From f17644dd15bd8999196c994c1634fe27d621fabc Mon Sep 17 00:00:00 2001
From: Maxim Slipenko <no-reply@maxim.slipenko.com>
Date: Thu, 22 Feb 2024 15:15:56 +0300
Subject: [PATCH] =?UTF-8?q?fix:=20=D0=B7=D0=B0=D0=BC=D0=B5=D0=BD=D1=91?=
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---
 .pylintrc                       |   1 +
 poetry.lock                     | 705 ++++++++++++++++++--------------
 pyproject.toml                  |   1 +
 statapp/_vendor/multipolyfit.py | 124 ------
 statapp/calculations.py         | 132 ++++--
 5 files changed, 484 insertions(+), 479 deletions(-)
 delete mode 100644 statapp/_vendor/multipolyfit.py

diff --git a/.pylintrc b/.pylintrc
index 8e46801..10603a1 100644
--- a/.pylintrc
+++ b/.pylintrc
@@ -23,6 +23,7 @@ disable=
     fixme,
     missing-docstring,
     too-many-public-methods,
+    too-many-locals,
     too-few-public-methods,
     too-many-arguments,
     broad-except
diff --git a/poetry.lock b/poetry.lock
index 667dc7b..c6c7457 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "altgraph"
@@ -137,119 +137,120 @@ tests = ["pytest", "pytest-cov", "pytest-xdist"]
 
 [[package]]
 name = "dill"
-version = "0.3.7"
+version = "0.3.8"
 description = "serialize all of Python"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "dill-0.3.7-py3-none-any.whl", hash = "sha256:76b122c08ef4ce2eedcd4d1abd8e641114bfc6c2867f49f3c41facf65bf19f5e"},
-    {file = "dill-0.3.7.tar.gz", hash = "sha256:cc1c8b182eb3013e24bd475ff2e9295af86c1a38eb1aff128dac8962a9ce3c03"},
+    {file = "dill-0.3.8-py3-none-any.whl", hash = "sha256:c36ca9ffb54365bdd2f8eb3eff7d2a21237f8452b57ace88b1ac615b7e815bd7"},
+    {file = "dill-0.3.8.tar.gz", hash = "sha256:3ebe3c479ad625c4553aca177444d89b486b1d84982eeacded644afc0cf797ca"},
 ]
 
 [package.extras]
 graph = ["objgraph (>=1.7.2)"]
+profile = ["gprof2dot (>=2022.7.29)"]
 
 [[package]]
 name = "distlib"
-version = "0.3.7"
+version = "0.3.8"
 description = "Distribution utilities"
 optional = false
 python-versions = "*"
 files = [
-    {file = "distlib-0.3.7-py2.py3-none-any.whl", hash = "sha256:2e24928bc811348f0feb63014e97aaae3037f2cf48712d51ae61df7fd6075057"},
-    {file = "distlib-0.3.7.tar.gz", hash = "sha256:9dafe54b34a028eafd95039d5e5d4851a13734540f1331060d31c9916e7147a8"},
+    {file = "distlib-0.3.8-py2.py3-none-any.whl", hash = "sha256:034db59a0b96f8ca18035f36290806a9a6e6bd9d1ff91e45a7f172eb17e51784"},
+    {file = "distlib-0.3.8.tar.gz", hash = "sha256:1530ea13e350031b6312d8580ddb6b27a104275a31106523b8f123787f494f64"},
 ]
 
 [[package]]
 name = "filelock"
-version = "3.12.4"
+version = "3.13.1"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.12.4-py3-none-any.whl", hash = "sha256:08c21d87ded6e2b9da6728c3dff51baf1dcecf973b768ef35bcbc3447edb9ad4"},
-    {file = "filelock-3.12.4.tar.gz", hash = "sha256:2e6f249f1f3654291606e046b09f1fd5eac39b360664c27f5aad072012f8bcbd"},
+    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
+    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
 ]
 
 [package.extras]
-docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
-typing = ["typing-extensions (>=4.7.1)"]
+docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
+testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
+typing = ["typing-extensions (>=4.8)"]
 
 [[package]]
 name = "fonttools"
-version = "4.43.1"
+version = "4.49.0"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
 files = [
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 graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
+interpolatable = ["munkres", "pycairo", "scipy"]
+lxml = ["lxml (>=4.0)"]
 pathops = ["skia-pathops (>=0.5.0)"]
 plot = ["matplotlib"]
 repacker = ["uharfbuzz (>=0.23.0)"]
 symfont = ["sympy"]
 type1 = ["xattr"]
 ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.0.0)"]
+unicode = ["unicodedata2 (>=15.1.0)"]
 woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
 
 [[package]]
 name = "identify"
-version = "2.5.30"
+version = "2.5.34"
 description = "File identification library for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.extras]
@@ -257,32 +258,32 @@ license = ["ukkonen"]
 
 [[package]]
 name = "importlib-metadata"
-version = "6.8.0"
+version = "7.0.1"
 description = "Read metadata from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = ">=0.5"
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
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 perf = ["ipython"]
 testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "6.1.0"
+version = "6.1.1"
 description = "Read resources from Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -294,20 +295,28 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)",
 
 [[package]]
 name = "isort"
-version = "5.12.0"
+version = "5.13.2"
 description = "A Python utility / library to sort Python imports."
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 python-versions = ">=3.8.0"
 files = [
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-plugins = ["setuptools"]
-requirements-deprecated-finder = ["pip-api", "pipreqs"]
+colors = ["colorama (>=0.4.6)"]
+
+[[package]]
+name = "joblib"
+version = "1.3.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.7"
+files = [
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+]
 
 [[package]]
 name = "kiwisolver"
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 [[package]]
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 [package.dependencies]
@@ -548,7 +558,6 @@ packaging = ">=20.0"
 pillow = ">=6.2.0"
 pyparsing = ">=2.3.1"
 python-dateutil = ">=2.7"
-setuptools_scm = ">=7"
 
 [[package]]
 name = "mccabe"
@@ -795,18 +804,18 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
 [[package]]
 name = "platformdirs"
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+version = "4.2.0"
 description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 optional = false
-python-versions = ">=3.7"
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 files = [
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 [[package]]
 name = "pre-commit"
@@ -828,49 +837,55 @@ virtualenv = ">=20.10.0"
 
 [[package]]
 name = "pyinstaller"
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 description = "PyInstaller bundles a Python application and all its dependencies into a single package."
 optional = false
 python-versions = "<3.13,>=3.8"
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 [package.dependencies]
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 importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
 macholib = {version = ">=1.8", markers = "sys_platform == \"darwin\""}
-packaging = ">=20.0"
+packaging = ">=22.0"
 pefile = {version = ">=2022.5.30", markers = "sys_platform == \"win32\""}
-pyinstaller-hooks-contrib = ">=2021.4"
+pyinstaller-hooks-contrib = ">=2024.0"
 pywin32-ctypes = {version = ">=0.2.1", markers = "sys_platform == \"win32\""}
 setuptools = ">=42.0.0"
 
 [package.extras]
+completion = ["argcomplete"]
 hook-testing = ["execnet (>=1.5.0)", "psutil", "pytest (>=2.7.3)"]
 
 [[package]]
 name = "pyinstaller-hooks-contrib"
-version = "2023.10"
+version = "2024.1"
 description = "Community maintained hooks for PyInstaller"
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
+[package.dependencies]
+importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
+packaging = ">=22.0"
+setuptools = ">=42.0.0"
+
 [[package]]
 name = "pylint"
 version = "2.17.7"
@@ -945,13 +960,13 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2023.3.post1"
+version = "2024.1"
 description = "World timezone definitions, modern and historical"
 optional = false
 python-versions = "*"
 files = [
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 ]
 
 [[package]]
@@ -990,6 +1005,7 @@ files = [
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@@ -1024,44 +1040,107 @@ files = [
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 ]
 
+[[package]]
+name = "scikit-learn"
+version = "1.3.2"
+description = "A set of python modules for machine learning and data mining"
+optional = false
+python-versions = ">=3.8"
+files = [
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 mpmath = ">=0.19"
 
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 ]
 
 [[package]]
@@ -1268,4 +1353,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.8,<3.9"
-content-hash = "e8ca19934d2ae7c8fbe0238a777e8d7b8882c86d38a700bdd0495b175f1458fe"
+content-hash = "2b8a026bc059c637b81c6e65b372f97283956248272a0c41af0b6d350eec14e8"
diff --git a/pyproject.toml b/pyproject.toml
index 541f73c..9ed723b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ pylint = { version = "^2", markers = "python_version < '3.9'" }
 sympy = "^1.12"
 matplotlib = "^3.7.0"
 pillow = "9.5"
+scikit-learn = "1.3.2"
 
 
 [build-system]
diff --git a/statapp/_vendor/multipolyfit.py b/statapp/_vendor/multipolyfit.py
deleted file mode 100644
index bcebcf9..0000000
--- a/statapp/_vendor/multipolyfit.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# Copyright (c) 2023 Matthew Rocklin
-# All rights reserved.
-# This source code is distributed under the terms of the BSD license,
-# which allows you to use, modify, and distribute it
-# as long as you comply with the license terms.
-
-# In addition, this code has been modified by Maxim Slipenko and
-# is now also licensed under the GPL-3.0.
-# See the GPL-3.0 license for details.
-
-import itertools
-from numpy import linalg, zeros, ones, hstack, asarray
-from sympy import symbols, Mul, Add, S
-
-
-def basisVector(n, i):
-    """ Return an array like [0, 0, ..., 1, ..., 0, 0]
-
-    >>> from statapp._vendor.multipolyfit import basisVector
-    >>> basis_vector(3, 1)
-    array([0, 1, 0])
-    >>> basisVector(5, 4)
-    array([0, 0, 0, 0, 1])
-    """
-    x = zeros(n, dtype=int)
-    x[i] = 1
-    return x
-
-
-def asTall(x):
-    """ Turns a row vector into a column vector """
-    return x.reshape(x.shape + (1,))
-
-
-def multipolyfit(xs, y, deg, full=False, modelOut=False, powersOut=False):
-    # pylint: disable-msg=too-many-locals
-    """
-    Least squares multivariate polynomial fit
-
-    Fit a polynomial like ``y = a**2 + 3a - 2ab + 4b**2 - 1``
-    with many covariates a, b, c, ...
-
-    Parameters
-    ----------
-
-    xs : array_like, shape (M, k)
-         x-coordinates of the k covariates over the M sample points
-    y :  array_like, shape(M,)
-         y-coordinates of the sample points.
-    deg : int
-         Degree o fthe fitting polynomial
-    modelOut : bool (defaults to True)
-         If True return a callable function
-         If False return an array of coefficients
-    powersOut : bool (defaults to False)
-         Returns the meaning of each of the coefficients in the form of an
-         iterator that gives the powers over the inputs and 1
-         For example if xs corresponds to the covariates a,b,c then the array
-         [1, 2, 1, 0] corresponds to 1**1 * a**2 * b**1 * c**0
-
-    See Also
-    --------
-        numpy.polyfit
-
-    """
-    # pylin
-    y = asarray(y).squeeze()
-    # rows = y.shape[0]
-    xs = asarray(xs)
-    numCovariates = xs.shape[1]
-    xs = hstack((ones((xs.shape[0], 1), dtype=xs.dtype) , xs))
-
-    generators = [basisVector(numCovariates + 1, i)
-                  for i in range(numCovariates+1)]
-
-    # All combinations of degrees
-    powers = [sum(x) for x in itertools.combinations_with_replacement(generators, deg)]
-
-    # Raise data to specified degree pattern, stack in order
-    a = hstack(asarray([asTall((xs ** p).prod(1)) for p in powers]))
-
-    result = linalg.lstsq(a, y, rcond=None)
-    beta = result[0]
-
-    if modelOut:
-        return mkModel(beta, powers)
-
-    if powersOut:
-        return beta, powers
-
-    if full:
-        return result, powers, a
-
-    return beta
-
-
-def mkModel(beta, powers):
-    """ Create a callable python function out of beta/powers from multipolyfit
-
-    This function is callable from within multipolyfit using the model_out flag
-    """
-    # Create a function that takes in many x values
-    # and returns an approximate y value
-    def model(*args):
-        numCovariates = len(powers[0]) - 1
-        if len(args) != numCovariates:
-            raise ValueError(f"Expected {numCovariates} inputs")
-        xs = asarray((1,) + args)
-        return sum(coeff * (xs**p).prod()
-                             for p, coeff in zip(powers, beta))
-    return model
-
-
-def mkSympyFunction(beta, powers):
-    terms = getTerms(powers)
-    return Add(*[coeff * term for term, coeff in zip(terms, beta)])
-
-
-def getTerms(powers):
-    numCovariates = len(powers[0])
-    xs = (S.One,) + symbols(f'x1:{numCovariates}')
-
-    terms = [Mul(*[x ** deg for x, deg in zip(xs, power)]) for power in powers]
-    return terms
diff --git a/statapp/calculations.py b/statapp/calculations.py
index 5c646ef..a0f9064 100644
--- a/statapp/calculations.py
+++ b/statapp/calculations.py
@@ -22,7 +22,10 @@ from dataclasses import dataclass
 import numpy as np
 import pandas as pd
 import sympy as sp
-from statapp._vendor.multipolyfit import multipolyfit, getTerms
+from sklearn.preprocessing import PolynomialFeatures
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
+
 
 DIRECT_LINK = 0
 INDIRECT_LINK = 1
@@ -78,64 +81,99 @@ class RegressionResult:
     monomials: list
 
 
-def commonPolynom(inputData, deg) -> RegressionResult:
-    x = inputData[:, 1:]
-    y = inputData[:, 0]
-    result, powers, data = multipolyfit(x, y, deg, full=True)
-    (out, mse, scaledResidualVariance,
-     rSquared, fStatistic) = calculateStats(data, result[0], result[1], y)
+def linearPolynom(data):
+    y = data[:, 0]
+    x = data[:, 1:]
+
+    polyFeatures = PolynomialFeatures(degree=1, include_bias=False)
+    xPoly = polyFeatures.fit_transform(x)
+
+    model = LinearRegression(fit_intercept=True)
+    model.fit(xPoly, y)
+
+    params = np.hstack([model.intercept_, model.coef_])
+
+    predictions = model.predict(xPoly)
+    residuals = y - predictions
+    mse = mean_squared_error(y, predictions)
+
+    rSquared = model.score(xPoly, y)
+
+    n = xPoly.shape[0]
+    k = xPoly.shape[1] + 1
+
+    fStatistic = (rSquared / (k - 1)) / ((1 - rSquared) / (n - k))
+
+    xWithIntercept = np.hstack([np.ones((n, 1)), xPoly])
+    varB = mse * np.linalg.inv(xWithIntercept.T @ xWithIntercept).diagonal()
+    seB = np.sqrt(varB)
+
+    tStats = params / seB
+
+    monomials = ['c'] + ['x' + str(i) for i in range(1, x.shape[1] + 1)]
+
+    residualVariance = np.var(residuals, ddof=k)
+    scaledResidualVariance = residualVariance / (n - k)
+
+    paramsAndTStats = np.vstack((params, tStats)).T
 
     return RegressionResult(
-        out.to_numpy(),
-        np.float64(mse),
-        np.float64(scaledResidualVariance),
-        np.float64(rSquared),
-        np.float64(fStatistic),
-        ['c' if str(x) == '1' else str(x) for x in getTerms(powers)]
+        paramsAndTStats,
+        residualVariance,
+        scaledResidualVariance,
+        rSquared,
+        fStatistic,
+        monomials
     )
 
 
-def linearPolynom(inputData) -> RegressionResult:
-    return commonPolynom(inputData, 1)
+def squaredPolynom(data):
+    y = data[:, 0]
+    x = data[:, 1:]
 
+    polyFeatures = PolynomialFeatures(degree=2, include_bias=False)
+    xPoly = polyFeatures.fit_transform(x)
 
-def squaredPolynom(inputData) -> RegressionResult:
-    return commonPolynom(inputData, 2)
+    model = LinearRegression(fit_intercept=True)
+    model.fit(xPoly, y)
 
+    params = np.hstack([model.intercept_, model.coef_])
 
-def calculateStats(data, params, residues, y):
-    # pylint: disable-msg=too-many-locals
+    predictions = model.predict(xPoly)
+    residuals = y - predictions
+    mse = mean_squared_error(y, predictions)
 
-    k = len(params)  # Количество оцениваемых параметров (коэффициентов)
-    n = len(data)  # Количество наблюдений
+    rSquared = model.score(xPoly, y)
 
-    # Степень свободы (degrees of freedom) для остатков
-    dof = n - k  # Количество наблюдений минус количество оцениваемых параметров
-    # Остаточная дисперсия (Mean Squared Error, MSE)
-    mse = residues / dof
-    # Среднее значение остатков
-    meanResiduals = np.sum(residues) / dof
-    # Масштабированная остаточная дисперсия
-    scaledResidualVariance = residues / meanResiduals ** 2
-    # Ковариационная матрица коэффициентов
-    cov = mse * np.diagonal(np.linalg.inv(data.T @ data))
-    # Стандартные ошибки коэффициентов
-    se = np.sqrt(cov)
-    # T-статистики для каждого коэффициента регрессии
-    tStatistics = params / se
+    n = xPoly.shape[0]
+    k = xPoly.shape[1] + 1
 
-    # R-squared (коэффициент множественной детерминации)
-    sst = np.sum((y - np.mean(y)) ** 2)  # Сумма квадратов отклонений
-    rSquared = 1 - (mse[0] / sst)
-
-    # F-statistic (статистика Фишера)
     fStatistic = (rSquared / (k - 1)) / ((1 - rSquared) / (n - k))
 
-    out = pd.DataFrame()
-    out[0] = params
-    out[1] = tStatistics
+    xWithIntercept = np.hstack([np.ones((n, 1)), xPoly])
+    varB = mse * np.linalg.pinv(xWithIntercept.T @ xWithIntercept).diagonal()
+    seB = np.sqrt(np.maximum(varB, 0))
 
-    return out, mse[0], scaledResidualVariance, rSquared, fStatistic
+    tStats = params / seB
+
+    monomials = ['c'] + list(
+        polyFeatures.get_feature_names_out(['x' + str(i) for i in range(1, x.shape[1] + 1)])
+    )
+    monomials = [monomial.replace(' ', '*') for monomial in monomials]
+
+    residualVariance = np.var(residuals, ddof=k)
+    scaledResidualVariance = residualVariance / (n - k)
+
+    paramsAndTStats = np.vstack((params, tStats)).T
+
+    return RegressionResult(
+        paramsAndTStats,
+        residualVariance,
+        scaledResidualVariance,
+        rSquared,
+        fStatistic,
+        monomials
+    )
 
 
 def prediction(inputData, result: RegressionResult):
@@ -153,8 +191,12 @@ def prediction(inputData, result: RegressionResult):
 
     results = []
 
+    numVars = inputs.shape[1]
+    symbolsStr = ' '.join([f'x{i}' for i in range(1, numVars + 1)])
+    symbols = sp.symbols(symbolsStr)
+
     for y, xValues in zip(outputs, inputs):
-        subsDict = dict(zip(expr.free_symbols, xValues))
+        subsDict = dict(zip(symbols, xValues))
         predictedResult = expr.subs(subsDict)
         difference = predictedResult - y