def test_PNN_avazu(data, train, test):
print("\nTesting PNN on avazu dataset...\n")
results_activation_function = {"auc": [], "logloss": [], "rmse": []}
results_dropout = {"auc": [], "logloss": [], "rmse": []}
results_number_of_neurons = {"auc": [], "logloss": [], "rmse": []}
auc = 0
logloss = 0
rmse = 0
features_labels = train.columns
sparse_features_labels = features_labels[1:23]
target_label = features_labels[0]
dnn_feature_columns = [
SparseFeat(
feat,
vocabulary_size=data[feat].nunique(),
embedding_dim=4,
) for feat in sparse_features_labels
]
feature_names = get_feature_names(dnn_feature_columns)
train_model_input = {name: train[name] for name in feature_names}
test_model_input = {name: test[name] for name in feature_names}
true_y = test[target_label].values
print("\t\t-- ACTIVATION FUNCTIONS --\t\t")
for dnn_activation in dnn_activation_list:
print("\nTesting {dnn_activation}...".format(
dnn_activation=dnn_activation))
# model = PNN(dnn_feature_columns, use_inner=False, use_outter=True, dnn_activation = dnn_activation, task='binary')
model = PNN(dnn_feature_columns,
use_inner=True,
use_outter=False,
dnn_activation=dnn_activation,
task='binary')
model.compile(
"adam",
"binary_crossentropy",
metrics=['binary_crossentropy'],
)
model.fit(
train_model_input,
train[target_label].values,
batch_size=256,
epochs=10,
verbose=0,
validation_split=TEST_PROPORTION,
)
pred_y = model.predict(test_model_input, batch_size=256)
auc = compute_auc(true_y, pred_y)
logloss = compute_log_loss(true_y, pred_y)
rmse = compute_rmse(true_y, pred_y)
results_activation_function["auc"].append(auc)
results_activation_function["logloss"].append(logloss)
results_activation_function["rmse"].append(rmse)
print("\t\t-- DROPOUT RATES --\t\t")
for dnn_dropout in dnn_dropout_list:
print("\nTesting {dnn_dropout}...".format(dnn_dropout=dnn_dropout))
# model = PNN(dnn_feature_columns, use_inner=False, use_outter=True, dnn_dropout = dnn_dropout, task='binary')
model = PNN(dnn_feature_columns,
use_inner=True,
use_outter=False,
dnn_dropout=dnn_dropout,
task='binary')
model.compile(
"adam",
"binary_crossentropy",
metrics=['binary_crossentropy'],
)
model.fit(
train_model_input,
train[target_label].values,
batch_size=256,
epochs=10,
verbose=0,
validation_split=TEST_PROPORTION,
)
pred_y = model.predict(test_model_input, batch_size=256)
auc = compute_auc(true_y, pred_y)
logloss = compute_log_loss(true_y, pred_y)
rmse = compute_rmse(true_y, pred_y)
results_dropout["auc"].append(auc)
results_dropout["logloss"].append(logloss)
results_dropout["rmse"].append(rmse)
print("\t\t-- HIDDEN UNITS --\t\t")
for dnn_hidden_units in dnn_hidden_units_list:
print("\nTesting {dnn_hidden_units}...".format(
dnn_hidden_units=dnn_hidden_units))
# model = PNN(dnn_feature_columns, use_inner=False, use_outter=True, dnn_hidden_units = dnn_hidden_units, task='binary')
model = PNN(dnn_feature_columns,
use_inner=True,
use_outter=False,
dnn_hidden_units=dnn_hidden_units,
task='binary')
model.compile(
"adam",
"binary_crossentropy",
metrics=['binary_crossentropy'],
)
model.fit(train_model_input,
train[target_label].values,
batch_size=256,
epochs=10,
verbose=0,
validation_split=TEST_PROPORTION)
pred_y = model.predict(test_model_input, batch_size=256)
auc = compute_auc(true_y, pred_y)
logloss = compute_log_loss(true_y, pred_y)
rmse = compute_rmse(true_y, pred_y)
results_number_of_neurons["auc"].append(auc)
results_number_of_neurons["logloss"].append(logloss)
results_number_of_neurons["rmse"].append(rmse)
if PLOT:
# create_plots("OPNN", "avazu", results_activation_function, "Activation Function", "activation_func", dnn_activation_list)
# create_plots("OPNN", "avazu", results_dropout, "Dropout Rate", "dropout", dnn_dropout_list)
# create_plots("OPNN", "avazu", results_number_of_neurons, "Number of Neurons per layer", "nr_neurons", dnn_hidden_units_list)
create_plots("PNN", "avazu", results_activation_function,
"Activation Function", "activation_func",
dnn_activation_list)
create_plots("PNN", "avazu", results_dropout, "Dropout Rate",
"dropout", dnn_dropout_list)
create_plots("PNN", "avazu", results_number_of_neurons,
"Number of Neurons per layer", "nr_neurons",
dnn_hidden_units_list)
linear_feature_columns = fixlen_feature_columns
feature_names = get_feature_names(linear_feature_columns + dnn_feature_columns)
# 3.generate input data for model
# train, test = train_test_split(data, test_size=0.2)
print("Spltting dataset into train and test sets...\n")
train, test = train_test_split(data, test_size=0.2)
# train, test = train_test_split(data_ohe, test_size=0.2)
train_model_input = {name:train[name] for name in feature_names}
test_model_input = {name:test[name] for name in feature_names}
# 4.Define Model,train,predict and evaluate
print("Defining PNN model...\n")
model = PNN(dnn_feature_columns, task='binary')
print("Compiling PNN model...\n")
model.compile("adam", "binary_crossentropy",
metrics=['binary_crossentropy'], )
print("Training the model...\n")
model.fit(train_model_input, train[target].values,
batch_size=256, epochs=10, verbose=1, validation_split=0.2, )
print("\nTesting the model...\n")
pred = model.predict(test_model_input, batch_size=256)
print("test LogLoss", round(log_loss(test[target].values, pred), 4))
print("test AUC", round(roc_auc_score(test[target].values, pred), 4))
print("\nProgram ended in {time}".format(time = datetime.now() - start_time))