Merge pull request #5 from OVVO-Financial/devel-spadim

Devel spadim

NNS/FSD.py

@@ -46,7 +46,6 @@ def NNS_FSD(x: pd.Series, y: pd.Series, type_cdf: str = "discrete", use_plot: bo
         plt.plot(Combined_sort, LPM_x_sort, label="<Combined Sort> vs <LPM X Sort>")
         plt.plot(Combined_sort, LPM_y_sort, label="<Combined Sort> vs <LPM Y Sort>")
         plt.legend()
-        ## TODO: plot
         # plot(
         #    Combined_sort,
         #    LPM_x_sort,

NNS/Numerical_Differentiation.py

@@ -1,14 +1,16 @@
 import numpy as np
 from typing import Callable
+import scipy.optimize
+import scipy.linalg
 
 
 def NNS_diff(
-    f: Callable,
-    point: float,
-    h: float = 0.1,
-    tol: float = 1e-10,
-    digits: int = 12,  # not in use
-    print_trace: bool = False,
+        f: Callable,
+        point: float,
+        h: float = 0.1,
+        tol: float = 1e-10,
+        digits: int = 12,  # not in use
+        print_trace: bool = False,
 ) -> dict:
     r"""NNS Numerical Differentiation
 
@@ -48,7 +50,7 @@ def Finite_step(p: float, h: float) -> dict:
     # Bs <- numeric()
     # Bl <- numeric()
     # Bu <- numeric()
-    Bs, Bl, Bu = 0, {}, {}
+    Bs, Bl, Bu = {}, {}, {}
     ### Step 1 initalize the boundaries for B
 
     ### Initial step size h
@@ -93,11 +95,14 @@ def Finite_step(p: float, h: float) -> dict:
         Bu[i] = upper_B
         # TODO: understand function(x)
         # new.f <- function(x) - f.x + ((f.x - f(point - x)) / x) * point + new.B
-        new_f = function(x) - f_x + ((f_x - f(point - x)) / x) * point + new_B
-
         ###  SOLVE FOR h, we just need the negative or positive sign from the tested B
-        # TODO: scipy root
-        inferred_h = uniroot(new_f, c(-2 * h, 2 * h), extendInt="yes")["root"]
+        # inferred_h = uniroot(new_f, c(-2 * h, 2 * h), extendInt="yes")["root"]
+
+        inferred_h = scipy.optimize.root_scalar(
+            f=lambda x: - f_x + ((f_x - f(point - x)) / x) * point + new_B,
+            # method='bisect',
+            bracket=[-2 * h, 2 * h],
+        ).root
 
         if print_trace:
             print("Iteration", i, "h", inferred_h, "Lower B", lower_B, "Upper B", upper_B)
@@ -108,7 +113,7 @@ def Finite_step(p: float, h: float) -> dict:
         if abs(inferred_h) < tol:
             final_B = np.mean([upper_B, lower_B])
             # TODO: scipy solver
-            slope = solve(point, f_x - final_B)
+            slope = scipy.linalg.solve(point, f_x - final_B)[0]
             z = complex(real=point, imag=inferred_h)
 
             # TODO: graph
@@ -147,7 +152,7 @@ def Finite_step(p: float, h: float) -> dict:
                     "Value of f(x) at point": f(point),
                     "Final y-intercept (B)": final_B,
                     "DERIVATIVE": slope,
-                    "Inferred h": inferred_h,
+                    "Inferred.h": inferred_h,
                     "iterations": i,
                     "Averaged Finite Step Initial h": Finite_step(point, h)["next"],
                     "Inferred h": Finite_step(point, inferred_h),

NNS/TSD.py

@@ -35,7 +35,6 @@ def NNS_TSD(x: pd.Series, y: pd.Series, use_plot: bool = True) -> str:
         plt.plot(Combined_sort, LPM_x_sort, label="<Combined Sort> vs <LPM X Sort>")
         plt.plot(Combined_sort, LPM_y_sort, label="<Combined Sort> vs <LPM Y Sort>")
         plt.legend()
-        ## TODO: plot
         # plot(
         #    LPM_x_sort,
         #    type="l",

pyproject.toml

@@ -0,0 +1,11 @@
+[build-system]
+requires = [
+    "wheel",
+    "pandas~=1.1.3",
+    "numpy~=1.19.2",
+    "scipy~=1.5.2",
+    "numba~=0.51.2",
+    "setuptools~=54.2.0",
+    "matplotlib~=3.3.2",
+]
+build-backend = "setuptools.build_meta"

setup.py

@@ -13,7 +13,7 @@
 # Fields marked as "Optional" may be commented out.
 setup(
     name="NNS",
-    version="1.0.0",
+    version="0.1.0",
     description="Nonlinear nonparametric statistics using partial moments",
     long_description=long_description,
     long_description_content_type="text/markdown",  # Optional (see note above)
@@ -69,4 +69,7 @@
     extras_require={
         "test": ["pytest-cov==2.10.0", "pytest==5.4.3"],
     },
+    url="https://github.com/OVVO-Financial/NNS-Python/",
+    # https://medium.com/@joel.barmettler/how-to-upload-your-python-package-to-pypi-65edc5fe9c56
+    download_url="https://github.com/OVVO-Financial/NNS-Python/archive/v_01.tar.gz",
 )

tests/unit/NNS/test_Numerical_differentiation.py

@@ -10,4 +10,21 @@ class TestNumerical_Differentiation(unittest.TestCase):
     COMPARISON_PRECISION = 7
 
     def test_Numerical_Differentiation(self):
+        # TODO
         r = NNS_diff(f=lambda x: x ** 2, point=5, h=0.1, tol=1e-10, digits=12, print_trace=True)
+        ret_ok = {
+            "Value of f(x) at point": 2.50e+01,
+            "Final y-intercept (B)": -2.50e+01,
+            "DERIVATIVE": 1.00e+01,
+            "Inferred h": 9.30e-11,
+            "iterations": 3.10e+01,
+            "f(x-h)": 9.90e+00,
+            "f(x+h)": 1.01e+01,
+            "Averaged Finite Step Initial h": 1.00e+01,
+            "Inferred h.f(x-h)": 1.00e+01,
+            "Inferred h.f(x+h)": 1.00e+01,
+            "Inferred h Averaged Finite Step": 1.00e+01,
+            "Complex Step Derivative (Initial h)": 1.00e+01,
+        }
+        self.assertDictEqual(r, ret_ok)
+        print(r)