Kaggle competition: OpenVaccine: COVID-19 mRNA Vaccine Degradation Prediction
<SOS> metric: MWCRMSE Kaggle data source: stanford-covid-vaccine<create_dataframe>
pretrain_dir = None
one_fold = False
run_test = False
denoise = True
ae_epochs = 20
ae_epochs_each = 5
ae_batch_size = 32
epochs_list = [50, 30, 20, 20, 15, 15]
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
output_notebook()
df_fimp_1_cc = pd.DataFrame({"feature": features_1, "importance": model_1_cc.feature_importance() , "model": "m01"})
 df_fimp_14_cc = pd.DataFrame({"feature": features_14, "importance": model_14_cc.feature_importance() , "model": "m14"})
 df_fimp_28_cc = pd.DataFrame({"feature": features_28, "importance": model_28_cc.feature_importance() , "model": "m28"})
 df_fimp_1_cc.sort_values("importance", ascending = False, inplace = True)
 df_fimp_14_cc.sort_values("importance", ascending = False, inplace = True)
 df_fimp_28_cc.sort_values("importance", ascending = False, inplace = True)
 v1 = figure(plot_width = 800, plot_height = 400, x_range = df_fimp_1_cc.feature, title = "Feature Importance of LGB Model 1")
 v1.vbar(x = df_fimp_1_cc.feature, top = df_fimp_1_cc.importance, width = 0.81)
 v1.xaxis.major_label_orientation = 1.3
v14 = figure(plot_width = 800, plot_height = 400, x_range = df_fimp_14_cc.feature, title = "Feature Importance of LGB Model 14")
 v14.vbar(x = df_fimp_14_cc.feature, top = df_fimp_14_cc.importance, width = 0.81)
 v14.xaxis.major_label_orientation = 1.3
v28 = figure(plot_width = 800, plot_height = 400, x_range = df_fimp_28_cc.feature, title = "Feature Importance of LGB Model 28")
 v28.vbar(x = df_fimp_28_cc.feature, top = df_fimp_28_cc.importance, width = 0.81)
 v28.xaxis.major_label_orientation = 1.3
v = column(v1, v14, v28)
 show(v )<feature_engineering>
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 )
df_test = df_panel.loc[~df_panel.ForecastId.isna() , ["ForecastId", "Country_Region", "Province_State", "Date",
"ConfirmedCases_test", "ConfirmedCases_test_lgb", "ConfirmedCases_test_mad",
"Fatalities_test", "Fatalities_test_lgb", "Fatalities_test_mad"]].reset_index()
df_test["ConfirmedCases"] = 0.6 * df_test.ConfirmedCases_test_lgb + 0.4 * df_test.ConfirmedCases_test_mad
df_test["Fatalities"] = 0.6 * df_test.Fatalities_test_lgb + 0.4 * df_test.Fatalities_test_mad
df_test.loc[df_test.Country_Region.isin(["Diamond Princess", "MS Zaandam"]), "ConfirmedCases"] = df_test[df_test.Country_Region.isin(["Diamond Princess", "MS Zaandam"])].ConfirmedCases_test_mad.values
df_test.loc[df_test.Country_Region.isin(["Diamond Princess", "MS Zaandam"]), "Fatalities"] = df_test[df_test.Country_Region.isin(["Diamond Princess", "MS Zaandam"])].Fatalities_test_mad.values
df_test.loc[df_test.Date.isin(df_train.Date.values), "ConfirmedCases"] = df_test[df_test.Date.isin(df_train.Date.values)].ConfirmedCases_test.values
df_test.loc[df_test.Date.isin(df_train.Date.values), "Fatalities"] = df_test[df_test.Date.isin(df_train.Date.values)].Fatalities_test.values
df_submission = df_test[["ForecastId", "ConfirmedCases", "Fatalities"]]
df_submission.ForecastId = df_submission.ForecastId.astype(int)
 df_submission 
<save_to_csv>
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
df_submission.to_csv(PATH_SUBMISSION, index = False)
 df_test.to_csv(PATH_OUTPUT, index = False )<load_from_csv>
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 )
df_train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
 df_test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
 df_sub = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv' )<data_type_conversions>
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
df_train['Date'] = pd.to_datetime(df_train['Date'], infer_datetime_format=True)
 df_test['Date'] = pd.to_datetime(df_test['Date'], infer_datetime_format=True)
 df_train.loc[:, 'Date'] = df_train.Date.dt.strftime("%m%d")
 df_train["Date"] = df_train["Date"].astype(int)
 df_test.loc[:, 'Date'] = df_test.Date.dt.strftime("%m%d")
 df_test["Date"] = df_test["Date"].astype(int )<data_type_conversions>
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 )
df_train['ConfirmedCases'] = df_train['ConfirmedCases'].apply(int)
 df_train['Fatalities'] = df_train['Fatalities'].apply(int )<data_type_conversions>
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='linear',
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(x_inner)
 x_K = L.Conv1D(n_factor, 1, activation='linear',
kernel_initializer='glorot_uniform',
bias_initializer='glorot_uniform',
)(x_outer)
 x_V = L.Conv1D(n_factor, 1, activation='linear',
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])
 res = tf.expand_dims(res, axis = 3)
 res = L.Conv2D(16, 3, 1, padding = "same", activation = "relu" )(res)
 res = L.Conv2D(1, 3, 1, padding = "same", activation = "relu" )(res)
 res = tf.squeeze(res, axis = 3)
 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, 16, 8]:
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
df_train['Province_State'] = df_train['Province_State'].fillna('unknown')
 df_test['Province_State'] = df_test['Province_State'].fillna('unknown' )<import_modules>
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" )
from sklearn.tree import DecisionTreeRegressor<prepare_x_and_y>
cv = StratifiedKFold(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(cv.split(X_node, train["SN_filter"])) :
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" )
features = ['Date']
submission = pd.DataFrame(columns=['ForecastId', 'ConfirmedCases', 'Fatalities'])
 for i in tqdm(df_train.Country_Region.unique()):
c_train = df_train[df_train['Country_Region'] == i]
c_test = df_test[df_test['Country_Region'] == i]
for j in c_train.Province_State.unique() :
p_train = c_train[c_train['Province_State'] == j]
p_test = c_test[c_test['Province_State'] == j]
x_train = p_train[features]
y_train_cc = p_train['ConfirmedCases']
y_train_ft = p_train['Fatalities']
model = DecisionTreeRegressor()
model.fit(x_train, y_train_cc)
 y_pred_cc = model.predict(p_test[features])
 model.fit(x_train, y_train_ft)
 y_pred_ft = model.predict(p_test[features])
 p_test['ConfirmedCases'] = y_pred_cc
p_test['Fatalities'] = y_pred_ft
submission = pd.concat([submission, p_test[['ForecastId', 'ConfirmedCases', 'Fatalities']]], axis=0 )<save_to_csv>
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])]
<save_to_csv><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()