proj_WeiReactions_JustNfp_MultipleLayers_PermutInv_BestSaved.py

# -*- coding: utf-8 -*-
"""
Created on Mon Aug  5 23:03:26 2019

@author: Andrei
"""

from keras.callbacks import ModelCheckpoint
import pandas as pd
import csv
#import processCSV as p
import numpy as np
from imp import reload
import rdkit.Chem.rdChemReactions as cR
from rdkit import Chem
import rdkit.DataStructs.cDataStructs as cS
from sklearn import preprocessing
from sklearn.preprocessing import OneHotEncoder
from sklearn.linear_model import LinearRegression
import sklearn.neural_network as nn
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestRegressor
from sklearn import datasets, linear_model
from sklearn.model_selection import cross_val_score, KFold
#from keras.models import Sequential
from sklearn.metrics import accuracy_score
#from keras.layers import Dense, Conv2D, Flatten,MaxPooling2D
#from keras.layers import Dense
#import  keras
#from keras.wrappers.scikit_learn import KerasRegressor
#from keras.layers import Input,Dropout,Embedding
#from keras.models import Model
#from keras.wrappers.scikit_learn import KerasRegressor
import pickle
from rdkit.Chem.Fingerprints import FingerprintMols
from sklearn.preprocessing import StandardScaler,MinMaxScaler
import pydot
from tensorflow.keras.layers import Input, Dropout,Embedding,Dense, Conv2D, Flatten,MaxPooling2D
from tensorflow.keras.layers import Add,Concatenate,Conv1D,Reshape,average,maximum,multiply,BatchNormalization
from tensorflow.keras.models import Model,Sequential
from tensorflow.keras.optimizers import Adam,Adagrad
from tensorflow.keras.initializers import RandomNormal
#from tensorflow.keras.layers.normalization import BatchNormalization
from tensorflow.keras.initializers import glorot_uniform
from tensorflow.keras.utils import plot_model
from keras.regularizers import l1,l2
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
from superkeras import permutational_layer as pl
from matplotlib import pyplot
from scipy.stats import pearsonr

#from tensorflow.keras.models import Sequential
import os
os.environ["PATH"] += os.pathsep + 'C:/Program Files (x86)/Graphviz2.38/bin/'

seed = 1

#reload(p)
suffix="\\"
#file="FinalCleaned.csv"
file="WeiReactionsAndAllFps.pkl"
filename="FinalAllcsv.csv"

#pCSV = p.processCSV(filename)


#readOriginalDataSetAndCreatePickledSmilesOnly
#pCSV.readPickleAndTranslateToMols()


#pCSV = p.processCSV(filename)
#pCSV = p.processCSV(file)
#pCSV.readOriginalDataSetAndCreatePickledSmilesOnly(file)
#df=pickle.load(open("WeiReactionsAndAllFps.pkl",'rb'))
df=pd.read_pickle("./WeiReactionsNeuralFp.pkl")
#df=pd.read_pickle("./WeiReactionsAndAllFps.pkl")


fpSize=767
FpLen=256
FpLen1=256
#pCSV.readPickleAndTranslateToMols(fpSize,1)


maxL=3



#for i in range(len(pCSV.Ws)):
#    pCSV.Ws[i]=pCSV.Ws[i]+[0.]*(maxL-len(pCSV.Ws[i]))
##################################### FC newtork ###########################
#X_train, X_test, y_train, y_test = train_test_split(pCSV.df, test_size=0.2, random_state=0)
df_train, df_test = train_test_split(df, test_size=0.8, random_state=0)


#scaler = StandardScaler()
#scaler = MinMaxScaler()


#ff0=df_train['Morgan'].to_numpy()
#ff00=df_test['Morgan'].to_numpy()

ff0=df_train['NeuralFp'].to_numpy()
ff00=df_test['NeuralFp'].to_numpy()

X_train=np.asarray([list(i) for i in ff0])
X_test=np.asarray([list(i) for i in ff00])



#X_train= df_train['Morgan']
#X_test=df_test['Morgan']

#X_train=np.asarray([[j for j in i] for i in df_train['fp']])





#X_train=np.asarray([[j for j in i] for i in df_train['fp']])

#X_test=np.asarray([[j for j in i] for i in df_test['fp']])


#y1_train=np.asarray([[j for j in i] for i in df_train['Ws']])
y_train=np.asarray([i for i in df_train['Target']])
y_test=np.asarray([i for i in df_test['Target']])
#y_train=np.asarray(df_train['Target'])
#y_test=np.asarray(df_test['Target'])

#y_train=np.asarray([float(i) for i in df_train['Loss']])
#y_test=np.asarray([float(i) for i in df_test['Loss']])

#aa=df_train['Ws'].to_numpy()
#aa1=df_test['Ws'].to_numpy()

#ff=df_train['NeuralFp'].to_numpy()
#ff1=df_test['NeuralFp'].to_numpy()


#fp_train=df_train['Mol2Vec']
#fp_test=df_test['Mol2Vec']
#fp_train=np.asarray([list(i) for i in ff])
#fp_test=np.asarray([list(i) for i in ff1])


#ff_1=df_train['Mol2Vec'].to_numpy()
#ff1_1=df_test['Mol2Vec'].to_numpy()


#fp1_train=np.asarray([list(i) for i in ff_1])
#fp1_test=np.asarray([list(i) for i in ff1_1])




#ff1_1=df_test['Mol2Vec'].to_numpy()

#ff=df_train['fpss'].to_numpy()
#ff1=df_test['fpss'].to_numpy()


#fp_train=np.asarray([list(i)+[[0. for i in range(len(i[0]))]]*(maxL-len(i)) for i in ff])
#fp_test=np.asarray([list(i)+[[0. for i in range(len(i[0]))]]*(maxL-len(i)) for i in ff1])


#fp1_train=np.asarray([list(i)+[[0. for i in range(len(i[0]))]]*(maxL-len(i)) for i in ff_1])
#fp1_test=np.asarray([list(i)+[[0. for i in range(len(i[0]))]]*(maxL-len(i)) for i in ff1_1])


#y1_train=np.asarray([list(i)+[0.]*(maxL-len(i)) for i in aa])
#y2_train=np.asarray(df_train['pH'].to_numpy())
#y3_train=np.asarray(df_train['InCnc'].to_numpy())

#trainMask=np.asarray(y1_train!=0).astype(float)
#y1_test=np.asarray([list(i)+[0.]*(maxL-len(i)) for i in aa1])
#y2_test=np.asarray(df_test['pH'].to_numpy())
#y3_test=np.asarray(df_test['InCnc'].to_numpy())







#scaler = StandardScaler()
#scaler1 = scaler.fit(y1_train)
#y1_train = scaler.transform(y1_train)
#y1_test = scaler.transform(y1_test)
#sc = StandardScaler()
#sc = scaler.fit(y2_train)
#y2_train = sc.transform(y2_train)
#y2_test = sc.transform(y2_test)
my_init=glorot_uniform(seed=42)



def MT_model(perm):
 
    #def funx1(i,maxL):

     #if i<maxL:
         #return Input(shape=(fpSize,),name=f"MRg_{i}")
     #elif i<2*maxL-3 and i!=maxL+2:
     
    # elif maxL<=i< 2*maxL:
         #return Input(shape=(FpLen,),name=f"Ml_{i}")
     #else:   
     #    return Input(shape=(FpLen1,),name=f"Sq_{i}")
    
    def funx1(i,maxL):

     if i<maxL:
         return Input(shape=(156,),name=f"MoR_{i}")
    



    
    
    
    x2 = [funx1(i,maxL) for i in range(1*maxL)]
    
    
#    x2_1=Concatenate()([x2[0],x2[1],x2[2]])

    xs=[]
    xs=[x2[perm[0]],x2[perm[1]],x2[perm[2]]]
    #xs=[x2[1],x2[2],x2[0]]
    #xs=[x2[1],x2[0],x2[2]]
    #xs=[x2[2],x2[0],x2[1]]
    #xs=[x2[2],x2[1],x2[0]]
    #xs=[x2[2],x2[0],x2[1]]
    #xs1=[]
    xs2=[]
    #xs.append(x2)
    #for i in range(maxL):
    #    xs.append(x2[i])
    #    xs.append(x2[maxL+i])    
    #    xs.append(x2[2*maxL+i])    
    
    #list1=[100,100,100,100]    
    list1=[100,100,100,100]    
    #list1=[100,58,58,58]    
 #   list2=[112,112,112,112]    
    pairwise_model = pl.PairwiseModel(
        (156,), pl.repeat_layers(Dense, list1, name="hidden",activation='relu'), name="pairwise_model"
    )



    perm_encoder = pl.PermutationalEncoder(pairwise_model, maxL, name="permutational_encoder")
    perm_layer = pl.PermutationalLayer1(perm_encoder, name="permutational_layer")
    outputs = perm_layer.model(xs)
    
    
    #outputs = average(outputs)
    outputs = maximum(outputs)
    #  print("x2",x2)
    output_51 = Dense(100, activation='relu',kernel_initializer=my_init)(outputs)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    #output_51 = Dropout(0.5)(output_51)
#    output_51 = Dense(200, activation='relu',kernel_initializer=my_init)(output_51)
#    
    
    output_Loss=Dense(17,name='Loss_output',activation='softmax',kernel_initializer=my_init)(output_51)
#    output_Loss=Dense(17,name='Loss_output',activation='linear',kernel_initializer=my_init)(output_51)
    #output_Loss=Dense(17,name='Loss_output',activation='linear',kernel_initializer=my_init)(output_51)


    model=Model(inputs=x2,outputs=output_Loss)
    

#    model.compile(loss={'Loss_output':'categorical_crossentropy'},
#              optimizer=Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-8),  loss_weights = {'Loss_output':1.}
#              ,metrics=['accuracy']
#              )

    model.compile(loss={'Loss_output':'categorical_crossentropy'},
              optimizer=Adam(lr=0.002976, beta_1=0.9, beta_2=0.999, epsilon=1e-8),  loss_weights = {'Loss_output':1.}
                  #optimizer=Adam(lr=0.00176, beta_1=0.9, beta_2=0.999, epsilon=1e-8),  loss_weights = {'Loss_output':1.}
                  #optimizer=Adam(lr=0.00005, beta_1=0.9, beta_2=0.999, epsilon=1e-8),  loss_weights = {'Loss_output':1.}
              ,metrics=['accuracy']
              )


    model.summary()
    plot_model(model, 'Config3Mod.png', show_shapes=True)
    
    return model




#model, model1=MT_model()
#model=MT_model()
#model=cnv_model()    


#results = cross_val_score(estimator, np.asarray(pCSV.features), pCSV.labels, cv=kfold)
#print("Results: %.2f (%.2f) MSE" % (results.mean(), results.std()))
#model.fit(X_train, y_train, epochs=1220,batch_size=20,validation_data=(X_test, y_test))
#model.fit({'Fp':X_train['fp']},{'Loss_output':X_train['Loss'],'Ws_output':X_train['Ws']}, epochs=1220,batch_size=20,validation_data=(X_test, y_test))


#model.fit({'Fp':X_train},{'Loss_output':y_train,'Ws_output':y1_train}, epochs=1220,batch_size=16, 
#          validation_data=({'Fp':X_test},{'Loss_output':y_test,'Ws_output':y1_test}))
#model.fit({'Fp':X_train,'WsI':y1_train},{'Loss_output':y_train,'Ws_output':y1_train}, epochs=1220,batch_size=20, 
#          validation_data=({'Fp':X_test},{'Loss_output':y_test,'Ws_output':y1_test}))

#es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,patience=150)
#reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.4,
                              #patience=5, min_lr=0.00001,verbose=1)
#model.fit({'Fp':X_train,'WsI':y1_train},{'Loss_output':y_train}, epochs=1220,batch_size=20, 
#          validation_data=({'Fp':X_test,'WsI':y1_test},{'Loss_output':y_test}),callbacks=[es])




#

#model.fit([y1_train[:,i] if i<maxL else X_train for i in range(maxL+1)],y_train, epochs=1220,batch_size=20, 
#          validation_data=([y1_test[:,i] if i<maxL else X_test for i in range(maxL+1)],y_test),callbacks=[es])

#def funx(i,maxL,Test=False):
#   if Test: 
#    if(i<maxL):
#        #return (y1_test[:,i],fp_test[:,i])
#        return y1_test[:,i]
#    elif i==maxL:
#        return y2_test
#    elif i==maxL+1:
#        return y3_test
#    elif i==maxL+2:
#        return X_test
#    else:
#       return fp_test[:,i-maxL-3]
# 
#   else:    
#       
#    if(i<maxL):
#        #return (y1_train[:,i],fp_train[:,i])
#        return y1_train[:,i]
#    elif i==maxL :
#        return y2_train
#    elif i==maxL+1:
#        return y3_train
#    elif i==maxL+2 :
#        return X_train
#    else:
#      return  fp_train[:,i-maxL-3]
#
#
#
#def funx1(i,maxL,Test=False):
#   if Test: 
#    if(i<maxL):
#        #return (y1_test[:,i],fp_test[:,i])
#        return y1_test[:,i]
#    elif i==maxL:
#        return y2_test
#    elif i==maxL+1:
#        return y3_test
#    elif i==maxL+2:
#        return X_test
#    else:
#       return fp_test[:,i-maxL-3]
# 
#   else:    
#       
#    if(i<maxL):
#        #return (y1_train[:,i],fp_train[:,i])
#        return y1_train[:,i]
#    elif i==maxL :
#        return y2_train
#    elif i==maxL+1:
#        return y3_train
#    elif i==maxL+2 :
#        return X_train
#    else:
#      return  fp_train[:,i-maxL-3]


def funx(i,maxL,Test=False):
       if Test: 
        if(i<maxL):

            return X_test[:,i]

            
       else:    
           
        if(i<maxL):
           return X_train[:,i]
            

def accuracy(preds, targs):
    isMaxPred = [[val == max(row) for val in row] for row in preds]
    isMaxTarg = [[val == max(row) for val in row] for row in targs]
    return float(sum([isMaxPred[ii] == isMaxTarg[ii] for ii in range(len(preds))]))/len(preds)




dP=0.1

def accuracy1(preds, targs):
    isMaxPred = [[max(row)-dP<= val <= max(row)+dP for val in row] for row in preds]
    isMaxTarg = [[val == max(row) for val in row] for row in targs]
    return float(sum([isMaxPred[ii] == isMaxTarg[ii] for ii in range(len(preds))]))/len(preds)


#model.fit([funx(i,maxL) for i in range(2*maxL)],y_train, epochs=1220,batch_size=20, 
#           validation_data=([funx(i,maxL,Test=True) for i in range(2*maxL)],y_test),
#           callbacks=[es,reduce_lr])

filepath="weights1.best.hdf5"
#checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')

accTest=[[],[],[],[],[],[]]
accTrain=[[],[],[],[],[],[]]
accTest1=[[],[],[],[],[],[]]
accTrain1=[[],[],[],[],[],[]]

#permutations=[[0,1,2],[0,2,1],[1,0,2],[1,2,0],[2,1,0],[2,0,1]]
permutations=[[0,1,2]]

#for counter, value in enumerate(my_list):
for cnt, pr in enumerate(permutations):
  #accTest
     
   for i in range(1):
    model=MT_model(pr)
    checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')
    es = EarlyStopping(monitor='val_loss', mode='min', verbose=1,patience=20)
    reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.4,
                              patience=10, min_lr=0.00001,verbose=1)

    for i in range(1):

 
#    model.fit([funx(i,maxL) for i in range(maxL)],y_train, epochs=1220,batch_size=100, 
#           validation_data=([funx(i,maxL,Test=True) for i in range(maxL)],y_test),
#           callbacks=[es,reduce_lr])

        model.fit([funx(i,maxL) for i in range(maxL)],y_train, epochs=100,batch_size=100, 
           validation_data=([funx(i,maxL,Test=True) for i in range(maxL)],y_test),
           callbacks=[es,reduce_lr,checkpoint])
           #callbacks=[es,reduce_lr])



    #model.load_weights("weights1.best.hdf5")


    data1=model.predict([funx(i,maxL,Test=True) for i in range(maxL)])
    data2=model.predict([funx(i,maxL,Test=False) for i in range(maxL)])

    accTest[cnt].append(accuracy(data1,y_test))
    accTrain[cnt].append(accuracy(data2,y_train))
    accTest1[cnt].append(accuracy1(data1,y_test))
    accTrain1[cnt].append(accuracy1(data2,y_train))
    print("###########       Permutation,count: ###############",pr,cnt )
    print("###########     !!!!!!!!!!!!!!!!!!!!!!!!! ###############")
    print("###########       Train,Test Accuracy: ###############",accTrain[cnt][-1],accTest[cnt][-1])
    print("###########       Corrected Train,Test Accuracy: ###############",accTrain1[cnt][-1],accTest1[cnt][-1])
    print("###########     !!!!!!!!!!!!!!!!!!!!!!!!! ###############")
    del model
    del es
    del reduce_lr

for i in range(len(permutations)):
    print(np.mean(accTrain[i]),np.mean(accTest[i]),np.mean(accTrain1[i]),np.mean(accTest1[i]))


#for i in range(200):
#
# 
##    model.fit([funx(i,maxL) for i in range(maxL)],y_train, epochs=1220,batch_size=100, 
##           validation_data=([funx(i,maxL,Test=True) for i in range(maxL)],y_test),
##           callbacks=[es,reduce_lr])
#
#    model.fit([funx(i,maxL) for i in range(maxL)],y_train, epochs=10,batch_size=100, 
#           validation_data=([funx(i,maxL,Test=True) for i in range(maxL)],y_test),
#           callbacks=[es,reduce_lr])
#
#
#
#    data1=model.predict([funx(i,maxL,Test=True) for i in range(maxL)])
#
#    accTest=accuracy(data1,y_test)
#
#
#    print("Test Accuracy:",accTest)