Merge pull request #182 from jakobrunge/developer

fix bug regarding causal_effects.py and masking

setup.py

@@ -59,7 +59,7 @@ def run(self):
 # Run the setup
 setup(
     name="tigramite",
-    version="5.0.0.3",
+    version="5.0.0.4",
     packages=["tigramite", "tigramite.independence_tests", "tigramite.toymodels"],
     license="GNU General Public License v3.0",
     description="Tigramite causal discovery for time series",

tigramite/causal_effects.py

@@ -1843,6 +1843,7 @@ def fit_total_effect(self,
                 conditions=self.listS,
                 tau_max=self.tau_max,
                 cut_off='max_lag_or_tau_max',
+                remove_missing_upto_maxlag=False,
                 return_data=False)
 
         return self
@@ -1998,6 +1999,7 @@ def fit_wright_effect(self,
                         Y=[medy], X=[par], Z=oset,
                         tau_max=self.tau_max,
                         cut_off='max_lag_or_tau_max',
+                        remove_missing_upto_maxlag=False,
                         return_data=False)
                     coeffs[medy][par] = fit_res[medy]['model'].coef_[0]
                     # print(mediators, par, medy, coeffs[medy][par])
@@ -2018,6 +2020,7 @@ def fit_wright_effect(self,
                     conditions=None,
                     tau_max=self.tau_max,
                     cut_off='max_lag_or_tau_max',
+                    remove_missing_upto_maxlag=False,
                     return_data=False)
 
                 for ipar, par in enumerate(all_parents):

tigramite/data_processing.py

@@ -120,6 +120,7 @@ def construct_array(self, X, Y, Z, tau_max,
                         return_cleaned_xyz=False,
                         do_checks=True,
                         cut_off='2xtau_max',
+                        remove_missing_upto_maxlag=True,
                         verbosity=0):
         """Constructs array from variables X, Y, Z from data.
 
@@ -155,6 +156,9 @@ def construct_array(self, X, Y, Z, tau_max,
             which uses the maximum of tau_max and the conditions, which is
             useful to compare multiple models on the same sample.  Last,
             'max_lag' uses as much samples as possible.
+        remove_missing_upto_maxlag : bool, optional (default: True)
+            Whether to remove not only missing samples, but also all neighboring
+            samples up to max_lag (as given by cut_off).
         verbosity : int, optional (default: 0)
             Level of verbosity.
 
@@ -228,11 +232,17 @@ def construct_array(self, X, Y, Z, tau_max,
         # slices that occur up to max_lag after
         if self.missing_flag is not None:
             missing_anywhere = np.any(np.isnan(self.values), axis=1)
-            for tau in range(max_lag+1):
+            if remove_missing_upto_maxlag:
+                for tau in range(max_lag+1):
+                    if self.bootstrap is None:
+                        use_indices[missing_anywhere[tau:T-max_lag+tau]] = 0
+                    else:
+                        use_indices[missing_anywhere[self.bootstrap - max_lag + tau]] = 0
+            else:
                 if self.bootstrap is None:
-                    use_indices[missing_anywhere[tau:T-max_lag+tau]] = 0
+                    use_indices[missing_anywhere] = 0
                 else:
-                    use_indices[missing_anywhere[self.bootstrap - max_lag + tau]] = 0
+                    use_indices[missing_anywhere[self.bootstrap]] = 0
 
         # Use the mask override if needed
         _use_mask = mask

tigramite/models.py

@@ -86,6 +86,7 @@ def get_general_fitted_model(self,
                 conditions=None,
                 tau_max=None,
                 cut_off='max_lag_or_tau_max',
+                remove_missing_upto_maxlag=True,
                 return_data=False):
         """Fit time series model.
 
@@ -108,6 +109,9 @@ def get_general_fitted_model(self,
             sample. Other options are '2xtau_max', which guarantees that MCI
             tests are all conducted on the same samples. Last, 'max_lag' uses
             as much samples as possible.
+        remove_missing_upto_maxlag : bool, optional (default: True)
+            Whether to remove not only missing samples, but also all neighboring
+            samples up to max_lag (as given by cut_off).
         return_data : bool, optional (default: False)
             Whether to save the data array.
 
@@ -160,6 +164,7 @@ def get_general_fitted_model(self,
                                                tau_max=self.tau_max,
                                                mask_type=self.mask_type,
                                                cut_off=self.cut_off,
+                                               remove_missing_upto_maxlag=remove_missing_upto_maxlag,
                                                verbosity=self.verbosity)