def runMain():
boston_data = datasets.load_boston()
X = boston_data.data
y = boston_data.target
# print '样本'
# print X[:5,:]
# print '标签'
# print y[:5]
lr_model = LinearRegression()
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=1 / 3.,
random_state=3)
lr_model.fit(X_train, y_train)
print 'xtrain====', X_train
print 'X_test====', X_test
print 'y_train====', y_train
print 'y_test====', y_test
#返回参数
params = lr_model.get_params()
print '参数：'
print params
train_scor = lr_model.score(X_train, y_train)
test_score = lr_model.score(X_test, y_test)
print '训练集打分：'
print train_scor
print '测试集打分：'
print test_score
return ''
class LinearRegressionModel:
# initialize a LinearRegressionModel object with "model" attribute containing an actual LinearRegression object from the skLearn module
def __init__(self,*args,**kwargs):
self.model=LinearRegression(*args,**kwargs)
# a function that returns the actual LinearRegression object which the called LinearRegressionModel object wraps around
def get_model(self):
return self.model
def fit(self,X,y,sample_weight=None):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
if (isinstance(y,TabularData)):
y=DataConversion.extract_y(y)
self.model.fit(X,y,sample_weight)
return self
def get_params(self,deep=True):
return self.model.get_params(deep)
def predict(self,X):
# if statement added to avoid converting TabularData twice when predict() is called inside score()
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
return self.model.predict(X)
def score(self,X,y,sample_weight=None):
if (isinstance(X,TabularData)):
X=DataConversion.extract_X(X)
if (isinstance(y,TabularData)):
y=DataConversion.extract_y(y)
return self.model.score(X,y,sample_weight)
def set_params(self,**params):
return self.model.set_params(**params)
'''
# for testing purposes
def __getattribute__(self,item):
logging.info("The function being called: "+str(item))
if (item in ('fit','predict','model','get_model','score')):
return super().__getattribute__(item)
'''
def __getattribute__(self,item):
# if the called function/attribute does not require X,y tabularData conversion, get the attribute value by calling the function on the actual LinearRegression model in skLearn module
# check if this object has the requested attribute
try:
return super().__getattribute__(item)
except:
pass;
# otherwise fetch it from the actual linear regression object
return getattr(self.model,item)
'''
def search_bestparam_LinearRegression(X_train, y_train, df_search_best_param):
print(f"Search best params for LinearRegression ...")
model = LinearRegression()
print("Supported params", model.get_params())
param_grid = {
'normalize' : [True,False],
'fit_intercept': [True,False]
}
search_bestparam(model, param_grid, X_train, y_train, df_search_best_param)
def sklearn_mode():
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
model = LinearRegression()
model.fit(data_X, data_y)
print(model.coef_) # y=0.1x+0.3
print(model.intercept_)
print(model.get_params())
print(model.score(data_X, data_y)) # R^2 coefficient of determination
def main():
x, y = datasets.make_regression(100, 1, noise=5)
x = preprocessing.scale(x)
y = preprocessing.scale(y)
print(x.shape, y.shape)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3)
lr = LinearRegression(n_jobs=8)
lr.fit(x_train, y_train)
print(lr.coef_, lr.intercept_)
print(lr.get_params())
print(lr.score(x_test, y_test))
class LinearRegression(Model):
# X represents the features, Y represents the labels
X = None
Y = None
prediction = None
model = None
def __init__(self, X=None, Y=None, cfg=False):
if X is not None:
self.X = X
if Y is not None:
self.Y = Y
self.model = LinearRegressionModel()
self.cfg = cfg
def fit(self, X=None, Y=None):
if X is not None:
self.X = X
if Y is not None:
self.Y = Y
print('Linear Regression Train started............')
self.model.fit(self.X, self.Y)
print('Linear Regression completed..........')
return self.model
def predict(self, test_features):
print('Prediction started............')
self.predictions = self.model.predict(test_features)
print('Prediction completed..........')
return self.predictions
def save(self):
if self.cfg:
f = open('linearregression_configs.txt', 'w')
f.write(json.dumps(self.model.get_params()))
f.close()
print('No models will be saved for lasso')
def featureImportance(self):
#if X_headers is None:
# X_headers = list(self.X)
#print(self.model.coef_)
#feature_importance_ = zip(self.model.coef_[0], X_headers)
#feature_importance = set(feature_importance_)
return self.model.coef_
def run_regression(n_classes):
X, y = get_scaled_data()
y = clean_y_data(y, n_classes)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = LinearRegression().fit(X_train, y_train)
print(model.get_params())
y_pred = model.predict(X_test)
for i in range(len(y_pred)):
y_pred[i] = int(y_pred[i])
print_model_info(y_test, y_pred,
("Run LinearRegression for", n_classes, "classes:"))
def train(stock, x_df, y_df):
# check nan
print(x_df.isnull().values.any())
print(y_df.isnull().values.any())
# LinearRegression
x = x_df[:].values
y = y_df[:].values
reg = LinearRegression().fit(x, y)
res = {}
res['stock'] = stock
res['score'] = reg.score(x, y)
res['params'] = reg.get_params()
# res['coef_'] = reg.coef_
linear_regression_results.append(res)
def train(stock, x_df, y_df):
# check nan
# print(x_df.isnull().values.any())
# print(y_df.isnull().values.any())
# LinearRegression
x = x_df.values
y = y_df.values
print(stock)
# print(x.shape)
# print(y.shape)
# x = x_df[:].values
# y = y_df[:].values
tc = 180
x_train = x[:tc]
y_train = y[:tc]
x_test = x[tc:]
y_test = y[tc:]
try:
reg = LinearRegression().fit(x_train, y_train)
except Exception as e:
print(e)
res = {}
res['stock'] = stock
res['score'] = reg.score(x_test, y_test)
res['params'] = reg.get_params()
# test prediction
pY = reg.predict(x)
print(pY.shape)
py_df = pd.DataFrame(pY)
pred_df = pd.concat([y_df, py_df], axis=1)
pred_df.to_csv(ROOT + '/data/test_pred/' + stock[0:4] + '.csv')
# custom score
sum = 0
for i, predY in enumerate(pY):
diff = predY - y[i]
# print(diff)
sum += (diff * diff)
print(sum)
res['custom_training_error_sum'] = sum
res['custom_training_error'] = math.sqrt(sum / len(pY))
# res['coef_'] = reg.coef_
linear_regression_results.append(res)
class LinRegClassifierSKLearn(BaseEstimator, ClassifierMixin, TransformerMixin):
def __init__(self, *args, **kwargs):
self.clf = LinearRegression(*args, **kwargs)
self.threshold = 0.0
def fit(self, X, y):
y = (2 * y) - 1
self.clf.fit(X, y)
return self
def predict(self, X):
y = self.clf.predict(X)
y = (2 * (y > self.threshold)) - 1
y[y == -1] = 0
return y
def get_params(self, deep=True):
return self.clf.get_params(deep=deep)
class LinearRegression():
def __init__(self, fit_intercept=True, normalize=False, copy_X=True, n_jobs=1):
self.LR = LR(fit_intercept, normalize, copy_X, n_jobs)
def decision_function(self, x):
return self.LR.decision_function(x)
def fit(self, x, y):
return self.LR.fit(x, y)
def get_params(self):
return self.LR.get_params()
def predict(self, x):
return self.LR.predict(x)
def set_params(self, **params):
self.LR.set_params(params)
class LinRegClassifierSKLearn(BaseEstimator, ClassifierMixin,
TransformerMixin):
def __init__(self, *args, **kwargs):
self.clf = LinearRegression(*args, **kwargs)
self.threshold = 0.0
def fit(self, X, y):
y = (2 * y) - 1
self.clf.fit(X, y)
return self
def predict(self, X):
y = self.clf.predict(X)
y = (2 * (y > self.threshold)) - 1
y[y == -1] = 0
return y
def get_params(self, deep=True):
return self.clf.get_params(deep=deep)
def RegressionModel(model, params, features, target, scoring, kFold):
if model == "linear regression":
model = LinearRegression(fit_intercept=params['fit_intercept'], \
normalize=params['normalize'], copy_X=params['copy_X'])
print('************************************************')
print(model.get_params())
print('************************************************')
else:
print('Sorry, we are still developing other regression methods.')
if kFold == 0:
x_train,x_test,y_train,y_test = train_test_split(features,target, random_state = 1)
model.fit(x_train,y_train)
model_train_pred = model.predict(x_train)
model_test_pred = model.predict(x_test)
results = str()
if "neg_mean_absolute_error" in scoring:
results = 'MAE train data: %.3f, MAE test data: %.3f' % (
mean_absolute_error(y_train,model_train_pred),
mean_absolute_error(y_test,model_test_pred))
if "neg_mean_squared_error" in scoring:
results = results + '\n' + 'MSE train data: %.3f, MSE test data: %.3f' % (
mean_squared_error(y_train,model_train_pred),
mean_squared_error(y_test,model_test_pred))
if "neg_mean_squared_log_error" in scoring:
results = results + '\n' + 'MSLE train data: %.3f, MSLE test data: %.3f' % (
mean_squared_log_error(y_train,model_train_pred),
mean_squared_log_error(y_test,model_test_pred))
if "r2" in scoring:
results = results + '\n' +'R2 train data: %.3f, R2 test data: %.3f' % (
r2_score(y_train,model_train_pred),
r2_score(y_test,model_test_pred))
return results
elif kFold > 2:
results = cross_validate(model, features, target, scoring=scoring, cv=kFold,error_score=np.nan)
return results
else:
print("K-Fold has to be an integer (>=3) or 0 (No cross validation)")
def simple_linear(X_train, y_train, X_test, y_test):
linear = LinearRegression()
linear.fit(X_train, y_train)
y_pred = linear.predict(X_test)
print('\nLinear Regression Summary')
print('R2:', linear.score(X_test, y_test))
print('Intercept:', linear.intercept_, '\nCoefficients:', linear.coef_)
print('Parameters:', linear.get_params())
'''Predict how well model will perfom on test data'''
# http://scikit-learn.org/stable/modules/model_evaluation.html
# http://scikit-learn.org/stable/modules/classes.html#module-sklearn.metrics
# scoring='wrong' to see valid scoring options
# Common regression metrics: R2, RMSE, quantiles/MAPE, precision accuracy
score = cross_val_score(estimator=linear,
X=X_train,
y=y_train,
fit_params=None,
scoring='r2',
cv=5,
n_jobs=-1)
print('Mean Cross Validation Score:', score.mean())
class LinRegClassifierSKLearn(BaseEstimator, ClassifierMixin, TransformerMixin):
"""
This class implements Linear Regression classifer.
Specifically, this class uses Linear Regression matcher from
scikit-learn, wraps it up to form a classifier.
"""
def __init__(self, *args, **kwargs):
# Set the classifier to the scikit-learn Linear Regression matcher.
self.clf = LinearRegression(*args, **kwargs)
# Set the threshold to 0
self.threshold = 0.0
def fit(self, X, y):
# Convert 0 and 1s to -1, and 1s
y = (2 * y) - 1
# Call the fit method of Linear Regression matcher
self.clf.fit(X, y)
# Return the wrapper object
return self
def predict(self, X):
# Call the predict method from the underlying matcher
y = self.clf.predict(X)
# Convert back the predictions a number between -1 and 1 to -1 and -1
y = (2 * (y > self.threshold)) - 1
# Convert all the -1 to 0s
y[y == -1] = 0
# Return back the predictions
return y
def get_params(self, deep=True):
"""
Function to get params. This will be used by other scikit-learn
matchers.
"""
return self.clf.get_params(deep=deep)
def linear_regression(df, significant_cols, target, cat_cols, num_cols):
ss = StandardScaler()
ohe = OneHotEncoder(drop='first', sparse=False)
X = df[significant_cols]
y = df[target]
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=0)
X_train_cat = ohe.fit_transform(X_train[cat_cols])
X_train_num = ss.fit_transform(X_train[num_cols])
X_test_cat = ohe.transform(X_test[cat_cols])
X_test_num = ss.transform(X_test[num_cols])
train_data = np.c_[X_train_cat, X_train_num]
test_data = np.c_[X_test_cat, X_test_num]
estimator = LinearRegression(n_jobs=-1)
r2_cv_scores = cross_val_score(estimator,
train_data,
y_train,
scoring='r2',
cv=3,
n_jobs=-1)
rmse_cv_scores = cross_val_score(estimator,
train_data,
y_train,
scoring='neg_root_mean_squared_error',
cv=3,
n_jobs=-1)
r2 = np.mean(r2_cv_scores)
rmse = np.abs(np.mean(rmse_cv_scores))
r2_variance = np.var(r2_cv_scores, ddof=1)
rmse_variance = np.abs(np.var(rmse_cv_scores, ddof=1))
estimator.fit(train_data, y_train)
y_pred = estimator.predict(test_data)
r2_validation = r2_score(y_test, y_pred)
rmse_validation = np.sqrt(mean_squared_error(y_test, y_pred))
params = estimator.get_params()
return r2, rmse, r2_variance, rmse_variance, r2_validation, rmse_validation, params
def sklearn_dateset_test():
# 测试sklearn的数据库
# 导入数据
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
# 定义模型（默认参数），后续可通过预测的准确度，进行调整，尝试不同的model和参数
model = LinearRegression()
# 训练模型
model.fit(data_X, data_y)
# 打印X的前4个预测值
print("predict target:")
print(model.predict(data_X[:4, :]))
print("actual data:")
print(data_y[:4])
# LinearRegressor model的参数：斜率和截距
print("model.coef_:\n", model.coef_)
print("model.intercept_:\n", model.intercept_)
# 取出之前定义的参数
print("model.get_params:\n", model.get_params())
# model.score(data_X, data_y)可以对Model以R^2的方式进行打分，输出精确度
# R2越大（接近于1），所拟合的回归方程越优
print("model.score:\n", model.score(data_X, data_y))
# 创建虚拟数据，100个样本，1个特征，1个目标，noise越大，点越离散
X1, y1 = datasets.make_regression(n_samples=100,
n_features=1,
n_targets=1,
noise=10)
X2, y2 = datasets.make_regression(n_samples=100,
n_features=1,
n_targets=1,
noise=50)
plt.scatter(X1, y1)
plt.scatter(X2, y2)
plt.show()
return 0
def linear_regression():
"""
"""
loaded_data = datasets.load_boston()
data_x = loaded_data.data
data_y = loaded_data.target
model = LinearRegression()
model.fit(data_x, data_y)
# 斜率
print(model.coef_)
# 截距
print(model.intercept_)
print(model.get_params())
print(model.predict(data_x[:4, :]))
print(data_x[:4, :])
x, y = datasets.make_regression(n_samples=100,
n_features=1,
n_targets=1,
noise=1)
plt.scatter(x, y)
plt.show()
class LinearRegressionModel(object):
def __init__(self):
self.name = 'Linear Regression'
self.clf = LinearRegression()
def get_params(self):
return self.clf.get_params()
def train(self, dataframe):
X = get_features(dataframe)
y = get_response(dataframe)
self.clf.fit(X, y)
def predict(self, X):
y_pred = self.clf.predict(X)
return y_pred
def save(self, filename):
with open(filename, 'wb') as output_file:
pickle.dump(self.clf, output_file, pickle.HIGHEST_PROTOCOL)
def load(self, filename):
with open(filename, 'rb') as input_file:
self.clf = pickle.load(input_file)
def train_model(xy):
# linear
x = xy[:, 0].reshape(-1, 1)
y = xy[:, 1]
model = LinearRegression()
model.fit(x, y)
pred_y = model.predict(x)
coef = model.coef_
intercept = model.intercept_
params = model.get_params()
print(params)
linear_r2 = sm.r2_score(y, pred_y) # r2得分
linear_absolute = sm.mean_absolute_error(y, pred_y) # 平均绝对值误差
linear_squared = sm.mean_squared_error(y, pred_y) # 均方误差
linear_median = sm.median_absolute_error(y, pred_y) # 中值绝对误差
drawing(xy, x, pred_y)
return {
'linear_score': {
'linear_r2': round(linear_r2, 5),
'linear_absolute': round(linear_absolute, 5),
'linear_squared': round(linear_squared, 5),
'linear_median': round(linear_median, 5)
}
}
train_x, train_y = data[:, 2:], data[:, 0]
return train_x, train_y, data
# '''
# X, y = make_regression(random_state=0)
# X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
train_x, train_y, data = generator_data(data)
reg = LinearRegression()
print(reg)
# for i in range(10):
reg.fit(train_x, train_y)
print('the whole parameter of the model : ', reg.get_params())
# GradientBoostingRegressor(random_state=0)
pre = reg.predict(train_x)
# print('Predict regression target for x :', pre)
# print(pre.shape)
r = reg.score(train_x, train_y)
print('Return the coefficient of determination R2 of the prediction : ', r)
re_index(observed_v=train_y, predicted_v=pre)
print(keys[7:])
print(reg.coef_)
feature_importance = reg.coef_
# feature_importance=(feature_importance/feature_importance.max())
print features.shape
values = np.empty(features.shape[0], float)
for i, res in enumerate(result.Result.all()):
if i % 100 == 0: print i,
if i >= num_results: break
for j, param in enumerate(param_features):
features[i, j] = getattr(res.spec, param)
#values[i] = analysis.horizontal_surface_area.compute_by_result(res, flush=False)
values[i] = analysis.horizontal_surface_area.func(res)
print
regression = LinearRegression()
regression.fit(features, values, n_jobs=-1)
print 'Features', param_features
print 'Coeff', regression.coef_
print 'Intercept', regression.intercept_
print 'Params', regression.get_params()
print 'R2', regression.score(features, values)
elif arg == 'dump':
from biofilm import util
path = util.results_h5_path(sys.argv[2])
util.set_h5(path)
import numpy as np
from biofilm.model import spec, result, analysis
param_features = []
for param, r in param_ranges.iteritems():
if isinstance(r, tuple):
param_features.append(param)
with open(path + '.dump', 'w') as dump:
regressor = LinearRegression()
regressor.fit(X_poly, yTrain)
print('dvj')
# Calculate errors
XTest_poly = poly_reg.fit_transform(XTest)
yTestPredict = regressor.predict(XTest_poly)
mse = mean_squared_error(yTest, yTestPredict, squared=True)
rmse = mean_squared_error(yTest, yTestPredict, squared=False)
mae = mean_absolute_error(yTest, yTestPredict)
mape = mean_absolute_percentage_error(yTest, yTestPredict)
print("The mean squared error (MSE) on test set: {:.4f}".format(mse))
print("The root Mean Square Error (RMSE) on test set: {:.4f}".format(rmse))
print("The mean absolute error on test set: {:.4f}".format(mae))
print("The mean absolute percentage error on test set: {:.4f}".format(mape))
print(regressor.get_params(deep=True))
# prediction part
Order_API_Concurrency = 5
Carts_API_Concurrency = 5
Order_Cores = 0.2
Order_DB_Cores = 0.2
Carts_Cores = 0.2
Carts_DB_Cores = 0.2
new_X = [
Order_API_Concurrency, Carts_API_Concurrency, Order_Cores, Order_DB_Cores,
Carts_Cores, Carts_DB_Cores
]
print()
print('X value ', new_X)
from sklearn.linear_model import LinearRegression
# 通用的学习模式
loaded_data = datasets.load_boston() # 加载房价的数据库
data_X = loaded_data.data
data_y = loaded_data.target
model = LinearRegression() # 调用线性回归模式
model.fit(data_X, data_y) # 训练
print(model.predict(data_X[:4, :])) # 测试
print(data_y[:4])
print(model.coef_) # 斜率,即输入特征的各比重
print(model.intercept_) # 截距
print(model.get_params()) # 返回model定义时的参数
# {'copy_X': True, 'fit_intercept': True, 'n_jobs': 1, 'normalize': False}
print(model.score(data_X, data_y)) # 将数据及结果传入,给线性模型打分,准确度
import matplotlib.pyplot as plt
# 生成数据集X,对应的线性结果集y
X, y = datasets.make_regression(n_samples=100,
n_features=1,
n_targets=1,
noise=10)
print(X[:5, :])
plt.scatter(X, y)
plt.show()
from sklearn import preprocessing
#!/usr/bin/python3 # coding: utf-8 from sklearn import datasets from sklearn.linear_model import LinearRegression ################################################################## ## 加载数据 loaded_data = datasets.load_boston(); print(loaded_data) data_X = loaded_data.data data_y = loaded_data.target ################################################################## ## 加载 Model model = LinearRegression() model.fit(data_X, data_y) ################################################################## ## Model attribute && method # 下面的要在 Model fit() 结束以后执行 print(model.predict(data_X[:4, :])) print(model.coef_) # 系数, 很多; y = 0.1x + 0.3 中的 0.1 print(model.intercept_) # 常数; 和 y 轴的交点 print(model.get_params()) # LinearRegression() 定义的参数 print(model.score(data_X, data_y)) # R^2 coefficient of determination; 使用参数进行打分
# %% [markdown]
# # Load Data:
# %%
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.8,
random_state=0)
# %% [markdown]
# # Linear Regression:
# %%
model_linreg = LinearRegression()
print_dict(model_linreg.get_params(), 'LinearRegressor params:')
model_linreg.fit(X_train, y_train)
y_predict_linreg = model_linreg.predict(X_test)
y_predict_linreg = np.round(y_predict_linreg).astype(
int) # Regressor -> classifier!
error_rate_linreg = test(y_predict_linreg, y_test)
print(f'Linear Regressor score: {model_linreg.score(X_test, y_test):.3g}')
# %% [markdown]
# # Naive Bayesian:
# %%
model_bayes = CategoricalNB()
print_dict(model_bayes.get_params(), 'CategoricalNB params:')
model_bayes.fit(X_train, y_train)
y_predict_bayes = model_bayes.predict(X_test)
error_rate_bayes = test(y_predict_bayes, y_test)
Y_Target = df.iloc[:, -1]
padronizacao = StandardScaler().fit(X_Data)
X_p = padronizacao.transform(X_Data)
d_Test = pd.read_csv('dados/test.csv')
colunas = ('NU_NOTA_CN', 'NU_NOTA_CH', 'NU_NOTA_LC', 'NU_NOTA_REDACAO')
#colunas = ('NU_NOTA_CN','NU_NOTA_LC')
df_test = d_Test.loc[:, colunas]
df_test.update(df_test.fillna(-236))
X_Data_Test = df_test
X_r = padronizacao.transform(X_Data_Test)
ols = LinearRegression()
print(ols.get_params().keys())
ols_params = {'fit_intercept': [True, False], 'normalize': [True, False]}
X_train, X_test, Y_train, Y_test = train_test_split(X_p,
Y_Target,
test_size=0.25,
random_state=5)
ols.fit(X_p, Y_Target)
pred_train = ols.predict(X_train)
pred_test = ols.predict(X_test)
final = ols.predict(X_r)
print(ols.score(X_test, Y_test))
lista = final
x = 0
#########################
# Create some simple data
# import pandas as pd
# df = pd.read_csv('09-regression-test.csv')
import numpy as np
X = [[50, 80], [80, 65], [60, 60], [95, 80], [95, 50], [40, 90]] # Features
y = [65, 83, 69, 92, 84, 55]
# Fit a linear regression to it
from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=True)
model.fit(X, y)
# Report Results
print 'intercept_ :', model.intercept_
print 'coef_ :', model.coef_
print 'get_params :', model.get_params()
# Model the prediction
predict_data = [[52, 81], [81, 66], [60, 62], [0, 8]]
y_hat = model.predict(predict_data)
# # Plot the data
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(X, y, 'o')
ax.plot(predict_data, y_hat)
plt.show()
lm_multi2.coef_
lm_multi1.score(X1,y)
lm_multi2.score(X2,y)
sns.regplot(x = 'highway-mpg', y = 'price', data = df)
sns.regplot(x = 'peak-rpm', y = 'price', data = df)
df[['highway-mpg','peak-rpm','price']].corr()
sns.residplot(x=df['highway-mpg'],y=df['price'],lowess=True)
lm.fit(X1,y)
dir(lm)
lm._decision_function(X1)
lm.get_params(True)
lm._get_tags()
y_hat = lm.predict(X1)
def PlotPolly(model, independent_variable, dependent_variabble, Name):
x_new = np.linspace(15, 55, 100)
y_new = model(x_new)
plt.plot(independent_variable, dependent_variabble, '.', x_new, y_new, '-')
plt.title('Polynomial Fit with Matplotlib for Price ~ Length')
ax = plt.gca()
ax.set_facecolor((0.898, 0.898, 0.898))
fig = plt.gcf()
plt.xlabel(Name)
plt.ylabel('Price of Cars')
#--------------------------------------------
# 交差検証：テスト実施
#--------------------------------------------
z = 0 # 訓練で一番良かったものをセット
y_pred = base_model[z].predict(X_test)
mse = mean_squared_error(y_test, y_pred)
mae = mean_absolute_error(y_test, y_pred)
print(
"**** Test set score( {} ): MSE={:.3f} RMSE={:.3f} MAE={:.3f} Score={:.3f} ****"
.format(z, round(mse, 3), round(np.sqrt(mse), 3), round(mae, 3),
regr.score(X_test, y_test)))
print('Parameters currently in use:')
from pprint import pprint
pprint(regr.get_params())
# 過学習気味？なのでチューニングしてみる
# # 5. RandomForest（ランダムフォレスト）
#
# * n_estimators =フォレスト内の樹木の数
# * max_features =ノードの分割に考慮されるフィーチャの最大数
# * max_depth =各決定木のレベルの最大数
# * min_samples_split =ノードが分割される前にノードに配置されたデータポイントの最小数
# * min_samples_leaf =リーフノードで許容されるデータポイントの最小数
# * bootstrap =データポイントをサンプリングする方法（置換の有無にかかわらず）
# In[26]:
# データをリセット
def scikit_tutorial():
"""
scikit-learn入门
:return:
"""
# 1.准备数据集
X = np.random.randint(0, 100, (10, 4))
y = np.random.randint(0, 3, 10)
y.sort()
print('样本：')
print(X)
print('标签：', y)
# 分割训练集、测试集
# random_state确保每次随机分割得到相同的结果 random_state：是随机数的种子
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=1 / 3.,
random_state=7)
print('训练集：')
print(X_train)
print(y_train)
print('测试集：')
print(X_test)
print(y_test)
# 特征归一化
x1 = np.random.randint(0, 1000, 5).reshape(5, 1)
x2 = np.random.randint(0, 10, 5).reshape(5, 1)
x3 = np.random.randint(0, 100000, 5).reshape(5, 1)
print(x1)
print(np.random.randint(0, 1000, (5, 1)))
X = np.concatenate([x1, x2, x3], axis=1)
print(X)
print(preprocessing.scale(X))
# 生成分类数据进行验证scale的必要性
X, y = make_classification(n_samples=300,
n_features=2,
n_redundant=0,
n_informative=2,
random_state=25,
n_clusters_per_class=1,
scale=100)
plt.scatter(X[:, 0], X[:, 1], c=y)
plt.show()
# 注释掉以下这句表示不进行特征归一化
# X = preprocessing.scale(X)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=1 / 3.,
random_state=7)
svm_classifier = svm.SVC()
svm_classifier.fit(X_train, y_train)
svm_classifier.score(X_test, y_test)
# 2.训练模型
# 回归模型
boston_data = datasets.load_boston()
X = boston_data.data
y = boston_data.target
print('样本：')
print(X[:5, :])
print('标签：')
print(y[:5])
# 选择线性回顾模型
lr_model = LinearRegression()
# 分割训练集、测试集
X_train, y_train, X_test, y_test = train_test_split(X,
y,
test_size=1 / 3,
random_state=7)
# 训练模型
lr_model.fit(X_train, y_train)
# 返回参数
lr_model.get_params()
lr_model.score(X_train, y_train)
lr_model.score(X_test, y_test)
# 3.交叉验证
# K最近邻分类
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=1 / 3.,
random_state=10)
k_range = range(1, 31)
cv_scores = []
for n in k_range:
knn = KNeighborsClassifier(n)
scores = cross_val_score(knn,
X_train,
y_train,
cv=10,
scoring='accuracy') # 分类问题使用
# scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='neg_mean_squared_error') # 回归问题使用
cv_scores.append(scores.mean())
plt.plot(k_range, cv_scores)
plt.xlabel('K')
plt.ylabel('Accuracy')
plt.show()
# 选择最优的K
best_knn = KNeighborsClassifier(n_neighbors=5)
best_knn.fit(X_train, y_train)
print(best_knn.score(X_test, y_test))
print(best_knn.predict(X_test))
def linear_model(x, attr, xvars, fit_intercept=None, name=None,
cut=None, residuals=True, quiet=True,
model='LinearRegression'):
"""Make a linear model for attr based on xvars as free parameters.
Currently only model='LinearRegression' implmented.
Uses scikit-learn.
residuals: Name of attribute for residuals (default: attr+"_residuals")
"""
if model is not 'LinearRegression':
raise Exception("Currently only model='LinearRegression' implmented.")
from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
import xarray as xr
lm = LinearRegression()
if not name:
name = '{:}_model'.format(attr)
if not quiet:
print '\nUsing scikit-learn LinearRegression to build model for {:} from variables:\n {:}'.format(attr, str(xvars))
allattrs = xvars + [attr]
if cut:
allattrs += [cut]
xx = x.reset_coords()[allattrs].where(np.isfinite(x.reset_coords()[attr]), drop=True)
df_xvars0 = xx[xvars].to_dataframe()
if cut:
df_xvars = xx[xvars].where(xx[cut] == 1, drop=True).to_dataframe()
xdata = xx[attr].where(xx[cut] == 1, drop=True).data
if not quiet:
print '\nUsing following cut in buildiing model'
print xx[cut]
else:
df_xvars = df_xvars0
xdata = xx[attr].data
if fit_intercept is not None:
lm.fit_intercept = fit_intercept
lm.fit(df_xvars, xdata)
#ft = pd.DataFrame(zip(df_xvars.columns,lm.coef_), columns=['params','estimatedCoefficients'])
x[name] = xr.DataArray(lm.predict(df_xvars0), coords=[('time', df_xvars0.index)])
#x[name] = (['time'], lm.predict(df_xvars0))
x[name].attrs.update(**lm.get_params())
x[name].attrs['unit'] = x[attr].attrs.get('unit','')
x[name].attrs['doc'] = 'LinearRegression scikit-learn model for {:} training data'.format(attr)
x[name].attrs['model'] = model
x[name].attrs['variables'] = xvars
x[name].attrs['coef_'] = lm.coef_
x[name].attrs['intercept_'] = lm.intercept_
x[name].attrs['score'] = lm.score(df_xvars, xdata)
if not quiet:
print '\n****Model Results****'
print x.reset_coords()[name]
if residuals:
if not isinstance(residuals, str):
residuals = '{:}_residuals'.format(attr)
x[residuals] = (['time'], x[attr]-x[name])
x[residuals].attrs['doc'] = 'Residuals for {:} based on LinearRegression model {:}'.format(attr, name)
if not quiet:
print '\n****Model Residuals****'
print x.reset_coords()[residuals]
return x
from sklearn import datasets
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
#使用以后的数据集进行线性回归
loaded_data = datasets.load_boston()
data_X = loaded_data.data
data_y = loaded_data.target
model = LinearRegression()
model.fit(data_X, data_y)
print(model.predict(data_X[:4, :]))
print(data_y[:4])
#参数
print(model.coef_) #如果y=0.1x+0.3 则此行输出的结果为0.1
print(model.intercept_) #此行输出的结果为0.3
print(model.get_params()) #模型定义时定义的参数，如果没有定义则返回默认值
print(model.score(
data_X,
data_y)) #给训练模型打分，注意用在LinearR中使用R^2 conefficient of determination打分
time_series['trend'] = range(time_series.shape[0])
time_series['month'] = time_series['month'].astype('category')
####dropping columns
X = time_series.drop(['week', 'year', 'date', 'total_sales'], axis=1)
names = pd.get_dummies(X).columns
X = pd.get_dummies(X).values
y = time_series.total_sales.values
model = LinearRegression()
model.fit(X, y)
model.get_params()
model.coef_
dict1 = list(zip(names, model.coef_))
prediction = model.predict(X)
time_series['prediction'] = prediction
import matplotlib.pyplot as plt
plt.plot(time_series.date, time_series.total_sales, label='Actual')
plt.plot(time_series.date, time_series.prediction, label='prediction')
plt.legend(loc='upperleft')
plt.show()
#####forecasting
from sklearn import datasets from sklearn.linear_model import LinearRegression import matplotlib.pyplot as plt loaded_data = datasets.load_boston() data_X = loaded_data.data data_Y = loaded_data.target model = LinearRegression() model.fit(data_X, data_Y) print(model.predict(data_X[:4, :])) print(data_Y[:4]) print(model.coef_) # y = 0.1x + 0.3 print(model.intercept_) print(model.get_params()) # R^2 Coefficient of Determnination, How similar between data and target print(model.score(data_X, data_Y)) # X, Y = datasets.make_regression( # n_samples=100, n_features=1, n_targets=1, noise=0.001) # plt.scatter(X, Y) # plt.show()
from sklearn import datasets from sklearn.linear_model import LinearRegression loaded_data = datasets.load_boston() data_X = loaded_data.data data_y = loaded_data.target model = LinearRegression() model.fit(data_X, data_y) print(model.coef_) # y=ax + b print out a print(model.intercept_) #print out b print(model.get_params()) print(model.score(data_X, data_y)) #R^2 coefficient of determination
class LinRegClassifierSKLearn(BaseEstimator, ClassifierMixin, TransformerMixin):
"""
This class implements Linear Regression classifer.
Specifically, this class uses Linear Regression matcher from
scikit-learn, wraps it up to form a classifier.
"""
def __init__(self, *args, **kwargs):
# Set the classifier to the scikit-learn Linear Regression matcher.
self.clf = LinearRegression(*args, **kwargs)
# Set the threshold to 0
self.threshold = 0.0
# Set the classes_
self.classes_ = np.array([0, 1], np.int64)
def fit(self, X, y):
# Convert 0 and 1s to -1, and 1s
y = (2 * y) - 1
# Call the fit method of Linear Regression matcher
self.clf.fit(X, y)
# Return the wrapper object
return self
def predict(self, X):
# Call the predict method from the underlying matcher
y = self.clf.predict(X)
# Convert back the predictions a number between -1 and 1 to -1 and -1
y = (2 * (y > self.threshold)) - 1
# Convert all the -1 to 0s
y[y == -1] = 0
# Return back the predictions
return y
def predict_proba(self, X):
# There is no proba function defined for Linear Regression Matcher in scikit
# learn. So we return the probs as 0 or 1
# give the warning to the user
logger.warning('There is no proba function defined for Linear Regression '
'Matcher in scikit learn. So we return the probs as 1')
y = self.predict(X)
p = np.ndarray(shape=[len(y), 2])
for i in range(len(y)):
if y[i] == 1:
p[i][0] = 0
p[i][1] = 1
elif y[i] == 0:
p[i][0] = 1
p[i][1] = 0
return p
def get_params(self, deep=True):
"""
Function to get params. This will be used by other scikit-learn
matchers.
"""
return self.clf.get_params(deep=deep)
