taxi

Winning entry to the Kaggle taxi competition
git clone https://esimon.eu/repos/taxi.git
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time_mlp_2_cswdtx.py (816B)

      1 from blocks.initialization import IsotropicGaussian, Constant
      2 
      3 import data
      4 from model.time_mlp import Model, Stream
      5 
      6 
      7 n_begin_end_pts = 5     # how many points we consider at the beginning and end of the known trajectory
      8 
      9 dim_embeddings = [
     10     ('origin_call', data.origin_call_train_size, 10),
     11     ('origin_stand', data.stands_size, 10),
     12     ('week_of_year', 52, 10),
     13     ('day_of_week', 7, 10),
     14     ('qhour_of_day', 24 * 4, 10),
     15     ('day_type', 3, 10),
     16     ('taxi_id', 448, 10),
     17 ]
     18 
     19 dim_input = n_begin_end_pts * 2 * 2 + sum(x for (_, _, x) in dim_embeddings)
     20 dim_hidden = [500, 100]
     21 dim_output = 1
     22 
     23 embed_weights_init = IsotropicGaussian(0.001)
     24 mlp_weights_init = IsotropicGaussian(0.01)
     25 mlp_biases_init = Constant(0.001)
     26 
     27 exp_base = 1.5
     28 
     29 learning_rate = 0.00001
     30 momentum = 0.99
     31 batch_size = 32
     32 
     33 max_splits = 100