f) 
                ##author and age to have different depth than gender and can be used for life phases
                if label in ['age']:
                    for key in res_dic[st][an][label]['labels'].keys():
                        ph = res_dic[st][an][label]['labels'][key]['life_phase']
                        sort = list(np.argsort(res_dic[st][an][label]['labels'][key]['feature_vec']))
                        array = res_dic[st][an][label]['labels'][key]['feature_vec']
                        most_pred[st][an][label][ph]['feature_vecs_pos'].append([i for i in sort if array[i] > 0 ])

                        most_pred[st][an][label][ph]['feature_vecs_neg'].append([i for i in sort if array[i] < 0 ])
                        if type(most_pred[st][an][label][ph]['val_array']) != type([]):
                            A = most_pred[st][an][label][ph]['val_array']
                            most_pred[st][an][label][ph]['val_array'] = np.vstack((A, array))
                        
                        else:
                            most_pred[st][an][label][ph]['val_array'] = array
                    for ph in phases:
                        most_pred[st][an][label][ph]['min_array'] = np.amin(most_pred[st][an][label][ph]['val_array'], axis=0)
                        most_pred[st][an][label][ph]['max_array'] = np.amax(most_pred[st][an][label][ph]['val_array'], axis=0)
                        most_pred[st][an][label][ph]['number'] = most_pred[st][an][label][ph]['val_array'].shape[0]
                        most_pred[st][an][label][ph]['val_array'] = np.mean(most_pred[st][an][label][ph]['val_array'], axis = 0)
      
                            
                elif label in ['author']:
                    for key in res_dic[st][an][label]['labels'].keys():
                        ph = res_dic[st][an][label]['labels'][key]['life_phase']
                        sex = res_dic[st][an][label]['labels'][key]['gender']
                        sort = list(np.argsort(res_dic[st][an][label]['labels'][key]['feature_vec']))
                        array = res_dic[st][an][label]['labels'][key]['feature_vec']
                        m_pred_pos = [i for i in sort if array[i] > 0 ]
                        m_pred_neg = [i for i in sort if array[i] < 0 ]
                        most_pred[st][an][label][ph]['feature_vecs_pos'].append(m_pred_pos)
                        most_pred[st][an][label][sex]['feature_vecs_pos'].append(m_pred_pos)
                        most_pred[st][an][label][ph][sex]['feature_vecs_pos'].append(m_pred_pos)
                        most_pred[st][an][label][ph]['feature_vecs_neg'].append(m_pred_neg)
                        most_pred[st][an][label][sex]['feature_vecs_neg'].append(m_pred_neg)
                        most_pred[st][an][label][ph][sex]['feature_vecs_neg'].append(m_pred_neg)
                        
                        if type(most_pred[st][an][label][ph]['val_array']) != type([]):
                            A = most_pred[st][an][label][ph]['val_array']
                            most_pred[st][an][label][ph]['val_array'] = np.vstack((A, array))
                        
                        else:
                            most_pred[st][an][label][ph]['val_array'] = array
                            
                        if type(most_pred[st][an][label][sex]['val_array']) != type([]):
                            A = most_pred[st][an][label][sex]['val_array']
                            most_pred[st][an][label][sex]['val_array'] = np.vstack((A, array))
                        
                        else:

                            most_pred[st][an][label][sex]['val_array'] = array
                            
                        if type(most_pred[st][an][label][ph][sex]['val_array']) != type([]):
                            A = most_pred[st][an][label][ph][sex]['val_array']
                            most_pred[st][an][label][ph][sex]['val_array'] = np.vstack((A, array))
                        
                        else:
                            most_pred[st][an][label][ph][sex]['val_array'] = array
                            
                            
                    check = True
                    for ph in phases:        
                        most_pred[st][an][label][ph]['min_array'] = np.amin(most_pred[st][an][label][ph]['val_array'], axis = 0)
                        most_pred[st][an][label][ph]['max_array'] = np.amax(most_pred[st][an][label][ph]['val_array'], axis = 0)
                        most_pred[st][an][label][ph]['number'] = most_pred[st][an][label][ph]['val_array'].shape[0]
                        most_pred[st][an][label][ph]['val_array'] = np.mean(most_pred[st][an][label][ph]['val_array'], axis = 0)
                        for sex in ['female', 'male']:
                            if check:
                                #print(sex)
                                #print(most_pred[st][an][label][sex]['val_array'])
                                most_pred[st][an][label][sex]['min_array'] = np.amin(most_pred[st][an][label][sex]['val_array'], axis = 0)
                                most_pred[st][an][label][sex]['max_array'] = np.amax(most_pred[st][an][label][sex]['val_array'], axis = 0)
                                most_pred[st][an][label][sex]['number'] = most_pred[st][an][label][sex]['val_array'].shape[0]
                                most_pred[st][an][label][sex]['val_array'] = np.mean(most_pred[st][an][label][sex]['val_array'], axis = 0)
                                #print(most_pred[st][an][label][sex]['val_array'])
                                #print(most_pred[st][an][label][sex]['min_array'])
                                #print(most_pred[st][an][label][sex]['max_array'])
        
                            most_pred[st][an][label][ph][sex]['min_array']= np.amin(most_pred[st][an][label][ph][sex]['val_array'], axis = 0)
                            most_pred[st][an][label][ph][sex]['max_array'] =np.amax(most_pred[st][an][label][ph][sex]['val_array'], axis = 0)
                            most_pred[st][an][label][ph][sex]['number'] = most_pred[st][an][label][ph][sex]['val_array'].shape[0]
                            most_pred[st][an][label][ph][sex]['val_array'] = np.mean(most_pred[st][an][label][ph][sex]['val_array'], axis = 0)
                        check = False
                         
                
                else:
                    
                    most_pred[st][an][label]['feature_vecs'].append(list(np.argsort(res_dic[st][an][label]['labels']['male']['feature_vec'])))


    #sys.exit(1)                
    for st in subsets:
        for ana in subana:
            an = ana.split('/')[0]
            for label in labels:
                if label == 'age':
                    for ph in phases:

                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_pos']:
                            c.update(list(vec))
                        most_pred[st][an][label][ph]['count_tot_pos'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_pos']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][ph]['count_top25_pos'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_neg']:
                            c.update(list(vec))
                        most_pred[st][an][label][ph]['count_tot_neg'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_neg']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][ph]['count_top25_neg'] = c.most_common()
          
                if label == 'author':
                    for ph in phases:
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_pos']:
                            c.update(list(vec))
                        most_pred[st][an][label][ph]['count_tot_pos'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_pos']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][ph]['count_top25_pos'] = c.most_common()
                        for sex in ['male', 'female']:
                            c = collections.Counter()
                            for vec in most_pred[st][an][label][ph][sex]['feature_vecs_pos']:
                                c.update(list(vec))
                            most_pred[st][an][label][ph][sex]['count_tot_pos'] = c.most_common()
                            c = collections.Counter()
                            for vec in most_pred[st][an][label][ph][sex]['feature_vecs_pos']:
                                c.update(list(vec[-25:]))
                            most_pred[st][an][label][ph][sex]['count_top25_pos'] = c.most_common()
                            
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_neg']:
                            c.update(list(vec))
                        most_pred[st][an][label][ph]['count_tot_neg'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][ph]['feature_vecs_neg']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][ph]['count_top25_neg'] = c.most_common()
                        for sex in ['male', 'female']:
                            c = collections.Counter()
                            for vec in most_pred[st][an][label][ph][sex]['feature_vecs_neg']:
                                c.update(list(vec))
                            most_pred[st][an][label][ph][sex]['count_tot_neg'] = c.most_common()
                            c = collections.Counter()
                            for vec in most_pred[st][an][label][ph][sex]['feature_vecs_neg']:
                                c.update(list(vec[-25:]))
                            most_pred[st][an][label][ph][sex]['count_top25_neg'] = c.most_common()                        
                    for sex in ['male', 'female']:
                        
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][sex]['feature_vecs_pos']:
                            c.update(list(vec))
                        most_pred[st][an][label][sex]['count_tot_pos'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][sex]['feature_vecs_pos']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][sex]['count_top25_pos'] = c.most_common()                       
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][sex]['feature_vecs_neg']:
                            c.update(list(vec))
                        most_pred[st][an][label][sex]['count_tot_neg'] = c.most_common()
                        c = collections.Counter()
                        for vec in most_pred[st][an][label][sex]['feature_vecs_neg']:
                            c.update(list(vec[-25:]))
                        most_pred[st][an][label][sex]['count_top25_neg'] = c.most_common()                           
              
                    










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    for st in subsets:
        for ana in subana:
            an = ana.split('/')[0]
            tmp = []
            index = []
            for label in labels:
                comp = {'accuracy': accuracy_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label+'_pred']).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label+'_pred'],
                                                 average='weighted').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label+'_pred'],
                                                 average='weighted').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_'+label+'_pred'],
                                                 average='weighted').round(3),
                       }
                tmp.append(comp)
                index.append(label)
                
                if label == 'age':
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                    tmp_sub = []
                    ind_sub = []
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                    comp = {'accuracy': accuracy_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred']).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred'],
                                                 average='weighted').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred'],
                                                 average='weighted').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred'],
                                                 average='weighted').round(3)
                           }
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                    for ph in phases:
                        df = df_dic[st][an]['df']
                        comp_sub = {'accuracy': accuracy_score(df.loc[df[an+'_'+str(st)+'_life_phase'] == ph][an+'_'+str(st)+'_life_phase'],
                                                  df.loc[df[an+'_'+str(st)+'_life_phase'] == ph][an+'_'+str(st)+'_life_phase_pred']).round(3),
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                            'precision': precision_score(df[an+'_'+str(st)+'_life_phase'],
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                                                  df[an+'_'+str(st)+'_life_phase_pred'], labels = [ph],
                                                     average='weighted').round(3),
                            'recall': recall_score(df[an+'_'+str(st)+'_life_phase'],
                                                  df[an+'_'+str(st)+'_life_phase_pred'],labels = [ph],
                                                     average='weighted').round(3),
                            'f1-score': f1_score(df[an+'_'+str(st)+'_life_phase'],
                                                  df[an+'_'+str(st)+'_life_phase_pred'],labels = [ph],
                                                     average='weighted').round(3)}

                        tmp_sub.append(comp_sub)
                        ind_sub.append('age phase {}'.format(ph))    



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                    f= plt.figure(figsize=(5,10))
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                    tmp_df =pd.DataFrame(tmp_sub, index = ind_sub)
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                    tmp_df.plot(kind='bar', colormap = cmap, ax=f.gca())
                    plt.legend(loc='upper right') #legend outside box
                    plt.xlabel(xlabel='Evaluation Measures for Different Groups',fontsize ='large', fontweight='roman')
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                    ax = plt.gca()
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                    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",rotation_mode="anchor")
                    plt.grid(True, axis ='y')
                    plt.ylim((0,0.6))
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                    plt.tight_layout()
                    plt.savefig(savedir+'barplots/age_scores_{}_{}.pdf'.format(st, an))
                    plt.savefig(savedir+'barplots/age_scores_{}_{}.png'.format(st, an))                                          

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                    tmp.append(comp)
                    index.append('age by life_phase')
                    
                    np.set_printoptions(precision=2)
                    cnf_matrix = confusion_matrix(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                                 df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred'],
                                                 labels=phases)
                    f = plt.figure()
                    ax = f.subplots()
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                    plot_confusion_matrix(cnf_matrix, classes=phases,title=None, ax=ax)
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                    plt.tight_layout()
                    #plt.show()
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                    #f.savefig('../Data/results/heatmaps/test.png')
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                    f.savefig(savedir+ 'heatmaps/cm_{st}_{an}_{label}_{group}.pdf'.format(st = st,
                                                                                                  an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase'))
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                    f.savefig(savedir+ 'heatmaps/cm_{st}_{an}_{label}_{group}.png'.format(st = st,
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                                                                                                  an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase'))
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                if label == 'author':
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                    tmp_sub = []
                    ind_sub = []


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                    gen_pred_auth = []
                    life_ph_pred_auth = []
                    gen_pred_auth_wrong = []
                    life_ph_pred_auth_wrong = []
                    auth = list(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_author_pred'])
                    sub_wrong = df_dic[st][an]['df'].loc[df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_author_pred'] != df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_author']]                
                    auth_sub_wrong = list(sub_wrong[an+'_'+str(st)+'_SVM_author_pred'])
                    
                    for au in auth:
                        gen_pred_auth.append(author_dic[st][an][au]['gender'])
                        life_ph_pred_auth.append(author_dic[st][an][au]['life_phase'])
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                    df_dic[st][an]['df'][an+'_'+str(st)+'_gender_pred_auth'] = gen_pred_auth
                    df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase_pred_auth'] = life_ph_pred_auth
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                    for au in auth_sub_wrong:
                        gen_pred_auth_wrong.append(author_dic[st][an][au]['gender'])
                        life_ph_pred_auth_wrong.append(author_dic[st][an][au]['life_phase']) 
                        
                        
                    comp = {'accuracy': accuracy_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              life_ph_pred_auth).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              life_ph_pred_auth,
                                                 average='weighted').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              life_ph_pred_auth,
                                                 average='weighted').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                              life_ph_pred_auth,
                                                 average='weighted').round(3)
                           }
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                    for ph in phases:
                        df = df_dic[st][an]['df']
                        comp_sub = {'accuracy': accuracy_score(df.loc[df[an+'_'+str(st)+'_life_phase'] == ph][an+'_'+str(st)+'_life_phase'],
                                                  df.loc[df[an+'_'+str(st)+'_life_phase'] == ph][an+'_'+str(st)+'_life_phase_pred_auth']).round(3),
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                            'precision': precision_score(df[an+'_'+str(st)+'_life_phase'],
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                                                  df[an+'_'+str(st)+'_life_phase_pred_auth'], labels = [ph],
                                                     average='weighted').round(3),
                            'recall': recall_score(df[an+'_'+str(st)+'_life_phase'],
                                                  df[an+'_'+str(st)+'_life_phase_pred_auth'],labels = [ph],
                                                     average='weighted').round(3),
                            'f1-score': f1_score(df[an+'_'+str(st)+'_life_phase'],
                                                  df[an+'_'+str(st)+'_life_phase_pred_auth'],labels = [ph],
                                                     average='weighted').round(3)}

                        tmp_sub.append(comp_sub)
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                        ind_sub.append('age phase {}'.format(ph))


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                    f= plt.figure(figsize=(5, 10))
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                    tmp_df =pd.DataFrame(tmp_sub, index = ind_sub)
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                    tmp_df.plot(kind='bar', colormap = cmap, ax=f.gca())
                    plt.legend(loc='upper right')
                    plt.xlabel(xlabel='Evaluation Measures for Different Groups',fontsize ='large', fontweight='roman')
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                    ax = plt.gca()
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                    plt.setp(ax.get_xticklabels(), rotation=45, ha="right",rotation_mode="anchor")
                    plt.grid(True, axis ='y')
                    plt.ylim((0,0.6))
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                    plt.tight_layout()
                    plt.savefig(savedir+'barplots/author_scores_{}_{}.pdf'.format(st, an))
                    plt.savefig(savedir+'barplots/author_scores_{}_{}.png'.format(st, an)) 

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                    tmp.append(comp)
                    index.append('author by life_phase')
                    
                    cnf_matrix = confusion_matrix(df_dic[st][an]['df'][an+'_'+str(st)+'_life_phase'],
                                                 life_ph_pred_auth,
                                                 labels=phases)
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                    f = plt.figure()
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                    ax = f.subplots()
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                    plot_confusion_matrix(cnf_matrix, classes=phases,title=None, ax=ax)
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                    plt.show()
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                    plt.tight_layout()
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}.pdf'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase'))
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}.png'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase'))
                    
                    comp = {'accuracy': accuracy_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth,
                                                 average='weighted').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth,
                                                 average='weighted').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth,
                                                 average='weighted').round(3)
                           }
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                    gend = [comp]
                    ind2 = ['author by gender']

                    gend_an = {'accuracy': accuracy_score(df_dic[st][an]['df'].loc[df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'] == 'male'][an+'_'+str(st)+'_SVM_gender'],
                                              df_dic[st][an]['df'].loc[df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'] == 'male'][an+'_'+str(st)+'_gender_pred_auth']).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'male',
                                                 average='binary').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'male',
                                                 average='binary').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'male',
                                                 average='binary').round(3)
                           }
                    gend.append(gend_an)
                    ind2.append('author by gender\n male')

                    gend_an = {'accuracy': accuracy_score(df_dic[st][an]['df'].loc[df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'] == 'female'][an+'_'+str(st)+'_SVM_gender'],
                                              df_dic[st][an]['df'].loc[df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'] == 'female'][an+'_'+str(st)+'_gender_pred_auth']).round(3),
                        'precision': precision_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'female',
                                                 average='binary').round(3),
                        'recall': recall_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'female',
                                                 average='binary').round(3),
                        'f1-score': f1_score(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                              gen_pred_auth, pos_label = 'female',
                                                 average='binary').round(3)
                           }
                    ind2.append('author by gender\n female')
                    gend.append(gend_an)
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                    tmp.append(comp)
                    index.append('author by gender')
                        
                    cnf_matrix = confusion_matrix(df_dic[st][an]['df'][an+'_'+str(st)+'_SVM_gender'],
                                                 gen_pred_auth,
                                                 labels=['female', 'male'])
                    f = plt.figure()
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                    ax = f.subplots()
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                    plot_confusion_matrix(cnf_matrix, classes=['female', 'male'],title=None, ax=ax)
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                    plt.tight_layout()
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}.pdf'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='gender'))
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}.png'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='gender'))
                    
                    ###make author cmap showing whether the author missclassified
                    ##were confused with authors of similar gender or life_phase
                    cnf_matrix = confusion_matrix(sub_wrong[an+'_'+str(st)+'_life_phase'],
                                                 life_ph_pred_auth_wrong,
                                                 labels=phases)
                    f = plt.figure()
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                    ax = f.subplots()
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                    plot_confusion_matrix(cnf_matrix, classes=phases,title=None, ax=ax)
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                    plt.tight_layout()
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}_false.pdf'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase')) 
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}_false.png'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='life_phase'))                
                    
                    
                    cnf_matrix = confusion_matrix(sub_wrong[an+'_'+str(st)+'_SVM_gender'],
                                                 gen_pred_auth_wrong,
                                                 labels=['female', 'male'])
                    f = plt.figure()
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                    ax = f.subplots()
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                    plot_confusion_matrix(cnf_matrix, classes=['female', 'male'],title=None, ax=ax)
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                    plt.tight_layout()
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}_false.pdf'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='gender'))
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                    f.savefig(savedir+'heatmaps/cm_{st}_{an}_{label}_{group}_false.png'.format(st = st, an=an,
                                                                                                  label=label,
                                                                                                  group='gender'))                
                                    
                    
                    
                    
                    
                    
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            f= plt.figure(figsize=(5,10))
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            tmp_df =pd.DataFrame(tmp, index = index)
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            tmp_df.plot(kind='bar', colormap = cmap, ax=f.gca())
            #plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5)) #legend outside box
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            plt.legend(loc='upper left')
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            plt.xlabel(xlabel='Evaluation Measures for Different Subsets',fontsize ='large', fontweight='roman')
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            ax = plt.gca()
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            plt.setp(ax.get_xticklabels(), rotation=45, ha="right",rotation_mode="anchor")
            plt.grid(True, axis ='y')
            plt.ylim((0,0.7))
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            plt.tight_layout()
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            plt.savefig(savedir+'barplots/overall_scores_{}_{}.pdf'.format(st, an))
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            plt.savefig(savedir+'barplots/overall_scores_{}_{}.png'.format(st, an))
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            f= plt.figure(figsize=(10,5))
            tmp_df =pd.DataFrame(gend, index = ind2)
            tmp_df.plot(kind='barh', colormap = cmap, ax=f.gca())
            plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))
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            plt.xlabel(xlabel='Evaluation Measures for Author Scores by Gender',fontsize ='large', fontweight='roman')
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            ax = plt.gca()
            plt.setp(ax.get_xticklabels(), rotation=45, ha="right",rotation_mode="anchor")
            plt.grid(True, axis ='y')
            plt.ylim((0,0.7))
            plt.tight_layout()
            plt.savefig(savedir+'barplots/author_gender_scores_{}_{}.pdf'.format(st, an))
            plt.savefig(savedir+'barplots/author_gender_scores_{}_{}.png'.format(st, an))
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    # Make plots with most predicitve features

    # In[ ]:


    #import matplotlib as mpl
    #mpl.rcParams['font.sans-serif'] = ['Segoe UI Emoji']
    #mpl.rcParams['font.serif'] = ['Segoe UI Emoji']
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    #sns.set_style({"font.sans-serif":['Segoe UI Emoji']}) 
    for st in subsets:
        for ana in subana:
            an = ana.split('/')[0]
            vocab = res_dic[st][an]['vocab_inverse']
            for label in labels:
                dic = {}
                ind_pos = []
                ind_neg = []
                if label == 'age':
                    phase_key = []
                    for ph in phases:
                        
                        leng = most_pred[st][an][label][ph]['number']
                        arr = most_pred[st][an][label][ph]['val_array']
                        maxi = most_pred[st][an][label][ph]['max_array']
                        mini = most_pred[st][an][label][ph]['min_array']
                        ##make it so that the values of heat are relatively to the min and max values of the feature
                        dic[ph+'\n({})'.format(leng)] = {el[0]:(arr[el[0]]/maxi[el[0]]).round(3) for el in most_pred[st][an][label][ph]['count_tot_pos'] if (el[1]/leng) >= .95}
                        dic[ph+'\n({})'.format(leng)].update({el[0]:-(abs(arr[el[0]])/abs(mini[el[0]])).round(3) for el in most_pred[st][an][label][ph]['count_tot_neg'] if (el[1]/leng) >= .95})        
                        ind_pos.extend([el[0] for el in most_pred[st][an][label][ph]['count_tot_pos'] if (el[1]/leng) >= .95])
                        ind_neg.extend([el[0] for el in most_pred[st][an][label][ph]['count_tot_neg'] if (el[1]/leng) >= .95]) 
                        phase_key.append(ph+'\n({})'.format(leng))
                    ind_pos = list(np.unique(ind_pos))
                    ind_neg = list(np.unique(ind_neg))
                        
                    tmp = {}
                    for ph in phase_key:
                        tmp_l = []
                        for el in ind_pos+ind_neg:
                            try:
                                tmp_l.append(dic[ph][el])
                            except:
                                tmp_l.append(0)
                        tmp[ph] = tmp_l
                    
                    #print([vocab[el] for el in ind_pos + ind_neg])
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                    ind = [vocab[el].replace('§', '') for el in ind_pos+ind_neg]
                    ind = [re.sub(r'\s', 'BLANK', el) for el in ind]
                    ind = [el.replace('$', r'\$') for el in ind]
                    ind = [el.replace('\n', 'BREAK') for el in ind]
                    #print(ind)
                    for i in range(0, len(ind)):
                        try:
                            ind[i].encode('ascii')
                        except:
                            ind[i] = ind[i].encode('unicode-escape')
                    
                        
                    df = pd.DataFrame(tmp , index = ind)
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                    ##select only informative features (i.e. those which are 0 across less than all columns of type)
                    mask = (df == 0.0).T
                    ls = []
                    for col in mask.columns:
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                        if collections.Counter(mask[col])[True] < 1:
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                            ls.append(col)
                    df.drop(ls, inplace = True)
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                    f,ax = plt.subplots(figsize=(18, len(ind)/6))
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                    sns.heatmap(df, fmt= '.1f',ax=ax, center = 0, yticklabels = True)
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_phases.pdf'.format(st, an, label))
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_phases.png'.format(st, an, label))
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                elif label == 'author':
                    sex_key = []
                    phase_key = []
                    for sex in ['male', 'female']:
                        leng = most_pred[st][an][label][sex]['number']
                        arr = most_pred[st][an][label][sex]['val_array']
                        maxi = most_pred[st][an][label][sex]['max_array']
                        mini = most_pred[st][an][label][sex]['min_array']
                        ##make it so that the values of heat are relatively to the min and max values of the feature
                        dic[sex+'\n({})'.format(leng)] = {el[0]:(arr[el[0]]/maxi[el[0]]).round(3) for el in most_pred[st][an][label][sex]['count_tot_pos'] if (el[1]/leng) >= .95}
                        dic[sex+'\n({})'.format(leng)].update({el[0]:-(abs(arr[el[0]])/abs(mini[el[0]])).round(3) for el in most_pred[st][an][label][sex]['count_tot_neg'] if (el[1]/leng) >= .95})        
                        ind_pos.extend([el[0] for el in most_pred[st][an][label][sex]['count_tot_pos'] if (el[1]/leng) >= .95])
                        ind_neg.extend([el[0] for el in most_pred[st][an][label][sex]['count_tot_neg'] if (el[1]/leng) >= .95]) 
                        sex_key.append(sex+'\n({})'.format(leng))

                    for ph in phases:
                        leng = most_pred[st][an][label][ph]['number']
                        arr = most_pred[st][an][label][ph]['val_array']
                        maxi = most_pred[st][an][label][ph]['max_array']
                        mini = most_pred[st][an][label][ph]['min_array']
                        ##make it so that the values of heat are relatively to the min and max values of the feature
                        dic[ph+'\n({})'.format(leng)] = {el[0]:(arr[el[0]]/maxi[el[0]]).round(3) for el in most_pred[st][an][label][ph]['count_tot_pos'] if (el[1]/leng) >= 0.95}
                        dic[ph+'\n({})'.format(leng)].update({el[0]:-(abs(arr[el[0]])/abs(mini[el[0]])).round(3) for el in most_pred[st][an][label][ph]['count_tot_neg'] if (el[1]/leng) >= .95})        
                        ind_pos.extend([el[0] for el in most_pred[st][an][label][ph]['count_tot_pos'] if (el[1]/leng) >= .95])
                        ind_neg.extend([el[0] for el in most_pred[st][an][label][ph]['count_tot_neg'] if (el[1]/leng) >= .95]) 
                        phase_key.append(ph+'\n({})'.format(leng))
                    ind_pos = list(np.unique(ind_pos))
                    ind_neg = list(np.unique(ind_neg))
                    
                    tmp = {}
                    for ph in phase_key+sex_key:
                        tmp_l = []
                        for el in ind_pos+ind_neg:
                            try:
                                tmp_l.append(dic[ph][el])
                            except:
                                tmp_l.append(0)
                        tmp[ph] = tmp_l
                                          
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                    #print([vocab[el] for el in ind_pos + ind_neg])
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                    ind = [vocab[el].replace('§', '') for el in ind_pos+ind_neg]
                    ind = [re.sub(r'\s', 'BLANK', el) for el in ind]       
                    ind = [el.replace('$', r'\$') for el in ind]
                    ind = [el.replace('\n', 'BREAK') for el in ind]
                    #print(ind)
                    for i in range(0, len(ind)):
                        try:
                            ind[i].encode('ascii')
                        except:
                            ind[i] = ind[i].encode('unicode-escape')
                    #print([vocab[el] for el in ind_pos + ind_neg])
                    df = pd.DataFrame(tmp, index = ind)
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                    ##select only informative features (i.e. those which are 0 across less than all columns of type)
                    mask = (df == 0.0).T
                    ls = []
                    for col in mask.columns:
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                        if collections.Counter(mask[col])[True] < 1:
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                            ls.append(col)
                    df.drop(ls, inplace = True)   


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                    f,ax = plt.subplots(figsize=(18, len(ind_pos+ind_neg)/6))
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                    sns.heatmap(df, fmt= '.1f',ax=ax, center = 0, yticklabels = True)
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_phases.pdf'.format(st, an, label))
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_phases.png'.format(st, an, label))
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                    dic = {}
                    ind_pos = []
                    ind_neg = []
                    phase_key = []
                    for ph in phases:
                        for sex in ['female', 'male']:
                            leng = most_pred[st][an][label][ph][sex]['number']
                            arr = most_pred[st][an][label][ph][sex]['val_array']
                            maxi = most_pred[st][an][label][ph][sex]['max_array']
                            mini = most_pred[st][an][label][ph][sex]['min_array']
                            ##make it so that the values of heat are relatively to the min and max values of the feature
                            dic[sex+'_'+ph+'\n({})'.format(leng)] = {el[0]:(arr[el[0]]/maxi[el[0]]).round(3) for el in most_pred[st][an][label][ph][sex]['count_tot_pos'] if (el[1]/leng) >= 0.95}
                            dic[sex+'_'+ph+'\n({})'.format(leng)].update({el[0]:-(abs(arr[el[0]])/abs(mini[el[0]])).round(3) for el in most_pred[st][an][label][ph][sex]['count_tot_neg'] if (el[1]/leng) >= .95})        
                            ind_pos.extend([el[0] for el in most_pred[st][an][label][ph][sex]['count_tot_pos'] if (el[1]/leng) >= .95])
                            ind_neg.extend([el[0] for el in most_pred[st][an][label][ph][sex]['count_tot_neg'] if (el[1]/leng) >= .95])                
                            phase_key.append(sex+'_'+ph+'\n({})'.format(leng))
                    ind_pos = list(np.unique(ind_pos))
                    ind_neg = list(np.unique(ind_neg))
                    
                    tmp = {}
                    for ph in phase_key:
                        tmp_l = []
                        for el in ind_pos+ind_neg:
                            try:
                                tmp_l.append(dic[ph][el])
                            except:
                                tmp_l.append(0)
                        tmp[ph] = tmp_l                                    
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                    #print([vocab[el] for el in ind_pos + ind_neg])
                    ind = [vocab[el].replace('§', '') for el in ind_pos+ind_neg]
                    ind = [re.sub(r'\s', 'BLANK', el) for el in ind]
                    ind = [el.replace('$', r'\$') for el in ind]
                    ind = [el.replace('\n', 'BREAK') for el in ind]
                    #print(ind)
                    for i in range(0, len(ind)):
                        try:
                            ind[i].encode('ascii')
                        except:
                            ind[i] = ind[i].encode('unicode-escape')
                    df = pd.DataFrame(tmp, index = ind)
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                    ##select only informative features (i.e. those which are 0 across less than all columns of type)
                    mask = (df == 0.0).T
                    ls = []
                    for col in mask.columns:
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                        if collections.Counter(mask[col])[True] < 1:
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                            ls.append(col)
                    df.drop(ls, inplace = True)

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                    f,ax = plt.subplots(figsize=(18, len(ind_pos+ind_neg)/6))
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                    sns.heatmap(df, fmt= '.1f',ax=ax, center = 0, yticklabels = True)
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_gender_phases.pdf'.format(st, an, label)) 
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                    f.savefig(savedir+'featureplots/features_heat_{}_{}_{}_gender_phases.png'.format(st, an, label))                      
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    return 1
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def main():
                        
    cpus = mp.cpu_count()
    pool = mp.Pool(cpus)
    #fire off workers
    jobs = []
    #create jobs
    print('make job queue...')
    sys.stdout.flush()
    print('enter cycle...')
    for subsets in subsets_l:
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        for subana in subana_l:
            job = pool.apply_async(plotter,([subsets], [subana], phases_l, labels_l))
            jobs.append(job)
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    print('collect results from jobs...')
    sys.stdout.flush()
    # collect results from the workers through the pool result queue
    for j in range(0, len(jobs)):
        tmp = jobs.pop(0)
        tmp = tmp.get()
        del tmp

    print('kill all remaining workers...')
    sys.stdout.flush()
    print('closing down the pool')
    sys.stdout.flush()
    pool.close()   
    pool.join()
    print('done and exit :)')
    sys.stdout.flush()




if __name__ == "__main__":
    main()