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amazon_main_xgboost_count_2D.py
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amazon_main_xgboost_count_2D.py
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""" Amazon Access Challenge Code for ensemble
Marios Michaildis script for Amazon .
Uses counts as features and 2way interactions and xgboost
based on Paul Duan's Script.
"""
from __future__ import division
import numpy as np
from sklearn import preprocessing
from sklearn.metrics import roc_auc_score
import XGBoostClassifier as xg
from sklearn.cross_validation import StratifiedKFold
import pandas as pd
SEED = 42 # always use a seed for randomized procedures
def load_datacount2D(tr,te):
#w ewill use pandas
train = pd.read_csv(tr, sep=',',quotechar='"')
test = pd.read_csv(te, sep=',',quotechar='"')
label= np.array(train['ACTION']).astype(float)
train.drop('ACTION', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
test.drop('ROLE_CODE', axis=1, inplace=True)
train.drop('ROLE_CODE', axis=1, inplace=True)
train_s = train
test_s = test
headers=[f for f in train_s.columns]
for t, col in enumerate(headers):
for t1, col1 in enumerate(headers):
if t1 <= t:
continue
print col + "_" + col1
train_s[col+"_"+col1] = train_s[[col, col1]].apply(lambda x: int(x[0]) + int(x[1]), axis=1)
test_s[col+"_"+col1] = test_s[[col, col1]].apply(lambda x: int(x[0]) + int(x[1]), axis=1)
result = pd.concat([test_s,train_s])
headers=[f for f in result.columns]
for i in range(train_s.shape[1]):
print headers[i], len(np.unique(result[headers[i]]))
cnt = result[headers[i]].value_counts().to_dict()
#cnt = dict((k, -1) if v < 3 else (k,v) for k, v in cnt.items() ) # if u want to encode rare values as "special"
train_s[headers[i]].replace(cnt, inplace=True)
test_s[headers[i]].replace(cnt, inplace=True)
train = np.array(train_s).astype(float)
test = np.array(test_s).astype(float)
print train.shape
print test.shape
return label, train,test
def save_results(predictions, filename):
"""Given a vector of predictions, save results in CSV format."""
with open(filename, 'w') as f:
f.write("id,ACTION\n")
for i, pred in enumerate(predictions):
f.write("%d,%f\n" % (i + 1, pred))
def bagged_set(X_t,y_c,model, seed, estimators, xt, update_seed=True):
# create array object to hold predictions
baggedpred=[ 0.0 for d in range(0, (xt.shape[0]))]
#loop for as many times as we want bags
for n in range (0, estimators):
#shuff;e first, aids in increasing variance and forces different results
#X_t,y_c=shuffle(Xs,ys, random_state=seed+n)
if update_seed: # update seed if requested, to give a slightly different model
model.set_params(random_state=seed + n)
model.fit(X_t,y_c) # fit model0.0917411475506
preds=model.predict_proba(xt)[:,1] # predict probabilities
# update bag's array
for j in range (0, (xt.shape[0])):
baggedpred[j]+=preds[j]
# divide with number of bags to create an average estimate
for j in range (0, len(baggedpred)):
baggedpred[j]/=float(estimators)
# return probabilities
return np.array(baggedpred)
# using numpy to print results
def printfilcsve(X, filename):
np.savetxt(filename,X)
def main():
"""
Fit models and make predictions.
We'll use one-hot encoding to transform our categorical features
into binary features.
y and X will be numpy array objects.
"""
filename="main_xgboos_count_2D" # nam prefix
#model = linear_model.LogisticRegression(C=3) # the classifier we'll use
model=xg.XGBoostClassifier(num_round=1000 ,nthread=25, eta=0.02, gamma=1,max_depth=20, min_child_weight=0.1, subsample=0.9,
colsample_bytree=0.5,objective='binary:logistic',seed=1)
# === load data in memory === #
print "loading data"
y, X,X_test = load_datacount2D('train.csv','test.csv')
# === one-hot encoding === #
# we want to encode the category IDs encountered both in
# the training and the test set, so we fit the encoder on both
# if you want to create new features, you'll need to compute them
# before the encoding, and append them to your dataset after
#create arrays to hold cv an dtest predictions
train_stacker=[ 0.0 for k in range (0,(X.shape[0])) ]
# === training & metrics === #
mean_auc = 0.0
bagging=25 # number of models trained with different seeds
n = 5 # number of folds in strattified cv
kfolder=StratifiedKFold(y, n_folds= n,shuffle=True, random_state=SEED)
i=0
for train_index, test_index in kfolder: # for each train and test pair of indices in the kfolder object
# creaning and validation sets
X_train, X_cv = X[train_index], X[test_index]
y_train, y_cv = np.array(y)[train_index], np.array(y)[test_index]
#print (" train size: %d. test size: %d, cols: %d " % ((X_train.shape[0]) ,(X_cv.shape[0]) ,(X_train.shape[1]) ))
# if you want to perform feature selection / hyperparameter
# optimization, this is where you want to do it
# train model and make predictions
preds=bagged_set(X_train,y_train,model, SEED , bagging, X_cv, update_seed=True)
# compute AUC metric for this CV fold
roc_auc = roc_auc_score(y_cv, preds)
print "AUC (fold %d/%d): %f" % (i + 1, n, roc_auc)
mean_auc += roc_auc
no=0
for real_index in test_index:
train_stacker[real_index]=(preds[no])
no+=1
i+=1
mean_auc/=n
print (" Average AUC: %f" % (mean_auc) )
print (" printing train datasets ")
printfilcsve(np.array(train_stacker), filename + ".train.csv")
# === Predictions === #
# When making predictions, retrain the model on the whole training set
preds=bagged_set(X, y,model, SEED, bagging, X_test, update_seed=True)
#create submission file
printfilcsve(np.array(preds), filename+ ".test.csv")
#save_results(preds, filename+"_submission_" +str(mean_auc) + ".csv")
if __name__ == '__main__':
main()