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Edit and run

Contest entry by Wouter Kimman

Strategy: 





In [218]:



    


from numpy.fft import rfft
from scipy import signal

import numpy as np
import matplotlib.pyplot as plt
import plotly.plotly as py


import pandas as pd
import timeit
from sqlalchemy.sql import text
from sklearn import tree
from sklearn.model_selection import LeavePGroupsOut

#from sklearn import cross_validation
#from sklearn.cross_validation import train_test_split
from sklearn import metrics

from sklearn.tree import export_graphviz
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn import linear_model
#import sherlock.filesystem as sfs
#import sherlock.database as sdb

from sklearn import preprocessing
from sklearn.model_selection import cross_val_score



    











In [2]:



    


filename = 'training_data.csv'
training_data0 = pd.read_csv(filename)
training_data1=training_data0.copy()



    











In [3]:



    


def magic(df):
    df1=df.copy()
    b, a = signal.butter(2, 0.2, btype='high', analog=False)
    feats00=['GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']
    feats01=['GR','DeltaPHI','PHIND']
    for ii in feats01:
        df1[ii]=df[ii]
        name1=ii + '_1'
        name2=ii + '_2'
        name3=ii + '_3'
        name4=ii + '_4'
        xx1 = list(df[ii])
        xx_mf= signal.medfilt(xx1,9)
        x_min3=np.roll(xx_mf, 3)    
        xx1a=xx1-np.mean(xx1)
        xx_fil = signal.filtfilt(b, a, xx1)
        xx_grad=np.gradient(xx1a) 
        if ii in feats01: 
            df1[name1]=x_min3
            df1[name2]=xx_fil
            df1[name3]=xx_grad
            df1[name4]=xx_mf     
    return df1



    











In [4]:



    


def run_test(remove_well, df_train):
    
    df_test=training_data1
    blind = df_test[df_test['Well Name'] == remove_well]      
    training_data = df_train[df_train['Well Name'] != remove_well]  
    
    correct_facies_labels_train = training_data['Facies'].values
    feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
    #rf = RandomForestClassifier(max_depth = 15, n_estimators=600) 
    rf = RandomForestClassifier(max_depth = 5, n_estimators=600)     
    rf.fit(feature_vectors, correct_facies_labels_train)

    correct_facies_labels = blind['Facies'].values
    features_blind = blind.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
    scaler = preprocessing.StandardScaler().fit(feature_vectors)
    scaled_features =feature_vectors
    predicted_random_forest = rf.predict(features_blind)

    out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
    return out_f1



    











In [5]:



    


def dothererun(selected_pred_facies):
    training_data_dodgy = blind[selected_data==True]  

    # PART BLIND WELL TO TRAIN
    #correct_facies_labels_train = training_data_dodgy['Facies'].values  #THIS WOULD OBVIOUSLY NOT BE AVAILABLE
    correct_facies_labels_train = selected_pred_facies
    feature_vectors = training_data_dodgy.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
    #rf = RandomForestClassifier(max_depth = 25, n_estimators=5000) 
    rf = RandomForestClassifier(max_depth = 5, n_estimators=1000)     
    rf.fit(feature_vectors, correct_facies_labels_train)

    # ENTIRE BLIND WELL TO PREDICT
    correct_facies_labels = blind['Facies'].values
    features_blind = blind.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
    
    predicted_random_forest_new = rf.predict(features_blind)
    
    out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest_new,average = 'micro')
    props=rf.predict_proba(features_blind)
    print "in the rerun f1 is: "
    print out_f1
    return out_f1



    











In [ ]:



    


df=training_data0.copy()
training_data1=magic(df)



    











In [6]:



    


df_train=training_data1

wells=['CHURCHMAN BIBLE','SHANKLE','NOLAN','NEWBY','Recruit F9' ,'CROSS H CATTLE','LUKE G U','SHRIMPLIN']
#wells=['CHURCHMAN BIBLE']
wells=['NOLAN']
for remove_well in wells:
    all=[]
    print("well : %s, f1 for different runs:" % (remove_well))
    for ii in range(4):
        out_f1=run_test(remove_well,df_train)   
        #out_f1=run_bayes2(remove_well,df_train) 
        all.append(out_f1)
    av1=np.mean(all) 
    print("average f1 is %f, 2*std is %f" % (av1, 2*np.std(all)) )



    


















    






well : NOLAN, f1 for different runs:
average f1 is 0.525904, 2*std is 0.010434

















In [337]:



    


df_test=training_data1
blind = df_test[df_test['Well Name'] == remove_well]      
training_data = df_train[df_train['Well Name'] != remove_well]  
    
correct_facies_labels_train = training_data['Facies'].values
feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
rf = RandomForestClassifier(max_depth = 5, n_estimators=1000) 
rf.fit(feature_vectors, correct_facies_labels_train)

correct_facies_labels = blind['Facies'].values
features_blind = blind.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
#scaler = preprocessing.StandardScaler().fit(feature_vectors)
#scaled_features =feature_vectors
predicted_random_forest = rf.predict(features_blind)

out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
print out_f1

props=rf.predict_proba(features_blind)
props_best=props.max(axis=1)



    


















    






0.532530120482

















In [342]:



    


plt.plot(props[:,3])
plt.plot(props_best)
plt.show()
print props_best.mean()
print props_best.mean()+props_best.std()



    


















    

















    






0.449710576641
0.559843757226

RERUNNING PREDICTION FROM PART BLIND ONLY:





In [343]:



    


#decs=[0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5]
#decs=[0.30, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42]
decs=[0.20, 0.22, 0.24, 0.26, 0.28, 0.30, 0.32, 0.34, 0.36, 0.38, 0.4, 0.42, 0.44]
#decs=[0.32, 0.325, 0.33, 0.335, 0.34, 0.345, 0.35, 0.355, 0.36, 0.38, 0.4, 0.42, 0.44]
decs=[0.15]
decs=[0.625]
for vals in decs:
    new_array=[]
    selected_data=[]
    selected_data=0*predicted_random_forest
    count=0
    for idx, val in enumerate(props_best):
        if val > vals:
            new_array.append(predicted_random_forest[idx])
            selected_data[idx]=True
            count+=1
        else:
            selected_data[idx]=False
            

    selected_pred_facies=predicted_random_forest[selected_data==True]
    selected_true_labels=correct_facies_labels[selected_data==True]    
    out_f1=metrics.f1_score(selected_true_labels, selected_pred_facies,average = 'micro')
    
    print("vals of decs is  %.5f " %  (vals) ) 
    print("nr of samples %d " %  (count) )     
    print("total nr of facies predicted is %d" % (len(np.unique(selected_pred_facies))))
    print("total nr of facies present is %d" % (len(np.unique(selected_true_labels))))    
    print("f1 is  %.5f " %  (out_f1) ) 
    #dothererun(selected_pred_facies)   
    print "-------------------------------------------------"



    


















    






vals of decs is  0.62500 
nr of samples 24 
total nr of facies predicted is 2
total nr of facies present is 2
f1 is  0.66667 
-------------------------------------------------

















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In [ ]:

Leave 2 out





In [314]:



    


wells = training_data["Well Name"].values      #DOES NOT CONTAIN THE BLIND
y = training_data['Facies']
X = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)

#wells = training_data1["Well Name"].values    #DOES CONTAIN THE BLIND
#y = training_data1['Facies']
#X = training_data1.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)

#lpgo = LeavePGroupsOut(n_groups=2)
lpgo = LeavePGroupsOut(n_groups=4)
pred_array=0*correct_facies_labels
for train, test in lpgo.split(X, y, groups=wells):
    xx0=X.iloc[train]
    yy0=y.iloc[train]
    
    rf = RandomForestClassifier(max_depth = 7, n_estimators=2000) 
    rf.fit(xx0, yy0)
    xx1=X.iloc[test]
    yy1=y.iloc[test]
    predicted_random_forest = rf.predict(xx1)  #prediction on the leave-out dataset
    out_f1=metrics.f1_score(yy1, predicted_random_forest,average = 'micro')
    
    #cross_val_score(rf, xx1, yy1, cv=3)
    #predicted = cross_val_predict(rf, xx1, cv=10)    
    predicted_random_forest = rf.predict(features_blind)    #prediction on blind
    out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
    
    pred_array = np.vstack((predicted_random_forest, pred_array))
    print out_f1
    
med_pred=np.median(pred_array,axis=0)
med_pred=med_pred.astype(int)
out_f1=metrics.f1_score(correct_facies_labels, med_pred,average = 'micro')
print "prediction from median, leave p out:"
print out_f1



    


















    






0.50843373494
0.566265060241
0.503614457831
0.402409638554
0.525301204819
0.501204819277
0.38313253012
0.498795180723
0.421686746988
0.35421686747
0.501204819277
0.424096385542
0.318072289157
0.568674698795
0.484337349398
0.395180722892
0.527710843373
0.477108433735
0.424096385542
0.532530120482
0.549397590361
0.522891566265
0.4
0.530120481928
0.486746987952
0.424096385542
0.530120481928
0.453012048193
0.390361445783
0.433734939759
0.515662650602
0.436144578313
0.41686746988
0.493975903614
0.489156626506
prediction from median, leave p out:
0.55421686747

BREAK

Combining part blind+leave 2 out:





In [344]:



    


training_data_dodgy = blind[selected_data==True] 
training_data_dodgy1 = training_data_dodgy.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
correct_facies_labels_train = selected_pred_facies
#----------------------------------------------------------------------------

wells = training_data["Well Name"].values      #DOES NOT CONTAIN THE BLIND
y = training_data['Facies']
X = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)

lpgo = LeavePGroupsOut(n_groups=5)
pred_array=0*correct_facies_labels
for train, test in lpgo.split(X, y, groups=wells):
    xx0=X.iloc[train]
    yy0=y.iloc[train]
    XTRAIN = pd.concat([xx0, training_data_dodgy1], ignore_index=True)
    YTRAIN=np.concatenate((yy0.values, correct_facies_labels_train))
    
    selected_pred_facies=predicted_random_forest[selected_data==True]
    selected_true_labels=correct_facies_labels[selected_data==True] 
    
    rf = RandomForestClassifier(max_depth = 7, n_estimators=2000) 
    rf.fit(XTRAIN, YTRAIN)
    
    predicted_random_forest = rf.predict(features_blind)    #prediction on blind
    #predicted = cross_val_predict(rf, features_blind, cv=10)
    out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
    
    pred_array = np.vstack((predicted_random_forest, pred_array))
    
    #predicted = cross_val_predict(rf, xx1, cv=10)
    print out_f1
    
med_pred=np.median(pred_array,axis=0)
med_pred=med_pred.astype(int)
out_f1=metrics.f1_score(correct_facies_labels, med_pred,average = 'micro')
print "prediction from median, leave p out+augmentation:"
print out_f1



    


















    






0.544578313253
0.522891566265
0.366265060241
0.561445783133
0.489156626506
0.402409638554
0.542168674699
0.436144578313
0.375903614458
0.404819277108
0.477108433735
0.414457831325
0.363855421687
0.518072289157
0.481927710843
0.546987951807
0.469879518072
0.457831325301
0.467469879518
0.438554216867
0.450602409639
prediction from median, leave p out+augmentation:
0.556626506024

















In [284]:



    


pred_array.shape



    


















    
Out[284]:








(8, 415)

















In [292]:



    


pred_array[:,70]



    


















    
Out[292]:








array([8, 8, 8, 8, 8, 8, 8, 0])

THE END

http://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py





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In [1665]:



    


def predict_final(test_well, training_data,test_data):
    blind = test_data[test_data['Well Name'] == test_well]      
    correct_facies_labels_train = training_data['Facies'].values
    feature_vectors_train = training_data.drop(['Formation', 'Well Name', 'Depth','Facies','FaciesLabels'], axis=1)
    rf = RandomForestClassifier(max_depth = 15, n_estimators=600) 
    rf.fit(feature_vectors_train, correct_facies_labels_train)

    # the blind well
    feature_vectors_blind = blind.drop(['Formation', 'Well Name', 'Depth'], axis=1)
    predicted_random_forest = rf.predict(feature_vectors_blind)
    #out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
    return predicted_random_forest

Train for the test data





In [406]:



    


filename = 'training_data.csv'
training_data0 = pd.read_csv(filename)
training_data1=training_data0.copy()



    











In [407]:



    


filename = 'validation_data_nofacies.csv'
test_data = pd.read_csv(filename)



    











In [408]:



    


test_data1=magic(test_data)
training_data1=magic(training_data0)



    











In [409]:



    


#test_well='STUART'
test_well='CRAWFORD'



    











In [410]:



    


blind = test_data1[test_data1['Well Name'] == test_well]      
training_data = training_data1
    
correct_facies_labels_train = training_data['Facies'].values
feature_vectors = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)
rf = RandomForestClassifier(max_depth = 5, n_estimators=1000) 
rf.fit(feature_vectors, correct_facies_labels_train)

#correct_facies_labels = blind['Facies'].values
features_blind = blind.drop(['Formation', 'Well Name', 'Depth'], axis=1)
predicted_random_forest = rf.predict(features_blind)

#out_f1=metrics.f1_score(correct_facies_labels, predicted_random_forest,average = 'micro')
#print out_f1

props=rf.predict_proba(features_blind)
props_best=props.max(axis=1)



    











In [411]:



    


plt.plot(props[:,3])
plt.plot(props_best)
plt.show()
print props_best.mean()
print props_best.mean()+props_best.std()



    


















    

















    






0.420450262308
0.568342574221

















In [419]:



    


decs=[0.5, 0.54, 0.58, 0.62, 0.66, 0.68, 0.70, 0.74]
decs=[0.66]
decs=[0.65]
for vals in decs:
    new_array=[]
    selected_data=[]
    selected_data=0*predicted_random_forest
    count=0
    for idx, val in enumerate(props_best):
        if val > vals:
            new_array.append(predicted_random_forest[idx])
            selected_data[idx]=True
            count+=1
        else:
            selected_data[idx]=False
            

    selected_pred_facies=predicted_random_forest[selected_data==True]
    #selected_true_labels=correct_facies_labels[selected_data==True]    
    #out_f1=metrics.f1_score(selected_true_labels, selected_pred_facies,average = 'micro')
    
    print("vals of decs is  %.5f " %  (vals) ) 
    print("nr of samples %d " %  (count) )     
    print("total nr of facies predicted is %d" % (len(np.unique(selected_pred_facies))))
    #print("total nr of facies present is %d" % (len(np.unique(selected_true_labels))))    
    #print("f1 is  %.5f " %  (out_f1) ) 
    #dothererun(selected_pred_facies)   
    print "-------------------------------------------------"



    


















    






vals of decs is  0.65000 
nr of samples 24 
total nr of facies predicted is 1
-------------------------------------------------

















In [422]:



    


training_data_dodgy = blind[selected_data==True] 
training_data_dodgy1 = training_data_dodgy.drop(['Formation', 'Well Name', 'Depth'], axis=1)
correct_facies_labels_train = selected_pred_facies
#----------------------------------------------------------------------------

wells = training_data["Well Name"].values      #DOES NOT CONTAIN THE BLIND
y = training_data['Facies']
X = training_data.drop(['Formation', 'Well Name', 'Depth','Facies'], axis=1)

count=0

lpgo = LeavePGroupsOut(n_groups=5)
#pred_array=np.zeros(474)    #stu
pred_array=np.zeros(356)    #craw
for train, test in lpgo.split(X, y, groups=wells):
    xx0=X.iloc[train]
    yy0=y.iloc[train]
    XTRAIN = pd.concat([xx0, training_data_dodgy1], ignore_index=True)
    YTRAIN=np.concatenate((yy0.values, correct_facies_labels_train))
    
    selected_pred_facies=predicted_random_forest[selected_data==True]

    rf = RandomForestClassifier(max_depth = 7, n_estimators=2000) 
    rf.fit(XTRAIN, YTRAIN)
    
    predicted_random_forest = rf.predict(features_blind)    #prediction on blind  
    pred_array = np.vstack((predicted_random_forest, pred_array))
    count+=1
    print count
    
med_pred=np.median(pred_array,axis=0)
med_pred=med_pred.astype(int)
#out_f1=metrics.f1_score(correct_facies_labels, med_pred,average = 'micro')
#print "prediction from median, leave p out+augmentation:"
#print out_f1



    


















    






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In [423]:



    


predicted_craw=med_pred
predicted_craw



    


















    
Out[423]:








array([8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 5, 5, 7, 4, 4, 4, 4, 4, 4, 4,
       4, 4, 4, 4, 4, 4, 4, 6, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 6, 6,
       8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
       6, 4, 8, 8, 8, 8, 4, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 8, 8, 8, 8, 6,
       6, 6, 6, 6, 6, 6, 6, 6, 6, 3, 2, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 8,
       6, 8, 8, 8, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 8, 8, 8, 8, 7,
       7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 3, 2, 8, 8, 8, 8, 8, 8, 8, 8, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
       7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 7, 7, 4, 4, 4, 4, 4, 7, 7, 7, 7, 7, 7,
       4, 4, 4, 7, 7, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 2, 2, 3, 3, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3])

















In [405]:



    


predicted_stu=med_pred
predicted_stu



    


















    
Out[405]:








array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 2, 2, 2, 8, 8, 8,
       8, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 4, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6,
       6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6,
       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 6, 6, 6,
       6, 6, 6, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 6, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 2, 2,
       2, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 3, 3, 3, 3, 3, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 2, 2, 2,
       3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, 8, 8,
       8, 8, 8, 8, 6, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3,
       3, 3, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 6, 8, 3, 3, 3, 3, 8, 8, 8, 8,
       8, 8, 8, 8, 8, 8, 8, 7, 7, 4, 7, 8, 8, 8, 6, 6, 8, 8, 8, 8, 8, 8, 9,
       9, 8, 8, 8, 8, 8, 8, 6, 6, 6, 6, 6, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
       3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 3, 3, 3, 2, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 8, 8, 8, 8, 6, 6, 6, 6, 6, 6, 6, 6,
       6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
       6, 6, 6, 6, 8, 8, 6, 8, 6, 6, 6, 8, 6, 6, 8, 8, 8, 8, 8, 8, 8, 8, 8,
       8, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4])

















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Content source: esa-as/2016-ml-contest

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