def predict_age():
mask = ~np.isnan(train["Age"])
age_train = train[mask]
age_test = train[~mask]
features = []
features.append(embarked_enc.transform(age_train["Embarked"]))
features.append(sex_enc.transform(age_train["Sex"]))
features.append(title_enc.transform(age_train["Title"]))
features.append(pclass_enc.transform(age_train["Pclass"]))
age_clf = SGDRegressor()
X = np.hstack(features)
y = np.array(train["Age"][mask]).T
age_clf.fit(X, y)
features = []
features.append(embarked_enc.transform(age_test["Embarked"]))
features.append(sex_enc.transform(age_test["Sex"]))
features.append(title_enc.transform(age_test["Title"]))
features.append(pclass_enc.transform(age_test["Pclass"]))
ages = age_clf.predict(np.hstack(features))
j = 0
for i in range(len(train)):
if ~mask[i]:
train.loc[i, "Age"] = ages[j]
j += 1
def ls_sklearn_sgd(x, y):
# Parameter estimation by sklearn SGD
sgd = SGDRegressor(fit_intercept=True)
sgd.fit(x.reshape((N, 1)), y)
beta_0_sk = sgd.intercept_
beta_1_sk = sgd.coef_[0]
return beta_0_sk, beta_1_sk
def sgd(X, y, weight, X_test=False):
from sklearn.linear_model import SGDRegressor
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
#X_train, X_test, y_train, y_test, weight_train, weight_test = cross_validation.train_test_split(
# X, y, weight, test_size=0.2, random_state=0)
clf = SGDRegressor(loss="huber", n_iter=100, penalty="l1")
#clf = LogisticRegression( max_iter=100)
X_train = X
y_train = y
scaler = StandardScaler(with_mean=False)
scaler.fit(X_train) # Don't cheat - fit only on training data
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test) # apply same transformation to test data
clf.fit(X_train, y_train, sample_weight=weight)
print(clf.score(X_train,y_train,weight))
y_pred = clf.predict(X_test)
from sklearn.externals import joblib
import scipy.io as sio
joblib.dump(clf, 'models/sgd_.pkl')
sio.savemat('predict_y_forward.mat', {'y':y_pred})
def predict(self, df):
# get time frame
time_frame = settings.time_frame
# copy of data
df_copy = df.copy()
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_absolute_error, mean_squared_error
# partition data
X_train, y_train, X_val, y_val, X_test, y_test = self.partition(df_copy)
# normalize features
X_train_std, X_val_std, X_test_std = self.feature_scale(X_train, X_val, X_test)
# instance of Linear Regression classifier
lr = SGDRegressor()
# fit model
lr.fit(X_train_std, y_train)
# predictions on validation set
predictions = lr.predict(X_val_std)
# R^2 score
score = lr.score(X_val_std, y_val)
# error
test_error = (mean_squared_error(y_val, predictions)**.5)
print test_error
def predictScores(trainFeatures,trainTargets,testFeatures,testItemIds,isRegression = False):
logging.info("Feature preparation done, fitting model...")
predicted_scores = []
if isRegression:
clf = SGDRegressor( penalty="l2",
alpha=1e-4)
print("trainFeatures rows::"+str(trainFeatures.shape[0]))
print("trainTargets rows::"+str(len(trainTargets)))
clf.fit(trainFeatures,trainTargets)
logging.info("Predicting...")
predicted_scores = clf.predict(testFeatures)
else:
clf = SGDClassifier( loss="log",
penalty="l2",
alpha=1e-4,
class_weight="auto")
print("trainFeatures rows::"+str(trainFeatures.shape[0]))
print("trainTargets rows::"+str(len(trainTargets)))
clf.fit(trainFeatures,trainTargets)
logging.info("Predicting...")
predicted_scores = clf.predict_proba(testFeatures).T[1]
logging.info("Write results...")
output_file = "avito_starter_solution.csv"
logging.info("Writing submission to %s" % output_file)
f = open(os.path.join(dataFolder,output_file), "w")
f.write("id\n")
for pred_score, item_id in sorted(zip(predicted_scores, testItemIds), reverse = True):
f.write("%d\n" % (item_id))
f.close()
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
clf = SGDRegressor(n_iter = 20)
clf.fit(features, values)
intercept,params = clf.intercept_,clf.coef_
return intercept, params
def slim_train(A, l1_reg=0.001, l2_reg=0.0001):
"""
Computes W matrix of SLIM
This link is useful to understand the parameters used:
http://web.stanford.edu/~hastie/glmnet_matlab/intro.html
Basically, we are using this:
Sum( yi - B0 - xTB) + ...
As:
Sum( aj - 0 - ATwj) + ...
Remember that we are wanting to learn wj. If you don't undestand this
mathematical notation, I suggest you to read section III of:
http://glaros.dtc.umn.edu/gkhome/slim/overview
"""
alpha = l1_reg + l2_reg
l1_ratio = l1_reg / alpha
model = SGDRegressor(
penalty='elasticnet',
fit_intercept=False,
alpha=alpha,
l1_ratio=l1_ratio,
)
# TODO: get dimensions in the right way
m, n = A.shape
# Fit each column of W separately
W = lil_matrix((n, n))
for j in range(n):
if j % 50 == 0:
print('-> %2.2f%%' % ((j / float(n)) * 100))
aj = A[:, j].copy()
# We need to remove the column j before training
A[:, j] = 0
model.fit(A, aj.toarray().ravel())
# We need to reinstate the matrix
A[:, j] = aj
w = model.coef_
# Removing negative values because it makes no sense in our approach
w[w < 0] = 0
for el in w.nonzero()[0]:
W[(el, j)] = w[el]
return W
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
model = SGDRegressor()
model.fit(features, values)
intercept = model.intercept_
params = model.coef_
return intercept, params
def sgd(pd, pl, qd, ql):
params = {'loss':['squared_loss', 'huber', 'epsilon_insensitive',
'squared_epsilon_insensitive'],
'alpha':expon(scale=1),
'epsilon':expon(scale=1),
'l1_ratio':uniform(),
'penalty':[ 'l2', 'l1', 'elasticnet']}
clf = SGDRegressor()
#clf = RandomizedSearchCV(clf, params, n_jobs=2, n_iter=10, verbose=10)
print("Training Linear SVM Randomly")
clf.fit(pd, pl)
print("Score: " + str(clf.score(qd, ql)))
return clf
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
###########################
### YOUR CODE GOES HERE ###
###########################
classifier = SGDRegressor(n_iter = 20)
classifier.fit(features, values)
intercept = classifier.intercept_
params = classifier.coef_
return intercept, params
def main(train_file, model_file):
#train_x, train_y = load_sparse_trainingData_memory(train_file, 2 * get_len_vector())
train_x, train_y = load_long_training_data_memory()
#train_x, train_y = load_trainingData(train_file)
logging('len of y: %d' % train_y.shape)
logging(train_x.shape)
#LR = LinearRegression(copy_X = False, normalize = True)
LR = SGDRegressor(verbose=1)
logging("training model...")
starttime = datetime.now()
LR.fit(train_x, train_y)
logging("training model, eplased time:%s" % str(datetime.now() - starttime))
logging("saving model")
joblib.dump(LR, model_file)
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
y = values
X = features
clf = SGDRegressor()
clf.fit(X, y)
intercept = clf.intercept_
params = clf.coef_
return intercept, params
def sgd_regressor(x, y, alpha):
kf = KFold(len(x), n_folds=3)
scores = []
for train_index, test_index in kf:
X_train, X_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
scaler = StandardScaler()
scaler.fit(X_train)
x_train = scaler.transform(X_train)
x_test = scaler.transform(X_test)
clf = SGDRegressor(loss='squared_loss', alpha=alpha)
clf.fit(x_train, y_train)
scores.append(mean_squared_error(clf.predict(x_test), y_test) ** 0.5)
# print 'SGDRegressor'
return np.mean(scores)
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
#features = sm.add_constant(features)
model = SGDRegressor(n_iter=30)
#normalize_res = normalize_features(features)
model.fit(features,values)
###########################
### YOUR CODE GOES HERE ###
###########################
intercept = model.intercept_
params = model.coef_
return intercept, params
def build_sgd_regressor(X_test, X_train_full, y_train_full):
#print "Building SGD regressor..."
rf = SGDRegressor(loss="modified_huber", penalty="elasticnet", n_iter=20000, alpha=0.1, epsilon=0.01)
probas_rf = rf.fit(X_train_full, y_train_full).predict(X_test)
return probas_rf
def linear_regression_GD(features, values):
means, std_devs, features = normalized_features(features)
model = SGDRegressor(eta0=0.001)
results = model.fit(features, values)
intercept = results.intercept_
params = results.coef_
return intercept, params
def predictLinearRegress(attributeList, starTargetList):
print("\nLinear Regression")
starTargetList = np.array(starTargetList)
Xtrain, Xtest, Ytrain, Ytest = ml.splitData(attributeList, starTargetList, 0.75)
lr = ml.linear.linearRegress(Xtrain, Ytrain)
yHatInitial = lr.predict(Xtest)
print("MSE test: ", mean_squared_error(yHatInitial, Ytest))
print("RMSE test: ", math.sqrt(mean_squared_error(yHatInitial, Ytest)))
incorrect = 0
total = 0
for i, value in enumerate(yHatInitial):
if(abs(yHatInitial[i] - Ytest[i]) > 0.5):
incorrect += 1
total += 1
ratioIncorrect = float(float(incorrect) / float(total))
print("Ratio incorrect: " + str(ratioIncorrect))
onesCol = np.ones((len(Xtrain),1))
Xtrain = np.concatenate((onesCol, Xtrain), 1)
onesCol = np.ones((len(Xtest),1))
Xtest = np.concatenate((onesCol, Xtest), 1)
m, n = np.shape(Xtrain)
clf = SGDRegressor(loss="squared_loss")
clf.fit(Xtrain, Ytrain)
yHat = clf.predict(Xtest)
print("MSE after GD: ", mean_squared_error(yHat, Ytest))
print("RMSE after GD: ", math.sqrt(mean_squared_error(yHat, Ytest)))
incorrect = 0
total = 0
for i, value in enumerate(yHat):
if(abs(yHat[i] - Ytest[i]) > 0.5):
incorrect += 1
total += 1
ratioIncorrect = float(float(incorrect) / float(total))
print("Ratio incorrect: " + str(ratioIncorrect))
class EdenRegressor(BaseEstimator, RegressorMixin):
"""Build a regressor for graphs."""
def __init__(self, r=3, d=8, nbits=16, discrete=True,
normalization=True, inner_normalization=True,
penalty='elasticnet', loss='squared_loss'):
"""construct."""
self.set_params(r, d, nbits, discrete,
normalization, inner_normalization,
penalty, loss)
def set_params(self, r=3, d=8, nbits=16, discrete=True,
normalization=True, inner_normalization=True,
penalty='elasticnet', loss='squared_loss'):
"""setter."""
self.r = r
self.d = d
self.nbits = nbits
self.normalization = normalization
self.inner_normalization = inner_normalization
self.discrete = discrete
self.model = SGDRegressor(
loss=loss, penalty=penalty,
average=True, shuffle=True,
max_iter=5, tol=None)
self.vectorizer = Vectorizer(
r=self.r, d=self.d,
normalization=self.normalization,
inner_normalization=self.inner_normalization,
discrete=self.discrete,
nbits=self.nbits)
return self
def transform(self, graphs):
"""transform."""
x = self.vectorizer.transform(graphs)
return x
@timeit
def kernel_matrix(self, graphs):
"""kernel_matrix."""
x = self.transform(graphs)
return metrics.pairwise.pairwise_kernels(x, metric='linear')
def fit(self, graphs, targets, randomize=True):
"""fit."""
x = self.transform(graphs)
self.model = self.model.fit(x, targets)
return self
def predict(self, graphs):
"""predict."""
x = self.transform(graphs)
preds = self.model.predict(x)
return preds
def decision_function(self, graphs):
"""decision_function."""
return self.predict(graphs)
def gradiantDescent(trainData,testData,trainOuts,testOuts): clf = SGDRegressor(loss="squared_loss") print(clf.fit(trainData,trainOuts)) print(clf.coef_) predictions = clf.predict(testData) print(predictions) misses,error = sup.crunchTestResults(predictions,testOuts,.5) print(1-error)
def linear_regression(features, values):
sgd = SGDRegressor()
results = sgd.fit(values, features)
intercept = sgd.intercept_
params = results.get_params()
return intercept, params
def train(self):
X_train = np.vstack([self.lang_1_w2v[p[0]] for p in self.bilingual_mappings])
Z_train = np.vstack([self.lang_2_w2v[p[1]] for p in self.bilingual_mappings])
# there's a trick here -- train each y column separately (as a separate SGD problem. They are all independent
# so this should be ok
Z_cols = [Z_train[:,i] for i in range(Z_train.shape[1])]
# train a model for each row of W, and get the W coefficients
trained_coef_rows = []
for z in Z_cols:
clf = SGDRegressor()
clf.fit(X_train, z)
trained_coef_rows.append(clf.coef_)
# now stack all the rows together to reconstruct W
self.W = np.vstack(trained_coef_rows)
def sgd_text_model(x_train, y_train, x_test, x_valid, cache_name, use_cache=False):
if use_cache:
fhand = open(cache_name, 'r')
data_dict = pickle.load(fhand)
return data_dict['test_pred'], data_dict['valid_pred']
np.random.seed(seed=123)
model = SGDRegressor(eta0=1000, fit_intercept=True, l1_ratio=0.15,
learning_rate='invscaling', loss='huber', n_iter=200,
p=None, penalty='l1', power_t=.1, random_state=123,
rho=None, shuffle=True, verbose=0, warm_start=False)
model.fit(x_train, y_train)
test_pred = model.predict(x_test)
valid_pred = model.predict(x_valid)
data_dict = {'test_pred': test_pred, 'valid_pred': valid_pred}
fhand = open(cache_name, 'w')
pickle.dump(data_dict, fhand)
fhand.close()
return test_pred, valid_pred
def fit(self, train_features, train_labels, N, c_val=0.0001, tol_val=0.001):
# break features into N sets
feat_dim = train_features.shape[1]
feat_per_bag = feat_dim / N
self.SVRs = []
for i in range(N):
if i < N-1:
cur_train_feat_bag = train_features[:,i*feat_per_bag:(i+1)*feat_per_bag]
else:
cur_train_feat_bag = train_features[:,i*feat_per_bag:]
# now train individual SVR
# model = svm.SVR(C=c_val, kernel='linear', tol=tol_val)
# model = LSVR(C=c_val, tol=tol_val)
model = SGDR(loss='epsilon_insensitive',alpha=c_val)
print 'current training on dimensionality: ', cur_train_feat_bag.shape[1], '\n'
model.fit(cur_train_feat_bag, train_labels)
self.SVRs.append(model)
return self.SVRs
def linear_regression(features, values):
model = SGDRegressor(n_iter=1000)
results = model.fit(features, values)
intercept = results.intercept_
params = results.coef_
return intercept, params
def apply_sgd_( X_train, Y_train, alpha=0.0003, shuffle=True):
n_iter = np.ceil(10**6 / len(Y_train))
model = SGDRegressor(loss='squared_loss',
penalty='l2',
alpha=alpha,
epsilon=0.01,
fit_intercept=True,
n_iter=n_iter, shuffle=shuffle, random_state=int(time.time()*8192)%8192, warm_start=False,
verbose=0,
learning_rate='invscaling' )
# model.fit_transform( X_train, Y_train )
# model.partial_fit_transform( X_train, Y_train )
# sample_weights = [ 1/float(m) for x in Y ]
model.fit( X_train, Y_train, sample_weight=None )
Theta = [ float( model.intercept_ ) , ]
Theta.extend( [ float( x ) for x in model.coef_])
( model, Theta, J, SCORE ) = performance_analysis( model, Theta, X_train, Y_train, debug=1 )
return ( model, Theta, J, SCORE )
def SGD_Regression(kf,data,label,k):
val=0
for train, test in kf:
X_train, X_test, y_train, y_test = data[train,:], data[test,:], label[train], label[test]
log = SGDRegressor(loss='squared_loss', penalty='l2', alpha=0.0001, l1_ratio=0.15,n_iter=5)
logit = log.fit(X_train,y_train)
y_pred = logit.predict(X_test)
val += metrics.mean_squared_error(y_test, y_pred)
return val/3
# print "SGD_Regression, Mean Squared Error ", "{0:.4f}".format(val/3)
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
regressor = SGDRegressor()
result = regressor.fit(features, values)
intercept = result.intercept_
params = result.coef_
return intercept, params
def sgd_cv_id_fold(trn, params={}, tst=None, model_seed=CONST.SEED):
if tst is not None:
preds = tst[['Engine']].copy()
cv_id = utils.get_cv_id(model_seed)
trn = trn.merge(cv_id, on=['Engine'], how='left')
assert trn.cv_id.notnull().all()
valid_preds = pd.DataFrame({
'preds': [np.nan] * trn.shape[0],
'actual_RUL': trn.RUL
})
features = [c for c in trn.columns if c not in CONST.EX_COLS]
scaler = preprocessing.StandardScaler()
trn.loc[:, features] = scaler.fit_transform(trn.loc[:, features])
if tst is not None:
tst.loc[:, features] = scaler.transform(tst.loc[:, features])
for i in list(range(1, utils.get_config()['nfold'] + 1)):
print(f"CV ID = {i}")
X_train, y_train = trn.loc[trn.cv_id != i,
features], trn.loc[trn.cv_id != i, 'RUL']
X_valid, y_valid = trn.loc[trn.cv_id == i,
features], trn.loc[trn.cv_id == i, 'RUL']
model = SGDRegressor(**params, random_state=seed)
model.fit(X_train, y_train)
valid_preds.loc[trn.cv_id == i, 'preds'] = model.predict(X_valid)
if tst is not None:
preds[f'fold{i + 1}'] = model.predict(tst[features])
valid_preds.dropna(inplace=True)
if tst is None:
print("CV MAE Score :",
mean_absolute_error(valid_preds.actual_RUL, valid_preds.preds))
return mean_absolute_error(valid_preds.actual_RUL, valid_preds.preds)
else:
return mean_absolute_error(valid_preds.actual_RUL,
valid_preds.preds), preds
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
###########################
### YOUR CODE GOES HERE ###
###########################
clf = SGDRegressor(alpha=0.1, n_iter=20)
clf.fit(features, values)
params = clf.get_params()
intercept = 0
#print params
#print len(features[0]), len(clf.coef_)
#print clf.coef_
#print clf.intercept_
return clf.intercept_, clf.coef_
class SGDPlainNystromRegressor:
def __init__(self,
kernel: str = 'rbf',
m: int = 100,
lambda_reg: int = 0,
**kwargs):
self.projector = PlainNystrom(kernel=kernel, m=m)
self.lambda_reg = lambda_reg
self.coeffs = None
self.regressor = SGDRegressor(fit_intercept=False, **kwargs)
self.kwargs = kwargs
def fit(self, X: np.ndarray, y: np.array = None, **kwargs):
k_nm = self.projector.fit_transform(X=X, y=y, **kwargs)
self.regressor.fit(k_nm, y)
return self
def predict(self, X):
projection = self.projector.transform(X=X)
return self.regressor.predict(projection)
def fit(self, U, Y):
self.initialize()
#learn X
#X = self.getX(U,Y)
X = self.getXBatched(U,Y,TSData.batchSize)
print("Starting to train the model...")
#clf = ElasticNet(alpha=5,l1_ratio=0.5,max_iter=50000)
#for x1,y1 in izip(X,Y):
# clf.partial_fit(x1[np.newaxis,:], y1)
#If not using generator
X = np.array([i for i in X])
#X = np.array(X)
print(X.shape)
print(Y.shape)
clf = SGDRegressor(n_iter=100)
clf.fit(X,np.ravel(Y))
print(metrics.mean_absolute_error(clf.predict(X),Y))
print(TSData().getScore(Y, clf.predict(X)))
self.clf = clf
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
clf = SGDRegressor(n_iter=20)
results = clf.fit(features, values)
intercept = results.intercept_
params = results.coef_
return intercept, params
def test_not_robust_regression(loss, weighting):
reg = RobustWeightedRegressor(
loss=loss,
max_iter=100,
weighting=weighting,
k=0,
c=1e7,
burn_in=0,
random_state=rng,
)
reg_not_rob = SGDRegressor(loss=loss, random_state=rng)
reg.fit(X_r, y_r)
reg_not_rob.fit(X_r, y_r)
pred1 = reg.predict(X_r)
pred2 = reg_not_rob.predict(X_r)
difference = [
np.linalg.norm(pred1[i] - pred2[i]) for i in range(len(pred1))
]
assert np.mean(difference) < 1
assert_almost_equal(reg.score(X_r, y_r), r2_score(y_r, reg.predict(X_r)))
def test_huber_and_sgd_same_results():
# Test they should converge to same coefficients for same parameters
X, y = make_regression_with_outliers(n_samples=10, n_features=2)
# Fit once to find out the scale parameter. Scale down X and y by scale
# so that the scale parameter is optimized to 1.0
huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100,
epsilon=1.35)
huber.fit(X, y)
X_scale = X / huber.scale_
y_scale = y / huber.scale_
huber.fit(X_scale, y_scale)
assert_almost_equal(huber.scale_, 1.0, 3)
sgdreg = SGDRegressor(
alpha=0.0, loss="huber", shuffle=True, random_state=0, max_iter=10000,
fit_intercept=False, epsilon=1.35, tol=None)
sgdreg.fit(X_scale, y_scale)
assert_array_almost_equal(huber.coef_, sgdreg.coef_, 1)
def test_huber_and_sgd_same_results():
# Test they should converge to same coefficients for same parameters
X, y = make_regression_with_outliers(n_samples=10, n_features=2)
# Fit once to find out the scale parameter. Scale down X and y by scale
# so that the scale parameter is optimized to 1.0
huber = HuberRegressor(fit_intercept=False, alpha=0.0, max_iter=100,
epsilon=1.35)
huber.fit(X, y)
X_scale = X / huber.scale_
y_scale = y / huber.scale_
huber.fit(X_scale, y_scale)
assert_almost_equal(huber.scale_, 1.0, 3)
sgdreg = SGDRegressor(
alpha=0.0, loss="huber", shuffle=True, random_state=0, max_iter=10000,
fit_intercept=False, epsilon=1.35, tol=None)
sgdreg.fit(X_scale, y_scale)
assert_array_almost_equal(huber.coef_, sgdreg.coef_, 1)
def perform_sgd_regression(features, values):
clf = SGDRegressor(n_iter=20)
clf = clf.fit(features, values)
intercept = clf.intercept_
params = clf.coef_
print "intercept:"
print intercept
print "params:"
for i in range(len(params)):
print "%s: %f" %(features.columns.values[i], params[i])
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
model = SGDRegressor()
sgd = model.fit(features, values)
intercept = sgd.intercept_
params = sgd.coef_
return intercept, params
def early_stoping(X, y):
from sklearn.base import clone
from sklearn.linear_model import SGDRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
# 当warm_start=True时，调用fit()方法后，训练会从停下来的地方继续，而不是从头重新开始。
sgd_reg = SGDRegressor(max_iter=1, warm_start=True, penalty=None, learning_rate="constant", eta0=0.0005)
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2)
minimum_val_error = float("inf")
best_epoch = None
best_model = None
for epoch in range(1000):
sgd_reg.fit(X_train, y_train.ravel())
y_val_predict = sgd_reg.predict(X_val)
val_error = mean_squared_error(y_val_predict, y_val)
if val_error < minimum_val_error:
minimum_val_error = val_error
best_epoch = epoch
best_model = clone(sgd_reg)
print('stopping in:', best_epoch)
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
###########################
### YOUR CODE GOES HERE ###
###########################
clf = SGDRegressor(alpha=0.1, n_iter=20)
clf.fit(features, values)
params = clf.get_params()
intercept = 0
#print params
#print len(features[0]), len(clf.coef_)
#print clf.coef_
#print clf.intercept_
return clf.intercept_, clf.coef_
def sgd_test():
# 获取数据
lb = load_boston()
# 分割数据集到训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(lb.data,
lb.target,
test_size=0.25)
print(y_train, '\n', y_test)
# 进行数据标准化处理(特征值与目标值都得标准化-目标值根据特征值求出[参考公式]) [两个标准化api:特征值[多列]/目标值[因为只有一列]]
# 特征值 标准化
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)
# 目标值 标准化
std_y = StandardScaler()
# 参数得是二维
y_train = std_y.fit_transform(y_train.reshape(-1, 1))
y_test = std_y.transform(y_test.reshape(-1, 1))
# 梯度下降进行预测
sg = SGDRegressor()
sg.fit(x_train, y_train)
print(sg.coef_)
# 进行预测
x_predict_res = sg.predict(x_test)
# 将标准化结果反转为非标准化之前的
stand_pre = std_y.inverse_transform(x_predict_res)
print(stand_pre)
# 真实样本 分割数据集的预测结果 与 梯度下降的结果
print(
'均方误差测试：\n',
mean_absolute_error(std_y.inverse_transform(y_test),
std_y.inverse_transform(x_predict_res)))
pass
def train(training_pandas_data, test_pandas_data, label_col,
feat_cols, alpha, l1_ratio, max_iter, tol, training_data_path, test_data_path):
print("train: " + training_data_path)
print("test: " + test_data_path)
print("alpha: ", alpha)
print("l1-ratio: ", l1_ratio)
print("max_iter: ", max_iter)
print("tol: ", tol)
print("label-col: " + label_col)
for col in feat_cols:
print("feat-cols: " + col)
# Split data into training labels and testing labels.
trainingLabels = training_pandas_data[label_col].values
trainingFeatures = training_pandas_data[feat_cols].values
testLabels = test_pandas_data[label_col].values
testFeatures = test_pandas_data[feat_cols].values
#We will use an SGD model.
en = SGDRegressor(alpha=alpha, l1_ratio=l1_ratio, warm_start=True, max_iter=max_iter, tol=tol)
# Here we train the model.
en.fit(trainingFeatures, trainingLabels)
# Calculating the scores of the model.
test_rmse = mean_squared_error(testLabels, en.predict(testFeatures))**0.5
r2_score_training = en.score(trainingFeatures, trainingLabels)
r2_score_test = en.score(testFeatures, testLabels)
print("Test RMSE:", test_rmse)
print("Training set score:", r2_score_training)
print("Test set score:", r2_score_test)
#Logging the RMSE and r2 scores.
mlflow.log_metric("Test RMSE", test_rmse)
mlflow.log_metric("Train R2", r2_score_training)
mlflow.log_metric("Test R2", r2_score_test)
#Saving the model as an artifact.
sklearn.log_model(en, "model")
def main():
vitals = ['LABEL_RRate', 'LABEL_ABPm', 'LABEL_SpO2', 'LABEL_Heartrate']
features_train = pd.read_csv('data/train_engineered_4.csv')
labels_train = pd.read_csv('data/train_labels.csv')
features_predict = pd.read_csv('data/test_engineered_4.csv')
# set reduced_size to reduce batch size
reduced_size = len(features_predict)
# reduced_size = 800
prediction = pd.DataFrame(features_predict['pid']).iloc[0:reduced_size,:]
metrics_summary = pd.DataFrame(columns=vitals)
hyperparams = pd.DataFrame(columns=vitals)
for label in vitals:
X_train = np.array(features_train)[0:reduced_size]
y_train = np.array(labels_train[label])[0:reduced_size]
X_predict = np.array(features_predict)[0:reduced_size]
# scaling data
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_predict = scaler.fit_transform(X_predict)
model = SGDRegressor(penalty='elasticnet', alpha=0.05, l1_ratio=0.1)
print()
print('learning : ', label)
model.fit(X_train, y_train)
#predict on the provided test set
y_predicted = model.predict(X_predict)
# y_predicted = grid.predict(X_predict)
prediction[label] = y_predicted
print(prediction)
return prediction
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
y = values
X = features
clf = SGDRegressor(n_iter=100)
result = clf.fit(X, y)
params = result.coef_
intercept = result.intercept_
return intercept, params
def SGD_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)
sgd = SGDRegressor()
sgd.fit(train_x, train_y)
score = sgd.score(test_x, test_y)
predict_y = sgd.predict(test_x)
print(score)
print(predict_y[:20])
print(test_y[:20])
# print(sgd.coef_)
# print(sgd.intercept_)
return None
def do_lreg_training_runs(d, cfg):
times = np.array([])
for i in range(cfg.num_runs):
print('***Run #%d***' % i)
start = time.perf_counter()
model = SGDRegressor(
eta0=cfg.learning_rate, #learning rate bias
max_iter=cfg.epochs,
random_state=42)
model.fit(d.x_train, d.y_train)
elapsed = time.perf_counter() - start
times = np.append(times, elapsed)
trace_model_filename = f'{model_filename(cfg, i)}.joblib'
dump(model, trace_model_filename)
print(f"#### dump model [{trace_model_filename}]")
print_times(times)
def test_regression():
torch.set_default_tensor_type('torch.DoubleTensor')
net_ctor = lambda: N.Linear(13, 1)
loss = F.mse_loss
# Supports fit, predict, and score
x, y = load_boston(return_X_y=True)
model = TorchEstimator(net_ctor, loss, opt_ctor='Adam', lr=1e-3)
model.fit(x, y, epochs=5)
model.predict(x)
model.score(x, y)
# Comparable to sklearn linear regression
theirs = SGDRegressor(max_iter=5, eta0=1e-3)
theirs.fit(x, y)
h_theirs = theirs.predict(x)
h_ours = model.predict(x)
mse_theirs = mean_squared_error(y, h_theirs)
mse_ours = mean_squared_error(y, h_ours)
assert mse_ours < mse_theirs # torch is better than sklearn by a lot
def test_mbsgd_regressor_default(datatype, nrows,
column_info):
ncols, n_info = column_info
X, y = make_regression(n_samples=nrows, n_features=ncols,
n_informative=n_info, random_state=0)
X = X.astype(datatype)
y = y.astype(datatype)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8,
random_state=0)
cu_mbsgd_regressor = cumlMBSGRegressor()
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test).to_array()
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
if nrows < 500000:
skl_sgd_regressor = SGDRegressor()
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
class TestingExercise3_06(unittest.TestCase):
def setUp(self) -> None:
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
self.data = pd.read_csv(
os.path.join(ROOT_DIR, '..', 'Datasets', 'synth_temp.csv'))
def test_SGD(self):
self.data = self.data.loc[self.data.Year > 1901]
self.data_group_year = self.data.groupby(['Year'
]).agg({'RgnAvTemp': 'mean'})
self.data_group_year['Year'] = self.data_group_year.index
self.data_group_year = self.data_group_year.rename(
columns={'RgnAvTemp': 'AvTemp'})
self.X_min = self.data_group_year.Year.min()
self.X_range = self.data_group_year.Year.max(
) - self.data_group_year.Year.min()
self.Y_min = self.data_group_year.AvTemp.min()
self.Y_range = self.data_group_year.AvTemp.max(
) - self.data_group_year.AvTemp.min()
self.scale_X = (self.data_group_year.Year - self.X_min) / self.X_range
self.train_X = self.scale_X.ravel()
self.train_Y = ((self.data_group_year.AvTemp - self.Y_min) /
self.Y_range).ravel()
np.random.seed(42)
self.model = SGDRegressor(loss='squared_loss',
max_iter=100,
learning_rate='constant',
eta0=0.0005,
tol=0.00009,
penalty='none')
self.model.fit(self.train_X.reshape((-1, 1)), self.train_Y)
self.Beta0 = (
self.Y_min + self.Y_range * self.model.intercept_[0] -
self.Y_range * self.model.coef_[0] * self.X_min / self.X_range)
self.Beta1 = self.Y_range * self.model.coef_[0] / self.X_range
self.pred_X = self.data_group_year['Year']
self.pred_Y = self.model.predict(self.train_X.reshape((-1, 1)))
self.r2 = r2_score(self.train_Y, self.pred_Y)
self.assertEqual(round(self.r2, 3), (0.544))
def myLinear():
""" 线性回归直接预测房子价格 """
# 获取数据
lb = load_boston()
# 分割数据集到训练集和测试集
x_train, x_test, y_train, y_test = train_test_split(lb.data,
lb.target,
test_size=0.25)
# 进行标准化处理, 目标值需要进行标准化处理吗
# 实例化两个标准化API
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.fit_transform(x_test)
# 目标值
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train)
y_test = std_y.fit_transform(y_test)
# estimator预测
# 正规方程求解1
lr = LinearRegression()
lr.fit(x_train, y_train)
# 权重参数
print(lr.coef_)
# 预测价格
y_lr_predict = std_y.inverse_transform(lr.predict(x_test))
print('测试集里面每个房子的预测价格: ', y_lr_predict)
print('正规方程的均方误差:',
mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict))
# 梯度下降进行房子价格预测
sgd = SGDRegressor()
sgd.fit(x_train, y_train)
print(sgd.coef_)
# 预测测试集的价格
y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))
print('测试集里面每个房子的预测价格: ', y_sgd_predict)
print('梯度下降的均方误差:',
mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))
return None
def line_lineregression():
# 读取并分割数据
data = load_boston()
x_train, x_test, y_train, y_test = train_test_split(data.data,
data.target,
test_size=0.25)
# 标准化(与分类问题有所不同，目标值也要标准化，在最终显示时，可用obj.inverse_transform()反标准化)
std1 = StandardScaler()
std2 = StandardScaler()
# 对特征值标准化 -- fit_transform()及transform()的使用可参考 "0.0.训练集、测试集的特征工程.py"
x_train = std1.fit_transform(x_train)
x_test = std1.transform(x_test)
# 对目标值标准化
y_train = std2.fit_transform(y_train.reshape((-1, 1))) # 由一维变二维,算法要求的格式
# y_test = std2.transform(y_test.reshape((-1,1))) # 同上
# 实例化预预估器
lr_1 = LinearRegression()
sgd_1 = SGDRegressor() # 学习率默认
# 1.正规方程求解
lr_1.fit(x_train, y_train) # 训练模型
print(lr_1.coef_) # 训练得到的模型参数
result_1 = lr_1.predict(x_test) # 得到的预测值
result_1 = std2.inverse_transform(result_1) # 反标准化
print("正规求解预测房价为：", result_1)
print("正规求解均方误差为：", mean_squared_error(y_test, result_1))
print("*" * 30)
# 2.梯度下降求解
sgd_1.fit(x_train, y_train)
print(sgd_1.coef_)
result_2 = sgd_1.predict(x_test)
result_2 = std2.inverse_transform(result_2) # 反标准化
print("梯度下降求解预测房价为：", result_2)
print("梯度下降求解均方误差为：", mean_squared_error(y_test, result_2))
def get_sgd(X_train, X_test, y_train, y_test):
temp_max_itr = 100000
dest_eta = 1e-5
dest_tol = 1e-3
temp_coef = 0.01
dest_coef = temp_coef
dest_intercept = 0.0
max = -1000
# mode = 'w'
# cnt = 1
while temp_coef <= 2.0:
temp_intercept = 0.0
while temp_intercept <= 50.0:
sgd = SGDRegressor(random_state=15,
max_iter=temp_max_itr,
eta0=dest_eta,
tol=dest_tol,
n_iter_no_change=6)
sgd.fit(X_train,
y_train,
coef_init=temp_coef,
intercept_init=temp_intercept)
scr = sgd.score(X_test, y_test)
#Checking if scored more than previous max score
if max < scr:
max = scr
dest_coef = temp_coef
dest_intercept = temp_intercept
# if cnt > 1 :
# mode = 'a'
# cnt += 1
# write_to_file(scr,dest_coef, dest_intercept, mode)
temp_intercept += 1.0
temp_coef += 0.1
sgd1 = SGDRegressor(random_state=15,
max_iter=temp_max_itr,
eta0=dest_eta,
tol=dest_tol,
n_iter_no_change=6)
return sgd1, dest_coef, dest_intercept
class SGDRegressionModel(RegressionModel): def __init__(self, train_data): RegressionModel.__init__(self, train_data) self.model = SGDRegressor() def train(self, x=None, y=None): x = x if x is not None else self.train_x y = y if y is not None else self.train_y self.model.fit(x, y) def predict(self, x_in): return self.model.predict(x_in) def evaluate(self, x_in, y_out): return self.model.score(x_in, y_out) def save(self, filename): joblib.dump(self.model, filename) def load(self, filename): self.model = joblib.load(filename)
def test_sklreandata():
x, y = data_xy(sklearn_regdata=True)
# My reg
lr = SGDLinear_reg(100, eta=0.01, batch_size=10)
lr.fit(x, y)
lr.plot_train_loss()
## sklearn sgd
sgd_reg = SGDRegressor(max_iter = 100, # 迭代次数
penalty = None, # 正则项为空
eta0 = 0.01, # 学习率
early_stopping=True
)
sgd_reg.fit(x,y)
## sklearn reg
lrskt = LinearRegression(fit_intercept=True)
lrskt.fit(x, y)
print(f'my linear loss: {lr.cost(y, lr.predict(x)):.2f}')
print(f'sklearn loss: {lr.cost(y, lrskt.predict(x)):.2f}')
print(f'sklearn sgd loss: {lr.cost(y, sgd_reg.predict(x)):.2f}')
def test_mbsgd_regressor(lrate, penalty, make_dataset):
nrows, datatype, X_train, X_test, y_train, y_test = make_dataset
cu_mbsgd_regressor = cumlMBSGRegressor(learning_rate=lrate, eta0=0.005,
epochs=100, fit_intercept=True,
batch_size=2, tol=0.0,
penalty=penalty)
cu_mbsgd_regressor.fit(X_train, y_train)
cu_pred = cu_mbsgd_regressor.predict(X_test).to_array()
cu_r2 = r2_score(cu_pred, y_test, convert_dtype=datatype)
if nrows < 500000:
skl_sgd_regressor = SGDRegressor(learning_rate=lrate, eta0=0.005,
max_iter=100, fit_intercept=True,
tol=0.0, penalty=penalty,
random_state=0)
skl_sgd_regressor.fit(X_train, y_train)
skl_pred = skl_sgd_regressor.predict(X_test)
skl_r2 = r2_score(skl_pred, y_test, convert_dtype=datatype)
assert abs(cu_r2 - skl_r2) <= 0.02
def runSGD(X_train, X_test, y_train, y_test,dataname):
all_epsilon = [0.001, 0.1 ,0.5, 0.9]
best_model=None
max_score=0
for epsilon in all_epsilon:
regressor = SGDRegressor(loss='epsilon_insensitive',epsilon=epsilon)
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)
# plt.show()
plt.scatter(y_test, y_pred)
plt.plot([y_test.min(), y_test.max()], [y_pred.min(), y_pred.max()], 'r', lw=2)
score = regressor.score(X_test, y_test)
if score>max_score:
best_model=regressor
plt.title('SGD - {0}\n epsilon ={1} \nScore = {2:.3f} '.format(str(dataname),epsilon, score))
plt.xlabel('Actual ')
plt.ylabel('Predict')
# plt.show()
plt.savefig('runSGD_{}_{}.png'.format(strftime("%H_%M_%S", gmtime()),epsilon))
plt.close()
return best_model
def regularization():
"""Plot error vs iterations."""
alphaList = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]
xTrain = db.load('train', 'data')[:, 1:].astype(int)
yTrain = np.squeeze(db.load('train', 'y')[:, 1:])
xValid = db.load('valid', 'data')[:, 1:].astype(int)
yValid = np.squeeze(db.load('valid', 'y')[:, 1:])
eTrain = np.zeros(len(alphaList))
eValid = np.zeros(len(alphaList))
for i in range(len(alphaList)):
for _ in range(5):
model = SGDRegressor(penalty='l2', learning_rate='constant', eta0=0.006,
max_iter=100)
model.fit(xTrain, yTrain)
eTrain[i] += np.sqrt(mean_squared_error(yTrain, model.predict(xTrain)))
eValid[i] += np.sqrt(mean_squared_error(yValid, model.predict(xValid)))
eTrain[i] /= 5
eValid[i] /= 5
plt.semilogx(alphaList, eTrain, label='Training')
plt.semilogx(alphaList, eValid, label='Validation')
plt.legend()
plt.show()
def linear():
"""
线性回归直接预测房子价格
:return:
"""
lb=load_boston()
#分割数据集到训练集和测试集
x_train,x_test,y_train,y_test=train_test_split(lb.data,lb.target,test_size=0.25)
#进行标准化处理
#必须对特征值和目标值进行标准化处理，实例化两个标准化API
std_x=StandardScaler()
x_train=std_x.fit_transform(x_train)
x_test=std_x.transform(x_test)
#目标值
std_y=StandardScaler()
#sklearn0.19版本传进的必须是二维数组reshape(-1,1)，由于样本数不知道，则直接填-1，目标值只有一个
y_train=std_y.fit_transform(y_train.reshape(-1,1))
y_test=std_y.transform(y_test.reshape(-1,1).reshape(-1,1))
print(y_train)
#estimator预测
#正规方程求解方式预测结果
lr=LinearRegression()
lr.fit(x_train,y_train)
print(lr.coef_)
#预测测试集的房子价格
y_predict=lr.predict(x_test)
#转化回标准化之前的数据形式
y_predict=std_y.inverse_transform(y_predict)
print("正规方程预测每个房子的价格：",y_predict)
print("正规方程的均方误差:",mean_squared_error(std_y.inverse_transform(y_test),y_predict))
#梯度下降
sgd=SGDRegressor()
sgd.fit(x_train,y_train)
print(sgd.coef_)
#岭回归
#alpha正则化力度
rd=Ridge(alpha=1.0)
rd.fit(x_train,y_train)
print(rd.coef_)
def Linear():
# 获取数据
lb = load_boston()
# 分割数据---将数据分割成为“训练集”和“测试集” 返回结果 （训练集、测试集的特征值）,(训练集、测试集的目标值)
x_train, x_test, y_train, y_test = tts(lb.data, lb.target, test_size=0.25)
# print(y_train, 'weqw\n', y_test)
# 标准化处理(特征值和目标值都要标准化处理) 要实例化2个的api
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)
std_y = StandardScaler()
# 要转化为 二维数组
y_train = std_y.fit_transform(y_train.reshape(-1, 1))
y_test = std_y.transform(y_test.reshape(-1, 1))
# 正规方程
lr = LinearRegression()
lr.fit(x_train, y_train)
print("LR各个特征的权重", lr.coef_)
predict = lr.predict(x_test)
# 要进行反标准化得到值
print("LR预测测试集的房子价格", std_y.inverse_transform(predict))
# 参数是 真实值 和预测值
print(
"正规方程的均方误差",
err(std_y.inverse_transform(y_test), std_y.inverse_transform(predict)))
# 梯度下降
SGD = SGDRegressor()
SGD.fit(x_train, y_train)
print("SGD各个特征的权重", SGD.coef_)
_predict = SGD.predict(x_test)
# 要进行反标准化得到值
print("SGD预测测试集的房子价格", std_y.inverse_transform(_predict))
print(
"梯度下降的均方误差",
err(std_y.inverse_transform(y_test),
std_y.inverse_transform(_predict)))
return None
def linear_model2():
#1.
data = load_boston()
#2.
x_train, x_test, y_train, y_test = train_test_split(data.data,
data.target,
test_size=0.2,
random_state=22)
#3.
Stan = StandardScaler()
x_train = Stan.fit_transform(x_train)
x_test = Stan.fit_transform(x_test)
#4
linear = SGDRegressor(max_iter=1000) #最大迭代次数
linear.fit(x_train, y_train)
#5
ss = linear.predict(x_test)
#print('梯度下降预测值是：',ss)
print('梯度下降均方误差:', mean_squared_error(y_test, ss))
print('梯度下降系数：', linear.coef_)
def test_both_fit_and_score_contain_sample_weight(sample_weight_passed_as):
mlflow.sklearn.autolog()
from sklearn.linear_model import SGDRegressor
# ensure that we use an appropriate model for this test
assert "sample_weight" in _get_arg_names(SGDRegressor.fit)
assert "sample_weight" in _get_arg_names(SGDRegressor.score)
mock_obj = mock.Mock()
def mock_score(self, X, y, sample_weight=None): # pylint: disable=unused-argument
mock_obj(X, y, sample_weight)
return 0
assert inspect.signature(
SGDRegressor.score) == inspect.signature(mock_score)
SGDRegressor.score = mock_score
model = SGDRegressor()
X, y = get_iris()
sample_weight = abs(np.random.randn(len(X)))
with mlflow.start_run() as run:
if sample_weight_passed_as == "positional":
model.fit(X, y, None, None, sample_weight)
elif sample_weight_passed_as == "keyword":
model.fit(X, y, sample_weight=sample_weight)
mock_obj.assert_called_once_with(X, y, sample_weight)
run_id = run.info.run_id
params, metrics, tags, artifacts = get_run_data(run_id)
assert params == truncate_dict(
stringify_dict_values(model.get_params(deep=True)))
assert {TRAINING_SCORE: model.score(X, y)}.items() <= metrics.items()
assert tags == get_expected_class_tags(model)
assert MODEL_DIR in artifacts
assert_predict_equal(load_model_by_run_id(run_id), model, X)