def fit_ridge_cv(theta_matrix, X_matrix, alpha=[0]):
reg = RidgeCV(alphas=alpha, fit_intercept=False)
reg.fit(theta_matrix, X_matrix)
gamma_vector = reg.coef_
loss = np.mean(np.square(reg.predict(theta_matrix) - X_matrix))
score = reg.score(theta_matrix, X_matrix)
return gamma_vector, loss, score, reg.alpha_
def Ridge_model(train_linear, test_linear):
ridgecv = RidgeCV(alphas = np.logspace(-5, 4, 400))
ridgecv.fit(train_linear_fea, train_linear_tar)
ridgecv_score = ridgecv.score(train_linear_fea, train_linear_tar)
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.set_params(alpha=6,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))
write_pkl(ridgecv_alpha, '/Users/vickywinter/Documents/NYC/Machine Learning Proj/Pickle/ridge_params.pkl')
return test_prediction_ridge
def fitModel(self, Xindex, Findex, X, Y):
if not hasattr(self, "model"):
self.model = []
if "ridge_alpha" in self.args:
alpha = self.args['ridge_alpha']
else:
ridgecv = RidgeCV(alphas=(10., 50., 100.))
ridgecv.fit(X, Y[:, 0])
logging.info("Ridge cv %f", ridgecv.alpha_)
logging.info("Ridge score %f", ridgecv.score(X, Y[:, 0]))
alpha = ridgecv.alpha_
global ridgeX, ridgeY, ridgeAlpha
ridgeX = X
ridgeY = Y
ridgeAlpha = alpha
for idx in self.divide(list(range(len(self.model), Y.shape[1])), 18):
with mp.Pool(6) as pool:
self.model += pool.map(train_ridge, idx)
logging.info("Ridge group %d", idx[0])
self.saveModel()
logging.info("Ridge ok")
def fitRidge(self, X, Y, name="ridge"):
if not hasattr(self, name):
ridge = []
setattr(self, name, ridge)
else:
ridge = getattr(self, name)
if "ridge_alpha" in self.args:
alpha = self.args['ridge_alpha']
else:
ridgecv = RidgeCV(alphas=(10., 50., 100.))
ridgecv.fit(X, Y[:, 24])
logging.info("Ridge cv %f", ridgecv.alpha_)
logging.info("Ridge score %f", ridgecv.score(X, Y[:, 24]))
alpha = ridgecv.alpha_
global ridgeX, ridgeY, ridgeAlpha
ridgeX = X
ridgeY = Y
ridgeAlpha = alpha
for idx in self.divide(list(range(len(ridge), Y.shape[1])), 18):
with mp.Pool(6) as pool:
ridge += pool.map(train_ridge, idx)
logging.info("Ridge group %d", idx[0])
self.saveModule(name, False)
logging.info("Ridge ok")
def train_rigdeCV(self, data):
train, validacion = data
x_tr, y_tr = train
x_val, y_val = validacion
#print("El set de train tiene {} filas y {} columnas".format(x_tr.shape[0],x_tr.shape[1]))
#print("El set de validacion tiene {} filas y {} columnas".format(x_val.shape[0],x_val.shape[1]))
print('Start training Ridge...')
start_time = self.timer()
ridge = RidgeCV(normalize=True, alphas=[0.0000999], cv=10)
ridge.fit(x_tr, y_tr)
print("The R2 is: {}".format(ridge.score(x_tr, y_tr)))
print("The alpha choose by CV is:{}".format(ridge.alpha_))
self.timer(start_time)
print("Making prediction on validation data")
y_val = np.expm1(y_val)
y_val_pred = np.expm1(ridge.predict(x_val))
mae = mean_absolute_error(y_val, y_val_pred)
print("El mean absolute error de es {}".format(mae))
print('Saving model into a pickle')
try:
os.mkdir('pickles')
except:
pass
with open('pickles/RidgeCV.pkl', 'wb') as f:
pickle.dump(ridge, f)
print('Making prediction and saving into a csv')
y_test = ridge.predict(self.x_test)
return y_test
def Ridge_Data(context,x,y):
x,y = data_process(x,y)
# standardize independent variables and response variable (necessary for ridge regularization)
from sklearn.preprocessing import StandardScaler
x_std = StandardScaler().fit_transform(x)
y_std = StandardScaler().fit_transform(y.reshape(-1,1))
# Ridge Regression RidgeCV() default:
# alphas=(0.1, 1.0, 10.0)
# fit_intercept=True
# cv=None: to use the efficient Leave-One-Out cross-validation
# cv=int: to specify the number of folds
from sklearn.linear_model import RidgeCV
ridgecv = RidgeCV()
ridgecv.fit(x_std, y_std)
ridgecv_score = ridgecv.score(x_std, y_std)
# ridge panelty: alpha
ridgecv_alpha = ridgecv.alpha_
print('Ridge R square', ridgecv_score)
print('Ridge Alpha', ridgecv_alpha )
print('Ridge Coefficients',ridgecv.coef_)
# Estimated coefficients are the same!!
import math
k = len(x_std[0])
y_ridge = np.append(y_std,np.zeros(k))
x_ridge = np.append(x_std,np.identity(k)*math.sqrt(ridgecv_alpha),axis=0)
def ridge_regression_cv(problem, **kwargs):
r"""High level description.
Parameters
----------
problem : type
Description
kwargs : dictionary
kwargs['ridge_reg_coefs'] must be a list of nonnegative float. These
are the multipliers for the penalty term in cross-validation of ridge
regression
kwargs['coef_tolerance'] must be a nonnegative float
Returns
-------
output : tuple
(optimum, maximum)
"""
data_list = [datum['data']['values'] for datum in problem.data]
data = numpy.array(data_list)
ridge = RidgeCV(kwargs['ridge_reg_coefs'])
ridge.fit(data.T, problem.goal['data']['values'])
ridge_regression_coefficients = ridge.coef_
optimum = [problem.data[index] for index,element in
enumerate(ridge_regression_coefficients)
if abs(element) > kwargs['coef_tolerance']]
maximum = ridge.score(data.T, problem.goal['data']['values'])
output = (optimum, maximum)
return output
def Ridge_model(train_linear, test_linear):
ridgecv = RidgeCV(alphas=np.logspace(-5, 4, 400))
ridgecv.fit(train_linear_fea, train_linear_tar)
ridgecv_score = ridgecv.score(train_linear_fea, train_linear_tar)
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.set_params(alpha=6,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))
write_pkl(
ridgecv_alpha,
'/Users/vickywinter/Documents/NYC/Machine Learning Proj/Pickle/ridge_params.pkl'
)
return test_prediction_ridge
def regularizedreg(Xtrain,Xtest,ytrain,ytest):
Rclf = RidgeCV(alphas=[1,2,20,40,50]) # RidgeCV(alphas=[0.1, 1.0, 2.0, 4.0, 20.0], cv=None, fit_intercept=True, scoring=None, normalize=False)
Rclf.fit(Xtrain,ytrain);
print("Residual sum of squares: %.2f"
% np.mean((Rclf.predict(Xtest) - ytest) ** 2))
print('Regularization choosen, alpha = %.2f' % Rclf.alpha_);
print(' Coef values = ', Rclf.coef_);
print('Variance score: %.2f' % Rclf.score(Xtest, ytest))
def forregCV(X, y, regressor):
kf = RepeatedKFold(n_splits=5, n_repeats=30)
if regressor == ridge_regression:
print("ridge")
regressor = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10], cv=kf).fit(X, y)
print('Ridge CV: ', regressor.score(X, y))
return
if regressor == lasso_regression:
print("lasso")
regressor = LassoCV(alphas=[1e-3, 1e-2, 1e-1, 1, 10], cv=kf).fit(X, y)
print('Lasso CV: ', regressor.score(X, y))
return
#REPEATED KFOLD CROSS-VAL
# midl = []
scores = cross_val_score(regressor, X, y, cv=kf, n_jobs=-1)
print("---> ", scores.mean())
def RidgeKFold(data=data, city='all', label="label_activity_density"):
if city == 'all':
data2 = data.copy()
else:
data2 = data[data["city_district"].str.contains(city)].copy()
target = data2[["city_district", label]]
features = data2[features_columns]
X = features.values
y = target[label].values
clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1]).fit(X, y)
print(clf.score(X, y))
return (clf.score(X, y))
def ridge_reg(x, y):
ridgecv = RidgeCV(alphas=(0.1, 10, 50), cv=20)
ridgecv.fit(x, y)
ridgecv_score = ridgecv.score(x, y)
ridgecv_alpha = ridgecv.alpha_
print('Ridge R square', ridgecv_score)
print('Ridge Alpha', ridgecv_alpha)
return ridgecv.coef_
def feature_ridge(self):
model = RidgeCV()
model.fit(self.x, self.y)
coefficients = pd.Series(model.coef_, index=self.x.columns)
print("Beta weights/co-efficients (L2 regularisation)")
print("-----------------------------------------")
print(coefficients)
print('\n')
print('R2 score is {}'.format(model.score(self.x, self.y)))
def linear_reg_single_meter(X_train, X_test, y_train, y_test):
# Fit your model using the training set
linear = LinearRegression()
lasso_cv = LassoCV(cv=5, random_state=0)
ridge_cv = RidgeCV(alphas=(0.1, 1.0, 10.0))
linear.fit(X_train, y_train)
lasso_cv.fit(X_train, y_train)
ridge_cv.fit(X_train, y_train)
print("Variance Inflation Factors")
print(vifs(X_test))
print('\n')
print('Features')
print('\n')
print(list(X_test.columns))
print(
'Linear regression score on train set with all parameters: {}'.format(
linear.score(X_train, y_train)))
print('Linear regression score on test set with all parameters: {}'.format(
linear.score(X_test, y_test)))
# print('Linear regression crossVal score on train set with all parameters: {}'.format(linear.score(X_train, y_train)))
# print('Linear regression crossVal score on test set with all parameters: {}'.format(linear.score(X_test, y_test)))
print(
'LassoCV regression score on train set with all parameters: {}'.format(
lasso_cv.score(X_train, y_train)))
print(
'LassoCV regression score on test set with all parameters: {}'.format(
lasso_cv.score(X_test, y_test)))
# print('LassoCV regression crossVal score on train set with all parameters: {}'.format(lasso_cv.score(X_train, y_train)))
# print('LassoCV regression crossVal score on test set with all parameters: {}'.format(lasso_cv.score(X_test, y_test)))
print(
'RidgeCV regression score on train set with all parameters: {}'.format(
ridge_cv.score(X_train, y_train)))
print(
'RidgeCV regression score on test set with all parameters: {}'.format(
ridge_cv.score(X_test, y_test)))
# print('RidgeCV regression crossVal score on train set with all parameters: {}'.format(ridge_cv.score(X_train, y_train)))
# print('RidgeCV regression crossVal score on test set with all parameters: {}'.format(ridge_cv.score(X_test, y_test)))
return ridge_cv, lasso_cv, linear
def calculate_slopes(data, osc_idx, osc_idx_type, N):
# Initialization of an array where values are saved
slope = np.zeros((data.shape[0], osc_idx.shape[0]))
pval = np.zeros((data.shape[0], osc_idx.shape[0]))
rsquared = np.zeros((data.shape[0], 1))
# If, crop yield data exists, calculate the slope coefficient for each oscillation and FPU
# using regularized ridge regression.
for k in range(0, slope.shape[0]):
y = data[k, :]
if all(~np.isnan(y)):
if 'multiv' in osc_idx_type:
X0 = np.hstack(
(osc_idx.T, np.ones((osc_idx[1, :][:, np.newaxis].shape))))
ridge_model = RidgeCV(
alphas=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10]).fit(
X0, y)
coeffs = ridge_model.coef_
rsquared_temp = ridge_model.score(X0, y)
coeffs_sample = np.empty((0, 4))
for j in range(0, N):
rand_idx_data = np.random.choice(data.shape[1],
data.shape[1],
replace=True)
y_sample = y[rand_idx_data]
X0_sample = X0[rand_idx_data, :]
ridge_model_sample = RidgeCV(
alphas=[1e-6, 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1, 10
]).fit(X0_sample, y_sample)
coeffs_sample = np.vstack(
(coeffs_sample,
ridge_model_sample.coef_[np.newaxis, :]))
# Calculate how many of the sampled slope values are
# smaller or larger than zero.
bstrp_test = np.zeros((2, coeffs.shape[0]))
bstrp_test[0, :] = np.sum(coeffs_sample > 0, axis=0)
bstrp_test[1, :] = np.sum(coeffs_sample < 0, axis=0)
pvals = (1 - np.amax(bstrp_test, axis=0) / N) * 2.0
# Select the maximum of the array that stores the number of sampled correlations
# larger or smaller than zero, and divide that with the total number of samples.
# Then substract this value from 1, and multiply this value by two (two-sided test) to get the p-value.
slope[k, :] = coeffs[0:3]
pval[k, :] = pvals[0:3]
rsquared[k, 0] = rsquared_temp
else:
slope[k, 0] = np.nan
return slope, pval, rsquared
def ridge_regression(X_train, y_train, X_test, y_test, plot):
"""
Perfomring a ridge regression with built in CV and plotting the feature importance
"""
# Fit the ridge regression
reg = RidgeCV()
reg.fit(X_train, y_train)
print("Best alpha using built-in RidgeCV: %f" % reg.alpha_)
print("Best score using built-in RidgeCV: %f" % reg.score(X_train, y_train))
coef = pd.Series(reg.coef_, index=X_train.columns)
print(
"Ridge picked "
+ str(sum(coef != 0))
+ " variables and eliminated the other "
+ str(sum(coef == 0))
+ " variables"
)
# Extract the feature importance
imp_coef = coef.sort_values()
# Plot the feature importance
if plot:
plt.rcParams["figure.figsize"] = (8.0, 10.0)
imp_coef.plot(kind="barh")
plt.title("Feature importance using Ridge Model")
plt.show()
# Visualizing the regression
visualizer = ResidualsPlot(reg, size=(1080, 720))
visualizer.fit(X_train, y_train) # Fit the training data to the visualizer
visualizer.score(X_test, y_test) # Evaluate the model on the test data
visualizer.show() # Finalize and render the figure
# Using the test data to calculate a score
y_pred = reg.predict(X_test)
# Return metrics
return {
"name": "Ridge Regression",
"R squared": reg.score(X_test, y_test),
"R squared training": reg.score(X_train, y_train),
"RMSE": rmse(y_test, y_pred),
"MAE": mean_absolute_error(y_test, y_pred),
}
def scale_test_and_train_ridge(X, y):
"""
Run a ridge regression on the model
"""
X, X_test, y, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=42)
X_train, X_val, y_train, y_val = train_test_split(X,
y,
test_size=.25,
random_state=3)
X_train_scale = X_train.values
X_val_scale = X_val.values
X_test_scale = X_test.values
scale = StandardScaler()
X_train_scale = scale.fit_transform(X_train_scale)
X_test_scale = scale.transform(X_test_scale)
X_val_scale = scale.transform(X_val_scale)
ridge = RidgeCV(cv=5)
ridge.fit(X_train_scale, y_train)
ridge.score(X_train_scale, y_train)
y_pred = ridge.predict(X_val_scale)
print(f'Ridge Regression val R^2: {ridge.score(X_val_scale, y_val):.3f}')
print(
f'Ridge Regression val RME: {sqrt(mean_squared_error(y_val,y_pred)):.3f}'
)
return ridge.coef_
def doRidgeRegressionCV(X_train, y_train, X_test, y_test):
print('Now doing Ridge cross validation')
alpha_ridge = [0.001, 0.01, 0.1, 1, 10, 100, 500, 1000]
rf = RidgeCV(alphas=alpha_ridge,
store_cv_values=True).fit(X_train, y_train)
print(rf.score(X_test, y_test))
cv_mse = np.mean(rf.cv_values_, axis=0)
print("alphas: %s" % alpha_ridge)
print("CV MSE: %s" % cv_mse)
print("Best alpha using built-in RidgeCV: %f" % rf.alpha_)
plt.plot(np.array(alpha_ridge, dtype="float64"),
np.array(cv_mse, dtype="float64")[0])
plt.xlabel('Value of alpha for Ridge')
plt.ylabel('Cross-validated MSE')
def build_ts_regression(self, feature_list, target, dt_index, model_list=['ridge']):
"""
Takes features and target
Does train test split
Reports on preformance
Returns: model
"""
# Fix missing values
print('\n[INFO] Imputing missing values')
self.fix_missing()
print('\nMissing Values:')
print(bo.df.isna().sum())
test_size = 0.3
self.df.sort_values(dt_index, ascending=True, inplace=True)
nrows = self.df.shape[0]
train_idx = int(nrows*(1-test_size))
test_idx = nrows - train_idx
X = self.df[feature_list]
y = self.df[target]
self.X_train = X.iloc[0:train_idx]
self.X_test = X.iloc[0:test_idx]
self.y_train = y.iloc[0:train_idx]
self.y_test = y.iloc[0:test_idx]
print('Xtrain size:', self.X_train.shape[0], 'Xtest size:', self.X_test.shape[0])
for m in model_list:
if m == 'ridge':
from sklearn.linear_model import RidgeCV
# Choosing a CV number
if self.df.shape[0] > 100:
cv = 3
elif self.df.shape[0] > 500:
cv = 5
else:
cv = 1
model = RidgeCV(alphas=(0.1, 1.0, 10.0), cv=cv)
model.fit(self.X_train, self.y_train)
print('\nRidge Regression R-squared:', model.score(self.X_test, self.y_test))
# Add the model to the output list
self.models.append(model)
def run():
# Data preprocessing
train = DataPrep.prep_data(headless_run)
# Scale data: https://scikit-learn.org/stable/modules/svm.html#tips-on-practical-use
target = train.SalePrice
train = train.drop(columns='SalePrice')
X_train, X_test, y_train, y_test = train_test_split(
train, target, test_size=0.25, random_state=0)
# Trying L2 regularization
clf = RidgeCV(cv=5).fit(X_train, y_train)
# print(rmse_cv(clf).mean())
# Lasso gives us an alpha of 0.1231, picks some coefficients and gives the rest a 0 value
coef = pd.Series(clf.coef_, index=X_train.columns)
# Metrics
variance_score = clf.score(X_test, y_test)
MSEscore = mean_squared_error(clf.predict(X_test), y_test)
MAEscore = median_absolute_error(clf.predict(X_test), y_test)
R2score = r2_score(clf.predict(X_test), y_test)
if not headless_run:
print('Variance score: {}'.format(variance_score))
# print("CLF best: {}".format(clf.best_score_)) grid search only
print('MSE score: {}'.format(MSEscore))
print('MAE score: {}'.format(MAEscore))
print('R2 score: {}'.format(R2score))
# Plotting Residuals
plt.scatter(clf.predict(X_train), clf.predict(X_train) - y_train,
color="green", s=10, label='Train data')
plt.scatter(clf.predict(X_test), clf.predict(X_test) - y_test,
color="blue", s=10, label='Test data')
plt.hlines(y=0, xmin=10, xmax=14, linewidth=2)
plt.legend(loc='upper right')
plt.title("Residual errors")
plt.show()
else:
return [variance_score,MSEscore,MAEscore,R2score]
def ridge_regression(y_train, x_train, df_test):
ridge = RidgeCV(
alphas=[0.01, 0.03, 0.06, 0.1, 0.3, 0.6, 1, 3, 6, 10, 30, 60])
ridge.fit(x_train, y_train)
alpha = ridge.alpha_
ridge = RidgeCV(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
],
cv=10)
ridge.fit(x_train, y_train)
alpha = ridge.alpha_
print('ALPHA', alpha)
acc_log = round(ridge.score(x_train, y_train) * 100, 2)
print('SCORE', acc_log)
pred = ridge.predict(df_test)
return pred
def ridge_boston():
boston = load_boston()
x = boston.data
y = boston.target
train_x, test_x, train_y, test_y = \
train_test_split(x, y, test_size=.25)
std_s = StandardScaler()
train_x = std_s.fit_transform(train_x)
test_x = std_s.fit_transform(test_x)
# ridge = Ridge(alpha=1.5)
ridge = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1], cv=4)
ridge.fit(train_x, train_y)
score = ridge.score(test_x, test_y)
predict_y = ridge.predict(test_x)
print(score)
print(predict_y[:20])
print(test_y[:20])
return None
class RidgeWithPost(BaseEstimator, TransformerMixin):
def __init__(self, weight=1.0):
self.ridge = RidgeCV(weight)
def fit(self, X, y, sample_weight=None):
self.ridge.fit(X, y)
return self
def predict(self, X):
y = self.ridge.predict(X)
ranged = np.empty(len(y))
for i in range(0, len(y)):
if y[i] < 18:
ranged[i] = 18
else:
ranged[i] = y[i]
return ranged
def score(self, X, y, sample_weight=None):
return self.ridge.score(X, y)
def ridge_reg():
from sklearn.linear_model import RidgeCV
n_alphas = 100
alpha_vals = np.logspace(-1, 3, n_alphas)
rr = RidgeCV(alphas=alpha_vals, cv=10)
rr.fit(X_train_scaled, y_train)
y_pred_train = rr.predict(X_train_scaled)
#y_pred_train_round = np.round(y_pred_train)
y_pred_test = rr.predict(X_test_scaled)
#y_pred_test_round = np.round(y_pred_test)
print(rr.alpha_)
print(rr.score(X_test_scaled, y_test))
#plot_conf_mat(y_test, _pred_round)
global metrics_ridge
metrics_ridge = [
accuracy_score(y_test, np.round(y_pred_test)),
mean_squared_error(y_test, y_pred_test),
r2_score(y_test, y_pred_test)
]
return scores_results(y_train, y_test, y_pred_train, y_pred_test)
def NLP(self):
"""Performs a linear regression on a TF-IDF matrix of property titles vs. revenue potential"""
# load and vectorize titles
corpus = self.comps['title']
target = self.comps['rev_pot']
vec = TfidfVectorizer(tokenizer=self.spacy_tokenizer,
max_features=15,
max_df=1.0,
ngram_range=(1, 1))
matrix = vec.fit_transform(corpus)
# perform ridge regression and return dataframe of coefficients
ls = RidgeCV()
ls.fit(matrix, target)
coefficients_df = pd.DataFrame.from_dict(
dict(zip(vec.get_feature_names(),
ls.coef_)), orient='index').sort_values(by=0)
score = ls.score(matrix, target)
print(f"R^2 = {score: .3f} \n Alpha: {ls.alpha_ : .3f}")
return coefficients_df
def eval_score(self, X, n):
""" RidgeCV
Parameters
-------------
X: pandas dataframe
n: train_test_splitの回数
Return
-------------
score: average score
"""
scores = []
for _ in range(n):
X_train, X_test, y_train, y_test = train_test_split(X,
self.y,
test_size=0.4)
model = RidgeCV()
model.fit(X_train, y_train)
scores.append(model.score(X_test, y_test))
score = np.array(scores).mean()
return score
def build_regression(self, feature_list, target, model_list=['ridge']):
"""
Takes features and target
Does train test split
Reports on preformance
Returns: model
"""
# Fix missing values
print('\n[INFO] Imputing missing values')
self.fix_missing()
print('\nMissing Values:')
print(self.df.isna().sum())
seed = 4784
from sklearn.model_selection import train_test_split
X = self.df[feature_list]
y = self.df[target]
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(X, y, random_state=seed)
for m in model_list:
if m == 'ridge':
from sklearn.linear_model import RidgeCV
# Choosing a CV number
if self.df.shape[0] > 100:
cv = 3
elif self.df.shape[0] > 500:
cv = 5
else:
cv = 1
model = RidgeCV(alphas=(0.1, 1.0, 10.0), cv=cv)
model.fit(self.X_train, self.y_train)
print('\nRidge Regression R-squared:', model.score(self.X_test, self.y_test))
# Add the model to the output list
self.models.append(model)
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)
X = X[:-predPeriod] #re-sizing the features for training
dataset.dropna(inplace=True) # get rid of naN for 'label' column
# create label
y = np.array(dataset['label'])
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.2, random_state=1)
# use linearRegression as algrithm
#clf = LinearRegression()
clf = RidgeCV (alphas =[0.1, 0.5, 1, 10])
clf.fit(X_train, y_train)
#start_time = time.time()
y_pred = clf.predict(X_pred)
#print time.time() - start_time
accuracy = clf.score(X_test, y_test)
# visualize Learning Curves
#ML.ModelLearning(X, y)
#ML.ModelComplexity(X_train, y_train)
#Linear slope calculation
#print clf.alpha_
#print clf
#print clf.coef_
#print clf.intercept_
print 'predict accuracy is: {:0.2f}'.format(accuracy)
# build a column in data for predict result
data['predict/Adj Close'] = data['Adj Close'] # add column for predict value/Adj Close
# RidgeCV Regression with 10 fold cross-validation along alpha values of 0.1, 1 and 10
Ridge_CV = RidgeCV(alphas=(0.1, 1.0, 10.0), cv=10)
Ridge_CV.fit(x_train, y_train)
predicted_Ridge_CV = Ridge_CV.predict(x_test)
plt.scatter(y_test, predicted_Ridge_CV)
plt.plot([-1, 5], [-1, 5], "g--", lw=1, alpha=0.4)
plt.xlabel("True prices (EUR)")
plt.ylabel("Predicted prices (EUR)")
plt.text(
-1, 2.5,
' R-squared = {}'.format(round(float(Ridge_CV.score(x_test, y_test)), 2)))
plt.text(
-1, 3, ' MSE = {}'.format(
round(float(mean_squared_error(y_test, predicted_Ridge_CV)), 2)))
plt.title(
'Ridge (Alpha = {}) - Predicted prices (EUR) vs. True prices (EUR)'.format(
Ridge_CV.alpha_))
plt.show()
# 10 folds cross-validation along the previous Ridge regression
ridge = Ridge(alpha=0.1)
shuffle = KFold(n_splits=10, shuffle=True, random_state=0)
cv_scores = cross_val_score(ridge, x, y, cv=shuffle)
print(cv_scores)
print(cv_scores.mean())
model1 = LinearRegression().fit(X_m1,y_m)
print(f"R2 Score: {model1.score(X_m1,y_m)}")
"""## Regularization
1. Lasso
2. Ridge
3. ElasticNet
### Ridge
"""
# higher the alpha value, more restriction on the coefficients;
# lower the alpha > more generalization, coefficients are barely
rr = RidgeCV(cv=5,fit_intercept=False)
rr.fit(X_m, y_m)
rr.score(X_m,y_m)
rr.alpha_
plt.plot(rr.coef_,alpha=0.7,marker='*',markersize=10,color='red',label=r'Ridge; $\alpha =10$')
plt.grid(True)
plt.xticks(range(0,28,1))
plt.legend()
plt.show()
"""# Model Accuracy Metrics
You must use the Mean Squared Error & Mean Absolute Error for your model evaluations. You may also include extra metrics for calculating the scores.
"""
def MSE(model_preds, ground_truths):
print("---------------------------------------------------------------------")
#岭回归，参数估计，固定岭参数
X = dfx.iloc[:, 0:5]
#print(X)
y = dfy_scaled
reg01 = Ridge(alpha=0.15).fit(X, y)
print('Ridge(alpha=0.15) score:', reg01.score(X, y).round(5)) #0.98513
print('Ridge(alpha=0.15) coefficients:', reg01.coef_.round(5),
'\n') #[-0.05087 0.54623 0.39501 -0.12857 -0.03614]
print("---------------------------------------------------------------------")
#岭回归，按 CV 标准自动选择岭参数
alphas = np.linspace(0.0001, 0.5, 1000)
reg02 = RidgeCV(alphas).fit(X, y)
print('RidgeCV score:', reg02.score(X, y).round(5))
print('RidgeCV alpha:', reg02.alpha_.round(5)) #0.33737
print('RidgeCV coefficients:', reg02.coef_.round(5), '\n')
print("---------------------------------------------------------------------")
#lasso求解
count = 0
lamb = 0.05
lasso_reg = Lasso(alpha=lamb)
lasso_reg.fit(dfx, dfy)
print('Lasso Intercept:', lasso_reg.intercept_)
print('Lasso Coef:', '\n', lasso_reg.coef_)
#print(type(lasso_reg.coef_))
for n in lasso_reg.coef_:
if ((n > 1e-5) or (n < -1e-5)):
count = count + 1
##################
# CLASSIFICATION #
##################
cv = 5
clf = RidgeCV(alphas=[1e-3, 1e-2, 1e-1, 1])
cv_outer = KFold(n_splits=10, shuffle=True, random_state=1)
outer_results = list()
outer_scores = list()
for train_ix, test_ix in cv_outer.split(X):
# split data
X_train, X_test = X[train_ix, :], X[test_ix, :]
y_train, y_test = y[train_ix], y[test_ix]
outer_results.append(clf.fit(X_train, y_train))
outer_scores.append(clf.score(X_test, y_test))
#################
# PRINT RESULTS #
#################
for i_split, results in enumerate(outer_results):
coefs_abs = np.abs(results.coef_)
IX_coefs = np.argsort(-coefs_abs)
coef_abs_sorted = coefs_abs[IX_coefs]
print('Split ', i_split + 1)
print(results.alpha_)
print(1 + IX_coefs[:10])
print(coef_abs_sorted[:10])
print(outer_scores)
print(np.mean(outer_scores))
print(ridge)
print("Percent variance explained: {0}".format(ridge.score(X_aging, y_aging)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")
print(ols)
print("Percent variance explained: {0}".format(ols.score(X_aging, y_aging)))
print("Coefficients found: \n{0}\n".format(prettyprint(ols.coef_, col, sort=True)))
print("WHOLE DATASET //////////////////////////")
print("SUPER AGERS //////////////////////////")
ridge = RidgeCV(alphas=alpha_params, cv=7, scoring=score)
ridge.fit(X_sa, y_sa)
ols = LinearRegression()
ols.fit(X_sa, y_sa)
print("RIDGE REGRESSION")
print("Percent variance explained: {0}".format(ridge.score(X_sa, y_sa)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")
print("Percent variance explained: {0}".format(ols.score(X_sa, y_sa)))
print("Coefficients found: \n{0}\n".format(prettyprint(ols.coef_, col, sort=True)))
print("SUPER AGERS //////////////////////////")
print("MCIS //////////////////////////")
ridge = RidgeCV(alphas=alpha_params, cv=7, scoring=score)
ridge.fit(X_mci, y_mci)
ols = LinearRegression()
ols.fit(X_mci, y_mci)
print("RIDGE REGRESSION")
print("Percent variance explained: {0}".format(ridge.score(X_mci, y_mci)))
print("Coefficients found: \n{0}\n".format(prettyprint(ridge.coef_, col, sort=True)))
print("ORDINARY LEAST SQUARES")
