Module imodels.irf.irf
Expand source code
import numpy as np
class IRFClassifier():
def __init__(self):
self.model = wrf()
self.predict = self.model.predict
self.predict_proba = self.model.predict_proba
def fit(self, X, y, lambda_reg=0.1, sample_weight=None):
'''fit a linear model with integer coefficient and L1 regularization
Params
------
sample_weight: np.ndarray (n,)
weight for each individual sample
'''
if 'pandas' in str(type(X)):
X = X.values
if 'pandas' in str(type(y)):
y = y.values
assert type(X) == np.ndarray, 'inputs should be ndarrays'
assert type(y) == np.ndarray, 'inputs should be ndarrays'
self.model.fit(X, y, keep_record=False)
Classes
class IRFClassifier
-
Expand source code
class IRFClassifier(): def __init__(self): self.model = wrf() self.predict = self.model.predict self.predict_proba = self.model.predict_proba def fit(self, X, y, lambda_reg=0.1, sample_weight=None): '''fit a linear model with integer coefficient and L1 regularization Params ------ sample_weight: np.ndarray (n,) weight for each individual sample ''' if 'pandas' in str(type(X)): X = X.values if 'pandas' in str(type(y)): y = y.values assert type(X) == np.ndarray, 'inputs should be ndarrays' assert type(y) == np.ndarray, 'inputs should be ndarrays' self.model.fit(X, y, keep_record=False)
Methods
def fit(self, X, y, lambda_reg=0.1, sample_weight=None)
-
fit a linear model with integer coefficient and L1 regularization
Params
sample_weight
:np.ndarray
(n
,)- weight for each individual sample
Expand source code
def fit(self, X, y, lambda_reg=0.1, sample_weight=None): '''fit a linear model with integer coefficient and L1 regularization Params ------ sample_weight: np.ndarray (n,) weight for each individual sample ''' if 'pandas' in str(type(X)): X = X.values if 'pandas' in str(type(y)): y = y.values assert type(X) == np.ndarray, 'inputs should be ndarrays' assert type(y) == np.ndarray, 'inputs should be ndarrays' self.model.fit(X, y, keep_record=False)