def score(self, X, confounder_types, assignment='assignment', store_model_fit=False, intercept=True):
df = X[[assignment]]
regression_confounders = []
for confounder, var_type in confounder_types.items():
if var_type == 'o' or var_type == 'u':
c_dummies = pd.get_dummies(X[[confounder]], prefix=confounder)
if len(c_dummies.columns) == 1:
df[c_dummies.columns] = c_dummies[c_dummies.columns]
regression_confounders.extend(c_dummies.columns)
else:
df[c_dummies.columns[1:]] = c_dummies[c_dummies.columns[1:]]
regression_confounders.extend(c_dummies.columns[1:])
else:
regression_confounders.append(confounder)
df.loc[:,confounder] = X[confounder].copy() #
df.loc[:,confounder] = X[confounder].copy() #
if intercept:
df.loc[:,'intercept'] = 1.
regression_confounders.append('intercept')
logit = Logit(df[assignment], df[regression_confounders])
result = logit.fit()
if store_model_fit:
self.model_fit = result
X.loc[:,'propensity score'] = result.predict(df[regression_confounders])
return X
def SM_logit(X, y):
"""Computing logit function using statsmodels Logit and
output is coefficient array."""
logit = Logit(y, X)
result = logit.fit()
coeff = result.params
return coeff
def logit_reg(): X_smoted, X_test, y_smoted, y_test = prep_X_y(df, constant=True) lm = Logit(y_smoted, X_smoted).fit(method = 'powell') y_pred = lm.predict(X_test).round(0) print 'Statsmodels Logit Regression--------------------------------' print 'Confusion Matrix:', confusion_matrix(y_test, y_pred) print 'Accuracy:', accuracy_score(y_test, y_pred) print 'Precision:', precision_score(y_test, y_pred) print 'Recall:', recall_score(y_test, y_pred) return lm
def _initialize(cls):
y, x = cls.y, cls.x
modp = Logit(y, x)
cls.res2 = modp.fit(disp=0)
mod = LogitPenalized(y, x, penal=cls.penalty)
mod.pen_weight = 0
cls.res1 = mod.fit(disp=0)
cls.atol = 1e-4 # why not closer ?
def _initialize(cls):
y, x = cls.y, cls.x
modp = Logit(y, x[:, :cls.k_nonzero])
cls.res2 = modp.fit(disp=0)
mod = LogitPenalized(y, x, penal=cls.penalty)
mod.pen_weight *= .5
mod.penal.tau = 0.05
cls.res1 = mod.fit(method='bfgs', maxiter=100, disp=0)
cls.exog_index = slice(None, cls.k_nonzero, None)
cls.atol = 5e-3
def test_perfect_prediction():
cur_dir = os.path.dirname(os.path.abspath(__file__))
iris_dir = os.path.join(cur_dir, '..', '..', 'genmod', 'tests', 'results')
iris_dir = os.path.abspath(iris_dir)
iris = np.genfromtxt(os.path.join(iris_dir, 'iris.csv'), delimiter=",",
skip_header=1)
y = iris[:,-1]
X = iris[:,:-1]
X = X[y != 2]
y = y[y != 2]
X = sm.add_constant(X, prepend=True)
mod = Logit(y,X)
assert_raises(PerfectSeparationError, mod.fit)
#turn off raise PerfectSeparationError
mod.raise_on_perfect_prediction = False
mod.fit(disp=False) #should not raise
class LogReg:
def __init__(self):
self.coef_=None
def fit(self,X,y):
X=add_constant(X)
self.lr=Logit(y,X)
self.l_fitted=self.lr.fit()
self.coef_=self.l_fitted.params[:-1]
def predict(self,X):
if self.coef_ is None:
print('you must first fit the model')
return
X=add_constant(X)
return(self.lr.predict(self.l_fitted.params,X))
class LogisticRegression(object):
def __init__(self):
pass
def fit(self, X, y, **kwargs):
self.model = Logit(y, X)
self.result = self.model.fit()
def predict_proba(self, X):
return self.result.predict(X)
class LogisticRegression(object):
def __init__(self):
pass
def fit(self, X, y, **kwargs):
from statsmodels.discrete.discrete_model import Logit
self.model = Logit(y, X)
self.result = self.model.fit()
def predict_proba(self, X):
return self.result.predict(X)
def regressOnFeatureSM(self):
'''
What: create a regression model with Statsmodels and try and find the Betas,
ie, try to find feature causation.
In: trimmed-down data
Out: print statements and a linear model regression
'''
df_dummies = pd.get_dummies(self.df_data)
df_dummies = pd.concat([df_dummies, self.df_data], axis=0)
X_train, X_test, y_train, y_test = train_test_split(df_dummies, self.df_data[self.predict].values,
test_size=0.3, random_state=42)
X_train = tools.add_constant(X_train)
modelSM = Logit(y_train, X_train).fit()
print modelSM.summary()
def fit(self, X, y, **kwargs):
from statsmodels.discrete.discrete_model import Logit
self.model = Logit(y, X)
self.result = self.model.fit()
def fit(self, X, y, **kwargs):
self.model = Logit(y, X)
self.result = self.model.fit()
def fit(self,X,y):
X=add_constant(X)
self.lr=Logit(y,X)
self.l_fitted=self.lr.fit()
self.coef_=self.l_fitted.params[:-1]
df.hist() plt.show() # Part 2 # 2.1 from statsmodels.discrete.discrete_model import Logit from statsmodels.tools import add_constant X = df[['gre', 'gpa', 'rank']].values X_const = add_constant(X, prepend=True) y = df['admit'].values logit_model = Logit(y, X_const).fit() # 2.2 logit_model.summary() # 2.3 import numpy as np from sklearn.cross_validation import KFold from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score, precision_score, recall_score kfold = KFold(len(y)) accuracies = []