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Stick with current features, add a few classifiers and stack with linear regression





In [39]:



    


import os
import pandas as pd
import numpy as np
from sklearn import preprocessing, ensemble, cross_validation, grid_search
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.grid_search import GridSearchCV
from sklearn import cross_validation, svm
from sklearn.linear_model import LinearRegression, BayesianRidge, SGDRegressor
import xgboost as xgb



    











In [2]:



    


print os.listdir('.')
directory = os.path.join(os.getcwd(),'competition_data')
filename = 'train_set.csv'
path = os.path.join(directory,filename)
print path



    


















    






['.ipynb_checkpoints', 'benchmark.csv', 'competition_data', 'Feat2OheXgb.ipynb', 'Feat2wXGB.ipynb', 'Feat3wXGB.ipynb', 'MoreFeatures.ipynb', 'MoreFeatures2.ipynb', 'MoreFeatureswXgb.ipynb', 'Prelim.ipynb', 'StackV1.ipynb']
C:\Users\Victor\Desktop\Kaggle\Caterpillar\competition_data\train_set.csv

















In [3]:



    


train = pd.read_csv(os.path.join(directory,'train_set.csv'), parse_dates=[2,])
test = pd.read_csv(os.path.join(directory,'test_set.csv'), parse_dates=[3,])



    











In [4]:



    


tubes = pd.read_csv(os.path.join(directory,'tube.csv'))

train = pd.merge(train,tubes,on='tube_assembly_id',how='inner')
test = pd.merge(test,tubes,on='tube_assembly_id',how='inner')

train['material_id'].fillna('SP-9999',inplace=True)
test['material_id'].fillna('SP-9999',inplace=True)

Featurizing Bill of Materials





In [5]:



    


materials = pd.read_csv(os.path.join(directory,'bill_of_materials_with_labels.csv'))



    











In [6]:



    


materials.columns



    


















    
Out[6]:








Index([u'tube_assembly_id', u'component_id', u'label'], dtype='object')

















In [7]:



    


comp_filenames = ['comp_adaptor.csv',
              'comp_boss.csv',
              'comp_elbow.csv',
              'comp_float.csv',
              'comp_hfl.csv',
              'comp_nut.csv',
              'comp_other.csv',
              'comp_sleeve.csv',
              'comp_straight.csv',
              'comp_tee.csv',
              'comp_threaded.csv']

comp_files = [pd.read_csv(os.path.join(directory,filename)) for filename in comp_filenames]



    











In [8]:



    


"""filename = 'comp_adaptor.csv'
filtered_materials = materials[materials['label']==6].drop('label',1)
comp_file = pd.read_csv(os.path.join(directory,filename)
pd.merge(filtered_materials,comp_file,on='component_id', how='inner').shape[0]"""



    


















    
Out[8]:








"filename = 'comp_adaptor.csv'\nfiltered_materials = materials[materials['label']==6].drop('label',1)\ncomp_file = pd.read_csv(os.path.join(directory,filename)\npd.merge(filtered_materials,comp_file,on='component_id', how='inner').shape[0]"

















In [9]:



    


for i in range(1,7):
    #filter by instances
    filtered_materials = materials[materials['label']==i].drop('label',1)
    for comp_file in comp_files:
        #join by component type files
        mat_comp = pd.merge(filtered_materials,comp_file,on='component_id', how='inner')
        #join onto train and test sets
        if mat_comp.shape[0]!=0:
            train = pd.merge(train,mat_comp,on='tube_assembly_id', how='left')
            test = pd.merge(test,mat_comp,on='tube_assembly_id', how='left')



    











In [10]:



    


print train.shape



    


















    






(30213, 440)

















In [11]:



    


train.fillna(0,inplace=True)
test.fillna(0,inplace=True)

End Bill of Materials stuff





In [12]:



    


# drop useless columns and create labels
idx = test.id.values.astype(int)
test = test.drop(['id', 'tube_assembly_id', 'quote_date'], axis = 1)
labels = train.cost.values
train = train.drop(['quote_date', 'cost', 'tube_assembly_id'], axis = 1)



    











In [13]:



    


# convert data to numpy array
train = np.array(train)
test = np.array(test)



    











In [14]:



    


rangeset = train.shape[1]
for i in range(rangeset):
    #if type(sorted(train[:,i],reverse=True)[1]) is str:
    if any(type(x) is str for x in train[:,i]) or any(type(x) is str for x in test[:,i]):
        train[:,i][train[:,i]==0] = 'zzz'
        test[:,i][test[:,i]==0] = 'zzz'
        lbl = preprocessing.LabelEncoder()
        lbl.fit(list(train[:,i]) + list(test[:,i]))
        train[:,i] = lbl.transform(train[:,i])
        test[:,i] = lbl.transform(test[:,i])



    











In [15]:



    


# object array to float
train = train.astype(float)
test = test.astype(float)

# i like to train on log(1+x) for RMSLE ;) 
# The choice is yours :)
label_log = np.log1p(labels)

Classifier section





In [16]:



    


X_train, X_test, y_train, y_test = cross_validation.train_test_split(train, label_log, test_size = 0.2, random_state = 0)



    











In [46]:



    


#RMSLE error function
import math

#A function to calculate Root Mean Squared Logarithmic Error (RMSLE)
def rmsle(y, y_pred):
	assert len(y) == len(y_pred)
	terms_to_sum = [(math.log(y_pred[i] + 1) - math.log(y[i] + 1)) ** 2.0 for i,pred in enumerate(y_pred)]
	return (sum(terms_to_sum) * (1.0/len(y))) ** 0.5

from sklearn.metrics import make_scorer

custom_score = make_scorer(rmsle,False)



    











In [47]:



    


testRun = 'No'



    











In [48]:



    


# sci-kit random forests

parameters = {'n_estimators': [1000] }

model_rfr = RandomForestRegressor(n_estimators=100)

if testRun == 'Yes':
    model_rfr.fit(X_train, y_train)
else:
    model_rfr.fit(train,label_log)



    











In [49]:



    


if testRun == 'Yes':
    val_preds_rfr = np.expm1(model_rfr.predict(X_test))
    norm_labels = np.expm1(y_test)
    score_val_rfr = rmsle(norm_labels,val_preds_rfr)
    print 'Base score is 0.249'
    print score_val_rfr



    











In [50]:



    


# xgboost random forest model

params = {}
params["objective"] = "reg:linear"
params["eta"] = 0.1
params["min_child_weight"] = 5
params["subsample"] = 1.0
params["scale_pos_weight"] = 1.0
params["silent"] = 1
params["max_depth"] = 7

plst = list(params.items())

if testRun == 'Yes':
    xgtrain = xgb.DMatrix(X_train, label = y_train)
    xgtest = xgb.DMatrix(X_test)
else:
    xgtrain = xgb.DMatrix(train, label=label_log)
    xgtest = xgb.DMatrix(test)

num_rounds = 500
model_xgb = xgb.train(plst, xgtrain, num_rounds)



    











In [51]:



    


if testRun == 'Yes':
    val_preds_xgb = np.expm1(model_xgb.predict(xgtest))
    norm_labels = np.expm1(y_test)
    score_val_xgb = rmsle(norm_labels,val_preds_xgb)
    print 'Base score is 0.207'
    print score_val_xgb



    











In [52]:



    


model_gbr = GradientBoostingRegressor()

if testRun == 'Yes':
    model_gbr.fit(X_train, y_train)
else:
    model_gbr.fit(train,label_log)



    











In [53]:



    


if testRun == 'Yes':
    val_preds_gbr = np.expm1(model_gbr.predict(X_test))
    norm_labels = np.expm1(y_test)
    score_val_gbr = rmsle(norm_labels,val_preds_gbr)
    print 'Base score is 0.327'
    print score_val_gbr



    











In [54]:



    


if testRun == 'Yes':
    X_stack_train = [list(t) for t in zip(model_rfr.predict(X_train),model_xgb.predict(xgtrain),model_gbr.predict(X_train))]
    X_stack_test = [list(t) for t in zip(model_rfr.predict(X_test),model_xgb.predict(xgtest),model_gbr.predict(X_test))]
else:
    X_stack_train = [list(t) for t in zip(model_rfr.predict(train),model_xgb.predict(xgtrain),model_gbr.predict(train))]
    X_stack_test = [list(t) for t in zip(model_rfr.predict(test),model_xgb.predict(xgtest),model_gbr.predict(test))]



    











In [55]:



    


model_stack = LinearRegression()

if testRun == 'Yes':
    model_stack.fit(X_stack_train,y_train)
else:
    model_stack.fit(X_stack_train,label_log)



    











In [56]:



    


if testRun == 'Yes':
    val_preds_stack = np.expm1(model_stack.predict(X_stack_test))
    norm_labels = np.expm1(y_test)
    score_val_stack = rmsle(norm_labels,val_preds_stack)
    print 'Base score is 0.237'
    print score_val_stack



    











In [57]:



    


if testRun == 'No':
    preds = np.expm1(model_stack.predict(X_stack_test))
    preds = pd.DataFrame({"id": idx, "cost": preds})
    preds.to_csv('benchmark.csv', index=False)

Looking into the data.

An analysis into the relationships between the different data files:

test_set is root

sheets with tube assembly ids:
bill_of_materials; components for every tube assembly
specs; specifications for every tube assembly
tube; description for every tube, including spec id, size and bend information, end of tube info

sheets without tube assembly ids with component ids:

  • components; component type list summary

comp_adaptor; detailed component specs, adaptor type
comp_boss; detailed component specs, boss type
comp_elbow; detailed component specs, elbow type
comp_float; detailed component specs, float type
comp_hfl; detailed component specs, hfl type
comp_nut; detailed component specs, nut type
comp_other; detailed component specs, other type
comp_sleeve; detailed component specs, sleeve type
comp_straight; detailed component specs, straight type
comp_tee; detailed component specs, tee type
comp_threaded; detailed component specs, threaded type

sheets without tube assembly ids with other ids
tube_end_form; end of tube types, yes/no on forming
type_component; component type id with name
type_connection; connection type id (in some components) with name
type_end_form; end form id (in some components) with name

Content source: VicCrome/Caterpillar

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