def main():
if (use_low_mem == 1):
data_iter = cu.iter_data_frames(train_file, _chunksize)
i = _chunksize
fea = None
y = []
for train_data in data_iter:
print "About to have processed: " + str(i)
print("Extracting features")
if fea is None:
fea = features.extract_features(feature_names, train_data)
else:
fea = fea.append(
features.extract_features(feature_names, train_data))
for element in train_data['OpenStatus']:
for index, status in enumerate(ques_status):
if element == status: y.append(index)
i = i + _chunksize
else:
print("Reading the data from:" + train_file)
data = cu.get_dataframe(train_file)
print("Extracting features")
fea = features.extract_features(feature_names, data)
y = []
for element in data['OpenStatus']:
for index, status in enumerate(ques_status):
if element == status: y.append(index)
if do_cross_validation == 1:
depth = len(feature_names)
print "depth=" + str(depth)
rf = GradientBoostingClassifier(loss='deviance',
learn_rate=0.1,
n_estimators=100,
subsample=1.0,
min_samples_split=1,
min_samples_leaf=1,
max_depth=depth,
init=None,
random_state=None)
print 'starting 10 fold verification'
# Dividing the dataset into k = 10 folds for cross validation
kf = KFold(len(y), k=10)
fold = 0
result_sum = 0
for train_index, test_index in kf:
fold += 1
X_train = []
X_test = []
y_train = []
y_test = []
for i in train_index:
temp = []
for feature_name in feature_names:
if feature_name == 'BodyLength':
temp.append(fea['BodyMarkdown'][i])
elif feature_name == 'TitleLength':
temp.append(fea['Title'][i])
else:
temp.append(fea[feature_name][i])
X_train.append(temp)
y_train.append(y[i])
for i in test_index:
temp = []
for feature_name in feature_names:
if feature_name == 'BodyLength':
temp.append(fea['BodyMarkdown'][i])
elif feature_name == 'TitleLength':
temp.append(fea['Title'][i])
else:
temp.append(fea[feature_name][i])
X_test.append(temp)
y_test.append(y[i])
print "Fitting for fold " + str(fold)
rf.fit(X_train, y_train)
y_test = vectorize_actual(y_test)
_pred_probs = rf.predict_proba(X_test)
print("Calculating priors and updating posteriors")
#new_priors = cu.get_priors(full_train_file)
# priors distribution over classes based on the training set
#new_priors = [0.00913477057600471, 0.004645859639795308, 0.005200965546050945, 0.9791913907850639, 0.0018270134530850952]
# priors distribution over classes based on the updated training set's last month
new_priors = [
0.03410911204982466, 0.01173872976800856, 0.018430671606251586,
0.926642216133641, 0.009079270442274271
]
old_priors = cu.get_priors(train_file)
_pred_probs = cu.cap_and_update_priors(old_priors, _pred_probs,
new_priors, 0.001)
# evaluating the performance
result = eval.mcllfun(y_test, _pred_probs)
result_sum += result
print "MCLL score for fold %d = %0.11f" % (fold, result)
print "depth=" + str(depth)
print "Average MCLL score for this classifier = %0.11f" % (result_sum /
10)
else:
#rf = RandomForestClassifier(n_estimators=50, verbose=0, compute_importances=True, n_jobs=-1)
rf = GradientBoostingClassifier(loss='deviance',
learn_rate=0.1,
n_estimators=100,
subsample=1.0,
min_samples_split=1,
min_samples_leaf=1,
max_depth=len(feature_names),
init=None,
random_state=None)
rf.fit(fea, y)
print("Reading test file " + test_file + " and making predictions")
data = cu.get_dataframe(test_file)
test_features = features.extract_features(feature_names, data)
probs = rf.predict_proba(test_features)
# commented out, because we want to adjust probabilities to the last month data anyway
#if do_full_train == 0:
print("Calculating priors and updating posteriors")
new_priors = cu.get_priors(full_train_file)
old_priors = cu.get_priors(train_file)
probs = cu.cap_and_update_priors(old_priors, probs, new_priors, 0.001)
print("Saving submission to %s" % submission_file)
cu.write_submission(submission_file, probs)
def main():
start = time.time()
print "Reading train data and its features from: " + train_file
data = cu.get_dataframe(train_file)
global fea
fea = features.extract_features(feature_names, data)
mnbayes = MultinomialNB(alpha=1.0, fit_prior=True)
'''Make sure you have the up to date version of sklearn; v0.12 has the
predict_proba method; http://scikit-learn.org/0.11/install.html '''
# Must be array type object. Strings must be converted to
# to integer values, otherwise fit method raises ValueError
y = []
for element in data['OpenStatus']:
for index, status in enumerate(ques_status):
if element == status: y.append(index)
if do_cross_validation == 1:
print 'starting 10 fold verification'
# Dividing the dataset into k = 10 folds for cross validation
#skf = StratifiedKFold(y,k = 10)
skf = KFold(len(y), k=10)
fold = 0
result_sum = 0
for train_index, test_index in skf:
fold += 1
X_train = []
X_test = []
y_train = []
y_test = []
for i in train_index:
temp = []
for feature_name in feature_names:
temp.append(fea[feature_name][i])
X_train.append(temp)
y_train.append(y[i])
for i in test_index:
temp = []
for feature_name in feature_names:
temp.append(fea[feature_name][i])
X_test.append(temp)
y_test.append(y[i])
mnbayes.fit(
X_train, y_train
) #, sample_weight=None, class_prior=[0.0091347705760047, 0.0046458596397953, 0.0052009655460509, 0.9791913907850639, 0.0018270134530851])
y_test = vectorize_actual(y_test) # vectorize y_test
_pred_probs = mnbayes.predict_proba(X_test)
# evaluating the performance
result = eval.mcllfun(y_test, _pred_probs)
result_sum += result
print "MCLL score for fold %d = %0.11f" % (fold, result)
print "Average MCLL score for this classifier = %0.11f" % (result_sum /
10)
print "Reading test data and features"
test_data = cu.get_dataframe(test_file)
test_fea = features.extract_features(feature_names, test_data)
print "Fitting"
mnbayes.fit(
fea, y
) #, class_prior=[0.0091347705760047, 0.0046458596397953, 0.0052009655460509, 0.9791913907850639, 0.0018270134530851])
print "Making predictions"
global probs
probs = mnbayes.predict_proba(test_fea)
#if is_full_train_set == 0:
# print("Calculating priors and updating posteriors")
# new_priors = cu.get_priors(full_train_file)
# old_priors = cu.get_priors(train_file)
# probs = cu.cap_and_update_priors(old_priors, probs, new_priors, 0.001)
print "writing submission to " + submission_file
cu.write_submission(submission_file, probs)
finish = time.time()
print "completed in %0.4f seconds" % (finish - start)
def main():
start = time.time()
if (use_low_mem == 1):
data_iter = cu.iter_data_frames(train_file, _chunksize)
i = _chunksize
fea = None
y = []
for train_data in data_iter:
print "About to have processed: " + str(i)
print("Extracting features")
if fea is None:
fea = features.extract_features(feature_names, train_data)
else:
fea = fea.append(features.extract_features(feature_names, train_data))
for element in train_data['OpenStatus']:
for index, status in enumerate(ques_status):
if element == status: y.append(index)
i = i + _chunksize
else:
print "Reading train data and its features from: " + train_file
data = cu.get_dataframe(train_file)
fea = features.extract_features(feature_names,data)
print "Collecting statuses"
y = []
for element in data["OpenStatus"]:
for index, status in enumerate(ques_status):
if element == status:
y.append(index)
if do_cross_validation == 1:
logit = LogisticRegression(penalty='l2', dual=False, C=1.0, class_weight=None,
fit_intercept=True, intercept_scaling=1, tol=0.0001)
print 'starting 10 fold verification'
# Dividing the dataset into k = 10 folds for cross validation
kf = KFold(len(y),k = 10)
fold = 0
result_sum = 0
for train_index,test_index in kf:
fold += 1
X_train = []
X_test = []
y_train = []
y_test = []
for i in train_index:
temp = []
for feature_name in feature_names:
if feature_name == 'BodyLength':
temp.append(fea['BodyMarkdown'][i])
elif feature_name == 'TitleLength':
temp.append(fea['Title'][i])
else:
temp.append(fea[feature_name][i])
X_train.append(temp)
y_train.append(y[i])
for i in test_index:
temp = []
for feature_name in feature_names:
if feature_name == 'BodyLength':
temp.append(fea['BodyMarkdown'][i])
elif feature_name == 'TitleLength':
temp.append(fea['Title'][i])
else:
temp.append(fea[feature_name][i])
X_test.append(temp)
y_test.append(y[i])
print "fitting this fold's data"
rf.fit(X_train, y_train)
y_test = vectorize_actual(y_test)
#_pred_probs = denormalize(rf.predict_proba(X_test))
_pred_probs = rf.predict_proba(X_test)
print("Calculating priors and updating posteriors")
#new_priors = cu.get_priors(full_train_file)
new_priors = [0.00913477057600471, 0.004645859639795308, 0.005200965546050945, 0.9791913907850639, 0.0018270134530850952]
old_priors = cu.get_priors(train_file)
_pred_probs = cu.cap_and_update_priors(old_priors, _pred_probs, new_priors, 0.001)
# evaluating the performance
result = eval.mcllfun(y_test,_pred_probs)
result_sum += result
print "MCLL score for fold %d = %0.11f" % (fold,result)
print "Average MCLL score for this classifier = %0.11f" % (result_sum/10)
else:
logit = LogisticRegression(penalty='l2', dual=False, C=1.0, class_weight=None,
fit_intercept=True, intercept_scaling=1, tol=0.0001) # not available: compute_importances=True
print "Fitting"
logit.fit(fea, y)
print "Reading test data and features"
test_data = cu.get_dataframe(test_file)
test_fea = features.extract_features(feature_names,test_data)
print "Making predictions"
global probs
probs = logit.predict_proba(test_fea)
if is_full_train_set == 0:
print("Calculating priors and updating posteriors")
#new_priors = cu.get_priors(full_train_file)
new_priors = [0.00913477057600471, 0.004645859639795308, 0.005200965546050945, 0.9791913907850639, 0.0018270134530850952]
old_priors = cu.get_priors(train_file)
probs = cu.cap_and_update_priors(old_priors, probs, new_priors, 0.001)
print "writing submission to " + submission_file
cu.write_submission(submission_file, probs)
finish = time.time()
print "completed in %0.4f seconds" % (finish-start)
def main():
start = time.time()
print "Reading train data and its features from: " + train_file
data = cu.get_dataframe(train_file)
global fea
fea = features.extract_features(feature_names,data)
mnbayes = MultinomialNB(alpha=1.0, fit_prior=True)
'''Make sure you have the up to date version of sklearn; v0.12 has the
predict_proba method; http://scikit-learn.org/0.11/install.html '''
# Must be array type object. Strings must be converted to
# to integer values, otherwise fit method raises ValueError
y = []
for element in data['OpenStatus']:
for index, status in enumerate(ques_status):
if element == status: y.append(index)
if do_cross_validation == 1:
print 'starting 10 fold verification'
# Dividing the dataset into k = 10 folds for cross validation
#skf = StratifiedKFold(y,k = 10)
skf = KFold(len(y),k = 10)
fold = 0
result_sum = 0
for train_index,test_index in skf:
fold += 1
X_train = []
X_test = []
y_train = []
y_test = []
for i in train_index:
temp = []
for feature_name in feature_names:
temp.append(fea[feature_name][i])
X_train.append(temp)
y_train.append(y[i])
for i in test_index:
temp = []
for feature_name in feature_names:
temp.append(fea[feature_name][i])
X_test.append(temp)
y_test.append(y[i])
mnbayes.fit(X_train, y_train) #, sample_weight=None, class_prior=[0.0091347705760047, 0.0046458596397953, 0.0052009655460509, 0.9791913907850639, 0.0018270134530851])
y_test = vectorize_actual(y_test) # vectorize y_test
_pred_probs = mnbayes.predict_proba(X_test)
# evaluating the performance
result = eval.mcllfun(y_test,_pred_probs)
result_sum += result
print "MCLL score for fold %d = %0.11f" % (fold,result)
print "Average MCLL score for this classifier = %0.11f" % (result_sum/10)
print "Reading test data and features"
test_data = cu.get_dataframe(test_file)
test_fea = features.extract_features(feature_names,test_data)
print "Fitting"
mnbayes.fit(fea,y)#, class_prior=[0.0091347705760047, 0.0046458596397953, 0.0052009655460509, 0.9791913907850639, 0.0018270134530851])
print "Making predictions"
global probs
probs = mnbayes.predict_proba(test_fea)
#if is_full_train_set == 0:
# print("Calculating priors and updating posteriors")
# new_priors = cu.get_priors(full_train_file)
# old_priors = cu.get_priors(train_file)
# probs = cu.cap_and_update_priors(old_priors, probs, new_priors, 0.001)
print "writing submission to " + submission_file
cu.write_submission(submission_file, probs)
finish = time.time()
print "completed in %0.4f seconds" % (finish-start)
