Vasilis/docs (#370)

* restructured modules into folders. Deprecate warning for old naming conventions.

* re-structured public module in docs

* removed automl for now from docs until import is fixed

* fixed wording in hyperparm tuning

* fixed reference to hoenst forest in docs

* added verbosity to bootstrap

* mvoed bootstrap to private

Author: vsyrgkanis

README.md

@@ -162,7 +162,7 @@ To install from source, see [For Developers](#for-developers) section below.
   <summary>Orthogonal Random Forests (click to expand)</summary>
 
   ```Python
-  from econml.ortho_forest import DMLOrthoForest, DROrthoForest
+  from econml.orf import DMLOrthoForest, DROrthoForest
   from econml.sklearn_extensions.linear_model import WeightedLasso, WeightedLassoCV
   # Use defaults
   est = DMLOrthoForest()
@@ -233,7 +233,7 @@ To install from source, see [For Developers](#for-developers) section below.
 * Linear final stage
 
 ```Python
-from econml.drlearner import LinearDRLearner
+from econml.dr import LinearDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = LinearDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -246,7 +246,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Sparse linear final stage
 
 ```Python
-from econml.drlearner import SparseLinearDRLearner
+from econml.dr import SparseLinearDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = SparseLinearDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -259,7 +259,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Nonparametric final stage
 
 ```Python
-from econml.drlearner import ForestDRLearner
+from econml.dr import ForestDRLearner
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 
 est = ForestDRLearner(model_propensity=GradientBoostingClassifier(),
@@ -276,7 +276,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05)
 * Intent to Treat Doubly Robust Learner (discrete instrument, discrete treatment)
 
 ```Python
-from econml.ortho_iv import LinearIntentToTreatDRIV
+from econml.iv.dr import LinearIntentToTreatDRIV
 from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier
 from sklearn.linear_model import LinearRegression
 
@@ -295,7 +295,7 @@ lb, ub = est.effect_interval(X_test, alpha=0.05) # OLS confidence intervals
 
 ```Python
 import keras
-from econml.deepiv import DeepIVEstimator
+from econml.iv.nnet import DeepIV
 
 treatment_model = keras.Sequential([keras.layers.Dense(128, activation='relu', input_shape=(2,)),
                                     keras.layers.Dropout(0.17),
@@ -310,11 +310,11 @@ response_model = keras.Sequential([keras.layers.Dense(128, activation='relu', in
                                   keras.layers.Dense(32, activation='relu'),
                                   keras.layers.Dropout(0.17),
                                   keras.layers.Dense(1)])
-est = DeepIVEstimator(n_components=10, # Number of gaussians in the mixture density networks)
-                      m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])), # Treatment model
-                      h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # Response model
-                      n_samples=1 # Number of samples used to estimate the response
-                      )
+est = DeepIV(n_components=10, # Number of gaussians in the mixture density networks)
+             m=lambda z, x: treatment_model(keras.layers.concatenate([z, x])), # Treatment model
+             h=lambda t, x: response_model(keras.layers.concatenate([t, x])), # Response model
+             n_samples=1 # Number of samples used to estimate the response
+             )
 est.fit(Y, T, X=X, Z=Z) # Z -> instrumental variables
 treatment_effects = est.effect(X_test)
 ```

doc/conf.py

@@ -212,7 +212,8 @@
 intersphinx_mapping = {'python': ('https://docs.python.org/3', None),
                        'numpy': ('https://docs.scipy.org/doc/numpy/', None),
                        'sklearn': ('https://scikit-learn.org/stable/', None),
-                       'matplotlib': ('https://matplotlib.org/', None)}
+                       'matplotlib': ('https://matplotlib.org/', None),
+                       'shap': ('https://shap.readthedocs.io/en/stable/', None)}
 
 # -- Options for todo extension ----------------------------------------------