Kaggle competition: Jane Street Market Prediction
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SEED = 1111
np.random.seed(SEED)
 train = pd.read_csv('.. /input/jane-street-market-prediction/train.csv')
 train = train.query('date > 85' ).reset_index(drop = True)
 train = train[train['weight'] != 0]
train.fillna(train.mean() ,inplace=True)
 train['action'] =(( train['resp'].values)> 0 ).astype(int)
 features = [c for c in train.columns if "feature" in c]
f_mean = np.mean(train[features[1:]].values,axis=0)
 resp_cols = ['resp_1', 'resp_2', 'resp_3', 'resp', 'resp_4']
X_train = train.loc[:, train.columns.str.contains('feature')]
y_train = np.stack([(train[c] > 0 ).astype('int')for c in resp_cols] ).T
def create_mlp(
 num_columns, num_labels, hidden_units, dropout_rates, label_smoothing, learning_rate 
):
inp = tf.keras.layers.Input(shape=(num_columns,))
x = tf.keras.layers.BatchNormalization()(inp)
 x = tf.keras.layers.Dropout(dropout_rates[0] )(x)
 for i in range(len(hidden_units)) :
x = tf.keras.layers.Dense(hidden_units[i] )(x)
 x = tf.keras.layers.BatchNormalization()(x)
 x = tf.keras.layers.Activation(tf.keras.activations.swish )(x)
 x = tf.keras.layers.Dropout(dropout_rates[i + 1] )(x)
 x = tf.keras.layers.Dense(num_labels )(x)
 out = tf.keras.layers.Activation("sigmoid" )(x)
 model = tf.keras.models.Model(inputs=inp, outputs=out)
 model.compile(
 optimizer=tfa.optimizers.RectifiedAdam(learning_rate=learning_rate),
loss=tf.keras.losses.BinaryCrossentropy(label_smoothing=label_smoothing),
metrics=tf.keras.metrics.AUC(name="AUC"), 
)
 return model
epochs = 200
batch_size = 4096
hidden_units = [160, 160, 160]
dropout_rates = [0.2, 0.2, 0.2, 0.2]
label_smoothing = 1e-2
learning_rate = 1e-3
tf.keras.backend.clear_session()
tf.random.set_seed(SEED)
 clf = create_mlp(
 len(features), 5, hidden_units, dropout_rates, label_smoothing, learning_rate 
)
 clf.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, verbose=2)
 clf.save(f'model.h5')
 th = 0.502
models = [clf]
f = np.median
env = janestreet.make_env()
for(test_df, pred_df)in tqdm(env.iter_test()):
if test_df['weight'].item() > 0:
x_tt = test_df.loc[:, features].values
if np.isnan(x_tt[:, 1:].sum()):
x_tt[:, 1:] = np.nan_to_num(x_tt[:, 1:])+ np.isnan(x_tt[:, 1:])* f_mean
pred = np.mean([model(x_tt, training = False ).numpy() for model in models],axis=0)
 pred = f(pred)
 pred_df.action = np.where(pred >= th, 1, 0 ).astype(int)
 else:
pred_df.action = 0
env.predict(pred_df )