fixed negative treatment bug (#23)

.travis.yml

@@ -13,7 +13,7 @@ env:
 
 before_install:
 # Here we download miniconda and install the dependencies
-- pip install black coverage future joblib numpy numpydoc pandas patsy progressbar2 pygam pytest python-dateutil python-utils pytz scikit-learn scipy six statsmodels
+- pip install black coverage future joblib numpy numpydoc pandas patsy progressbar2 pygam pytest python-dateutil python-utils pytz scikit-learn scipy six sphinx_rtd_theme statsmodels
 
 install:
 - python setup.py install

causal_curve/core.py

@@ -1,6 +1,7 @@
 """
 Core classes (with basic methods) that will be invoked when other, model classes are defined
 """
+import pkg_resources
 
 
 class Core:
@@ -24,3 +25,5 @@ def get_params(self):
         return dict(
             [(k, v) for k, v in list(attrs.items()) if (k[0] != "_") and (k[-1] != "_")]
         )
+
+    __version__ = pkg_resources.require("causal-curve")[0].version

causal_curve/gps.py

@@ -429,43 +429,8 @@ def fit(self, T, X, y):
         # Create grid_values
         self.grid_values = self._grid_values()
 
-        # Estimating the GPS
-        self.best_gps_family = self.gps_family
-
-        # If no family specified, pick the best family
-        if self.gps_family == None:
-            if self.verbose:
-                print(f"Fitting several GPS models and picking the best fitting one...")
-
-            (
-                self.best_gps_family,
-                self.gps_function,
-                self.gps_deviance,
-            ) = self._find_best_gps_model()
-
-            if self.verbose:
-                print(
-                    f"Best fitting model was {self.best_gps_family}, which "
-                    f"produced a deviance of {self.gps_deviance}"
-                )
-
-        # Otherwise, go with the what the user provided...
-        else:
-            if self.verbose:
-                print(f"Fitting GPS model of family '{self.best_gps_family}'...")
-
-            if self.best_gps_family == "normal":
-                (
-                    self.gps_function,
-                    self.gps_deviance,
-                ) = self._create_normal_gps_function()
-            elif self.best_gps_family == "lognormal":
-                (
-                    self.gps_function,
-                    self.gps_deviance,
-                ) = self._create_lognormal_gps_function()
-            elif self.best_gps_family == "gamma":
-                self.gps_function, self.gps_deviance = self._create_gamma_gps_function()
+        # Determine which GPS family to use
+        self._determine_gps_function()
 
         # Estimate the GPS
         if self.verbose:
@@ -710,6 +675,58 @@ def _fit_gam(self):
             lam=self.lambda_,
         ).fit(X, y)
 
+    def _determine_gps_function(self):
+        """Based on the user input, distribution of treatment values, and/or model deviances,
+        this function determines which GPS function family should be used.
+        """
+
+        # If any negative values in treatment, you must use the normal GLM family.
+        if any(self.T <= 0):
+            self.best_gps_family = "normal"
+            self.gps_function, self.gps_deviance = self._create_normal_gps_function()
+            if self.verbose:
+                print(
+                    f"Must fit `normal` GLM family to model treatment since treatment takes on zero or negative values..."
+                )
+
+        # If treatment has no negative values and user provides in put, use that.
+        elif (all(self.T > 0)) & (not isinstance(self.gps_family, type(None))):
+            if self.verbose:
+                print(f"Fitting GPS model of family '{self.gps_family}'...")
+
+            if self.gps_family == "normal":
+                self.best_gps_family = "normal"
+                (
+                    self.gps_function,
+                    self.gps_deviance,
+                ) = self._create_normal_gps_function()
+            elif self.gps_family == "lognormal":
+                self.best_gps_family = "lognormal"
+                (
+                    self.gps_function,
+                    self.gps_deviance,
+                ) = self._create_lognormal_gps_function()
+            elif self.gps_family == "gamma":
+                self.best_gps_family = "gamma"
+                self.gps_function, self.gps_deviance = self._create_gamma_gps_function()
+
+        # If no zero or negative treatment values and user didn't provide input, figure out best-fitting family
+        elif (all(self.T > 0)) & (isinstance(self.gps_family, type(None))):
+            if self.verbose:
+                print(f"Fitting several GPS models and picking the best fitting one...")
+
+            (
+                self.best_gps_family,
+                self.gps_function,
+                self.gps_deviance,
+            ) = self._find_best_gps_model()
+
+            if self.verbose:
+                print(
+                    f"Best fitting model was {self.best_gps_family}, which "
+                    f"produced a deviance of {self.gps_deviance}"
+                )
+
     def _create_normal_gps_function(self):
         """Models the GPS using a GLM of the Gaussian family"""
         normal_gps_model = sm.GLM(

docs/changelog.rst

@@ -4,6 +4,14 @@
 Change Log
 ==========
 
+
+Version 0.4.1
+-------------
+- When using GPS tool with a treatment with negative values, only the normal GLM family can be picked
+- Added 'sphinx_rtd_theme' to dependency list in `.travis.yml` and `install.rst`
+- core.py base class now has __version__ attribute 
+
+
 Version 0.4.0
 -------------
 - Added support for binary outcomes in GPS tool

docs/conf.py

@@ -22,7 +22,7 @@
 author = 'Roni Kobrosly'
 
 # The full version, including alpha/beta/rc tags
-release = '0.4.0'
+release = '0.4.1'
 
 # -- General configuration ---------------------------------------------------
 

docs/install.rst

@@ -26,6 +26,7 @@ causal-curve requires:
 - scikit-learn
 - scipy
 - six
+- sphinx_rtd_theme
 - statsmodels
 
 

setup.py

@@ -5,7 +5,7 @@
 
 setuptools.setup(
     name="causal-curve",
-    version="0.4.0",
+    version="0.4.1",
     author="Roni Kobrosly",
     author_email="[email protected]",
     description="A python library with tools to perform causal inference using \