How to use the gseapy.gsea.GSEAbase function in gseapy

# Plotting
        if not self._noplot:
            self._plotting(rank_metric=dat2, results=self.results,
                           graph_num=self.graph_num, outdir=self.outdir,
                           figsize=self.figsize, format=self.format,
                           pheno_pos=self.pheno_pos, pheno_neg=self.pheno_neg)

        self._logger.info("Congratulations. GSEApy runs successfully................\n")
        if self._outdir is None:
            self._tmpdir.cleanup()

        return


class SingleSampleGSEA(GSEAbase):
    """GSEA extension: single sample GSEA"""
    def __init__(self, data, gene_sets, outdir="GSEA_SingleSample", sample_norm_method='rank',
                 min_size=15, max_size=2000, permutation_num=0, weighted_score_type=0.25,
                 scale=True, ascending=False, processes=1, figsize=(7,6), format='pdf',
                 graph_num=20, no_plot=True, seed=None, verbose=False):
        self.data=data
        self.gene_sets=gene_sets
        self.outdir=outdir
        self.sample_norm_method=sample_norm_method
        self.weighted_score_type=weighted_score_type
        self.scale = scale
        self.min_size=min_size
        self.max_size=max_size
        self.permutation_num=int(permutation_num) if int(permutation_num) > 0 else 0
        self.ascending=ascending
        self.figsize=figsize

f.write('# scale the enrichment scores by number of genes in the gene sets\n')
                f.write('# this normalization has not effects on the final NES, ' + \
                        'as indicated by Barbie et al., 2009, online methods, pg. 2\n')
            else:
                f.write('# raw enrichment scores of all data\n')
                f.write('# no scale es by numbers of genes in the gene sets\n')
            samplesRawES.to_csv(f, sep='\t')

        outNESfile = os.path.join(outdir, "gseapy.samples.normalized.es.txt")
        with open(outNESfile, 'a') as f:
            f.write('# normalize enrichment scores by using the entire data set\n')
            f.write('# as indicated by Barbie et al., 2009, online methods, pg. 2\n')
            samplesNES.to_csv(f, sep='\t')
        return

class Replot(GSEAbase):
    """To reproduce GSEA desktop output results."""
    def __init__(self, indir, outdir='GSEApy_Replot', weighted_score_type=1,
                  min_size=3, max_size=1000, figsize=(6.5,6), graph_num=20, format='pdf', verbose=False):
        self.indir=indir
        self.outdir=outdir
        self.weighted_score_type=weighted_score_type
        self.min_size=min_size
        self.max_size=max_size
        self.figsize=figsize
        self.fignum=int(graph_num)
        self.format=format
        self.verbose=bool(verbose)
        self.module='replot'
        self.gene_sets=None
        self.ascending=False
        # init logger

pheno_pos=phenoPos, pheno_neg=phenoNeg)
        # Plotting
        if not self._noplot:
            self._plotting(rank_metric=dat2, results=self.results,
                           graph_num=self.graph_num, outdir=self.outdir,
                           figsize=self.figsize, format=self.format,
                           pheno_pos=phenoPos, pheno_neg=phenoNeg)

        self._logger.info("Congratulations. GSEApy ran successfully.................\n")
        if self._outdir is None:
            self._tmpdir.cleanup()

        return


class Prerank(GSEAbase):
    """GSEA prerank tool"""
    def __init__(self, rnk, gene_sets, outdir='GSEA_prerank',
                 pheno_pos='Pos', pheno_neg='Neg', min_size=15, max_size=500,
                 permutation_num=1000, weighted_score_type=1,
                 ascending=False, processes=1, figsize=(6.5,6), format='pdf',
                 graph_num=20, no_plot=False, seed=None, verbose=False):

        self.rnk =rnk
        self.gene_sets=gene_sets
        self.outdir=outdir
        self.pheno_pos=pheno_pos
        self.pheno_neg=pheno_neg
        self.min_size=min_size
        self.max_size=max_size
        self.permutation_num=int(permutation_num) if int(permutation_num) > 0 else 0
        self.weighted_score_type=weighted_score_type

if self._outdir is None: return
        out = os.path.join(outdir,'gseapy.{b}.{c}.report.csv'.format(b=module, c=permutation_type))
        if self.module == 'ssgsea':
            out = out.replace(".csv",".txt")
            msg = "# normalize enrichment scores calculated by random permutation procedure (GSEA method)\n" +\
                  "# It's not proper for publication. Please check the original paper!\n"
            self._logger.warning(msg)
            with open(out, 'a') as f:
                f.write(msg)
                res_df.to_csv(f, sep='\t')
        else:
            res_df.to_csv(out)

        return

class GSEA(GSEAbase):
    """GSEA main tool"""
    def __init__(self, data, gene_sets, classes, outdir='GSEA_ouput',
                 min_size=15, max_size=500, permutation_num=1000,
                 weighted_score_type=1, permutation_type='gene_set',
                 method='log2_ratio_of_classes', ascending=False,
                 processes=1, figsize=(6.5,6), format='pdf', graph_num=20,
                 no_plot=False, seed=None, verbose=False):

        self.data = data
        self.gene_sets=gene_sets
        self.classes=classes
        self.outdir=outdir
        self.permutation_type=permutation_type
        self.method=method
        self.min_size=min_size
        self.max_size=max_size