Fixed module

__init__.py

@@ -1,107 +0,0 @@
-#
-#   Statistics test
-#
-
-import numpy as np
-from scipy.stats.distributions import chi2
-import warnings
-from functools import reduce
-from operator import mul
-
-# chisquare_trend_contingency
-
-def chisquare_trend_contingency (observed, tendencies_values, axis = None):
-
-    """
-        chisquare_trend_contingency
-        Apply a Chi2 square Cochran-Armitage trend test with variable in a contingency table.
-        It measure the effect of an tendencie variable on a evaluated variable.
-        The evaluated variable should be a categorial variable with 2 modalities. It should be of shape 2.
-        The tendencies values should be of shape >= 2 an be numeric. 
-
-        Validation conditions
-        ----------
-            - Monotonous evolution of the tendencie (not checked by the function)
-            - Theorical value >= 5 : checked by the function
-
-        Parameters
-        ----------
-            observed, numpy matrix : contingency table of observed value. Should have an axis with a shape of 2 as the test is designed to evaluate tendencies on 2 modalities categorial variable. If only an axis is of shape 2, this axis is used for applying the test, otherwise you should specify the axis containing the tested variable with the axis parameter.
-            tendencies_values, numpy vector : values of the tendencie variable, should be an 1D vector of shape >= 2
-
-        Returns
-        ----
-        chi2 : float
-            The test statistic.
-        p : float
-            The p-value of the test
-        dof : int
-            Degrees of freedom
-        expected : ndarray, same shape as `observed`
-            The expected frequencies, based on the marginal sums of the table.
-    """
-
-    # Making all numpy
-    observed = np.array(observed)
-    tendencies_values = np.array(tendencies_values)
-
-    # Checking data
-
-    ## Observed
-    observed_shape = np.array(observed.shape)
-    if ((observed_shape == 2).sum() == 0):
-        # Exception if absence of shape 2
-        raise Exception("Observed should contains at list of element of shape 2.")
-    
-    ## Tendencies values
-    if(tendencies_values.ndim != 1):
-        # Exception if dim different of 1
-        raise Exception("Tendencies values should be an 1D array")
-    elif(tendencies_values.shape not in observed_shape):
-        raise Exception("Tendencies values should be contains the same number of element than observed")
-
-    # Calculating parameters
-    n = observed.sum() # Number of observations
-    
-    ## Getting axis containing the evaluated variable
-    tmp_axis_id = (np.array(observed.shape) == 2) ## Check which axis is of shape 2
-    if (tmp_axis_id.sum() == 1):
-        # If there is only one axis with shape 2 : it is the evaluated variable
-        axis_id = np.where(tmp_axis_id)[0][0]
-    else:
-        # Taking the content of the axis variables
-        axis_id = axis
-
-        if (type(axis_id) != type(int())):
-            raise Exception("Observed values are of shape (2,2), you should specify the axis of the evaluated variable with the axis parameter (0 or 1)")
-
-    # Getting theorical values
-    expected = (observed.sum(axis = 1)/n).reshape(-1, 1)*observed.sum(axis = 0)
-
-    # Getting dof
-    dof = 1 # By desing in this test, this is a 1 dof
-
-    # Checking condition parameter
-    ## Every expected value should be >= 5
-    if ((expected < 5).sum() != 0):
-        warnings.warn("Test condition not respected, every expected value should be >= 5")
-
-    # Getting test parameter
-    chi2_parameter = (
-        (
-            np.power(n, 3)
-            *np.square(sum(tendencies_values*(np.take(observed, 0, axis_id)-np.take(expected, 0, axis_id))))
-        )/(
-            reduce(mul, observed.sum(axis = (1-axis_id)))*
-            (
-                n*(observed.sum(axis = axis_id)*np.square(tendencies_values)).sum()
-                -np.square((observed.sum(axis = axis_id)*(tendencies_values)).sum())
-            )
-        )
-    )
-
-    # Getting p
-    p = chi2.sf(chi2_parameter, dof)
-
-    # Returning result
-    return(chi2_parameter, p, dof, expected)

requirements.txt

@@ -1,3 +1,2 @@
 numpy
-scipy
-warnings
+scipy

setup.py

@@ -8,5 +8,5 @@ setup(name='statisticsTest',
         author_email='contact@alibellamine.me',
         description='Additional statistics test absent from Scipy.',
         long_description=open('README.md').read(),
-        packages = ["."]
+        packages = ["statisticsTest"]
     )

statisticsTest/__init__.py

@@ -0,0 +1,107 @@
+#
+#   Statistics test
+#
+
+import numpy as np
+from scipy.stats.distributions import chi2
+import warnings
+from functools import reduce
+from operator import mul
+
+# chisquare_trend_contingency
+
+def chisquare_trend_contingency (observed, tendencies_values, axis = None):
+
+    """
+        chisquare_trend_contingency
+        Apply a Chi2 square Cochran-Armitage trend test with variable in a contingency table.
+        It measure the effect of an tendencie variable on a evaluated variable.
+        The evaluated variable should be a categorial variable with 2 modalities. It should be of shape 2.
+        The tendencies values should be of shape >= 2 an be numeric. 
+
+        Validation conditions
+        ----------
+            - Monotonous evolution of the tendencie (not checked by the function)
+            - Theorical value >= 5 : checked by the function
+
+        Parameters
+        ----------
+            observed, numpy matrix : contingency table of observed value. Should have an axis with a shape of 2 as the test is designed to evaluate tendencies on 2 modalities categorial variable. If only an axis is of shape 2, this axis is used for applying the test, otherwise you should specify the axis containing the tested variable with the axis parameter.
+            tendencies_values, numpy vector : values of the tendencie variable, should be an 1D vector of shape >= 2
+
+        Returns
+        ----
+        chi2 : float
+            The test statistic.
+        p : float
+            The p-value of the test
+        dof : int
+            Degrees of freedom
+        expected : ndarray, same shape as `observed`
+            The expected frequencies, based on the marginal sums of the table.
+    """
+
+    # Making all numpy
+    observed = np.array(observed)
+    tendencies_values = np.array(tendencies_values)
+
+    # Checking data
+
+    ## Observed
+    observed_shape = np.array(observed.shape)
+    if ((observed_shape == 2).sum() == 0):
+        # Exception if absence of shape 2
+        raise Exception("Observed should contains at list of element of shape 2.")
+    
+    ## Tendencies values
+    if(tendencies_values.ndim != 1):
+        # Exception if dim different of 1
+        raise Exception("Tendencies values should be an 1D array")
+    elif(tendencies_values.shape not in observed_shape):
+        raise Exception("Tendencies values should be contains the same number of element than observed")
+
+    # Calculating parameters
+    n = observed.sum() # Number of observations
+    
+    ## Getting axis containing the evaluated variable
+    tmp_axis_id = (np.array(observed.shape) == 2) ## Check which axis is of shape 2
+    if (tmp_axis_id.sum() == 1):
+        # If there is only one axis with shape 2 : it is the evaluated variable
+        axis_id = np.where(tmp_axis_id)[0][0]
+    else:
+        # Taking the content of the axis variables
+        axis_id = axis
+
+        if (type(axis_id) != type(int())):
+            raise Exception("Observed values are of shape (2,2), you should specify the axis of the evaluated variable with the axis parameter (0 or 1)")
+
+    # Getting theorical values
+    expected = (observed.sum(axis = 1)/n).reshape(-1, 1)*observed.sum(axis = 0)
+
+    # Getting dof
+    dof = 1 # By desing in this test, this is a 1 dof
+
+    # Checking condition parameter
+    ## Every expected value should be >= 5
+    if ((expected < 5).sum() != 0):
+        warnings.warn("Test condition not respected, every expected value should be >= 5")
+
+    # Getting test parameter
+    chi2_parameter = (
+        (
+            np.power(n, 3)
+            *np.square(sum(tendencies_values*(np.take(observed, 0, axis_id)-np.take(expected, 0, axis_id))))
+        )/(
+            reduce(mul, observed.sum(axis = (1-axis_id)))*
+            (
+                n*(observed.sum(axis = axis_id)*np.square(tendencies_values)).sum()
+                -np.square((observed.sum(axis = axis_id)*(tendencies_values)).sum())
+            )
+        )
+    )
+
+    # Getting p
+    p = chi2.sf(chi2_parameter, dof)
+
+    # Returning result
+    return(chi2_parameter, p, dof, expected)