team cool

.travis.yml

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+language: python
+
+# ===== Linux ======
+dist: xenial
+python:
+  - 3.7
+matrix:
+  include:
+    - name: "Python 3.7 on linux"
+      os: linux
+      language: python
+      before_install:
+          - cd Projects/project_2_packages/Team_cool
+          - pip install -U pip
+          - pip install -r requirements.txt
+          - pip install .
+      script: python -m unittest -v
+    # ======= OSX ========
+    - name: "Python 3.7.3 on macOS 10.14"
+      os: osx
+      osx_image: xcode10.2  # Python 3.7.3 running on macOS 10.14.3
+      language: shell       # 'language: python' is an error on Travis CI macOS
+      before_install:
+        - cd Projects/project_2_packages/Team_cool
+        - python3 --version
+        - pip3 install -U pip
+        - pip3 install -r requirements.txt
+        - pip3 install .
+      script: python3 -m unittest -v
+    #  ====== WINDOWS =========
+    - name: "Python 3.7.4 on Windows"
+      os: windows           # Windows 10.0.17134 N/A Build 17134
+      language: shell       # 'language: python' is an error on Travis CI Windows
+      before_install:
+        - cd Projects/project_2_packages/Team_cool
+        - choco install python --version 3.7.4
+        - python -m pip install --upgrade pip
+        - pip3 install -r requirements.txt
+        - pip install .
+      env: PATH=/c/Python37:/c/Python37/Scripts:$PATH
+      script: python -m unittest -v

Projects/project_2_packages/Team_cool/OLS_team_cool.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: OLS-team-cool
+Version: 0.0.31
+Summary: A small example package for OLS.
+Home-page: https://github.com/pypa/sampleproject
+Author: Team Cool
+Author-email: [email protected]
+License: UNKNOWN
+Project-URL: NYU Predoctoral repo, https://github.com/nyupredocs
+Description: # README
+
+
+
+        This is simple OLS package made by Team Cool.
+
+        © Team Cool
+
+
+
+
+
+
+
+
+Platform: UNKNOWN
+Classifier: Programming Language :: Python :: 3
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: >=3.6
+Description-Content-Type: text/markdown

Projects/project_2_packages/Team_cool/OLS_team_cool.egg-info/SOURCES.txt

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+README.md
+setup.py
+OLS_team_cool/__init__.py
+OLS_team_cool/logit.py
+OLS_team_cool/ols.py
+OLS_team_cool.egg-info/PKG-INFO
+OLS_team_cool.egg-info/SOURCES.txt
+OLS_team_cool.egg-info/dependency_links.txt
+OLS_team_cool.egg-info/top_level.txt
+test/__init__.py
+test/test_ols.py

Projects/project_2_packages/Team_cool/OLS_team_cool.egg-info/dependency_links.txt

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+

Projects/project_2_packages/Team_cool/OLS_team_cool.egg-info/top_level.txt

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+OLS_team_cool
+test

Projects/project_2_packages/Team_cool/OLS_team_cool/__init__.py

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+from . import ols
+
+from .ols import OLS

Projects/project_2_packages/Team_cool/OLS_team_cool/logit.py

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+import numpy as np
+import scipy.stats as st
+import statsmodels as sm
+from scipy import optimize
+
+y = np.random.randint(2, size=(100,1))
+x = np.random.normal(0,1,(100,2))
+
+res_correct = sm.discrete.discrete_model.Logit(y,x).fit()
+res_correct.params
+
+
+def Logit(b,y,x):
+    # y = np.random.randint(2, size=(100,1))
+    # x = np.random.normal(0,1,(100,2))
+    n = x.shape[0]
+    # b = np.zeros((s,1))
+    # log_likelihood = (y.T @ x @ b)[0] - np.log(1 + np.exp(x.T @ b))
+    log_likelihood = -y.T @ np.log(1 + np.exp(-x @ b)) + (np.ones((n,1)) - y).T @ np.log(1 - 1 / (1 + np.exp(- x @ b)))
+
+    return -log_likelihood[0]
+
+Logit(y,x,np.array((2,1)))
+
+s = x.shape[1]
+b_0 = np.array((0,0))
+
+optimize.minimize(Logit,x0=b_0,args=(y,x))
+
+
+optimize.fmin_bfgs(Logit, b_0,args=(y,x,))
+
+y.shape
+
+
+# def OLS(y,x,cf=0.95):
+#     """
+#     OLS estimation.
+#
+#     Parameters
+#     −−−−−−−−−−
+#     y : Dependent variable
+#     x : Explanatory variable
+#     cf: Confidence level
+#
+#     Returns
+#     −−−−−−−
+#     beta : Beta
+#     se: Standard Error
+#     confidence: Confidence Interval
+#
+#     See Also
+#     −−−−−−−−
+#     other_function : This is a related function
+#     """
+#
+#     beta = np.linalg.inv(x.T @ x) @ (x.T @ y)
+#
+#     se_term1 = ((y - x @ beta).T @ (y - x @ beta)) / (x.shape[0] - 1)
+#     se_term2 = x.T @ x
+#     cov_matrix = se_term1 * se_term2
+#     se = np.sqrt(np.diag(cov_matrix))
+#
+#     confidence = [beta - st.norm.ppf(1 - (1-0.95)/2) * se, beta \
+#      + st.norm.ppf(1 - (1-0.95)/2) * se]
+#
+#     return {"Beta":beta, "Standard Error":se, "Confidence Interval":confidence}

Projects/project_2_packages/Team_cool/OLS_team_cool/ols.py

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+import numpy as np
+import scipy.stats as st
+import statsmodels.api as sm
+
+# x = np.random.randn(100,2)
+# eps = np.random.normal(0,1,(100,1))
+# y = (np.array([5, 10]) @ x.T).reshape(100,1) + eps
+# cf = 0.95
+
+def OLS(y,x,cf=0.95):
+    """
+    OLS estimation.
+
+    Parameters
+    −−−−−−−−−−
+    y : Dependent variable
+    x : Explanatory variable
+    cf: Confidence level
+
+    Returns
+    −−−−−−−
+    beta : Beta
+    se: Standard Error
+    confidence: Confidence Interval
+
+    See Also
+    −−−−−−−−
+    other_function : This is a related function
+    """
+
+    beta = np.linalg.inv(x.T @ x) @ (x.T @ y)
+
+    se_term1 = ((y - x @ beta).T @ (y - x @ beta)) / (x.shape[0] - (x.shape[1]))
+    se_term2 = x.T @ x
+    cov_matrix = se_term1 * np.linalg.inv(se_term2)
+    se = np.sqrt(np.diag(cov_matrix))
+
+    confidence = [beta - st.norm.ppf(1 - (1-0.95)/2) * se, beta \
+     + st.norm.ppf(1 - (1-0.95)/2) * se]
+
+    return {"Beta":beta, "Standard Error":se, "Confidence Interval":confidence}

Projects/project_2_packages/Team_cool/README.md

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+# README
+
+
+
+This is simple OLS package made by Team Cool.
+
+© Team Cool
+
+
+
+
+
+
+

Projects/project_2_packages/Team_cool/build/lib/OLS_team_cool/__init__.py

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+from . import ols
+
+from .ols import OLS

Projects/project_2_packages/Team_cool/build/lib/OLS_team_cool/logit.py

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+import numpy as np
+import scipy.stats as st
+import randrange
+irand = randrange (0,1)
+ x = np.random.randn(100,2)
+# eps = np.random.normal(0,1,(100,1))
+ y = (np.array([5, 10]) @ x.T).reshape(100,1) + eps
+# cf = 0.95
+-y.T
+def OLS(y,x,cf=0.95):
+    """
+    OLS estimation.
+
+    Parameters
+    −−−−−−−−−−
+    y : Dependent variable
+    x : Explanatory variable
+    cf: Confidence level
+
+    Returns
+    −−−−−−−
+    beta : Beta
+    se: Standard Error
+    confidence: Confidence Interval
+
+    See Also
+    −−−−−−−−
+    other_function : This is a related function
+    """
+
+    beta = np.linalg.inv(x.T @ x) @ (x.T @ y)
+
+    se_term1 = ((y - x @ beta).T @ (y - x @ beta)) / (x.shape[0] - 1)
+    se_term2 = x.T @ x
+    cov_matrix = se_term1 * se_term2
+    se = np.sqrt(np.diag(cov_matrix))
+
+    confidence = [beta - st.norm.ppf(1 - (1-0.95)/2) * se, beta \
+     + st.norm.ppf(1 - (1-0.95)/2) * se]
+
+    return {"Beta":beta, "Standard Error":se, "Confidence Interval":confidence}

Projects/project_2_packages/Team_cool/build/lib/OLS_team_cool/ols.py

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+import numpy as np
+import scipy.stats as st
+import statsmodels.api as sm
+
+# x = np.random.randn(100,2)
+# eps = np.random.normal(0,1,(100,1))
+# y = (np.array([5, 10]) @ x.T).reshape(100,1) + eps
+# cf = 0.95
+
+def OLS(y,x,cf=0.95):
+    """
+    OLS estimation.
+
+    Parameters
+    −−−−−−−−−−
+    y : Dependent variable
+    x : Explanatory variable
+    cf: Confidence level
+
+    Returns
+    −−−−−−−
+    beta : Beta
+    se: Standard Error
+    confidence: Confidence Interval
+
+    See Also
+    −−−−−−−−
+    other_function : This is a related function
+    """
+
+    beta = np.linalg.inv(x.T @ x) @ (x.T @ y)
+
+    se_term1 = ((y - x @ beta).T @ (y - x @ beta)) / (x.shape[0] - (x.shape[1]))
+    se_term2 = x.T @ x
+    cov_matrix = se_term1 * np.linalg.inv(se_term2)
+    se = np.sqrt(np.diag(cov_matrix))
+
+    confidence = [beta - st.norm.ppf(1 - (1-0.95)/2) * se, beta \
+     + st.norm.ppf(1 - (1-0.95)/2) * se]
+
+    return {"Beta":beta, "Standard Error":se, "Confidence Interval":confidence}

Projects/project_2_packages/Team_cool/build/lib/test/test_ols.py

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+import numpy as np
+import scipy.stats as st
+import unittest
+import statsmodels.api as sm
+from OLS_team_cool import ols
+
+# x = np.random.randn(100,2)
+# eps = np.random.normal(0,1,(100,1))
+# y = (np.array([5, 10]) @ x.T).reshape(100,1) + eps
+# cf = 0.95
+
+#We are creating a class to define the variables we are testing.
+class TestOLS(unittest.TestCase):
+
+    def setUp(self):
+        self.x = np.random.randn(100,2)
+        self.eps = np.random.normal(0,1,(100,1))
+        self.y = (np.array([5, 10]) @ (self.x).T).reshape(100,1) + self.eps
+        self.cf = 0.95
+        self.model = sm.OLS(self.y,self.x)
+        self.result = self.model.fit()
+        self.ourmodel = ols.OLS(self.y,self.x)
+
+# This is checking the OLS model that comes with python
+    def test_beta(self):
+        betacrt = self.result.params.reshape(-1,1)
+
+# assertAlmostEqual accounts for float errors
+        ourbeta = self.ourmodel['Beta'].reshape(-1,1)
+        testresults =np.testing.assert_almost_equal(betacrt,ourbeta)
+        self.assertIsNone(testresults)
+
+    def test_se(self):
+        se_correct = self.result.bse.reshape(-1,1)
+        our_se = self.ourmodel.get("Standard Error").reshape(-1,1)
+        test_se_res =np.testing.assert_almost_equal(se_correct,our_se)
+        self.assertIsNone(test_se_res)
+
+
+# #This is checking the OLS model that team_cool has created
+# #    def test_example2(self):
+if __name__ == '__main__':
+    unittest.main()

Projects/project_2_packages/Team_cool/docs/Makefile

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+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

Projects/project_2_packages/Team_cool/docs/_build/html/.buildinfo

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+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 8817f0fe47cd223d4e8a37b9abaaac61
+tags: 645f666f9bcd5a90fca523b33c5a78b7

Projects/project_2_packages/Team_cool/docs/_build/html/_sources/index.rst.txt

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+.. OLS_team_cool documentation master file, created by
+   sphinx-quickstart on Fri Dec  6 15:10:41 2019.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to OLS_team_cool's documentation!
+=========================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`