def stg2_XTR(fobj, train_x, hold_x, sub_test, ntrees=100):
ntrees = ntrees
e = 40
njobs = 2
r = 0.3
arr = [0, 1, 2, 21, 22, 23, 24, 25, 26]
hold_test_x = hold_x[:, arr]
hold_test_y = hold_x[:, -1]
sub_test = sub_test[:, arr]
fobj.write('Trees: %r' % ntrees)
print 'Stage 2: Exp Threshold - %r' % e
fobj.write('Stage 2: Exp Threshold - %r\n' % e)
c_train = train_x[(train_x[:, -1] <= e)]
c_arr = [0, 1, 2, 21, 22, 23, 24, 25, 26, 27]
c_train = c_train[:, c_arr]
c_train_y = c_train[:, -1]
c_train_x = c_train[:, :-1]
kaggle_file = 'Kaggle_XTR_Ankit_e' + str(e) + '_r' + str(r) + '_t_' + str(
ntrees) + str(len(arr)) + '.csv'
df_kaggle_file = 'Kaggle_df_XTR_Ankit_e' + str(e) + '_r' + str(
r) + '_t_' + str(ntrees) + str(len(arr)) + '.csv'
print 'Stage 2: Rate - %r' % r
fobj.write('XTR exp: %r rate: %r' % (e, r))
rf_model = etr(n_estimators=ntrees, n_jobs=njobs)
est = rf_model.fit(c_train_x, c_train_y)
train_y_pred = est.predict(c_train_x)
error = mt.mean_absolute_error(c_train_y, train_y_pred)
print 'XTR Train Error: %r\n' % (error)
fobj.write('XTR Train-2 Error: %r\n' % (error))
train_y_pred = est.predict(hold_test_x)
error = mt.mean_absolute_error(hold_test_y, train_y_pred)
print 'XTR 20 percent Hold Error: %r\n' % (error)
fobj.write('XTR 20 percent Hold Error: %r\n' % (error))
print 'Test Size:%r' % len(sub_test)
test_y_pred = est.predict(sub_test)
id_col = np.arange(1., len(test_y_pred) + 1, dtype=np.int)
all_data = np.column_stack((id_col, test_y_pred))
np.savetxt(os.path.join(param.CURRENT_FOLDER, kaggle_file),
all_data,
delimiter=',',
header='Id,Expected')
df = pd.read_csv(os.path.join(param.CURRENT_FOLDER, kaggle_file))
df.to_csv(os.path.join(param.CURRENT_FOLDER, df_kaggle_file),
header=True,
index=False)
def stg2_XTR(fobj, train_x, hold_x, sub_test, ntrees=100):
ntrees = ntrees
e = 40
njobs=2
r = 0.3
arr = [0,1,2,21,22,23,24,25,26]
hold_test_x = hold_x[:,arr]
hold_test_y = hold_x[:,-1]
sub_test = sub_test[:,arr]
fobj.write('Trees: %r'%ntrees)
print 'Stage 2: Exp Threshold - %r' % e
fobj.write('Stage 2: Exp Threshold - %r\n'%e)
c_train = train_x[(train_x[:,-1]<=e)]
c_arr = [0,1,2,21,22,23,24,25,26,27]
c_train = c_train[:,c_arr]
c_train_y = c_train[:,-1]
c_train_x = c_train[:,:-1]
kaggle_file = 'Kaggle_XTR_Ankit_e'+str(e)+'_r'+str(r)+'_t_'+str(ntrees)+str(len(arr)) +'.csv'
df_kaggle_file = 'Kaggle_df_XTR_Ankit_e'+str(e)+'_r'+str(r)+'_t_'+str(ntrees)+str(len(arr))+ '.csv'
print 'Stage 2: Rate - %r' % r
fobj.write('XTR exp: %r rate: %r'%(e,r))
rf_model = etr(n_estimators=ntrees, n_jobs=njobs)
est = rf_model.fit(c_train_x, c_train_y)
train_y_pred = est.predict(c_train_x)
error = mt.mean_absolute_error(c_train_y, train_y_pred)
print 'XTR Train Error: %r\n' % (error)
fobj.write('XTR Train-2 Error: %r\n' % (error))
train_y_pred = est.predict(hold_test_x)
error = mt.mean_absolute_error(hold_test_y, train_y_pred)
print 'XTR 20 percent Hold Error: %r\n' % (error)
fobj.write('XTR 20 percent Hold Error: %r\n' % (error))
print 'Test Size:%r' % len(sub_test)
test_y_pred = est.predict(sub_test)
id_col = np.arange(1.,len(test_y_pred)+1, dtype=np.int)
all_data = np.column_stack((id_col, test_y_pred))
np.savetxt(os.path.join(param.CURRENT_FOLDER, kaggle_file), all_data, delimiter=',', header='Id,Expected')
df = pd.read_csv(os.path.join(param.CURRENT_FOLDER, kaggle_file))
df.to_csv(os.path.join(param.CURRENT_FOLDER, df_kaggle_file), header=True, index=False)
def runXTR(train_1_x, test_x, hold_out, sub_test, fobj):
ntrees = 100
njobs = 2
exp = 40
c_train_1_x = train_1_x[(train_1_x[:, -1] <= exp)]
c_train_y = c_train_1_x[:, -1]
c_train_x = c_train_1_x[:, :-1]
rf_model = etr(n_estimators=ntrees, n_jobs=-1)
est = rf_model.fit(c_train_x, c_train_y)
train_y_pred = est.predict(c_train_x)
error = mt.mean_absolute_error(c_train_y, train_y_pred)
print 'XTR Train-1 Error: %r\n' % (error)
fobj.write('XTR Train-1 Error: %r\n' % (error))
valid_y_pred = est.predict(test_x)
hold_y = est.predict(hold_out)
sub_y = est.predict(sub_test)
return est, valid_y_pred, hold_y, sub_y
def runXTR(train_1_x, test_x, hold_out, sub_test, fobj):
ntrees = 100
njobs = 2
exp = 40
c_train_1_x = train_1_x[(train_1_x[:,-1]<=exp)]
c_train_y = c_train_1_x[:,-1]
c_train_x = c_train_1_x[:,:-1]
rf_model = etr(n_estimators=ntrees, n_jobs=-1)
est = rf_model.fit(c_train_x, c_train_y)
train_y_pred = est.predict(c_train_x)
error = mt.mean_absolute_error(c_train_y, train_y_pred)
print 'XTR Train-1 Error: %r\n' % (error)
fobj.write('XTR Train-1 Error: %r\n' % (error))
valid_y_pred = est.predict(test_x)
hold_y = est.predict(hold_out)
sub_y = est.predict(sub_test)
return est, valid_y_pred, hold_y, sub_y
def driver(comp_mat,
pps_mth='ORIGINAL',
exp_thresh=[40],
regression_mth=[],
test_size=0.2,
f_selection=False,
n_features=3,
reg_methods=[]):
global fobj
fobj.write('Total Number of records: %r\n' % (len(comp_mat)))
#step 1 split Train and test records
train_x, u_test_x, train_y, u_test_y = cv.train_test_split(
comp_mat, comp_mat[:, -1], random_state=52, test_size=test_size)
#removing the predictor column from the matrix
u_test_x = u_test_x[:, :-1]
for exp in exp_thresh:
fobj.write('\n\nExpected Threshold Limit: %r\n' % (exp))
#step 2 prning to required exppected threshold
c_train_x = train_x[(train_x[:, -1] <= exp)]
c_train_y = c_train_x[:, -1]
c_train_x = c_train_x[:, :-1]
#step1 split Train
ttrain_x, ttest_x, ttrain_y, ttest_y = cv.train_test_split(
c_train_x, c_train_y, random_state=32, test_size=test_size)
fobj.write('Total Number of Constrained test records: %r\n' %
len(ttest_y))
fobj.write('Total Number of Unconstrained test records: %r\n' %
len(u_test_y))
fobj.write('Total Constrained Training Records: %r\n' % len(ttrain_y))
print 'Fitting Model Measurements'
trees_array = [100]
kf = cv.KFold(len(ttrain_x), n_folds=5)
st_time = time.time()
l_rate = [0.1, 0.05, 0.02, 0.01]
max_depth = [None]
min_samples_leaf = [1]
for dpth in max_depth:
fobj.write('\n\nmax _depth: %r\n' % (dpth))
print 'max _depth: %r' % (dpth)
for msl in min_samples_leaf:
fobj.write('\nmin_samples_leaf: %r\n' % (msl))
print 'min_samples_leaf: %r' % (msl)
for ntrees in trees_array:
valid_acc = []
test_acc = []
train_acc = []
est_arr = []
unconst_acc = []
fold = 0
for train_idx, test_idx in kf:
fobj.write('\nfold: %r\n' % (fold))
#rf_model = rfr(n_estimators=ntrees, n_jobs=-1) Random forest regressor
#rf_model = etr(n_estimators=ntrees, n_jobs=3, bootstrap=False)
rf_model = etr(n_estimators=ntrees,
max_depth=dpth,
min_samples_split=msl)
vacc, tacc, est = rf_regressor(rf_model,
ttrain_x[train_idx],
ttrain_y[train_idx],
ttrain_x[test_idx],
ttrain_y[test_idx],
f_selection=f_selection,
n_features=n_features)
valid_acc.append(vacc)
train_acc.append(tacc)
est_arr.append(est)
fobj.write('Train Size:%r\n' % (len(train_idx)))
fobj.write('Validation Size:%r\n' % (len(test_idx)))
fobj.write('Validation Error: %r\n' % (vacc))
fobj.write('Train Error: %r\n' % (tacc))
fobj.write('Constrained Test data size:%r\n' %
(len(ttest_x)))
test_acc.append(
mt.mean_absolute_error(ttest_y,
est.predict(ttest_x)))
fobj.write('Constrained Test Error for fold: %r\n' %
(test_acc[-1]))
unconst_acc.append(
mt.mean_absolute_error(u_test_y,
est.predict(u_test_x)))
fobj.write('Complete test accuracy:%r\n' %
(unconst_acc[-1]))
fold += 1
break
et_time = time.time()
fobj.write('..Statistics..\n')
fobj.write('Expected Threshold Limit: %r\n' % (exp))
fobj.write('Trees:%r\n' % ntrees)
fobj.write('Train Average: %r\n' % (np.mean(train_acc)))
fobj.write('Validation Average: %r\n' %
(np.mean(valid_acc)))
fobj.write('Constrained Test Average: %r\n' %
(np.mean(test_acc)))
fobj.write('Unconstrained Test Avg: %r\n' %
(np.mean(unconst_acc)))
fobj.write('Total Time Taken: %r mins\n' %
((et_time - st_time) / 60))
#Print to console
print('..Statistics..\n')
print('Expected Threshold Limit: %r\n' % (exp))
print('Trees:%r\n' % ntrees)
print('Train Average: %r\n' % (np.mean(train_acc)))
print('Validation Average: %r\n' % (np.mean(valid_acc)))
print('Constrained Test Average: %r\n' %
(np.mean(test_acc)))
print('Unconstrained Test Avg: %r\n' %
(np.mean(unconst_acc)))
print('Total Time Taken: %r mins\n' %
((et_time - st_time) / 60))
print 'Generating Solution Result:'
test_x = np.loadtxt(
'C:\Users\saura\Desktop\ML_Project\data\\norm_test_fmat.csv',
delimiter=',')
print 'Test Size:%r' % len(test_x)
test_y_pred = est.predict(test_x)
id_col = np.arange(1., len(test_y_pred) + 1, dtype=np.int)
all_data = np.column_stack((id_col, test_y_pred))
np.savetxt(
'C:\Users\saura\Desktop\ML_Project\ensemble_data\\mytest_solution.csv',
all_data,
delimiter=',',
header='Id,Expected')
df = pd.read_csv(
'C:\Users\saura\Desktop\ML_Project\ensemble_data\\mytest_solution.csv'
)
df.to_csv(
'C:\Users\saura\Desktop\ML_Project\\ensemble_data\\final_solution.csv',
header=True,
index=False)
def driver(comp_mat, pps_mth='ORIGINAL', exp_thresh=[25], regression_mth=[], test_size=0.2, f_selection=False, n_features=3, reg_methods=[], remove_mp=False):
global fobj
fobj.write('Total Number of records: %r\n' % (len(comp_mat)))
#remove marshal palmer result column:
if remove_mp:
print 'Before Removing label size: ',np.shape(comp_mat)
y = comp_mat[:,-1]
print 'Before Removing label size: ', np.shape(comp_mat)
comp_mat = comp_mat[:,:-2]
print 'Before Appending label size: ',np.shape(comp_mat)
comp_mat = np.column_stack((comp_mat, y))
print 'After appending label',np.shape(comp_mat)
#step 1 split Train and test records
train_x, u_test_x, train_y, u_test_y = cv.train_test_split(comp_mat, comp_mat[:,-1], random_state = 52, test_size=test_size)
#removing the predictor column from the matrix
u_test_x = u_test_x[:,:-1]
#for exp in exp_thresh:
exp = 40
fobj.write('\n\nExpected Threshold Limit: %r\n' % (exp))
#step 2 prning to required exppected threshold
c_train_x = train_x[(train_x[:,-1]<=exp)]
c_train_y = c_train_x[:,-1]
c_train_x = c_train_x[:,:-1]
#step1 split Train
ttrain_x, ttest_x, ttrain_y, ttest_y = cv.train_test_split(c_train_x, c_train_y, random_state = 32, test_size=test_size )
fobj.write('Total Number of Constrained test records: %r\n' % len(ttest_y))
fobj.write('Total Number of Unconstrained test records: %r\n' % len(u_test_y))
fobj.write('Total Constrained Training Records: %r\n' % len(ttrain_y))
print 'Fitting Model Measurements'
trees_array = [100]
kf = cv.KFold(len(ttrain_x), n_folds=5)
st_time = time.time()
l_rate = [0.3]
max_depth = [4,6]
min_samples_leaf = [3, 5, 9, 17]
for rate in l_rate:
fobj.write('\n\nLearning Rate: %r\n' % (rate))
print '\n\nLearning Rate: %r\n' % (rate)
for ntrees in trees_array:
valid_acc = []
test_acc = []
train_acc = []
est_arr = []
unconst_acc = []
fold = 0
for train_idx, test_idx in kf:
fobj.write('\nfold: %r\n'%(fold))
print '\nfold: %r\n'%(fold)
#rf_model = rfr(n_estimators=ntrees, n_jobs=-1) Random forest regressor
#rf_model = etr(n_estimators=ntrees, n_jobs=3, bootstrap=False)
rf_model = etr(n_estimators=ntrees, max_depth=dpth, min_samples_split=msl)
vacc, tacc, est = rf_regressor(rf_model, ttrain_x[train_idx], ttrain_y[train_idx], ttrain_x[test_idx], ttrain_y[test_idx], f_selection=f_selection, n_features=n_features)
valid_acc.append(vacc)
train_acc.append(tacc)
est_arr.append(est)
fobj.write('Train Size:%r\n' % (len(train_idx)))
print 'Train Size:%r\n' % (len(train_idx))
fobj.write('Validation Size:%r\n' % (len(test_idx)))
fobj.write('Validation Error: %r\n' % (vacc))
print 'Validation Error: %r\n' % (vacc)
fobj.write('Train Error: %r\n' % (tacc))
fobj.write('Constrained Test data size:%r\n' % (len(ttest_x)))
test_acc.append(mt.mean_absolute_error(ttest_y, est.predict(ttest_x)))
fobj.write('Constrained Test Error for fold: %r\n' % (test_acc[-1]))
print 'Constrained Test Error for fold: %r\n' % (test_acc[-1])
y_res = est.predict(u_test_x)
unconst_acc.append(mt.mean_absolute_error(u_test_y, y_res))
fobj.write('Complete test accuracy:%r\n' % (unconst_acc[-1]))
print 'Complete test accuracy:%r\n' % (unconst_acc[-1])
fold+=1
et_time = time.time()
fobj.write('..Statistics..\n')
fobj.write('Expected Threshold Limit: %r\n' % (exp))
fobj.write('Trees:%r\n' % ntrees)
fobj.write('Train Average: %r\n' % (np.mean(train_acc)))
fobj.write('Validation Average: %r\n' % (np.mean(valid_acc)))
fobj.write('Constrained Test Average: %r\n' % (np.mean(test_acc)))
fobj.write('Unconstrained Test Avg: %r\n' % (np.mean(unconst_acc)))
fobj.write('Total Time Taken: %r mins\n' % ((et_time-st_time)/60))
#Print to console
print('..Statistics..\n')
print('Expected Threshold Limit: %r\n' % (exp))
print('Trees:%r\n' % ntrees)
print('Train Average: %r\n' % (np.mean(train_acc)))
print('Validation Average: %r\n' % (np.mean(valid_acc)))
print('Constrained Test Average: %r\n' % (np.mean(test_acc)))
print('Unconstrained Test Avg: %r\n' % (np.mean(unconst_acc)))
print('Total Time Taken: %r mins\n' % ((et_time-st_time)/60))
print 'Generating Solution Result:'
test_x = np.loadtxt('ensemble_data\\norm_test_fmat.csv',delimiter=',')
print 'Test Size:%r' % len(test_x)
test_y_pred = est.predict(test_x)
id_col = np.arange(1.,len(test_y_pred)+1, dtype=np.int)
all_data = np.column_stack((id_col, test_y_pred))
np.savetxt('C:\Users\saura\Desktop\ML_Project\ensemble_data\\gbr_mytest_solution.csv', all_data, delimiter=',', header='Id,Expected')
df = pd.read_csv('C:\Users\saura\Desktop\ML_Project\ensemble_data\\gbr_mytest_solution.csv')
df.to_csv('C:\Users\saura\Desktop\ML_Project\\ensemble_data\\gbr_final_solution.csv', header=True, index=False)
print("CV score: {:<8.8f}".format(mean_squared_error(oof_gbr_263, target)))
# ExtraTreesRegressor 极端随机森林回归
folds = KFold(n_splits=5, shuffle=True, random_state=13)
oof_etr_263 = np.zeros(train_shape)
predictions_etr_263 = np.zeros(len(X_test_263))
for fold_, (trn_idx, val_idx) in enumerate(folds.split(X_train_263, y_train)):
print("fold n°{}".format(fold_ + 1))
tr_x = X_train_263[trn_idx]
tr_y = y_train[trn_idx]
etr_263 = etr(n_estimators=1000,
max_depth=8,
min_samples_leaf=12,
min_weight_fraction_leaf=0.0,
max_features=0.4,
verbose=1,
n_jobs=-1)
etr_263.fit(tr_x, tr_y)
oof_etr_263[val_idx] = etr_263.predict(X_train_263[val_idx])
predictions_etr_263 += etr_263.predict(X_test_263) / folds.n_splits
print("CV score: {:<8.8f}".format(mean_squared_error(oof_etr_263, target)))
train_stack2 = np.vstack(
[oof_lgb_263, oof_xgb_263, oof_gbr_263, oof_rfr_263,
oof_etr_263]).transpose()
# transpose()函数的作用就是调换x,y,z的位置,也就是数组的索引值
test_stack2 = np.vstack([
if (Env_var.get('Pca') == 1):
pca = PCA(n_components = 300).fit(train_data_X)
train_data_X = pca.transform(train_data_X)
test_data_X = pca.transform(test_data_X)
###############################--------Model Setup--------###############################
ann_regressor = KerasRegressor(build_fn=ann_model, epochs=30, batch_size=10, verbose=1)
xgb_regressor = xgb(learning_rate = 0.0825, min_child_weight = 1, max_depth = 7, subsample = 0.8, verbose = 10, random_state = 2017, n_jobs = -1, eval_metric = "rmse")
rfr_regressor = rfr(max_features = 0.9, min_samples_leaf = 50)
gbr_regressor = gbr(n_estimators = 200, verbose = 5, learning_rate = 0.08, max_depth = 7, max_features = 0.5, min_samples_leaf = 50, subsample = 0.8, random_state = 2017)
etr_regressor = etr(n_estimators = 200, verbose = 10, max_depth = 7, min_samples_leaf = 100, max_features = 0.9, min_impurity_split = 100, random_state = 2017)
lr_regressor = lr()
svr_regressor = svr(verbose = 10)
ensemble = Ensemble(n_folds = 5,stacker = lr_regressor,base_models = [ann_regressor, xgb_regressor, rfr_regressor, gbr_regressor, etr_regressor])
###############################--------Grid Search--------###############################
if (Env_var.get('GridSearch') == 1):
if (Env_var.get('Model') == 'ann'):
dropout_rate = [0.0, 0.001, 0.01]
