def test_model_poisson_regressor(self):
X, y = make_regression(n_features=5,
n_samples=100,
n_targets=1,
random_state=42,
n_informative=3)
y = numpy.abs(y)
y = y / y.max() + 1e-5
model = linear_model.PoissonRegressor().fit(X, y)
model_onnx = convert_sklearn(
model,
"linear regression",
[("input", FloatTensorType([None, X.shape[1]]))],
target_opset=TARGET_OPSET)
self.check_model(model_onnx, X.astype(numpy.float32))
dump_data_and_model(X.astype(numpy.float32),
model,
model_onnx,
basename="SklearnPoissonRegressor-Dec4")
model_onnx = convert_sklearn(
model,
"linear regression",
[("input", DoubleTensorType([None, X.shape[1]]))],
target_opset=TARGET_OPSET)
dump_data_and_model(X.astype(numpy.float64),
model,
model_onnx,
basename="SklearnPoissonRegressor64")
def fit_glm(self, tol=1e-12, pretest=False): # added on D5
clf = linear_model.PoissonRegressor(fit_intercept=False,
tol=tol,
verbose=3,
alpha=0)
clf.fit(spr.diags(1 / self.d_a) @ self.C(),
self.μhat_a,
sample_weight=self.d_a)
return clf.coef_[0:-self.nbk], clf.coef_[-self.nbk:]
def solveGLM(self, σ, tol=1e-9): # added on D3
ptm = time()
muhat_a = (self.n_x.reshape((self.nbx, -1)) @ self.m_y.reshape(
(-1, self.nby))).flatten() / self.n_x.sum()
ot_as_glm = linear_model.PoissonRegressor(fit_intercept=False,
tol=tol,
verbose=3,
alpha=0)
ot_as_glm.fit(-self.M_z_a().T,
muhat_a * np.exp(-self.Φ_a / σ),
sample_weight=np.exp(self.Φ_a / σ))
p = σ * ot_as_glm.coef_
u_x, v_y = p[:self.nbx] - p[0], p[self.nbx:] + p[0]
μ_x_y = np.exp((self.Φ_a.reshape((self.nbx, -1)) - u_x.reshape(
(-1, 1)) - v_y.reshape((1, -1))) / σ)
valobs = self.Φ_a.dot(μ_x_y.flatten())
valtot = valobs - σ * sum_xlogx(μ_x_y)
taken = time() - ptm
return μ_x_y, u_x, v_y, valobs, valtot, None, taken, 'GLM'
def generate_model(pred_vars,
log_transform=True,
one_hot_week=False,
method="lm"):
"""
Generate the model for transforming and predicting.
...
"""
assert method in ['lm',
'poisson'], "method must be one of 'lm' or 'poisson'"
if log_transform:
ft = preprocessing.FunctionTransformer(np.log)
else:
ft = preprocessing.FunctionTransformer()
if one_hot_week:
model_prep = compose.ColumnTransformer(
[("onehot_categorical", preprocessing.OneHotEncoder(),
["week_num"]), ("num_scaler", ft, pred_vars)],
remainder="drop",
)
else:
model_prep = compose.ColumnTransformer(
[("num_scaler", ft, pred_vars + ['ca_prop'])],
remainder="drop",
)
if method == 'lm':
pipe = pipeline.Pipeline([("preprocessor", model_prep),
("regressor",
linear_model.LinearRegression())])
elif method == 'poisson':
pipe = pipeline.Pipeline([
("preprocessor", model_prep),
("regressor",
linear_model.PoissonRegressor(alpha=1e-12, max_iter=10000))
])
return pipe
regression(linear_model.ElasticNetCV(random_state=RANDOM_SEED)),
regression(linear_model.GammaRegressor()),
regression(linear_model.HuberRegressor()),
regression(linear_model.Lars()),
regression(linear_model.LarsCV()),
regression(linear_model.Lasso(random_state=RANDOM_SEED)),
regression(linear_model.LassoCV(random_state=RANDOM_SEED)),
regression(linear_model.LassoLars()),
regression(linear_model.LassoLarsCV()),
regression(linear_model.LassoLarsIC()),
regression(linear_model.LinearRegression()),
regression(linear_model.OrthogonalMatchingPursuit()),
regression(linear_model.OrthogonalMatchingPursuitCV()),
regression(
linear_model.PassiveAggressiveRegressor(random_state=RANDOM_SEED)),
regression(linear_model.PoissonRegressor()),
regression(
linear_model.RANSACRegressor(
base_estimator=tree.ExtraTreeRegressor(**TREE_PARAMS),
random_state=RANDOM_SEED)),
regression(linear_model.Ridge(random_state=RANDOM_SEED)),
regression(linear_model.RidgeCV()),
regression(linear_model.SGDRegressor(random_state=RANDOM_SEED)),
regression(linear_model.TheilSenRegressor(random_state=RANDOM_SEED)),
regression(linear_model.TweedieRegressor(power=0.0)),
regression(linear_model.TweedieRegressor(power=1.0)),
regression(linear_model.TweedieRegressor(power=1.5)),
regression(linear_model.TweedieRegressor(power=2.0)),
regression(linear_model.TweedieRegressor(power=3.0)),
# Statsmodels Linear Regression
import matplotlib.pyplot as plt
from sklearn import linear_model
from sklearn.model_selection import train_test_split
from helper import prepare_data
df = prepare_data()
y = df["Berri1"]
X = df[[
"day", "month", "day_of_week", "Mean Temp (°C)", "Total Precip (mm)",
"Snow on Grnd (cm)", "Min Temp (°C)", "Max Temp (°C)"
]]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
clf = linear_model.PoissonRegressor(max_iter=200)
clf.fit(X_train, y_train)
print(clf.score(X_test, y_test))
result = clf.predict(X)
plt.plot(list(y.index), y, label="true")
plt.plot(list(y.index), result, label="predicted")
plt.legend()
plt.show()
# The coefficients
# print('Coefficients: \n', regr.coef_)
# The mean squared error
print('Mean squared error: %.2f'
# % mean_squared_error(y_test, y_pred))
# The coefficient of determination: 1 is perfect prediction
print('Coefficient of determination: %.2f'
# % r2_score(y_test, y_pred))
scores_length_no_reg = cross_val_score(regr, X_train_std, y_train, cv=5, scoring='r2')
regr.fit(X_train_std, y_train)
#The mean score and the standard deviation are hence given by:
print("%0.2f (alpha = 1.0) accuracy with a standard deviation of %0.2f" % (scores_length_no_reg.mean(), scores_length_no_reg.std()))
# Alpha = 0.1
regr_l1_1 = linear_model.PoissonRegressor(alpha=0.01)
scores_length_l1_1_reg = cross_val_score(regr_l1_1, X_train_std, y_train, cv=5, scoring='r2')
regr_l1_1.fit(X_train_std, y_train)
#The mean score and the standard deviation are hence given by:
print("%0.2f (alpha = 0.01) accuracy with a standard deviation of %0.2f" % (scores_length_l1_1_reg.mean(), scores_length_l1_1_reg.std()))
# Alpha = 30
regr_l1_30 = linear_model.PoissonRegressor(alpha=30)
scores_length_l1_30_reg = cross_val_score(regr_l1_30, X_train_std, y_train, cv=5, scoring='r2')
regr_l1_30.fit(X_train_std, y_train)
#The mean score and the standard deviation are hence given by:
print("%0.2f (alpha = 30) accuracy with a standard deviation of %0.2f" % (scores_length_l1_30_reg.mean(), scores_length_l1_30_reg.std()))
# Alpha = 100
regr_l1_100 = linear_model.PoissonRegressor(alpha=0.001)
scores_length_l1_100_reg = cross_val_score(regr_l1_100, X_train_std, y_train, cv=5, scoring='r2')
def _init_model(self):
return linear_model.PoissonRegressor(alpha=1e-4, warm_start=True)
plt.scatter(X, y, color='blue', alpha=0.5, label='ToyData')
#plt.legend()
plt.show()
df = pd.DataFrame([X, y]).T.rename({0: 'x', 1: 'y'}, axis=1)
# df['x_round']=df['x'].apply(lambda x:np.round(x,1))
# for i in range(x_min, x_max):
# plt.hist( df[df['x_round']==i]['y'])
# plt.title(np.exp(w[0]+w[1]*i))
# plt.show()
lr = linear_model.LinearRegression()
df['logy'] = np.log(df['y'])
lr.fit(df[['x']], df['logy'])
print([lr.intercept_, lr.coef_[0]])
pr = linear_model.PoissonRegressor(alpha=0, fit_intercept=True, max_iter=300)
pr.fit(df[['x']], df['y'])
print([pr.intercept_, pr.coef_[0]])
plt.scatter(X, y, color='blue', alpha=0.5)
plt.plot(x, lam, color='red', label='True')
plt.plot(x,
lam + np.sqrt(lam),
color='red',
label='True+sd',
linestyle='dashed')
plt.plot(x,
lam - np.sqrt(lam),
color='red',
label='True-sd',
linestyle='dashed')
