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kaggle4.py
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import matplotlib
matplotlib.use("PS")
import numpy as np # linear algebra
import modin.pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt # Matlab-style plotting
import seaborn as sns
color = sns.color_palette()
sns.set_style("darkgrid")
import warnings
def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn # ignore annoying warning (from sklearn and seaborn)
from scipy import stats
from scipy.stats import norm, skew # for some statistics
pd.set_option(
"display.float_format", lambda x: "{:.3f}".format(x)
) # Limiting floats output to 3 decimal points
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
train.head(5)
test.head(5)
print("The train data size before dropping Id feature is : {} ".format(train.shape))
print("The test data size before dropping Id feature is : {} ".format(test.shape))
train_ID = train["Id"]
test_ID = test["Id"]
train.drop("Id", axis=1, inplace=True)
test.drop("Id", axis=1, inplace=True)
print("\nThe train data size after dropping Id feature is : {} ".format(train.shape))
print("The test data size after dropping Id feature is : {} ".format(test.shape))
fig, ax = plt.subplots()
ax.scatter(x=train["GrLivArea"], y=train["SalePrice"])
plt.ylabel("SalePrice", fontsize=13)
plt.xlabel("GrLivArea", fontsize=13)
plt.show()
train = train.drop(
train[(train["GrLivArea"] > 4000) & (train["SalePrice"] < 300000)].index
)
fig, ax = plt.subplots()
ax.scatter(train["GrLivArea"], train["SalePrice"])
plt.ylabel("SalePrice", fontsize=13)
plt.xlabel("GrLivArea", fontsize=13)
plt.show()
sns.distplot(train["SalePrice"], fit=norm)
(mu, sigma) = norm.fit(train["SalePrice"])
print("\n mu = {:.2f} and sigma = {:.2f}\n".format(mu, sigma))
plt.legend(
[r"Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )".format(mu, sigma)],
loc="best", # noqa: W605
)
plt.ylabel("Frequency")
plt.title("SalePrice distribution")
fig = plt.figure()
res = stats.probplot(train["SalePrice"], plot=plt)
plt.show()
train["SalePrice"] = np.log1p(train["SalePrice"])
sns.distplot(train["SalePrice"], fit=norm)
(mu, sigma) = norm.fit(train["SalePrice"])
print("\n mu = {:.2f} and sigma = {:.2f}\n".format(mu, sigma))
plt.legend(
[r"Normal dist. ($\mu=$ {:.2f} and $\sigma=$ {:.2f} )".format(mu, sigma)],
loc="best", # noqa: W605
)
plt.ylabel("Frequency")
plt.title("SalePrice distribution")
fig = plt.figure()
res = stats.probplot(train["SalePrice"], plot=plt)
plt.show()
ntrain = train.shape[0]
ntest = test.shape[0]
y_train = train.SalePrice.values
all_data = pd.concat((train, test)).reset_index(drop=True)
all_data.drop(["SalePrice"], axis=1, inplace=True)
print("all_data size is : {}".format(all_data.shape))
all_data_na = (all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(
ascending=False
)[:30]
missing_data = pd.DataFrame({"Missing Ratio": all_data_na})
missing_data.head(20)
corrmat = train.corr()
plt.subplots(figsize=(12, 9))
sns.heatmap(corrmat, vmax=0.9, square=True)
all_data["PoolQC"] = all_data["PoolQC"].fillna("None")
all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None")
all_data["Alley"] = all_data["Alley"].fillna("None")
all_data["Fence"] = all_data["Fence"].fillna("None")
all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None")
all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(
lambda x: x.fillna(x.median())
)
for col in ("GarageType", "GarageFinish", "GarageQual", "GarageCond"):
all_data[col] = all_data[col].fillna("None")
for col in ("GarageYrBlt", "GarageArea", "GarageCars"):
all_data[col] = all_data[col].fillna(0)
for col in (
"BsmtFinSF1",
"BsmtFinSF2",
"BsmtUnfSF",
"TotalBsmtSF",
"BsmtFullBath",
"BsmtHalfBath",
):
all_data[col] = all_data[col].fillna(0)
for col in ("BsmtQual", "BsmtCond", "BsmtExposure", "BsmtFinType1", "BsmtFinType2"):
all_data[col] = all_data[col].fillna("None")
all_data["MasVnrType"] = all_data["MasVnrType"].fillna("None")
all_data["MasVnrArea"] = all_data["MasVnrArea"].fillna(0)
all_data["MSZoning"] = all_data["MSZoning"].fillna(all_data["MSZoning"].mode()[0])
all_data = all_data.drop(["Utilities"], axis=1)
all_data["Functional"] = all_data["Functional"].fillna("Typ")
all_data["Electrical"] = all_data["Electrical"].fillna(all_data["Electrical"].mode()[0])
all_data["KitchenQual"] = all_data["KitchenQual"].fillna(
all_data["KitchenQual"].mode()[0]
)
all_data["Exterior1st"] = all_data["Exterior1st"].fillna(
all_data["Exterior1st"].mode()[0]
)
all_data["Exterior2nd"] = all_data["Exterior2nd"].fillna(
all_data["Exterior2nd"].mode()[0]
)
all_data["SaleType"] = all_data["SaleType"].fillna(all_data["SaleType"].mode()[0])
all_data["MSSubClass"] = all_data["MSSubClass"].fillna("None")
all_data_na = (all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(
ascending=False
)
missing_data = pd.DataFrame({"Missing Ratio": all_data_na})
missing_data.head()
all_data["MSSubClass"] = all_data["MSSubClass"].apply(str)
all_data["OverallCond"] = all_data["OverallCond"].astype(str)
all_data["YrSold"] = all_data["YrSold"].astype(str)
all_data["MoSold"] = all_data["MoSold"].astype(str)
from sklearn.preprocessing import LabelEncoder
cols = (
"FireplaceQu",
"BsmtQual",
"BsmtCond",
"GarageQual",
"GarageCond",
"ExterQual",
"ExterCond",
"HeatingQC",
"PoolQC",
"KitchenQual",
"BsmtFinType1",
"BsmtFinType2",
"Functional",
"Fence",
"BsmtExposure",
"GarageFinish",
"LandSlope",
"LotShape",
"PavedDrive",
"Street",
"Alley",
"CentralAir",
"MSSubClass",
"OverallCond",
"YrSold",
"MoSold",
)
for c in cols:
lbl = LabelEncoder()
lbl.fit(list(all_data[c].values))
all_data[c] = lbl.transform(list(all_data[c].values))
print("Shape all_data: {}".format(all_data.shape))
all_data["TotalSF"] = (
all_data["TotalBsmtSF"] + all_data["1stFlrSF"] + all_data["2ndFlrSF"]
)
numeric_feats = all_data.dtypes[all_data.dtypes != "object"].index
skewed_feats = (
all_data[numeric_feats]
.apply(lambda x: skew(x.dropna()))
.sort_values(ascending=False)
)
print("\nSkew in numerical features: \n")
skewness = pd.DataFrame({"Skew": skewed_feats})
skewness.head(10)
skewness = skewness[abs(skewness) > 0.75]
print(
"There are {} skewed numerical features to Box Cox transform".format(
skewness.shape[0]
)
)
from scipy.special import boxcox1p
skewed_features = skewness.index
lam = 0.15
for feat in skewed_features:
# all_data[feat] += 1
all_data[feat] = boxcox1p(all_data[feat], lam)
all_data = pd.get_dummies(all_data)
print(all_data.shape)
train = all_data[:ntrain]
test = all_data[ntrain:]
from sklearn.linear_model import ElasticNet, Lasso # BayesianRidge, LassoLarsIC
from sklearn.ensemble import GradientBoostingRegressor # RandomForestRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.model_selection import KFold, cross_val_score # train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import lightgbm as lgb
n_folds = 5
def rmsle_cv(model):
kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)
rmse = np.sqrt(
-cross_val_score(
model, train.values, y_train, scoring="neg_mean_squared_error", cv=kf
)
)
return rmse
lasso = make_pipeline(RobustScaler(), Lasso(alpha=0.0005, random_state=1))
ENet = make_pipeline(
RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=0.9, random_state=3)
)
KRR = KernelRidge(alpha=0.6, kernel="polynomial", degree=2, coef0=2.5)
GBoost = GradientBoostingRegressor(
n_estimators=1,
learning_rate=0.05,
max_depth=4,
max_features="sqrt",
min_samples_leaf=15,
min_samples_split=10,
loss="huber",
random_state=5,
)
model_xgb = xgb.XGBRegressor(
colsample_bytree=0.4603,
gamma=0.0468,
learning_rate=0.05,
max_depth=3,
min_child_weight=1.7817,
n_estimators=1,
reg_alpha=0.4640,
reg_lambda=0.8571,
subsample=0.5213,
silent=1,
random_state=7,
nthread=-1,
)
model_lgb = lgb.LGBMRegressor(
objective="regression",
num_leaves=5,
learning_rate=0.05,
n_estimators=1,
max_bin=55,
bagging_fraction=0.8,
bagging_freq=5,
feature_fraction=0.2319,
feature_fraction_seed=9,
bagging_seed=9,
min_data_in_leaf=6,
min_sum_hessian_in_leaf=11,
)
score = rmsle_cv(lasso)
print("\nLasso score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(ENet)
print("ElasticNet score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(KRR)
print("Kernel Ridge score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(GBoost)
print("Gradient Boosting score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(model_xgb)
print("Xgboost score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
score = rmsle_cv(model_lgb)
print("LGBM score: {:.4f} ({:.4f})\n".format(score.mean(), score.std()))
class AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, models):
self.models = models
def fit(self, X, y):
self.models_ = [clone(x) for x in self.models]
for model in self.models_:
model.fit(X, y)
return self
def predict(self, X):
predictions = np.column_stack([model.predict(X) for model in self.models_])
return np.mean(predictions, axis=1)
averaged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))
score = rmsle_cv(averaged_models)
print(
" Averaged base models score: {:.4f} ({:.4f})\n".format(score.mean(), score.std())
)
class StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):
def __init__(self, base_models, meta_model, n_folds=5):
self.base_models = base_models
self.meta_model = meta_model
self.n_folds = n_folds
def fit(self, X, y):
self.base_models_ = [[] for _ in self.base_models]
self.meta_model_ = clone(self.meta_model)
kfold = KFold(n_splits=self.n_folds, shuffle=True, random_state=156)
out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))
for i, model in enumerate(self.base_models):
for train_index, holdout_index in kfold.split(X, y):
instance = clone(model)
self.base_models_[i].append(instance)
instance.fit(X[train_index], y[train_index])
y_pred = instance.predict(X[holdout_index])
out_of_fold_predictions[holdout_index, i] = y_pred
self.meta_model_.fit(out_of_fold_predictions, y)
return self
def predict(self, X):
meta_features = np.column_stack(
[
np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)
for base_models in self.base_models_
]
)
return self.meta_model_.predict(meta_features)
stacked_averaged_models = StackingAveragedModels(
base_models=(ENet, GBoost, KRR), meta_model=lasso
)
score = rmsle_cv(stacked_averaged_models)
print(
"Stacking Averaged models score: {:.4f} ({:.4f})".format(score.mean(), score.std())
)
def rmsle(y, y_pred):
return np.sqrt(mean_squared_error(y, y_pred))
stacked_averaged_models.fit(train.values, y_train)
stacked_train_pred = stacked_averaged_models.predict(train.values)
stacked_pred = np.expm1(stacked_averaged_models.predict(test.values))
print(rmsle(y_train, stacked_train_pred))
model_xgb.fit(train, y_train)
xgb_train_pred = model_xgb.predict(train)
xgb_pred = np.expm1(model_xgb.predict(test))
print(rmsle(y_train, xgb_train_pred))
model_lgb.fit(train, y_train)
lgb_train_pred = model_lgb.predict(train)
lgb_pred = np.expm1(model_lgb.predict(test.values))
print(rmsle(y_train, lgb_train_pred))
print("RMSLE score on train data:")
print(
rmsle(
y_train,
stacked_train_pred * 0.70 + xgb_train_pred * 0.15 + lgb_train_pred * 0.15,
)
)
ensemble = stacked_pred * 0.70 + xgb_pred * 0.15 + lgb_pred * 0.15
sub = pd.DataFrame()
sub["Id"] = test_ID
sub["SalePrice"] = ensemble
sub.to_csv("submission.csv", index=False)