Python numpy2ri.activate方法代码示例

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def activate():
    global original_converter
    # If module is already activated, there is nothing to do.
    if original_converter is not None:
        return

    original_converter = conversion.Converter(
        'snapshot before pandas conversion',
        template=conversion.converter)
    numpy2ri.activate()
    new_converter = conversion.Converter('snapshot before pandas conversion',
                                         template=conversion.converter)
    numpy2ri.deactivate()

    for k, v in py2rpy.registry.items():
        if k is object:
            continue
        new_converter.py2rpy.register(k, v)

    for k, v in rpy2py.registry.items():
        if k is object:
            continue
        new_converter.rpy2py.register(k, v)

    conversion.set_conversion(new_converter) 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def setUp(self):
        numpy2ri.activate()
        ro.r('set.seed({})'.format(0))
        np.random.seed(0) 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def check_rpy2(self):
        try:
            from rpy2 import robjects
            from rpy2.robjects import numpy2ri
            numpy2ri.activate()
        except ImportError:
            return True, "WARNING: rpy2 is not installed correctly; SVs will not be visualized"

        return True, "rpy2 detected and appears to be correctly installed" 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def _build(self, **kwargs):
        from rpy2.robjects import numpy2ri, pandas2ri
        match_it = self.install_matchit()

        self.num_treatments = kwargs["num_treatments"]
        self.batch_size = kwargs["batch_size"]
        self.match_it = match_it
        numpy2ri.activate()
        pandas2ri.activate()

        return super(PSM, self)._build(**kwargs) 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def _build(self, **kwargs):
        from rpy2.robjects import numpy2ri
        from sklearn import linear_model
        grf = self.install_grf()

        self.grf = grf
        numpy2ri.activate()

        num_treatments = kwargs["num_treatments"]
        return [linear_model.Ridge(alpha=.5)] +\
               [None for _ in range(num_treatments)] 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def _build(self, **kwargs):
        from rpy2.robjects import numpy2ri, pandas2ri
        n_jobs = int(np.rint(kwargs["n_jobs"]))

        bart = self.install_bart()
        bart.set_bart_machine_num_cores(n_jobs)

        self.bart = bart
        numpy2ri.activate()
        pandas2ri.activate()

        return None 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def numpy_conversion():
    rpyn.activate()
    yield
    rpyn.deactivate() 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def test_activate(self):
        rpyn.deactivate()
        #FIXME: is the following still making sense ?
        assert rpyn.py2rpy != conversion.py2rpy
        l = len(conversion.py2rpy.registry)
        k = set(conversion.py2rpy.registry.keys())
        rpyn.activate()
        assert len(conversion.py2rpy.registry) > l
        rpyn.deactivate()
        assert len(conversion.py2rpy.registry) == l
        assert set(conversion.py2rpy.registry.keys()) == k 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def test_activate_twice(self):
        rpyn.deactivate()
        #FIXME: is the following still making sense ?
        assert rpyn.py2rpy != conversion.py2rpy
        l = len(conversion.py2rpy.registry)
        k = set(conversion.py2rpy.registry.keys())
        rpyn.activate()
        rpyn.deactivate()
        rpyn.activate()
        assert len(conversion.py2rpy.registry) > l
        rpyn.deactivate()
        assert len(conversion.py2rpy.registry) == l
        assert set(conversion.py2rpy.registry.keys()) == k 

# 需要导入模块: from rpy2.robjects import numpy2ri [as 别名]
# 或者: from rpy2.robjects.numpy2ri import activate [as 别名]
def fitRCausalImpact(Y_pre, Y_post, treated_units):
        import rpy2.robjects as ro #ro.r is the R instace
        #Automatically convert numpy arrays to R vector/matrix
        from rpy2.robjects import numpy2ri
        numpy2ri.activate()

        control_units = [u for u in range(Y_pre.shape[0]) if u not in treated_units]

        #np.matrix is not automatically converted so use ndarray
        if type(Y_pre).__name__=="matrix":
            Y_pre = Y_pre.A
        if type(Y_post).__name__=="matrix":
            Y_post = Y_post.A

        Y = np.hstack((Y_pre, Y_post))
        Y_sc = np.full(Y.shape, np.nan)
        Y_c = Y[control_units,:]
        T0 = Y_pre.shape[1]
        N,T = Y.shape

        try:
            CausalImpact = importr('CausalImpact')
        except:
            raise RuntimeError("Need the 'CausalImpact' package loaded in the rpy2 R environment. Use test.test_simulation.installRCausalImpact")
        
        for unit in range(N): #
            if unit in treated_units:
                data = np.hstack((Y[unit,:].T, Y_c.T))
            else:
                data = np.hstack((Y[unit,:].T, np.delete(Y_c, unit, 0).T))
            r_data = ro.r.matrix(data, nrow=df.shape[0], ncol=df.shape[1])

            r_casaulimpact_out = CausalImpact.CausalImpact(data=r_data, pre_period=ro.IntVector([1, T0]), post_period=ro.IntVector([T0+1, T]))
            #can't seem to get weights from impact$model$bsts.model
            #r_summary = r_casaulimpact_out[r_casaulimpact_out.names.index('summary')]
            #te = r_summary[r_summary.names.index('AbsEffect')]
            #p = r_summary[r_summary.names.index('p')]
            #te_ci_l = r_summary[r_summary.names.index('AbsEffect.lower')]
            #te_ci_u = r_summary[r_summary.names.index('AbsEffect.upper')]
            point_pred = np.array(r_casaulimpact_out[r_casaulimpact_out.names.index('series')])[:,2] #'point.pred' from 'zoo' object
            Y_sc[unit,:] = point_pred

        class RCausalImpact:
            def __self__(self, Y_sc):
                self.Y_sc = Y_sc

        return RCausalImpact(Y_sc)