watex.view.plotProjection#

watex.view.plotProjection(X, Xt=None, *, columns=None, test_kws=None, **baseplot_kws)[source]#

Visualize train and test dataset based on the geographical coordinates.

Since there is geographical information(latitude/longitude or easting/northing), it is a good idea to create a scatterplot of all instances to visualize data.

Parameters:

  • X (Ndarray ( M x N matrix where M=m-samples, & N=n-features)) – training set; Denotes data that is observed at training and prediction time, used as independent variables in learning. The notation is uppercase to denote that it is ordinarily a matrix. When a matrix, each sample may be represented by a feature vector, or a vector of precomputed (dis)similarity with each training sample.

  • Xt (Ndarray ( M x N matrix where M=m-samples, & N=n-features)) – Shorthand for “test set”; data that is observed at testing and prediction time, used as independent variables in learning. The notation is uppercase to denote that it is ordinarily a matrix.

  • columns (list of str or index, optional) – columns is usefull when a dataframe is given with a dimension size greater than 2. If such data is passed to X or Xt, columns must hold the name to considered as ‘easting’, ‘northing’ when UTM coordinates are given or ‘latitude’ , ‘longitude’ when latlon are given. If dimension size is greater than 2 and columns is None , an error will raises to prevent the user to provide the index for ‘y’ and ‘x’ coordinated retrieval.

  • test_kws (dict,) – keywords arguments passed to matplotlib.plot.scatter() as test location font and colors properties.

  • baseplot_kws (dict,) – All all the keywords arguments passed to the peroperty watex.property.BasePlot class.

Examples

>>> from watex.datasets import fetch_data
>>> from watex.view.mlplot import plotProjection
>>> # Discard all the non-numeric data
>>> # then inut numerical data
>>> from watex.utils import to_numeric_dtypes, naive_imputer
>>> X, Xt, *_ = fetch_data ('bagoue', split_X_y =True, as_frame =True)
>>> X =to_numeric_dtypes(X, pop_cat_features=True )
>>> X= naive_imputer(X)
>>> Xt = to_numeric_dtypes(Xt, pop_cat_features=True )
>>> Xt= naive_imputer(Xt)
>>> plot_kws = dict (fig_size=(8, 12),
                 lc='k',
                 marker='o',
                 lw =3.,
                 font_size=15.,
                 xlabel= 'easting (m) ',
                 ylabel='northing (m)' ,
                 markerfacecolor ='k',
                 markeredgecolor='r',
                 alpha =1.,
                 markeredgewidth=2.,
                 show_grid =True,
                 galpha =0.2,
                 glw=.5,
                 rotate_xlabel =90.,
                 fs =3.,
                 s =None )
>>> plotProjection( X, Xt , columns= ['east', 'north'],
                    trainlabel='train location',
                    testlabel='test location', **plot_kws
                   )