switch to tfcausalimpact

README.md

@@ -12,7 +12,10 @@ intent of eventually deploying pipelines)
 
 The functionality that has been implemented so far is essentially a Python translation of the
 features available in the R library: https://github.com/klarsen1/MarketMatching (v.1.1.7 - as of Dec 2020),
-which combines 2 packages: https://github.com/dafiti/causalimpact and https://github.com/DynamicTimeWarping/dtw-python
+which combines 2 packages: https://github.com/WillianFuks/tfcausalimpact and https://github.com/DynamicTimeWarping/dtw-python
+
+(I switched to tfcausalimpact in v0.0.4, from pycausalimpact, which was available at https://github.com/dafiti/causalimpact,
+  but has now been removed)
 
 The DTW package is used for selection of most suitable control groups.
 
@@ -42,7 +45,7 @@ rmm.inference(**kwargs) # returns
 
 ```
 
-This package has only been tested for **a single test market** (I will test it for multiple test markets soon)
+This package has only been tested for **a single test market** (I will test it for multiple test markets soon).
 
 
 ## Example Use case
@@ -71,7 +74,7 @@ under the notebooks/examples folder. I will keep updating this example as I deve
 
 - [ ] Add Streamlit and Dash app
 
-- [ ] switch to https://github.com/WillianFuks/tfcausalimpact
+- [x] switch to https://github.com/WillianFuks/tfcausalimpact
 
 - [ ] add remaining functionality of the R package
 
@@ -88,7 +91,7 @@ Please make sure to update tests as appropriate.
 [MIT](https://choosealicense.com/licenses/mit/)
 
 
-#### Project Organization
+#### Project Organization (partially implemented)
 ------------
 
     ├── LICENSE

pycausalmatch/__version__.py

@@ -1,5 +1,5 @@
 
 
 
-VERSION = (0, 0, 3)
-__version__ = '.'.join([str(e) for e in VERSION])
+VERSION = (0, 0, 4)
+__version__ = '.'.join([str(e) for e in VERSION])

pycausalmatch/main.py

@@ -12,7 +12,7 @@
 
 
 class R_MarketMatching():
-    
+
     """
     TODO:
 
@@ -364,7 +364,7 @@ def best_matches(data=None, markets_to_be_matched=None, id_variable=None, date_v
         else:
             #TODO add parallel execution
             print("Parallel execution has not been added yet - executing sequentially")
-            
+
             for i in range(len(markets_to_be_matched)):
                 dist_op = R_MarketMatching.calculate_distances(markets_to_be_matched, ip_df, id_variable,
                                                 i, warping_limit, matches, dtw_emphasis
@@ -456,7 +456,8 @@ def best_matches(data=None, markets_to_be_matched=None, id_variable=None, date_v
 
 
     def inference(matched_markets=None, bsts_modelargs=None, test_market=None, end_post_period=None,
-                alpha=0.05, prior_level_sd=0.01, control_matches=5, analyze_betas=False, nseasons=None
+                alpha=0.05, prior_level_sd=0.01, control_matches=5, analyze_betas=False, nseasons=None,
+                ci_fit_method = 'vi'
                 ):
 
 
@@ -558,7 +559,7 @@ def inference(matched_markets=None, bsts_modelargs=None, test_market=None, end_p
 
 
         #TODO add alpha=alpha, model.args=bsts_modelargs
-        impact = CausalImpact(ts, pre_period, post_period)
+        impact = CausalImpact(ts, pre_period, post_period, model_args={'fit_method': ci_fit_method})
 
         #TODO implement analyze_betas = True
         if analyze_betas:
@@ -586,5 +587,3 @@ def inference(matched_markets=None, bsts_modelargs=None, test_market=None, end_p
         print('Matching (pre) Period MAPE: ')
 
         return results
-
-

requirements.txt

@@ -1,4 +1,4 @@
 numpy>=1.18.1
 pandas>=1.0.3
 dtw-python==1.1.6
-pycausalimpact==0.0.14
+tfcausalimpact==0.0.5

setup.py

@@ -3,11 +3,11 @@
 with open("README.md", "r") as readme_file:
     readme = readme_file.read()
 
-requirements = ["numpy>=1.18.1", "pandas>=1.0.3", "dtw-python==1.1.6", "pycausalimpact==0.0.14"]
+requirements = ["numpy>=1.18.1", "pandas>=1.0.3", "dtw-python==1.1.6", "tfcausalimpact==0.0.5"]
 
 setup(
     name="pycausalmatch",
-    version="0.0.3",
+    version="0.0.4",
     author="Rishi Jumani",
     author_email="[email protected]",
     description="Causal Impact of an intervention integrated with control group selection",
@@ -21,4 +21,4 @@
         "Programming Language :: Python :: 3.8",
         "License :: OSI Approved :: MIT License",
     ],
-)
+)