Kaggle competition: Mercari Price Suggestion Challenge
test.to_csv('submission.csv', index=False )<import_modules>
!apt-get install p7zip
!p7zip -d -f -k /kaggle/input/mercari-price-suggestion-challenge/train.tsv.7z
import numpy as np
import pandas as pd
from pathlib import Path
from fastai import *
from fastai.vision import *
import torchvision
import torch<define_variables>
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
data_root_path = Path(".. /input" )<load_from_csv>
print("Loading data...")
 train = pd.read_table("train.tsv", sep="\t")
 test = pd.read_csv(".. /input/mercari-price-suggestion-challenge/test_stg2.tsv.zip" , sep='\t')
 print(train.shape)
 print(test.shape )
train_df = pd.read_csv(data_root_path/"train_labels.csv")
 test_df = pd.read_csv(data_root_path/"sample_submission.csv")
 train_df.id = train_df.id + '.tif'
test_df.id = test_df.id + '.tif'<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 )
transforms = get_transforms(
 do_flip=True,
flip_vert=True,
max_rotate=15.0,
max_lighting=0.2,
max_warp=0.2 
)
 train_imgs = ImageList.from_df(train_df, path=data_root_path, folder='train')
 test_imgs = ImageList.from_df(test_df, path=data_root_path, folder='test')
 train_imgs =(train_imgs 
.split_by_rand_pct(0.01)
.label_from_df() 
.add_test(test_imgs)
.transform(transforms, size=128)
.databunch(path='.', bs=64, device= torch.device('cuda:0')) 
.normalize(imagenet_stats))<choose_model_class>
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 )
learn = cnn_learner(train_imgs, torchvision.models.densenet169, metrics=[error_rate, accuracy] )<train_model>
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 )
lr = 3e-02
learn.fit_one_cycle(10, slice(lr))<predict_on_test>
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
preds,_ = learn.get_preds(ds_type=DatasetType.Test )<prepare_x_and_y>
dtrain, dvalid = train_test_split(train, random_state=123, train_size=0.99)
 print(dtrain.shape)
 print(dvalid.shape )
test_df.label = preds.numpy() [:, 0]<feature_engineering>
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 )
test_df['id'] = test_df['id'].str.replace('.tif','' )<save_to_csv>
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 = 0.1
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()
test_df.to_csv('submission.csv', index=False )<import_modules>
BATCH_SIZE = 20000
epochs = 5
model = get_model()
model.fit(X_train, dtrain.target, epochs=epochs, batch_size=BATCH_SIZE
, validation_data=(X_valid, dvalid.target)
 , verbose=1 )
from fastai.tabular import *
from fastai.callbacks import ReduceLROnPlateauCallback, EarlyStoppingCallback, SaveModelCallback
from sklearn.metrics import roc_auc_score
import gc<load_from_csv>
val_preds = model.predict(X_valid)
 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))
dense161 = pd.read_csv(".. /input/cancer-densenet161-v2-for-ensemble/validation_0.976066529750824.csv")
 dense161_test = pd.read_csv(".. /input/cancer-densenet161-v2-for-ensemble/submission_0.976066529750824.csv" )<load_from_csv>
preds = model.predict(X_test, batch_size=BATCH_SIZE)
 preds = np.exp(preds)-1
submission = test[["test_id"]]
submission["price"] = preds
dense201 = pd.read_csv(".. /input/cancer-densenet201-v2-for-ensemble/validation_0.9749373197555542.csv")
 dense201_test = pd.read_csv(".. /input/cancer-densenet201-v2-for-ensemble/submission_0.9749373197555542.csv" )<load_from_csv>
submission.to_csv("./submission.csv", index=False)
 submission.price.hist()