def lasso_cv_rmse(X, y, l_alphas):
lasso_rmse = []
# initialize model
lasso = Lasso()
# iterate through Lasso alphas
for la in l_alphas:
# set the current alpha to the model
lasso.set_params(alpha=la)
# keep track of fold RMSE
lasso_scores = cross_val_score(lasso,
X,
y,
scoring="neg_mean_squared_error")
lasso_rmse.append(np.sqrt(np.mean(-1 * lasso_scores)))
# We can compared against a multiple linear regression without regularisation
linreg = LinearRegression()
linreg_scores = cross_val_score(linreg,
X,
y,
scoring="neg_mean_squared_error")
linreg_rmse = np.sqrt(np.mean(-1 * linreg_scores))
return lasso_rmse, linreg_rmse
def fit_lasso(self, split_frac=0.5, cv=10):
# Split into training and test sets
X_train, X_test, y_train, y_test = model_selection.train_test_split(
self.X, self.y, test_size=split_frac, random_state=1)
#Lasso CV
lasso = Lasso(max_iter=10000, normalize=False, fit_intercept=False)
lassocv = LassoCV(alphas=None,
cv=cv,
max_iter=10000,
normalize=False,
fit_intercept=False)
lassocv.fit((X_train), y_train)
lasso.set_params(alpha=lassocv.alpha_)
lasso.fit((X_train), y_train)
pred_train = np.squeeze(lasso.predict((X_train)))
pred = np.squeeze(lasso.predict((X_test)))
train_corr = np.corrcoef(y_train, pred_train)[0, 1]
mse = mean_squared_error(y_test, pred)
test_corr = np.corrcoef(y_test, pred)[0, 1]
nnz_coef = len(lasso.coef_[np.abs(lasso.coef_ > 1e-10)])
return lassocv.alpha_, train_corr, test_corr, lasso.coef_, nnz_coef, mse
def Lasso_regression(X_train, y_train, X_test, params):
# Поиск по сетке для Lasso в виду малого количества гиперпараметров 'GridSearch' и 'RandomGridSearch' - одно и то же
if hyperparameters == 'RandomGridSearch' or hyperparameters == 'GridSearch':
# Осуществляем поиск по сетке с кросс-валидацией (число фолдов равно 3)
alphas = np.arange(1, 800, 50)
param_grid = {'alpha': alphas}
# Задаем модель, которую будем обучать
estimator = Lasso()
# Производим обучение модели с заданными вариантами параметров (осуществляем поиск по сетке)
optimizer = GridSearchCV(estimator, param_grid, iid = 'deprecated', cv = 3, scoring = 'neg_mean_absolute_error')
optimizer.fit(X_train, y_train)
regression = optimizer.best_estimator_
predicted = regression.predict(X_test)
validation_score = optimizer.best_score_
elif hyperparameters == 'Custom':
estimator = Lasso()
# Задаем нужные параметры
estimator.set_params(**params)
# Проверка по кросс-валидации
fold = KFold(n_splits = 3, shuffle = True)
validation_score = cross_val_score(estimator = estimator, X = X_train, y = y_train, cv = fold, scoring = 'neg_mean_absolute_error')
# Обучаем модель уже на всех данных
estimator.fit(X_train, np.ravel(y_train))
predicted = estimator.predict(X_test)
return(predicted, validation_score)
def est_coefs(i):
#coefs=[]
cross_val_par = 5
lasso = Lasso(fit_intercept=False, max_iter=10000, normalize=True)
lassocv = LassoCV(alphas=None,
fit_intercept=False,
cv=cross_val_par,
max_iter=100000,
normalize=True)
y = Y[:, i]
y = y.reshape(-1, 1)
X_train = lagged_ret[train_index, :]
X_test = lagged_ret[test_index, :]
y_train = y[train_index, :].ravel()
y_test = y[test_index, :]
lassocv.fit(X_train, y_train)
# print("\n")
# print("Optimal cv parameter: ", lassocv.alpha_)
lasso.set_params(alpha=lassocv.alpha_)
lasso.fit(X_train, y_train.ravel())
#coefs.append([col_names[i], lasso.coef_])
coefs = lasso.coef_
# coefs = lasso.coef_.reshape(1, -1)
# print("MSE: ", mean_squared_error(y_test, lasso.predict(X_test)))
# print("Lasso coefs: ", coefs)
return coefs
def get_estimator(self):
estimator = self.kwargs.get("estimator", self.ESTIMATOR)
if estimator == "Lasso":
model = Lasso()
elif estimator == "Ridge":
model = Ridge()
elif estimator == "Linear":
model = LinearRegression()
elif estimator == "GBM":
model = GradientBoostingRegressor()
elif estimator == "RandomForest":
model = RandomForestRegressor()
self.model_params = { # 'n_estimators': [int(x) for x in np.linspace(start = 50, stop = 200, num = 10)],
'max_features': ['auto', 'sqrt'],
'n_estimators': range(60, 220, 40)
}
# 'max_depth' : [int(x) for x in np.linspace(10, 110, num = 11)]}
else:
model = Lasso()
estimator_params = self.kwargs.get("estimator_params", {})
self.mlflow_log_param("estimator", estimator)
model.set_params(**estimator_params)
print(colored(model.__class__.__name__, "red"))
return model
def _compare_with_lasso(self,
lasso_X,
lasso_y,
wlasso_X,
wlasso_y,
sample_weight,
alpha_range=[0.01],
params={}):
for alpha in alpha_range:
lasso = Lasso(alpha=alpha)
lasso.set_params(**params)
lasso.fit(lasso_X, lasso_y)
wlasso = WeightedLasso(alpha=alpha)
wlasso.set_params(**params)
wlasso.fit(wlasso_X, wlasso_y, sample_weight=sample_weight)
# Check results are similar with tolerance 1e-6
if np.ndim(lasso_y) > 1:
for i in range(lasso_y.shape[1]):
np.testing.assert_allclose(lasso.coef_[i], wlasso.coef_[i])
if lasso.get_params()["fit_intercept"]:
self.assertAlmostEqual(lasso.intercept_[i],
wlasso.intercept_[i])
else:
np.testing.assert_allclose(lasso.coef_, wlasso.coef_)
self.assertAlmostEqual(lasso.intercept_, wlasso.intercept_)
def vendor_lasso(df):
'''
Creating a subsetted version of the full dataset ('new_train') and running lasso regression on data with OneHotEncoded vendor column
'''
new_train = df[[
'Order.Month', 'Order.Day', 'Order.Year', 'Vendor Number', 'Pack',
'Bottle Volume (L)', 'Volume Sold (Liters)', 'Profit'
]].copy()
sale_data = new_train.loc[:, new_train.columns != 'Profit']
sale_target = new_train['Profit']
X_train, X_test, y_train, y_test = train_test_split(sale_data,
sale_target,
test_size=0.2,
random_state=3)
new_train_cols = X_train[[
'Order.Day', 'Order.Year', 'Order.Month', 'Bottle Volume (L)', 'Pack',
'Volume Sold (Liters)'
]]
new_train_nomcols = X_train[['Vendor Number']]
enc = OneHotEncoder(drop='first', sparse=False)
encodecols = enc.fit_transform(new_train_nomcols)
feature_names = enc.get_feature_names(['Vendor Number'])
pd.DataFrame(encodecols, columns=feature_names)
X_train = pd.concat([
new_train_cols.reset_index(drop=True),
pd.DataFrame(encodecols,
columns=feature_names).astype(int).reset_index(drop=True)
],
axis=1)
'''lasso = Lasso()
lasso.set_params(alpha=1, normalize=True)
lasso.fit(X_train, y_train)
print('The intercept is %.4f' %(lasso.intercept_))
lassoCoef = pd.Series(lasso.coef_, index=X_train.columns).sort_values(ascending = False)
print('The slopes are \n%s' %(lassoCoef))'''
lasso = Lasso()
coefs = []
alphaRange = np.linspace(1e-3, 20, 20)
for alpha in alphaRange:
lasso.set_params(alpha=alpha, normalize=True)
lasso.fit(X_train, y_train)
coefs.append(lasso.coef_)
coefs = pd.DataFrame(np.array(coefs), columns=X_train.columns)
for name in coefs.columns:
plt.plot(alphaRange, coefs[name])
plt.xlabel('Alpha')
plt.ylabel("Coefficients")
plt.title('Change of Lasso Slopes Varying Alpha')
def trylasso5times(array_alphas, cv=5, max_iter=1e6, normalize=True):
lassocv = LassoCV(alphas=array_alphas, cv=5, max_iter=1e6, normalize=True)
lassocv.fit(X_train, y_train)
lasso = Lasso()
lasso.set_params(alpha=lassocv.alpha_)
lasso.fit(X_train, y_train)
return lasso, lassocv
def LinearModelLasso(X_train, y_train, X_test, y_test):
alphas = 10**np.linspace(10, -2, 100) * 0.5
lasso_cv = LassoCV(alphas=alphas, random_state=0, max_iter=100000, cv=10)
lasso_cv.fit(X_train, y_train)
print("Value of lasso tuning parameter", lasso_cv.alpha_)
lasso = Lasso()
lasso.set_params(alpha=lasso_cv.alpha_)
lasso.fit(X_train, y_train)
print_evaluation_metrics(lasso, "Lasso Model", X_train, y_train, X_test,
y_test)
print("\n")
print("--- Feature Selection -----")
coef = pd.Series(lasso.coef_, index=X_train.columns)
print("Lasso picked " + str(sum(coef != 0)) +
" variables and eliminated the other " + str(sum(coef == 0)) +
" variables")
for e in sorted(list(zip(list(X_train), lasso.coef_)),
key=lambda e: -abs(e[1])):
if e[1] != 0:
print("\t{}, {:.3f}".format(e[0], e[1]))
def lasso(x, y):
lassocv = LassoCV(alphas=None, cv=10, max_iter=10000, normalize=True)
lassocv.fit(x, y)
lassoreg = Lasso(alpha=.00005, normalize=True, max_iter=1e5)
lassoreg.set_params(alpha=lassocv.alpha_)
lassoreg.fit(x, y)
r2 = round(lassoreg.score(x, y), 2)
return r2, lassoreg.coef_
def lasso_coefs(X, Y, alphas):
coefs = []
lasso_reg = Lasso()
for a in alphas:
lasso_reg.set_params(alpha=a)
lasso_reg.fit(X, Y)
coefs.append(lasso_reg.coef_)
return coefs
def DoLasso(alphas, currentCountry, otherCountries):
lasso = Lasso(alpha=alphas, normalize=True)
coefs = []
for a in alphas:
lasso.set_params(alpha=a)
lasso.fit(otherCountries.transpose(), currentCountry)
coefs.append(lasso.coef_)
return coefs
def lasso_coefs(X, Y, alphas):
coefs = []
lasso_reg = Lasso()
for a in alphas:
lasso_reg.set_params(alpha=a) # We set the hyperparameter alpha
lasso_reg.fit(X, Y)
coefs.append(lasso_reg.coef_)
return coefs
def lasso_regression(X_train,
y_train,
X_test,
params,
use_cv: bool = True):
# If there are not enough points for cross validation
if use_cv is False:
if params is None:
model = Lasso()
else:
model = Lasso(**params)
model.fit(X_train, y_train)
predicted = model.predict(X_test)
# Calculate score on train
train_predicted = model.predict(X_train)
validation_score = mean_absolute_error(
np.ravel(y_train), np.ravel(train_predicted))
return predicted, validation_score
# Grid search for Lasso due to the small number of hyperparameters 'GridSearch' and 'RandomGridSearch' are the same
if hyperparameters == 'RandomGridSearch' or hyperparameters == 'GridSearch':
# We search the grid with cross-validation (the number of folds is 3)
alphas = np.arange(1, 800, 50)
param_grid = {'alpha': alphas}
# Setting the model to train
estimator = Lasso()
# We train the model with the specified parameter options (we search the grid)
optimizer = GridSearchCV(estimator,
param_grid,
iid='deprecated',
cv=3,
scoring='neg_mean_absolute_error')
optimizer.fit(X_train, y_train)
regression = optimizer.best_estimator_
predicted = regression.predict(X_test)
validation_score = optimizer.best_score_
elif hyperparameters == 'Custom':
estimator = Lasso()
# Setting the necessary parameters
estimator.set_params(**params)
# Cross-validation check
fold = KFold(n_splits=3, shuffle=True)
validation_score = cross_val_score(
estimator=estimator,
X=X_train,
y=y_train,
cv=fold,
scoring='neg_mean_absolute_error')
# Training the model already on all data
estimator.fit(X_train, np.ravel(y_train))
predicted = estimator.predict(X_test)
return predicted, validation_score
class LassoModel(Model):
def create_model(self):
self.lasso = Lasso()
def fit(self, train_x, train_y):
self.lasso_pip = make_pipeline(RobustScaler(), self.lasso)
self.lasso_pip.fit(train_x, train_y)
def set_config(self, config):
self.lasso.set_params(**config)
def predict(self, test_x):
return self.lasso_pip.predict(test_x)
def lasso_prediction(x, y):
lasso = Lasso(normalize = "True", tol = 0.01, max_iter = 500000)
w, q = x.shape
coefs = []
preds = []
i = 0
while i < q:
start = timer()
x_i = x
print("\n------------------------------------\n")
name ="Fitting for country no.: %s" %i
print(name)
alpha = 100
lasso.set_params(alpha = alpha)
population_vector = x.iloc[:, i]
x_i = x.drop([i], axis = 1)
y_i = y.drop([i], axis = 1)
lasso.fit(x_i, population_vector)
while np.count_nonzero(lasso.coef_) > 5:
alpha = alpha + 500
lasso.set_params(alpha = alpha)
population_vector = x.iloc[:, i]
x_i = x.drop([i], axis = 1)
lasso.fit(x_i, population_vector)
prediction = lasso.predict(y_i)
coefs.append(lasso.coef_)
preds.append(prediction)
end_timer = timer() - start
print("Alpha value: " + str(alpha))
time_statement = "Fitting time for country no. %s: " %i
print(time_statement + str(round(end_timer, 4)) + " seconds.")
print("Number of countries used for fitting: " + str(np.count_nonzero(lasso.coef_)))
i = i + 1
np.savetxt("population_parameters.csv", coefs, delimiter=",")
np.savetxt("population_prediction.csv", preds, delimiter=",")
return coefs
def lasso_prediction(x, y):
lasso = Lasso(normalize="True", tol=0.001, max_iter=5000000)
w, q = x.shape
coefs = []
preds = []
i = 0
alphas = 10**(np.linspace(0.01, 8, 1000))
while i < q:
x_i = x
print("\n------------------------------------\n")
name = "Fitting for country no.: %s" % (i + 1)
print(name)
lasso.set_params(alpha=1000000000)
population_vector = x.iloc[:, i]
y_true = y.iloc[:, i]
lasso.fit(x_i, population_vector)
prediction = lasso.predict(y)
error = mse(y_true, prediction)
for a in alphas:
lasso.set_params(alpha=a)
population_vector = x.iloc[:, i]
y_true = y.iloc[:, i]
lasso.fit(x, population_vector)
prediction = lasso.predict(y)
mean_error = mse(y_true, prediction)
if (mean_error < error) and 0 < np.count_nonzero(lasso.coef_) <= 5:
coefs_best = lasso.coef_
pred_best = prediction
error = mean_error
alpha_value = a
country_number = np.count_nonzero(lasso.coef_)
coefs.append(coefs_best)
preds.append(pred_best)
print("Aplha value: " + str(alpha_value))
print("Number of countries used for fitting: " + str(country_number))
i = i + 1
return coefs, preds
def Lasso_Reg(Phi_train, Y_train, Phi_test, Y_test, alphas):
reg = Lasso()
coefs = []
train_MSE = []
test_MSE = []
for a in alphas:
reg.set_params(alpha=a)
reg.fit(Phi_train, Y_train)
coefs.append(reg.coef_)
train_pred = (reg.predict(Phi_train))
train_MSE.append(mean_squared_error(Y_train, train_pred))
test_pred = (reg.predict(Phi_test))
test_MSE.append(mean_squared_error(Y_test, test_pred))
return {'coefs': coefs, 'train_MSE': train_MSE, 'test_MSE': test_MSE}
def lasso_regress(X, y, a=None, b=None):
"""
This function returns regression of input data by LASSO regression
method.
Parameters
----------
X: an array or array-like predictors.
It should be scaled by StandardScaler.
y: an array or array-like target.
It should has compatible dimension with input X.
a, b: an array or array-like, optional.
another set of data, such as a = X_test, b = y_test.
Returns
-------
coefs_LASSO: list.
a list of coefficients from LASSO with different lambdas
lambdas_LASSO: list.
a list of lambdas used in this LASSO
error1_LASSO: list.
a list of MSE of prediction from first input set (X, y)
error2_LASSO: list.
a list of MSE of prediction from second input set (a, b).
Return as None if a and b are not defined.
modelLASSO: modelLASSO = Lasso(), the LASSO model command
"""
# lASSO vs lambda
coefs_LASSO = []
error1_LASSO = []
error2_LASSO = []
# Tunning parameter(lambda)
lambdas_LASSO = np.logspace(-4, 8, 200)
modelLASSO = Lasso(max_iter=1e5)
# loop over lambda values to determine the best by mse
for l in lambdas_LASSO:
modelLASSO.set_params(alpha=l)
modelLASSO.fit(X, y)
coefs_LASSO.append(modelLASSO.coef_)
error1_LASSO.append(mean_squared_error(y, modelLASSO.predict(X)))
if a.any() and b.any() is not None:
error2_LASSO.append(mean_squared_error(b, modelLASSO.predict(a)))
else:
error2_LASSO = None
return coefs_LASSO, lambdas_LASSO, error1_LASSO, error2_LASSO, modelLASSO
def get_estimator(self):
estimator = self.kwargs.get("estimator", self.ESTIMATOR)
if estimator == "Lasso":
model = Lasso()
elif estimator == "Ridge":
model = Ridge()
elif estimator == "Linear":
model = LinearRegression()
elif estimator == "GBM":
model = GradientBoostingRegressor()
else:
model = Lasso()
estimator_params = self.kwargs.get("estimator_params", {})
model.set_params(**estimator_params)
print(model.__class__.__name__)
return model
def binarySearch(self, X, y, select_num = 50):
model = Lasso()
betaM = np.zeros([X.shape[1]])
#max(abs(crossprod(xx, yy / sqrt(sum(yy ^ 2)) / sqrt(n))))
iteration = 0
min_lambda = 1e-15
max_lambda = 1e15
minFactor = 0.9
maxFactor = 1.1
stuckCount = 1
previousC = -1
patience = 20
while min_lambda < max_lambda and iteration < 50:
iteration += 1
lmbd = np.exp((np.log(min_lambda) + np.log(max_lambda)) / 2.0)
# print "\t\tIter:{}\tlambda:{}".format(iteration, lmbd),
model.set_params(alpha=lmbd)
model.fit(X, y)
beta = model.coef_
c = len(np.where(np.abs(beta) > 0)[0]) # we choose regularizers based on the number of non-zeros it reports
# print "# Chosen:{}".format(c)
if c < select_num * minFactor: # Regularizer too strong
max_lambda = lmbd
betaM = beta
elif c > select_num * maxFactor: # Regularizer too weak
min_lambda = lmbd
betaM = beta
else:
betaM = beta
break
if c == previousC:
stuckCount += 1
else:
previousC = c
stuckCount = 1
if stuckCount > patience:
# print 'Run out of patience'
break
return betaM
def build_models(x, y):
"""
Build a Ridge regression model
"""
# Alpha values uniformly
# spaced between 0.01 and 0.02
alpha_range = np.linspace(0, 0.5, 200)
model = Lasso(normalize=True)
coeffiecients = []
# Fit a model for each alpha value
for alpha in alpha_range:
model.set_params(alpha=alpha)
model.fit(x, y)
# Track the coeffiecients for plot
coeffiecients.append(model.coef_)
# Plot coeffients weight decay vs alpha value
# Plot model RMSE vs alpha value
coeff_path(alpha_range, coeffiecients)
def build_models(x,y):
"""
Build a Ridge regression model
"""
# Alpha values uniformly
# spaced between 0.01 and 0.02
alpha_range = np.linspace(0,0.5,200)
model = Lasso(normalize=True)
coeffiecients = []
# Fit a model for each alpha value
for alpha in alpha_range:
model.set_params(alpha=alpha)
model.fit(x,y)
# Track the coeffiecients for plot
coeffiecients.append(model.coef_)
# Plot coeffients weight decay vs alpha value
# Plot model RMSE vs alpha value
coeff_path(alpha_range,coeffiecients)
def Lasso_model(train_linear, test_linear):
train_linear_fea = train_linear.drop(columns=['SalePrice'])
train_linear_tar = train_linear.SalePrice
real_train_tar = np.expm1(train_linear_tar)
x_train, x_test, y_train, y_test = train_test_split(train_linear_fea,
train_linear_tar,
test_size=0.2,
random_state=0)
real_train_tar = np.expm1(train_linear_tar)
"""
. Lasso model
"""
lassocv = LassoCV(alphas=np.logspace(-5, 4, 400), )
lassocv.fit(train_linear_fea, train_linear_tar)
lassocv_score = lassocv.score(train_linear_fea, train_linear_tar)
lassocv_alpha = lassocv.alpha_
print("Best alpha : ", lassocv_alpha, "Score: ", lassocv_score)
start = time.time()
lasso = Lasso(normalize=True)
lasso.set_params(alpha=lassocv_alpha, max_iter=10000)
lasso.fit(x_train, y_train)
end = time.time()
mean_squared_error(y_test, lasso.predict(x_test))
coef_lasso = pd.Series(lassocv.coef_,
index=x_train.columns).sort_values(ascending=False)
evaluate(lasso, x_test, y_test, x_train, y_train)
print('Time elapsed: %.4f seconds' % (end - start))
y_lasso_predict = lasso.predict(train_linear_fea)
x_line = np.arange(700000)
y_line = x_line
plt.scatter(real_train_tar, np.expm1(y_lasso_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_lasso = np.expm1(lasso.predict(test_linear))
write_pkl(
lassocv_alpha,
'/Users/vickywinter/Documents/NYC/Machine Learning Proj/Pickle/lasso_params.pkl'
)
return test_prediction_lasso
class LassoRegressor(LinearRegressor):
"""
Linear Regression via Lasso Regression (OLS Regression w/ L1 penalty)
-------------------------------------------------------------------------------
This class is a wrapper for scikit-learn's "sklearn.linear_model.Lasso" class.
"""
def __init__(self, std=True, max_iter=10000):
self.regressor_type = "Lasso"
self.sklearn_Lasso = Lasso(fit_intercept=std,
normalize=std,
max_iter=max_iter)
super().__init__(std)
self.std = False # let scikit-learn handle preprocessing
# regress X on Y
def compute_coeff(self):
self.sklearn_Lasso.set_params(alpha=self.alpha)
self.sklearn_Lasso.fit(self.X, self.Y)
self.coeff = self.sklearn_Lasso.coef_
def lasso_reg(alphas, x, y):
lasso = Lasso(normalize="True", tol=0.001, max_iter=50000)
w, q = x.shape
f = 1
error = []
for a in alphas:
coefs = []
preds = []
lasso.set_params(alpha=a)
i = 0
while i < q:
population_vector = x.iloc[:, i]
train1 = x.drop([i], axis=1)
lasso.fit(train1, population_vector)
test1 = y.drop([i], axis=1)
prediction = lasso.predict(test1)
coefs.append(lasso.coef_)
preds.append(prediction)
i = i + 1
name_coef = "coef_no_%s.csv" % f
name_preds = "pred_no_%s.csv" % f
np.savetxt(name_coef, coefs, delimiter=",")
np.savetxt(name_preds, preds, delimiter=",")
y_true = y.values
result = mse(y_true, np.transpose(preds))
error.append(result)
f = f + 1
return lasso, preds, error
def fit(x, y):
a = 50000000
coefs = []
model = Lasso(tol=0.02, max_iter=500000)
x = x.T
w, q = x.shape
i = 0
while i < q:
t = x.iloc[:, i]
x_i = x[x != t]
model.set_params(alpha=a)
model.fit(x_i, t)
coefs.append(model.coef_)
i = i + 1
return coefs
def best_parameter(x, y, method, alphas):
Xm = x.as_matrix()
ym = y.as_matrix()
if method == "lasso":
model = Lasso(fit_intercept=True)
elif method == "ridge":
model = Ridge(fit_intercept=True)
k_fold = cross_validation.KFold(len(Xm), 10)
best_cv_mse = float("inf")
for a in alphas:
model.set_params(alpha=a)
mse_list_k10 = [
MSE(model.fit(Xm[train], ym[train]).predict(Xm[val]), ym[val])
for train, val in k_fold]
if np.mean(mse_list_k10) < best_cv_mse:
best_cv_mse = np.mean(mse_list_k10)
best_alpha = a
print method, "BEST PARAMETER=%f, MSE(CV)=%f" % (best_alpha, best_cv_mse)
def get_lasso(X, Y):
#running lasso with alpha 0 (MLR)
lasso = Lasso()
lasso.set_params(alpha=0, normalize=True)
lasso.fit(X, Y)
#Grid search
alphas_lasso = np.linspace(0, 10, 50)
tuned_parameters_r = [{'alpha': alphas_lasso}]
n_folds = 5
cv = KFold(n_splits=n_folds, shuffle=True)
tune_lasso = GridSearchCV(lasso,
tuned_parameters_r,
cv=cv,
refit=True,
return_train_score=True,
scoring='neg_mean_squared_error')
tune_lasso.fit(X, Y)
print(tune_lasso.best_params_)
print(np.max(tune_lasso.cv_results_['mean_test_score']))
print(np.min(tune_lasso.cv_results_['mean_test_score']))
lasso_best = tune_lasso.best_estimator_
lasso_best.fit(X, Y)
print(lasso_best.score(X, Y))
suffix = str(datetime.datetime.now())
model_filename = 'lasso' + suffix + '.sav'
pickle.dump(lasso_best, open(model_filename, 'wb'))
csv_filename = 'ridge ' + suffix + '.csv'
raw_test, test_IDs = load_test()
predict = lasso_best.predict(raw_test)
predict = np.exp(predict)
predict = pd.DataFrame(predict)
predict = pd.concat([test_IDs, predict], axis=1)
predict.columns = ['Id', 'SalePrice']
predict.to_csv(csv_filename, index=False)
def Lasso_model(train_linear, test_linear):
train_linear_fea=train_linear.drop(columns=['SalePrice'])
train_linear_tar=train_linear.SalePrice
real_train_tar=np.expm1(train_linear_tar)
x_train, x_test, y_train, y_test = train_test_split(train_linear_fea, train_linear_tar,test_size=0.2, random_state=0)
real_train_tar=np.expm1(train_linear_tar)
"""
. Lasso model
"""
lassocv = LassoCV(alphas = np.logspace(-5, 4, 400), )
lassocv.fit(train_linear_fea, train_linear_tar)
lassocv_score = lassocv.score(train_linear_fea, train_linear_tar)
lassocv_alpha = lassocv.alpha_
print("Best alpha : ", lassocv_alpha, "Score: ",lassocv_score)
start=time.time()
lasso =Lasso(normalize = True)
lasso.set_params(alpha=lassocv_alpha,max_iter = 10000)
lasso.fit(x_train, y_train)
end=time.time()
mean_squared_error(y_test, lasso.predict(x_test))
coef_lasso=pd.Series(lassocv.coef_, index=x_train.columns).sort_values(ascending =False)
evaluate(lasso,x_test,y_test,x_train,y_train)
print('Time elapsed: %.4f seconds' % (end-start))
y_lasso_predict=lasso.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_lasso_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_lasso=np.expm1(lasso.predict(test_linear))
write_pkl(lassocv_alpha, '/Users/vickywinter/Documents/NYC/Machine Learning Proj/Pickle/lasso_params.pkl')
return test_prediction_lasso
def lr(self):
self.alphas = np.logspace(self.model_params_obj.lasso_params_obj.minAlpha,
self.model_params_obj.lasso_params_obj.maxAlpha,
num=self.model_params_obj.lasso_params_obj.lasso_n_alphas,base=np.e)
print('alphas', self.alphas)
lasso = Lasso(max_iter=self.model_params_obj.lasso_params_obj.lasso_max_iter,
normalize=False, fit_intercept=True)
#initialize scores for each alpha
self.scores = np.empty_like(self.alphas)
self.weights = []
for i,a in enumerate(self.alphas):
lasso.set_params(alpha=a)
lasso.fit(self.X_train, self.y_train)
self.weights.append(lasso.coef_)
#add scores for each alpha
self.scores[i] = lasso.score(self.X_train, self.y_train)
print('r-2',self.scores)
tuned_parameters = [{'alpha': self.alphas}]
n_folds = 6
gridsearch_lasso_cv = GridSearchCV(lasso, tuned_parameters,
scoring=['neg_mean_squared_error', 'explained_variance', 'r2'],
cv=n_folds, refit='neg_mean_squared_error')
gridsearch_lasso_cv.fit(self.X_train, self.y_train)
#plot_lasso_permutation_importance(gridsearch_lasso_cv, _X_train, _y_train)
self.rmse["lr"] = gridsearch_lasso_cv.cv_results_['mean_test_neg_mean_squared_error'].tolist()
self.r2["lr"] = gridsearch_lasso_cv.cv_results_['mean_test_r2'].tolist()
self.expvarscore["lr"] = gridsearch_lasso_cv.cv_results_['mean_test_explained_variance'].tolist()
self.plotsobj.weights_vs_alphas(self.weights, self.alphas, self.features)
self.compute_results(gridsearch_lasso_cv, "Lasso Regression", 'lr')
plot_lasso_permutation_importance([gridsearch_lasso_cv, self.X_train, self.y_train], self.plotsobj, self.features)
return {'results': [self.plotsobj.results['lr_train'], self.plotsobj.results['lr_test']], \
'rmse':self.rmse['lr'], 'r2':self.r2['lr'], 'expvar': self.expvarscore['lr']}
def set_params(self, C=None):
if self.l1:
Lasso.set_params(self, alpha=C)
else:
Ridge.set_params(self, alpha=1.0/(2.0*C))
return self
def Model(train_linear, test_linear):
train_linear_fea=train_linear.drop(columns=['SalePrice'])
train_linear_tar=train_linear.SalePrice
x_train, x_test, y_train, y_test = train_test_split(train_linear_fea, train_linear_tar,test_size=0.2, random_state=0)
def evaluate(model, test_features, test_labels,train_features, train_labels):
predictions = model.predict(test_features)
errors = abs(predictions - test_labels)
mape = 100 * np.mean(errors / test_labels)
accuracy = 100 - mape
print('Model Performance')
print('Average Error: {:0.4f} degrees.'.format(np.mean(errors)))
print('Accuracy = {:0.2f}%.'.format(accuracy))
print("MSE for train data is: %f" % mean_squared_error(y_train, model.predict(x_train)))
print("MSE for validation data is: %f" % mean_squared_error(y_test, model.predict(x_test)))
return accuracy
real_train_tar=np.expm1(train_linear_tar)
"""
. Lasso model
"""
lassocv = LassoCV(alphas = np.logspace(-5, 4, 400), )
lassocv.fit(train_linear_fea, train_linear_tar)
lassocv_score = lassocv.score(train_linear_fea, train_linear_tar)
lassocv_alpha = lassocv.alpha_
print("Best alpha : ", lassocv_alpha, "Score: ",lassocv_score)
start=time.time()
lasso =Lasso(normalize = True)
lasso.set_params(alpha=lassocv_alpha,max_iter = 10000)
lasso.fit(x_train, y_train)
end=time.time()
mean_squared_error(y_test, lasso.predict(x_test))
coef_lasso=pd.Series(lassocv.coef_, index=x_train.columns).sort_values(ascending =False)
evaluate(lasso,x_test,y_test,x_train,y_train)
print('Time elapsed: %.4f seconds' % (end-start))
y_lasso_predict=lasso.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_lasso_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_lasso=np.expm1(lasso.predict(test_linear))
"""
. Ridge model
"""
ridgecv = RidgeCV(alphas = np.logspace(-5, 4, 400))
ridgecv.fit(x_train, y_train)
ridgecv_score = ridgecv.score(x_train, y_train)
ridgecv_alpha = ridgecv.alpha_
print("Best alpha : ", ridgecv_alpha, "Score: ",ridgecv_score)
coef=pd.Series(ridgecv.coef_, index=x_train.columns).sort_values(ascending =False)
start=time.time()
ridge =Ridge(normalize = True)
ridge.set_params(alpha=ridgecv_alpha,max_iter = 10000)
ridge.fit(x_train, y_train)
end=time.time()
mean_squared_error(y_test, ridge.predict(x_test))
coef_ridge=pd.Series(ridgecv.coef_, index=x_train.columns).sort_values(ascending =False)
evaluate(ridge,x_test,y_test,x_train,y_train)
print('Time elapsed: %.4f seconds' % (end-start))
y_ridge_predict=ridge.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_ridge_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
test_prediction_ridge=np.expm1(ridge.predict(test_linear))
"""
. Random Forest
"""
#train=train.drop(columns=['DateSold'])
#test=test.drop(columns=['DateSold'])
#X_train=train.drop(columns=['SalePrice'])
#Y_train=train['SalePrice']
X_train=train_linear_fea
Y_train=train_linear_tar
x_train_rf, x_test_rf, y_train_rf, y_test_rf = train_test_split(X_train, Y_train,test_size=0.2, random_state=0)
n_estimators = [int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
max_features = ['auto', 'sqrt']
max_depth = [int(x) for x in np.linspace(10, 110, num = 11)]
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators,
'max_features': max_features,
'max_depth': max_depth,
'min_samples_split': min_samples_split,
'min_samples_leaf': min_samples_leaf,
'bootstrap': bootstrap}
rf = RandomForestRegressor()
# Random search of parameters, using 3 fold cross validation,
# search across 100 different combinations, and use all available cores
#
rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 100, cv = 3, verbose=2, random_state=42, n_jobs = -1)
rf_random.fit(X_train, Y_train)
#rf_random.fit(x_train_rf, y_train_rf)
rf_random.best_params_
#Random search allowed us to narrow down the range for each hyperparameter. Now that we know where to concentrate our search,
# we can explicitly specify every combination of settings to try.
param_grid = {
'bootstrap': [False],
'max_depth': [80, 90, 100, 110,120,130],
'max_features': [2, 3],
'min_samples_leaf': [1,2,3, 4],
'min_samples_split': [2,4,6,8, 10, 12],
'n_estimators': [600,700, 800, 900, 1000]
}
# Create a based model
rf = RandomForestRegressor()
# Instantiate the grid search model
grid_search = GridSearchCV(estimator = rf, param_grid = param_grid, cv = 3, n_jobs = -1, verbose = 2)
#grid_search.fit(x_train, y_train)
grid_search.fit(X_train, Y_train)
grid_search.best_params_
best_random = grid_search.best_estimator_
start=time.time()
best_random.fit(x_train_rf,y_train_rf)
end=time.time()
evaluate(best_random, x_test_rf, y_test_rf,x_train_rf,y_train_rf)
print('Time elapsed: %.4f seconds' % (end-start))
y_rf_predict=best_random.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_rf_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
importance_rf = pd.DataFrame({'features':train_linear_fea.columns, 'imp':best_random.feature_importances_}).\
sort_values('imp',ascending=False)
importance_top20_rf = importance_rf.iloc[:20,]
plt.barh(importance_top20_rf.features, importance_top20_rf.imp)
plt.xlabel('Feature Importance')
test_prediction_rf=np.expm1(best_random.predict(test_linear))
"""
. Xgboost
"""
learning_rate = [round(float(x), 2) for x in np.linspace(start = .1, stop = .2, num = 11)]
# Minimum for sum of weights for observations in a node
min_child_weight = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
# Maximum nodes in each tree
max_depth = [int(x) for x in np.linspace(1, 10, num = 10)]
n_estimators=[int(x) for x in np.linspace(start = 100, stop = 2000, num = 20)]
subsample=[0.3, 0.4,0.5,0.6, 0.7]
model = xgb.XGBRegressor()
random_grid = {'learning_rate': learning_rate,
'max_depth': max_depth,
'min_child_weight': min_child_weight,
'subsample': subsample,
'n_estimators':n_estimators
}
# Make a RandomizedSearchCV object with correct model and specified hyperparams
xgb_random = RandomizedSearchCV(estimator=model, param_distributions=random_grid, n_iter=1000, cv=5, verbose=2, random_state=42, n_jobs=-1)
start = time.time()
# Fit models
xgb_random.fit(X_train, Y_train)
xgb_random.best_params_
"""
best_params_={'learning_rate': 0.1,
'max_depth': 2,
'min_child_weight': 4,
'n_estimators': 900,
'subsample': 0.5}
"""
model_xgb = XGBRegressor(**xgb_random.best_params_)
#model_xgb = XGBRegressor(**best_params_)
start=time.time()
model_xgb.fit(x_train_rf,y_train_rf)
end=time.time()
evaluate(model_xgb, x_test_rf, y_test_rf,x_train_rf,y_train_rf)
print('Time elapsed: %.4f seconds' % (end-start))
y_xgb_predict=model_xgb.predict(train_linear_fea)
x_line = np.arange(700000)
y_line=x_line
plt.scatter(real_train_tar,np.expm1(y_xgb_predict))
plt.plot(x_line, y_line, color='r')
plt.xlabel('Actual Sale Price')
plt.ylabel('Predict Sle Price')
importance_xgb = pd.DataFrame({'features':train_linear_fea.columns, 'imp':model_xgb.feature_importances_}).\
sort_values('imp',ascending=False)
importance_top20_xgb = importance_xgb.iloc[:20,]
plt.barh(importance_top20_xgb.features, importance_top20_xgb.imp)
plt.xlabel('Feature Importance')
test_prediction_xgb=np.expm1(model_xgb.predict(test_linear))
return(test_prediction_lasso, test_prediction_ridge, test_prediction_rf, test_prediction_xgb,y_lasso_predict, y_ridge_predict, y_rf_predict, y_xgb_predict)
df = df.drop("train", axis=1)
scaler = StandardScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(df), columns=df.columns)
df_scaled["lpsa"] = df["lpsa"]
X = df_scaled.ix[:, :-1]
N = X.shape[0]
X.insert(X.shape[1], "intercept", np.ones(N))
y = df_scaled["lpsa"]
names_regressors = ["Lcavol", "Lweight", "Age", "Lbph", "Svi", "Lcp", "Gleason", "Pgg45"]
X = X.drop("intercept", axis=1)
Xtrain = X[istrain]
ytrain = y[istrain]
names_regressors = ["Lcavol", "Lweight", "Age", "Lbph", "Svi", "Lcp", "Gleason", "Pgg45"]
alphas_ = np.logspace(1, -2, base=10)
Xm = Xtrain.as_matrix()
ym = ytrain.as_matrix()
k_fold = cross_validation.KFold(len(Xm), 10)
best_cv_mse = float("inf")
model = Lasso(fit_intercept=True)
for a in alphas_:
model.set_params(alpha=a)
mse_list_k10 = [MSE(model.fit(Xm[train], ym[train]).predict(Xm[val]), ym[val]) for train, val in k_fold]
if np.mean(mse_list_k10) < best_cv_mse:
best_cv_mse = np.mean(mse_list_k10)
best_alpha = a
print "LASSO BEST PARAMETER=%f, MSE(CV)=%f" % (best_alpha, best_cv_mse)
ax.set_xscale('log')
ax.set_xlim(ax.get_xlim()[::-1]) #reverse axis
plt.xlabel('Alpha')
plt.ylabel('Weights')
plt.title('Regularization Path RIDGE')
plt.axis('tight')
plt.legend(loc=2)
plt.show()
#Pregunta B
clf = Lasso(fit_intercept=True)
alphas_2 = np.logspace(1,-2,base=10)
coefs = []
for a in alphas_2:
clf.set_params(alpha=a)
clf.fit(Xtrain,ytrain)
coefs.append(clf.coef_)
ax = plt.gca()
for y_arr, label in zip(np.squeeze(coefs).T, names_regressors):
plt.plot(alphas_2, y_arr, label=label)
plt.legend()
ax.set_xscale('log')
ax.set_xlim(ax.get_xlim()[::-1]) #reverse axis
plt.xlabel('Alpha')
plt.ylabel('Weights')
plt.title('Regularization Path LASSO')
plt.axis('tight')
plt.legend(loc=2)
plt.show()