Kaggle competition: Mercari Price Suggestion Challenge
test = df[df['rentals'].isnull() ]<filter>
def rmsle(y_true, yz_pred):
assert len(y_true)== len(y_pred)
 return(np.square(np.log(y_pred + 1)- np.log(y_true + 1)).mean())**0.5
df = df[~df['rentals'].isnull() ]<import_modules>
print("Loading data...")
 train = pd.read_csv('.. /input/train.tsv', sep='\t')
 test = pd.read_csv('.. /input/test.tsv', sep='\t')
 train['target'] = np.log1p(train['price'])
 print(train.shape)
 print(test.shape )
from sklearn.model_selection import train_test_split<import_modules>
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 )
from sklearn.model_selection import train_test_split<split>
print("Handling categorical variables...")
 le = LabelEncoder()
le.fit(np.hstack([train.category_name, test.category_name]))
train['category'] = le.transform(train.category_name)
 test['category'] = 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, valid = train_test_split(df, random_state=42 )<define_variables>
print("Text to seq process...")
 print(" Fitting tokenizer...")
 raw_text = np.hstack([train.category_name.str.lower() ,
train.item_description.str.lower() ,
train.name.str.lower() ])
 tok_raw = Tokenizer()
tok_raw.fit_on_texts(raw_text)
 print(" Transforming text to seq...")
 train["seq_category_name"] = tok_raw.texts_to_sequences(train.category_name.str.lower())
 test["seq_category_name"] = tok_raw.texts_to_sequences(test.category_name.str.lower())
 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 )
removed_cols = ['rentals', 'casual', 'registered', 'datetime']<define_variables>
MAX_NAME_SEQ = 15
MAX_ITEM_DESC_SEQ = 65
MAX_CATEGORY_NAME_SEQ = 20
MAX_TEXT = np.max([np.max(train.seq_name.max())
 , np.max(test.seq_name.max())
 , np.max(train.seq_category_name.max())
 , np.max(test.seq_category_name.max())
 , np.max(train.seq_item_description.max())
 , np.max(test.seq_item_description.max())])+2
MAX_CATEGORY = np.max([train.category.max() , test.category.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
feats = [c for c in df.columns if c not in removed_cols]<import_modules>
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()
from sklearn.tree import DecisionTreeRegressor<choose_model_class>
dtrain, dvalid = train_test_split(train, random_state=123, train_size=0.985)
 print(dtrain.shape)
 print(dvalid.shape )
dt = DecisionTreeRegressor(random_state=42, max_depth=2 )<train_model>
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': np.array(dataset.category)
 ,'category_name': pad_sequences(dataset.seq_category_name
, maxlen=MAX_CATEGORY_NAME_SEQ)
 ,'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 )
dt.fit(train[feats], train['rentals'] )<import_modules>
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 = 0.05
dr_d = 0.05
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 = Input(shape=[1], name="category")
 category_name = Input(shape=[X_train["category_name"].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_size = 55
emb_name = Embedding(MAX_TEXT, 60 )(name)
 emb_item_desc = Embedding(MAX_TEXT, 60 )(item_desc)
 emb_category_name = Embedding(MAX_TEXT, emb_size//3 )(category_name)
 emb_brand_name = Embedding(MAX_BRAND, 10 )(brand_name)
 emb_category = Embedding(MAX_CATEGORY, 10 )(category)
 emb_item_condition = Embedding(MAX_CONDITION, 5 )(item_condition)
 rnn_layer1 = GRU(16 )(emb_item_desc)
 rnn_layer2 = GRU(16 )(emb_category_name)
 rnn_layer3 = GRU(8 )(emb_name)
 main_l = concatenate([
Flatten()(emb_brand_name)
 , Flatten()(emb_category)
 , Flatten()(emb_item_condition)
 , rnn_layer1
, rnn_layer2
, rnn_layer3
, num_vars
])
 main_l = Dropout(dr_r )(Dense(512,activation='elu' )(main_l))
main_l = Dropout(dr_d )(Dense(16,activation='elu' )(main_l))
output = Dense(1, activation="linear" )(main_l)
 model = Model([name, item_desc, brand_name
,category, category_name, item_condition, num_vars], output)
 model.compile(loss="mse", optimizer="adam", metrics=["mae", rmsle_cust])
 return model
model = get_model()
model.summary()
from fastai.structured import draw_tree<import_modules>
BATCH_SIZE = 512 * 3
epochs = 2
model = get_model()
model.fit(X_train, dtrain.target, epochs=epochs, batch_size=BATCH_SIZE
, validation_data=(X_valid, dvalid.target)
 , verbose=1 )
from sklearn.ensemble import RandomForestRegressor<import_modules>
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))
from sklearn.ensemble import RandomForestRegressor<choose_model_class>
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
<train_model><EOS> 
submission.to_csv("./NNsubmission.csv", index=False)
 submission.price.hist()