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overnighttrainingsession.py
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overnighttrainingsession.py
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params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'max_depth': 10,
'min_child_weight': 1.15,
'gamma': 1.15,
'subsample': 0.90,
'eta' :0.3,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v3.model')
gbm.dump_model('model\\base_v3.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'max_depth': 5,
'min_child_weight': 1.15,
'gamma': 1.15,
'subsample': 0.90,
'eta' :0.3,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v6.model')
gbm.dump_model('model\\base_v6.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'nthread': 4,
'silent':1,
'num_class': 38}
num_rounds = 300
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v2.model')
gbm.dump_model('model\\base_v2.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'max_depth': 5,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v4.model')
gbm.dump_model('model\\base_v4.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'max_depth': 6,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v10.model')
gbm.dump_model('model\\base_v10.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta' :0.3,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v5.model')
gbm.dump_model('model\\base_v5.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'eta' :0.005,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v7.model')
gbm.dump_model('model\\base_v7.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'subsample' : 100,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v8.model')
gbm.dump_model('model\\base_v8.dmp', with_stats=True)
params = {'objective': 'multi:softprob',
'eval_metric': 'mlogloss',
'subsample' :0.90,
'silent':1,
'num_class': 38}
num_rounds = 3000
#dvalid = xgb.DMatrix(X_test[features], np.log(X_test["Sales"] + 1))
#dtest = xgb.DMatrix(test[features])
watchlist = [(dvalid, 'eval'), (dtrain, 'train')]
gbm = xgb.train(params, dtrain, num_rounds, evals=watchlist, early_stopping_rounds=50)
gbm.save_model('model\\base_v9.model')
gbm.dump_model('model\\base_v9.dmp', with_stats=True)