Kaggle competition: Mercari Price Suggestion Challenge
sample_submission = pd.read_csv('/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv')
 sample_submission.tail()<load_pretrained>
def rmsle(y, y_pred):
assert len(y)== len(y_pred)
 to_sum = [(math.log(y_pred[i] + 1)- math.log(y[i] + 1)) ** 2.0 for i,pred in enumerate(y_pred)]
return(sum(to_sum)*(1.0/len(y)))** 0.5
train_image_list = np.load('.. /input/cats-and-dogs-embedded-data/cats_and_dogs_1/train_image_list.npy')
 test_image_list = np.load('.. /input/cats-and-dogs-embedded-data/cats_and_dogs_1/test_image_list.npy' )<define_variables>
print("Loading data...")
 train = pd.read_table(".. /input/train.tsv")
 test = pd.read_table(".. /input/test.tsv")
 print(train.shape)
 print(test.shape )
ids = [int(x.split('.')[0])for x in test_image_list]<load_pretrained>
print("Handling missing values...")
 def handle_missing(dataset):
dataset.category_name.fillna(value="missing", inplace=True)
 dataset.brand_name.fillna(value="missing", inplace=True)
 dataset.item_description.fillna(value="missing", inplace=True)
 return(dataset)
 train = handle_missing(train)
 test = handle_missing(test)
 print(train.shape)
 print(test.shape )
target = np.load('.. /input/cats-and-dogs-embedded-data/cats_and_dogs_1/target.npy' )<load_pretrained>
print("Handling categorical variables...")
 le = LabelEncoder()
le.fit(np.hstack([train.category_name, test.category_name]))
train.category_name = le.transform(train.category_name)
 test.category_name = le.transform(test.category_name)
 le.fit(np.hstack([train.brand_name, test.brand_name]))
train.brand_name = le.transform(train.brand_name)
 test.brand_name = le.transform(test.brand_name)
 del le
train.head(3)
train_EB7_ns = np.load('.. /input/cats-and-dogs-embedded-data/cats_and_dogs_4/cats_and_dogs_4/train_EB7_ns.npy')
 test_EB7_ns = np.load('.. /input/cats-and-dogs-embedded-data/cats_and_dogs_4/cats_and_dogs_4/test_EB7_ns.npy' )<prepare_x_and_y>
print("Text to seq process...")
 raw_text = np.hstack([train.item_description.str.lower() , train.name.str.lower() ])
 print(" Fitting tokenizer...")
 tok_raw = Tokenizer()
tok_raw.fit_on_texts(raw_text)
 print(" Transforming text to seq...")
 train["seq_item_description"] = tok_raw.texts_to_sequences(train.item_description.str.lower())
 test["seq_item_description"] = tok_raw.texts_to_sequences(test.item_description.str.lower())
 train["seq_name"] = tok_raw.texts_to_sequences(train.name.str.lower())
 test["seq_name"] = tok_raw.texts_to_sequences(test.name.str.lower())
 train.head(3 )
y_oof = np.zeros(train_EB7_ns.shape[0])
 y_test = 0<choose_model_class>
MAX_NAME_SEQ = 10
MAX_ITEM_DESC_SEQ = 75
MAX_TEXT = np.max([np.max(train.seq_name.max())
 , np.max(test.seq_name.max())
 , np.max(train.seq_item_description.max())
 , np.max(test.seq_item_description.max())])+2
MAX_CATEGORY = np.max([train.category_name.max() , test.category_name.max() ])+1
MAX_BRAND = np.max([train.brand_name.max() , test.brand_name.max() ])+1
MAX_CONDITION = np.max([train.item_condition_id.max() , test.item_condition_id.max() ])+1
NUM_FOLDS = 20
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=137 )<predict_on_test>
train["target"] = np.log(train.price+1)
 target_scaler = MinMaxScaler(feature_range=(-1, 1))
train["target"] = target_scaler.fit_transform(train.target.reshape(-1,1))
pd.DataFrame(train.target ).hist()
%%time
for f,(train_ind, val_ind)in enumerate(kf.split(train_EB7_ns, target)) :
print(f)
 X_train, X_val = train_EB7_ns[train_ind], train_EB7_ns[val_ind]
y_train, y_val = target[train_ind], target[val_ind]
lr = LogisticRegression(C=0.026, max_iter=10000)
 lr.fit(X_train, y_train)
 val_preds = lr.predict_proba(X_val)[:,1]
test_preds = lr.predict_proba(test_EB7_ns)[:,1]
y_oof[val_ind] = val_preds
y_test += test_preds/NUM_FOLDS<compute_test_metric>
dtrain, dvalid = train_test_split(train, random_state=123, train_size=0.99)
 print(dtrain.shape)
 print(dvalid.shape )
roc_auc_score(target, y_oof )<compute_test_metric>
def get_keras_data(dataset):
X = {
'name': pad_sequences(dataset.seq_name, maxlen=MAX_NAME_SEQ)
 ,'item_desc': pad_sequences(dataset.seq_item_description, maxlen=MAX_ITEM_DESC_SEQ)
 ,'brand_name': np.array(dataset.brand_name)
 ,'category_name': np.array(dataset.category_name)
 ,'item_condition': np.array(dataset.item_condition_id)
 ,'num_vars': np.array(dataset[["shipping"]])
 }
return X
X_train = get_keras_data(dtrain)
 X_valid = get_keras_data(dvalid)
 X_test = get_keras_data(test )
log_loss(target, y_oof )<prepare_x_and_y>
def get_callbacks(filepath, patience=2):
es = EarlyStopping('val_loss', patience=patience, mode="min")
 msave = ModelCheckpoint(filepath, save_best_only=True)
 return [es, msave]
def rmsle_cust(y_true, y_pred):
first_log = K.log(K.clip(y_pred, K.epsilon() , None)+ 1.)
second_log = K.log(K.clip(y_true, K.epsilon() , None)+ 1.)
return K.sqrt(K.mean(K.square(first_log - second_log), axis=-1))
def get_model() :
dr_r = 0.15
name = Input(shape=[X_train["name"].shape[1]], name="name")
 item_desc = Input(shape=[X_train["item_desc"].shape[1]], name="item_desc")
 brand_name = Input(shape=[1], name="brand_name")
 category_name = Input(shape=[1], name="category_name")
 item_condition = Input(shape=[1], name="item_condition")
 num_vars = Input(shape=[X_train["num_vars"].shape[1]], name="num_vars")
 emb_name = Embedding(MAX_TEXT, 50 )(name)
 emb_item_desc = Embedding(MAX_TEXT, 50 )(item_desc)
 emb_brand_name = Embedding(MAX_BRAND, 10 )(brand_name)
 emb_category_name = Embedding(MAX_CATEGORY, 10 )(category_name)
 emb_item_condition = Embedding(MAX_CONDITION, 5 )(item_condition)
 rnn_layer1 = GRU(16 )(emb_item_desc)
 rnn_layer2 = GRU(8 )(emb_name)
 main_l = concatenate([
Flatten()(emb_brand_name)
 , Flatten()(emb_category_name)
 , Flatten()(emb_item_condition)
 , rnn_layer1
, rnn_layer2
, num_vars
])
 main_l = Dropout(dr_r )(Dense(128 )(main_l))
main_l = Dropout(dr_r )(Dense(64 )(main_l))
output = Dense(1, activation="linear" )(main_l)
 model = Model([name, item_desc, brand_name
, category_name, item_condition, num_vars], output)
 model.compile(loss="mse", optimizer="adam", metrics=["mae", rmsle_cust])
 return model
model = get_model()
model.summary()
5
y_oof_xgb_1 = np.zeros(train_EB7_ns.shape[0])
 y_test_xgb_1 = 0 
<split>
BATCH_SIZE = 10000
epochs = 3
model = get_model()
model.fit(X_train, dtrain.target, epochs=epochs, batch_size=BATCH_SIZE
, validation_data=(X_valid, dvalid.target)
 , verbose=1 )
%%time
xgb_params = {
'eta': 0.05,
'max_depth': 4,
'subsample': 0.8,
'colsample_bytree': 0.6,
'alpha': 0.01,
'lambda': 1.00,
'gamma' : 0.02,
'max_bin': 256,
'objective': 'reg:logistic',
'eval_metric': 'auc',
'verbosity': 0,
'tree_method': 'gpu_hist',
'predictor': 'gpu_predictor'
}
dtest = xgb.DMatrix(test_EB7_ns)
 for f,(train_ind, val_ind)in enumerate(kf.split(train_EB7_ns, target)) :
print(f)
 X_train, X_val = train_EB7_ns[train_ind], train_EB7_ns[val_ind]
y_train, y_val = target[train_ind], target[val_ind]
dtrain = xgb.DMatrix(X_train, y_train)
 dval = xgb.DMatrix(X_val, y_val)
 clf = xgb.train(xgb_params, dtrain, num_boost_round=100)
 val_preds = clf.predict(dval)
 test_preds = clf.predict(dtest)
 y_oof_xgb_1[val_ind] = val_preds
y_test_xgb_1 += test_preds/NUM_FOLDS
print(roc_auc_score(target, y_oof_xgb_1))
print(log_loss(target, y_oof_xgb_1))<predict_on_test>
val_preds = model.predict(X_valid)
 val_preds = target_scaler.inverse_transform(val_preds)
 val_preds = np.exp(val_preds)+1
y_true = np.array(dvalid.price.values)
 y_pred = val_preds[:,0]
v_rmsle = rmsle(y_true, y_pred)
 print(" RMSLE error on dev test: "+str(v_rmsle))
%%time
y_oof_knn = np.zeros(train_EB7_ns.shape[0])
 y_test_knn = 0
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=137)
 for f,(train_ind, val_ind)in enumerate(kf.split(train_EB7_ns, target)) :
print(f)
 X_train, X_val = train_EB7_ns[train_ind], train_EB7_ns[val_ind]
y_train, y_val = target[train_ind], target[val_ind]
knc = KNeighborsClassifier(n_neighbors=400)
 knc.fit(X_train, y_train)
 val_preds = knc.predict_proba(X_val)[:,1]
test_preds = knc.predict_proba(test_EB7_ns)[:,1]
y_oof_knn[val_ind] = val_preds
y_test_knn += test_preds/NUM_FOLDS
print(roc_auc_score(target, y_oof_knn))
print(log_loss(target, y_oof_knn))<compute_test_metric>
preds = model.predict(X_test, batch_size=BATCH_SIZE)
 preds = target_scaler.inverse_transform(preds)
 preds = np.exp(preds)-1
submission = test[["test_id"]]
submission["price"] = preds
<save_to_csv><EOS> 
submission.to_csv("./myNNsubmission.csv", index=False)
 submission.price.hist()