Wann-Jiun

from xgboost import plot_importance
from matplotlib import pyplot

xgb_regr = xgb.XGBRegressor(
                 colsample_bytree=0.2,
                 gamma=0.0,
                 learning_rate=0.01,
                 max_depth=4,
                 min_child_weight=1.5,
                 n_estimators=7200,                                                                  

Wann-Jiun

# creating matrices for sklearn:
x_train = np.array(train_df_munged)
x_test = np.array(test_df_munged)
y_train = label_df.values

ntrain = x_train.shape[0]
ntest = x_test.shape[0]

kf = KFold(ntrain, n_folds=NFOLDS, shuffle=True, random_state=SEED)

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y_final = (1*np.ravel(y_test_pred_xgb) + 1*np.ravel(y_test_pred_kridge) + 1*np.ravel(y_test_pred_lasso))/3
y_final.shape

y_pred = np.exp(y_final)

# Final Conversion.
output_file = 'xgboost_lasso_kridge_weights_1_1_1'
final_file = '0108_'+ output_file +'.csv'

pred_df = pd.DataFrame(y_pred, index=test_df["Id"], columns=["SalePrice"])

Wann-Jiun

# Kernel Ridge GridSearch
from sklearn.kernel_ridge import KernelRidge

kridge_grid = KernelRidge()

parameter_grid = {'alpha': [0.0001,0.001,0.01,0.1],
                 'degree': [1,2,3,4],
                 'kernel': ['polynomial']
                 #'n_estimators': [200,210,240,250],
                 #'min_child_weight': [1,2,3,4]

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train_df["New_House"] = (train_df["YearRemodAdd"] == train_df["YrSold"]) * 1

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train_df["Aggregate_OverallQual"] = train_df.OverallQual.replace(
{1 : 1, 2 : 1, 3 : 1, 4 : 2, 5 : 2, 6 : 2, 7 : 3, 8 : 3, 9 : 3, 10 : 3})

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train_df["Is_Electrical_SBrkr"] = (df["Electrical"] == "SBrkr") * 1

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dummies = pd.get_dummies(train_df[column_name], prefix = "_" + column_name)
train_df = train_df.join(dummies)
train_df = train_df.drop([column_name], axis=1)

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quality_dict = {None: 0, "Po": 1, "Fa": 2, "TA": 3, "Gd": 4, "Ex": 5}

train_df["ExterQual"] = df["ExterQual"].map(quality_dict).astype(int)
train_df["ExterCond"] = df["ExterCond"].map(quality_dict).astype(int)
train_df["BsmtQual"] = df["BsmtQual"].map(quality_dict).astype(int)
train_df["BsmtCond"] = df["BsmtCond"].map(quality_dict).astype(int)
train_df["HeatingQC"] = df["HeatingQC"].map(quality_dict).astype(int)
train_df["KitchenQual"] = df["KitchenQual"].map(quality_dict).astype(int)
train_df["FireplaceQu"] = df["FireplaceQu"].map(quality_dict).astype(int)
train_df["GarageQual"] = df["GarageQual"].map(quality_dict).astype(int)

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all_df["CentralAir"] = (df["CentralAir"] == "Y") * 1.0