Merge pull request #180 from OpenSourceEconomics/develop_segsell

develop_segsell

Author: segsell

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ repos:
     rev: 19.3b0
     hooks:
     - id: black
-      language_version: python3.6
+      language_version: python3.7
       exclude: ^docs/
 -   repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v1.2.3

.travis.yml

@@ -1,7 +1,7 @@
 language: python
 
 python:
-  - "3.6"
+  - "3.7"
 
 install:
   - sudo apt-get update

README.md

@@ -16,6 +16,44 @@ $ git clone https://github.com/OpenSourceEconomics/grmpy.git
 $ pip install -e .
 ```
 
+---
+## Quick Start
+> Initialization File
+
+```grmpy``` relies on an ```"initialization.yml"``` file (referred to as ``ìnit_file`` below)
+to perform both simulation and estimation.
+For example, check out these two ``init_files`` for
+[simulation and parametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial.grmpy.yml) as well as 
+a [semiparametric estimation](https://github.com/OpenSourceEconomics/grmpy/blob/master/promotion/grmpy_tutorial_notebook/files/tutorial_semipar.yml) setup.
+
+Below you'll find some example code you can copy to jump-start your project.  
+
+> Simulation
+```
+import grmpy
+
+# Specify the initilaization file you want to use, e.g.:
+init_file = "ProjectFiles/simulation.yml"
+
+data = grmpy.simulate(init_file)
+```
+> Estimation
+
+```
+import grmpy
+
+# Specify the initilaization file you want to use, e.g.:
+init_file = "ProjectFiles/estimation.yml"
+
+# Parametric Normal Model
+rslt = grmpy.fit(init_file, semipar=False)
+grmpy.plot_mte(rslt, init_file, color="blue", semipar=False, save_plot="MTE_par.png")
+
+# Local Instrumental Variables (Semiparametric Model)
+rslt = grmpy.fit(init_file, semipar=True)
+grmpy.plot_mte(rslt, init_file, color="orange", semipar=True, nboot= 250, save_plot="MTE_semipar.png")
+```
+
 Please visit our [online documentation](http://grmpy.readthedocs.io/) for tutorials and more.
 
 -----

environment.yml

@@ -1,7 +1,7 @@
 name: grmpy
 
 dependencies:
-  - python=3.6
+  - python=3.7
   - numpy
   - pandas
   - statsmodels

grmpy/KernReg/locpoly.py

@@ -2,13 +2,14 @@
 This module provides a function for the estimation of a local polynomial
 kernel regression.
 """
+from grmpy.KernReg.locpoly_auxiliary import (
+    combine_bincounts_kernelweights,
+    get_curve_estimator,
+    get_kernelweights,
+    is_sorted,
+)
 from grmpy.KernReg.locpoly_linbin import linear_binning
 
-from grmpy.KernReg.locpoly_auxiliary import combine_bincounts_kernelweights
-from grmpy.KernReg.locpoly_auxiliary import get_curve_estimator
-from grmpy.KernReg.locpoly_auxiliary import get_kernelweights
-from grmpy.KernReg.locpoly_auxiliary import is_sorted
-
 
 def locpoly(
     x,
@@ -87,10 +88,7 @@ def locpoly(
 
     Returns
     -------
-    gridpoints: np.ndarry
-        Array of sorted x values, i.e. grid points, at which the estimate
-        of E[Y|X] (or its derivative) is computed.
-    curvest: np.ndarry
+    curvest: np.ndarray
         Array of M local estimators.
     """
     # The input arrays x (predictor) and y (response variable)

grmpy/estimate/estimate.py

@@ -1,26 +1,44 @@
 """The module provides an estimation process given the simulated data set and the
 initialization file.
 """
-from grmpy.check.check import check_presence_estimation_dataset
-from grmpy.check.check import check_presence_init
-from grmpy.check.check import check_est_init_dict
-from grmpy.check.check import check_par_init_file
 from grmpy.check.auxiliary import read_data
-
-from grmpy.read.read import read, check_append_constant
-
-from grmpy.estimate.estimate_semipar import semipar_fit
+from grmpy.check.check import (
+    check_est_init_dict,
+    check_par_init_file,
+    check_presence_estimation_dataset,
+)
 from grmpy.estimate.estimate_par import par_fit
+from grmpy.estimate.estimate_semipar import semipar_fit
+from grmpy.read.read import check_append_constant, read
 
 
 def fit(init_file, semipar=False):
     """This function estimates the MTE based on a parametric normal model
     or, alternatively, via the semiparametric method of
     local instrumental variables (LIV).
+
+    Parameters
+    ----------
+    init_file: yaml
+        Initialization file containing parameters for the estimation
+        process.
+
+    Returns
+    ------
+    rslt: dict
+        Result dictionary containing
+        - quantiles
+        - mte
+        - mte_x
+        - mte_u
+        - mte_min
+        - mte_max
+        - X
+        - b1
+        - b0
     """
 
     # Load the estimation file
-    check_presence_init(init_file)
     dict_ = read(init_file, semipar)
 
     # Perform some consistency checks given the user's request