regression module

Regression module.

Regression

Class object for storing regression results

__init__(self) special

Instantiate the Regression object.

Source code in mygeopackage/regression.py

def __init__(self):
    """Instantiate the Regression object.
    """
    self.identifier = 0
    self.dependent = []
    self.independent = []
    self.coef = []
    self.intercept = []
    self.score = []
    self.residuals = []

show(self, map=None)

Show regression results on interactive web map.

Parameters:

Name	Type	Description	Default
map	Folium map object	If set to None, the function will create a new map object. If given the map object, the layer will be drawn on the map. Defaults to None.	None

Source code in mygeopackage/regression.py

def show(self,map=None):
    """Show regression results on interactive web map.

    Args:
        map (Folium map object, optional): If set to None, the function will create a new map object. If given the map object, the layer will be drawn on the map. Defaults to None.
    """
    if map == None:
        m = folium.Map(location=[self.residuals[0]['Location'][1],self.residuals[0]['Location'][0]],zoom_start=10)
    else:
        m = map
    max_value = max([self.residuals[i]['Residual'] for i in range(len(self.residuals))])
    min_value = min([self.residuals[i]['Residual'] for i in range(len(self.residuals))])
    for feature in self.residuals:

        interval = (max_value-min_value)/5
        if feature['Residual'] >= min_value and feature['Residual'] < min_value + interval:
            folium.CircleMarker((feature['Location'][1],feature['Location'][0]),radius=6,popup='Residual: '+str(feature['Residual']),fill=True,color='Purple',fill_color='Purple',fill_opacity=1).add_to(m)
        if feature['Residual'] >= min_value + interval and feature['Residual'] < min_value + 2 * interval:
            folium.CircleMarker((feature['Location'][1],feature['Location'][0]),radius=6,popup='Residual: '+str(feature['Residual']),fill=True,color='Blue',fill_color='Blue',fill_opacity=1).add_to(m)
        if feature['Residual'] >= min_value + 2 * interval and feature['Residual'] < min_value + 3 * interval:
            folium.CircleMarker((feature['Location'][1],feature['Location'][0]),radius=6,popup='Residual: '+str(feature['Residual']),fill=True,color='Yellow',fill_color='Yellow',fill_opacity=1).add_to(m)
        if feature['Residual'] >= min_value + 3 * interval and feature['Residual'] < min_value + 4 * interval:
            folium.CircleMarker((feature['Location'][1],feature['Location'][0]),radius=6,popup='Residual: '+str(feature['Residual']),fill=True,color='Orange',fill_color='Orange',fill_opacity=1).add_to(m)
        if feature['Residual'] >= min_value + 4 * interval and feature['Residual'] < max_value:
            folium.CircleMarker((feature['Location'][1],feature['Location'][0]),radius=6,popup='Residual: '+str(feature['Residual']),fill=True,color='Red',fill_color='Red',fill_opacity=1).add_to(m)
    display(m)

ols(geo, dependent, independent, identifier)

Perform ordinary least square linear regression.

Parameters:

Name	Type	Description	Default
geo	type	GEO class object that stored spatial data and attributes.	required
dependent	int	The index of the column of dependent variable.	required
independent	list	The list of indices of the columns of independent variables.	required
identifier	int	The index of the column of id.	required

Returns:

Type	Description
class Regression	Return the regression results.

Source code in mygeopackage/regression.py

def ols(geo: type(Geo),dependent,independent,identifier):
    """Perform ordinary least square linear regression.

    Args:
        geo (class GEO): GEO class object that stored spatial data and attributes.
        dependent (int): The index of the column of dependent variable.
        independent (list): The list of indices of the columns of independent variables.
        identifier (int): The index of the column of id.

    Returns:
        class Regression: Return the regression results.
    """
    ols_results = Regression()
    ols_results.identifier = identifier
    ols_results.dependent = geo.attributes[dependent]
    ols_results.independent = [geo.attributes[i] for i in independent]
    reg = LinearRegression().fit(geo.data[:,independent],geo.data[:,dependent])
    ols_results.coef = reg.coef_
    ols_results.intercept = reg.intercept_
    ols_results.score = reg.score(geo.data[:,independent],geo.data[:,dependent])

    residuals = geo.data[:,dependent] - reg.predict(geo.data[:,independent])
    residuals_dict = {}
    for i in range(len(residuals)):
        residuals_dict['ID'] = geo.data[i][identifier]
        residuals_dict['Location'] = [geo.data[i][0],geo.data[i][1]]
        residuals_dict['Residual'] = residuals[i]
        ols_results.residuals.append(residuals_dict.copy())

    return ols_results

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Last update: 2021-05-03