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%matplotlib inline
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.chained_assignment = None
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filename = './../training_data.csv'
training_data = pd.read_csv(filename)
print(set(training_data["Well Name"]))
training_data.head()
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well_data = pd.read_csv('./../validation_data_nofacies.csv')
print(set(well_data["Well Name"]))
print(well_data.shape)
well_data.head()
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# concat train and test for processing
well_data["origin"] = 'test'
training_data["origin"] = 'train'
df = pd.concat([well_data,training_data],axis=0,ignore_index=True)[list(training_data.columns)]
df['Well Name'] = df['Well Name'].astype('category')
df.head(10)
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# add some features based on the well data.
# nb points : can be correlated with how soft soil is ?
print("session")
sessionsize = df.groupby(["Well Name",'Formation']).size().reset_index()
sessionsize.columns = ["Well Name",'Formation','formation_size']
df = pd.merge(df,sessionsize,how='left',on = ["Well Name",'Formation'])
# depth :
print("depth")
sessionsize = df.groupby(["Well Name",'Formation'])["Depth"].min().reset_index()
sessionsize.columns = ["Well Name",'Formation','minimum_depth']
df = pd.merge(df,sessionsize,how='left',on = ["Well Name",'Formation'])
sessionsize = df.groupby(["Well Name",'Formation'])["Depth"].max().reset_index()
sessionsize.columns = ["Well Name",'Formation','maximum_depth']
df = pd.merge(df,sessionsize,how='left',on = ["Well Name",'Formation'])
df['formation_depth'] = df["maximum_depth"] - df["minimum_depth"]
df["soft_indic"] = df['formation_depth'] / df["formation_size"]
# add avgs of feat
print("add avgs of feat")
list_to_avg = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']
for val in list_to_avg :
df[val + "_min"] = df.groupby(["Well Name",'Formation'])[val].transform(np.min)
df[val + "_max"] = df.groupby(["Well Name",'Formation'])[val].transform(np.max)
df[val + "_mean"] = df.groupby(["Well Name",'Formation'])[val].transform(np.mean)
df[val + "_var"] = df.groupby(["Well Name",'Formation'])[val].transform(np.var)
# add distances feat. = an attempt at regulariation.
print("add distances feat.")
for val in list_to_avg :
df[val + "_min_dist"] = df[val] -df[val + "_min"]
df[val + "_max_dist"] = df[val] -df[val + "_max"]
df[val + "_mean_dist"] = df[val] -df[val + "_mean"]
# add lag and lead !
print("lag lead")
list_to_lag = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'NM_M', 'RELPOS']
for val in list_to_lag:
for lag in range(1,11):
df[val+'_lag_'+str(lag)]=df[val]-df.groupby("Well Name")[val].shift(periods=lag)
df[val+'_lead_'+str(lag)]=df[val]-df.groupby("Well Name")[val].shift(periods=-lag)
# adding some Formation lag and lead.
for lag in range(1,3):
df['Formation'+'_lag_'+str(lag)]=df.groupby("Well Name")['Formation'].shift(periods=lag)
df['Formation'+'_lead_'+str(lag)]=df.groupby("Well Name")['Formation'].shift(periods=-lag)
df['Formation'+'_lag_'+str(lag) + 'equal'] = (df['Formation'+'_lag_'+str(lag)] == df["Formation"]).astype(int)
df['Formation'+'_lead_'+str(lag) + 'equal'] = (df['Formation'+'_lead_'+str(lag)] == df["Formation"]).astype(int)
print("rolling")
#Add rolling mean
list_to_roll = ['Depth', 'GR', 'ILD_log10', 'DeltaPHI', 'PHIND', 'PE', 'RELPOS']
window_size = 5
for val in list_to_roll:
df[val+'_rollingmean_'+str(lag)]=df.groupby("Well Name")[val].apply(lambda x:x.rolling(window=window_size).mean())
df[val+'_rollingmax_'+str(lag)]=df.groupby("Well Name")[val].apply(lambda x:x.rolling(window=window_size).max())
df[val+'_rollingmin_'+str(lag)]=df.groupby("Well Name")[val].apply(lambda x:x.rolling(window=window_size).min())
df[val+'_rollingstd_'+str(lag)]=df.groupby("Well Name")[val].apply(lambda x:x.rolling(window=window_size).std())
print("filling na")
df = df.groupby(["Well Name"], as_index=False).apply(lambda group: group.bfill())
df = df.groupby(["Well Name"], as_index=False).apply(lambda group: group.ffill())
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tokeep =['Facies','origin','Formation','Formation_lag_1','Formation_lead_1','Formation_lag_2','Formation_lead_2',
'Well Name','Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']
nums = ['Depth','GR','ILD_log10','DeltaPHI','PHIND','PE','NM_M','RELPOS']
tokeep = tokeep + [x+'_lag_1' for x in nums] +[x+'_lead_1' for x in nums]
df = df[tokeep]
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from sklearn.feature_extraction.text import CountVectorizer
list_formation = ['Formation',
'Formation_lag_1',
'Formation_lead_1',
'Formation_lag_2',
'Formation_lead_2']
for l in list_formation:
cv = CountVectorizer()
counts = cv.fit_transform(df[l].values)
cols = [c+"_"+l for c in cv.get_feature_names()]
counts = pd.DataFrame(counts.toarray(),columns = cols)
df = df.drop(l,axis = 1)
df = pd.concat([df,counts],axis=1)
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# params
max_depth = 10
n_estimators = 1000
min_samples_leaf = 10
clf = RandomForestClassifier(max_depth = max_depth,
n_estimators=n_estimators,
min_samples_leaf=min_samples_leaf)
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ytrain = df[(df['origin']=='train')&(df['Well Name']<>'CHURCHMAN BIBLE')]['Facies']
yvalid = df[(df['origin']=='train')&(df['Well Name']=='CHURCHMAN BIBLE')]['Facies']
xtrain = df[(df['origin']=='train')&(df['Well Name']<>'CHURCHMAN BIBLE')].drop(['Well Name','origin','Facies'],axis=1)
xvalid = df[(df['origin']=='train')&(df['Well Name']=='CHURCHMAN BIBLE')].drop(['Well Name','origin','Facies'],axis=1)
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clf.fit(xtrain,ytrain)
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preds = clf.predict(xvalid)
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from sklearn.metrics import classification_report
print classification_report(yvalid, preds)
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# this time let's use all the training set
ytrain = df[(df['origin']=='train')]['Facies']
yvalid = df[(df['origin']=='test')]['Facies']
xtrain = df[(df['origin']=='train')].drop(['Well Name','origin','Facies'],axis=1)
xvalid = df[(df['origin']=='test')].drop(['Well Name','origin','Facies'],axis=1)
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#clf.fit(xtrain,ytrain)
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preds = clf.predict(xvalid.values)
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xvalid['Facies']=preds
xvalid.to_csv('XmasPreds_2.csv')