def __init__(self):
bank_full = pd.read_csv('data/bank_full_w_dummy_vars.csv')
X = bank_full.ix[:,(18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36)].values
y = bank_full.ix[:,17].values
LogReg = LogisticRegression()
LogReg.fit(X, y)
self.model = LogReg
def test_logreg():
X_train, Y_train, X_test, Y_test = import_census(CENSUS_FILE_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
# my_x=np.array([[3,4],[5,6],[7,8],[9,10],[22,22],[12,23]])
# my_y=np.array([0,1,2,0,2,1]).reshape(6,)
# #print(my_y)
# my_x = np.append(my_x, np.ones((len(my_x), 1)), axis=1)
# test_model = LogisticRegression(2, 3, 2, CONV_THRESHOLD)
# #print(test_model.predict(my_x))
# test_model.train(my_x, my_y)
## Logistic Regression ###
#print(X_train_b.shape)
#print(Y_train.shape)
model = LogisticRegression(num_features, NUM_CLASSES, BATCH_SIZE,
CONV_THRESHOLD)
#print(model.loss(X_train_b, Y_train))
num_epochs = model.train(X_train_b, Y_train)
acc = model.accuracy(X_test_b, Y_test) * 100
print("Test Accuracy: {:.1f}%".format(acc))
print("Number of Epochs: " + str(num_epochs))
acc = 0
return acc
def lg_k_folds(X_train, y_train, lr, b, epochs, lamda, bias, k=5, verbose=False):
results = {
'accuracy': [],
'recall': [],
'precision': []
}
metric_means = {}
accuracy = Accuracy()
recall = Recall()
precision = Precision()
chunk_size = int(len(X_train) / k)
logistic_regression = LogisticRegression(bias)
for i in range(0, len(X_train), chunk_size):
end = i + chunk_size if i + chunk_size <= len(X_train) else len(X_train)
new_X_valid = X_train[i: end]
new_y_valid = y_train[i: end]
new_X_train = np.concatenate([X_train[: i], X_train[end:]])
new_y_train = np.concatenate([y_train[: i], y_train[end:]])
logistic_regression.fit(new_X_train, new_y_train, lr, b, epochs, lamda, verbose=verbose)
predictions = logistic_regression.predict(new_X_valid)
results['accuracy'].append(accuracy(new_y_valid, predictions))
results['recall'].append(recall(new_y_valid, predictions))
results['precision'].append(precision(new_y_valid, predictions))
metric_means['accuracy'] = np.mean(results['accuracy'])
metric_means['recall'] = np.mean(results['recall'])
metric_means['precision'] = np.mean(results['precision'])
return metric_means
class LogisticRegressionExperiment(object):
def __init__(self):
self._data_set = get_pick_data("LogisticRegression")
self._num_features = self._data_set.dynamic_features.shape[1]
self._time_steps = 1
self._n_output = 1
self._model_format()
self._check_path()
def _model_format(self):
learning_rate, max_loss, max_pace, ridge, batch_size, hidden_size, epoch, dropout = lr_setup.all
self._model = LogisticRegression(
num_features=self._num_features,
time_steps=self._time_steps,
n_output=self._n_output,
batch_size=batch_size,
epochs=epoch,
output_n_epoch=ExperimentSetup.output_n_epochs,
learning_rate=learning_rate,
max_loss=max_loss,
dropout=dropout,
max_pace=max_pace,
ridge=ridge)
def _check_path(self):
if not os.path.exists("result_9_16_0"):
os.makedirs("result_9_16_0")
self._filename = "result_9_16_0" + "/" + self._model.name + " " + \
time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
def do_experiments(self):
n_output = 1
dynamic_features = self._data_set.dynamic_features
labels = self._data_set.labels
# tol_test_index = np.zeros(shape=0, dtype=np.int32)
tol_pred = np.zeros(shape=(0, n_output))
tol_label = np.zeros(shape=(0, n_output), dtype=np.int32)
train_dynamic_features, test_dynamic_features, train_labels, test_labels = \
split_logistic_data(dynamic_features,labels)
for i in range(5):
train_dynamic_res, train_labels_res = imbalance_preprocess(
train_dynamic_features[i], train_labels[i],
'LogisticRegression')
train_set = DataSet(train_dynamic_res, train_labels_res)
test_set = DataSet(test_dynamic_features[i].reshape(-1, 92),
test_labels[i].reshape(-1, 1))
self._model.fit(train_set, test_set)
y_score = self._model.predict(test_set)
tol_pred = np.vstack((tol_pred, y_score))
tol_label = np.vstack((tol_label, test_labels[i]))
print("Cross validation: {} of {}".format(i, 5),
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
tol_test_index = np.arange(labels.shape[0] * labels.shape[1])
evaluate(tol_test_index, tol_label, tol_pred, self._filename)
self._model.close()
def train_logistic_regression(self,
distribution,
fan_in,
fan_out,
learning_rate=0.013):
######################
# BUILD ACTUAL MODEL #
######################
print('... building the model.')
X = tt.fmatrix('X') # data, presented as rasterized images
y = tt.fmatrix('y') # labels, presented as 1-hot matrix of labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(X, distribution, fan_in, fan_out)
# the cost we minimize during training
cost = classifier.neg_log_like(y)
# compute the gradient of cost with respect to theta = (W,b)
grads = [tt.grad(cost=cost, wrt=param) for param in classifier.params]
# specify how to update the parameters of the model as a list of
# (variable, update expression) pairs.
updates = [(param, param - learning_rate * gparam)
for param, gparam in zip(classifier.params, grads)]
# compiling a Theano function `train_model` that returns the cost,
# in the same time updates the parameter based on the rules
# defined in `updates`
train = theano.function(inputs=[X, y],
outputs=cost,
updates=updates,
allow_input_downcast=True)
# function that computes the mistakes that are made by
# the model on a minibatch
test = theano.function(inputs=[X, y],
outputs=classifier.errors(y),
allow_input_downcast=True)
validate = theano.function(inputs=[X, y],
outputs=classifier.errors(y),
allow_input_downcast=True)
predict = theano.function(inputs=[X],
outputs=classifier.y_pred,
allow_input_downcast=True)
self.early_stopping(classifier, train, test, validate, predict,
learning_rate)
def _model_format(self):
learning_rate, max_loss, max_pace, ridge, batch_size, hidden_size, epoch, dropout = lr_setup.all
self._model = LogisticRegression(
num_features=self._num_features,
time_steps=self._time_steps,
n_output=self._n_output,
batch_size=batch_size,
epochs=epoch,
output_n_epoch=ExperimentSetup.output_n_epochs,
learning_rate=learning_rate,
max_loss=max_loss,
dropout=dropout,
max_pace=max_pace,
ridge=ridge)
def main():
Dataset = namedtuple('Dataset', ['inputs', 'labels'])
# Reading in data. You do not need to touch this.
with open("data/train-images-idx3-ubyte.gz",
'rb') as f1, open("data/train-labels-idx1-ubyte.gz", 'rb') as f2:
buf1 = gzip.GzipFile(fileobj=f1).read(16 + 60000 * 28 * 28)
buf2 = gzip.GzipFile(fileobj=f2).read(8 + 60000)
inputs = np.frombuffer(buf1, dtype='uint8',
offset=16).reshape(60000, 28 * 28)
inputs = np.where(inputs > 99, 1, 0)
labels = np.frombuffer(buf2, dtype='uint8', offset=8)
data_train = Dataset(inputs, labels)
with open("data/t10k-images-idx3-ubyte.gz",
'rb') as f1, open("data/t10k-labels-idx1-ubyte.gz", 'rb') as f2:
buf1 = gzip.GzipFile(fileobj=f1).read(16 + 10000 * 28 * 28)
buf2 = gzip.GzipFile(fileobj=f2).read(8 + 10000)
inputs = np.frombuffer(buf1, dtype='uint8',
offset=16).reshape(10000, 28 * 28)
inputs = np.where(inputs > 99, 1, 0)
labels = np.frombuffer(buf2, dtype='uint8', offset=8)
data_test = Dataset(inputs, labels)
# run naive bayes
model = NaiveBayes(10)
model.train(data_train)
print("{:.1f}%".format(model.accuracy(data_test) * 100))
# run logistic regression
model = LogisticRegression(784, 10)
model.train(data_train)
print("{:.1f}%".format(model.accuracy(data_test) * 100))
def test_LogisticRegression(dim):
model_name = "LogisticRegression"
x, y = make_classification(n_samples=1000, n_features=dim)
model = LogisticRegression(dim)
check_model(model, model_name, x, y, category="binary_classification")
def train(args):
"""
This function trains the models
:param args: the command line arguments defining the desired actions
"""
# load data
train_data_all, dev_data_all, _ = load(args.data_dir,
cachedir=args.cachedir,
override_cache=args.override_cache,
text_only=(args.model.lower()
in ["bi-lstm", "bert"]),
include_tfidf=args.include_tfidf,
balanced=args.balanced)
train_data, train_labels = train_data_all.X, train_data_all.y
dev_data, dev_labels = dev_data_all.X, dev_data_all.y
# Build model
apx = get_appendix(args.include_tfidf, args.balanced)
if args.model.lower() == "simple-ff":
model = FeedForward(args.ff_hunits, train_data.shape[1])
train_pytorch(args,
model,
train_data,
train_labels,
dev_data,
dev_labels,
save_model_path=f"models/simple-ff{apx}.torch")
elif args.model.lower() == "bi-lstm":
model = BiLSTM(epochs=args.num_epochs,
batch_size=args.batch_size,
max_seq_len=args.max_seq_len)
model.train(train_data, train_labels, dev_data, dev_labels)
elif args.model.lower() == "logreg":
model = LogisticRegression()
model.train(train_data,
train_labels,
dev_data,
dev_labels,
save_model_path=f"models/logreg{apx}.pkl")
elif args.model.lower() == "majority-vote":
model = MajorityVote()
model.train(train_labels, dev_labels)
elif args.model.lower() == "bert":
model = Bert(epochs=args.num_epochs,
batch_size=args.batch_size,
max_seq_len=args.max_seq_len,
learning_rate=args.learning_rate)
model.train(train_data,
train_labels,
dev_data,
dev_labels,
save_model_path=f"models/bert.pkl")
elif args.model.lower() == "svm":
model = SVM()
model.train(train_data,
train_labels,
save_model_path=f"models/svm{apx}.sav")
else:
raise Exception("Unknown model type passed in!")
def classifier_log_reg(features, config, train_mode, **kwargs):
if train_mode:
features_train, features_valid = features
if config.random_search.log_reg.n_runs:
transformer = RandomSearchOptimizer(
LogisticRegression,
config.log_reg,
train_input_keys=[],
valid_input_keys=['X_valid', 'y_valid'],
score_func=roc_auc_score,
maximize=True,
n_runs=config.random_search.log_reg.n_runs,
callbacks=[
NeptuneMonitor(**config.random_search.log_reg.callbacks.
neptune_monitor),
SaveResults(
**config.random_search.log_reg.callbacks.save_results),
])
else:
transformer = LogisticRegression(**config.log_reg)
log_reg = Step(name='log_reg',
transformer=transformer,
input_data=['input'],
input_steps=[features_train, features_valid],
adapter={
'X': ([(features_train.name, 'X')]),
'y': ([('input', 'y')], to_numpy_label_inputs),
'X_valid': ([(features_valid.name, 'X')]),
'y_valid':
([('input', 'y_valid')], to_numpy_label_inputs),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
else:
log_reg = Step(name='log_reg',
transformer=LogisticRegression(**config.log_reg),
input_steps=[features],
adapter={
'X': ([(features.name, 'features')]),
},
cache_dirpath=config.env.cache_dirpath,
**kwargs)
return log_reg
def test_logreg():
X_train, Y_train, X_test, Y_test = import_census(CENSUS_FILE_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
### Logistic Regression ###
model = LogisticRegression(num_features, NUM_CLASSES, BATCH_SIZE,
CONV_THRESHOLD)
num_epochs = model.train(X_train_b, Y_train)
acc = model.accuracy(X_test_b, Y_test) * 100
print("Test Accuracy: {:.1f}%".format(acc))
print("Number of Epochs: " + str(num_epochs))
return acc
def test_logreg():
X_train, Y_train, X_test, Y_test = import_mnist(MNIST_TRAIN_INPUTS_PATH,
MNIST_TRAIN_LABELS_PATH,
MNIST_TEST_INPUTS_PATH,
MNIST_TEST_LABELS_PATH)
num_features = X_train.shape[1]
# Add a bias
X_train_b = np.append(X_train, np.ones((len(X_train), 1)), axis=1)
X_test_b = np.append(X_test, np.ones((len(X_test), 1)), axis=1)
### Logistic Regression ###
print('--------- LOGISTIC REGRESSION w/ SGD ---------')
model = LogisticRegression(num_features, MNIST_CLASSES)
model.train(X_train_b, Y_train)
print("Test Accuracy: {:.1f}%".format(
model.accuracy(X_test_b, Y_test) * 100))
def _model_format(self):
if self._event_type == "qx":
learning_rate, max_loss, max_pace, lasso, ridge = lr_qx_setup.all
elif self._event_type == "cx":
learning_rate, max_loss, max_pace, lasso, ridge = lr_cx_setup.all
else:
learning_rate, max_loss, max_pace, lasso, ridge = lr_xycj_setup.all
self._model = LogisticRegression(
num_features=self._num_features,
time_steps=self._time_steps,
n_output=self._n_output,
batch_size=ExperimentSetup.batch_size,
epochs=ExperimentSetup.epochs,
output_n_epoch=ExperimentSetup.output_n_epochs,
learning_rate=learning_rate,
max_loss=max_loss,
max_pace=max_pace,
lasso=lasso,
ridge=ridge)
def run_with_model(dataset, args):
"""
Running a particular choice of model, saves to /results folder.
"""
cut_data, all_data = cut_dataset(dataset, args.cens_time)
if args.model == "xlearner":
model = RFXLearner()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"], all_data["w"], False, True)
elif args.model == "cox":
model = CoxAIC()
model.train(all_data["X"], all_data["w"], all_data["y"], all_data["t"])
pred_rr = model.predict(args.cens_time, all_data["X"])
elif args.model == "survrf":
model = SurvRF()
model.train(all_data["X"], all_data["w"], all_data["y"], all_data["t"])
pred_rr = model.predict(args.cens_time)
elif args.model == "causalforest":
model = CausalForest()
model.train(cut_data["X"], cut_data["w"], cut_data["y"])
pred_rr = np.r_[model.predict(),
model.predict(all_data["X"][all_data["cens"] == 1])]
elif args.model == "logreg":
model = LogisticRegression()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"])
elif args.model == "linearxlearner":
model = LinearXLearner()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"], all_data["w"], False)
else:
raise ValueError("Not a supported model.")
return {
"pred_rr": pred_rr,
"X": all_data["X"],
"w": all_data["w"],
"y": all_data["y"],
"t": all_data["t"],
"y_cut": all_data["y_cut"],
"cens": all_data["cens"],
}
def test(args):
"""
This function tests our models
:param args: the command line arguments with the desired actions
"""
_, _, test_data_all = load(args.data_dir,
cachedir=args.cachedir,
override_cache=args.override_cache,
text_only=(args.model.lower()
in ["bi-lstm", "bert"]),
include_tfidf=args.include_tfidf,
balanced=args.balanced)
test_data, test_labels = test_data_all.X, test_data_all.y
apx = get_appendix(args.include_tfidf, args.balanced)
if args.model.lower() == "simple-ff":
preds = test_pytorch(
test_data,
test_labels,
load_model_path=f"models/simple-ff{apx}.torch",
predictions_file=f"preds/simple-ff-preds{apx}.txt")
elif args.model.lower() == "bi-lstm":
model = BiLSTM(load_model_path="models/bilstm.keras",
tokenizer_path='models/bilstm-tokenizer.json')
preds = model.test(test_data, y_test=test_labels)
elif args.model.lower() == "logreg":
model = LogisticRegression(load_model_path=f"models/logreg{apx}.pkl")
preds = model.test(
test_data,
test_labels,
save_predictions_path=f"preds/logreg-preds{apx}.txt")
elif args.model.lower() == "majority-vote":
model = MajorityVote(load_model_path="models/majority-class.txt")
preds = model.test(test_labels)
elif args.model.lower() == "bert":
model = Bert(load_model_path="models/bert.pkl")
preds = model.test(test_data,
test_labels,
save_predictions_path="preds/bert-preds.txt")
elif args.model.lower() == "svm":
model = SVM(load_model_path=f"models/svm{apx}.sav")
preds = model.test(test_data,
save_predictions_path=f"preds/svm-preds{apx}.txt")
else:
raise Exception("Unknown model type passed in!")
metrics = classification_report(test_labels, preds, output_dict=True)
pprint(metrics)
with open(f"scores/{args.model.lower()}{apx}.json", "w") as fout:
json.dump(metrics, fout, indent=4)
def run_for_optimism(original_dataset, bootstrap_dataset, args):
"""
Calculates difference between performance on a bootstrapped dataset (upon
which the model is trained) and the original dataset. Optimism is defined
as the mean difference over many bootstrap datasets.
"""
cut_data, all_data = cut_dataset(bootstrap_dataset, args.cens_time)
cut_data_orig, all_data_orig = cut_dataset(original_dataset,
args.cens_time)
if args.model == "cox":
model = CoxAIC()
model.train(all_data["X"], all_data["w"], all_data["y"], all_data["t"])
pred_rr = model.predict(args.cens_time, all_data["X"])
pred_rr_orig = model.predict(args.cens_time, all_data_orig["X"])
elif args.model == "logreg":
model = LogisticRegression()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"])
pred_rr_orig = model.predict(all_data_orig["X"])
elif args.model == "linearxlearner":
model = LinearXLearner()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"], all_data["w"], False)
pred_rr_orig = model.predict(all_data_orig["X"], all_data_orig["w"],
False)
else:
raise ValueError("Not a supported model.")
c_stat_bootstrap = c_statistic(pred_rr[all_data["cens"] == 0],
cut_data["y"], cut_data["w"])
c_stat_original = c_statistic(pred_rr_orig[all_data_orig["cens"] == 0],
cut_data_orig["y"], cut_data_orig["w"])
rmst_bootstrap = decision_value_rmst(pred_rr, all_data["y"], all_data["w"],
all_data["t"], args.cens_time)
rmst_original = decision_value_rmst(pred_rr_orig, all_data_orig["y"],
all_data_orig["w"], all_data_orig["t"],
args.cens_time)
return {
"c_stat_diff": c_stat_bootstrap - c_stat_original,
"decision_value_rmst_diff": rmst_bootstrap - rmst_original
}
def run_for_optimism(original_dataset, bootstrap_dataset, args):
"""
Calculate optimism for a particular bootstrap run.
"""
cut_data, all_data = cut_dataset_at_cens_time(bootstrap_dataset,
args.cens_time)
cut_data_orig, all_data_orig = cut_dataset_at_cens_time(
original_dataset, args.cens_time)
if args.model == "cox":
model = CoxAIC()
model.train(all_data["X"], all_data["w"], all_data["y"], all_data["t"])
pred_rr = model.predict(args.cens_time, all_data["X"])
pred_rr_orig = model.predict(args.cens_time, all_data_orig["X"])
elif args.model == "logreg":
model = LogisticRegression()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"])
pred_rr_orig = model.predict(all_data_orig["X"])
elif args.model == "linearxlearner":
model = LinearXLearner()
model.train(cut_data["X"], cut_data["w"], cut_data["y"],
cut_data["ipcw"])
pred_rr = model.predict(all_data["X"], all_data["w"], False)
pred_rr_orig = model.predict(all_data_orig["X"], all_data_orig["w"],
False)
else:
raise ValueError("Not a supported model.")
c_stat_bootstrap = c_statistic(pred_rr[all_data["cens"] == 0],
cut_data["y"], cut_data["w"])
c_stat_original = c_statistic(pred_rr_orig[all_data_orig["cens"] == 0],
cut_data_orig["y"], cut_data_orig["w"])
rmst_bootstrap = decision_value_rmst(pred_rr, all_data["y"], all_data["w"],
all_data["t"], args.cens_time)
rmst_original = decision_value_rmst(pred_rr_orig, all_data_orig["y"],
all_data_orig["w"], all_data_orig["t"],
args.cens_time)
return {
"c_stat_diff": c_stat_bootstrap - c_stat_original,
"decision_value_rmst_diff": rmst_bootstrap - rmst_original
}
def load_model(json_file):
model_data = utils.load_json_data(json_file)
if model_data['model_type'] == 'logistic_regression':
model = LogisticRegression(model_data)
elif model_data['model_type'] == 'decision_tree':
model = DecisionTree(model_data)
elif model_data['model_type'] == 'scoring':
model = ScoringModel(model_data)
elif model_data['model_type'] == 'nomogram':
model = NomogramModel(model_data)
elif model_data['model_type'] == 'NOCOS':
model = NOCOS(model_data)
else:
raise Exception('model type [{0}] not recognised'.format(model_data['model_type']))
logging.info('{0} loaded as a {1} model'.format(json_file, model.model_type))
return model
def train_lr(self, cid):
params = {
"offline_model_dir": PROJECT_ROOT+"/ltr/weights/lr",
}
params.update(self.params_common)
X_train, X_valid = self.load_data_by_id("train", cid), self.load_data_by_id("vali", cid)
model = LogisticRegression("ranking", params, self.logger)
model.fit(X_train, validation_data=X_valid)
model.save_session()
def _initialize_models(self, data_generator):
"""Initializes models prior to training."""
models = {
"Linear Regression":
LinearRegression(),
"Logistic Regression":
LogisticRegression(),
"Quadratic Regression":
QuadraticRegression(),
"Naive Bayes'":
NaiveBayes(std_X=data_generator.std_X,
m0=data_generator.m0s,
m1=data_generator.m1s),
"kNN CV":
kNNCV(n_folds=self.n_folds)
}
return models
def confusion_matrix_plot(outputdir, X_train, Y_train, cv=5):
f, ax = plt.subplots(2, 3, figsize=(12, 10))
y_pred = cross_val_predict(svm.SVC(kernel='linear'),
X_train,
Y_train,
cv=cv)
sns.heatmap(confusion_matrix(Y_train, y_pred),
ax=ax[0, 0],
annot=True,
fmt='2.0f')
ax[0, 0].set_title('Matrix for Linear-SVM')
y_pred = cross_val_predict(rfgd.best_estimator_, X_train, Y_train, cv=cv)
sns.heatmap(confusion_matrix(Y_train, y_pred),
ax=ax[0, 1],
annot=True,
fmt='2.0f')
ax[0, 1].set_title('Matrix for Random-Forests')
y_pred = cross_val_predict(LogisticRegression(), X_train, Y_train, cv=cv)
sns.heatmap(confusion_matrix(Y_train, y_pred),
ax=ax[0, 2],
annot=True,
fmt='2.0f')
ax[0, 2].set_title('Matrix for Logistic Regression')
y_pred = cross_val_predict(gd.best_estimator_, X_train, Y_train, cv=cv)
sns.heatmap(confusion_matrix(Y_train, y_pred),
ax=ax[1, 0],
annot=True,
fmt='2.0f')
ax[1, 0].set_title('Matrix for Ada Boosting')
y_pred = cross_val_predict(ensemble_lin_rbf, X_train, Y_train, cv=cv)
sns.heatmap(confusion_matrix(Y_train, y_pred),
ax=ax[1, 1],
annot=True,
fmt='2.0f')
ax[1, 1].set_title('Matrix for Ensemble-classfier')
plt.subplots_adjust(hspace=0.2, wspace=0.2)
f.savefig(outputdir + '/confusion_plot.png') # save the figure to file
plt.close(f)
print('confusion matrix plot is saved to {}'.format(outputdir +
'/confusion_plot.png'))
def main(grid): # Get Clean Data X, Y = read_clean_data() # Linear Regression try: LinearRegression(X, Y, grid) except Exception as e: print(e) # Binarize Y Y_binary = BinaryY(Y) # Logistic Regression try: LogisticRegression(X, Y_binary, grid) except Exception as e: print(e) # Decision Tree try: DecisionTree(X, Y_binary, grid) except Exception as e: print(e) # Support Vector Machine try: SVM(X, Y_binary, grid) except Exception as e: print(e) # Random Forest try: RandomForest(X, Y_binary, grid) except Exception as e: print(e) # Bagging Classifier try: Bagging(X, Y_binary, grid) except Exception as e: print(e) # Neural Network try: NeuralNet(X, Y_binary, grid) except Exception as e: print(e)
def get_model(model_type: str, model_config: Dict, w2v: torch.Tensor, vocab_list: List, model_name: str) -> nn.Module:
# Instantiate model and configuration
train_config = {
"num_epochs": 30,
"lr_rate": 2e-5,
"log_step": 100,
"l2norm": False,
"l2factor": 3.,
"lambda": 0.01,
}
if model_type == "nb":
model = NaiveBayes(model_config)
elif model_type == "lr":
model = LogisticRegression(model_config)
train_config["lr_rate"] = 2e-3
elif model_type == "ff":
model = feedforwardNN(model_config, w2v)
train_config["num_epochs"] = 50
train_config["lr_rate"] = 2e-4
elif model_type == "cnn":
model = convolutionalNN(model_config, w2v)
train_config["num_epochs"] = 30
train_config["lr_rate"] = 2e-4
train_config["l2norm"] = False
elif model_type == "bertff":
model = BERTfeedforward(model_config, vocab_list)
train_config["num_epochs"] = 30
train_config["lr_rate"] = 1e-5
else:
raise ValueError("Model type is not supported.")
# Load model
if model_name is not "":
model = torch.load("./models/"+model_name)
return model, train_config
)
dataset = getattr(
__import__('datasets'), args.dataset.capitalize() + 'Dataset'
)
train_dataset = dataset('train', args)
val_dataset = dataset('validation', args)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True
)
val_loader = torch.utils.data.DataLoader(
val_dataset, batch_size=args.batch_size, shuffle=False
)
autoencoder = Autoencoder(args.encoder_layers, args.decoder_layers)
classifier = LogisticRegression(args.encoder_layers[-1])
oracle = DL2_Oracle(
learning_rate=args.dl2_lr, net=autoencoder,
use_cuda=torch.cuda.is_available(),
constraint=ConstraintBuilder.build(
autoencoder, train_dataset, args.constraint
)
)
binary_cross_entropy = nn.BCEWithLogitsLoss(
pos_weight=train_dataset.pos_weight('train') if args.balanced else None
)
optimizer = torch.optim.Adam(
list(autoencoder.parameters()) + list(classifier.parameters()),
lr=args.learning_rate, weight_decay=args.weight_decay
def logistic_test():
n_samples = 100
np.random.seed(0)
X_train = np.random.normal(size=n_samples)
y_train = (X_train > 0).astype(float)
X_train[X_train > 0] *= 4
X_train += 0.3 * np.random.normal(size=n_samples)
X_train = X_train[:, np.newaxis]
X, y = make_classification(
n_features=1,
n_classes=2,
n_redundant=0,
n_informative=1,
n_clusters_per_class=1,
class_sep=0.75,
shuffle=True,
random_state=0,
)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.33,
random_state=0)
df_test = pd.DataFrame(data=[X_test.flatten(), y_test]).T
df_test.columns = ["X", "y"]
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
score = [1 if yi == yi_pred else 0 for yi, yi_pred in zip(y_test, y_pred)]
print(np.sum(score) / len(score))
# and plot the result
plt.figure(1, figsize=(4, 3))
plt.clf()
plt.scatter(X_train.ravel(), y_train, color="black", zorder=20)
df_test["loss"] = expit(X_test * lr.theta + lr.bias).ravel()
df_test = df_test.sort_values("X")
plt.plot(df_test["X"], df_test["loss"], color="red", linewidth=3)
ols = LinearRegression()
ols.fit(X_train, y_train)
plt.plot(X_test, ols.theta * X_test + ols.bias, linewidth=1)
plt.axhline(0.5, color=".5")
plt.ylabel("y")
plt.xlabel("X")
plt.xticks(range(-5, 10))
plt.yticks([0, 0.5, 1])
plt.ylim(-0.25, 1.25)
plt.xlim(-2, 2)
plt.legend(
("Logistic Regression Model", "Linear Regression Model"),
loc="lower right",
fontsize="small",
)
plt.tight_layout()
plt.show()
def _convolutional_mlp(new_digit, nkerns=[20, 50], batch_size=1):
rng = numpy.random.RandomState(23455)
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
# start-snippet-1
x = T.matrix('x') # the data is presented as rasterized images
y = T.ivector('y') # the labels are presented as 1D vector of
# [int] labels
######################
# BUILD ACTUAL MODEL #
######################
print '... building the model'
# Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
# to a 4D tensor, compatible with our LeNetConvPoolLayer
# (28, 28) is the size of MNIST images.
#layer0_input = x.reshape((batch_size, 1, 28, 28))
layer0_input = new_digit.reshape((1, 1, 28, 28))
# Construct the first convolutional pooling layer:
# filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
# maxpooling reduces this further to (24/2, 24/2) = (12, 12)
# 4D output tensor is thus of shape (batch_size, nkerns[0], 12, 12)
layer0 = LeNetConvPoolLayer(
rng,
input=layer0_input,
image_shape=(batch_size, 1, 28, 28),
filter_shape=(nkerns[0], 1, 5, 5),
poolsize=(2, 2)
)
# Construct the second convolutional pooling layer
# filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
# maxpooling reduces this further to (8/2, 8/2) = (4, 4)
# 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
layer1 = LeNetConvPoolLayer(
rng,
input=layer0.output,
image_shape=(batch_size, nkerns[0], 12, 12),
filter_shape=(nkerns[1], nkerns[0], 5, 5),
poolsize=(2, 2)
)
# the HiddenLayer being fully-connected, it operates on 2D matrices of
# shape (batch_size, num_pixels) (i.e matrix of rasterized images).
# This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
# or (500, 50 * 4 * 4) = (500, 800) with the default values.
layer2_input = layer1.output.flatten(2)
# construct a fully-connected sigmoidal layer
layer2 = HiddenLayer(
rng,
input=layer2_input,
n_in=nkerns[1] * 4 * 4,
n_out=500,
activation=T.tanh
)
# classify the values of the fully-connected sigmoidal layer
layer3 = LogisticRegression(input=layer2.output, n_in=500, n_out=10)
# the cost we minimize during training is the NLL of the model
cost = layer3.negative_log_likelihood(y)
predict = theano.function(inputs=[], outputs=layer3.y_pred)
print 'Loading the model ...'
f = file('trained_models/models/convolutional_mlp.mnist.trained.pickle', 'rb')
# model_data = {layer0: {W: ..., b: ...}, layer1: ..., layer2: ..., layer3: ...}
model_data = cPickle.load(f)
f.close()
print 'Loaded the model.'
layers = [layer0, layer1, layer2, layer3]
for i in range(4):
layers[i].W.set_value(model_data['layer%s' % i]['W'])
layers[i].b.set_value(model_data['layer%s' % i]['b'])
print 'Restored model parameters.'
print 'Predicting'
return predict()[0]
def __init__(self, **kwargs):
self.n_in_src = kwargs.pop('nembed_src')
self.n_in_trg = kwargs.pop('nembed_trg')
self.n_hids_src = kwargs.pop('nhids_src')
self.n_hids_trg = kwargs.pop('nhids_trg')
self.src_vocab_size = kwargs.pop('src_vocab_size')
self.trg_vocab_size = kwargs.pop('trg_vocab_size')
self.method = kwargs.pop('method')
self.dropout = kwargs.pop('dropout')
self.maxout_part = kwargs.pop('maxout_part')
self.path = kwargs.pop('saveto')
self.clip_c = kwargs.pop('clip_c')
self.mkl = kwargs.pop('mkl')
self.with_attention = kwargs.pop('with_attention')
self.with_coverage = kwargs.pop('with_coverage')
self.coverage_dim = kwargs.pop('coverage_dim')
self.coverage_type = kwargs.pop('coverage_type')
self.max_fertility = kwargs.pop('max_fertility')
if self.coverage_type is 'linguistic':
# make sure the dimension of linguistic coverage is always 1
self.coverage_dim = 1
self.with_context_gate = kwargs.pop('with_context_gate')
self.params = []
self.layers = []
self.table_src = LookupTable(self.src_vocab_size, self.n_in_src, name='table_src')
self.layers.append(self.table_src)
self.encoder = BidirectionalEncoder(self.n_in_src, self.n_hids_src, self.table_src, self.mkl, name='birnn_encoder')
self.layers.append(self.encoder)
self.table_trg = LookupTable(self.trg_vocab_size, self.n_in_trg, name='table_trg')
self.layers.append(self.table_trg)
self.decoder = Decoder(self.mkl,
self.n_in_trg,
self.n_hids_trg,
2 * self.n_hids_src,
with_attention=self.with_attention,
with_coverage=self.with_coverage,
coverage_dim=self.coverage_dim,
coverage_type=self.coverage_type,
max_fertility=self.max_fertility,
with_context_gate=self.with_context_gate,
maxout_part=self.maxout_part,
name='rnn_decoder')
self.layers.append(self.decoder)
self.logistic_layer = LogisticRegression(self.n_in_trg, self.trg_vocab_size)
self.layers.append(self.logistic_layer)
# for reconstruction
self.with_reconstruction = kwargs.pop('with_reconstruction')
self.reconstruction_weight = kwargs.pop('reconstruction_weight')
if self.with_reconstruction:
# note the source and target sides are reversed
self.inverse_decoder = InverseDecoder(self.n_in_src, 2 * self.n_hids_src, self.n_hids_trg,
with_attention=self.with_attention,
maxout_part=self.maxout_part, name='rnn_inverse_decoder')
self.layers.append(self.inverse_decoder)
self.inverse_logistic_layer = LogisticRegression(self.n_in_src, self.src_vocab_size, name='inverse_LR')
self.layers.append(self.inverse_logistic_layer)
for layer in self.layers:
self.params.extend(layer.params)
transform=transform)
batch_numbers = len(train_set) // BATCH_SIZE + 1
train_loader = torch.utils.data.DataLoader(train_set, **kwargs)
valid_loader = torch.utils.data.DataLoader(val_set, **kwargs)
test_loader = torch.utils.data.DataLoader(test_dataset, **kwargs)
" MODEL SETTINGS "
if model_name == 'MLP':
model = MLP(dropout=True).to(device)
elif model_name == 'ResNet':
# model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).to(device)
model = MnistResNet().to(device)
elif model_name == 'logistic':
model = LogisticRegression().to(device)
if include_temperature_scaling:
model = ModelWithTemperature(model, valid_loader)
optimizer = torch.optim.Adam(model.parameters(), lr=lr) # 10−3
# resnet18 = models.resnet18()
loss_fn = nn.CrossEntropyLoss().to(device)
# nll_criterion = nn.CrossEntropyLoss().cuda()
mean_train_losses = []
mean_test_losses = []
valid_acc_list = []
batch_size=batch_size,
shuffle=False)
input_size = train_dataset.get_len_cvec()
print('input_size: ', input_size)
# set hyper parameter
hidden_size = 10000
num_class = 2
learning_rate = 0.0001
num_epoch = 5
# device configuration
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
model = LogisticRegression(input_size, hidden_size, num_class).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# train model
total_step = len(train_loader)
for epoch in range(num_epoch):
print('epoch start')
for i, (data, label) in enumerate(train_loader):
label = label.squeeze().to(device)
data = data.to(device)
outputs = model(data)
loss = criterion(outputs, label)
optimizer.zero_grad()
loss.backward()
optimizer.step()
def __init__(self, rng, **kwargs):
self.n_in_src = kwargs.pop('nembed_src')
self.n_in_trg = kwargs.pop('nembed_trg')
self.n_hids_src = kwargs.pop('nhids_src')
self.n_hids_trg = kwargs.pop('nhids_trg')
self.src_vocab_size = kwargs.pop('src_vocab_size')
self.trg_vocab_size = kwargs.pop('trg_vocab_size')
self.method = kwargs.pop('method')
self.dropout = kwargs.pop('dropout')
self.maxout_part = kwargs.pop('maxout_part')
self.path = kwargs.pop('saveto')
self.clip_c = kwargs.pop('clip_c')
self.rng = rng
self.trng = RandomStreams(rng.randint(1e5))
# added by Zhaopeng Tu, 2016-06-09
self.with_attention = kwargs.pop('with_attention')
# added by Zhaopeng Tu, 2016-04-29
self.with_coverage = kwargs.pop('with_coverage')
self.coverage_dim = kwargs.pop('coverage_dim')
self.coverage_type = kwargs.pop('coverage_type')
self.max_fertility = kwargs.pop('max_fertility')
if self.coverage_type is 'linguistic':
# make sure the dimension of linguistic coverage is always 1
self.coverage_dim = 1
# added by Zhaopeng Tu, 2016-05-30
self.with_context_gate = kwargs.pop('with_context_gate')
self.params = []
self.layers = []
self.table_src = LookupTable(self.rng,
self.src_vocab_size,
self.n_in_src,
name='table_src')
self.layers.append(self.table_src)
self.encoder = BidirectionalEncoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='birnn_encoder')
self.layers.append(self.encoder)
# added by Longyue
self.encoder_hist_1 = Encoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_1')
self.layers.append(self.encoder_hist_1)
self.encoder_hist_2 = Encoder(self.rng,
self.n_hids_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_2')
self.layers.append(self.encoder_hist_2)
self.table_trg = LookupTable(self.rng,
self.trg_vocab_size,
self.n_in_trg,
name='table_trg')
self.layers.append(self.table_trg)
self.decoder = Decoder(self.rng, self.n_in_trg, self.n_hids_trg, 2*self.n_hids_src, self.n_hids_src, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
# added by Zhaopeng Tu, 2016-04-29
with_coverage=self.with_coverage, coverage_dim=self.coverage_dim, coverage_type=self.coverage_type, max_fertility=self.max_fertility, \
# added by Zhaopeng Tu, 2016-05-30
with_context_gate=self.with_context_gate, \
maxout_part=self.maxout_part, name='rnn_decoder')
self.layers.append(self.decoder)
self.logistic_layer = LogisticRegression(self.rng, self.n_in_trg,
self.trg_vocab_size)
self.layers.append(self.logistic_layer)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
self.with_reconstruction = kwargs.pop('with_reconstruction')
if self.with_reconstruction:
# added by Zhaopeng Tu, 2016-07-27
self.reconstruction_weight = kwargs.pop('reconstruction_weight')
# note the source and target sides are reversed
self.inverse_decoder = InverseDecoder(self.rng, self.n_in_src, 2*self.n_hids_src, self.n_hids_trg, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
maxout_part=self.maxout_part, name='rnn_inverse_decoder')
self.layers.append(self.inverse_decoder)
self.srng = RandomStreams(rng.randint(1e5))
self.inverse_logistic_layer = LogisticRegression(
self.rng,
self.n_in_src,
self.src_vocab_size,
name='inverse_LR')
self.layers.append(self.inverse_logistic_layer)
for layer in self.layers:
self.params.extend(layer.params)
class EncoderDecoder(object):
def __init__(self, rng, **kwargs):
self.n_in_src = kwargs.pop('nembed_src')
self.n_in_trg = kwargs.pop('nembed_trg')
self.n_hids_src = kwargs.pop('nhids_src')
self.n_hids_trg = kwargs.pop('nhids_trg')
self.src_vocab_size = kwargs.pop('src_vocab_size')
self.trg_vocab_size = kwargs.pop('trg_vocab_size')
self.method = kwargs.pop('method')
self.dropout = kwargs.pop('dropout')
self.maxout_part = kwargs.pop('maxout_part')
self.path = kwargs.pop('saveto')
self.clip_c = kwargs.pop('clip_c')
self.rng = rng
self.trng = RandomStreams(rng.randint(1e5))
# added by Zhaopeng Tu, 2016-06-09
self.with_attention = kwargs.pop('with_attention')
# added by Zhaopeng Tu, 2016-04-29
self.with_coverage = kwargs.pop('with_coverage')
self.coverage_dim = kwargs.pop('coverage_dim')
self.coverage_type = kwargs.pop('coverage_type')
self.max_fertility = kwargs.pop('max_fertility')
if self.coverage_type is 'linguistic':
# make sure the dimension of linguistic coverage is always 1
self.coverage_dim = 1
# added by Zhaopeng Tu, 2016-05-30
self.with_context_gate = kwargs.pop('with_context_gate')
self.params = []
self.layers = []
self.table_src = LookupTable(self.rng,
self.src_vocab_size,
self.n_in_src,
name='table_src')
self.layers.append(self.table_src)
self.encoder = BidirectionalEncoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='birnn_encoder')
self.layers.append(self.encoder)
# added by Longyue
self.encoder_hist_1 = Encoder(self.rng,
self.n_in_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_1')
self.layers.append(self.encoder_hist_1)
self.encoder_hist_2 = Encoder(self.rng,
self.n_hids_src,
self.n_hids_src,
self.table_src,
name='rnn_encoder_hist_2')
self.layers.append(self.encoder_hist_2)
self.table_trg = LookupTable(self.rng,
self.trg_vocab_size,
self.n_in_trg,
name='table_trg')
self.layers.append(self.table_trg)
self.decoder = Decoder(self.rng, self.n_in_trg, self.n_hids_trg, 2*self.n_hids_src, self.n_hids_src, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
# added by Zhaopeng Tu, 2016-04-29
with_coverage=self.with_coverage, coverage_dim=self.coverage_dim, coverage_type=self.coverage_type, max_fertility=self.max_fertility, \
# added by Zhaopeng Tu, 2016-05-30
with_context_gate=self.with_context_gate, \
maxout_part=self.maxout_part, name='rnn_decoder')
self.layers.append(self.decoder)
self.logistic_layer = LogisticRegression(self.rng, self.n_in_trg,
self.trg_vocab_size)
self.layers.append(self.logistic_layer)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
self.with_reconstruction = kwargs.pop('with_reconstruction')
if self.with_reconstruction:
# added by Zhaopeng Tu, 2016-07-27
self.reconstruction_weight = kwargs.pop('reconstruction_weight')
# note the source and target sides are reversed
self.inverse_decoder = InverseDecoder(self.rng, self.n_in_src, 2*self.n_hids_src, self.n_hids_trg, \
# added by Zhaopeng Tu, 2016-06-09
with_attention=self.with_attention, \
maxout_part=self.maxout_part, name='rnn_inverse_decoder')
self.layers.append(self.inverse_decoder)
self.srng = RandomStreams(rng.randint(1e5))
self.inverse_logistic_layer = LogisticRegression(
self.rng,
self.n_in_src,
self.src_vocab_size,
name='inverse_LR')
self.layers.append(self.inverse_logistic_layer)
for layer in self.layers:
self.params.extend(layer.params)
def build_trainer(self, src, src_mask, src_hist, src_hist_mask, trg,
trg_mask, ite):
# added by Longyue
# checked by Zhaopeng: sentence dim = n_steps, hist_len, batch_size (4, 3, 25)
# hist = (bath_size, sent_num, sent_len) --.T-->
# hist = (sent_len, sent_num, bath_size) --lookup table-->
# (sent_len, sent_num, bath_size, word_emb) --reshape-->
# (sent_len, sent_num*bath_size, word_emb) --word-level rnn-->
# (sent_len, sent_num*bath_size, hidden_size) --reshape-->
# (sent_len, sent_num, bath_size, hidden_size) --[-1]-->
# (sent_num, bath_size, hidden_size) --sent-level rnn-->
# (sent_num, bath_size, hidden_size) --[-1]-->
# (bath_size, hidden_size) = cross-sent context vector
annotations_1 = self.encoder_hist_1.apply_1(src_hist, src_hist_mask)
annotations_1 = annotations_1[-1] # get last hidden states
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1] # get last hidden states
#modified by Longyue
annotations = self.encoder.apply(src, src_mask, annotations_3)
# init_context = annotations[0, :, -self.n_hids_src:]
# modification #1
# mean pooling
init_context = (annotations *
src_mask[:, :, None]).sum(0) / src_mask.sum(0)[:, None]
#added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
trg_emb = self.table_trg.apply(trg)
trg_emb_shifted = T.zeros_like(trg_emb)
trg_emb_shifted = T.set_subtensor(trg_emb_shifted[1:], trg_emb[:-1])
# modified by Longyue
hiddens, readout, alignment = self.decoder.run_pipeline(
state_below=trg_emb_shifted,
mask_below=trg_mask,
init_context=init_context,
c=annotations,
c_mask=src_mask,
hist=annotations_3)
# apply dropout
if self.dropout < 1.0:
logger.info('Apply dropout with p = {}'.format(self.dropout))
readout = Dropout(self.trng, readout, 1, self.dropout)
p_y_given_x = self.logistic_layer.get_probs(readout)
self.cost = self.logistic_layer.cost(p_y_given_x, trg,
trg_mask) / trg.shape[1]
# self.cost = theano.printing.Print('likilihood cost:')(self.cost)
# added by Zhaopeng Tu, 2016-07-12
# for reconstruction
if self.with_reconstruction:
# now hiddens is the annotations
inverse_init_context = (hiddens * trg_mask[:, :, None]
).sum(0) / trg_mask.sum(0)[:, None]
src_emb = self.table_src.apply(src)
src_emb_shifted = T.zeros_like(src_emb)
src_emb_shifted = T.set_subtensor(src_emb_shifted[1:],
src_emb[:-1])
inverse_hiddens, inverse_readout, inverse_alignment = self.inverse_decoder.run_pipeline(
state_below=src_emb_shifted,
mask_below=src_mask,
init_context=inverse_init_context,
c=hiddens,
c_mask=trg_mask)
# apply dropout
if self.dropout < 1.0:
# logger.info('Apply dropout with p = {}'.format(self.dropout))
inverse_readout = Dropout(self.srng, inverse_readout, 1,
self.dropout)
p_x_given_y = self.inverse_logistic_layer.get_probs(
inverse_readout)
self.reconstruction_cost = self.inverse_logistic_layer.cost(
p_x_given_y, src, src_mask) / src.shape[1]
# self.reconstruction_cost = theano.printing.Print('reconstructed cost:')(self.reconstruction_cost)
self.cost += self.reconstruction_cost * self.reconstruction_weight
self.L1 = sum(T.sum(abs(param)) for param in self.params)
self.L2 = sum(T.sum(param**2) for param in self.params)
params_regular = self.L1 * 1e-6 + self.L2 * 1e-6
# params_regular = theano.printing.Print('params_regular:')(params_regular)
# train cost
train_cost = self.cost + params_regular
# gradients
grads = T.grad(train_cost, self.params)
# apply gradient clipping here
grads = grad_clip(grads, self.clip_c)
# updates
updates = adadelta(self.params, grads)
# train function
# modified by Longyue
inps = [src, src_mask, src_hist, src_hist_mask, trg, trg_mask]
self.train_fn = theano.function(inps, [train_cost],
updates=updates,
name='train_function')
# self.train_fn = theano.function(inps, [train_cost], updates=updates, name='train_function', mode=NanGuardMode(nan_is_error=True, inf_is_error=True, big_is_error=True))
def build_sampler(self):
# added by Longyue
x_hist = T.ltensor3()
x_hist_mask = T.tensor3()
annotations_1 = self.encoder_hist_1.apply_1(x_hist, x_hist_mask)
annotations_1 = annotations_1[-1]
annotations_2 = self.encoder_hist_2.apply_2(annotations_1)
annotations_3 = annotations_2[-1]
x = T.lmatrix()
# Build Networks
# src_mask is None
c = self.encoder.apply(x, None, annotations_3)
#init_context = ctx[0, :, -self.n_hids_src:]
# mean pooling
init_context = c.mean(0)
# added by Longyue
init_context = concatenate([init_context, annotations_3],
axis=annotations_3.ndim - 1)
init_state = self.decoder.create_init_state(init_context)
outs = [init_state, c, annotations_3]
if not self.with_attention:
outs.append(init_context)
# compile function
print 'Building compile_init_state_and_context function ...'
self.compile_init_and_context = theano.function(
[x, x_hist, x_hist_mask], outs, name='compile_init_and_context')
print 'Done'
y = T.lvector()
cur_state = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
trg_emb = T.switch(y[:, None] < 0, T.alloc(0., 1, self.n_in_trg),
self.table_trg.apply(y))
# added by Zhaopeng Tu, 2016-06-09
# for with_attention=False
if self.with_attention and self.with_coverage:
cov_before = T.tensor3()
if self.coverage_type is 'linguistic':
print 'Building compile_fertility ...'
fertility = self.decoder._get_fertility(c)
fertility = T.addbroadcast(fertility, 1)
self.compile_fertility = theano.function(
[c], [fertility], name='compile_fertility')
print 'Done'
else:
fertility = None
else:
cov_before = None
fertility = None
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
# [next_state, ctxs] = self.decoder.apply(state_below=trg_emb,
results = self.decoder.apply(
state_below=trg_emb,
init_state=cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None if self.with_attention else init_context,
c=c if self.with_attention else None,
hist=annotations_3, # added by Longyue
one_step=True,
# added by Zhaopeng Tu, 2016-04-27
cov_before=cov_before,
fertility=fertility)
next_state = results[0]
if self.with_attention:
ctxs, alignment = results[1], results[2]
if self.with_coverage:
cov = results[3]
else:
# if with_attention=False, we always use init_context as the source representation
ctxs = init_context
readout = self.decoder.readout(next_state, ctxs, trg_emb)
# maxout
if self.maxout_part > 1:
readout = self.decoder.one_step_maxout(readout)
# apply dropout
if self.dropout < 1.0:
readout = Dropout(self.trng, readout, 0, self.dropout)
# compute the softmax probability
next_probs = self.logistic_layer.get_probs(readout)
# sample from softmax distribution to get the sample
next_sample = self.trng.multinomial(pvals=next_probs).argmax(1)
# compile function
print 'Building compile_next_state_and_probs function ...'
inps = [y, cur_state]
if self.with_attention:
inps.append(c)
else:
inps.append(init_context)
# added by Longyue
inps.append(annotations_3)
outs = [next_probs, next_state, next_sample]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(alignment)
# added by Zhaopeng Tu, 2016-04-29
if self.with_coverage:
inps.append(cov_before)
if self.coverage_type is 'linguistic':
inps.append(fertility)
outs.append(cov)
self.compile_next_state_and_probs = theano.function(
inps, outs, name='compile_next_state_and_probs')
print 'Done'
# added by Zhaopeng Tu, 2016-07-18
# for reconstruction
if self.with_reconstruction:
# Build Networks
# trg_mask is None
inverse_c = T.tensor3()
# mean pooling
inverse_init_context = inverse_c.mean(0)
inverse_init_state = self.inverse_decoder.create_init_state(
inverse_init_context)
outs = [inverse_init_state]
if not self.with_attention:
outs.append(inverse_init_context)
# compile function
print 'Building compile_inverse_init_state_and_context function ...'
self.compile_inverse_init_and_context = theano.function(
[inverse_c], outs, name='compile_inverse_init_and_context')
print 'Done'
src = T.lvector()
inverse_cur_state = T.matrix()
trg_mask = T.matrix()
# if it is the first word, emb should be all zero, and it is indicated by -1
src_emb = T.switch(src[:, None] < 0, T.alloc(0., 1, self.n_in_src),
self.table_src.apply(src))
# apply one step
# modified by Zhaopeng Tu, 2016-04-29
inverse_results = self.inverse_decoder.apply(
state_below=src_emb,
init_state=inverse_cur_state,
# added by Zhaopeng Tu, 2016-06-09
init_context=None
if self.with_attention else inverse_init_context,
c=inverse_c if self.with_attention else None,
c_mask=trg_mask,
one_step=True)
inverse_next_state = inverse_results[0]
if self.with_attention:
inverse_ctxs, inverse_alignment = inverse_results[
1], inverse_results[2]
else:
# if with_attention=False, we always use init_context as the source representation
inverse_ctxs = init_context
inverse_readout = self.inverse_decoder.readout(
inverse_next_state, inverse_ctxs, src_emb)
# maxout
if self.maxout_part > 1:
inverse_readout = self.inverse_decoder.one_step_maxout(
inverse_readout)
# apply dropout
if self.dropout < 1.0:
inverse_readout = Dropout(self.srng, inverse_readout, 0,
self.dropout)
# compute the softmax probability
inverse_next_probs = self.inverse_logistic_layer.get_probs(
inverse_readout)
# sample from softmax distribution to get the sample
inverse_next_sample = self.srng.multinomial(
pvals=inverse_next_probs).argmax(1)
# compile function
print 'Building compile_inverse_next_state_and_probs function ...'
inps = [src, trg_mask, inverse_cur_state]
if self.with_attention:
inps.append(inverse_c)
else:
inps.append(inverse_init_context)
outs = [
inverse_next_probs, inverse_next_state, inverse_next_sample
]
# added by Zhaopeng Tu, 2016-06-09
if self.with_attention:
outs.append(inverse_alignment)
self.compile_inverse_next_state_and_probs = theano.function(
inps, outs, name='compile_inverse_next_state_and_probs')
print 'Done'
def save(self, path=None):
if path is None:
path = self.path
filenpz = open(path, "w")
val = dict([(value.name, value.get_value())
for index, value in enumerate(self.params)])
logger.info("save the model {}".format(path))
numpy.savez(path, **val)
filenpz.close()
def load(self, path=None):
if path is None:
path = self.path
if os.path.isfile(path):
logger.info("load params {}".format(path))
val = numpy.load(path)
for index, param in enumerate(self.params):
logger.info('Loading {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
if param.name not in val.keys():
logger.info('Adding new param {} with shape {}'.format(
param.name,
param.get_value(borrow=True).shape))
continue
if param.get_value().shape != val[param.name].shape:
logger.info("Error: model param != load param shape {} != {}".format(\
param.get_value().shape, val[param.name].shape))
raise Exception("loading params shape mismatch")
else:
param.set_value(val[param.name], borrow=True)
else:
logger.error("file {} does not exist".format(path))
self.save()
