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prepare:
folderlocation: 'terra-mystica/'
vp-data-dir: 'data/faction-picker-bot/vpdata.csv'
feature-data-dir: 'data/faction-picker-bot/featdata.csv'
player-drop-dir: 'data/faction-picker-bot/unfinishedgames.csv'
prepare-step2:
round-features: 'one-hot' # choose between: ['one-hot', 'ordinal']
map-features: 'one-hot' # choose between: ['one-hot', 'ordinal']
playercount-features: 'ordinal' # choose between: ['one-hot', 'ordinal']
pickle-dir: 'data/faction-picker-bot/each-faction-data.pkl'
training:
model-dir: 'data/faction-picker-bot/models/'
model-metrics-dir: 'data/faction-picker-bot/model-metrics/'
training-routine: 'lgb_train_method' # either lgb_train_method or lgb_kfolds_scikitlearn
train-proportion: 0.8
val-proportion: 0.1
test-proportion: 0.1
split-rounds: 1500 # used only when lgb_kfolds_scikitlearn is selected
num-rounds: 40
lgbt-model-kwargs:
num_leaves: 31
learning_rate: 0.1
max_depth: -1
boosting_type: 'dart'
objective: 'regression'
feature_fraction: 0.7
nn-model-kwargs:
hidden_units: [8, 8]
learning_rate: 0.001
batch_size: 256
num_epochs: 100
loss: 'mse'
create-metrics:
metrics-dir: 'data/faction-picker-bot/metrics/'
metrics-dir2: 'data/faction-picker-bot/model_metrics.json'
metrics-dir3: 'data/faction-picker-bot/model_plot_data.csv'
shap-metrics:
shap-dir: 'data/faction-picker-bot/shap-metrics/'
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