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In [2]:



    


import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
import pandas as pd
import numpy as np
import os

Gen0





In [6]:



    


#upload geneotypes and phenotypes 
Gen0_genotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen0+genotypes.p'))
Gen0_phenotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen0+phenotypes.p'))
Gen0_phenotypes.sort_values('test_accuracy',ascending=False).head()



    


















    
Out[6]:










  
    

      
      gene_name
      misclassed
      test_accuracy
      test_loss
      time
      train_accuracy
      train_loss
    

  
  
    

      0
      lab3000_n1e1p1b2+Gen0+gene13
      {'true_class': [4, 4, 5, 23, 20, 1, 40, 15, 1,...
      0.803651
      0.883051
      47.459044
      0.965607
      0.120601
    

    

      0
      lab3000_n1e1p1b2+Gen0+gene6
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 21...
      0.780944
      1.239399
      31.594541
      0.962266
      0.149377
    

    

      0
      lab3000_n1e1p1b2+Gen0+gene24
      {'true_class': [4, 4, 5, 23, 10, 20, 1, 40, 15...
      0.752449
      1.030251
      194.952801
      0.827910
      0.682031
    

    

      0
      lab3000_n1e1p1b2+Gen0+gene25
      {'true_class': [4, 4, 5, 23, 8, 3, 6, 10, 20, ...
      0.743544
      1.087753
      141.736290
      0.838550
      0.620869
    

    

      0
      lab3000_n1e1p1b2+Gen0+gene11
      {'true_class': [10, 4, 4, 5, 23, 8, 6, 20, 1, ...
      0.723063
      1.521470
      26.861886
      0.932327
      0.261401

Gen1





In [7]:



    


#upload geneotypes and phenotypes 
Gen1_genotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen1+genotypes.p'))
Gen1_phenotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen1+phenotypes.p'))
Gen1_phenotypes.sort_values('test_accuracy',ascending=False).head()



    


















    
Out[7]:










  
    

      
      gene_name
      misclassed
      test_accuracy
      test_loss
      time
      train_accuracy
      train_loss
    

  
  
    

      0
      lab3000_n1e1p1b2+Gen1+gene15
      {'true_class': [10, 4, 5, 23, 20, 1, 40, 15, 1...
      0.803651
      0.897263
      122.271955
      0.964122
      0.125870
    

    

      0
      lab3000_n1e1p1b2+Gen1+gene8
      {'true_class': [4, 4, 5, 23, 20, 1, 40, 15, 1,...
      0.803206
      0.934038
      225.305663
      0.961772
      0.174198
    

    

      0
      lab3000_n1e1p1b2+Gen1+gene29
      {'true_class': [4, 4, 5, 23, 20, 1, 40, 15, 1,...
      0.801425
      0.937885
      52.716231
      0.957442
      0.177796
    

    

      0
      lab3000_n1e1p1b2+Gen1+gene20
      {'true_class': [3, 4, 4, 5, 23, 20, 1, 40, 15,...
      0.792520
      1.012021
      124.393423
      0.958184
      0.165223
    

    

      0
      lab3000_n1e1p1b2+Gen1+gene10
      {'true_class': [4, 4, 5, 23, 20, 1, 40, 15, 1,...
      0.787622
      1.014112
      127.006645
      0.956328
      0.164417

Gen2





In [9]:



    


#upload geneotypes and phenotypes 
Gen2_genotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen2+genotypes.p'))
Gen2_phenotypes = pd.read_pickle(os.path.expanduser('~/Experiments/lab3000_n1e1p1b2/lab3000_n1e1p1b2+Gen2+phenotypes.p'))
Gen2_phenotypes.sort_values('test_accuracy',ascending=False).head()



    


















    
Out[9]:










  
    

      
      gene_name
      misclassed
      test_accuracy
      test_loss
      time
      train_accuracy
      train_loss
    

  
  
    

      0
      lab3000_n1e1p1b2+Gen2+gene26
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 4,...
      0.814782
      0.854156
      141.992026
      0.927378
      0.342601
    

    

      0
      lab3000_n1e1p1b2+Gen2+gene2
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 21...
      0.808994
      0.834695
      28.404398
      0.955586
      0.233952
    

    

      0
      lab3000_n1e1p1b2+Gen2+gene16
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 21...
      0.808549
      0.933977
      58.579753
      0.960040
      0.165074
    

    

      0
      lab3000_n1e1p1b2+Gen2+gene12
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 21...
      0.806768
      0.912981
      41.787451
      0.967710
      0.090800
    

    

      0
      lab3000_n1e1p1b2+Gen2+gene25
      {'true_class': [4, 5, 23, 20, 1, 40, 15, 1, 4,...
      0.806322
      0.878398
      128.751011
      0.965236
      0.121338

Plots





In [18]:



    


sns.set(font_scale=1.3);
sns.set_style("whitegrid");
fig = plt.figure(figsize=(6,16));

ax1 = plt.subplot2grid((4,1),(0,0));
ax1 = sns.distplot(Gen2_phenotypes.test_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[2],label='Gen2_test',kde=False);
ax1 = sns.distplot(Gen2_phenotypes.train_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[4],label='Gen2_train',kde=False);
plt.ylabel("Count");
plt.xlabel("Accuracy");
plt.legend(loc='upper right',fontsize=16);
plt.xlim([0,1.0])

ax1 = plt.subplot2grid((4,1),(1,0));
ax1 = sns.distplot(Gen1_phenotypes.test_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[1],label='Gen1_test',kde=False);
ax1 = sns.distplot(Gen1_phenotypes.train_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[4],label='Gen1_train',kde=False)
plt.ylabel("Count");
plt.xlabel("Accuracy");
plt.legend(loc='upper right',fontsize=16);
plt.xlim([0,1.0])

ax1 = plt.subplot2grid((4,1),(2,0));
ax1 = sns.distplot(Gen0_phenotypes.test_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[0],label='Gen0_test',kde=False);
ax1 = sns.distplot(Gen0_phenotypes.train_accuracy,norm_hist=True,bins=50,
                    color=sns.color_palette()[4],label='Gen0_train',kde=False)
plt.ylabel("Count");
plt.xlabel("Accuracy");
plt.legend(loc='upper right',fontsize=16);
plt.xlim([0,1.0]);
plt.savefig('Gen0_Gen1_Gen2_population_accuracies.png')



    


















    
























In [ ]:

Content source: lab3000/deeplearngene

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