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parameters.yml
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parameters.yml
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pytorch_model:
=: torch.nn.Sequential
_:
- =: pipelinex.ModuleProd
_:
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, start: 18 }
- { =: pipelinex.TensorSqueeze, dim: 1 }
- { =: pipelinex.TensorSlice, start: 1, end: 2 }
- { =: pipelinex.TensorSqueeze, dim: 1 }
- { =: pipelinex.TensorSlice, end: 1 }
- { =: pipelinex.TensorSqueeze, dim: 1 }
# -> [N] # YardsToGoalP10Val
- { =: pipelinex.StepBinary, size: 100, desc: True }
# -> [N, 100] # YardsToGoal mask
- =: torch.nn.Sequential
_:
- =: pipelinex.ModuleSum
_:
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, start: 18 }
- { =: pipelinex.TensorSqueeze, dim: 1 }
- { =: pipelinex.TensorSlice, start: 0, end: 1 }
- { =: pipelinex.TensorFlatten, _ }
- { =: pipelinex.TensorSlice, start: 0, end: 50 }
# -> [N, 50] # - Base Probas
- =: torch.nn.Sequential
_:
- =: pipelinex.ModuleConcat
_:
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, start: 18 }
- { =: pipelinex.TensorSqueeze, dim: 1 }
- { =: pipelinex.TensorSlice, start: 1, end: 6 }
- { =: pipelinex.TensorFlatten, _ }
- { =: pipelinex.TensorSlice, start: 1 }
- { =: torch.nn.Linear, in_features: 269, out_features: 200 }
- { =: torch.nn.CELU, alpha: 1.0 }
- { =: torch.nn.Linear, in_features: 200, out_features: 200 }
- { =: torch.nn.CELU, alpha: 1.0 }
# - =: pipelinex.ModuleConcat
# _:
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 0, end: 1}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 25}
# - {=: torch.nn.CELU, alpha: 1.0}
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 1, end: 2}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 15}
# - {=: torch.nn.Sigmoid, _: }
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 2, end: 3}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 15}
# - {=: torch.nn.Sigmoid, _: }
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 3, end: 4}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 15}
# - {=: torch.nn.CELU, alpha: 1.0}
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 4, end: 5}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 15}
# - {=: torch.nn.CELU, alpha: 1.0}
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 5, end: 6}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 5}
# - {=: torch.nn.CELU, alpha: 1.0}
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 6, end: 7}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 5}
# - {=: torch.nn.CELU, alpha: 1.0}
# - =: torch.nn.Sequential
# _:
# - {=: pipelinex.TensorSlice, start: 7, end: 8}
# - {=: pipelinex.TensorFlatten, _: }
# - {=: torch.nn.Linear, in_features: 54, out_features: 5}
# - {=: torch.nn.CELU, alpha: 1.0}
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, end: 18 }
- { =: pipelinex.TensorGlobalAvgPool2d, keepdim: False }
- { =: torch.nn.Linear, in_features: 18, out_features: 15 }
- { =: torch.nn.CELU, alpha: 1.0 }
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, end: 18 }
- { =: pipelinex.TensorGlobalMaxPool2d, keepdim: False }
- { =: torch.nn.Linear, in_features: 18, out_features: 15 }
- { =: torch.nn.Sigmoid, _ }
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, end: 18 }
- { =: pipelinex.TensorGlobalMinPool2d, keepdim: False }
- { =: torch.nn.Linear, in_features: 18, out_features: 15 }
- { =: torch.nn.Sigmoid, _ }
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, end: 18 }
- { =: pipelinex.TensorGlobalRangePool2d, keepdim: False }
- { =: torch.nn.Linear, in_features: 18, out_features: 15 }
- { =: torch.nn.Sigmoid, _ }
- =: torch.nn.Sequential
_:
- { =: pipelinex.TensorSlice, end: 18 }
- =: pipelinex.ModuleConcat
_:
# - {=: kaggle_nfl.kaggle_nfl.GaussianBlur2d, kernel_size: "(15, 15)", sigma: "(5.0, 5.0)"}
- { =: pipelinex.TensorConv2d, in_channels: 18, out_channels: 10, kernel_size: [3, 3] }
- { =: pipelinex.TensorConv2d, in_channels: 18, out_channels: 10, kernel_size: [7, 7] }
- { =: pipelinex.TensorConv2d, in_channels: 18, out_channels: 10, kernel_size: [3, 9] }
- { =: torch.nn.CELU, alpha: 1.0 }
- =: pipelinex.ModuleConcat
_:
- { =: pipelinex.TensorAvgPool2d, stride: [1, 2], kernel_size: [3, 3] }
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 30,
out_channels: 10,
kernel_size: [3, 3],
}
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 30,
out_channels: 10,
kernel_size: [7, 7],
}
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 30,
out_channels: 10,
kernel_size: [3, 9],
}
- { =: torch.nn.CELU, alpha: 1.0 }
# - =: pipelinex.ModuleConcat
# _:
# - {=: pipelinex.TensorConv2d, in_channels: 60, out_channels: 60, kernel_size: [1, 1]}
# - {=: pipelinex.TensorConv2d, in_channels: 60, out_channels: 20, kernel_size: [3, 3]}
# - {=: pipelinex.TensorConv2d, in_channels: 60, out_channels: 20, kernel_size: [7, 7]}
# - {=: pipelinex.TensorConv2d, in_channels: 60, out_channels: 20, kernel_size: [5, 15]}
# - {=: torch.nn.CELU, alpha: 1.0}
- =: pipelinex.ModuleConcat
_:
- { =: pipelinex.TensorAvgPool2d, stride: [1, 2], kernel_size: [3, 3] }
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 60,
out_channels: 20,
kernel_size: [3, 3],
}
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 60,
out_channels: 20,
kernel_size: [7, 7],
}
- {
=: pipelinex.TensorConv2d,
stride: [1, 2],
in_channels: 60,
out_channels: 20,
kernel_size: [3, 9],
}
# -> [N, 120, 30, 14]
- { =: torch.nn.CELU, alpha: 1.0 }
- =: pipelinex.ModuleConcat
_:
- =: torch.nn.Sequential
_:
- { =: torch.nn.AvgPool2d, stride: [1, 2], kernel_size: [3, 14] }
# -> [N, 120, 28, 1]
- {
=: pipelinex.TensorConv2d,
in_channels: 120,
out_channels: 20,
kernel_size: [1, 1],
}
- { =: pipelinex.TensorFlatten, _ }
- { =: torch.nn.CELU, _ }
- =: torch.nn.Sequential
_:
- { =: torch.nn.MaxPool2d, stride: [1, 2], kernel_size: [3, 14] }
# -> [N, 120, 28, 1]
- {
=: pipelinex.TensorConv2d,
in_channels: 120,
out_channels: 20,
kernel_size: [1, 1],
}
- { =: pipelinex.TensorFlatten, _ }
- { =: torch.nn.CELU, _ }
- { =: torch.nn.Linear, in_features: 1380, out_features: 50 }
- { =: torch.nn.Tanhshrink, _ }
- { =: pipelinex.TensorNearestPad, lower: 0, upper: 50 }
- { =: torch.nn.ConstantPad1d, padding: [89, 10], value: 0.0 } # 199 = 89 + 100 + 10
# - {=: torch.nn.Softmax, dim: 1}
- { =: torch.nn.ReLU, _ }
- { =: pipelinex.TensorProba, dim: 1 }
- { =: pipelinex.TensorCumsum, dim: 1 }
- { =: pipelinex.TensorClamp, min: 0.0, max: 1.0 }
augmentation:
horizontal_flip_proba: 0
horizontal_shift_std: 1.0
vertical_shift_std: 0.1
tta: 0
train_batch_size: 32
train_params:
epochs: 12 # number of epochs to train
time_limit: 12000
model_checkpoint: { =: pipelinex.extras.ops.ignite.handlers.flexible_checkpoint.FlexibleModelCheckpoint }
model_checkpoint_params:
dirname: ../checkpoint
filename_prefix: "%Y-%m-%dT%H-%M-%S"
offset_hours: 8
n_saved: 12
atomic: True
require_empty: True
create_dir: True
save_as_state_dict: False
early_stopping_params:
metric: crps
minimize: True
patience: 1000
scheduler:
=: ignite.contrib.handlers.param_scheduler.LinearCyclicalScheduler
# =: ignite.contrib.handlers.param_scheduler.CosineAnnealingScheduler
scheduler_params:
param_name: lr
start_value:
=: operator.mul
_:
- 0.000_001
- $: train_batch_size
end_value:
=: operator.mul
_:
- 0.000_01
- $: train_batch_size
cycle_epochs: 2 # cycle_size: int(cycle_epochs * len(train_loader))
cycle_mult: 1.0
start_value_mult: 1.0
end_value_mult: 1.0
save_history: False
optimizer:
=: torch.optim.Adam
# optimizer:
# =: torch.optim.SGD
optimizer_params:
weight_decay:
=: operator.truediv
_:
- 0.001
- $: train_batch_size
loss_fn:
=: kaggle_nfl.kaggle_nfl.NflCrpsLossFunc
min: -10
max: 19
# desc_penalty: 10.0
evaluation_metrics:
crps:
=: ignite.metrics.Loss
loss_fn:
=: kaggle_nfl.kaggle_nfl.nfl_crps_loss
# l1crps:
# =: ignite.metrics.Loss
# loss_fn:
# =: kaggle_nfl.kaggle_nfl.nfl_l1crps_loss
# train_dataset_size_limit: 128
# val_dataset_size_limit: 128
train_data_loader_params:
batch_size:
$: train_batch_size
num_workers: 1
val_data_loader_params:
batch_size:
$: train_batch_size
num_workers: 1
evaluate_train_data: COMPLETED
evaluate_val_data: EPOCH_COMPLETED
progress_update: True
seed: 0 #
#q_transformer:
# =: pipelinex.DfQuantileTransformer
# n_quantiles: 1000
# output_distribution: normal
# subsample: 100000
# random_state: 42
# =: pipelinex.DfStandardScaler
# with_mean: False
# with_std: True
# zero_to_zero: FalseQ
# cols:
# - _A
# - _S_X
# - _S_Y
# - X_Defense_Max
# - X_RR_Defense_Max
# - Y_RR_Defense_Max
# - X_Offense_Max
# - X_RR_Offense_Max
# - Y_RR_Offense_Max
# - X_Defense_Min
# - X_RR_Defense_Min
# - Y_RR_Defense_Min
# - X_Offense_Min
# - X_RR_Offense_Min
# - Y_RR_Offense_Min
# - X_Defense_Mean
# - X_RR_Defense_Mean
# - Y_RR_Defense_Mean
# - X_Offense_Mean
# - X_RR_Offense_Mean
# - Y_RR_Offense_Mean
# - X_RR_Defense_Stdev
# - Y_RR_Defense_Stdev
# - X_RR_Offense_Stdev
# - Y_RR_Offense_Stdev
# - X_Rusher
# - Y_Rusher
RUN_CONFIG:
pipeline_name: __default__
only_missing: True
runner: SequentialRunner # None
tags: # None
node_names: # None
from_nodes: # None
to_nodes: # None
from_inputs: # None
load_versions: # None
MLFLOW_LOGGING_CONFIG:
offset_hours: 8
logging_artifacts: # None
PIPELINES:
__default__:
=: pipelinex.FlexiblePipeline
parameters_in_inputs: False
module: kaggle_nfl.kaggle_nfl
decorator: pipelinex.log_time
nodes:
- inputs: train
outputs: df_020
- inputs: df_020
func:
- preprocess
outputs: df_030
# - inputs: df_030
# func:
# =: pipelinex.DfSample
# frac: 0.5
# random_state: 191024
# col_sample: PlayId
# col_assign: Validation
# outputs: df_040
- inputs: df_030
func:
- =: pipelinex.DfHead
groupby: Season
n: 22
outputs: df_031_head
- inputs: df_030
func:
- =: pipelinex.DfQuery
expr: "TimeHandoff < '2018-11-30T08:00:00'"
outputs: df_031_train
- inputs: df_030
func:
- =: pipelinex.DfQuery
expr: "'2018-11-30T08:00:00' <= TimeHandoff"
outputs: df_031_val
- inputs:
- df_031_train
- df_031_val
func:
=: pipelinex.DfConcat
new_col_name: Validation
new_col_values: [0, 1]
outputs: df_040
- inputs: [df_040, parameters]
func: generate_datasets
outputs:
- train_dataset
- val_dataset
- base_probas
- inputs:
- "params:pytorch_model"
- train_dataset
- val_dataset
func:
=: pipelinex.NetworkTrain
train_params:
$: train_params
mlflow_logging: True
outputs: pytorch_model
# - inputs: [pytorch_model, base_probas, transformer, parameters]
# func: infer
# outputs: infered_first_iter
- inputs:
- val_dataset
- pytorch_model
- parameters
func: final_validation
outputs: loss_df
- inputs: df_020
outputs: describe
- inputs: df_020
func:
=: pipelinex.DfQuery
expr: "Season == 2017"
outputs: describe_s2017
- inputs: df_020
func:
=: pipelinex.DfQuery
expr: "Season == 2018"
outputs: describe_s2018
- inputs: df_020
func:
- =: pipelinex.DfQuery
expr: "NflId == NflIdRusher"
outputs: rusher_df_raw
- inputs: df_030
func:
=: pipelinex.DfQuery
expr: "Season == 2017"
outputs: describe_prep_s2017
- inputs: df_030
func:
=: pipelinex.DfQuery
expr: "Season == 2018"
outputs: describe_prep_s2018
- inputs: df_030
func:
- =: pipelinex.DfQuery
expr: "PlayerCategory == 2"
outputs: rusher_df
- inputs: rusher_df
func:
- =: pipelinex.DfQuery
expr: "Season == 2017"
outputs: rusher_2017_describe
- inputs: rusher_df
func:
- =: pipelinex.DfQuery
expr: "Season == 2018"
outputs: rusher_2018_describe
# - inputs: play_df
# func:
# =: pipelinex.DfDrop
# columns: TimeHandoff
# outputs: pairplot
# - inputs: parameters
# func: get_test_df
# outputs: test_df
#
# - inputs:
# - train
# - test_df
# func:
# =: pipelinex.DfConcat
# _:
# outputs: eda_df
#
# - inputs: eda_df
# func:
# - =: pipelinex.DfFilter
# items:
# - GameId
# - PlayId
# - X
# - Y
# - S
# - A
# - Dis
# - Dir
# - Season
# - YardLine
# - Quarter
# - PossessionTeam
# - Down
# - Distance
# - FieldPosition
# - PlayDirection
# - Yards
# - Week
# outputs: df_020_filtered
#