Kaggle competition: OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
<SOS> metric: MWCRMSE Kaggle data source: stanford-covid-vaccine<compute_train_metric>
pretrain_dir = None
one_fold = False
run_test = False
denoise = True
ae_epochs = 20
ae_epochs_each = 5
ae_batch_size = 32
epochs_list = [30, 10, 3, 3, 5, 5]
batch_size_list = [8, 16, 32, 64, 128, 256]
if pretrain_dir is not None:
for d in glob.glob(pretrain_dir + "*"):
shutil.copy(d, ".")
 %matplotlib inline
metrics_df = nbeats_pred02_df[['id_str']]
error =(np.array(all_pred_agg.values[:,1:])- np.array(nbeats_pred02_df.values[:,1:]))
successive_diff = np.diff(nbeats_pred01_df.values[:,1:])** 2
denom = successive_diff.mean(1)
 num = error.mean(1)**2
rmsse = num / denom
metrics_df['rmsse'] = rmsse
metrics_df['mean_error'] = error.mean(1)
 metrics_df['mean_abs_error'] = np.absolute(error ).mean(1)
 squared_error = error **2
mean_squ_err = np.array(squared_error.mean(1), dtype=np.float64)
 metrics_df['rmse'] = np.sqrt(mean_squ_err)
 metrics_df<save_to_csv>
train = pd.read_json("/kaggle/input/stanford-covid-vaccine/train.json",lines=True)
 if denoise:
train = train[train.signal_to_noise > 1].reset_index(drop = True)
 test = pd.read_json("/kaggle/input/stanford-covid-vaccine/test.json",lines=True)
 test_pub = test[test["seq_length"] == 107]
test_pri = test[test["seq_length"] == 130]
sub = pd.read_csv("/kaggle/input/stanford-covid-vaccine/sample_submission.csv")
 if run_test:
train = train[:30]
test_pub = test_pub[:30]
test_pri = test_pri[:30]
As = []
for id in tqdm(train["id"]):
a = np.load(f"/kaggle/input/stanford-covid-vaccine/bpps/{id}.npy")
 As.append(a)
 As = np.array(As)
 As_pub = []
for id in tqdm(test_pub["id"]):
a = np.load(f"/kaggle/input/stanford-covid-vaccine/bpps/{id}.npy")
 As_pub.append(a)
 As_pub = np.array(As_pub)
 As_pri = []
for id in tqdm(test_pri["id"]):
a = np.load(f"/kaggle/input/stanford-covid-vaccine/bpps/{id}.npy")
 As_pri.append(a)
 As_pri = np.array(As_pri )
submission_pred_df.to_csv('m5-final-submission.csv', index=False)
<import_modules>
targets = list(sub.columns[1:])
 print(targets)
 y_train = []
seq_len = train["seq_length"].iloc[0]
seq_len_target = train["seq_scored"].iloc[0]
ignore = -10000
ignore_length = seq_len - seq_len_target
for target in targets:
y = np.vstack(train[target])
 dummy = np.zeros([y.shape[0], ignore_length])+ ignore
y = np.hstack([y, dummy])
 y_train.append(y)
 y = np.stack(y_train, axis = 2)
 y.shape
from datetime import datetime, timedelta
import gc
import numpy as np, pandas as pd
import lightgbm as lgb<define_variables>
def get_structure_adj(train):
Ss = []
for i in tqdm(range(len(train))):
seq_length = train["seq_length"].iloc[i]
structure = train["structure"].iloc[i]
sequence = train["sequence"].iloc[i]
cue = []
a_structures = { 
("A", "U"): np.zeros([seq_length, seq_length]), 
("C", "G"): np.zeros([seq_length, seq_length]), 
("U", "G"): np.zeros([seq_length, seq_length]), 
("U", "A"): np.zeros([seq_length, seq_length]), 
("G", "C"): np.zeros([seq_length, seq_length]), 
("G", "U"): np.zeros([seq_length, seq_length]),
}
a_structure = np.zeros([seq_length, seq_length])
 for i in range(seq_length):
if structure[i] == "(":
cue.append(i)
 elif structure[i] == ")":
start = cue.pop()
a_structures[(sequence[start], sequence[i])][start, i] = 1
a_structures[(sequence[i], sequence[start])][i, start] = 1
a_strc = np.stack([a for a in a_structures.values() ], axis = 2)
 a_strc = np.sum(a_strc, axis = 2, keepdims = True)
 Ss.append(a_strc)
 Ss = np.array(Ss)
 print(Ss.shape)
 return Ss
Ss = get_structure_adj(train)
 Ss_pub = get_structure_adj(test_pub)
 Ss_pri = get_structure_adj(test_pri )
CAL_DTYPES={"event_name_1": "category",
"event_name_2": "category",
"event_type_1": "category",
"event_type_2": "category",
"weekday": "category",
'wm_yr_wk': 'int16',
"wday": "int16",
"month": "int16",
"year": "int16",
"snap_CA": "float32",
'snap_TX': 'float32',
'snap_WI': 'float32' }
PRICE_DTYPES = {"store_id": "category",
"item_id": "category",
"wm_yr_wk": "int16",
"sell_price":"float32" }<set_options>
def get_distance_matrix(As):
idx = np.arange(As.shape[1])
 Ds = []
for i in range(len(idx)) :
d = np.abs(idx[i] - idx)
 Ds.append(d)
 Ds = np.array(Ds)+ 1
Ds = 1/Ds
Ds = Ds[None, :,:]
Ds = np.repeat(Ds, len(As), axis = 0)
 Dss = []
for i in [1, 2, 4]:
Dss.append(Ds ** i)
 Ds = np.stack(Dss, axis = 3)
 print(Ds.shape)
 return Ds
Ds = get_distance_matrix(As)
 Ds_pub = get_distance_matrix(As_pub)
 Ds_pri = get_distance_matrix(As_pri )
pd.options.display.max_columns = 50<define_variables>
As = np.concatenate([As[:,:,:,None], Ss, Ds], axis = 3 ).astype(np.float32)
 As_pub = np.concatenate([As_pub[:,:,:,None], Ss_pub, Ds_pub], axis = 3 ).astype(np.float32)
 As_pri = np.concatenate([As_pri[:,:,:,None], Ss_pri, Ds_pri], axis = 3 ).astype(np.float32)
 del Ss, Ds, Ss_pub, Ds_pub, Ss_pri, Ds_pri
As.shape, As_pub.shape, As_pri.shape
NDAYS = 28
MAX_LAGS = 57
TRAINING_LAST = 1913
FDAY = datetime(2016,4, 25)
 FDAY<load_from_csv>
def return_ohe(n, i):
tmp = [0] * n
tmp[i] = 1
return tmp
def get_input(train):
mapping = {}
vocab = ["A", "G", "C", "U"]
for i, s in enumerate(vocab):
mapping[s] = return_ohe(len(vocab), i)
 X_node = np.stack(train["sequence"].apply(lambda x : list(map(lambda y : mapping[y], list(x)))))
 mapping = {}
vocab = ["S", "M", "I", "B", "H", "E", "X"]
for i, s in enumerate(vocab):
mapping[s] = return_ohe(len(vocab), i)
 X_loop = np.stack(train["predicted_loop_type"].apply(lambda x : list(map(lambda y : mapping[y], list(x)))))
 mapping = {}
vocab = [".", "(", ")"]
for i, s in enumerate(vocab):
mapping[s] = return_ohe(len(vocab), i)
 X_structure = np.stack(train["structure"].apply(lambda x : list(map(lambda y : mapping[y], list(x)))))
 X_node = np.concatenate([X_node, X_loop], axis = 2)
 a = np.sum(X_node *(2 ** np.arange(X_node.shape[2])[None, None, :]), axis = 2)
 vocab = sorted(set(a.flatten()))
print(vocab)
 ohes = []
for v in vocab:
ohes.append(a == v)
 ohes = np.stack(ohes, axis = 2)
 X_node = np.concatenate([X_node, ohes], axis = 2 ).astype(np.float32)
 print(X_node.shape)
 return X_node
X_node = get_input(train)
 X_node_pub = get_input(test_pub)
 X_node_pri = get_input(test_pri )
def create_calendar_df() :
calendar_df = pd.read_csv(".. /input/m5-forecasting-accuracy/calendar.csv", dtype = CAL_DTYPES)
 calendar_df["date"] = pd.to_datetime(calendar_df["date"])
 for col, col_dtype in CAL_DTYPES.items() :
if col_dtype == "category":
calendar_df[col] = calendar_df[col].cat.codes.astype("int16")
 calendar_df[col] -= calendar_df[col].min()
return calendar_df
def create_prices_df() :
prices_df = pd.read_csv(".. /input/m5-forecasting-accuracy/sell_prices.csv", dtype = PRICE_DTYPES)
 for col, col_dtype in PRICE_DTYPES.items() :
if col_dtype == "category":
prices_df[col] = prices_df[col].cat.codes.astype("int16")
 prices_df[col] -= prices_df[col].min()
return prices_df
def create_dt(is_train = True, nrows = None, first_day = 1200):
start_day = max(1 if is_train else TRAINING_LAST - MAX_LAGS, first_day)
 numeric_cols = [f"d_{day}" for day in range(start_day, TRAINING_LAST + 1)]
category_cols = ['id', 'item_id', 'dept_id','store_id', 'cat_id', 'state_id']
dtype = {numcol:"float32" for numcol in numeric_cols}
dtype.update({col: "category" for col in category_cols if col != "id"})
 sales_df = pd.read_csv(".. /input/m5-forecasting-accuracy/sales_train_validation.csv",
nrows = nrows, usecols = category_cols + numeric_cols, dtype = dtype)
 for col in category_cols:
if col != "id":
sales_df[col] = sales_df[col].cat.codes.astype("int16")
 sales_df[col] -= sales_df[col].min()
if not is_train:
for day in range(TRAINING_LAST + 1, TRAINING_LAST + NDAYS +1):
sales_df[f"d_{day}"] = np.nan
sales_df = pd.melt(sales_df,
id_vars = category_cols,
value_vars = [col for col in sales_df.columns if col.startswith("d_")],
var_name = "d",
value_name = "sales")
 calendar_df = create_calendar_df()
prices_df = create_prices_df()
sales_df = sales_df.merge(calendar_df, on= "d", copy = False)
 sales_df = sales_df.merge(prices_df, on = ["store_id", "item_id", "wm_yr_wk"], copy = False)
 return sales_df<define_variables>
def mcrmse(t, p, seq_len_target = seq_len_target):
score = np.mean(np.sqrt(np.mean(( p - y_va)** 2, axis = 2)) [:, :seq_len_target])
 return score
def mcrmse_loss(t, y, seq_len_target = seq_len_target):
t = t[:, :seq_len_target]
y = y[:, :seq_len_target]
loss = tf.reduce_mean(tf.sqrt(tf.reduce_mean(( t - y)** 2, axis = 2)))
 return loss
def attention(x_inner, x_outer, n_factor, dropout):
x_Q = L.Conv1D(n_factor, 1, activation='relu',
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(x_inner)
 x_K = L.Conv1D(n_factor, 1, activation='relu',
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(x_outer)
 x_V = L.Conv1D(n_factor, 1, activation='relu',
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(x_outer)
 x_KT = L.Permute(( 2, 1))(x_K)
 res = L.Lambda(lambda c: K.batch_dot(c[0], c[1])/ np.sqrt(n_factor))([x_Q, x_KT])
 att = L.Lambda(lambda c: K.softmax(c, axis=-1))(res)
 att = L.Lambda(lambda c: K.batch_dot(c[0], c[1]))([att, x_V])
 return att
def multi_head_attention(x, y, n_factor, n_head, dropout):
if n_head == 1:
att = attention(x, y, n_factor, dropout)
 else:
n_factor_head = n_factor // n_head
heads = [attention(x, y, n_factor_head, dropout)for i in range(n_head)]
att = L.Concatenate()(heads)
 att = L.Dense(n_factor,
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(att)
 x = L.Add()([x, att])
 x = L.LayerNormalization()(x)
 if dropout > 0:
x = L.Dropout(dropout )(x)
 return x
def res(x, unit, kernel = 3, rate = 0.1):
h = L.Conv1D(unit, kernel, 1, padding = "same", activation = None )(x)
 h = L.LayerNormalization()(h)
 h = L.LeakyReLU()(h)
 h = L.Dropout(rate )(h)
 return L.Add()([x, h])
 def forward(x, unit, kernel = 3, rate = 0.1):
h = L.Conv1D(unit, kernel, 1, padding = "same", activation = None )(x)
 h = L.LayerNormalization()(h)
 h = L.Dropout(rate )(h)
 h = L.LeakyReLU()(h)
 h = res(h, unit, kernel, rate)
 return h
def adj_attn(x, adj, unit, n = 2, rate = 0.1):
x_a = x
x_as = []
for i in range(n):
x_a = forward(x_a, unit)
 x_a = tf.matmul(adj, x_a)
 x_as.append(x_a)
 if n == 1:
x_a = x_as[0]
else:
x_a = L.Concatenate()(x_as)
 x_a = forward(x_a, unit)
 return x_a
def get_base(config):
node = tf.keras.Input(shape =(None, X_node.shape[2]), name = "node")
 adj = tf.keras.Input(shape =(None, None, As.shape[3]), name = "adj")
 adj_learned = L.Dense(1, "relu" )(adj)
 adj_all = L.Concatenate(axis = 3 )([adj, adj_learned])
 xs = []
xs.append(node)
 x1 = forward(node, 128, kernel = 3, rate = 0.0)
 x2 = forward(x1, 64, kernel = 6, rate = 0.0)
 x3 = forward(x2, 32, kernel = 15, rate = 0.0)
 x4 = forward(x3, 16, kernel = 30, rate = 0.0)
 x = L.Concatenate()([x1, x2, x3, x4])
 for unit in [64, 32]:
x_as = []
for i in range(adj_all.shape[3]):
x_a = adj_attn(x, adj_all[:, :, :, i], unit, rate = 0.0)
 x_as.append(x_a)
 x_c = forward(x, unit, kernel = 30)
 x = L.Concatenate()(x_as + [x_c])
 x = forward(x, unit)
 x = multi_head_attention(x, x, unit, 4, 0.0)
 xs.append(x)
 x = L.Concatenate()(xs)
 model = tf.keras.Model(inputs = [node, adj], outputs = [x])
 return model
def get_ae_model(base, config):
node = tf.keras.Input(shape =(None, X_node.shape[2]), name = "node")
 adj = tf.keras.Input(shape =(None, None, As.shape[3]), name = "adj")
 x = base([L.SpatialDropout1D(0.3 )(node), adj])
 x = forward(x, 64, rate = 0.3)
 p = L.Dense(X_node.shape[2], "sigmoid" )(x)
 loss = - tf.reduce_mean(20 * node * tf.math.log(p + 1e-4)+(1 - node)* tf.math.log(1 - p + 1e-4))
model = tf.keras.Model(inputs = [node, adj], outputs = [loss])
 opt = get_optimizer()
model.compile(optimizer = opt, loss = lambda t, y : y)
 return model
def get_model(base, config):
node = tf.keras.Input(shape =(None, X_node.shape[2]), name = "node")
 adj = tf.keras.Input(shape =(None, None, As.shape[3]), name = "adj")
 x = base([node, adj])
 x = forward(x, 128, rate = 0.4)
 x = L.Dense(5, None )(x)
 model = tf.keras.Model(inputs = [node, adj], outputs = [x])
 opt = get_optimizer()
model.compile(optimizer = opt, loss = mcrmse_loss)
 return model
def get_optimizer() :
adam = tf.optimizers.Adam()
return adam
FIRST_DAY = 350<feature_engineering>
config = {}
if ae_epochs > 0:
base = get_base(config)
 ae_model = get_ae_model(base, config)
 for i in range(ae_epochs//ae_epochs_each):
print(f"------ {i} ------")
 print("--- train ---")
 ae_model.fit([X_node, As], [X_node[:,0]],
epochs = ae_epochs_each,
batch_size = ae_batch_size)
 print("--- public ---")
 ae_model.fit([X_node_pub, As_pub], [X_node_pub[:,0]],
epochs = ae_epochs_each,
batch_size = ae_batch_size)
 print("--- private ---")
 ae_model.fit([X_node_pri, As_pri], [X_node_pri[:,0]],
epochs = ae_epochs_each,
batch_size = ae_batch_size)
 gc.collect()
print("****** save ae model ******")
 base.save_weights("./base_ae" )
def create_features(dt):
lags = [7, 28]
lag_cols = [f"lag_{lag}" for lag in lags ]
for lag, lag_col in zip(lags, lag_cols):
dt[lag_col] = dt[["id","sales"]].groupby("id")["sales"].shift(lag)
 wins = [7, 28]
for win in wins :
for lag,lag_col in zip(lags, lag_cols):
dt[f"rmean_{lag}_{win}"] = dt[["id", lag_col]].groupby("id")[lag_col].transform(lambda x : x.rolling(win ).mean())
 date_features = {
"wday": "weekday",
"week": "weekofyear",
"month": "month",
"quarter": "quarter",
"year": "year",
"mday": "day",
}
for date_feat_name, date_feat_func in date_features.items() :
if date_feat_name in dt.columns:
dt[date_feat_name] = dt[date_feat_name].astype("int16")
 else:
dt[date_feat_name] = getattr(dt["date"].dt, date_feat_func ).astype("int16" )<correct_missing_values>
kfold = KFold(5, shuffle = True, random_state = 42)
 scores = []
preds = np.zeros([len(X_node), X_node.shape[1], 5])
 for i,(tr_idx, va_idx)in enumerate(kfold.split(X_node, As)) :
print(f"------ fold {i} start -----")
 print(f"------ fold {i} start -----")
 print(f"------ fold {i} start -----")
 X_node_tr = X_node[tr_idx]
X_node_va = X_node[va_idx]
As_tr = As[tr_idx]
As_va = As[va_idx]
y_tr = y[tr_idx]
y_va = y[va_idx]
base = get_base(config)
 if ae_epochs > 0:
print("****** load ae model ******")
 base.load_weights("./base_ae")
 model = get_model(base, config)
 if pretrain_dir is not None:
d = f"./model{i}"
print(f"--- load from {d} ---")
 model.load_weights(d)
 for epochs, batch_size in zip(epochs_list, batch_size_list):
print(f"epochs : {epochs}, batch_size : {batch_size}")
 model.fit([X_node_tr, As_tr], [y_tr],
validation_data=([X_node_va, As_va], [y_va]),
epochs = epochs,
batch_size = batch_size, validation_freq = 3)
 model.save_weights(f"./model{i}")
 p = model.predict([X_node_va, As_va])
 scores.append(mcrmse(y_va, p))
print(f"fold {i}: mcrmse {scores[-1]}")
 preds[va_idx] = p
if one_fold:
break
pd.to_pickle(preds, "oof.pkl" )
df.dropna(inplace = True)
 df.shape<prepare_x_and_y>
p_pub = 0
p_pri = 0
for i in range(5):
model.load_weights(f"./model{i}")
 p_pub += model.predict([X_node_pub, As_pub])/ 5
p_pri += model.predict([X_node_pri, As_pri])/ 5
if one_fold:
p_pub *= 5
p_pri *= 5
break
for i, target in enumerate(targets):
test_pub[target] = [list(p_pub[k, :, i])for k in range(p_pub.shape[0])]
test_pri[target] = [list(p_pri[k, :, i])for k in range(p_pri.shape[0])]
<create_dataframe><EOS> 
preds_ls = []
for df, preds in [(test_pub, p_pub),(test_pri, p_pri)]:
for i, uid in enumerate(df.id):
single_pred = preds[i]
single_df = pd.DataFrame(single_pred, columns=targets)
 single_df['id_seqpos'] = [f'{uid}_{x}' for x in range(single_df.shape[0])]
preds_ls.append(single_df)
 preds_df = pd.concat(preds_ls)
 preds_df.to_csv("submission.csv", index = False)
 preds_df.head()