def parameter_importance(): import parameter_importance as pi # find datasets to fill in the selector selections = utils.select_init_data(request.args.get("id", "")) dataset = selections["current_dataset"] # get parameters for this dataset (cols, low_var_cols) = pi.get_params(dataset) # rank parameters (rank1, _, rank2, scores) = pi.rank(dataset) # correlation analysis (rank0, correlation_plots) = pi.correlation_analysis(dataset) # plot scatter_plot = pi.scatter_plot(dataset, request.args.get("varx", rank1[0]), request.args.get("vary", rank1[1])) scores_plot = pi.rlasso_scores_plot(rank2, scores) return render_template( "parameter_importance.html", cols=cols, low_var_cols=low_var_cols.tolist(), selections=selections, scatter_plot=scatter_plot, scores_plot=scores_plot, correlation_plots=correlation_plots, rank0=rank0[:9], rank1=rank1[:9], )
def scatter_plot(): import parameter_importance xname = request.args.get("xname") yname = request.args.get("yname") dataset = request.args.get("dataset") scatter_plot = parameter_importance.scatter_plot(dataset, xname, yname) return jsonify(result=scatter_plot)