""" ===================================================== Data exploratory: Quick view ===================================================== Real-world examples for data exploratory, visualization, ... """ # Author: L.Kouadio # Licence: BSD-3-clause #%% # Import required modules import matplotlib.pyplot as plt from watex.view import ExPlot, QuickPlot, TPlot from watex.datasets import fetch_data , load_bagoue , load_edis from watex.transformers import StratifiedWithCategoryAdder #%% # Data Exploratory with :class:`~watex.view.ExPlot` # --------------------------------------------------- # Explore data for analysis purpose # `ExPlot` is a shadow class. Exploring data is needed to create a model since # it gives a feel for the data and is also at great excuse to meet and discuss # issues with business units that control the data. `ExPlot` methods i.e. # return an instanced object that inherits from :class:`~watex.property.Baseplots` # ABC (Abstract Base Class) for visualization # It gives some data exploration tricks. Here are a few examples for analysis # and visualization #%% # * Use parallel coordinates in multivariates for clustering visualization # (Need yelowbrick to be installed if 'pkg' argument is set to 'yb') data =fetch_data('original data').get('data=dfy1') p = ExPlot (tname ='flow').fit(data) p.plotparallelcoords(pkg='pd') #%% # * Plot each sample on a circle or square, with features on the circumference # to visualize separately between targets. data2 = fetch_data('bagoue original').get('data=dfy2') p = ExPlot(tname ='flow').fit(data2) p.plotradviz(classes= None, pkg='pd' ) #%% # * Create pairwise comparisons between features. # Plots shows a ['pearson'|'spearman'|'covariance'] correlation. data = fetch_data ('bagoue original').get('data=dfy1') p= ExPlot(tname='flow').fit(data) p.plotpairwisecomparison(fmt='.2f', corr='spearman', annot=True, cmap='RdBu_r', vmin=-1, vmax=1 ) #%% # Create a pair grid. # Is a matrix of columns and kernel density estimations. # To colorize by columns from a data frame, use the 'hue' parameter. data = fetch_data ('bagoue original').get('data=dfy1') p= ExPlot(tname='flow').fit(data) p.plotpairgrid (vars = ['magnitude', 'power', 'ohmS'] ) #%% # Features analysis with :class:`~watex.view.QuickPlot` # --------------------------------------------------------- # Special class dealing with analysis modules for quick diagrams, # histograms, and bar visualization. # Originally, it was designed for the flow rate prediction, however, it still # works with any other dataset by following the details of the parameters. Here are # some quick features analysis examples. #%% # * Create a plot of naive visualization df = load_bagoue ().frame stratifiedNumObj= StratifiedWithCategoryAdder('flow') strat_train_set , *_= stratifiedNumObj.fit_transform(X=df) pd_kws ={'alpha': 0.4, 'label': 'flow m3/h', 'c':'flow', 'cmap':plt.get_cmap('jet'), 'colorbar':True} qkObj=QuickPlot(fs=25.) qkObj.fit(strat_train_set) qkObj.naiveviz( x= 'east', y='north', **pd_kws) #%% # * Provide the names of the features at least 04 and discuss their distribution. # This method maps a dataset onto multiple axes arrayed in a grid of # rows and columns that correspond to levels of features in the dataset. # The plots it produces are often called “lattice”, “trellis”, or # 'small multiple graphics. data = load_bagoue ().frame qkObj = QuickPlot( leg_kws={'loc':'upper right'}, fig_title = '`sfi` vs`ohmS|`geol`', ) qkObj.tname='flow' # target the DC-flow rate prediction dataset qkObj.mapflow=True # to hold category FR0, FR1 etc.. qkObj.fit(data) sns_pkws={'aspect':2 , "height": 2, } map_kws={'edgecolor':"w"} qkObj.discussingfeatures(features =['ohmS', 'sfi','geol', 'flow'], map_kws=map_kws, **sns_pkws ) #%% # * Joint method allows the visualization correlation of two features. # Draw a plot of two features with bivariate and univariate graphs. data = load_bagoue ().frame qkObj = QuickPlot( lc='b', sns_style ='darkgrid', fig_title='Quantitative features correlation' ).fit(data) sns_pkws={ 'kind':'reg' , #'kde', 'hex' # "hue": 'flow', } joinpl_kws={"color": "r", 'zorder':0, 'levels':6} plmarg_kws={'color':"r", 'height':-.15, 'clip_on':False} qkObj.joint2features(features=['ohmS', 'lwi'], join_kws=joinpl_kws, marginals_kws=plmarg_kws, **sns_pkws, ) #%% # Tensors recovery with :class:`~watex.view.TPlot` # --------------------------------------------------------- # Tensor plot from EM processing data # `TPlot` is a Tensor (Impedances, resistivity, and phases ) plot class. # Explore SEG ( Society of Exploration Geophysicist ) class data. Plot recovery # tensors. `TPlot` method returns an instanced object that inherits # from :class:`watex.property.Baseplots` ABC (Abstract Base Class) for # visualization. Here are a few demonstration examples. #%% # * Plot multiple sites/stations with signal recovery. # takes the 03 samples of EDIs edi_data = load_edis (return_data= True, samples =3 ) TPlot(fig_size =(5, 3), font_size=7., sns_style='ticks').fit(edi_data).plot_multi_recovery ( sites =['S00'], colors =['o', 'ok--']) #%% # * Plot two-dimensional recovery tensor # get some 12 samples of EDI for the demo edi_data = load_edis (return_data =True, samples =12 ) # customize the plot by adding plot_kws plot_kws = dict( ylabel = '$Log_{10}Frequency [Hz]$', xlabel = '$Distance(m)$', cb_label = '$Log_{10}Rhoa[\Omega.m$]', fig_size =(7, 4), font_size =7. ) t= TPlot(**plot_kws ).fit(edi_data) # plot recovery2d using the log10 resistivity t.plot_tensor2d (to_log10=True) #%% # * Plot two-dimensional filtered tensors using the default trimming moving-average (AMA) filter # take the 12 samples of EDI and plot the corrected tensors edi_data = load_edis (return_data =True, samples =12 ) # customize plot by adding plot_kws plot_kws = dict( ylabel = '$Log_{10}Frequency [Hz]$', xlabel = '$Distance(m)$', cb_label = '$Log_{10}Rhoa[\Omega.m$]', fig_size =(7, 4), font_size =7. ) t= TPlot(**plot_kws ).fit(edi_data) # plot filtered tensor using the log10 resistivity t.plot_ctensor2d (to_log10=True) #%% # Model evaluation with :class:`~watex.view.EvalPlot` # --------------------------------------------------------- # Metric and dimensionality Evaluation Plots # `EvalPlot` Inherited from :class:`BasePlot`. Dimensional reduction and metric # plots. The class works only with numerical features. #%% # * Plot ROC for RandomForest classifier from watex.exlib.sklearn import RandomForestClassifier from watex.datasets.dload import load_bagoue from watex.utils import cattarget from watex.view.mlplot import EvalPlot X , y = load_bagoue(as_frame =True ) rdf_clf = RandomForestClassifier(random_state= 42) # our estimator b= EvalPlot(scale = True , encode_labels=True) b.fit_transform(X, y) # binarize the label b.y ybin = cattarget(b.y, labels= 2 ) # can also use labels =[0, 1] b.y = ybin b.font_size=7. b.lc ='r' b.lw =7. b.sns_style='ticks' b.plotROC(rdf_clf , label =1, method ="predict_proba") # class=1 #%% # * Plot confusion matrix # customize plot matshow_kwargs ={ 'aspect': 'auto', # 'auto'equal 'interpolation': None, 'cmap':'cool'} plot_kws ={'lw':3, 'lc':(.9, 0, .8), 'font_size':15., 'cb_format':None, 'xlabel': 'Predicted classes', 'ylabel': 'Actual classes', 'font_weight':None, 'tp_labelbottom':False, 'tp_labeltop':True, 'tp_bottom': False } # replace the integer identifier with a litteral string b.litteral_classes = ['FR0', 'FR1']# 'FR2', 'FR3'] b.plotConfusionMatrix(clf=rdf_clf, matshow_kws = matshow_kwargs, **plot_kws)