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In [117]:

    
import pandas as pd
import numpy as np
import nolearn
import matplotlib.pyplot as plt
import seaborn
import sklearn.linear_model as lm
import scipy.stats as sps
import math

from Bio import SeqIO
from collections import Counter
from decimal import Decimal
from lasagne import layers, nonlinearities
from lasagne.updates import nesterov_momentum
from lasagne import layers
from nolearn.lasagne import NeuralNet
from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
from sklearn.cross_validation import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.svm import SVR

seaborn.set_style('white')
seaborn.set_context('poster')

%matplotlib inline



In [91]:

    
# Read in the protease inhibitor data
widths = [8]
widths.extend([4]*8)
widths.extend([4]*99)
data = pd.read_csv('hiv-nnrt-data.csv', index_col='SeqID')
drug_cols = data.columns[0:4]
feat_cols = data.columns[4:]



In [92]:

    
# Read in the consensus data
consensus = SeqIO.read('hiv-rt-consensus.fasta', 'fasta')

consensus_map = {i+1:letter for i, letter in enumerate(str(consensus.seq))}



In [93]:

    
# Because there are '-' characters in the dataset, representing consensus sequence at each of the positions, 
# they need to be replaced with the actual consensus letter.

for i, col in enumerate(feat_cols):
    # Replace '-' with the consensus letter.
    data[col] = data[col].replace({'-':consensus_map[i+1]})
    
    # Replace '.' with np.nan
    data[col] = data[col].replace({'.':np.nan})
    
    # Replace 'X' with np.nan
    data[col] = data[col].replace({'X':np.nan})



In [94]:

    
# Drop any feat_cols that have np.nan inside them. We don't want low quality sequences.
data.dropna(inplace=True, subset=feat_cols)



In [95]:

    
data









    Out[95]:






  
    
      
      EFV
      NVP
      ETR
      RPV
      P1
      P2
      P3
      P4
      P5
      P6
      ...
      P231
      P232
      P233
      P234
      P235
      P236
      P237
      P238
      P239
      P240
    
    
      SeqID
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
  
  
    
      4427
      0.4
      0.4
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      4433
      0.7
      0.7
      NaN
      NaN
      P
      I
      S
      P
      I
      ED
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      4483
      0.3
      0.4
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      4487
      7.0
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      4689
      0.5
      0.3
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      4697
      0.5
      0.5
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5071
      200.0
      200.0
      9.6
      6.0
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5222
      0.3
      0.3
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5280
      0.5
      1.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5445
      200.0
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5463
      1.0
      0.7
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5465
      17.4
      42.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5641
      0.4
      0.5
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5682
      154.0
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      5708
      200.0
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6029
      200.0
      79.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6485
      2.8
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6519
      18.0
      25.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      LH
      H
      P
      D
      K
      W
      T
    
    
      6540
      1.0
      53.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6569
      45.6
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6709
      0.4
      0.6
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6796
      0.2
      0.3
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6820
      4.3
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      E
      K
      W
      T
    
    
      6859
      0.4
      0.6
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      6876
      0.3
      0.5
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      7328
      24.0
      200.0
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      7347
      0.5
      0.6
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      7348
      0.4
      0.3
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      7350
      1.0
      1.1
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      7360
      0.6
      0.9
      NaN
      NaN
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
      ...
    
    
      259222
      11.7
      144.0
      44.0
      19.2
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259224
      1.4
      1.3
      1.3
      1.5
      P
      I
      S
      P
      I
      ED
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259228
      0.6
      0.4
      0.7
      0.7
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259230
      0.7
      153.0
      53.2
      27.7
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259232
      0.7
      180.0
      67.7
      32.1
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259234
      1.2
      0.9
      1.1
      1.2
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259236
      18.2
      22.2
      11.1
      10.4
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      LI
      H
      P
      D
      K
      W
      T
    
    
      259238
      0.9
      0.7
      0.8
      0.7
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259242
      0.8
      0.8
      0.8
      0.9
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259244
      119.0
      91.8
      11.6
      15.6
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259246
      0.8
      0.9
      0.9
      0.9
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259250
      0.6
      0.2
      0.6
      0.6
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259254
      1.0
      0.7
      1.0
      1.1
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259256
      0.8
      153.0
      17.7
      25.9
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259258
      1.1
      1.4
      1.3
      1.3
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259266
      0.8
      0.4
      0.7
      0.8
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      259268
      1.1
      194.0
      10.0
      22.8
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268425
      1.2
      1.0
      1.0
      1.1
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268426
      1500.0
      20.7
      6.5
      17.4
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268427
      1500.0
      51.3
      8.2
      13.8
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268428
      6.8
      2.1
      1.3
      0.7
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268429
      6.8
      34.8
      1.1
      1.2
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268430
      14.2
      16.5
      3.1
      2.8
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268431
      9.3
      1.3
      1.0
      1.0
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      268432
      1500.0
      26.4
      20.3
      6.8
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
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      270334
      2.1
      3.3
      3.1
      3.2
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      270335
      1.6
      0.8
      3.3
      3.2
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      270336
      1.9
      2.0
      3.2
      3.3
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      270337
      1.3
      0.9
      2.5
      2.3
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
      K
      W
      T
    
    
      270338
      2.1
      170.0
      4.9
      3.0
      P
      I
      S
      P
      I
      E
      ...
      G
      Y
      E
      L
      H
      P
      D
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      W
      T
    
  

1495 rows × 244 columns



In [96]:

    
# Drop any feat_cols that are completely conserved.

# The nonconserved_cols list will serve as a convenient selector for the X- data from the 
# original dataframe.
nonconserved_cols = []
for col in feat_cols:
    if len(pd.unique(data[col])) == 1:
        data.drop(col, axis=1, inplace=True)
        
    else:
        nonconserved_cols.append(col)



In [97]:

    
drug_cols









    Out[97]:





Index(['EFV', 'NVP', 'ETR', 'RPV'], dtype='object')



In [98]:

    
def x_equals_y(y_test):
    """
    A function that returns a range from minimum to maximum of y_test.
    
    Used below in the plotting below.
    """
    floor = math.floor(min(y_test))
    ceil = math.ceil(max(y_test))
    x_eq_y = range(floor, ceil)
    return x_eq_y

TWOPLACES = Decimal(10) ** -2



In [170]:

    
colnum = 3

drug_df = pd.DataFrame()
drug_df[drug_cols[colnum]] = data[drug_cols[colnum]]
drug_df[nonconserved_cols] = data[nonconserved_cols]
for col in nonconserved_cols:
    drug_df[col] = drug_df[col].apply(lambda x: np.nan if len(x) > 1 else x)
drug_df.dropna(inplace=True)

drug_X = drug_df[nonconserved_cols]
drug_Y = drug_df[drug_cols[colnum]].apply(lambda x:np.log(x))
# drug_Y.values



In [171]:

    
from isoelectric_point import isoelectric_points
from molecular_weight import molecular_weights

# Standardize pI matrix. 7 is neutral
drug_X_pi = drug_X.replace(isoelectric_points)

# Standardize MW matrix.
drug_X_mw = drug_X.replace(molecular_weights)

# Binarize drug_X matrix.
from sklearn.preprocessing import LabelBinarizer
drug_X_bi = pd.DataFrame()
binarizers = dict()

for col in drug_X.columns:
    lb = LabelBinarizer()
    binarized_cols = lb.fit_transform(drug_X[col])
    # print(binarized_cols)
    if len(lb.classes_) == 2:
        # print(binarized_cols)
        drug_X_bi[col] = pd.Series(binarized_cols[:,0])
    else:
        for i, c in enumerate(lb.classes_):
            # print(col + c)
            # print(binarized_cols[:,i])

            drug_X_bi[col + '_' + c] = binarized_cols[:,i]
        
drug_X_bi









    Out[171]:






  
    
      
      P1_P
      P2_I
      P3_S
      P4
      P5_I
      P6
      P7
      P8_V
      P9_P
      P10_V
      ...
      P230
      P232_Y
      P233_E
      P234_L
      P235_H
      P236_P
      P237
      P238_K
      P239_W
      P240
    
  
  
    
      0
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      1
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      2
      0
      0
      0
      0
      0
      1
      1
      0
      0
      0
      ...
      1
      0
      0
      0
      0
      0
      0
      0
      0
      1
    
    
      3
      0
      0
      0
      0
      0
      1
      0
      0
      0
      0
      ...
      1
      0
      0
      0
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81 rows × 301 columns



In [172]:

    
fig = plt.figure(figsize=(3,3))
drug_Y.hist(grid=False)
plt.xlabel('Value')
plt.ylabel('Count')
plt.title('{0} Distribution'.format(drug_cols[colnum]))









    Out[172]:





<matplotlib.text.Text at 0x7f4b4a6a2eb8>



In [173]:

    
# Here, let's try the Random Forest Regressor. This will be the baseline.
x_train, x_test, y_train, y_test = train_test_split(drug_X_bi, drug_Y)

rfr = RandomForestRegressor(n_estimators=500, n_jobs=-1, oob_score=True)

rfr.fit(x_train, y_train)
rfr_preds = rfr.predict(x_test)
print(rfr.score(x_test, y_test), mean_squared_error(rfr_preds, y_test))
rfr_mse = mean_squared_error(rfr_preds, y_test)
# print(rfr.oob_score_)
sps.pearsonr(rfr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(y_test, rfr_preds,)
plt.title('{0} Random Forest'.format(drug_cols[colnum]))
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(rfr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()









    



-0.0326395088037 0.997215655263



In [174]:

    
plt.bar(range(len(rfr.feature_importances_)), rfr.feature_importances_)
plt.xlabel('Position')
plt.ylabel('Relative Importance')
plt.title('{0} Random Forest'.format(drug_cols[colnum]))









    Out[174]:





<matplotlib.text.Text at 0x7f4b481912e8>



In [175]:

    
# Get back the importance of each feature.
feat_impt = [(p, i) for p, i in zip(drug_X_bi.columns, rfr.feature_importances_)]
sorted(feat_impt, key=lambda x:x[1], reverse=True)









    Out[175]:





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 ('P83', 6.6591835798483035e-05),
 ('P211_S', 4.6261360503589671e-05),
 ('P169', 3.6468480649915733e-05),
 ('P189', 3.2871185323285835e-05),
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 ('P35_R', 1.0797965754040618e-05),
 ('P165_L', 1.067082457928276e-05),
 ('P162_D', 9.0406616431302377e-06),
 ('P7', 8.3956447528148519e-06),
 ('P1_P', 0.0),
 ('P2_I', 0.0),
 ('P3_S', 0.0),
 ('P4', 0.0),
 ('P5_I', 0.0),
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In [176]:

    
# # Here, let's try a parameter grid search, to figure out what would be the best 
# from sklearn.grid_search import GridSearchCV
# import numpy as np

# param_grid = [{'n_estimators':[100, 500, 1000],
#                #'max_features':['auto', 'sqrt', 'log2'],
#                #'min_samples_leaf':np.arange(1,20,1),
#               }]

# x_train, x_test, y_train, y_test = train_test_split(fpv_X_bi, fpv_Y)


# rfr_gs = GridSearchCV(RandomForestRegressor(), param_grid=param_grid, n_jobs=-1)
# rfr_gs.fit(x_train, y_train)
# print(rfr_gs.best_estimator_)
# print(rfr_gs.best_params_)



In [177]:

    
# Try Bayesian Ridge Regression
# x_train, x_test, y_train, y_test = train_test_split(drug_X_bi, drug_Y)

brr = lm.BayesianRidge()
brr.fit(x_train, y_train)
brr_preds = brr.predict(x_test)
print(brr.score(x_test, y_test), mean_squared_error(brr_preds, y_test))
print(sps.pearsonr(brr_preds, y_test))
brr_mse = mean_squared_error(brr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(y_test, brr_preds)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} Bayesian Ridge'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(brr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()









    



0.127821841659 0.842258800154
(0.43881339541178765, 0.046588693002101465)



In [178]:

    
# Try ARD regression
ardr = lm.ARDRegression()
ardr.fit(x_train, y_train)
ardr_preds = ardr.predict(x_test)
ardr_mse = mean_squared_error(ardr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(y_test, ardr_preds)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} ARD Regression'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(ardr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()



In [179]:

    
# Try Gradient Boost
# x_train, x_test, y_train, y_test = train_test_split(drug_X_bi, drug_Y)

gbr = GradientBoostingRegressor()
gbr.fit(x_train, y_train)
gbr_preds = gbr.predict(x_test)
print(gbr.score(x_test, y_test), mean_squared_error(gbr_preds, y_test))
print(sps.pearsonr(gbr_preds, y_test))
gbr_mse = mean_squared_error(gbr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(y_test, gbr_preds)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} Grad. Boost'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(gbr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()









    



0.0103796304678 0.95567225237
(0.39743565271055858, 0.074410522130373941)



In [180]:

    
plt.bar(range(len(gbr.feature_importances_)), gbr.feature_importances_)









    Out[180]:





<Container object of 301 artists>



In [181]:

    
# Try AdaBoost
# x_train, x_test, y_train, y_test = train_test_split(drug_X_bi, drug_Y)

abr = AdaBoostRegressor()
abr.fit(x_train, y_train)
abr_preds = abr.predict(x_test)
print(abr.score(x_test, y_test), mean_squared_error(abr_preds, y_test))
print(sps.pearsonr(abr_preds, y_test))
abr_mse = mean_squared_error(abr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(x=y_test, y=abr_preds)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} AdaBoost'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(abr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()









    



-0.141282096696 1.10213134806
(0.025866145510122118, 0.91138362307506238)



In [182]:

    
plt.bar(range(len(abr.feature_importances_)), abr.feature_importances_)









    Out[182]:





<Container object of 301 artists>



In [183]:

    
# Try support vector regression
svr = SVR()
svr.fit(x_train, y_train)
svr_preds = svr.predict(x_test)

svr_mse = mean_squared_error(svr_preds, y_test)

plt.figure(figsize=(3,3))
plt.scatter(y_test, svr_preds, )
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} SVR'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(svr_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()



In [184]:

    
# Neural Network 1 Specification: Feed Forward ANN with 1 hidden layer.
# x_train, x_test, y_train, y_test = train_test_split(drug_X_bi, drug_Y)

x_train = x_train.astype(np.float32)
y_train = y_train.astype(np.float32)
x_test = x_test.astype(np.float32)
y_test = y_test.astype(np.float32)

net1 = NeuralNet(
    layers=[  # three layers: one hidden layer
        ('input', layers.InputLayer),
        ('hidden1', layers.DenseLayer),
        ('dropout1', layers.DropoutLayer),
        #('hidden2', layers.DenseLayer),
        #('dropout2', layers.DropoutLayer),
        ('nonlinear', layers.NonlinearityLayer),
        ('output', layers.DenseLayer),
        ],
    # layer parameters:
    input_shape=(None, x_train.shape[1]),  # 
    hidden1_num_units=math.ceil(x_train.shape[1] / 2),  # number of units in hidden layer
    hidden1_nonlinearity=nonlinearities.tanh,
    dropout1_p = 0.5,
    #hidden2_num_units=math.ceil(x_train.shape[1] / 2),
    #dropout2_p = 0.5,
    output_nonlinearity=None,  # output layer uses identity function
    output_num_units=1,  # 30 target values
    
    # optimization method:
    update=nesterov_momentum,
    update_learning_rate=0.01,
    update_momentum=0.95,

    regression=True,  # flag to indicate we're dealing with regression problem
    max_epochs=500,  # we want to train this many epochs
    verbose=1,
    )
net1.fit(x_train.values, y_train.values)









    



# Neural Network with 45754 learnable parameters

## Layer information

  #  name         size
---  ---------  ------
  0  input         301
  1  hidden1       151
  2  dropout1      151
  3  nonlinear     151
  4  output          1

  epoch    train loss    valid loss    train/val  dur
-------  ------------  ------------  -----------  -----
      1       3.09138       1.87169      1.65165  0.00s
      2       2.23954       2.15974      1.03695  0.00s
      3       2.21964       1.49758      1.48215  0.00s
      4       2.05813       1.13670      1.81062  0.00s
      5       2.11053       1.05192      2.00636  0.00s
      6       1.89762       0.93186      2.03637  0.00s
      7       1.51066       1.14422      1.32025  0.00s
      8       1.66632       1.12656      1.47912  0.00s
      9       1.38603       0.90850      1.52563  0.00s
     10       1.59926       0.80087      1.99690  0.00s
     11       1.42572       0.74973      1.90164  0.00s
     12       1.45986       0.68320      2.13679  0.00s
     13       1.15931       0.84708      1.36858  0.00s
     14       1.28390       0.74891      1.71436  0.00s
     15       1.16442       0.60978      1.90957  0.00s
     16       0.91826       0.53520      1.71576  0.00s
     17       1.03375       0.52452      1.97086  0.00s
     18       0.88357       0.55902      1.58055  0.00s
     19       1.03875       0.51289      2.02530  0.00s
     20       0.55124       0.44621      1.23538  0.00s
     21       0.81158       0.40679      1.99511  0.00s
     22       0.74313       0.52161      1.42470  0.00s
     23       0.52532       0.47521      1.10546  0.00s
     24       0.57771       0.49559      1.16570  0.00s
     25       0.60192       0.55973      1.07538  0.00s
     26       0.65065       0.60645      1.07288  0.00s
     27       0.51811       0.55793      0.92862  0.00s
     28       0.69553       0.62857      1.10653  0.00s
     29       0.60919       0.56841      1.07174  0.00s
     30       0.51942       0.64703      0.80278  0.00s
     31       0.46033       0.56315      0.81741  0.00s
     32       0.53810       0.50626      1.06289  0.00s
     33       0.59144       0.49816      1.18724  0.00s
     34       0.42397       0.53463      0.79302  0.00s
     35       0.47340       0.58048      0.81553  0.00s
     36       0.44709       0.62009      0.72101  0.00s
     37       0.44645       0.52953      0.84312  0.00s
     38       0.40098       0.60618      0.66149  0.00s
     39       0.46663       0.46865      0.99569  0.00s
     40       0.51329       0.46833      1.09599  0.00s
     41       0.41557       0.50491      0.82306  0.00s
     42       0.32113       0.56337      0.57001  0.00s
     43       0.49427       0.49019      1.00833  0.00s
     44       0.50834       0.52046      0.97672  0.00s
     45       0.46611       0.47508      0.98111  0.00s
     46       0.23285       0.49051      0.47471  0.00s
     47       0.39066       0.55530      0.70351  0.00s
     48       0.34065       0.61602      0.55299  0.00s
     49       0.35575       0.68071      0.52262  0.00s
     50       0.46853       0.54531      0.85920  0.00s
     51       0.32417       0.53988      0.60045  0.00s
     52       0.35954       0.54593      0.65858  0.00s
     53       0.29628       0.59466      0.49823  0.00s
     54       0.39856       0.53345      0.74714  0.00s
     55       0.30403       0.56129      0.54166  0.00s
     56       0.33263       0.50355      0.66058  0.00s
     57       0.48097       0.57542      0.83587  0.00s
     58       0.23384       0.54406      0.42981  0.00s
     59       0.36108       0.51444      0.70188  0.00s
     60       0.20788       0.46661      0.44552  0.00s
     61       0.28687       0.49280      0.58212  0.00s
     62       0.24756       0.51272      0.48285  0.00s
     63       0.34467       0.47157      0.73088  0.00s
     64       0.25737       0.46671      0.55146  0.00s
     65       0.22398       0.47088      0.47566  0.00s
     66       0.27373       0.52378      0.52261  0.00s
     67       0.30621       0.50902      0.60156  0.00s
     68       0.32212       0.54688      0.58900  0.00s
     69       0.22235       0.57572      0.38621  0.00s
     70       0.30402       0.54859      0.55418  0.00s
     71       0.32921       0.57469      0.57285  0.00s
     72       0.43078       0.59750      0.72097  0.00s
     73       0.30278       0.41999      0.72094  0.00s
     74       0.22134       0.44486      0.49754  0.00s
     75       0.21523       0.47938      0.44897  0.00s
     76       0.24382       0.54551      0.44696  0.00s
     77       0.24298       0.46924      0.51782  0.00s
     78       0.25483       0.42821      0.59510  0.00s
     79       0.23100       0.44146      0.52325  0.00s
     80       0.27043       0.48716      0.55512  0.00s
     81       0.26689       0.40340      0.66161  0.00s
     82       0.23394       0.38443      0.60855  0.00s
     83       0.25832       0.41631      0.62050  0.00s
     84       0.27356       0.42058      0.65043  0.00s
     85       0.28602       0.43938      0.65097  0.00s
     86       0.42921       0.49612      0.86514  0.00s
     87       0.22773       0.43381      0.52496  0.00s
     88       0.33308       0.44382      0.75048  0.00s
     89       0.20340       0.44419      0.45790  0.00s
     90       0.33828       0.47155      0.71737  0.00s
     91       0.21919       0.52556      0.41706  0.00s
     92       0.25578       0.60145      0.42527  0.00s
     93       0.15603       0.59955      0.26024  0.00s
     94       0.29586       0.55079      0.53715  0.00s
     95       0.16876       0.51785      0.32588  0.00s
     96       0.19347       0.50411      0.38377  0.00s
     97       0.19466       0.51190      0.38027  0.00s
     98       0.20521       0.56051      0.36612  0.00s
     99       0.22197       0.55149      0.40249  0.00s
    100       0.25016       0.53098      0.47113  0.00s
    101       0.24881       0.52797      0.47126  0.00s
    102       0.10956       0.54175      0.20224  0.00s
    103       0.23224       0.49617      0.46805  0.00s
    104       0.19941       0.50594      0.39413  0.00s
    105       0.18224       0.48829      0.37323  0.00s
    106       0.15698       0.51548      0.30452  0.00s
    107       0.29843       0.51177      0.58313  0.00s
    108       0.27643       0.52835      0.52320  0.00s
    109       0.19681       0.49970      0.39385  0.00s
    110       0.21056       0.42282      0.49798  0.00s
    111       0.21823       0.38774      0.56284  0.00s
    112       0.26796       0.39494      0.67848  0.00s
    113       0.19231       0.41157      0.46726  0.00s
    114       0.27135       0.41391      0.65556  0.00s
    115       0.29406       0.34087      0.86267  0.00s
    116       0.16451       0.35832      0.45911  0.00s
    117       0.23582       0.37801      0.62385  0.00s
    118       0.10371       0.44939      0.23078  0.00s
    119       0.19807       0.45647      0.43393  0.00s
    120       0.22335       0.45223      0.49389  0.00s
    121       0.12276       0.46000      0.26687  0.00s
    122       0.23238       0.41623      0.55830  0.00s
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    464       0.15090       0.39636      0.38072  0.00s
    465       0.12370       0.36297      0.34079  0.00s
    466       0.12268       0.34934      0.35119  0.00s
    467       0.09910       0.37316      0.26557  0.00s
    468       0.09775       0.34401      0.28414  0.00s
    469       0.17952       0.32719      0.54866  0.00s
    470       0.14320       0.36042      0.39732  0.00s
    471       0.20605       0.45740      0.45049  0.00s
    472       0.12161       0.41507      0.29298  0.00s
    473       0.17274       0.37693      0.45827  0.00s
    474       0.12220       0.40690      0.30033  0.00s
    475       0.13222       0.41404      0.31933  0.00s
    476       0.19081       0.48656      0.39216  0.00s
    477       0.13357       0.47658      0.28027  0.00s
    478       0.08921       0.44526      0.20035  0.00s
    479       0.12012       0.40888      0.29378  0.00s
    480       0.13888       0.37450      0.37085  0.00s
    481       0.15324       0.37782      0.40560  0.00s
    482       0.25653       0.35523      0.72214  0.00s
    483       0.12568       0.35882      0.35027  0.00s
    484       0.15253       0.35128      0.43421  0.00s
    485       0.20506       0.42219      0.48570  0.00s
    486       0.13871       0.46994      0.29516  0.00s
    487       0.19386       0.42608      0.45499  0.00s
    488       0.18317       0.36988      0.49522  0.00s
    489       0.11097       0.34740      0.31945  0.00s
    490       0.18076       0.34716      0.52068  0.00s
    491       0.21016       0.42074      0.49951  0.00s
    492       0.07795       0.41984      0.18566  0.00s
    493       0.14279       0.41075      0.34762  0.00s
    494       0.15156       0.46324      0.32718  0.00s
    495       0.12480       0.46665      0.26745  0.00s
    496       0.18079       0.41748      0.43306  0.00s
    497       0.10472       0.42136      0.24853  0.00s
    498       0.17465       0.42295      0.41294  0.00s
    499       0.12050       0.40118      0.30037  0.00s
    500       0.14382       0.40962      0.35109  0.00s






    Out[184]:





NeuralNet(X_tensor_type=None,
     batch_iterator_test=<nolearn.lasagne.base.BatchIterator object at 0x7f4bf326e0b8>,
     batch_iterator_train=<nolearn.lasagne.base.BatchIterator object at 0x7f4bf326e908>,
     custom_score=None, dropout1_p=0.5,
     hidden1_nonlinearity=<function tanh at 0x7f4ba9069f28>,
     hidden1_num_units=151, input_shape=(None, 301),
     layers=[('input', <class 'lasagne.layers.input.InputLayer'>), ('hidden1', <class 'lasagne.layers.dense.DenseLayer'>), ('dropout1', <class 'lasagne.layers.noise.DropoutLayer'>), ('nonlinear', <class 'lasagne.layers.dense.NonlinearityLayer'>), ('output', <class 'lasagne.layers.dense.DenseLayer'>)],
     loss=None, max_epochs=500, more_params={},
     objective=<function objective at 0x7f4bf3270510>,
     objective_loss_function=<function squared_error at 0x7f4ba8bcd840>,
     on_epoch_finished=[<nolearn.lasagne.handlers.PrintLog object at 0x7f4b481a0400>],
     on_training_finished=[],
     on_training_started=[<nolearn.lasagne.handlers.PrintLayerInfo object at 0x7f4b481a04a8>],
     output_nonlinearity=None, output_num_units=1, regression=True,
     train_split=<nolearn.lasagne.base.TrainSplit object at 0x7f4bf326e6a0>,
     update=<function nesterov_momentum at 0x7f4ba8bd40d0>,
     update_learning_rate=0.01, update_momentum=0.95,
     use_label_encoder=False, verbose=1,
     y_tensor_type=TensorType(float32, matrix))



In [185]:

    
nn1_preds = net1.predict(x_test)
nn1_mse = float(mean_squared_error(nn1_preds, y_test))

plt.figure(figsize=(3,3))
plt.scatter(y_test, nn1_preds)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('{0} Neural Network'.format(drug_cols[colnum]))
plt.gca().set_aspect('equal', 'datalim')
plt.annotate(s='mse: {0}'.format(str(Decimal(nn1_mse).quantize(TWOPLACES))), xy=(1,0), xycoords='axes fraction', ha='right', va='bottom')
plt.plot(x_equals_y(y_test), x_equals_y(y_test), color='red')
plt.show()



In [186]:

    
sps.pearsonr(nn1_preds, y_test.reshape(y_test.shape[0],1))









    Out[186]:





(array([ 0.43451592], dtype=float32), array([ 0.04903143], dtype=float32))



In [ ]:



In [ ]:

	EFV	NVP	ETR	RPV	P1	P2	P3	P4	P5	P6	...	P231	P232	P233	P234	P235	P236	P237	P238	P239	P240
SeqID
4427	0.4	0.4	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
4433	0.7	0.7	NaN	NaN	P	I	S	P	I	ED	...	G	Y	E	L	H	P	D	K	W	T
4483	0.3	0.4	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
4487	7.0	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
4689	0.5	0.3	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
4697	0.5	0.5	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5071	200.0	200.0	9.6	6.0	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5222	0.3	0.3	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5280	0.5	1.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5445	200.0	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5463	1.0	0.7	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5465	17.4	42.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5641	0.4	0.5	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5682	154.0	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
5708	200.0	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6029	200.0	79.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6485	2.8	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6519	18.0	25.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	LH	H	P	D	K	W	T
6540	1.0	53.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6569	45.6	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6709	0.4	0.6	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6796	0.2	0.3	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6820	4.3	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	E	K	W	T
6859	0.4	0.6	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
6876	0.3	0.5	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
7328	24.0	200.0	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
7347	0.5	0.6	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
7348	0.4	0.3	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
7350	1.0	1.1	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
7360	0.6	0.9	NaN	NaN	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
259222	11.7	144.0	44.0	19.2	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259224	1.4	1.3	1.3	1.5	P	I	S	P	I	ED	...	G	Y	E	L	H	P	D	K	W	T
259228	0.6	0.4	0.7	0.7	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259230	0.7	153.0	53.2	27.7	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259232	0.7	180.0	67.7	32.1	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259234	1.2	0.9	1.1	1.2	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259236	18.2	22.2	11.1	10.4	P	I	S	P	I	E	...	G	Y	E	LI	H	P	D	K	W	T
259238	0.9	0.7	0.8	0.7	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259242	0.8	0.8	0.8	0.9	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259244	119.0	91.8	11.6	15.6	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259246	0.8	0.9	0.9	0.9	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259250	0.6	0.2	0.6	0.6	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259254	1.0	0.7	1.0	1.1	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259256	0.8	153.0	17.7	25.9	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259258	1.1	1.4	1.3	1.3	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259266	0.8	0.4	0.7	0.8	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
259268	1.1	194.0	10.0	22.8	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268425	1.2	1.0	1.0	1.1	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268426	1500.0	20.7	6.5	17.4	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268427	1500.0	51.3	8.2	13.8	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268428	6.8	2.1	1.3	0.7	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268429	6.8	34.8	1.1	1.2	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268430	14.2	16.5	3.1	2.8	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268431	9.3	1.3	1.0	1.0	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
268432	1500.0	26.4	20.3	6.8	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
270334	2.1	3.3	3.1	3.2	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
270335	1.6	0.8	3.3	3.2	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
270336	1.9	2.0	3.2	3.3	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
270337	1.3	0.9	2.5	2.3	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T
270338	2.1	170.0	4.9	3.0	P	I	S	P	I	E	...	G	Y	E	L	H	P	D	K	W	T

	P1_P	P2_I	P3_S	P4	P5_I	P6	P7	P8_V	P9_P	P10_V	...	P230	P232_Y	P233_E	P234_L	P235_H	P236_P	P237	P238_K	P239_W	P240
0	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
1	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
2	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
3	0	0	0	0	0	1	0	0	0	0	...	1	0	0	0	0	0	0	0	0	1
4	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
6	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
7	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	1	0	0	1
8	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
9	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
10	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
11	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
12	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
13	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
14	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
15	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
16	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
17	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
18	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
19	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
20	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
21	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
22	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
23	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
24	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
25	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
26	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
27	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
28	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
29	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
52	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
53	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
54	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
55	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
56	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
57	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
58	0	0	0	1	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
59	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
60	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
61	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
62	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
63	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
64	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
65	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
66	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
67	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
68	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
69	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
70	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
71	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
72	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
73	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
74	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
75	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
76	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
77	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
78	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
79	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
80	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1

	P1_P	P2_I	P3_S	P4	P5_I	P6	P7	P8_V	P9_P	P10_V	...	P230	P232_Y	P233_E	P234_L	P235_H	P236_P	P237	P238_K	P239_W	P240
0	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
1	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
2	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
3	0	0	0	0	0	1	0	0	0	0	...	1	0	0	0	0	0	0	0	0	1
4	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
6	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
7	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	1	0	0	1
8	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
9	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
10	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
11	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
12	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
13	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
14	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
15	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
16	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
17	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
18	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
19	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
20	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
21	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
22	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
23	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
24	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
25	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
26	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
27	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
28	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
29	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
52	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
53	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
54	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
55	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
56	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
57	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
58	0	0	0	1	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
59	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
60	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
61	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
62	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
63	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
64	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
65	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
66	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
67	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
68	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
69	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
70	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
71	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
72	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
73	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
74	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
75	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
76	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
77	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
78	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
79	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
80	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1

	P1_P	P2_I	P3_S	P4	P5_I	P6	P7	P8_V	P9_P	P10_V	...	P230	P232_Y	P233_E	P234_L	P235_H	P236_P	P237	P238_K	P239_W	P240
0	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
1	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
2	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
3	0	0	0	0	0	1	0	0	0	0	...	1	0	0	0	0	0	0	0	0	1
4	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
5	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
6	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
7	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	1	0	0	1
8	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
9	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
10	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
11	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
12	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
13	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
14	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
15	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
16	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	0
17	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
18	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
19	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
20	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
21	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
22	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
23	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
24	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
25	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
26	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
27	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
28	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
29	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
51	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
52	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
53	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
54	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
55	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
56	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
57	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
58	0	0	0	1	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
59	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
60	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
61	0	0	0	0	0	0	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
62	0	0	0	0	0	1	1	0	0	0	...	0	0	0	0	0	0	0	0	0	1
63	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
64	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
65	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
66	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
67	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
68	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
69	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
70	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
71	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
72	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
73	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
74	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
75	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
76	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
77	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
78	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
79	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1
80	0	0	0	0	0	1	1	0	0	0	...	1	0	0	0	0	0	0	0	0	1