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Classification of ECOLI - UCI ML Archive





In [1]:



    


import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')



    


















    






Populating the interactive namespace from numpy and matplotlib

















In [2]:



    


url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/ecoli/ecoli.data'

data_df = pd.read_csv(url, header=None, delim_whitespace=True)

Attribute Information

  1. Sequence Name: Accession number for the SWISS-PROT database
  2. mcg: McGeoch's method for signal sequence recognition.
  3. gvh: von Heijne's method for signal sequence recognition.
  4. lip: von Heijne's Signal Peptidase II consensus sequence score. Binary attribute.
  5. chg: Presence of charge on N-terminus of predicted lipoproteins. Binary attribute.
  6. aac: score of discriminant analysis of the amino acid content of outer membrane and periplasmic proteins.
  7. alm1: score of the ALOM membrane spanning region prediction program.
  8. alm2: score of ALOM program after excluding putative cleavable signal regions from the sequence.




In [3]:



    


columns = ['seq_name', 'mcg', 'gvh', 'lip','chg', 'aac', 'alm1', 'alm2', 'type']



    











In [4]:



    


data_df.head()



    


















    
Out[4]:










  
    

      
      0
      1
      2
      3
      4
      5
      6
      7
      8
    

  
  
    

      0
      AAT_ECOLI
      0.49
      0.29
      0.48
      0.5
      0.56
      0.24
      0.35
      cp
    

    

      1
      ACEA_ECOLI
      0.07
      0.40
      0.48
      0.5
      0.54
      0.35
      0.44
      cp
    

    

      2
      ACEK_ECOLI
      0.56
      0.40
      0.48
      0.5
      0.49
      0.37
      0.46
      cp
    

    

      3
      ACKA_ECOLI
      0.59
      0.49
      0.48
      0.5
      0.52
      0.45
      0.36
      cp
    

    

      4
      ADI_ECOLI
      0.23
      0.32
      0.48
      0.5
      0.55
      0.25
      0.35
      cp
    

  




















In [5]:



    


data_df.columns = columns



    











In [6]:



    


data_df.head()



    


















    
Out[6]:










  
    

      
      seq_name
      mcg
      gvh
      lip
      chg
      aac
      alm1
      alm2
      type
    

  
  
    

      0
      AAT_ECOLI
      0.49
      0.29
      0.48
      0.5
      0.56
      0.24
      0.35
      cp
    

    

      1
      ACEA_ECOLI
      0.07
      0.40
      0.48
      0.5
      0.54
      0.35
      0.44
      cp
    

    

      2
      ACEK_ECOLI
      0.56
      0.40
      0.48
      0.5
      0.49
      0.37
      0.46
      cp
    

    

      3
      ACKA_ECOLI
      0.59
      0.49
      0.48
      0.5
      0.52
      0.45
      0.36
      cp
    

    

      4
      ADI_ECOLI
      0.23
      0.32
      0.48
      0.5
      0.55
      0.25
      0.35
      cp
    

  




















In [7]:



    


data_df['type'].value_counts().plot(kind='bar')



    


















    
Out[7]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b38dfc18>










    
























In [8]:



    


from sklearn.feature_selection import f_classif, chi2

f_test_statistic, f_p_val = f_classif(data_df.drop(['seq_name', 'type'], axis=1), data_df['type'])



    











In [9]:



    


f_test_series = pd.Series(f_test_statistic, index=[c for c  in data_df.columns if c not in ['seq_name', 'type']])



    











In [10]:



    


f_test_series.sort_values(ascending=True).plot(kind='bar')



    


















    
Out[10]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b56329e8>










    
























In [11]:



    


f_test_p_vals = pd.Series(f_p_val, index=[c for c in data_df.columns if c not in ['seq_name', 'type']])
f_test_p_vals.sort_values(ascending=True)



    


















    
Out[11]:








alm1    1.026237e-108
lip      6.842695e-82
alm2     2.364261e-74
gvh      2.652127e-56
mcg      8.304073e-50
chg      1.321999e-45
aac      2.762051e-30
dtype: float64

















In [12]:



    


avg_aac = data_df.groupby(by='type')['aac'].describe().loc[:, 'mean', :]
avg_aac.plot(kind='barh', title='Average AAC values per feature')



    


















    
Out[12]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b56ca6d8>










    
























In [13]:



    


avg_alm1 = data_df.groupby(by='type')['alm1'].describe().loc[:, 'mean', :]
avg_alm1.plot(kind='barh', title='Average ALM1 values per feature')



    


















    
Out[13]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b5746d68>










    
























In [21]:



    


# pd.crosstab(data_df['seq_name'], data_df['type'])



    











In [15]:



    


import seaborn as sns
corrs = data_df.drop(['type', 'seq_name'], axis=1).corr()

sns.heatmap(corrs, annot=True)



    


















    
Out[15]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b5ab2278>










    
























In [16]:



    


# Oversample the feature counts
feature_counts = data_df['type'].value_counts()
max_counts = feature_counts.max()

max_counts



    


















    
Out[16]:








143

















In [17]:



    


unique_labels = feature_counts.index

indexed_by_labels = data_df.set_index('type')
sampled = [indexed_by_labels.loc[label].sample(max_counts, replace=True) for label in unique_labels]
oversampled_data_df = pd.concat(sampled, axis=0).reset_index()



    











In [18]:



    


oversampled_data_df.head()



    


















    
Out[18]:










  
    

      
      type
      seq_name
      mcg
      gvh
      lip
      chg
      aac
      alm1
      alm2
    

  
  
    

      0
      cp
      NIRD_ECOLI
      0.44
      0.42
      0.48
      0.5
      0.42
      0.25
      0.20
    

    

      1
      cp
      XYLA_ECOLI
      0.16
      0.43
      0.48
      0.5
      0.54
      0.27
      0.37
    

    

      2
      cp
      PHOB_ECOLI
      0.41
      0.43
      0.48
      0.5
      0.45
      0.31
      0.41
    

    

      3
      cp
      GLNA_ECOLI
      0.28
      0.38
      0.48
      0.5
      0.50
      0.33
      0.42
    

    

      4
      cp
      ASG1_ECOLI
      0.42
      0.24
      0.48
      0.5
      0.57
      0.27
      0.37
    

  




















In [19]:



    


oversampled_data_df['type'].value_counts().plot(kind='barh')



    


















    
Out[19]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b574a8d0>










    
























In [20]:



    


from sklearn.model_selection import StratifiedKFold, cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.naive_bayes import GaussianNB

estimator = GaussianNB()
features = oversampled_data_df.drop(['type', 'seq_name'], axis=1)
labels = oversampled_data_df['type']
fold = StratifiedKFold(n_splits=5, shuffle=True)

scores = cross_val_score(estimator, features, labels, cv=fold)

pd.Series(scores).plot(kind='bar')



    


















    
Out[20]:








<matplotlib.axes._subplots.AxesSubplot at 0x1c0b5c939b0>

Content source: dipanjank/ml

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