def test_polynomial_features():
# Test Polynomial Features
X1 = np.arange(6)[:, np.newaxis]
P1 = np.hstack([np.ones_like(X1), X1, X1**2, X1**3])
deg1 = 3
X2 = np.arange(6).reshape((3, 2))
x1 = X2[:, :1]
x2 = X2[:, 1:]
P2 = np.hstack([
x1**0 * x2**0, x1**1 * x2**0, x1**0 * x2**1, x1**2 * x2**0,
x1**1 * x2**1, x1**0 * x2**2
])
deg2 = 2
for (deg, X, P) in [(deg1, X1, P1), (deg2, X2, P2)]:
P_test = PolynomialFeatures(deg, include_bias=True).fit_transform(X)
assert_array_almost_equal(P_test, P)
P_test = PolynomialFeatures(deg, include_bias=False).fit_transform(X)
assert_array_almost_equal(P_test, P[:, 1:])
interact = PolynomialFeatures(2, interaction_only=True, include_bias=True)
X_poly = interact.fit_transform(X)
assert_array_almost_equal(X_poly, P2[:, [0, 1, 2, 4]])
def fit(self, data, args):
self.model = PolynomialFeatures()
with Timer() as t:
self.model.fit(data.X_train, data.y_train)
return t.interval
def __gen_model(self, model = LinearRegression()):
model = Pipeline([('poly', PolynomialFeatures(degree=3)),
('linear', LinearRegression(fit_intercept=False))])
X_train, y_train, _ = self.getDataSet()
model.fit(X_train, y_train)
# print "mode coef: ",
# print model.named_steps['linear'].coef_
self.model = model
def polynomial(self):
poly = PolynomialFeatures(degree=3)
self.training_order_start_end_districts_and_time = poly.fit_transform(
self.training_order_start_end_districts_and_time,
self.training_number_of_orders)
predict = poly.transform(
self.testing_order_start_end_districts_and_time)
clf = linear_model.LinearRegression()
clf.fit(self.training_order_start_end_districts_and_time,
self.training_number_of_orders)
predicted_number_of_orders = clf.predict(predict)
current_ride_prediction_error = numpy.mean(
(predicted_number_of_orders - self.testing_number_of_orders)**2)
print(current_ride_prediction_error)
print(clf.coef_)
def test_ols_with_boston_dataset():
# load boston dataset
dataset = load_boston()
# create metamodel
input_names = list(dataset.inputs)
response_names = list(dataset.responses)
metamodel = metamodels.OLSModel(
preprocessors=[PolynomialFeatures(degree=2)],
input_names=input_names,
response_names=response_names)
# create trainer and fit metamodel to the dataset
result = Trainer().fit(metamodel, dataset)
print('score:', result.score)
# score: 0.682539990982
assert result.score > 0.68
assert result.score < 0.69
#selecting based on best performance
# predictors = np.column_stack((NBO[:,0],sa[:,0],sa[:,1],bv[:,0],CoHOMO[:,0],CoHOMO[:,1],CoHOMO[:,2],CoLUMO[:,0],CoLUMO[:,1],ba[:,0],ba[:,1],ba[:,2],ba[:,3],ba[:,4],ba[:,5],lt[:,0],lt[:,1],lt[:,2],lt[:,4],lt[:,5]))
#######Training targets ###
# hydricities = CoHOMO[:,1]
# hyduns = np.column_stack((therm[:,1])).reshape((-1,1))
# scaler = StandardScaler()
# hydricities2 = hydricities.reshape((-1,1))
# hydricities=scale(hydricities2)
# print(hyd1)
# compound features
polyFeatures = PolynomialFeatures(degree=1,interaction_only=False)
regressor = make_pipeline(polyFeatures, StandardScaler(), LassoCV(max_iter=60000, cv=KFold(n_splits=5, shuffle=True)))
# regressor = make_pipeline(polyFeatures, LassoCV(max_iter=60000, cv=KFold(n_splits=5, shuffle=True)))
# regressor = make_pipeline(polyFeatures, StandardScaler(), LassoCV(max_iter=60000))
# regressor = make_pipeline(polyFeatures, StandardScaler(), LassoCV(max_iter=60000))
# regressor = make_pipeline(polyFeatures, StandardScaler(), Ridge())
# regressor = make_pipeline(polyFeatures, StandardScaler(), Lasso(alpha=0.035, max_iter=70000))#, fit_intercept=True))
# regressor = make_pipeline(polyFeatures, StandardScaler(), Lasso(alpha=0, max_iter=70000))#, fit_intercept=True))
# regressor = make_pipeline(polyFeatures, StandardScaler(), LinearRegression())
# regressor = make_pipeline(polyFeatures, LinearRegression())
# regressor = RandomForestRegressor(oob_score=True,n_estimators=2000)
boston = load_boston()
#print(boston)
#通过DESCR属性可以查看数据集的详细情况，这里数据有14列，前13列为特征数据，最后一列为标签数据。
#print(boston.DESCR)
#boston的data和target分别存储了特征和标签
#print(boston.data)
#print(boston.target)
#切分数据集
X_train, X_test, y_train, y_test = train_test_split(boston.data,
boston.target,
test_size=0.2,
random_state=2)
#增加特征多项式让线性回归模型更好地拟合数据
#多项式的个数的不断增加，可以在训练集上有很好的效果，但很容易造成过拟合
poly = PolynomialFeatures(degree=2, include_bias=False)
X_train_poly = poly.fit_transform(X_train)
X_test_poly = poly.fit_transform(X_test)
#多项式线性回归
model2 = LinearRegression(normalize=True)
model2.fit(X_train_poly, y_train)
mutilScore = model2.score(X_test_poly, y_test)
print(mutilScore)
#模型测试，并利用均方根误差(MSE)对测试结果进行评价
#模型的拟合值
y_pred = model2.predict(X_test_poly)
print("MSE:", metrics.mean_squared_error(y_test, y_pred))
#交叉验证
predicted = cross_val_predict(model2, boston.data, boston.target, cv=10)
def __init__(self, degree):
self._poly = PolynomialFeatures(degree=degree)
with open(filename, "r") as filestream:
for line in filestream:
current = line.split(",")
Z.append([i for i in current[0:len(current)]])
#Shuffle Matrix for Cross Validation
Z = np.asarray(Z)
np.random.shuffle(Z)
#Split Matrix in X,Y
X = []
Y = []
X, B, Y = np.hsplit(Z, [Z.shape[1] - 1, Z.shape[1] - 1])
#Non-Linear
X = PolynomialFeatures(1).fit_transform(X)
#Get Float Data
X = X.astype(np.float)
classes = np.unique(Y)
for i in range(0, len(classes)):
classes[i] = classes[i].strip()
classes = np.unique(classes)
y = []
for i in range(0, len(Y)):
for j in range(0, len(classes)):
if (Y[i].item(0).strip() == classes[j]):
y.append(j)
Y = np.asarray(y)
Y = Y.astype(np.float)
def validate(params):
transf_type = params['transf_type']
if transf_type == 'drop':
transf = FunctionTransformer(drop_transform, validate=False)
elif transf_type == 'dr+inp+sc+pca':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
StandardScaler(),
PCA(n_components=params['n_pca_components']),
)
elif transf_type == 'dr+inp':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
)
elif transf_type == 'dr+inp+sc':
transf = make_pipeline(drop_transform, SimpleImputer(),
StandardScaler())
elif transf_type == 'union':
transf = create_union_transf(params)
elif transf_type == 'poly_kbest':
transf = make_pipeline(
drop_transform,
SimpleImputer(),
StandardScaler(),
PolynomialFeatures(degree=2, interaction_only=True),
SelectKBest(f_regression, params['best_features']),
)
else:
raise AttributeError(f'unknown transformer type: {transf_type}')
est_type = params['est_type']
if est_type == 'xgboost':
est = create_xgb_est(params)
elif est_type == 'gblinear':
est = create_gblinear_est(params)
elif est_type == 'exttree':
est = ExtraTreesRegressor(n_estimators=params['n_estimators'],
n_jobs=-1)
elif est_type == 'gp':
est = GaussianProcessRegressor()
elif est_type == 'ridge':
est = Ridge(alpha=params['alpha'])
else:
raise AttributeError(f'unknown estimator type: {est_type}')
if params['bagging']:
BaggingRegressor(est,
n_estimators=params['n_bag_estimators'],
max_features=1.,
max_samples=1.)
pl = make_pipeline(transf, est)
if params['per_group_regr']:
pl = PerGroupRegressor(estimator=pl,
split_condition=['os', 'cpuFreq', 'memSize_MB'],
n_jobs=1,
verbose=1)
return cv_test(pl, n_folds=params['n_folds'])
data.loc[data['Weekday'] == i, 'expensive than average weekday'] = data.loc[data['Weekday'] == i, 'Price'] - \
data.loc[data['Weekday'] == i, 'Price'].mean()
for i in range(1, 366):
data.loc[data['Date'] == i, 'expensive than average date'] = data.loc[data['Date'] == i, 'Price'] - \
data.loc[data['Date'] == i, 'Price'].mean()
for i in range(2):
data.loc[data['Apartment'] == i, 'expensive than average apartment'] = data.loc[data['Apartment'] == i, 'Price'] - \
data.loc[data['Apartment'] == i, 'Price'].mean()
for i in range(1, 5):
data.loc[data['Beds'] == i, 'expensive than average bed'] = data.loc[data['Beds'] == i, 'Price'] - \
data.loc[data['Beds'] == i, 'Price'].mean()
threshold1 = Binarizer(threshold=3.0)
res1 = pd.DataFrame(threshold1.transform(data['Review'].values.reshape(-1, 1)))
threshold2 = Binarizer(threshold=80)
res2 = pd.DataFrame(threshold2.transform(data['Price'].values.reshape(-1, 1)))
pf = PolynomialFeatures(degree=2, interaction_only=True, include_bias=False)
res3 = pd.DataFrame(
pf.fit_transform(
data[['Apartment', 'Beds', 'Review', 'Pic Quality', 'Price']]))
encoder = OneHotEncoder()
data_region1hot = encoder.fit_transform(data['Region'].values.reshape(-1, 1))
data_region = pd.DataFrame(data_region1hot.toarray())
data_weekday1hot = encoder.fit_transform(data['Weekday'].values.reshape(-1, 1))
data_weekday = pd.DataFrame(data_weekday1hot.toarray())
data_reformed = pd.concat(
[data.drop(columns=['ID']), data_region, data_weekday, res1, res2, res3],
axis=1)
Seed = 40
def create_model(
model_type,
feature_scaling=False,
polynomial_degree=1,
cross_validation=False,
alpha=1.0,
C=None,
kernel=None,
svr_epsilon=None,
svr_degree=None,
svr_gamma=None,
svr_coef0=None,
sparse=False,
):
""" Creates a new model of the specified type.
Args:
model_type (str): The type of model to create. Use one of the MODEL_TYPE_X constants.
feature_scaling (bool): If feature scaling is to be used.
polynomial_degree (int): If higher than 1, polynomial feature transformation will be applied.
cross_validation (bool): If cross validation is to be applied, if applicable to the model type.
alpha (float): The regularization parameter. Will only be used if applicable to the model type.
C: The regularization parameter für SVR. Will only be used if applicable to the model type.
kernel (str): The kernel to use, if applicable to the model type.
sparse (bool): If a sparse feature matrix is used.
svr_epsilon (float): Epsilon parameter for SVR. Specifies the epsilon tube. (see sklearn for more info)
svr_degree (int): Polynomial degree parameter for the SVR kernel 'poly'
svr_gamma (float): Kernel coefficient for SVR kernels 'rbf', 'poly' and 'sigmoid'
svr_coef0 (float): Independent term (or bias) for SVR kernels 'poly' and 'sigmoid'
Returns:
(sklearn.pipeline.Pipeline) The estimator model.
"""
assert polynomial_degree > 0, "Polynomial degree must be higher than 0!"
model_type = model_type.upper()
logging.debug("Creating model with type %s" % model_type)
if model_type == MODEL_TYPE_LINREG:
model = create_linear_regression_model()
elif model_type == MODEL_TYPE_RIDREG:
if cross_validation:
model = create_ridge_cv_model(alpha)
else:
model = create_ridge_model(alpha)
elif model_type == MODEL_TYPE_SVR:
if cross_validation:
model = create_svr_cv_model(C, kernel, svr_epsilon, svr_degree,
svr_gamma, svr_coef0)
else:
model = create_svr_model(C, kernel, svr_epsilon, svr_degree,
svr_gamma, svr_coef0)
else:
raise ValueError("The model type %s is not supported." % model_type)
steps = []
if polynomial_degree > 1:
if not sparse:
steps.append(
("poly", PolynomialFeatures(degree=polynomial_degree)))
else:
logging.warning(
"Polynomial Features for sparse matrices are not supported!")
if feature_scaling:
if sparse:
scaler = SparseScaler()
else:
scaler = StandardScaler()
steps.append(("scale", scaler))
steps.append((model_type, model))
return Pipeline(steps)