def train_wrapper(params):
cv_losses, _ = cross_validate(params, X, y)
# return an object to be recorded in hyperopt trials for future uses
return {
'loss': np.mean(cv_losses),
'status': STATUS_OK,
'eval_time': datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
'params': params
}
#----For smaller data sets------# (Do not work or have very long training times on large sparse datasets) Require .todense()
#clf = ensemble.RandomForestRegressor(n_estimators=50); clfname='RFReg_50'
#clf = ensemble.ExtraTreesRegressor(n_estimators=30) #n_jobs = -1 if running in a main() loop
#clf = ensemble.GradientBoostingRegressor(n_estimators=700, learning_rate=.1, max_depth=1, random_state=888, loss='ls');clf_name='GBM'
clf = ensemble.AdaBoostRegressor(base_estimator=tree.DecisionTreeRegressor(compute_importances=None, criterion='mse', max_depth=3,
max_features=None, min_density=None, min_samples_leaf=1,
min_samples_split=2, random_state=None, splitter='best'),
n_estimators=150, learning_rate=.5, loss='linear', random_state=None)
#clf = gaussian_process.GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100)
#clf = neighbors.KNeighborsRegressor(100, weights='uniform', algorithm = 'auto');clf_name='KNN_200'
################################################################################################
#---Different methods of cross validation---#
#May require mtxTrn.toarray()
cv_preds = train.cross_validate(hstack([sparse.csr_matrix(dfTrn.urlid.values).transpose(),mtxTrn]),mtxTrnTarget.ravel(),
folds=10,SEED=42,test_size=.1,clf=clf,clf_name=clf_name,pred_fg=True)
train.cross_validate(mtxTrn,mtxTrnTarget.ravel(),folds=8,SEED=888,test_size=.1,clf=clf,clf_name=clf_name,pred_fg=False)
train.cross_validate_temporal(mtxTrn,mtxTest,mtxTrnTarget.ravel(),mtxTestTarget.ravel(),clf=clf,
clf_name=clf_name,pred_fg=False)
train.cross_validate_using_benchmark('global_mean',dfTrn, mtxTrn,mtxTrnTarget,folds=20)
################################################################################################
#---Calculate the degree of variance between ground truth and the mean of the CV predictions.----#
#---Returns a list of all training records with their average variance---#
train.calc_cv_preds_var(dfTrn,cv_preds)
################################################################################################
#--Use estimator for manual predictions--#
dfTest, clf = train.predict(mtxTrn,mtxTrnTarget.ravel(),mtxTest,dfTest,clf,clf_name) #may require mtxTest.toarray()
#--------------Machine Learning (woohoo, we finally got to the good stuff)------------------------#
#quant_features = ['user_average_stars','user_review_count','calc_total_checkins','bus_stars','bus_review_count']
quant_features = ['bus_stars','user_average_stars','bus_review_count', 'user_review_count','calc_total_checkins','calc_cat_avg']
dfTrn_ML=dfTrn_All_5_8; dfTest_ML= dfTest_All_1_5;
mtxTrn,mtxTest = features.standardize(dfTrn_ML,dfTest_ML,quant_features)
#--Combine the standardized quant features and the vectorized categorical features--#
#mtxTrn = hstack([mtxTrn,vecTrn_BusOpen]) #vecTrn_BusOpen,vecTrn_Cats,vecTrn_Zip,
#mtxTest = hstack([mtxTest,vecTest_BusOpen]) #vecTest_Master_Cats,vecTest_Master_Zip,
#--Test without the vecZip and vecCats--#
#mtxTrn = hstack([mtxTrn,vecTrn_BusOpen])
#mtxTest = hstack([mtxTest,vecTest_Master_BusOpen])
#--select target--#
mtxTarget = dfTrn_ML.ix[:,['rev_stars']].as_matrix()
#--Use classifier for cross validation--#
train.cross_validate(mtxTrn,mtxTarget,clf,folds=10,SEED=42,test_size=.2) #may require mtxTrn.toarray()
#--Use classifier for predictions--#
dfTest_ML, clf = train.predict(mtxTrn,mtxTarget,mtxTest,dfTest_ML,clf,clf_name) #may require mtxTest.toarray()
#--Save predictions to file--#
train.save_predictions(dfTest_ML,clf_name,'_All_1_5_KitchenSink',submission_no)
#---------End Machine Learning Section-------------#
#------------------------------Optional Steps----------------------------------#
#--Memory cleanup prior to running the memory intensive classifiers--#
dfTrn,dfTest,dfAll = utils.data_garbage_collection(dfTrn,dfTest,dfAll)
#--use a benchmark instead of a classifier--#
benchmark_preds = train.cross_validate_using_benchmark('3.5', dfTrn, dfTrn[0].merge(dfTrn[1],how='inner',on='business_id').as_matrix(),dfTrn[0].ix[:,['rev_stars']].as_matrix(),folds=3,SEED=42,test_size=.15)
'calc_total_checkins', 'calc_cat_avg'
]
dfTrn_ML = dfTrn_All_5_8
dfTest_ML = dfTest_All_1_5
mtxTrn, mtxTest = features.standardize(dfTrn_ML, dfTest_ML, quant_features)
#--Combine the standardized quant features and the vectorized categorical features--#
#mtxTrn = hstack([mtxTrn,vecTrn_BusOpen]) #vecTrn_BusOpen,vecTrn_Cats,vecTrn_Zip,
#mtxTest = hstack([mtxTest,vecTest_BusOpen]) #vecTest_Master_Cats,vecTest_Master_Zip,
#--Test without the vecZip and vecCats--#
#mtxTrn = hstack([mtxTrn,vecTrn_BusOpen])
#mtxTest = hstack([mtxTest,vecTest_Master_BusOpen])
#--select target--#
mtxTarget = dfTrn_ML.ix[:, ['rev_stars']].as_matrix()
#--Use classifier for cross validation--#
train.cross_validate(mtxTrn, mtxTarget, clf, folds=10, SEED=42,
test_size=.2) #may require mtxTrn.toarray()
#--Use classifier for predictions--#
dfTest_ML, clf = train.predict(mtxTrn, mtxTarget, mtxTest, dfTest_ML, clf,
clf_name) #may require mtxTest.toarray()
#--Save predictions to file--#
train.save_predictions(dfTest_ML, clf_name, '_All_1_5_KitchenSink',
submission_no)
#---------End Machine Learning Section-------------#
#------------------------------Optional Steps----------------------------------#
#--Memory cleanup prior to running the memory intensive classifiers--#
dfTrn, dfTest, dfAll = utils.data_garbage_collection(dfTrn, dfTest, dfAll)
#----------------------------------------# #--------- Machine Learning--------------# #----------------------------------------# #--select target--# mtxTarget = frmTrn_All.ix[:,['rev_votes_useful']].as_matrix() #--select classifier--# ## Common options: ensemble -- RandomForestClassifier, RandomForestRegressor, ExtraTreesRegressor, GradientBoostingRegressor ## linear_model -- SGDRegressor, Lasso #clf = linear_model.LassoCV(cv=3) #clf = linear_model.ElasticNet() #clf = ensemble.RandomForestRegressor(n_estimators=50) clf = linear_model.SGDRegressor(alpha=0.001, n_iter=1000,shuffle=True); clfname='SGD_001_1000' #--Use classifier for cross validation--# train.cross_validate(mtxTrn,mtxTarget,clf,folds=10,SEED=42,test_size=.15) #--Use classifier for predictions--# frmTest_All = train.predict(mtxTrn,mtxTarget,mtxTest,frmTest_All,clf,clfname) #frmTest_NoVotes = train.predict(mtxTrn,mtxTarget,mtxTest,frmTest_NoVotes,clf,clfname) #frmTest_NoUser = train.predict(mtxTrn,mtxTarget,mtxTest,frmTest_NoUser,clf,clfname) #--Save predictions to file--# train.save_predictions(frmTest_All,clfname) #train.save_predictions(frmTest_NoVotes,clfname) #train.save_predictions(frmTest_NoUser,clfname) #--Save model to joblib file--# train.save_model(clf,clfname) #--Load model from joblib file--#
def main():
log.info('********New program instance started********')
#-------------Load Environment----------------------#
#Get program settings and model settings from SETTINGS.json file in root directory
settings, model_settings = utils.load_settings()
#If not using cached data, then load raw data, clean/munge it, create hand-crafted features, slice it for CV
if settings['use_cached_data'] == 'y':
log.info('==========LOADING CACHED FEATURES===========')
dfTrn = data_io.load_cached_object('dfTrn')
dfTest = data_io.load_cached_object('dfTest')
dfCV = data_io.load_flatfile_to_df('Data/CV.csv')
else:
#-------Data Loading/Cleaning/Munging------------#
#Load the data
log.info('===============LOADING DATA=================')
dfTrn = data_io.load_flatfile_to_df(settings['file_data_train'])
dfTest = data_io.load_flatfile_to_df(settings['file_data_test'])
dfCV = data_io.load_flatfile_to_df('Data/CV.csv')
#Clean/Munge the data
log.info('=======CLEANING AND MUNGING DATA============')
dfTrn = munge.clean(dfTrn)
dfTest = munge.clean(dfTest)
#-------Feature creation-------------------------#
#Add all currently used hand crafted features to dataframes
log.info('====CREATING HAND-CRAFTED DATA FEATURES=====')
features.add(dfTrn)
features.add(dfTest)
#---------Data slicing/parsing--------------------------#
#Split data for CV
if settings['generate_cv_score'] == 'y':
log.info('=====SPLITTING DATA FOR CROSS-VALIDATION====')
if settings['cv_method'] == 'april':
dfTrnCV, dfTestCV = munge.temporal_split(dfTrn, (2013, 04, 1))
elif settings['cv_method'] == 'march':
#take an addtional week from February b/c of lack of remote_api source issues in March
dfTrnCV, dfTestCV = munge.temporal_split(dfTrn, (2013, 02, 21))
elif settings['cv_method'] == 'list_split':
#load stored list of data points and use those for CV
dfCVlist = pd.DataFrame({'id': data_io.load_cached_object('Cache/cv_issue_ids.pkl'), 'dummy': 0})
dfTrnCV, dfTestCV = munge.list_split(dfTrn, dfCVlist)
#--------------Modeling-------------------------#
#If cached models exist then load them for reuse into segment_models. Then run through model_settings and for
# each model where 'use_cached_model' is false then clear the cached model and recreate it fresh
log.info('=========LOADING CACHED MODELS==============')
segment_models = data_io.load_cached_object('segment_models')
if segment_models == None:
log.info('=========CACHED MODELS NOT LOADED===========')
for model in model_settings:
model['use_cached_model'] = 'n'
segment_models = []
#Initialize new model for models not set to use cache
log.info('=======INITIALIZING UN-CACHED MODELS========')
index = 0
for model in model_settings:
if model_settings[model]['use_cached_model'] == 'n':
new_model = ensembles.Model(model_name=model,target=model_settings[model]['target'],
segment=model_settings[model]['segment'],
estimator_class=model_settings[model]['estimator_class'],
estimator_params=model_settings[model]['estimator_params'],
features=model_settings[model]['features'],
postprocess_scalar=model_settings[model]['postprocess_scalar'])
#Flag the model as not cached, so that it does not get skipped when running the modeling process
new_model.use_cached_model='n'
#Project specific model attributes not part of base class
new_model.KNN_neighborhood_threshold=model_settings[model]['KNN_neighborhood_threshold']
new_model.sub_zip_neighborhood=model_settings[model]['sub_zip_neighborhood']
segment_models[index] = new_model
log.info('Model %s intialized at index %i' % (model,index))
index += 1
#Cross validate all segment models (optional)
if settings['export_cv_predictions_all_models'] == 'y' or settings['export_cv_predictions_new_models'] == 'y':
log.info('============CROSS VALIDATION================')
for model in segment_models[:]:
#If model has cached CV predictions then skip predicting and just export them (if selected in settings)
if hasattr(model,'dfCVPredictions'):
log.info('Cached CV predictions found. Using cached CV predictions.')
if settings['export_cv_predictions_all_models'] == 'y':
data_io.save_predictions(model.dfCVPredictions,model.target,model_name=model.model_name,
directory=settings['dir_submissions'],
estimator_class=model.estimator_class, note='CV_list')
else:
print_model_header(model)
#Prepare segment model: segment and create feature vectors for the CV data set
dfTrn_Segment, dfTest_Segment = prepare_segment_model(dfTrnCV,dfTestCV,model)
#Generate CV predictions
train.cross_validate(model, settings, dfTrn_Segment, dfTest_Segment)
#Cache the CV predictions as a dataframe stored in each segment model
model.dfCVPredictions = dfTest_Segment.ix[:,['id',model.target]]
if settings['export_cv_predictions_new_models'] == 'y':
data_io.save_predictions(model.dfCVPredictions,model.target,model_name=model.model_name,
directory=settings['dir_submissions'],
estimator_class=model.estimator_class, note='CV_list')
#Generate predictions on test set for all segment models (optional)
if settings['export_predictions_all_models'] == 'y' or settings['export_predictions_new_models'] == 'y'\
or settings['export_predictions_total'] == 'y':
log.info('=======GENERATING TEST PREDICTIONS==========')
for model in segment_models[:]:
#If model has cached test predictions then skip predicting and just export them (if selected in settings)
if hasattr(model,'dfPredictions'):
log.info('Cached test predictions found for model %s. Using cached predictions.' % model.model_name)
if settings['export_predictions_all_models'] == 'y':
data_io.save_predictions(model.dfPredictions,model.target,model_name=model.model_name,
directory=settings['dir_submissions'],
estimator_class=model.estimator_class,note='TESTset')
else:
print_model_header(model)
#Prepare segment model: segment and create feature vectors for the full TEST data set
dfTrn_Segment, dfTest_Segment = prepare_segment_model(dfTrn,dfTest,model)
#Generate TEST set predictions
model.predict(dfTrn_Segment, dfTest_Segment)
if settings['export_predictions_all_models'] == 'y' or settings['export_predictions_new_models'] == 'y':
data_io.save_predictions(model.dfPredictions,model.target,model_name=model.model_name,
directory=settings['dir_submissions'],
estimator_class=model.estimator_class,note='TESTset')
log.info(utils.line_break())
#Cache the trained models and predictions to file (optional)
if settings['export_cached_models'] == 'y':
log.info('==========EXPORTING CACHED MODELS===========')
data_io.save_cached_object(segment_models,'segment_models')
#Merge each segment model's CV predictions into a master dataframe and export it (optional)----#
if settings['export_cv_predictions_total'] == 'y':
log.info('====MERGING CV PREDICTIONS FROM SEGMENTS====')
dfTestPredictionsTotal = merge_segment_predictions(segment_models, dfTestCV, cv=True)
#---Apply post process rules to master dataframe---#
#Set all votes and comments for remote_api segment to 1 and 0
dfTestPredictionsTotal = dfTestPredictionsTotal.merge(dfTest.ix[:][['source','id']], on='id', how='left')
for x in dfTestPredictionsTotal.index:
if dfTestPredictionsTotal.source[x] == 'remote_api_created':
dfTestPredictionsTotal.num_votes[x] = 1
dfTestPredictionsTotal.num_comments[x] = 0
#Export
timestamp = datetime.now().strftime('%m-%d-%y_%H%M')
filename = 'Submits/'+timestamp+'--bryan_CV_predictions.csv'
dfTestPredictionsTotal.to_csv(filename)
#Merge each segment model's TEST predictions into a master dataframe and export it (optional)----#
if settings['export_predictions_total'] == 'y':
log.info('===MERGING TEST PREDICTIONS FROM SEGMENTS===')
dfTestPredictionsTotal = merge_segment_predictions(segment_models, dfTest)
#---Apply post process rules to master dataframe---#
#Set all votes and comments for remote_api segment to 1 and 0
dfTestPredictionsTotal = dfTestPredictionsTotal.merge(dfTest.ix[:][['source','id']], on='id', how='left')
for x in dfTestPredictionsTotal.index:
if dfTestPredictionsTotal.source[x] == 'remote_api_created':
dfTestPredictionsTotal.num_votes[x] = 1
dfTestPredictionsTotal.num_comments[x] = 0
del dfTestPredictionsTotal['source']
#Export
filename = 'bryan_test_predictions.csv'
data_io.save_combined_predictions(dfTestPredictionsTotal, settings['dir_submissions'], filename)
#End main
log.info('********Program ran successfully. Exiting********')
def cross_validate_pipeline(X, y):
"""Trains and validates a model"""
train.cross_validate(X, y)
splitter='best'),
n_estimators=150,
learning_rate=.5,
loss='linear',
random_state=None)
#clf = gaussian_process.GaussianProcess(corr='cubic', theta0=1e-2, thetaL=1e-4, thetaU=1e-1, random_start=100)
#clf = neighbors.KNeighborsRegressor(100, weights='uniform', algorithm = 'auto');clf_name='KNN_200'
################################################################################################
#---Different methods of cross validation---#
#May require mtxTrn.toarray()
cv_preds = train.cross_validate(hstack(
[sparse.csr_matrix(dfTrn.urlid.values).transpose(), mtxTrn]),
mtxTrnTarget.ravel(),
folds=10,
SEED=42,
test_size=.1,
clf=clf,
clf_name=clf_name,
pred_fg=True)
train.cross_validate(mtxTrn,
mtxTrnTarget.ravel(),
folds=8,
SEED=888,
test_size=.1,
clf=clf,
clf_name=clf_name,
pred_fg=False)
train.cross_validate_temporal(mtxTrn,
mtxTest,
mtxTrnTarget.ravel(),
