def svm_cross_validate_category(X, y, category, C, penalty, sample_weights):
clf_svm_1 = SGDRegressor(loss=loss,
penalty=penalty,
epsilon=epsilon,
alpha=C,
shuffle=True)
clf_svm_2 = SGDRegressor(loss=loss,
penalty=penalty,
epsilon=epsilon,
alpha=C,
shuffle=True)
cv_indices = generate_cv_indices(category)
train_ids = cv_indices[0:N]
test_ids = cv_indices[N:2 * N]
clf_svm_1.fit(X[train_ids, :],
y[train_ids],
sample_weight=sample_weights[train_ids])
clf_svm_2.fit(X[test_ids, :],
y[test_ids],
sample_weight=sample_weights[test_ids])
score = np.zeros(2)
score[0] = clf_svm_1.score(X[test_ids, :], y[test_ids])
score[1] = clf_svm_2.score(X[train_ids, :], y[train_ids])
mean_score = np.mean(score)
y_1 = clf_svm_1.decision_function(X[test_ids, :])
y_2 = clf_svm_2.decision_function(X[train_ids, :])
u, indices = np.unique(category, return_inverse=True)
auc = np.zeros((2, len(u)))
for i in range(0, len(u)):
i_inds = indices == i
if (np.sum(test_ids & i_inds) != 0):
fpr, tpr, thresholds = metrics.roc_curve(y[test_ids & i_inds],
y_1[i_inds[test_ids]],
pos_label=1)
auc[0, i] = metrics.auc(fpr, tpr)
if (np.sum(train_ids & i_inds) != 0):
fpr, tpr, thresholds = metrics.roc_curve(y[train_ids & i_inds],
y_2[i_inds[train_ids]],
pos_label=1)
auc[1, i] = metrics.auc(fpr, tpr)
mean_auc = np.mean(auc, axis=0)
print("Finished running category cross-validation")
return mean_auc
def svm_cross_validate(X, y, category, C, penalty, sample_weights):
clf_svm_1 = SGDRegressor(loss=loss,
penalty=penalty,
epsilon=epsilon,
alpha=C,
shuffle=True)
clf_svm_2 = SGDRegressor(loss=loss,
penalty=penalty,
epsilon=epsilon,
alpha=C,
shuffle=True)
#N = len(category)
#half_data= np.floor(N/2)
#cv_indices_1= np.repeat([False],N)
#cv_indices_2= np.repeat([False],N)
#cv_indices_1[0:half_data] =True
#cv_indices_2[half_data:N] =True
#cv_indices= np.concatenate((cv_indices_1,cv_indices_2),axis=1)
cv_indices = generate_cv_indices_unbalanced(category)
train_ids = cv_indices[0:N]
test_ids = cv_indices[N:2 * N]
clf_svm_1.fit(X[train_ids, :],
y[train_ids],
sample_weight=sample_weights[train_ids])
clf_svm_2.fit(X[test_ids, :],
y[test_ids],
sample_weight=sample_weights[test_ids])
score = np.zeros(2)
score[0] = clf_svm_1.score(X[test_ids, :], y[test_ids])
score[1] = clf_svm_2.score(X[train_ids, :], y[train_ids])
mean_score = np.mean(score)
y_1 = clf_svm_1.decision_function(X[test_ids, :])
y_2 = clf_svm_2.decision_function(X[train_ids, :])
auc = np.zeros(2)
fpr, tpr, thresholds = metrics.roc_curve(y[test_ids], y_1, pos_label=1)
auc[0] = metrics.auc(fpr, tpr)
fpr, tpr, thresholds = metrics.roc_curve(y[train_ids], y_2, pos_label=1)
auc[1] = metrics.auc(fpr, tpr)
mean_auc = np.mean(auc, axis=0)
print("Finished running standard cross validation")
return mean_auc
class RBFSamplerSGDRegressorEstimator(BaseEstimator, TransformerMixin):
def __init__(self,
gamma=1.0,
n_components=100,
random_state=None,
**kwargs):
kwargs['random_state'] = random_state
self.rbf_sampler = RBFSampler(gamma=gamma,
n_components=n_components,
random_state=random_state)
self.sgdregressor = SGDRegressor(**kwargs)
def fit(self, X, y):
X = self.rbf_sampler.fit_transform(X)
self.sgdregressor.fit(X, y)
return self
def transform(self, X, y=None):
return np.sqrt(self.rbf_sampler.n_components) / np.sqrt(
2.) * self.rbf_sampler.transform(X)
def predict(self, X):
return self.sgdregressor.predict(self.transform(X))
def score(self, X, y):
return self.sgdregressor.score(self.transform(X), y)
def linear_model2():
"""
梯度下降法
:return: None
"""
# 1.获取数据
boston = load_boston()
# 2.数据基本处理
# 2.1 数据集划分
x_train, x_test, y_train, y_test = train_test_split(boston.data,
boston.target,
test_size=0.2)
# 3.特征工程 --标准化
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.fit_transform(x_test)
# 4.机器学习(线性回归)
estimator = SGDRegressor(max_iter=1000,
learning_rate="constant",
eta0=0.001)
estimator.fit(x_train, y_train)
print("这个模型的偏置是:\n", estimator.intercept_)
# 5.模型评估
# 5.1 预测值和准确率
y_pre = estimator.predict(x_test)
print("预测值是:\n", y_pre)
score = estimator.score(x_test, y_test)
print("准确率是:\n", score)
# 5.2 均方误差
ret = mean_squared_error(y_test, y_pre)
print("均方误差是:\n", ret)
class support_vector_machine:
_model = None
def __init__(self):
self._model = SGDRegressor()
def train(self, data_x, data_y):
self._model.fit(data_x, data_y)
joblib.dump(self._model, 'svm_model.pickle')
def predict(self, X):
ret = self._model.predict(X)
return ret
def score(self, X, y):
score = self._model.score(X, y)
return score
def load_model(self, path):
path = os.path.join(os.path.dirname(
os.path.abspath(__file__)), path)
print(path)
self._model = joblib.load(path)
return self._model
def get_model(self):
return self._model
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 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 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 test_call_fit_with_arguments_score_does_not_accept():
mlflow.sklearn.autolog()
from sklearn.linear_model import SGDRegressor
assert "intercept_init" in _get_arg_names(SGDRegressor.fit)
assert "intercept_init" not 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()
with mlflow.start_run() as run:
model.fit(X, y, intercept_init=0)
mock_obj.assert_called_once_with(X, y, None)
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)
def SGD(x,
y,
test_x,
test_y,
loss="squared_loss",
penalty="l1",
alpha=0.0001,
tol=0.001,
random_state=1,
eta0=0.01,
learning_rate='optimal',
power_t=0.25,
max_iter=1000):
sr = SGDRegressor(loss=loss,
penalty=penalty,
alpha=alpha,
tol=tol,
random_state=random_state,
eta0=eta0,
learning_rate=learning_rate,
power_t=power_t,
max_iter=max_iter)
sr.partial_fit(x, y)
y_pred_undersample = sr.predict(test_x)
y_pred_undersample[(y_pred_undersample > 0.5)] = 1
y_pred_undersample[(y_pred_undersample <= 0.5)] = 0
i = sr.n_iter_
Score = sr.score(test_x, test_y)
F1 = f1_score(test_y, y_pred_undersample)
P = precision_score(test_y, y_pred_undersample)
R = recall_score(test_y, y_pred_undersample)
tn, fp, fn, tp = confusion_matrix(test_y, y_pred_undersample).ravel()
return Score, F1, P, R, tn, fp, fn, tp, i
def SGDTrain():
model = SGDRegressor()
# model.fit(x_train_standard, y_train)
# print(model.coef_)
# print(model.intercept_)
data_generator = get_batch(x_train_standard, y_train)
sgd_curve_x = []
sgd_curve_y = []
for i in range(epochs): # Train for 100 epochs
x, y = next(data_generator)
# print(x)
# print(y)
model.partial_fit(x, y)
# print(model.score(x_test_standard, y_test))
# print(model.coef_)
sgd_curve_x.append(i)
sgd_curve_y.append(model.score(x_test_standard, y_test))
predicted = model.predict(x_test_standard)
plt.title('SGD result (4000 epochs)')
plt.scatter(y_test, predicted, color='y', marker='o')
plt.plot(y_test, y_test, color='g')
plt.xlabel('True value')
plt.ylabel('Predicted value')
# plt.savefig('./4000_1.png')
plt.show()
print('SGD RMSE为：', np.sqrt(mean_squared_error(y_test, predicted)))
return sgd_curve_x, sgd_curve_y
def mylinear():
'''
线性回归预测房价
:return:
'''
#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
#特征值和目标值是都必须进行标准化处理，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)
std_y = StandardScaler()
y_train = std_y.fit_transform(y_train.reshape(-1, 1))
y_test = std_y.transform(y_test.reshape(-1, 1))
#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_lr_preidct = std_y.inverse_transform(lr.predict(x_test))
print("正规方程测试集里面每个房子的价格：", y_lr_preidct)
print("分数：", lr.score(x_test, y_test))
print("正规方程的均方误差：",
mean_squared_error(std_y.inverse_transform(y_test), y_lr_preidct))
# 梯度下降求解方式预测结果
sgd = SGDRegressor()
sgd.fit(x_train, y_train)
print(sgd.coef_)
# 预测测试集的房子价格
y_sgd_preidct = std_y.inverse_transform(sgd.predict(x_test))
print("梯度下降测试集里面每个房子的价格：", y_sgd_preidct)
print("分数：", sgd.score(x_test, y_test))
print("梯度下降的均方误差：",
mean_squared_error(std_y.inverse_transform(y_test), y_sgd_preidct))
# 岭回归求解方式预测结果
rd = Ridge()
rd.fit(x_train, y_train)
print(rd.coef_)
# 预测测试集的房子价格
y_rd_preidct = std_y.inverse_transform(rd.predict(x_test))
print("梯度下降测试集里面每个房子的价格：", y_rd_preidct)
print("分数：", rd.score(x_test, y_test))
print("梯度下降的均方误差：",
mean_squared_error(std_y.inverse_transform(y_test), y_rd_preidct))
def linear():
# 获取数据，分割数据
lb = load_boston()
x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25, random_state=24)
# 标准化处理，对特征值和目标值都进行处理
std_x = StandardScaler()
x_train = std_x.fit_transform(x_train)
x_test = std_x.transform(x_test)
# 目标值先转为二位数组，并且转置，然后标准化
y_train = np.array([y_train])
y_test = np.array([y_test])
std_y = StandardScaler()
# 每一行的元素需要个数一致
y_train = std_y.fit_transform(y_train.T)
y_test = std_y.transform(y_test.T)
print('--------正规方程--------')
# 应用线性回归分析-正规方程
lr = LinearRegression()
lr.fit(x_train, y_train)
# 标准化之前的数据大小!!!!!!
# lr_predict = std_y.inverse_transform(lr.predict(x_test))
# print("预测结果：", lr.predict(x_test))
# print("真实结果：", y_test)
# print("参数/系数：", lr.coef_)
print("测试集准确率：", lr.score(x_test, y_test))
# 如果目标值集合不标准化使用下面这一句
# print("正规方程的均方误差：", mean_squared_error(y_test, lr.predict(x_test)))
# 目标值集合标准化需要把标准化之前的真实数据算出来
# 标准化之前的数据大小!!!!!!
lr_predict = std_y.inverse_transform(lr.predict(x_test))
print("正规方程的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), lr_predict))
print('--------SGD梯度下降--------')
# SGD梯度下降
sgd = SGDRegressor()
sgd.fit(x_train, y_train)
print("测试集准确率：", sgd.score(x_test, y_test))
print("参数/系数：", sgd.coef_)
# 均方误差
sgd_predict = std_y.inverse_transform(sgd.predict(x_test))
print("梯度下降的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), sgd_predict))
print('--------岭回归分析--------')
# 岭回归分析
r = Ridge(alpha=3.0)
r.fit(x_train, y_train)
print("测试集准确率：", r.score(x_test, y_test))
print("参数/系数：", r.coef_)
# 均方误差
r_predict = std_y.inverse_transform(r.predict(x_test))
print("岭回归的均方误差：", mean_squared_error(std_y.inverse_transform(y_test), r_predict))
def trainModel(ModelType, X, y):
if ModelType == SGDRegressor:
model = SGDRegressor(loss='epsilon_insensitive', max_iter=100)
else:
model = ModelType()
model.fit(X, y)
accuracy = model.score(X, y)
print("Model training score: {}".format(accuracy))
return model
def run():
iterations = 10001
learning_rate = 0.01
X_train, Y_train = readTrainingData()
X_test = readTestingData()
scaler = StandardScaler()
scaler.fit_transform(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
clf = SGDRegressor(n_iter=100)
#clf = AdaGradRegressor(n_iter=100)
clf.fit(X_train, Y_train)
print clf.score(X_train, Y_train)
predict = clf.predict(X_test)
write_to_file(predict)
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
clf = SGDRegressor(n_iter=100)
clf.fit(features,values)
print(clf.score(features,values))
intercept = clf.intercept_
params = clf.coef_
return intercept, params
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 SGDRegressor_test():
sgdr = SGDRegressor(max_iter=1000)
sgdr.fit(X_train, y_train.ravel())
sgdr_y_predict = sgdr.predict(X_test)
print("快速的随机梯度下降模型评价：{}".format(sgdr.score(X_test, y_test)))
print("使用R-squared评价标准：{}".format(r2_score(y_test, sgdr_y_predict)))
print("使用MAE评价标准：{}".format(
mean_absolute_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(sgdr_y_predict))))
print("使用MSE评价标准：{}".format(
mean_squared_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(sgdr_y_predict))))
def sgdregressor_sk(x_train, y_train, x_test, y_test, epochs):
#Create the SGD regressor with best hyperparameters
regressor = SGDRegressor(eta0=2, power_t=0.3, max_iter=epochs)
#fit the data
regressor.fit(x_train, y_train)
#predict the prices
y_pred = regressor.predict(x_test)
#get the accuarcy by r2
acc = regressor.score(x_test, y_test)
return y_pred, acc
def linear_regression(features, values):
"""
Perform linear regression given a data set with an arbitrary number of features.
"""
model = SGDRegressor(n_iter=100)
model.fit(features,values)
print 'SCORE: ',model.score(features,values)
intercept = model.intercept_
params = model.coef_
return intercept, params
def lineaReg():
boston = loadDataSet()
X = boston.data
y = boston.target.reshape((len(boston.target), 1))
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=33)
print('The max target value is: ', np.max(boston.target))
print('The min target value is: ', np.min(boston.target))
print('The average target value is: ', np.mean(boston.target))
ss_x = StandardScaler()
ss_y = StandardScaler()
X_train = ss_x.fit_transform(X_train)
X_test = ss_x.transform(X_test)
y_train = ss_y.fit_transform(y_train)
y_test = ss_y.transform(y_test)
lr = LinearRegression()
lr.fit(X_train, y_train)
lr_y_predict = lr.predict(X_test)
sgdr = SGDRegressor()
sgdr.fit(X_train, y_train)
sgdr_y_predcit = sgdr.predict(X_test)
# 回归问题的评估方法
print('dafault value of LR: ', lr.score(X_test, y_test))
print('R-squared of LR: ', r2_score(y_test, lr_y_predict))
print(
'Mean squared error of LR: ',
mean_squared_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(lr_y_predict)))
print(
'Mean absoluate of LR: ',
mean_absolute_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(lr_y_predict)))
print('------------------------------------------------------------------')
print('dafault value of SGDR: ', sgdr.score(X_test, y_test))
print('R-squared of SGDR: ', r2_score(y_test, sgdr_y_predcit))
print(
'Mean squared error of SGDR: ',
mean_squared_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(sgdr_y_predcit)))
print(
'Mean absoluate of SGDR: ',
mean_absolute_error(ss_y.inverse_transform(y_test),
ss_y.inverse_transform(sgdr_y_predcit)))
return None
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 sgd_regression(slef, x, y, prediction_set):
''' Perfoms SGD regression by taking x, and y and return the fit model. Attributes need to be called on sgd_regression
coef_, intercept_ ,average_coef_ : array, shape (n_features,) ,average_intercept_, n_iter_ : int'''
regr = SGDRegressor(max_iter=1000, tol=1e-3)
# SGDRegressor(alpha=0.0001, average=False, early_stopping=False,
# epsilon=0.1, eta0=0.01, fit_intercept=True, l1_ratio=0.15,
# learning_rate='invscaling', loss='squared_loss', max_iter=1000,
# n_iter_no_change=5, penalty='l2', power_t=0.25, random_state=None,
# shuffle=True, tol=0.001, validation_fraction=0.1, verbose=0,
# warm_start=False)
model = regr.fit(x, y)
if isinstance(prediction_set, np.ndarray):
y_pred = model.predict(prediction_set)
return [regr.score(x, y), regr.predict(prediction_set)]
else:
return [regr.score(x, y)]
def player_prediction(name):
data = pd.read_csv('../resources/newMERGED.csv',
sep=',',
encoding='utf-8',
index_col=0)
model = data[[
'player_id', 'name', 'season', 'pos', 'round', 'team_rank',
'opponent_team_rank', 'team_pot', 'opp_pot', 'concede_pot',
'opp_concede_pot', 'prev_points', 'form_points', 'total_points',
'long_form', 'ict_form'
]]
MidfielderModal = model.loc[model['pos'] == 'Midfielder']
MidfielderModal.drop('pos', axis=1, inplace=True)
MidfielderModal.sort_values(['season', 'round'],
ascending=True,
inplace=True)
# MidfielderModal.to_csv('../temp/MIDFIELDERS.csv', sep=',', encoding='utf-8')
players = MidfielderModal[7959:]
keys = MidfielderModal['round']
values = pd.cut(MidfielderModal['round'], 3, labels=[1, 2, 3])
dictionary = dict(zip(keys, values))
MidfielderModal['round'] = values
X = MidfielderModal.drop(['total_points', 'season', 'player_id', 'name'],
axis=1)
y = MidfielderModal[['total_points']]
X_train = X[:7958]
X_test = X[7959:]
y_train = y[:7958]
y_test = y[7959:]
regression_model = SGDRegressor()
regression_model.fit(X_train, y_train)
score = regression_model.score(X_test, y_test)
y_pred = regression_model.predict(X_test)
testing = pd.concat([X_test, y_test], 1)
testing['Predicted'] = np.round(y_pred, 1)
testing[
'Prediction_Error'] = testing['total_points'] - testing['Predicted']
testing['player_id'] = 0
testing['name'] = 0
testing['player_id'] = players.player_id
testing['name'] = players.name
print(testing[testing['name'] == name])
def SGD(x_train,x_test,y_train,y_test):
# 梯度下降法
# 自我修正的线性模型,默认学习率为0.01 这个就是碗的下滑速度
# 梯度方向，就是你朝着偏离正下滑的多少角度向碗底迂回前进的方向
sgd = SGDRegressor()
sgd.fit(x_train,y_train)
predict=sgd.predict(x_test)
score = sgd.score(x_test,y_test)
print(predict)
print(score)
k=sgd.coef_
b=sgd.intercept_
return k,b
def runSGDRegressor(self):
lm = SGDRegressor(loss='squared_loss',
penalty='l2',
fit_intercept=True)
print("SGDRegressor\n")
lm.fit(self.m_X_train, self.m_y_train)
predictY = lm.predict(self.m_X_test)
score = lm.score(self.m_X_test, self.m_y_test)
predictTraingY = lm.predict(self.m_X_train)
self.displayPredictPlot(predictY)
self.displayResidualPlot(predictY, predictTraingY)
self.dispalyModelResult(lm, predictY, score)
def SGDRegressor_pred(X_train, X_test, y_train_normalized, y_train_mean, y_test):
# The learning rate:
# ---constant: eta = eta0 [assign to the initial one, eta0]
# ---optimal: eta = 1.0/(t+t0)
# ---invscaling: eta = eta0 / pow(t, power_t) [default]
clf = SGDRegressor(alpha=0.0001, eta0=0.001, n_iter=150, fit_intercept=False, shuffle=True, verbose=0)
clf = clf.fit(X_train, y_train_normalized)
# Conveting to back, (could be used sklearn standardization function for both decoding and encoding)
predictions_train = clf.predict(X_train) + y_train_mean
predictions = clf.predict(X_test) + y_train_mean
score_test = clf.score(X_test, y_test)
return predictions, predictions_train, score_test
def SGD(task, data, split=0.3, lr="optimal", alpha=0.0001, seed=42):
task = task.lower()
X = data["X"]
y = data["y"]
if task == "r" or task == "reg" or task == "regression":
sgd = SGDRegressor(learning_rate=lr, alpha=alpha, random_state=seed)
elif task == "c" or task == "classify" or task == "classification":
sgd = SGDClassifier(learning_rate=lr, alpha=alpha, random_state=seed)
else:
raise NameError('Task should be either regression or classification')
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=split,
random_state=seed)
sgd.fit(X_train, y_train)
train_preds = sgd.score(X_train, y_train)
print("Boosting Training Accuracy: " + str(train_preds * 100) + "%")
preds = sgd.score(X_test, y_test)
print("Boosting Testing Accuracy: " + str(preds * 100) + "%")
return sgd
def main():
# setting up our data and removing unwanted instances
with open('auto_mobile_data.csv') as file:
data = list(csv.reader(file))
data = setup.remove_inst(data)
# creating a set of attributes to skip
skip_atr = setup.skip_attribute(data)
# adding attributes with no correlation to skip_atr (see scatter_plots\analysis.txt)
# remove: symboling, losses, car height, bore, stroke, compression ratio, peak rpm
skip_atr.update({0, 1, 12, 18, 19, 20, 22, 25})
# dictionary of the mean of values of attributes
mean_nums = setup.missing_values(data)
# arranging the x and y data
# x is a 2d list and y is 1d
x = []
y = [float(i[len(i) - 1]) for i in data]
for i in data:
thing = [
i[val] if i[val] != '?' else mean_nums[val]
for val in range(len(i))
]
x.append([
float(thing[val]) for val in range(len(thing))
if val not in skip_atr
])
# splitting our data into training and testing data
train_x, train_y, test_x, test_y = train_test(x, y)
# preparing data for regression
x = np.array(train_x)
y = np.array(train_y)
x = minmax_scale(x)
y = minmax_scale(y)
# fitting data to our model
sgd = SGDRegressor().fit(x, y)
# scoring our model
# we must score our model with unseen data to prevent over/under fitting
test_x = minmax_scale(test_x)
test_y = minmax_scale(test_y)
print('score of model: ', sgd.score(test_x, test_y))
def consider_SGD():
performances: List[ExperimentResult] = []
for rnd in range(3):
for penal in ["l1","l2","elasticnet"]:
for los in ["squared_loss","huber"]:
params = {
"random_state": rnd,
"penalty": penal,
"max_iter": 100,
"loss":los
}
f = SGDRegressor(**params)
f.fit(X_train, y_train)
vali_acc = f.score(X_vali, y_vali)
result = ExperimentResult(vali_acc, params, f)
performances.append(result)
return min(performances, key=lambda result: result.vali_acc)
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 SDGRegressionExample():
import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import SGDRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data,data.target)
X_scaler = StandardScaler()
y_scaler = StandardScaler()
X_train = X_scaler.fit_transform(X_train)
y_train = y_scaler.fit_transform(y_train)
X_test = X_scaler.transform(X_test)
y_test = y_scaler.transform(y_test)
regressor = SGDRegressor(loss='squared_loss')
scores = cross_val_score(regressor, X_train, y_train, cv=5)
print 'Cross validation r-squared scores:', scores
print 'Average cross validation r-squared score:', np.mean(scores)
regressor.fit_transform(X_train, y_train)
print 'Test set r-squared score', regressor.score(X_test, y_test)
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_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)
def SGDDemo(): import numpy as np from sklearn.datasets import load_boston from sklearn.linear_model import SGDRegressor from sklearn.cross_validation import cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.cross_validation import train_test_split data = load_boston() X_train,X_test,y_train,y_test = train_test_split(data.data,data.target) X_scaler = StandardScaler() y_scaler = StandardScaler() X_train = X_scaler.fit_transform(X_train) y_train = y_scaler.fit_transform(y_train) X_test = X_scaler.transform(X_test) y_test = y_scaler.transform(y_test) regressor = SGDRegressor(loss='squared_loss') scores = cross_val_score(regressor,X_train,y_train,cv=5) print "Cross validation r-sqr ",np.mean(scores) regressor.fit_transform(X_train,y_train) print "TEST score :",regressor.score(X_test,y_test)
Y_scaler = StandardScaler()
X_train = X_scaler.fit_transform(X_train)
Y_train = Y_scaler.fit_transform(Y_train)
X_test = X_scaler.transform(X_test)
Y_test = Y_scaler.transform(Y_test)
print X_train[0:5]
print len(X_train)
print Y_test
clf =SGDRegressor(loss="squared_loss")
scores = cross_val_score(clf,X_train,Y_train,cv=5)
print scores
print np.mean(scores)
clf.fit_transform(X_train,Y_train)
pred = clf.predict(X_test)
print clf.score(X_test,Y_test)
# correlation(X_train,Y_train)
# feature_selection(X_train,Y_train)
scatter_plot(X_train,Y_train)
import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import SGDRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
data = load_boston()
print(data)
x_train,x_test,y_train,y_test = train_test_split(data.data,data.target)
x_scaler = StandardScaler()
y_scaler = StandardScaler()
x_train=x_scaler.fit_transform(x_train)
y_train=y_scaler.fit_transform(y_train)
x_test = x_scaler.transform(x_test)
y_test=y_scaler.transform(y_test)
regressor = SGDRegressor(loss='squared_loss')
scores=cross_val_score(regressor,x_train,y_train,cv=5)
print('Cross Validation r-squared scores:',scores)
print('Average cross validation r-squared score',np.mean(scores))
regressor.fit_transform(x_train,y_train)
print('Test set r-squared score',regressor.score(x_test,y_test))
X_test = X[test]
y_train = y[train]
y_test = y[test]
#iris = datasets.load_iris()
#print(iris.data)
#X = iris.data[:,0:3]
#y = iris.data[:,3]
start_time = time.time()
sgd = SGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
loss='squared_loss', n_iter=1000, penalty='l2', power_t=0.25,
random_state=None, shuffle=True, verbose=0, warm_start=False)
sgd.fit(X_train.astype('float64'),y_train)
elapsed_time = time.time() - start_time
print("Time %s"%elapsed_time)
print(sgd.coef_,sgd.intercept_)
print("Accuracy %s"%sgd.score(X_test,y_test))
start_time = time.time()
psgd = ParallelSGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
loss='squared_loss', n_iter=1000, penalty='l2', power_t=0.25,
random_state=None, shuffle=True, verbose=0, warm_start=False)
psgd.fit(X_train.astype('float64'), y_train)
elapsed_time = time.time() - start_time
print("Time %s" %elapsed_time)
print(psgd.coef_,psgd.intercept_)
print("Accuracy %s"%psgd.score(X_test,y_test))
import numpy as np
from sklearn.datasets import load_boston
from sklearn.linear_model import SGDRegressor
from sklearn.cross_validation import cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.cross_validation import train_test_split
# load and split data
data = load_boston()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target)
# scale the features
X_scaler = StandardScaler()
y_scaler = StandardScaler()
X_train = X_scaler.fit_transform(X_train)
y_train = y_scaler.fit_transform(y_train)
X_test = X_scaler.fit_transform(X_test)
y_test = y_scaler.fit_transform(y_test)
# train
regressor = SGDRegressor(loss='squared_loss')
scores = cross_val_score(regressor, X_train, y_train, cv=5)
print('Cross validation r-squared scores: {0}'.format(scores))
print('Average cross validation r-squared score: {0}'.format(np.mean(scores)))
regressor.fit_transform(X_train, y_train)
print('Test set r-squared score: {0}'.format(regressor.score(X_test, y_test)))
# we wont use causal and registered in tutorial
df.drop(['registered', 'casual'], axis=1, inplace=True)
df.drop(['datetime'], axis=1, inplace=True)
from sklearn.cross_validation import train_test_split
# X are our features without 'count'
X = df.drop(['count'], axis=1)
# y is the target 'count'
y = df['count']
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.70, random_state=2)
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import SGDRegressor
X_train_transformed = StandardScaler().fit_transform(X_train)
X_test_transformed = StandardScaler().fit_transform(X_test)
clf = SGDRegressor()
# train you model
clf.fit(X_train_transformed, y_train)
# print out predicted values
print(clf.predict(X_test_transformed))
# print out how well the model fits for data
print("Model Score: ", clf.score(X_train_transformed, y_train))
from sklearn.pipeline import make_pipeline
clf = make_pipeline(StandardScaler(), SGDRegressor())
clf.fit(X_train, y_train)
print("Model Score: ", clf.score(X_train, y_train))
for idx in range(int(np.ceil(n_trainSamples / mini_batch))):
x_batch = train[ind[idx * mini_batch: min((idx + 1) * mini_batch, n_trainSamples)]]
y_batch = train_target[ind[idx * mini_batch: min((idx + 1) * mini_batch, n_trainSamples)]]
if idx > 0:
validationScore.append(clf.score(x_batch, y_batch))
clf.partial_fit(x_batch, y_batch)
if idx > 0:
trainScore.append(clf.score(x_batch, y_batch))
plt.plot(trainScore, label="train score")
plt.plot(validationScore, label="validation socre")
plt.xlabel("Mini_batch")
plt.ylabel("Score")
plt.legend(loc='best')
plt.title(title)
sgd_regresor = SGDRegressor(penalty='l2',alpha=0.001)
plot_learning(sgd_regresor,"SGDRegressor")
test = test_subset.drop(['EbayID','Price','SellerName'], axis=1)
test = scaler.fit_transform(test)
test_target = test_subset['Price']
print("SGD regressor prediction result on testing data: %f" % sgd_regresor.score(test,test_target))
plt.show()
X_scaler = StandardScaler() y_scaler = StandardScaler() X_train = X_scaler.fit_transform(X_train) y_train = y_scaler.fit_transform(y_train) X_test = X_scaler.transform(X_test) y_test = y_scaler.transform(y_test) regressor = SGDRegressor(loss='squared_loss') score = cross_val_score(regressor, X_train, y_train, cv=5) print score print np.mean(score) regressor.fit_transform(X_train, y_train) print regressor.score(X_test, y_test)
y.append(float(s_data[4]))
y2.append(float(s_data[1]))
pprint.pprint("Training the supervised learning model... Fit on training data")
print ("=========================================")
try:
clf = SGDRegressor(loss="huber")
pprint.pprint(clf.fit(X, y))
except:
raise
try:
clf2 = SGDRegressor(loss="huber")
pprint.pprint(clf2.fit(X, y2))
except:
raise
print ("=========================================")
print "Model testing itself! Confidence score on the training data used to construct:", clf.score(X, y)
pprint.pprint("Ready to predict")
print ("=========================================")
pprint.pprint("Testing with test data...")
test_data = list()
test_diff = list()
predict_diff = list()
for index in test_indices:
tmp = data[index][1:5]
my_tmp = list()
for item in tmp:
my_tmp.append(float(item))
test_data.append(my_tmp)
class Model(object):
def __init__(self, params):
self.model_class = params['class']
self.model = {}
self.feature_constructor = None
self.all_possible_decisions = []
self.X = []
self.y = []
self.buffer = 0
def initialize(self):
if self.model_class == 'scikit':
self.model = SGDRegressor(loss='squared_loss', alpha=0.1, n_iter=10, shuffle=True, eta0=0.0001)
self.feature_constructor = FeatureHasher(n_features=200, dtype=np.float64, non_negative=False, input_type='dict')
elif self.model_class == 'lookup':
self.model = {}
def clean_buffer(self):
self.X = []
self.y = []
self.buffer = 0
def return_design_matrix(self, all_decision_states, reward=None):
if self.model_class == 'lookup_table':
return all_decision_states, reward
elif self.model_class == 'scikit':
X, y = [], []
for decision_state in all_decision_states:
information, decision_taken = decision_state
tr = {}
tr['-'.join([str(information[1]), decision_taken])] = 1
tr['-'.join([str(information[0]), decision_taken])] = 1
tr['-'.join([str(information[0]), str(information[1]), decision_taken])] = 1
X.append(tr)
y.extend([reward])
X = self.feature_constructor.transform(X).toarray()
return X, y
def fit(self, X, y):
if self.model_class == 'scikit':
# X, y = self.shuffle_data(X, y)
self.model.partial_fit(X, y)
print self.model.score(X, y)
if self.model_class == 'lookup_table':
for decision_state in X:
if decision_state not in self.model:
for d in self.all_possible_decisions:
self.model[(decision_state[0], d)] = DecisionState()
self.model[decision_state].count += 1
updated_value = self.model[decision_state].value_estimate + (1.0 / self.model[decision_state].count) * (
y - self.model[decision_state].value_estimate)
self.model[decision_state].value_estimate = updated_value
def predict(self, X):
if self.model_class == 'scikit':
return self.model.predict(X)
if self.model_class == 'lookup_table':
if X not in self.model:
for d in self.all_possible_decisions:
self.model[(X[0], d)] = DecisionState()
return self.model[X].value_estimate
@staticmethod
def shuffle_data(a, b):
assert len(a) == len(b)
p = np.random.permutation(len(a))
return a[p], b[p]
import numpy as np from sklearn.datasets import load_boston from sklearn.linear_model import SGDRegressor from sklearn.cross_validation import cross_val_score from sklearn.preprocessing import StandardScaler from sklearn.cross_validation import train_test_split data = load_boston() # print data X_train, X_test, y_train, y_test = train_test_split(data.data, data.target) X_scaler = StandardScaler() y_scaler = StandardScaler() # print X_train X_train = X_scaler.fit_transform(X_train) y_train = y_scaler.fit_transform(y_train) # print X_train X_test = X_scaler.fit_transform(X_test) y_test = y_scaler.fit_transform(y_test) regressor = SGDRegressor(loss='squared_loss') scores = cross_val_score(regressor, X_train, y_train, cv=5) print X_train.shape print "CV ", scores print regressor.fit_transform(X_train, y_train).shape print "Test r-ss", regressor.score(X_test, y_test)
def fit_SGD(features_train, labels_train, features_pred): model = SGDRegressor() model.fit(features_train, labels_train) labels_pred = model.predict(features_pred) print "SGD - coefficient of determination R^2 of the prediction: ", model.score(features_train, labels_train) return labels_pred