YData = df_train.SalePrice
#Applying Lasso Model
from sklearn.cross_validation import cross_val_score
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV, LinearRegression
def rmse_cv(model):
rmse = np.sqrt(-cross_val_score(
model, trainData, YData, scoring="neg_mean_squared_error", cv=5))
return (rmse)
model_lasso = LassoCV(alphas=[1, 0.1, 0.001, 0.0005],
selection='random',
max_iter=15000).fit(trainData, YData)
res = rmse_cv(model_lasso)
#print(res)
print("Lasso Mean:", res.mean())
print("Lasso Min: ", res.min())
coef = pd.Series(model_lasso.coef_, index=trainData.columns)
#print("Lasso picked " + str(sum(coef != 0)) + " variables and eliminated the other " + str(sum(coef == 0)) + " variables")
imp_coef = pd.concat(
[coef.sort_values().head(10),
coef.sort_values().tail(10)])
matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)
imp_coef.plot(kind="barh", color='r')
plt.title("Coefficients used in the Lasso Model")
#trainFilled.plot.scatter(x='TotalBsmtSF', y='SalePrice')
#trainFilled.plot.scatter(x='MasVnrArea', y='SalePrice')
numRuns = 1
errors = [0] * numRuns
ridgeCV = RidgeCV(alphas=[21, 22, 23, 24, 25, 26, 27, 28, 29, 30],
scoring="neg_mean_squared_error",
normalize=False,
cv=nFolds)
ridge = Ridge(normalize=False)
alphasRidge = {"alpha": [13, 14, 15, 16, 17, 20, 21, 22, 23, 24, 25]}
alphasLasso = {"alpha": [0.0005, 0.001, 0.005]}
lasso = Lasso(max_iter=1000)
lassoCV = LassoCV(alphas=[0.0005, 0.001, 0.005],
normalize=False,
cv=nFolds,
max_iter=1000)
#lassoCV.fit(trainFilled.drop(columns='SalePrice'), trainFilled['SalePrice'])
testModel = ridge
testParams = alphasRidge
predModel = ridgeCV
Xtrain = trainFilled.drop(columns=['SalePrice', 'Id'])
ytrain = trainFilled['SalePrice']
testFilled = testFilled.drop(columns=['Id'])
print trainFilled.shape
print testFilled.shape
# error = nestedCrossValidation(Xtrain, ytrain,
def Lasso_Mode(X_train, y_train, X_test, y_test, num_class):
algo_name = 'Lasso Regression'
lasso_model = LassoCV(alphas=[0.01, 0.05, 0.10, 0.20, 0.50, 1])
lasso_model.fit(X_train, y_train)
y_pred_lm = lasso_model.predict(X_test)
PRAF(y_test, y_pred_lm, num_class, algo_name)
max_depth=15,
learning_rate=0.01,
subsample=1),
XGBRegressor(seed=0,
n_estimators=800,
max_depth=15,
learning_rate=0.01,
subsample=1,
colsample_bytree=0.75),
XGBRegressor(seed=0,
n_estimators=800,
max_depth=12,
learning_rate=0.01,
subsample=0.8,
colsample_bytree=0.75),
LassoCV(alphas=[1, 0.1, 0.001, 0.0005, 0.0002, 0.0001, 0.00005]),
KNeighborsRegressor(n_neighbors=5),
KNeighborsRegressor(n_neighbors=10),
KNeighborsRegressor(n_neighbors=15),
KNeighborsRegressor(n_neighbors=25),
LassoLarsCV(),
ElasticNet(),
SVR()
]
ensem = ensemble(n_folds=10,
stacker=linear_model.LinearRegression(),
base_models=base_models)
X_train, X_test, y_train = data_preprocess(train, test)
y_pred, score = ensem.fit_predict(X_train, X_test, y_train)
def QuickML_Stacking(X_train, y_train, X_test='', modeltype='Regression',Boosting_Flag=False,
scoring='', verbose=0):
"""
Quickly build Stacks of multiple model results
Input must be a clean data set (only numeric variables, no categorical or string variables).
"""
X_train = copy.deepcopy(X_train)
X_test = copy.deepcopy(X_test)
y_train = copy.deepcopy(y_train)
start_time = time.time()
seed = 99
if len(X_train) <= 100000 or X_train.shape[1] < 50:
NUMS = 100
FOLDS = 5
else:
NUMS = 200
FOLDS = 10
## create Stacking models
estimators = []
### This keeps tracks of the number of predict_proba columns generated by each model ####
estimator_length = []
if isinstance(X_test, str):
no_fit = True
else:
no_fit = False
if no_fit:
#### This is where you don't fit the model but just do cross_val_predict ####
if modeltype == 'Regression':
if scoring == '':
scoring = 'neg_mean_squared_error'
scv = KFold(n_splits=FOLDS, shuffle=False)
if Boosting_Flag:
###### Bagging models if Bagging is chosen ####
#model4 = BaggingRegressor(DecisionTreeRegressor(random_state=seed),
# n_estimators=NUMS,random_state=seed)
model4 = LinearSVR()
results = cross_val_predict(model4,X_train,y_train, cv=scv,n_jobs=-1)
estimators.append(('Linear_SVR',model4))
estimator_length.append(1)
elif Boosting_Flag is None:
#### Tree models if Linear chosen #####
model5 = DecisionTreeRegressor(random_state=seed,min_samples_leaf=2)
results = cross_val_predict(model5,X_train,y_train, cv=scv,n_jobs=-1)
estimators.append(('Decision Trees',model5))
estimator_length.append(1)
else:
#### Linear Models if Boosting is chosen #####
model6 = LassoCV(alphas=np.logspace(-5,-1,20), cv=scv,random_state=seed)
results = cross_val_predict(model6,X_train,y_train, cv=scv,n_jobs=-1)
estimators.append(('LassoCV Regularization',model6))
estimator_length.append(1)
else:
n_classes = len(Counter(y_train))
if n_classes > 2:
#### In multi-class setting, it makes sense to turn it into binary class in stage-1
#### In stage 2, a complex model will take the inputs of this model and try to predict
rare_class = find_rare_class(y_train)
if rare_class == 0:
majority_class = 1
else:
majority_class = 0
y_train = y_train.map(lambda x: rare_class if x==rare_class else majority_class)
if scoring == '':
scoring = 'accuracy'
scv = StratifiedKFold(n_splits=FOLDS, random_state=seed, shuffle=True)
if Boosting_Flag:
#### Linear Models if Boosting is chosen #####
model4 = LinearDiscriminantAnalysis()
results = cross_val_predict(model4,X_train,y_train, cv=scv,n_jobs=-1,
method='predict_proba')
estimators.append(('Linear Discriminant',model4))
estimator_length.append(results.shape[1])
elif Boosting_Flag is None:
#### Tree models if Linear chosen #####
model6 = DecisionTreeClassifier(min_samples_leaf=2)
results = cross_val_predict(model6,X_train,y_train, cv=scv,n_jobs=-1,
method='predict_proba')
estimators.append(('Decision Tree',model6))
estimator_length.append(results.shape[1])
else:
###### Naive Bayes models if Bagging is chosen ####
if n_classes <= 2:
try:
model7 = GaussianNB()
except:
model7 = DecisionTreeClassifier(min_samples_leaf=2)
else:
try:
model7 = MultinomialNB()
except:
model7 = DecisionTreeClassifier(min_samples_leaf=2)
results = cross_val_predict(model7,X_train,y_train, cv=scv,n_jobs=-1,
method='predict_proba')
estimators.append(('Naive Bayes',model7))
estimator_length.append(results.shape[1])
else:
#### This is where you fit the model and then predict ########
if modeltype == 'Regression':
if scoring == '':
scoring = 'neg_mean_squared_error'
scv = KFold(n_splits=FOLDS, shuffle=False)
if Boosting_Flag:
###### Bagging models if Bagging is chosen ####
#model4 = BaggingRegressor(DecisionTreeRegressor(random_state=seed),
# n_estimators=NUMS,random_state=seed)
model4 = LinearSVR()
results = model4.fit(X_train,y_train).predict(X_test)
estimators.append(('Linear_SVR',model4))
estimator_length.append(1)
elif Boosting_Flag is None:
#### Tree models if Linear chosen #####
model5 = DecisionTreeRegressor(random_state=seed,min_samples_leaf=2)
results = model5.fit(X_train,y_train).predict(X_test)
estimators.append(('Decision Trees',model5))
estimator_length.append(1)
else:
#### Linear Models if Boosting is chosen #####
model6 = LassoCV(alphas=np.logspace(-5,-1,20), cv=scv,random_state=seed)
results = model6.fit(X_train,y_train).predict(X_test)
estimators.append(('LassoCV Regularization',model6))
estimator_length.append(1)
else:
n_classes = len(Counter(y_train))
if n_classes > 2:
#### In multi-class setting, it makes sense to turn it into binary class in stage-1
#### In stage 2, a complex model will take the inputs of this model and try to predict
rare_class = find_rare_class(y_train)
if rare_class == 0:
majority_class = 1
else:
majority_class = 0
y_train = y_train.map(lambda x: rare_class if x==rare_class else majority_class)
if scoring == '':
scoring = 'accuracy'
scv = StratifiedKFold(n_splits=FOLDS, random_state=seed, shuffle=True)
if Boosting_Flag:
#### Linear Models if Boosting is chosen #####
model4 = LinearDiscriminantAnalysis()
results = model4.fit(X_train,y_train).predict_proba(X_test)
estimators.append(('Linear Discriminant',model4))
estimator_length.append(results.shape[1])
elif Boosting_Flag is None:
#### Tree models if Linear chosen #####
model6 = DecisionTreeClassifier(min_samples_leaf=2)
results = model6.fit(X_train,y_train).predict_proba(X_test)
estimators.append(('Decision Tree',model6))
estimator_length.append(results.shape[1])
else:
###### Naive Bayes models if Bagging is chosen ####
if n_classes <= 2:
try:
model7 = GaussianNB()
except:
model7 = DecisionTreeClassifier(min_samples_leaf=2)
else:
try:
model7 = MultinomialNB()
except:
model7 = DecisionTreeClassifier(min_samples_leaf=2)
results = model7.fit(X_train,y_train).predict_proba(X_test)
estimators.append(('Naive Bayes',model7))
estimator_length.append(results.shape[1])
#stacks = np.c_[results1,results2,results3]
estimators_list = [(tuples[0],tuples[1]) for tuples in estimators]
estimator_names = [tuples[0] for tuples in estimators]
#### Here is where we consolidate the estimator names and their results into one common list ###
ls = []
for x,y in dict(zip(estimator_names,estimator_length)).items():
els = [x+'_'+str(eachy) for eachy in range(y)]
ls += els
if verbose == 1:
print(' Time taken for Stacking: %0.1f seconds' %(time.time()-start_time))
return ls, results
def train_linear_model(X,
y,
random_state=1,
test_size=0.2,
regularization_type='elasticnet',
k_fold=5,
max_iter=1000000,
tol=0.0001,
l1_ratio=None):
"""
Function to train linear model with regularization and cross-validation.
Args:
X (pandas.DataFrame): dataframe of descriptors.
y (pandas.DataFrame): dataframe of cycle lifetimes.
random_state (int): seed for train/test split.
test_size (float): proportion of the dataset reserved for model evaluation.
regularization_type (str): lasso or ridge or elastic-net (with cv).
k_fold (int): k in k-fold cross-validation.
max_iter (int): maximum number of iterations for model fitting.
tol (float): tolerance for optimization.
l1_ratio ([float]): list of lasso to ridge ratios for elasticnet.
Returns:
sklearn.linear_model.LinearModel: fitted model.
mu (float): Mean value of descriptors used in training.
s (float): Std dev of descriptors used in training.
"""
if l1_ratio is None:
l1_ratio = [.1, .5, .7, .9, .95, 1]
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=test_size, random_state=random_state)
# Standardize (training) data after train/test split
mu = np.mean(X_train, axis=0)
s = np.std(X_train, axis=0)
X_scaled = (X_train - mu) / s
hyperparameters = {
'random_state': random_state,
'test_size': test_size,
'k_fold': k_fold,
'tol': tol,
'max_iter': max_iter
}
if regularization_type == 'lasso' and y.shape[1] == 1:
lassocv = LassoCV(fit_intercept=True,
alphas=None,
tol=tol,
cv=k_fold,
max_iter=max_iter)
lassocv.fit(X_scaled, y_train.values.ravel())
# Set optimal alpha and refit model
alpha_opt = lassocv.alpha_
linear_model = Lasso(fit_intercept=True,
alpha=alpha_opt,
max_iter=max_iter)
linear_model.fit(X_scaled, y_train.values)
hyperparameters['l1_ratio'] = 1
elif regularization_type == 'ridge' and y.shape[1] == 1:
ridgecv = RidgeCV(fit_intercept=True, alphas=None, cv=k_fold)
ridgecv.fit(X_scaled, y_train.values.ravel())
# Set optimal alpha and refit model
alpha_opt = ridgecv.alpha_
linear_model = Ridge(fit_intercept=True, alpha=alpha_opt)
linear_model.fit(X_scaled, y_train)
hyperparameters['l1_ratio'] = 0
elif regularization_type == 'elasticnet' and y.shape[1] == 1:
elasticnetcv = ElasticNetCV(fit_intercept=True,
normalize=False,
alphas=None,
cv=k_fold,
l1_ratio=l1_ratio,
max_iter=max_iter)
elasticnetcv.fit(X_scaled, y_train.values.ravel())
# Set optimal alpha and l1_ratio. Refit model
alpha_opt = elasticnetcv.alpha_
l1_ratio_opt = elasticnetcv.l1_ratio_
linear_model = ElasticNet(fit_intercept=True,
normalize=False,
l1_ratio=l1_ratio_opt,
alpha=alpha_opt,
max_iter=max_iter)
linear_model.fit(X_scaled, y_train)
hyperparameters['l1_ratio'] = l1_ratio_opt
# If more than 1 outcome present, perform multitask regression
elif regularization_type == 'elasticnet' and y.shape[1] > 1:
multi_elasticnet_CV = MultiTaskElasticNetCV(fit_intercept=True,
cv=k_fold,
normalize=False,
l1_ratio=l1_ratio,
max_iter=max_iter)
multi_elasticnet_CV.fit(X_scaled, y_train)
# Set optimal alpha and l1_ratio. Refit model
alpha_opt = multi_elasticnet_CV.alpha_
l1_ratio_opt = multi_elasticnet_CV.l1_ratio_
linear_model = MultiTaskElasticNet(fit_intercept=True,
normalize=False,
max_iter=max_iter)
linear_model.set_params(alpha=alpha_opt, l1_ratio=l1_ratio_opt)
linear_model.fit(X_scaled, y_train)
hyperparameters['l1_ratio'] = l1_ratio_opt
else:
raise NotImplementedError
y_pred = linear_model.predict((X_test - mu) / s)
Rsq = linear_model.score((X_test - mu) / s, y_test)
# Compute 95% confidence interval
# Multioutput = 'raw_values' provides prediction error per output
pred_actual_ratio = [x / y for x, y in zip(y_pred, np.array(y_test))]
relative_prediction_error = 1.96 * np.sqrt(
mean_squared_error(np.ones(y_pred.shape),
pred_actual_ratio,
multioutput='raw_values') / y_pred.shape[0])
hyperparameters['alpha'] = alpha_opt
return linear_model, mu, s, relative_prediction_error, Rsq, hyperparameters
rcv.fit(X_train, Y_train)
print('\nBest RidgeCV alpha value:')
print(rcv.alpha_)
#Ridge regression using best alpha#
rbest = Ridge(alpha=rcv.alpha_, normalize=True)
rbest.fit(X_train, Y_train)
print('\nBest Ridge MSE:')
print(mean_squared_error(Y_test, rbest.predict(X_test)))
###Lasso regression###
#LassoCV with 10-fold cross-validation(similar to ISLR)#
lcv = LassoCV(alphas=None, max_iter=100000, normalize=True, cv=kfcv, n_jobs=2)
lcv.fit(X_train, Y_train)
print('\nBest LassoCV alpha value:')
print(lcv.alpha_)
#Ridge regression using best alpha#
lbest = Lasso(alpha=lcv.alpha_, normalize=True)
lbest.fit(X_train, Y_train)
print('\nBest Lasso MSE:')
print(mean_squared_error(Y_test, lbest.predict(X_test)))
print('\nLasso Coeficients:')
print(pd.Series(lbest.coef_, index=xcols))
def test_linreg():
for m in [LinearRegression(), Ridge(), Lars(), LarsCV(), ElasticNet(), ElasticNetCV(), \
Lasso(), LassoCV(), LassoLars(), LassoLarsCV(), LassoLarsIC(), SVR(kernel='linear')]:
verify(m.fit(X, y), ['predict'])
if not isinstance(m, SVR):
verify(m.set_params(fit_intercept=False).fit(X, y), ['predict'])
#
# Another possibility to take into account correlated variables in the dataset,
# is to estimate sparse coefficients. In some way we already did it manually
# when we dropped the AGE column in a previous Ridge estimation.
#
# Lasso models (see the :ref:`lasso` User Guide section) estimates sparse
# coefficients. LassoCV applies cross validation in order to
# determine which value of the regularization parameter (`alpha`) is best
# suited for the model estimation.
from sklearn.linear_model import LassoCV
model = make_pipeline(
preprocessor,
TransformedTargetRegressor(
regressor=LassoCV(alphas=np.logspace(-10, 10, 21), max_iter=100000),
func=np.log10,
inverse_func=sp.special.exp10,
),
)
_ = model.fit(X_train, y_train)
# %%
# First we verify which value of :math:`\alpha` has been selected.
model[-1].regressor_.alpha_
# %%
# Then we check the quality of the predictions.
print(X_train.shape)
X_test0 = X_test0[:, i_keep_columns]
print(X_test0.shape)
X_test1 = X_test1[:, i_keep_columns]
print(X_test1.shape)
sys.exit()
################################################################################################################
### linear LASSO Model ###
################################################################################################################
# from sklearn import linear_model
from sklearn.linear_model import LassoCV
print("training")
# should be cv = 5
model = LassoCV(cv=5, verbose=1, n_jobs=-1).fit(X_train, y_train)
print("done training")
coef = model.coef_
type(coef)
len(coef)
np.savetxt(dir_lasso + "coef_lasso_linear_pronoun.txt", coef)
# predicted probability for a post being Female
'''ypred_train=model.predict_proba(X_train)[:,1] # Pr(female=1)
ypred_test0=model.predict_proba(X_test0)[:,1]
ypred_test1=model.predict_proba(X_test1)[:,1]
np.savetxt("linear_ypred_train.txt",ypred_train)
np.savetxt("linear_ypred_test0.txt",ypred_test0)
np.savetxt("linear_ypred_test1.txt",ypred_test1)'''
score_mse = metrics.mean_absolute_error(Y_cv, one_result)
print('Fold [%s] norm. Gini = %0.5f, MSE = %0.5f' %
(i, cv_score, score_mse))
blend_test_j[:, i] = clf.predict(X_test)
blend_test[:, j] = blend_test_j.mean(1)
print('Clf_%d Mean norm. Gini = %0.5f (%0.5f)' %
(j, cv_results[j, ].mean(), cv_results[j, ].std()))
end_time = datetime.now()
time_taken = (end_time - start_time)
print("Time taken for pre-blending calculations: {0}".format(time_taken))
print("CV-Results", cv_results)
print("Blending models.")
bclf = LassoCV(n_alphas=100,
alphas=None,
normalize=True,
cv=5,
fit_intercept=True,
max_iter=10000,
positive=True)
bclf.fit(blend_train, Y_dev)
Y_test_predict = bclf.predict(blend_test)
cv_score = cv_results.mean()
print('Avg. CV-Score = %s' % (cv_score))
submission = pd.DataFrame({"Id": test_ids, "Hazard": Y_test_predict})
submission = submission.set_index('Id')
submission.to_csv("farons_solution.csv")
def regression_regularization(Xs, y):
#---------------------------------------------
# RidgeCV
#----------------------------------------------
# optimal value for Ridge regression alpha using RidgeCV
ridge_alphas = np.logspace(0, 5, 200)
optimal_ridge = RidgeCV(alphas=ridge_alphas, cv=30)
optimal_ridge.fit(Xs, y)
print("Ridge Alpha: ", optimal_ridge.alpha_)
# Cross-validate the Ridge regression
ridge = Ridge(alpha=optimal_ridge.alpha_)
ridge_scores = cross_val_score(ridge, Xs, y, cv=10)
print(ridge_scores)
print("RidgeCV Mean: ", np.mean(ridge_scores))
print("-----------------------------------------------------")
print("\n")
#----------------------------------------------
# LassoCV
#----------------------------------------------
# Optimal value for Lasso regression alpha using LassoCV
optimal_lasso = LassoCV(n_alphas=500, cv=10, verbose=1)
optimal_lasso.fit(Xs, y)
print("Lasso Alpha: ",optimal_lasso.alpha_)
# Cross-validate the Lasso regression
lasso = Lasso(alpha=optimal_lasso.alpha_)
lasso_scores = cross_val_score(lasso, Xs, y, cv=30)
print(lasso_scores)
print("LassoCV Mean: ", np.mean(lasso_scores))
print("-----------------------------------------------------")
print("\n")
#----------------------------------------------
# ElasticNetCV
#----------------------------------------------
# Optimal value for Elastic Net regression alpha using ElasticNetCV
l1_ratios = np.linspace(0.01, 1.0, 25)
optimal_enet = ElasticNetCV(l1_ratio=l1_ratios, n_alphas=100, cv=10,
verbose=1)
optimal_enet.fit(Xs, y)
print("ElasticNet Alpha: ",optimal_enet.alpha_)
print(optimal_enet.l1_ratio_)
# Cross-validate the ElasticNet with L1_ratio
enet = ElasticNet(alpha=optimal_enet.alpha_, l1_ratio=optimal_enet.l1_ratio_)
enet_scores = cross_val_score(enet, Xs, y, cv=10)
print(enet_scores)
print("ElasticCV Mean: ", np.mean(enet_scores))
print("-----------------------------------------------------")
print("\n")
def sklearnLassoCV(params, alphas=(0.1, 1.0, 10.0), folds=10):
X = params['X_train']
Y = params['y_train']
name = params['name']
model = LassoCV(alphas=alphas, cv=folds)
return MachinLearningModel(model, X, Y, modelType="Linear", name=name)
Pipeline([
('scaler', RobustScaler()),
('knn', KNeighborsRegressor(5, weights='distance', n_jobs=-1))
]),
Pipeline([
('scaler', RobustScaler()),
('svr', SVR(
C=160,
gamma=0.1,
epsilon=0.1
))
]),
Pipeline([
('scaler', RobustScaler()),
('lasso', LassoCV(
cv=KFold(4, True, 0),
n_jobs=-1
))
])
]
model = Model(estimators, [.45, .05, .45, .05])
routine(
dir_path=os.path.dirname(__file__),
task_id=2,
model=model,
mode=os.environ.get('KDD_MODE', 'train')
)
print('task2 train done')
from sklearn.linear_model import Lasso
lasso = Lasso(alpha=np.float('{}'.format(i)),
max_iter=1000000).fit(house_price_train_X,
house_price_train_y) # 默认的lamda的参数为i
# 拉索模型训练的准确率
predict_result_lasso = lasso.predict(house_price_test_X)
predict_result_lasso1 = lasso.score(house_price_train_X,
house_price_train_y)
predict_result_lasso0 = lasso.score(house_price_test_X, house_price_test_y)
print('拉索回归惩罚参数为 {} ，训练集的准确率:'.format(i), predict_result_lasso1)
print('拉索回归惩罚参数为 {} ，测试集的准确率:'.format(i), predict_result_lasso0)
print('拉索回归惩罚参数为 {}，使用的特征属性有：{}'.format(i, np.sum(lasso.coef_ != 0)))
# 实现交叉检验拉索回归模型的导入
from sklearn.linear_model import LassoCV
lasso_cv = LassoCV(alphas=np.logspace(-3, 1, 2, 50), max_iter=1000000).fit(
house_price_train_X, house_price_train_y) # 默认的lamda的参数为i
# 交叉检验拉索模型训练的准确率
predict_result_lasso_cv = lasso_cv.predict(house_price_test_X)
predict_result_lasso_cv1 = lasso_cv.score(house_price_train_X,
house_price_train_y)
predict_result_lasso_cv0 = lasso_cv.score(house_price_test_X,
house_price_test_y)
print('交叉检验拉索回归 训练集的准确率:', predict_result_lasso_cv1)
print('交叉检验拉索回归 测试集的准确率:', predict_result_lasso_cv0)
from sklearn.linear_model import LinearRegression
# 训练多元线性回归模型
lr = LinearRegression().fit(house_price_train_X, house_price_train_y)
# 预测测试集房价结果
predict_result_lr = lr.predict(house_price_test_X)
# 模型训练的准确率
compact=False)
build_auto(
BaggingRegressor(DecisionTreeRegressor(random_state=13,
min_samples_leaf=5),
random_state=13,
n_estimators=3,
max_features=0.5), "DecisionTreeEnsembleAuto")
build_auto(DummyRegressor(strategy="median"), "DummyAuto")
build_auto(ElasticNetCV(random_state=13), "ElasticNetAuto")
build_auto(ExtraTreesRegressor(random_state=13, min_samples_leaf=5),
"ExtraTreesAuto")
build_auto(GradientBoostingRegressor(random_state=13, init=None),
"GradientBoostingAuto")
build_auto(HuberRegressor(), "HuberAuto")
build_auto(LarsCV(), "LarsAuto")
build_auto(LassoCV(random_state=13), "LassoAuto")
build_auto(LassoLarsCV(), "LassoLarsAuto")
build_auto(
OptimalLGBMRegressor(objective="regression",
n_estimators=17,
num_iteration=11), "LGBMAuto")
build_auto(LinearRegression(), "LinearRegressionAuto")
build_auto(
BaggingRegressor(LinearRegression(), random_state=13, max_features=0.75),
"LinearRegressionEnsembleAuto")
build_auto(OrthogonalMatchingPursuitCV(), "OMPAuto")
build_auto(RandomForestRegressor(random_state=13, min_samples_leaf=3),
"RandomForestAuto",
flat=True)
build_auto(RidgeCV(), "RidgeAuto")
build_auto(TheilSenRegressor(n_subsamples=15, random_state=13), "TheilSenAuto")
def exp(n):
COV_CLIP = 10 / n
X_data = {
colname: gen_data(datatype, n)
for colname, datatype in X_colnames.items()
}
X_data = pd.DataFrame({**X_data})
# Turn strings into categories for numeric mapping
X_data['os_type'] = X_data.os_type.astype('category').cat.codes
X_pre = X_data.values.astype('float')
true_fn = lambda X: (.8 + .5 * X[:, 0] - 3 * X[:, 6])
y, T, Z = dgp_binary(X_pre, n, true_fn)
X = QuantileTransformer(subsample=n // 10).fit_transform(X_pre)
true_ate = np.mean(true_fn(X_pre))
print("True ATE: {:.3f}".format(true_ate))
print("New members: in treatment = {:f}, in control = {:f}".format(
T[Z == 1].sum() / Z.sum(), T[Z == 0].sum() / (1 - Z).sum()))
print("Z treatment proportion: {:.5f}".format(np.mean(Z)))
# ### Defining some generic regressors and classifiers
# This a generic non-parametric regressor
# model = lambda: GradientBoostingRegressor(n_estimators=20, max_depth=3, min_samples_leaf=20,
# n_iter_no_change=5, min_impurity_decrease=.001, tol=0.001)
#model = lambda: XGBWrapper(XGBRegressor(gamma=0.001, n_estimators=50, min_child_weight=50, n_jobs=10),
# early_stopping_rounds=5, eval_metric='rmse', binary=False)
# model = lambda: RandomForestRegressor(n_estimators=100)
# model = lambda: Lasso(alpha=0.0001) #CV(cv=5)
# model = lambda: GradientBoostingRegressor(n_estimators=60)
# model = lambda: LinearRegression(n_jobs=-1)
model = lambda: LassoCV(cv=5, n_jobs=-1)
# This is a generic non-parametric classifier. We have to wrap it with the RegWrapper, because
# we want to use predict_proba and not predict. The RegWrapper calls predict_proba of the
# underlying model whenever predict is called.
# model_clf = lambda: RegWrapper(GradientBoostingClassifier(n_estimators=20, max_depth=3, min_samples_leaf=20,
# n_iter_no_change=5, min_impurity_decrease=.001, tol=0.001))
# model_clf = lambda: RegWrapper(XGBWrapper(XGBClassifier(gamma=0.001, n_estimators=50, min_child_weight=50, n_jobs=10),
# early_stopping_rounds=5, eval_metric='logloss', binary=True))
# model_clf = lambda: RandomForestClassifier(n_estimators=100)
# model_clf = lambda: RegWrapper(GradientBoostingClassifier(n_estimators=60))
# model_clf = lambda: RegWrapper(LogisticRegression(C=10, penalty='l1', solver='liblinear'))
model_clf = lambda: RegWrapper(
LogisticRegressionCV(n_jobs=-1, cv=5, scoring='neg_log_loss'))
model_clf_dummy = lambda: RegWrapper(DummyClassifier(strategy='prior'))
# We need to specify models to be used for each of these residualizations
model_Y_X = lambda: model() # model for E[Y | X]
model_T_X = lambda: model_clf(
) # model for E[T | X]. We use a classifier since T is binary
# model_Z_X = lambda: model_clf() # model for E[Z | X]. We use a classifier since Z is binary
model_Z_X = lambda: model_clf_dummy(
) # model for E[Z | X]. We use a classifier since Z is binary
# E[T | X, Z]
model_T_XZ = lambda: SeparateModel(model_clf(), model_clf())
# E[TZ | X]
model_TZ_X = lambda: model_clf()
# We fit DMLATEIV with these models and then we call effect() to get the ATE.
# n_splits determines the number of splits to be used for cross-fitting.
# # Algorithm 2 - Current Method
# In[121]:
dmlateiv_obj = DMLATEIV(
model_Y_X(),
model_T_X(),
model_Z_X(),
n_splits=
10, # n_splits determines the number of splits to be used for cross-fitting.
binary_instrument=
True, # a flag whether to stratify cross-fitting by instrument
binary_treatment=
True # a flag whether to stratify cross-fitting by treatment
)
dmlateiv_obj.fit(y, T, X, Z)
ta_effect = dmlateiv_obj.effect()
ta_effect_conf = dmlateiv_obj.normal_effect_interval(lower=2.5, upper=97.5)
print("{:.3f}, ({:.3f}, {:3f})".format(ta_effect, ta_effect_conf[0],
ta_effect_conf[1]))
# # Algorithm 3 - DRIV ATE
driv_model_effect = lambda: Pipeline(
[('poly', PolynomialFeatures(degree=0, include_bias=True)),
('reg', StatsModelLinearRegression())])
dmliv_featurizer = lambda: PolynomialFeatures(degree=1, include_bias=True)
dmliv_model_effect = lambda: SelectiveLasso(np.arange(1, X.shape[1] + 1),
LassoCV(cv=5, n_jobs=-1))
prel_model_effect = DMLIV(model_Y_X(),
model_T_X(),
model_T_XZ(),
dmliv_model_effect(),
dmliv_featurizer(),
n_splits=1)
#dmliv_model_effect = lambda: model()
#prel_model_effect = GenericDMLIV(model_Y_X(), model_T_X(), model_T_XZ(),
# dmliv_model_effect(),
# n_splits=1)
dr_cate = DRIV(
model_Y_X(),
model_T_X(),
model_Z_X(), # same as in DMLATEIV
prel_model_effect, # preliminary model for CATE, must support fit(y, T, X, Z) and effect(X)
model_TZ_X(), # model for E[T * Z | X]
driv_model_effect(), # model for final stage of fitting theta(X)
cov_clip=
COV_CLIP, # covariance clipping to avoid large values in final regression from weak instruments
n_splits=10, # number of splits to use for cross-fitting
binary_instrument=
True, # a flag whether to stratify cross-fitting by instrument
binary_treatment=
True # a flag whether to stratify cross-fitting by treatment
)
dr_cate.fit(y, T, X, Z)
dr_effect = dr_cate.effect_model.named_steps['reg'].coef_[0]
dr_effect_conf = dr_cate.effect_model.named_steps['reg'].model.conf_int(
alpha=0.05)[0]
print("{:.3f}, ({:.3f}, {:3f})".format(dr_effect, dr_effect_conf[0],
dr_effect_conf[1]))
return true_ate, ta_effect, ta_effect_conf[0], ta_effect_conf[
1], dr_effect, dr_effect_conf[0], dr_effect_conf[1]
N = 9
x = np.linspace(0, 6, N) + np.random.randn(N)
x = np.sort(x)
y = x**2 - 4*x - 3 + np.random.randn(N)
x.shape = -1, 1
y.shape = -1, 1
models = [Pipeline([
('poly', PolynomialFeatures()),
('linear', LinearRegression(fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', RidgeCV(alphas=np.logspace(-3, 2, 50), fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', LassoCV(alphas=np.logspace(-3, 2, 50), fit_intercept=False))]),
Pipeline([
('poly', PolynomialFeatures()),
('linear', ElasticNetCV(alphas=np.logspace(-3, 2, 50), l1_ratio=[.1, .5, .7, .9, .95, .99, 1],
fit_intercept=False))])
]
mpl.rcParams['font.sans-serif'] = [u'simHei']
mpl.rcParams['axes.unicode_minus'] = False
np.set_printoptions(suppress=True)
plt.figure(figsize=(18, 12), facecolor='w')
d_pool = np.arange(1, N, 1) # 阶
m = d_pool.size
clrs = [] # 颜色
for c in np.linspace(16711680, 255, m):
clrs.append('#%06x' % int(c))
learning_rate=0.07,
max_depth=20,
min_child_weight=1.5,
n_estimators=300,
reg_alpha=0.65,
reg_lambda=0.45,
subsample=0.95)
model_xgb.fit(X_train, y)
xgb_p = np.expm1(model_xgb.predict(X_test))
#-------------------------------------Use Lasso CV to tune Parameters----------------------------#
lasso = LassoCV(alphas=[
0.0001, 0.0003, 0.0006, 0.001, 0.003, 0.006, 0.01, 0.03, 0.06, 0.1, 0.3,
0.6, 1
],
max_iter=50000,
cv=10)
lasso.fit(X_train, y)
alpha = lasso.alpha_
print "Best alpha :", alpha
print "Trying alphas centered around " + str(alpha)
lasso = LassoCV(alphas=[
alpha * .6, alpha * .65, alpha * .7, alpha * .75, alpha * .8, alpha * .85,
alpha * .9, alpha * .95, alpha, alpha * 1.05, alpha * 1.1, alpha * 1.15,
alpha * 1.25, alpha * 1.3, alpha * 1.35, alpha * 1.4
],
max_iter=50000,
cv=10)
lasso.fit(X_train, y)
def main(args):
logfile = args['logfile']
directory = args['directory'] # full fly func path
pca_subfolder = args['pca_subfolder']
glm_date = args['glm_date']
printlog = getattr(flow.Printlog(logfile=logfile), 'print_to_log')
### Load PCA ###
pca_directory = os.path.join(directory, 'pca', pca_subfolder)
printlog("performing glm on {}".format(pca_directory))
file = os.path.join(pca_directory, 'scores_(spatial).npy')
pca_spatial = np.load(file)
file = os.path.join(pca_directory, 'loadings_(temporal).npy')
pca_loadings = np.load(file)
### Load Fictrac and Timstamps###
timestamps = bbb.load_timestamps(os.path.join(directory, 'imaging'))
fictrac_raw = bbb.load_fictrac(os.path.join(directory, 'fictrac'))
### Prepare Fictrac ###
resolution = 100 #desired resolution in ms
expt_len = 1000 * 30 * 60
fps = 50 #of fictrac camera
behaviors = ['dRotLabY', 'dRotLabZ']
fictrac = {}
for behavior in behaviors:
if behavior == 'dRotLabY': short = 'Y'
elif behavior == 'dRotLabZ': short = 'Z'
fictrac[short] = bbb.smooth_and_interp_fictrac(fictrac_raw,
fps,
resolution,
expt_len,
behavior,
timestamps=timestamps,
smoothing=51)
fictrac[short] = fictrac[short] / np.std(fictrac[short])
xnew = np.arange(0, expt_len, resolution)
### Fit GLM ###
Y_glm = {}
Y_glm['Y'] = fictrac['Y'].copy()
Y_glm['Z'] = np.abs(fictrac['Z'].copy())
models = {}
num_pcs = 1000
behaviors = ['Y', 'Z']
for behavior in behaviors:
t0 = time.time()
models[behavior] = {'num_pcs': num_pcs, 'model': LassoCV()}
X_glm = pca_loadings[:, :num_pcs]
models[behavior]['model'].fit(X_glm, Y_glm[behavior])
models[behavior]['score'] = models[behavior]['model'].score(
X_glm, Y_glm[behavior])
### Construct Spatial Map ###
coef = models[behavior]['model'].coef_
spatial_map = np.tensordot(coef, pca_spatial[:1000, :, :, :], axes=1)
### Save map ###
glm_directory = os.path.join(directory, 'glm')
if not os.path.exists(glm_directory):
os.mkdir(glm_directory)
save_file = os.path.join(glm_directory,
'{}_{}.nii'.format(glm_date, behavior))
nib.Nifti1Image(spatial_map, np.eye(4)).to_filename(save_file)
### Save scores ###
score_file = os.path.join(glm_directory,
'{}_score_{}.txt'.format(glm_date, behavior))
with open(score_file, "a") as f:
f.write("{}:{}".format(behavior, models[behavior]['score']))
print(__doc__)
import matplotlib.pyplot as plt
import numpy as np
from sklearn.datasets import load_boston
from sklearn.feature_selection import SelectFromModel
from sklearn.linear_model import LassoCV
# Load the boston dataset.
boston = load_boston()
X, y = boston['data'], boston['target']
# We use the base estimator LassoCV since the L1 norm promotes sparsity of features.
clf = LassoCV()
# Set a minimum threshold of 0.25
sfm = SelectFromModel(clf, threshold=0.25)
sfm.fit(X, y)
n_features = sfm.transform(X).shape[1]
# Reset the threshold till the number of features equals two.
# Note that the attribute can be set directly instead of repeatedly
# fitting the metatransformer.
while n_features > 2:
sfm.threshold += 0.1
X_transform = sfm.transform(X)
n_features = X_transform.shape[1]
# Plot the selected two features from X.
## 범주형 독립변수 EDA
#for g_i in cat_var_names:
# p1 = ggplot(all[-all["SalePrice"].isnull()], aes(x = g_i, y = "SalePrice")) + geom_boxplot()
# p1 = p1 + geom_hline(yintercept=all["SalePrice"].median(), linetype = "dashed", colour = "red")
# p1 = p1 + ggtitle("Boxplot of SalePrice ~ " + g_i) + theme(title=element_text(hjust=0.5))
# p1 = p1 + theme(axis_text_x = element_text(angle = 45, hjust = 1))
# p1.save("./OUT/EDA_BOX_SalePrice~"+g_i+".png", width = 7, height = 3)
#%% 변수 선택(feature selection)
#숫자형 독립변수 Lasso로 추출
train_x = all[-all["SalePrice"].isnull()][num_var_names]
train_y = all[-all["SalePrice"].isnull()]["SalePrice"]
lasso = LassoCV(normalize=True, random_state = 2019)
lasso.fit(train_x, train_y)
lasso_coef = lasso.coef_
imp_num_var_names = pd.DataFrame(num_var_names)[lasso_coef!=0][0].tolist()
#범주형 독립변수 Random Forest로 추출
train_x = all[-all["SalePrice"].isnull()][cat_var_names]
#범주형 독립변수 LabelEncoder 변형
for ind in cat_var_names:
a = LabelEncoder()
a.fit(train_x[ind])
train_x[ind] = a.transform(train_x[ind])
train_y = all[-all["SalePrice"].isnull()]["SalePrice"]
y_pred = ridgeregcv.predict(X_test) print np.sqrt(metrics.mean_squared_error(y_test, y_pred)) # Lasso regression # try alpha=0.001 and examine coefficients from sklearn.linear_model import Lasso lassoreg = Lasso(alpha=0.001, normalize=True) lassoreg.fit(X_train, y_train) print lassoreg.coef_ # try alpha=0.01 and examine coefficients lassoreg = Lasso(alpha=0.01, normalize=True) lassoreg.fit(X_train, y_train) print lassoreg.coef_ # calculate RMSE (for alpha=0.01) y_pred = lassoreg.predict(X_test) print np.sqrt(metrics.mean_squared_error(y_test, y_pred)) # select the best alpha with LassoCV from sklearn.linear_model import LassoCV lassoregcv = LassoCV(n_alphas=100, normalize=True, random_state=1) lassoregcv.fit(X_train, y_train) lassoregcv.alpha_ # examine the coefficients print lassoregcv.coef_ # predict method uses the best alpha value y_pred = lassoregcv.predict(X_test) print np.sqrt(metrics.mean_squared_error(y_test, y_pred))
plt.figure()
plot_ic_criterion(model_aic, 'AIC', 'b')
plot_ic_criterion(model_bic, 'BIC', 'r')
plt.legend()
plt.title('Information-criterion for model selection (training time %.3fs)' %
t_bic)
# #############################################################################
# LassoCV: coordinate descent
from sklearn.linear_model import LassoCV
# Compute paths
print("Computing regularization path using the coordinate descent lasso...")
LassoCV_fit = LassoCV(cv=20).fit(X, y)
LassoCV_pred = LassoCV_fit.predict(X_test)
R2_LassoCV = metrics.r2_score(LassoCV_pred, y_test)
# 0.60 pas top du tout
# Display results
m_log_alphas = -np.log10(model.alphas_ + EPSILON)
plt.figure()
ymin, ymax = 2300, 3800
plt.plot(m_log_alphas, model.mse_path_, ':')
plt.plot(m_log_alphas,
model.mse_path_.mean(axis=-1),
'k',
label='Average across the folds',
linewidth=2)
# Linear regression
clfreg = LinearRegression(n_jobs=-2)
clfreg.fit(X_train, y_train)
# Quadratic Regression 2
clfpoly2 = make_pipeline(PolynomialFeatures(2), Ridge())
clfpoly2.fit(X_train, y_train)
# Quadratic Regression 3
clfpoly3 = make_pipeline(PolynomialFeatures(3), Ridge())
clfpoly3.fit(X_train, y_train)
# LassoCV
clfLasso = LassoCV(eps=0.002,
n_alphas=100,
fit_intercept=True,
normalize=False)
clfLasso.fit(X_train, y_train)
# Score models
scores = []
modelNames = ["LinearRegression", "Quadratic2", "Quadratic3", "LassoCV"]
confidencereg = clfreg.score(X_test, y_test)
confidencepoly2 = clfpoly2.score(X_test, y_test)
confidencepoly3 = clfpoly3.score(X_test, y_test)
confLasso = clfLasso.score(X_test, y_test)
scores.append(confidencereg)
scores.append(confidencepoly2)
scores.append(confidencepoly3)
scores.append(confLasso)
x = np.linspace(0, 6, N) + np.random.randn(N)
x = np.sort(x)
y = x**2 - 4 * x - 3 + np.random.randn(N)
x.shape = -1, 1
y.shape = -1, 1
models = [
Pipeline([('poly', PolynomialFeatures()),
('linear', LinearRegression(fit_intercept=False))]),
Pipeline([('poly', PolynomialFeatures()),
('linear',
RidgeCV(alphas=np.logspace(-3, 2, 10),
fit_intercept=False))]),
Pipeline([('poly', PolynomialFeatures()),
('linear',
LassoCV(alphas=np.logspace(-3, 2, 10),
fit_intercept=False))]),
Pipeline([('poly', PolynomialFeatures()),
('linear',
ElasticNetCV(alphas=np.logspace(-3, 2, 10),
l1_ratio=[.1, .5, .7, .9, .95, .99, 1],
fit_intercept=False))])
]
mpl.rcParams['font.sans-serif'] = ['simHei']
mpl.rcParams['axes.unicode_minus'] = False
np.set_printoptions(suppress=True)
plt.figure(figsize=(18, 12), facecolor='w')
d_pool = np.arange(1, N, 1) # 阶
m = d_pool.size
clrs = [] # 颜色
for c in np.linspace(16711680, 255, m, dtype=int):
beta_est = ridCV.coef_
else:
# Estimating beta using Ordinary Least Squares
linReg = LinearRegression().fit(X, y_true)
beta_est = linReg.coef_[0]
# Weights
gam_w = 2
w = 1/np.abs(beta_est)**gam_w
X_prim = np.empty(shape=np.shape(X))
for c in range(np.shape(X)[1]):
X_prim[:, c] = X[:, c]/w[c]
lasPCV = LassoCV(max_iter=10000).fit(X_prim, y_true.ravel())
betaP_las = lasPCV.coef_
beta_adp = betaP_las/w
lasCV = LassoCV(max_iter=10000).fit(X, y_true.ravel())
coefAdp = beta_adp
coefLas = lasCV.coef_
coefAdpIX = np.where(coefAdp != 0)[0]
coefLasIX = np.where(coefLas != 0)[0]
# print(lasPCV.alpha_)
# print(A_true)
# print(np.where(coefAdp != 0)[0])
# print(np.where(coefLas != 0)[0])
imprtc = model.feature_importances_
imprtc = pd.DataFrame(imprtc, index=keys[2:], columns=["Importance"])
imprtc["Std"] = np.std(
[tree.feature_importances_ for tree in model.estimators_], axis=0)
x = range(imprtc.shape[0])
y = imprtc.ix[:, 0]
yerr = imprtc.ix[:, 1]
plt.xticks(x, keys[2:], rotation=90)
plt.bar(x, y, yerr=yerr, align="center")
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5)
plt.show()
######### Filter out Important Variables ############
model = LassoCV()
rfe1 = RFE(model, 25)
rfe2 = rfe1.fit(df[keys[2:]], df[keys[1]])
x = rfe2.transform(df[keys[2:]])
new_features = df[keys[2:]].columns[rfe2.get_support()]
######### Create dummy variables ############
dummy = []
for i in df[new_features]:
ct = df[i].nunique()
if ct > 2 and ct <= 10:
dummy.append(i)
df_with_dummies = pd.get_dummies(df[keys[2:]], columns=dummy, drop_first=True)
df_with_dummies = df_with_dummies.drop('target', axis=1)
## RidgeCV和Ridge的区别是：前者可以进行交叉验证
models = [
Pipeline([
# include_bias: 是否添加多项式扩展中的1
('Poly', PolynomialFeatures(include_bias=True)),
('Linear', LinearRegression(fit_intercept=False))
]),
Pipeline([
('Poly', PolynomialFeatures(include_bias=True)),
# alpha给定的是Ridge算法中，L2正则项的权重值，也就是ppt中的兰姆达
# alphas是给定CV交叉验证过程中，Ridge算法的alpha参数值的取值的范围
('Linear', RidgeCV(alphas=np.logspace(-3,2,50), fit_intercept=False))
]),
Pipeline([
('Poly', PolynomialFeatures(include_bias=True)),
('Linear', LassoCV(alphas=np.logspace(0,1,10), fit_intercept=False))
]),
Pipeline([
('Poly', PolynomialFeatures(include_bias=True)),
# la_ratio：给定EN算法中L1正则项在整个惩罚项中的比例，这里给定的是一个列表；
# 表示的是在CV交叉验证的过程中，EN算法L1正则项的权重比例的可选值的范围
('Linear', ElasticNetCV(alphas=np.logspace(0,1,10), l1_ratio=[.1, .5, .7, .9, .95, 1], fit_intercept=False))
])
]
## 线性回归、Lasso回归、Ridge回归、ElasticNet比较
N = 2
plt.figure(facecolor='w')
degree = np.arange(1, N, 2) # 阶， 多项式扩展允许给定的阶数
dm = degree.size
colors = [] # 颜色
# is to estimate sparse coefficients. In some way we already did it manually
# when we dropped the AGE column in a previous ridge estimation.
#
# Lasso models (see the :ref:`lasso` User Guide section) estimates sparse
# coefficients. :class:`~sklearn.linear_model.LassoCV` applies cross
# validation in order to determine which value of the regularization parameter
# (`alpha`) is best suited for the model estimation.
from sklearn.linear_model import LassoCV
alphas = np.logspace(-10, 10,
21) # alpha values to be chosen from by cross-validation
model = make_pipeline(
preprocessor,
TransformedTargetRegressor(
regressor=LassoCV(alphas=alphas, max_iter=100_000),
func=np.log10,
inverse_func=sp.special.exp10,
),
)
_ = model.fit(X_train, y_train)
# %%
# First we verify which value of :math:`\alpha` has been selected.
model[-1].regressor_.alpha_
# %%
# Then we check the quality of the predictions.