train_df = pd.read_csv('/home/fotis/dev_projects/explanation_framework/input/Crimes_Workload/{0}'.format(train_datasets[p]), index_col=0)
sub = np.load('/home/fotis/dev_projects/explanation_framework/input/Subqueries/{0}'.format(sub_datasets[p]))
logger.info('Finished loading\nCommencing Evaluation')
aggregates = ['count','sum_','avg']
agg_map = {'count' :4, 'sum_':5, 'avg':6}
for agg in aggregates:
logger.info("Evaluating Aggregates : {0}".format(agg))
X_train = train_df[['x','y','x_range','y_range']].values
y_train = train_df[agg].values
sc = StandardScaler()
sc.fit(X_train)
X_train = sc.transform(X_train)
#Training Models
logger.info("Model Training Initiation\n=====================")
mars_ = Earth(feature_importance_type='gcv',)
vigilance_x = np.linspace(0.01, 3, Config.vigilance_x_frequency)
for sens_x in vigilance_x:
lsnr = PR(mars_,vigil_x=sens_x)
lsnr.fit(X_train,y_train)
logger.info("Accuracy Evaluation on Test set with vigil_x={0}\n=====================".format(sens_x))
for i in range(1000):
#Obtain query from test-set
dataset = p
printProgressBar(i, 1000,prefix = 'Progress:', suffix = 'Complete', length = 50)
q = test_df.iloc[i].values[:4].reshape(1,-1)
q = sc.transform(q)
y = train['transactionRevenue']
X =train.drop(["fullVisitorId","transactionRevenue"],axis=1)
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from pyearth import Earth
regression_OLS=LinearRegression()
regression_Lasso=Lasso(precompute=True,max_iter=10000,alpha=3.0)
regression_RF=RandomForestRegressor(max_leaf_nodes=100,max_features=100)
#regression_SVR=SVR(kernel='rbf', C=1e3, gamma=0.1)
regression_spline = Earth()
from sklearn import model_selection
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)
print('training data has %d observation with %d features'% X_train.shape)
print('test data has %d observation with %d features'% X_test.shape)
from sklearn.metrics import mean_squared_error,explained_variance_score
model_names = ['Linear Regression OLS','Linear Regression Lasso','Random Forest','Spline_regression']
model_list = [regression_OLS,regression_Lasso, regression_RF,regression_spline]
count = 0
for regression in model_list:
model = regression.fit(X_train,y_train)
y_preds = model.predict(X_test)
df = pd.read_csv(dataset, sep='\t')
df = pd.read_table(dataset)
gt_mapping = {'0/0': 0, '0/1': 1, '1/1': 2}
df['GT_GATK'] = df['GT_GATK'].map(gt_mapping)
df['GT_Varscan'] = df['GT_Varscan'].map(gt_mapping)
df['GT_Freebayes'] = df['GT_Freebayes'].map(gt_mapping)
X = df.values[:100, 5:]
X = set_missing_values(X)
#print df.columns[12]
y = np.random.randint(2, size=(int(np.shape(X)[0]), ))
#print X
#print y
earth_classifier = Pipeline([('earth', Earth(allow_missing=True)),
('logistic', LogisticRegression())])
#earth_classifier = Pipeline([('earth', Earth(allow_missing=True)),
# ('logistic', RandomForestClassifier())])
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.4,
random_state=0)
ec = earth_classifier.fit(X_train, y_train)
y_hat = earth_classifier.predict(X_test)
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outElasticNet.csv')
regr_2.fit(X_train, y_train)
y_eval = regr_2.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions'
]).to_csv('outAdaBoostRegressor.csv')
clf = linear_model.Lars(n_nonzero_coefs=1)
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outLARS.csv')
"""
clf = AdaBoostRegressor(DecisionTreeRegressor(max_depth=4),n_estimators=300, random_state=rng)
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval, columns=['predictions']).to_csv('outAdaBoostRegressor.csv')
"""
from pyearth import Earth
clf = Earth()
clf.fit(X_train, y_train)
y_eval = clf.predict(X)
prediction = pd.DataFrame(y_eval,
columns=['predictions']).to_csv('outMARS.csv')
test = pd.read_csv('../data/modeltest.csv',index_col=0)
label = train['Response'].values
featextra= pd.read_csv('../feat/improve.csv',index_col=0)
train = pd.concat([train,featextra.loc[train.index]],axis=1)
test = pd.concat([test,featextra.loc[test.index]],axis=1)
featextra= pd.read_csv('../feat/duplicate.csv',index_col=0)
train = pd.concat([train,featextra.loc[train.index]],axis=1)
test = pd.concat([test,featextra.loc[test.index]],axis=1)
feat = train.columns.drop('Response',1)
#Build an Earth model with a logisticregression pipeline
earth_pipe = Pipeline([('earth',Earth(use_fast=True,allow_missing=True,penalty=0.5,max_degree=3)),('log',LogisticRegression())])
earth_pipe.fit(train[feat],label)
#Parameter tuning
#param_grid = {'earth__penalty': np.arange(1,11,2),'earth__max_degree': range(1,4)}
#
#gs1 = GridSearchCV(earth_pipe,param_grid,n_jobs=1,pre_dispatch=1,cv=StratifiedKFold(label, n_folds=5, shuffle=True),scoring='log_loss',verbose=2)
#
#
#gs1.fit(train[feat],label)
#
#print gs1.best_params_
#print gs1.best_score_
#
##----------------------------------------------------------
def translation_correction(cell_mesh, cell_mesh_2, buffer_cell,\
x_pos, y_pos, z_pos, x_pos_new, y_pos_new, z_pos_new, closest_no_conflict, directory ):
x_min = np.min([np.min(cell_mesh[:,0]),np.min(cell_mesh_2[:,0])]) - buffer_cell
x_max = np.max([np.max(cell_mesh[:,0]),np.max(cell_mesh_2[:,0])]) + buffer_cell
y_min = np.min([np.min(cell_mesh[:,1]),np.min(cell_mesh_2[:,1])]) - buffer_cell
y_max = np.max([np.max(cell_mesh[:,1]),np.max(cell_mesh_2[:,1])]) + buffer_cell
z_min = np.min([np.min(cell_mesh[:,2]),np.min(cell_mesh_2[:,2])]) - buffer_cell
z_max = np.max([np.max(cell_mesh[:,2]),np.max(cell_mesh_2[:,2])]) + buffer_cell
num_pts = len(x_pos)
X = []; Y = []; Z = []; U = []; V = []; W = []
for kk in range(0,num_pts):
idx = closest_no_conflict[kk]
if idx < len(closest_no_conflict):
U.append(x_pos_new[idx] - x_pos[kk])
V.append(y_pos_new[idx] - y_pos[kk])
W.append(z_pos_new[idx] - z_pos[kk])
X.append(x_pos_new[idx]); Y.append(y_pos_new[idx]); Z.append(z_pos_new[idx])
# --> limit to points that aren't too close to the cell
X_safe = []; Y_safe = []; Z_safe = []; U_safe = []; V_safe = []; W_safe = []
num_pts = len(U)
for kk in range(0,num_pts):
x_out = X[kk] < x_min or X[kk] > x_max
y_out = Y[kk] < y_min or Y[kk] > y_max
z_out = Z[kk] < z_min or Z[kk] > z_max
if x_out or y_out or z_out:
X_safe.append(X[kk])
Y_safe.append(Y[kk])
Z_safe.append(Z[kk])
U_safe.append(U[kk])
V_safe.append(V[kk])
W_safe.append(W[kk])
X_safe = np.asarray(X_safe); Y_safe = np.asarray(Y_safe); Z_safe = np.asarray(Z_safe)
U_safe = np.asarray(U_safe); V_safe = np.asarray(V_safe); W_safe = np.asarray(W_safe)
# --> fit MARS models
model_U = Earth(max_degree=2,max_terms=10)
model_U.fit(Z_safe,U_safe)
model_V = Earth(max_degree=2,max_terms=10)
model_V.fit(Z_safe,V_safe)
model_W = Earth(max_degree=2,max_terms=10)
model_W.fit(Z_safe,W_safe)
# --> re-define Z
pred_U = model_U.predict(z_pos_new)
pred_V = model_V.predict(z_pos_new)
pred_W = model_W.predict(z_pos_new)
# --> correct new bead positions
for kk in range(0,len(x_pos_new)):
x_pos_new[kk] = x_pos_new[kk] - pred_U[kk]
y_pos_new[kk] = y_pos_new[kk] - pred_V[kk]
z_pos_new[kk] = z_pos_new[kk] - pred_W[kk]
# --> correct new cell position
pred_cell_0 = model_U.predict(cell_mesh_2[:,0])
pred_cell_1 = model_V.predict(cell_mesh_2[:,1])
pred_cell_2 = model_W.predict(cell_mesh_2[:,2])
cell_mesh_2_new = np.zeros(cell_mesh_2.shape)
cell_mesh_2_new[:,0] = cell_mesh_2[:,0] - pred_cell_0
cell_mesh_2_new[:,1] = cell_mesh_2[:,1] - pred_cell_1
cell_mesh_2_new[:,2] = cell_mesh_2[:,2] - pred_cell_2
# --> plot MARS models
Z_line = np.linspace(np.min(Z),np.max(Z),100)
pred_line_U = model_U.predict(Z_line)
pred_line_V = model_V.predict(Z_line)
pred_line_W = model_W.predict(Z_line)
plt.figure(figsize=(15,5))
plt.subplot(1,3,1)
plt.plot(Z,U,'b.',label='x raw')
plt.plot(Z_line,pred_line_U,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('x displacements')
plt.subplot(1,3,2)
plt.plot(Z,V,'r.',label='y raw')
plt.plot(Z_line,pred_line_V,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('y displacements')
plt.subplot(1,3,3)
plt.plot(Z,W,'g.',label='z raw')
plt.plot(Z_line,pred_line_W,'k--',label='fit')
plt.xlabel('z position'); plt.ylabel('displacement')
plt.tight_layout(); plt.legend(); plt.title('z displacements')
plt.savefig(directory + '/translation_correction.png')
return x_pos_new, y_pos_new, z_pos_new, cell_mesh_2_new
def fit_and_predict(X_training,
Y_training,
X_validation,
hprm,
assignments={}):
#assert type(hprm['learning.model.benchmarks.independent_models']) == bool
#assert type(hprm['learning.model.benchmarks.individual_inputs']) == bool
# if not hprm['learning.model.benchmarks.independent_models']:
# Y_hat_training, Y_hat_validation, model = call_fitter(inputs_training,
# Y_training,
# inputs_validation,
# hprm,
# )
# Y_hat_training = pd.DataFrame(Y_hat_training,
# index = Y_training.index,
# columns = Y_training.columns,
# )
# Y_hat_validation = pd.DataFrame(Y_hat_validation,
# index = inputs_validation.index,
# columns = Y_training.columns,
# )
# else:
# Y_hat_training = pd.DataFrame(0,
# index = Y_training.index,
# columns = Y_training.columns,
# )
# Y_hat_validation = pd.DataFrame(0,
# index = inputs_validation.index,
# columns = Y_training.columns,
# )
# model = {}
# if hprm['learning.model.benchmarks.individual_inputs']:
# for ii, site_name in enumerate(Y_training.columns):
# print('\r{0}/{1}'.format(ii, Y_training.shape[1]), end = '')
# columns_to_keep = [
# (name_input, transformation, parameter, location)
# for (name_input, transformation, parameter, location) in inputs_training.columns
# if ( name_input not in assignments
# or location in assignments[name_input]
# )
# ]
# Y_hat_training[site_name], Y_hat_validation[site_name], model[site_name] = call_fitter(inputs_training[columns_to_keep],
# Y_training[site_name],
# inputs_validation[columns_to_keep],
# hprm,
# )
# else :
# for ii, site_name in enumerate(Y_training.columns):
# print('\r{0}/{1}'.format(ii, Y_training.shape[1]), end = '')
# Y_hat_training[site_name], Y_hat_validation[site_name], model[site_name] = call_fitter(inputs_training,
# Y_training[site_name],
# inputs_validation,
# hprm,
# )
# return Y_hat_training, Y_hat_validation, model
# def call_fitter(X_training,
# Y_training,
# X_validation,
# hprm,
# ):
X_mean = X_training.mean(axis=0)
X_std = X_training.std(axis=0)
X_training = (X_training - X_mean) / X_std
X_validation = (X_validation - X_mean) / X_std
method = hprm['learning.model']
if Y_training.ndim == 2 and Y_training.shape[1] == 1:
Y_training = Y_training[:, 0]
if method in {'random_forests', 'regression_tree'}:
pass
elif method in {'xgboost', 'svr', 'mars'}:
assert Y_training.ndim == 1
if method == 'mars':
model = Earth(
verbose=hprm['mars.verbose'],
thresh=hprm['mars.thresh'],
)
elif method == 'random_forests':
model = RandomForestRegressor(
n_estimators=hprm['random_forests.n_estimators'])
elif method == 'regression_tree':
model = DecisionTreeRegressor()
elif method == 'svr':
model = SVR(
C=hprm['svr.C'],
epsilon=hprm['svr.epsilon'],
)
elif method == 'xgboost':
model = XGBRegressor()
else:
raise ValueError
model.fit(
X_training.values,
Y_training.values,
)
Y_hat_training = model.predict(X_training.values)
Y_hat_validation = model.predict(X_validation.values)
return Y_hat_training, Y_hat_validation, model
model = Earth(max_terms=50, max_degree=3)
model.fit(X,y)
#Print the model
#print(model.trace())
print(model.summary())
print "MARS degree 5"
model = Earth(max_terms=20, max_degree=5)
model.fit(X,y)
#Print the model
#print(model.trace())
print(model.summary())
"""
print "====================================="
print "MARS degree 1"
model = Earth(max_terms=70, max_degree=1)
print "Score: {}".format ( crossValidation ( model, X, y ) )
print "MARS degree 3"
model = Earth(max_terms=50, max_degree=3)
crossValidation ( model, X, y )
print "Score: {}".format ( crossValidation ( model, X, y ) )
def accuracy_on_higgs():
logger.info("Starting Accuracy Tests on Higgs")
logger.info("================================")
df = pd.read_csv('input/sample_higgs_0.01.csv', index_col=0)
X = df[['m_bb','m_wwbb']].dropna().values
y = df['label']
min_ = np.min(X, axis=0)
max_ = np.max(X, axis=0)
X = (X-min_) / (max_-min_)
data = np.column_stack((X,y))
x = np.linspace(0.1,0.9,7)
xx,yy = np.meshgrid(x,x)
DIMS = X.shape[1]
cov = np.identity(DIMS)*0.001
cluster_centers = np.column_stack((xx.ravel(),yy.ravel()))
query_centers = []
#Generate queries over cluster centers
for c in cluster_centers:
queries = np.random.multivariate_normal(np.array(c), cov, size=40)
query_centers.append(queries)
query_centers = np.array(query_centers).reshape(-1,DIMS)
ranges = np.random.uniform(low=0.005**(1/3), high=0.25**(1/3), size=(query_centers.shape[0], DIMS))
queries = []
empty = 0
for q,r in zip(query_centers,ranges):
b = generate_boolean_vector(data,q,r,2)
res = data[b]
if res.shape[0]==0:
empty+=1
ans = float(np.mean(res[:,-1])) if res.shape[0]!=0 else 0
qt = q.tolist()
qt += r.tolist()
qt.append(ans)
queries.append(qt)
qs = np.array(queries).reshape(-1, 2*DIMS+1)
X_train, X_test, y_train, y_test = train_test_split(
qs[:,:qs.shape[1]-1], qs[:,-1], test_size=0.4, random_state=0)
earth = Earth()
lsnr = PR(earth)
lsnr.fit(X_train, y_train)
y_hat = np.array([float(lsnr.get_model(x.reshape(1,-1)).predict(x.reshape(1,-1))) for x in X_test])
r2 = metrics.r2_score(y_test,y_hat)
kl = kl_divergence_error(y_test, y_hat)
nrmse = np.sqrt(metrics.mean_squared_error(y_test, y_hat))/np.mean(y_test)
logger.info("R2 Score : {}\nNRMSE : {}\nKL-Divergence : {}".format(r2, nrmse, kl))
#Linear Regression comparsion
lr = LinearRegression()
lr.fit(X_train, y_train)
y_hat_lr = lr.predict(X_test)
r2_lr = metrics.r2_score(y_test, y_hat_lr)
kl_lr = kl_divergence_error(y_test, y_hat_lr)
nrmse_lr = np.sqrt(metrics.mean_squared_error(y_test, y_hat_lr))/np.mean(y_test)
logger.info("R2 Score : {}\nNRMSE : {}\nKL-Divergence : {}".format(r2_lr, kl_lr, nrmse_lr))
dic = {}
dic['LPM' ]= [('r2',r2), ('kl',kl), ('nrmse',nrmse)]
dic['LR'] = [('r2',r2_lr), ('kl',kl_lr), ('nrmse',nrmse_lr)]
#Polynomial regression comparsion
for count, degree in enumerate(np.arange(3,10,2)):
model = make_pipeline(PolynomialFeatures(degree), Ridge())
model.fit(X_train, y_train)
y_hat = model.predict(X_test)
r2_p = metrics.r2_score(y_test,y_hat)
kl_p = kl_divergence_error(y_test, y_hat)
nrmse_p = np.sqrt(metrics.mean_squared_error(y_test, y_hat))/np.mean(y_test)
dic["LR ({})".format(degree)] = [('r2',r2_p), ('kl',kl_p), ('nrmse',nrmse_p)]
print("R2 for degree {} : {}".format(degree, metrics.r2_score(y_test, y_hat)))
logger.info("==============================================")
with open('output/Accuracy/multiple_methods_higgs.pkl', 'wb') as handle:
pickle.dump(dic, handle)
10 * numpy.random.normal(size=m)
y2 = 100 * \
(numpy.cos((X[:, 5] + X[:, 6]) / 20) - 4.0) + \
10 * numpy.random.normal(size=m)
y = numpy.concatenate([y1[:, None], y2[:, None]], axis=1)
missing = numpy.random.binomial(1, .2, (m, n)).astype(bool)
X_full = X.copy()
X[missing] = None
idx5 = (1 - missing[:, 5]).astype(bool)
idx6 = (1 - missing[:, 6]).astype(bool)
# Fit an Earth model
model = Earth(max_degree=5,
minspan_alpha=.5,
allow_missing=True,
enable_pruning=True,
thresh=.001,
smooth=True,
verbose=True)
model.fit(X, y)
# Print the model
print(model.trace())
print(model.summary())
# Plot the model
y_hat = model.predict(X)
fig = plt.figure()
for j in [0, 1]:
ax1 = fig.add_subplot(3, 4, 1 + 2 * j)
def generate_data():
alpha1 = 0
beta1 = 1
n_samples = 50
noise1 = 2 * np.random.randn(n_samples)
x1 = np.linspace(1, 50, 50)
y1 = alpha1 + beta1 * x1 + noise1
alpha2 = (alpha1 + beta1 * 50) * 2
beta2 = -1
n_samples = 50
noise2 = 2 * np.random.randn(n_samples)
x2 = np.linspace(50, 100, 50)
y2 = alpha2 + beta2 * x2 + noise2
x = np.concatenate((x1, x2), axis=None)
y = np.concatenate((y1, y2), axis=None)
return x, y
x, y = generate_data()
mars = Earth(max_terms=2)
mars.fit(x, y)
Y_hat = mars.predict(x)
plt.scatter(x, y)
plt.scatter(x, Y_hat)
plt.show()
def getModel(config, modelname):
info("Getting {0} Model".format(modelname), ind=0)
problemType = config['problem']
modelData = getModelData(config, modelname)
modelParams = modelData.get('params')
retval = None
###########################################################################
# Classification
###########################################################################
if isClassification(problemType):
if modelname == "logistic":
retval = classifier(modelname, LogisticRegression(), modelParams)
if modelname == "sgd":
retval = classifier(modelname, SGDClassifier(), modelParams)
if modelname == "passagg":
retval = classifier(modelname, PassiveAggressiveClassifier(),
modelParams)
if modelname == "mlp":
retval = classifier(modelname, MLPClassifier(), modelParams)
if modelname == "xgboost":
retval = classifier(modelname, XGBClassifier(), modelParams)
if modelname == "gaussproc":
retval = classifier(modelname, GaussianProcessClassifier(),
modelParams)
if modelname == "lda":
retval = classifier(modelname, LinearDiscriminantAnalysis(),
modelParams)
if modelname == "qda":
retval = classifier(modelname, QuadraticDiscriminantAnalysis(),
modelParams)
if modelname == "nb":
retval = classifier(modelname, GaussianNB(), modelParams)
if modelname == "nbbern":
retval = classifier(modelname, BernoulliNB(), modelParams)
if modelname == "nbmulti":
retval = classifier(modelname, MultinomialNB(), modelParams)
if modelname == "dtree":
retval = classifier(modelname, DecisionTreeClassifier(),
modelParams)
if modelname == "kneighbors":
retval = classifier(modelname, KNeighborsClassifier(), modelParams)
if modelname == "rneighbors":
retval = classifier(modelname, RadiusNeighborsClassifier(),
modelParams)
if modelname == "svmlin":
retval = classifier(modelname, LinearSVC(), modelParams)
if modelname == "svmnupoly":
retval = classifier(modelname, NuSVC(), modelParams)
if modelname == "svmnulinear":
retval = classifier(modelname, NuSVC(), modelParams)
if modelname == "svmnusigmoid":
retval = classifier(modelname, NuSVC(), modelParams)
if modelname == "svmnurbf":
retval = classifier(modelname, NuSVC(), modelParams)
if modelname == "svmepspoly":
retval = classifier(modelname, SVC(), modelParams)
if modelname == "svmepslinear":
retval = classifier(modelname, SVC(), modelParams)
if modelname == "svmepssigmoid":
retval = classifier(modelname, SVC(), modelParams)
if modelname == "svmepsrbf":
retval = classifier(modelname, SVC(), modelParams)
if modelname == "rf":
retval = classifier(modelname, RandomForestClassifier(),
modelParams)
if modelname == "extratrees":
retval = classifier(modelname, ExtraTreesClassifier(), modelParams)
if modelname == "adaboost":
retval = classifier(modelname, AdaBoostClassifier(), modelParams)
if modelname == "gbm":
retval = classifier(modelname, GradientBoostingClassifier(),
modelParams)
if modelname == "tpot":
retval = classifier(modelname, TPOTClassifier(), modelParams)
#######################################################################
# Regression
#######################################################################
if modelname == "lightning":
retval = external.extlightning.createLightningClassifier(
modelParams)
###########################################################################
# Regression
###########################################################################
if isRegression(problemType):
if modelname == "linear":
retval = classifier(modelname, LinearRegression(), modelParams)
if modelname == "ridge":
retval = classifier(modelname, Ridge(), modelParams)
if modelname == "lasso":
retval = classifier(modelname, Lasso(), modelParams)
if modelname == "elasticnet":
retval = classifier(modelname, ElasticNet(), modelParams)
if modelname == "omp":
retval = classifier(modelname, OrthogonalMatchingPursuit(),
modelParams)
if modelname == "bayesridge":
retval = classifier(modelname, BayesianRidge(), modelParams)
if modelname == "ard":
retval = classifier(modelname, ARDRegression(), modelParams)
if modelname == "sgd":
retval = classifier(modelname, SGDRegressor(), modelParams)
if modelname == "passagg":
retval = classifier(modelname, PassiveAggressiveRegressor(),
modelParams)
if modelname == "perceptron":
retval = None
if modelname == "huber":
retval = classifier(modelname, HuberRegressor(), modelParams)
if modelname == "theilsen":
retval = classifier(modelname, TheilSenRegressor(), modelParams)
if modelname == "ransac":
retval = classifier(modelname, RANSACRegressor(), modelParams)
if modelname == "mlp":
retval = classifier(modelname, MLPRegressor(), modelParams)
if modelname == "xgboost":
retval = classifier(modelname, XGBRegressor(), modelParams)
if modelname == "gaussproc":
retval = classifier(modelname, GaussianProcessRegressor(),
modelParams)
if modelname == "dtree":
retval = classifier(modelname, DecisionTreeRegressor(),
modelParams)
if modelname == "kneighbors":
retval = classifier(modelname, KNeighborsRegressor(), modelParams)
if modelname == "rneighbors":
retval = classifier(modelname, RadiusNeighborsRegressor(),
modelParams)
if modelname == "svmlin":
retval = classifier(modelname, LinearSVR(), modelParams)
if modelname == "svmnupoly":
retval = classifier(modelname, NuSVR(), modelParams)
if modelname == "svmnulinear":
retval = classifier(modelname, NuSVR(), modelParams)
if modelname == "svmnusigmoid":
retval = classifier(modelname, NuSVR(), modelParams)
if modelname == "svmnurbf":
retval = classifier(modelname, NuSVR(), modelParams)
if modelname == "svmepspoly":
retval = classifier(modelname, SVR(), modelParams)
if modelname == "svmepslinear":
retval = classifier(modelname, SVR(), modelParams)
if modelname == "svmepssigmoid":
retval = classifier(modelname, SVR(), modelParams)
if modelname == "svmepsrbf":
retval = classifier(modelname, SVR(), modelParams)
if modelname == "rf":
retval = classifier(modelname, RandomForestRegressor(),
modelParams)
if modelname == "extratrees":
retval = classifier(modelname, ExtraTreesRegressor(), modelParams)
if modelname == "adaboost":
retval = classifier(modelname, AdaBoostRegressor(), modelParams)
if modelname == "gbm":
retval = classifier(modelname, GradientBoostingRegressor(),
modelParams)
if modelname == "isotonic":
retval = classifier(modelname, IsotonicRegression(), modelParams)
if modelname == "earth":
retval = classifier(modelname, Earth(), modelParams)
if modelname == "symbolic":
retval = classifier(modelname, SymbolicRegressor(), modelParams)
if modelname == "tpot":
retval = classifier(modelname, TPOTRegressor(), modelParams)
if retval is None:
raise ValueError(
"No model with name {0} was created".format(modelname))
model = retval.get()
return model
from pyearth import Earth
import joblib
y_s = 0
y_s = sys.argv[1]
n = y_s
ans = 0
for i in range(len(n)):
if n[i].isnumeric():
ans = ans + int(n[i]) * pow(10, (len(n) - 1) - i)
gdpdata = pd.read_csv(
"/home/cheeryluck/PycharmProjects/djangoProject2/data1/IndiaGDP.csv",
header=None)
labels = ['Year', 'GDP']
gdpdata.columns = labels
train, test = train_test_split(gdpdata)
model6 = Earth().fit(train.iloc[:, :1], train.iloc[:, 1:])
ycap6 = model6.predict(test.iloc[:, :1])
error = mean_squared_error(test.iloc[:, :1], ycap6)
model6.predict([[2019]])
joblib.dump(
model6,
'/home/cheeryluck/PycharmProjects/djangoProject2/data1/GDP_Model.sav')
impmodel = joblib.load(
'/home/cheeryluck/PycharmProjects/djangoProject2/data1/GDP_Model.sav')
print(impmodel.predict([[2019]]))
total_data = pd.concat([
total_category_data,
total_numeric_data.clip(total_numeric_data.quantile(0.01).to_dict(),
total_numeric_data.quantile(0.99).to_dict(),
axis=1)
],
axis=1)
print(total_data.shape)
total_data = total_data.fillna(total_data.mean())
print(total_data.head(5))
train_data = total_data[total_data.index < 1460]
test_data = total_data[total_data.index >= 1460]
rfe = RFE(Earth(), step=15, verbose=2).fit(train_data, train_Y)
validKeys = list(train_data.columns[rfe.support_])
train_data = train_data[validKeys]
test_data = test_data[validKeys]
model = Earth().fit(train_data, train_Y)
predict = model.predict(test_data)
predict = np.exp(predict)
submission = pd.DataFrame()
submission['Id'] = test_index
submission['SalePrice'] = predict
submission.to_csv(
"C:\\Users\\hongj\\Desktop\\kaggle\\house_price\\submission.csv",
index=False)
import matplotlib.pyplot as plt
from pyearth import Earth
np.random.seed(1)
m = 1000
n = 5
X = np.random.normal(size=(m, n))
# Make X[:,1] binary
X[:, 1] = np.random.binomial(1, .5, size=m)
# The response is a linear function of the inputs
y = 2 * X[:, 0] + 3 * X[:, 1] + np.random.normal(size=m)
# Fit the earth model
model = Earth().fit(X, y)
# Print the model summary, showing linear terms
print model.summary()
# Plot for both values of X[:,1]
y_hat = model.predict(X)
plt.figure()
plt.plot(X[:, 0], y, 'k.')
plt.plot(X[X[:, 1] == 0, 0], y_hat[X[:, 1] == 0], 'r.', label='$x_1 = 0$')
plt.plot(X[X[:, 1] == 1, 0], y_hat[X[:, 1] == 1], 'b.', label='$x_1 = 1$')
plt.legend(loc='best')
plt.xlabel('$x_0$')
plt.show()
X = np.array(X)
y = np.sin(X) + np.random.normal(size=X.shape[0])/10.0
#Defining different knots which will be used as a parameter for MARS model
knots = [2,4,5,10]
#Helpful in creating graph
axis = [[0,0],[0,1],[1,0],[1,1]]
#Defining different max_degree parameter for MARS model parameter
for degree in range(1,5):
fig,ax = plt.subplots(2,2,figsize=(10, 10))
for num_knot in range(4):
# Defining MARS model with max_term and max_degree parameter
model = Earth(max_terms=knots[num_knot],max_degree=degree,verbose=0)
#Fitting the dataset on the dataset
model.fit(X, y)
#Prediction model output
y_hat = model.predict(X)
#Potting graphs
ax[axis[num_knot][0],axis[num_knot][1]].title.set_text(f"degree = {degree}, knots = {knots[num_knot]}")
ax[axis[num_knot][0],axis[num_knot][1]].plot(X,y,'r.')
ax[axis[num_knot][0],axis[num_knot][1]].plot(X,y_hat,'b.')
plt.show()
# Plotting dataset distribution
plt.figure()
A simple example plotting a fit of the absolute value function. """ import numpy import matplotlib.pyplot as plt from pyearth import Earth # Create some fake data numpy.random.seed(2) m = 1000 n = 10 X = 80 * numpy.random.uniform(size=(m, n)) - 40 y = numpy.abs(X[:, 6] - 4.0) + 1 * numpy.random.normal(size=m) # Fit an Earth model model = Earth(max_degree=1) model.fit(X, y) # Print the model print(model.trace()) print(model.summary()) # Plot the model y_hat = model.predict(X) plt.figure() plt.plot(X[:, 6], y, 'r.') plt.plot(X[:, 6], y_hat, 'b.') plt.show()
st = 'CPY012'
target,start_p,stop_p,host_path=station_sel(st,mode)
if mode =='hour': n_past,n_future = 24*7,72
elif mode =='day': n_past,n_future = 60,30
data = df[start_p:stop_p]
data['Day'] = data.index.dayofyear #add day
data = data.interpolate(limit=300000000,limit_direction='both').astype('float32') #interpolate neighbor first, for rest NA fill with mean()
conclude_df=pd.DataFrame()
for n_out in range(1,n_future+1):
X,y,xlabels = to_supervise(data,target,n_out)
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(enable_pruning = True,
# max_degree=3,
# max_terms=20,
minspan_alpha=.5,
feature_importance_type=criteria,
verbose=True)
model.fit(X,y,xlabels=xlabels)
nbsub = model.summary_feature_importances(sort_by='nb_subsets')[:2000].split()[3:83]
gcv = model.summary_feature_importances(sort_by='gcv')[:2000].split()[3:83]
rss = model.summary_feature_importances(sort_by='rss')[:2000].split()[3:83]
rss,gcv,nbsub = toDF(rss),toDF(gcv),toDF(nbsub)
top20=pd.concat([rss,gcv,nbsub],ignore_index=True)
top20 = pd.concat([rss,gcv,nbsub],ignore_index=True).drop_duplicates('feature')
top20['timestep'] = n_out
#ADDED combine all result
conclude_df = pd.concat([conclude_df,top20],ignore_index=True)
if mode=='day':
A simple example plotting a fit of the sine function.
"""
import numpy
import matplotlib.pyplot as plt
from pyearth import Earth
# Create some fake data
numpy.random.seed(2)
m = 10000
n = 10
X = 80 * numpy.random.uniform(size=(m, n)) - 40
y = 100 * \
numpy.abs(numpy.sin((X[:, 6]) / 10) - 4.0) + \
10 * numpy.random.normal(size=m)
# Fit an Earth model
model = Earth(max_degree=3, minspan_alpha=.5)
model.fit(X, y)
# Print the model
print(model.trace())
print(model.summary())
# Plot the model
y_hat = model.predict(X)
plt.plot(X[:, 6], y, 'r.')
plt.plot(X[:, 6], y_hat, 'b.')
plt.show()
from matplotlib import pyplot
from sklearn import preprocessing
from sklearn.feature_extraction import DictVectorizer
from pyearth import Earth
from matplotlib import pyplot
df = pd.read_excel('relay-foods.xlsx', sheetname='Purchase Data - Full Study')
df['OrderId'] = df['OrderId'].astype('category')
df['CommonId'] = df['CommonId'].astype('category')
df['OrderId'] = df['OrderId'].astype('category')
df['CommonId'] = df['CommonId'].astype('category')
df.dtypes
col_names = ['OrderDate', 'PickupDate']
df = df.drop(col_names, axis=1)
y = df['TotalCharges']
df_2 = df[['OrderId', 'UserId', 'PupId']]
#del df['OrderDate']
X = [dict(r.iteritems()) for _, r in df_2.iterrows()]
train_fea = DictVectorizer().fit_transform(X)
#Fit an Earth model
model = Earth()
model.fit(train_fea, y)
#Print the model
print(model.trace())
print(model.summary())
#Plot the model
y_hat = model.predict(X)
def train(object_name,
data_dir,
output_dir,
train_type,
classifier_type,
learned_model=None,
debug=False):
from sklearn import linear_model, tree
from sklearn.svm import SVR
from sklearn.tree import DecisionTreeRegressor, DecisionTreeClassifier
from sklearn.ensemble import AdaBoostRegressor
if classifier_type == 'Earth':
from pyearth import Earth
import numpy as np
have_graphviz = True
try:
import graphviz
except:
have_graphviz = False
ans = None
saso_data = load_data_file(object_name, data_dir)
if train_type == 'gripper_status':
action_str = 'gs'
actions = range(CLOSE_ACTION_ID + 1)
x = []
y = []
x_index = []
for action in actions:
for sasor in saso_data[action]:
#x_entry = sasor['touch_prev'] + sasor['init_joint_values']
x_entry = sasor['next_joint_values']
x_entry = x_entry + sasor['next_gripper'] + sasor['next_object']
x_entry.append(sasor['next_object'][0] -
sasor['next_gripper'][0])
x_entry.append(sasor['next_object'][1] -
sasor['next_gripper'][1])
x.append(x_entry)
x_index.append(sasor['index'])
if action == CLOSE_ACTION_ID:
y.append(1)
else:
y.append(0) #gripper open
if train_type == 'pick_success_probability':
action_str = repr(PICK_ACTION_ID)
x = []
y = []
x_index = []
for sasor in saso_data[PICK_ACTION_ID]:
#x_entry = sasor['touch_prev'] + sasor['init_joint_values']
x_entry = sasor['init_joint_values']
x_entry = x_entry + sasor['init_gripper'][0:3] + sasor[
'init_object'][0:3]
x_entry.append(sasor['init_object'][0] - sasor['init_gripper'][0])
x_entry.append(sasor['init_object'][1] - sasor['init_gripper'][1])
x.append(x_entry)
x_index.append(sasor['index'])
if sasor['reward'] > 0:
y.append(1)
else:
y.append(0)
if train_type in ['pick_success_probability', 'gripper_status']:
if learned_model is not None:
logistic = learned_model
else:
print classifier_type
if classifier_type == 'DTC':
logistic = DecisionTreeClassifier(criterion='entropy')
else:
logistic = linear_model.LogisticRegression(max_iter=400, C=1.0)
logistic.fit(x, y)
joblib.dump(
logistic,
output_dir + '/' + classifier_type + '-' + action_str + '.pkl')
ans = logistic
print logistic.score(x, y)
print logistic.get_params()
print len(x)
if classifier_type != 'DTC':
print logistic.coef_
print logistic.intercept_
yaml_out = {}
yaml_out['coef'] = logistic.coef_.tolist()[0]
yaml_out['intercept'] = logistic.intercept_.tolist()[0]
write_config_in_file(
output_dir + '/' + classifier_type + '-' + action_str +
".yaml", yaml_out)
else:
print logistic.feature_importances_
#feature_names=['t1','t2', 'j1', 'j2']
feature_names = [
'j1', 'j2'
] #Touch not required when object coordinates are known
feature_names = feature_names + [
'gx', 'gy', 'gz', 'gxx', 'gyy', 'gzz', 'gw'
][0:3]
feature_names = feature_names + [
'ox', 'oy', 'oz', 'oxx', 'oyy', 'ozz', 'ow'
][0:3]
feature_names = feature_names + ['xrel', 'yrel']
if have_graphviz:
dot_data = tree.export_graphviz(logistic,
out_file=None,
feature_names=feature_names,
filled=True)
graph = graphviz.Source(dot_data)
graph.render(output_dir + '/' + classifier_type + '-' +
action_str)
yaml_out = {}
yaml_out["max_depth"] = logistic.tree_.max_depth
yaml_out["values"] = logistic.tree_.value
yaml_out['n_nodes'] = logistic.tree_.node_count
yaml_out['children_left'] = logistic.tree_.children_left
yaml_out['children_right'] = logistic.tree_.children_right
yaml_out['feature'] = logistic.tree_.feature
yaml_out['threshold'] = logistic.tree_.threshold
write_config_in_file(
output_dir + '/' + classifier_type + '-' + action_str +
".yaml", yaml_out)
if debug:
for i in range(0, len(x)):
y_bar = logistic.predict([x[i]])
if y_bar != y[i]:
print x_index[i]
print x[i]
print y[i]
print logistic.predict_proba([x[i]])
if classifier_type != 'DTC':
print logistic.decision_function([x[i]])
prob = (np.dot(logistic.coef_[0], x[i]) +
logistic.intercept_[0])
print prob
prob *= -1
prob = np.exp(prob)
prob += 1
prob = np.reciprocal(prob)
print prob
if 'next_state' in train_type:
actions = range(10)
# predictions can be 18, 7 for gripper pose, 7 for objct pose
# 2 for joint values
# 2 for touch values
predictions = range(NUM_PREDICTIONS)
train_type_array = train_type.split('_')
for s in train_type_array:
if 'action' in s:
actions = s.split('-')[1:]
if 'pred' in s:
predictions = s.split('-')[1:]
ans = {}
for action_ in actions:
action = int(action_)
x = []
y = []
y_c = []
l_reg = []
l_reg_c = []
x_index = []
for i in range(0, NUM_PREDICTIONS):
y.append([])
y_c.append([])
l_reg.append('')
l_reg_c.append('')
for sasor in saso_data[action]:
if sasor['reward'] > -999: #discard invalid states
x_entry = sasor['init_joint_values']
x_entry = x_entry + sasor['init_gripper'][0:3] + sasor[
'init_object'][0:3]
x_entry.append(sasor['init_object'][0] -
sasor['init_gripper'][0])
x_entry.append(sasor['init_object'][1] -
sasor['init_gripper'][1])
x.append(x_entry)
x_index.append(sasor['index'])
for p_ in predictions:
p = int(p_)
y[p].append(get_prediction_value(sasor, p))
y_default = get_default_value(sasor, p)
y_c[p].append(is_correct(p, y[p][-1], y_default))
"""
try:
check_array(x)
check_array(y[p])
except:
print x[-1]
print y[p][-1]
print sasor['index']
assert(0==1)
"""
print len(x)
ans[action] = {}
for p_ in predictions:
p = int(p_)
if learned_model is not None:
l_reg[p] = learned_model[action][p]
else:
if classifier_type == 'ridge':
l_reg[p] = linear_model.Ridge(alpha=0.5,
normalize=True)
elif classifier_type == 'SVR':
l_reg[p] = SVR(epsilon=0.2)
elif classifier_type in ['DTR', 'DTRM']:
l_reg[p] = DecisionTreeRegressor()
elif classifier_type == 'DTC':
l_reg[p] = DecisionTreeClassifier()
elif classifier_type == 'Earth':
l_reg[p] = Earth()
elif classifier_type == 'AdaLinear':
l_reg[p] = AdaBoostRegressor(
linear_model.LinearRegression())
else:
l_reg[p] = linear_model.LinearRegression()
if classifier_type == 'DTRM':
l_reg[p].fit(x, np.transpose(np.array(y)))
elif classifier_type == 'DTC':
l_reg[p].fit(x, y_c[p])
else:
l_reg[p].fit(x, y[p])
joblib.dump(
l_reg[p], output_dir + '/' + classifier_type + "-" +
repr(action) + "-" + repr(p) + '.pkl')
ans[action][p] = l_reg[p]
if classifier_type == 'DTRM':
print repr(action) + " " + repr(p) + " " + repr(
l_reg[p].score(x, np.transpose(np.array(y))))
elif classifier_type == 'DTC':
print repr(action) + " " + repr(p) + " " + repr(
l_reg[p].score(x, y_c[p]))
else:
print repr(action) + " " + repr(p) + " " + repr(
l_reg[p].score(x, y[p]))
print l_reg[p].get_params()
if classifier_type not in [
'SVR', 'DTR', 'DTRM', 'AdaLinear', 'DTC'
]:
print l_reg[p].coef_
if classifier_type not in [
'DTR', 'DTRM', 'AdaLinear', 'DTC', 'Earth'
]:
print l_reg[p].intercept_
if classifier_type in ['Earth']:
for j in range(0, len(x)):
predict_earth(l_reg[p], x[j])
print l_reg[p].summary()
if learned_model is None:
if classifier_type in ['DTR', 'DTRM', 'AdaLinear', 'DTC']:
print l_reg[p].feature_importances_
feature_names = ['j1', 'j2']
feature_names = feature_names + [
'gx', 'gy', 'gz', 'gxx', 'gyy', 'gzz', 'gw'
][0:3]
feature_names = feature_names + [
'ox', 'oy', 'oz', 'oxx', 'oyy', 'ozz', 'ow'
][0:3]
feature_names = feature_names + ['xrel', 'yrel']
if have_graphviz:
dot_data = tree.export_graphviz(
l_reg[p],
out_file=None,
feature_names=feature_names,
filled=True)
graph = graphviz.Source(dot_data)
graph.render(output_dir + '/' + classifier_type +
"-" + repr(action) + "-" + repr(p))
yaml_out = {}
yaml_out['max_depth'] = l_reg[p].tree_.max_depth
yaml_out["values"] = l_reg[p].tree_.value.tolist()
yaml_out['n_nodes'] = l_reg[p].tree_.node_count
yaml_out['children_left'] = l_reg[
p].tree_.children_left.tolist()
yaml_out['children_right'] = l_reg[
p].tree_.children_right.tolist()
yaml_out['feature'] = l_reg[p].tree_.feature.tolist()
yaml_out['threshold'] = l_reg[
p].tree_.threshold.tolist()
write_config_in_file(
output_dir + '/' + classifier_type + "-" +
repr(action) + "-" + repr(p) + ".yaml", yaml_out)
if classifier_type in ['Earth']:
yaml_out = get_yaml_earth(l_reg[p])
write_config_in_file(
output_dir + '/' + classifier_type + "-" +
repr(action) + "-" + repr(p) + ".yaml", yaml_out)
if classifier_type == 'DTRM':
i = 0
y_bar = l_reg[p].predict([x[i]])
print x_index[i]
print x[i]
y_t = np.transpose(np.array(y))
print repr(y_t[i]) + ' Prediction ' + repr(y_bar)
break
if debug:
for i in range(0, len(x)):
y_bar = l_reg[p].predict([x[i]])
if classifier_type == 'DTC':
if y_bar != y_c[p][i]:
print x_index[i]
print x[i]
print y_c[p][i]
print y[p][i]
print l_reg[p].predict_proba([x[i]])
else:
if is_correct(p, y_bar, y[p][i]) == 0:
print x_index[i]
print x[i]
print repr(
y[p][i]) + ' Prediction ' + repr(y_bar)
return ans
def test_score():
earth = Earth(**default_params)
model = earth.fit(X, y)
record = model.pruning_trace()
rsq = record.rsq(record.get_selected())
assert_almost_equal(rsq, model.score(X, y))
y = pm.reshape(-1, 1)
print y.shape
#获得y的统计信息
#statistics(y)
##频率分布图
#drawHist(y,'PM2.5','Frequency','the Frequency of PM2.5')
##频率累计图
#drawCumulativeHist(y,'PM2.5','Frequency','Curve cumulative of PM2.5')
##箱图
#drawBox(y,'PM2.5','BOX of PM2.5')
###########################################################
#MARS拟合
#1)Fit an Earth model
criteria = ('rss', 'gcv', 'nb_subsets')
model = Earth(max_degree=2, feature_importance_type=criteria)
model.fit(X, y) #这里用的是标准化之后的数据
#2)Print the model模型结果
print(model.trace())
print(model.summary())
print(model.summary_feature_importances(sort_by='gcv'))
#3)预测的y
y_hat = model.predict(X)
#评价指标
#R_2=R2((y_hat.reshape(-1,1)),(y.reshape(-1,1)))
R_square = metrics.r2_score((y.reshape(-1, 1)),
(y_hat.reshape(-1, 1))) #计算r2,来表示y与拟合y_hat的接近程度
RMSE = sqrt(metrics.mean_squared_error(y.reshape(-1, 1), y_hat.reshape(-1, 1)))
def test_export_python_function():
for smooth in (True, False):
model = Earth(penalty=1, smooth=smooth, max_degree=2).fit(X, y)
export_model = export_python_function(model)
for exp_pred, model_pred in zip(model.predict(X), export_model(X)):
assert_almost_equal(exp_pred, model_pred)
X_train['TotalSF'] = X_train['TotalBsmtSF'] + X_train['1stFlrSF'] + X_train[
'2ndFlrSF']
X_test['TotalSF'] = X_test['TotalBsmtSF'] + X_test['1stFlrSF'] + X_test[
'2ndFlrSF']
#normality check for the target
ax = sns.distplot(y_train)
plt.show()
#log transform the dependent variable for normality
y_train = np.log(y_train)
ax = sns.distplot(y_train)
plt.show()
#mars solution
model = Earth()
model = Earth(
max_degree=2,
penalty=1.0,
minspan_alpha=0.01,
endspan_alpha=0.01,
endspan=5
) #2nd degree formula is necessary to see interactions, penalty and alpha values for making model simple
model.fit(X_train, y_train)
model.score(X_train, y_train)
#y_pred = model.predict(train["SalePrice"])
y_pred = model.predict(X_test)
y_pred = np.exp(y_pred) # inverse log transform the results
)
# Select target and feature dataset(s) --> [target, feature1, feature2, ... ]
datasets = [
Dataset('runoff', database),
Dataset('runoff', database).normalized(),
Dataset('temp', database).normalized(),
Dataset('precip', database).normalized(),
Dataset('season', database).normalized()
]
# Select leadtimes for target and feature. negative:past/positive:future
leadtimes = [[1, 3], [-4, -1], [-4, -1], [-4, -1], [1, 1]]
# Select Model
model_type = Earth(max_degree=10, smooth=True)
#model_type= Lasso(alpha=0.05,normalize=True, max_iter=3000)
#model_type = Regressor(
# layers=[
# Layer("Sigmoid",units=5),
# Layer("Linear", units=1)],
# learning_rate=0.1,
# n_iter=1000)
# Set training interval
startyear = DateFormat(1900, 1)
endyear = DateFormat(2005, 36)
training_daterange = DateFormat.decadal_daterange(startyear, endyear)
# Set testing interval
startyear = DateFormat(2006, 1)
y1 = 120 * np.abs(np.sin((X[:, 6]) / 6) - 1.0) + 15 * np.random.normal(size=m)
y2 = 120 * np.abs(np.sin((X[:, 5]) / 6) - 1.0) + 15 * np.random.normal(size=m)
y1 = (y1 - y1.mean()) / y1.std()
y2 = (y2 - y2.mean()) / y2.std()
y_mix = np.concatenate((y1[:, np.newaxis], y2[:, np.newaxis]), axis=1)
alphas = [0.9, 0.8, 0.6, 0.4, 0.2, 0.1]
n_plots = len(alphas)
k = 1
fig = plt.figure(figsize=(10, 15))
for i, alpha in enumerate(alphas):
# Fit an Earth model
model = Earth(max_degree=5,
minspan_alpha=.05,
endspan_alpha=.05,
max_terms=10,
check_every=1,
thresh=0.)
output_weight = np.array([alpha, 1 - alpha])
model.fit(X, y_mix, output_weight=output_weight)
print(model.summary())
# Plot the model
y_hat = model.predict(X)
mse = ((y_hat - y_mix) ** 2).mean(axis=0)
ax = plt.subplot(n_plots, 2, k)
ax.set_ylabel("Run {0}".format(i + 1), rotation=0, labelpad=20)
plt.plot(X[:, 6], y_mix[:, 0], 'r.')
plt.plot(X[:, 6], model.predict(X)[:, 0], 'b.')
plt.title("MSE: {0:.3f}, Weight : {1:.1f}".format(mse[0], alpha))