python logistic步骤_Python api.Logit方法代码示例
本文整理匯總了Python中statsmodels.api.Logit方法的典型用法代碼示例。如果您正苦于以下問題：Python api.Logit方法的具體用法？Python api.Logit怎么用？Python api.Logit使用的例子？那么恭喜您, 這里精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在模塊statsmodels.api的用法示例。
在下文中一共展示了api.Logit方法的13個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點贊，您的評價將有助于我們的系統推薦出更棒的Python代碼示例。
示例1: compute
?點贊 6
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def compute(self, method='logistic'):
"""
Compute propensity score and measures of goodness-of-fit
Parameters
----------
method : str
Propensity score estimation method. Either 'logistic' or 'probit'
"""
predictors = sm.add_constant(self.covariates, prepend=False)
if method == 'logistic':
model = sm.Logit(self.treatment, predictors).fit(disp=False, warn_convergence=True)
elif method == 'probit':
model = sm.Probit(self.treatment, predictors).fit(disp=False, warn_convergence=True)
else:
raise ValueError('Unrecognized method')
return model.predict()
開發者ID:kellieotto，項目名稱:pscore_match，代碼行數:19，
示例2: Nagelkerke_Rsquare
?點贊 6
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def Nagelkerke_Rsquare(self,columns):
cols=columns.copy()
cols.append('intercept')
log_clf=sm.Logit(self.data[self.target],self.data[cols])
N=self.data.shape[0]
# result=log_clf.fit(disp=0,method='powell')
try:
result=log_clf.fit(disp=0)
except:
result=log_clf.fit(disp=0,method='powell')
llf=result.llf
llnull=result.llnull
lm=np.exp(llf)
lnull=np.exp(llnull)
naglkerke_rsquare=(1-(lnull/lm)**(2/N))/(1-lnull**(2/N))
return naglkerke_rsquare
開發者ID:dominance-analysis，項目名稱:dominance-analysis，代碼行數:18，
示例3: Cox_and_Snell_Rsquare
?點贊 6
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def Cox_and_Snell_Rsquare(self,columns):
cols=columns.copy()
cols.append('intercept')
log_clf=sm.Logit(self.data[self.target],self.data[cols])
N=self.data.shape[0]
# result=log_clf.fit(disp=0,method='powell')
try:
result=log_clf.fit(disp=0)
except:
result=log_clf.fit(disp=0,method='powell')
llf=result.llf
llnull=result.llnull
lm=np.exp(llf)
lnull=np.exp(llnull)
cox_and_snell_rsquare=(1-(lnull/lm)**(2/N))
return cox_and_snell_rsquare
開發者ID:dominance-analysis，項目名稱:dominance-analysis，代碼行數:18，
示例4: test_logistic_regressions
?點贊 6
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def test_logistic_regressions():
def _test_random_logistic_regression():
n_uncorr_features, n_corr_features, n_drop_features = (
generate_regression_hyperparamters())
X, y, parameters = make_logistic_regression(
n_samples=N_SAMPLES,
n_uncorr_features=n_uncorr_features,
n_corr_features=n_corr_features,
n_drop_features=n_drop_features)
lr = GLM(family=Bernoulli())
lr.fit(X, y)
#assert approx_equal(lr.coef_, parameters)
mod = sm.Logit(y, X)
res = mod.fit()
assert approx_equal(lr.coef_, res.params)
assert approx_equal(lr.coef_standard_error_, res.bse)
for _ in range(N_REGRESSION_TESTS):
_test_random_logistic_regression()
開發者ID:madrury，項目名稱:py-glm，代碼行數:22，
示例5: setup
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def setup(self):
#fit for each test, because results will be changed by test
x = self.exog
nobs = x.shape[0]
np.random.seed(987689)
y_bin = (np.random.rand(nobs) < 1.0 / (1 + np.exp(x.sum(1) - x.mean()))).astype(int)
model = sm.Logit(y_bin, x) #, exposure=np.ones(nobs), offset=np.zeros(nobs)) #bug with default
# use start_params to converge faster
start_params = np.array([-0.73403806, -1.00901514, -0.97754543, -0.95648212])
self.results = model.fit(start_params=start_params, method='bfgs', disp=0)
開發者ID:birforce，項目名稱:vnpy_crypto，代碼行數:12，
示例6: setup_class
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def setup_class(cls):
data = sm.datasets.spector.load()
data.exog = sm.add_constant(data.exog, prepend=False)
#mod = sm.Probit(data.endog, data.exog)
cls.mod = sm.Logit(data.endog, data.exog)
#res = mod.fit(method="newton")
cls.params = [np.array([1,0.25,1.4,-7])]
##loglike = mod.loglike
##score = mod.score
##hess = mod.hessian
開發者ID:birforce，項目名稱:vnpy_crypto，代碼行數:12，
示例7: run_logistic_regression
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def run_logistic_regression(df):
# Logistic regression
X = df['pageviews_cumsum']
X = sm.add_constant(X)
y = df['is_conversion']
logit = sm.Logit(y, X)
logistic_regression_results = logit.fit()
print(logistic_regression_results.summary())
return logistic_regression_results
開發者ID:thomhopmans，項目名稱:themarketingtechnologist，代碼行數:11，
示例8: run_logistic_regression
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def run_logistic_regression(self):
# Logistic regression
X = self.df['pageviews_cumsum']
X = sm.add_constant(X)
y = self.df['is_conversion']
logit = sm.Logit(y, X)
self.logistic_regression_results = logit.fit()
print self.logistic_regression_results.summary()
開發者ID:thomhopmans，項目名稱:themarketingtechnologist，代碼行數:10，
示例9: McFadden_RSquare
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def McFadden_RSquare(self,columns):
cols=columns.copy()
cols.append('intercept')
# print("model columns :",cols)
log_clf=sm.Logit(self.data[self.target],self.data[cols])
# result=log_clf.fit(disp=0,method='powell')
try:
result=log_clf.fit(disp=0)
except:
result=log_clf.fit(disp=0,method='powell')
mcfadden_rsquare=result.prsquared
return mcfadden_rsquare
開發者ID:dominance-analysis，項目名稱:dominance-analysis，代碼行數:14，
示例10: Estrella
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def Estrella(self,columns):
cols=columns.copy()
cols.append('intercept')
log_clf=sm.Logit(self.data[self.target],self.data[cols])
N=self.data.shape[0]
# result=log_clf.fit(disp=0,method='powell')
try:
result=log_clf.fit(disp=0)
except:
result=log_clf.fit(disp=0,method='powell')
llf=result.llf
llnull=result.llnull
estrella_rsquare=1-((llf/llnull)**(-(2/N)*llnull))
return estrella_rsquare
開發者ID:dominance-analysis，項目名稱:dominance-analysis，代碼行數:16，
示例11: Adjusted_McFadden_RSquare
?點贊 5
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def Adjusted_McFadden_RSquare(self,columns):
log_clf=sm.Logit(self.data[self.target],self.data[cols])
# result=log_clf.fit(disp=0,method='powell')
try:
result=log_clf.fit(disp=0)
except:
result=log_clf.fit(disp=0,method='powell')
llf=result.llf
llnull=result.llnull
adjusted_mcfadden_rsquare=1-((llf-len(columns))/llnull)
return adjusted_mcfadden_rsquare
開發者ID:dominance-analysis，項目名稱:dominance-analysis，代碼行數:13，
示例12: estimate_treatment_effect
?點贊 4
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def estimate_treatment_effect(covariates, treatment, outcome):
"""Estimate treatment effects using propensity weighted least-squares.
Parameters
----------
covariates: `np.ndarray`
Array of shape [num_samples, num_features] of features
treatment: `np.ndarray`
Binary array of shape [num_samples] indicating treatment status for each
sample.
outcome: `np.ndarray`
Array of shape [num_samples] containing the observed outcome for each sample.
Returns
-------
result: `whynot.framework.InferenceResult`
InferenceResult object for this procedure
"""
start_time = perf_counter()
# Compute propensity scores with logistic regression model.
features = sm.add_constant(covariates, prepend=True, has_constant="add")
logit = sm.Logit(treatment, features)
model = logit.fit(disp=0)
propensities = model.predict(features)
# IP-weights
treated = treatment == 1.0
untreated = treatment == 0.0
weights = treated / propensities + untreated / (1.0 - propensities)
treatment = treatment.reshape(-1, 1)
features = np.concatenate([treatment, covariates], axis=1)
features = sm.add_constant(features, prepend=True, has_constant="add")
model = sm.WLS(outcome, features, weights=weights)
results = model.fit()
stop_time = perf_counter()
# Treatment is the second variable (after the constant offset)
ate = results.params[1]
stderr = results.bse[1]
conf_int = tuple(results.conf_int()[1])
return InferenceResult(
ate=ate,
stderr=stderr,
ci=conf_int,
individual_effects=None,
elapsed_time=stop_time - start_time,
)
開發者ID:zykls，項目名稱:whynot，代碼行數:54，
示例13: estimate_treatment_effect
?點贊 4
?
# 需要導入模塊: from statsmodels import api [as 別名]
# 或者: from statsmodels.api import Logit [as 別名]
def estimate_treatment_effect(covariates, treatment, outcome):
"""Estimate treatment effects using propensity score matching.
Parameters
----------
covariates: `np.ndarray`
Array of shape [num_samples, num_features] of features
treatment: `np.ndarray`
Binary array of shape [num_samples] indicating treatment status for each
sample.
outcome: `np.ndarray`
Array of shape [num_samples] containing the observed outcome for each sample.
Returns
-------
result: `whynot.framework.InferenceResult`
InferenceResult object for this procedure
"""
start_time = perf_counter()
# Compute propensity scores with logistic regression model.
features = sm.add_constant(covariates, has_constant="add")
logit = sm.Logit(treatment, features)
model = logit.fit(disp=0)
propensity_scores = model.predict(features)
matched_treatment, matched_outcome, matched_weights = get_matched_dataset(
treatment, propensity_scores, outcome
)
ate = compute_ate(matched_outcome, matched_treatment, matched_weights)
# Bootstrap confidence intervals
samples = []
num_units = len(matched_treatment)
for _ in range(1000):
sample_idxs = np.random.choice(num_units, size=num_units, replace=True)
samples.append(
compute_ate(
matched_outcome[sample_idxs],
matched_treatment[sample_idxs],
matched_weights[sample_idxs],
)
)
conf_int = (np.quantile(samples, 0.025), np.quantile(samples, 0.975))
stop_time = perf_counter()
return InferenceResult(
ate=ate,
stderr=None,
ci=conf_int,
individual_effects=None,
elapsed_time=stop_time - start_time,
)
開發者ID:zykls，項目名稱:whynot，代碼行數:57，
注：本文中的statsmodels.api.Logit方法示例整理自Github/MSDocs等源碼及文檔管理平臺，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。
總結
以上是生活随笔為你收集整理的python logistic步骤_Python api.Logit方法代码示例的全部內容，希望文章能夠幫你解決所遇到的問題。
- 上一篇： python代码测试健康指数计算器_Py
- 下一篇： websocket python爬虫_p