import abc
import click
import sys
import os
import time
import warnings
import pandas as pd
import numpy as np
import sqlite3
import pickle

from .pyprophet import PyProphet
from .report import save_report
from .data_handling import is_sqlite_file, check_sqlite_table
from shutil import copyfile

try:
    profile
except NameError:
    def profile(fun):
        return fun


class PyProphetRunner(object):

    __metaclass__ = abc.ABCMeta

    """Base class for workflow of command line tool
    """

    def __init__(self, infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter, color_palette, main_score_selection_report):
        def read_tsv(infile):
            table = pd.read_csv(infile, "\t")
            return(table)

        def read_osw(infile, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn):
            con = sqlite3.connect(infile)

            if level == "ms2" or level == "ms1ms2":
                if not check_sqlite_table(con, "FEATURE_MS2"):
                    raise click.ClickException("MS2-level feature table not present in file.")

                con.executescript('''
CREATE INDEX IF NOT EXISTS idx_precursor_precursor_id ON PRECURSOR (ID);
CREATE INDEX IF NOT EXISTS idx_feature_precursor_id ON FEATURE (PRECURSOR_ID);
CREATE INDEX IF NOT EXISTS idx_feature_feature_id ON FEATURE (ID);
CREATE INDEX IF NOT EXISTS idx_feature_ms2_feature_id ON FEATURE_MS2 (FEATURE_ID);
''')

                table = pd.read_sql_query('''
SELECT *,
       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
FROM FEATURE_MS2
INNER JOIN
  (SELECT RUN_ID,
          ID,
          PRECURSOR_ID,
          EXP_RT
   FROM FEATURE) AS FEATURE ON FEATURE_ID = FEATURE.ID
INNER JOIN
  (SELECT ID,
          CHARGE AS PRECURSOR_CHARGE,
          DECOY
   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
INNER JOIN
  (SELECT PRECURSOR_ID AS ID,
          COUNT(*) AS TRANSITION_COUNT
   FROM TRANSITION_PRECURSOR_MAPPING
   INNER JOIN TRANSITION ON TRANSITION_PRECURSOR_MAPPING.TRANSITION_ID = TRANSITION.ID
   WHERE DETECTING==1
   GROUP BY PRECURSOR_ID) AS VAR_TRANSITION_SCORE ON FEATURE.PRECURSOR_ID = VAR_TRANSITION_SCORE.ID
ORDER BY RUN_ID,
         PRECURSOR.ID ASC,
         FEATURE.EXP_RT ASC;
''', con)
            elif level == "ms1":
                if not check_sqlite_table(con, "FEATURE_MS1"):
                    raise click.ClickException("MS1-level feature table not present in file.")

                con.executescript('''
CREATE INDEX IF NOT EXISTS idx_precursor_precursor_id ON PRECURSOR (ID);
CREATE INDEX IF NOT EXISTS idx_feature_precursor_id ON FEATURE (PRECURSOR_ID);
CREATE INDEX IF NOT EXISTS idx_feature_feature_id ON FEATURE (ID);
CREATE INDEX IF NOT EXISTS idx_feature_ms1_feature_id ON FEATURE_MS1 (FEATURE_ID);
''')

                table = pd.read_sql_query('''
SELECT *,
       RUN_ID || '_' || PRECURSOR_ID AS GROUP_ID
FROM FEATURE_MS1
INNER JOIN
  (SELECT RUN_ID,
          ID,
          PRECURSOR_ID,
          EXP_RT
   FROM FEATURE) AS FEATURE ON FEATURE_ID = FEATURE.ID
INNER JOIN
  (SELECT ID,
          CHARGE AS PRECURSOR_CHARGE,
          DECOY
   FROM PRECURSOR) AS PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
ORDER BY RUN_ID,
         PRECURSOR.ID ASC,
         FEATURE.EXP_RT ASC;
''', con)
            elif level == "transition":
                if not check_sqlite_table(con, "SCORE_MS2"):
                    raise click.ClickException("Transition-level scoring for IPF requires prior MS2 or MS1MS2-level scoring. Please run 'pyprophet score --level=ms2' or 'pyprophet score --level=ms1ms2' on this file first.")
                if not check_sqlite_table(con, "FEATURE_TRANSITION"):
                    raise click.ClickException("Transition-level feature table not present in file.")

                con.executescript('''
CREATE INDEX IF NOT EXISTS idx_transition_id ON TRANSITION (ID);
CREATE INDEX IF NOT EXISTS idx_score_ms2_feature_id ON SCORE_MS2 (FEATURE_ID);
CREATE INDEX IF NOT EXISTS idx_precursor_precursor_id ON PRECURSOR (ID);
CREATE INDEX IF NOT EXISTS idx_feature_precursor_id ON FEATURE (PRECURSOR_ID);
CREATE INDEX IF NOT EXISTS idx_feature_feature_id ON FEATURE (ID);
CREATE INDEX IF NOT EXISTS idx_feature_transition_feature_id ON FEATURE_TRANSITION (FEATURE_ID);
CREATE INDEX IF NOT EXISTS idx_feature_transition_transition_id ON FEATURE_TRANSITION (TRANSITION_ID);
''')

                table = pd.read_sql_query('''
SELECT TRANSITION.DECOY AS DECOY,
       FEATURE_TRANSITION.*,
       PRECURSOR.CHARGE AS PRECURSOR_CHARGE,
       TRANSITION.PRODUCT_CHARGE AS PRODUCT_CHARGE,
       RUN_ID || '_' || FEATURE_TRANSITION.FEATURE_ID || '_' || PRECURSOR_ID || '_' || TRANSITION_ID AS GROUP_ID
FROM FEATURE_TRANSITION
INNER JOIN
  (SELECT RUN_ID,
          ID,
          PRECURSOR_ID,
          EXP_RT
   FROM FEATURE) AS FEATURE ON FEATURE_TRANSITION.FEATURE_ID = FEATURE.ID
INNER JOIN PRECURSOR ON FEATURE.PRECURSOR_ID = PRECURSOR.ID
INNER JOIN SCORE_MS2 ON FEATURE.ID = SCORE_MS2.FEATURE_ID
INNER JOIN
  (SELECT ID,
          CHARGE AS PRODUCT_CHARGE,
          DECOY
   FROM TRANSITION) AS TRANSITION ON FEATURE_TRANSITION.TRANSITION_ID = TRANSITION.ID
WHERE RANK <= %s
  AND PEP <= %s
  AND VAR_ISOTOPE_OVERLAP_SCORE <= %s
  AND VAR_LOG_SN_SCORE > %s
  AND PRECURSOR.DECOY == 0
ORDER BY RUN_ID,
         PRECURSOR.ID,
         FEATURE.EXP_RT,
         TRANSITION.ID;
''' % (ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn), con)
            else:
                raise click.ClickException("Unspecified data level selected.")

            # Append MS1 scores to MS2 table if selected
            if level == "ms1ms2":
                if not check_sqlite_table(con, "FEATURE_MS1"):
                    raise click.ClickException("MS1-level feature table not present in file.")
                ms1_table = pd.read_sql_query('SELECT * FROM FEATURE_MS1;', con)

                ms1_scores = [c for c in ms1_table.columns if c.startswith("VAR_")]
                ms1_table = ms1_table[['FEATURE_ID'] + ms1_scores]
                ms1_table.columns = ['FEATURE_ID'] + ["VAR_MS1_" + s.split("VAR_")[1] for s in ms1_scores]

                table = pd.merge(table, ms1_table, how='left', on='FEATURE_ID')

            # Format table
            table.columns = [col.lower() for col in table.columns]

            # Mark main score column
            if ss_main_score.lower() in table.columns:
                table = table.rename(index=str, columns={ss_main_score.lower(): "main_"+ss_main_score.lower()})
            elif ss_main_score.lower() == "swath_pretrained":
                # Add a pretrained main score corresponding to the original implementation in OpenSWATH
                # This is optimized for 32-windows SCIEX TripleTOF 5600 data
                table['main_var_pretrained'] = -( -0.19011762 * table['var_library_corr']
                                                +  2.47298914 * table['var_library_rmsd']
                                                +  5.63906731 * table['var_norm_rt_score']
                                                + -0.62640133 * table['var_isotope_correlation_score']
                                                +  0.36006925 * table['var_isotope_overlap_score']
                                                +  0.08814003 * table['var_massdev_score']
                                                +  0.13978311 * table['var_xcorr_coelution']
                                                + -1.16475032 * table['var_xcorr_shape']
                                                + -0.19267813 * table['var_yseries_score']
                                                + -0.61712054 * table['var_log_sn_score'])
            else:
                raise click.ClickException("Main score column not present in data.")

            # Enable transition count & precursor / product charge scores for XGBoost-based classifier
            if classifier == 'XGBoost':
                click.echo("Info: Enable number of transitions & precursor / product charge scores for XGBoost-based classifier")
                table = table.rename(index=str, columns={'precursor_charge': 'var_precursor_charge', 'product_charge': 'var_product_charge', 'transition_count': 'var_transition_count'})

            con.close()
            return(table)

        # Check for auto main score selection
        if ss_main_score=="auto":
            # Set starting default main score
            ss_main_score = "var_xcorr_shape"
            use_dynamic_main_score = True
        else:
            use_dynamic_main_score = False

        # Main function
        if is_sqlite_file(infile):
            self.mode = 'osw'
            self.table = read_osw(infile, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn)
        else:
            self.mode = 'tsv'
            self.table = read_tsv(infile)    

        self.infile = infile
        self.outfile = outfile
        self.classifier = classifier
        self.xgb_hyperparams = xgb_hyperparams
        self.xgb_params = xgb_params
        self.xgb_params_space = xgb_params_space
        self.xeval_fraction = xeval_fraction
        self.xeval_num_iter = xeval_num_iter
        self.ss_initial_fdr = ss_initial_fdr
        self.ss_iteration_fdr = ss_iteration_fdr
        self.ss_num_iter = ss_num_iter
        self.ss_main_score = ss_main_score
        self.group_id = group_id
        self.parametric = parametric
        self.pfdr = pfdr
        self.pi0_lambda = pi0_lambda
        self.pi0_method = pi0_method
        self.pi0_smooth_df = pi0_smooth_df
        self.pi0_smooth_log_pi0 = pi0_smooth_log_pi0
        self.lfdr_truncate = lfdr_truncate
        self.lfdr_monotone = lfdr_monotone
        self.lfdr_transformation = lfdr_transformation
        self.lfdr_adj = lfdr_adj
        self.lfdr_eps = lfdr_eps
        self.level = level
        self.tric_chromprob = tric_chromprob
        self.threads = threads
        self.test = test
        self.ss_score_filter = ss_score_filter
        self.color_palette = color_palette
        self.main_score_selection_report = main_score_selection_report
        self.ss_use_dynamic_main_score = use_dynamic_main_score

        self.prefix = os.path.splitext(outfile)[0]

    @abc.abstractmethod
    def run_algo(self):
        pass

    @abc.abstractmethod
    def extra_writes(self):
        pass

    def run(self):

        extra_writes = dict(self.extra_writes())

        self.check_cols = [self.group_id, "run_id", "decoy"]

        start_at = time.time()
        with warnings.catch_warnings():
            warnings.simplefilter("ignore")
            (result, scorer, weights) = self.run_algo()

        needed = time.time() - start_at

        self.print_summary(result)

        if self.mode == 'tsv':
            self.save_tsv_results(result, extra_writes, scorer.pi0)
            if self.classifier == 'LDA':
                self.save_tsv_weights(weights, extra_writes)
            elif self.classifier == 'XGBoost':
                self.save_bin_weights(weights, extra_writes)

        elif self.mode == 'osw':
            self.save_osw_results(result, extra_writes, scorer.pi0)
            self.save_osw_weights(weights)

        seconds = int(needed)
        msecs = int(1000 * (needed - seconds))

        click.echo("Info: Total time: %d seconds and %d msecs wall time" % (seconds, msecs))

    def print_summary(self, result):
        if result.summary_statistics is not None:
            click.echo("=" * 80)
            click.echo(result.summary_statistics)
            click.echo("=" * 80)

    def save_tsv_results(self, result, extra_writes, pi0):
        summ_stat_path = extra_writes.get("summ_stat_path")
        if summ_stat_path is not None:
            result.summary_statistics.to_csv(summ_stat_path, sep=",", index=False)
            click.echo("Info: %s written." % summ_stat_path)

        full_stat_path = extra_writes.get("full_stat_path")
        if full_stat_path is not None:
            result.final_statistics.to_csv(full_stat_path, sep=",", index=False)
            click.echo("Info: %s written." % full_stat_path)

        output_path = extra_writes.get("output_path")
        if output_path is not None:
            result.scored_tables.to_csv(output_path, sep="\t", index=False)
            click.echo("Info: %s written." % output_path)

        if result.final_statistics is not None:
            cutoffs = result.final_statistics["cutoff"].values
            svalues = result.final_statistics["svalue"].values
            qvalues = result.final_statistics["qvalue"].values

            pvalues = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 0)]["p_value"].values
            top_targets = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 0)]["d_score"].values
            top_decoys = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 1)]["d_score"].values

            save_report(extra_writes.get("report_path"), output_path, top_decoys, top_targets, cutoffs, svalues, qvalues, pvalues, pi0, self.color_palette)
            click.echo("Info: %s written." % extra_writes.get("report_path"))

    def save_tsv_weights(self, weights, extra_writes):
        weights['level'] = self.level
        trained_weights_path = extra_writes.get("trained_weights_path")
        if trained_weights_path is not None:
            weights.to_csv(trained_weights_path, sep=",", index=False)
            click.echo("Info: %s written." % trained_weights_path)

    def save_osw_results(self, result, extra_writes, pi0):
        if self.infile != self.outfile:
            copyfile(self.infile, self.outfile)

        con = sqlite3.connect(self.outfile)

        if self.level == "ms2" or self.level == "ms1ms2":
            c = con.cursor()
            c.execute('DROP TABLE IF EXISTS SCORE_MS2;')
            con.commit()
            c.fetchall()

            df = result.scored_tables
            if 'h_score' in df.columns:
                df = df[['feature_id','d_score','h_score','h0_score','peak_group_rank','p_value','q_value','pep']]
                df.columns = ['FEATURE_ID','SCORE','HSCORE','H0SCORE','RANK','PVALUE','QVALUE','PEP']
            else:
                df = df[['feature_id','d_score','peak_group_rank','p_value','q_value','pep']]
                df.columns = ['FEATURE_ID','SCORE','RANK','PVALUE','QVALUE','PEP']
            table = "SCORE_MS2"
            df.to_sql(table, con, index=False)
        elif self.level == "ms1":
            c = con.cursor()
            c.execute('DROP TABLE IF EXISTS SCORE_MS1;')
            con.commit()
            c.fetchall()

            df = result.scored_tables
            if 'h_score' in df.columns:
                df = df[['feature_id','d_score','h_score','h0_score','peak_group_rank','p_value','q_value','pep']]
                df.columns = ['FEATURE_ID','SCORE','HSCORE','H0SCORE','RANK','PVALUE','QVALUE','PEP']
            else:
                df = df[['feature_id','d_score','peak_group_rank','p_value','q_value','pep']]
                df.columns = ['FEATURE_ID','SCORE','RANK','PVALUE','QVALUE','PEP']
            table = "SCORE_MS1"
            df.to_sql(table, con, index=False)
        elif self.level == "transition":
            c = con.cursor()
            c.execute('DROP TABLE IF EXISTS SCORE_TRANSITION;')
            con.commit()
            c.fetchall()

            df = result.scored_tables[['feature_id','transition_id','d_score','peak_group_rank','p_value','q_value','pep']]
            df.columns = ['FEATURE_ID','TRANSITION_ID','SCORE','RANK','PVALUE','QVALUE','PEP']
            table = "SCORE_TRANSITION"
            df.to_sql(table, con, index=False)

        con.close()
        click.echo("Info: %s written." % self.outfile)

        if result.final_statistics is not None:
            cutoffs = result.final_statistics["cutoff"].values
            svalues = result.final_statistics["svalue"].values
            qvalues = result.final_statistics["qvalue"].values

            pvalues = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 0)]["p_value"].values
            top_targets = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 0)]["d_score"].values
            top_decoys = result.scored_tables.loc[(result.scored_tables.peak_group_rank == 1) & (result.scored_tables.decoy == 1)]["d_score"].values

            save_report(os.path.join(self.prefix + "_" + self.level + "_report.pdf"), self.outfile, top_decoys, top_targets, cutoffs, svalues, qvalues, pvalues, pi0, self.color_palette)
            click.echo("Info: %s written." %  os.path.join(self.prefix + "_" + self.level + "_report.pdf"))

    def save_osw_weights(self, weights):
        if self.classifier == "LDA":
            weights['level'] = self.level
            con = sqlite3.connect(self.outfile)

            c = con.cursor()
            c.execute('SELECT count(name) FROM sqlite_master WHERE type="table" AND name="PYPROPHET_WEIGHTS";')
            if c.fetchone()[0] == 1:
                c.execute('DELETE FROM PYPROPHET_WEIGHTS WHERE LEVEL =="%s"' % self.level)
            c.close()

            weights.to_sql("PYPROPHET_WEIGHTS", con, index=False, if_exists='append')

        elif self.classifier == "XGBoost":
            con = sqlite3.connect(self.outfile)

            c = con.cursor()
            c.execute('SELECT count(name) FROM sqlite_master WHERE type="table" AND name="PYPROPHET_XGB";')
            if c.fetchone()[0] == 1:
                c.execute('DELETE FROM PYPROPHET_XGB WHERE LEVEL =="%s"' % self.level)
            else:
                c.execute('CREATE TABLE PYPROPHET_XGB (level TEXT, xgb BLOB)')

            c.execute('INSERT INTO PYPROPHET_XGB VALUES(?, ?)', [self.level, pickle.dumps(weights)])
            con.commit()
            c.close()

    def save_bin_weights(self, weights, extra_writes):
        trained_weights_path = extra_writes.get("trained_model_path_" + self.level)
        if trained_weights_path is not None:
            with open(trained_weights_path, 'wb') as file:
                self.persisted_weights = pickle.dump(weights, file)
            click.echo("Info: %s written." % trained_weights_path)

class PyProphetLearner(PyProphetRunner):

    def run_algo(self):
        (result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette, self.main_score_selection_report, self.outfile, self.level, self.ss_use_dynamic_main_score).learn_and_apply(self.table)
        return (result, scorer, weights)

    def extra_writes(self):
        yield "output_path", os.path.join(self.prefix + "_scored.tsv")
        yield "summ_stat_path", os.path.join(self.prefix + "_summary_stat.csv")
        yield "full_stat_path", os.path.join(self.prefix + "_full_stat.csv")
        yield "trained_weights_path", os.path.join(self.prefix + "_weights.csv")
        yield "trained_model_path_ms1", os.path.join(self.prefix + "_ms1_model.bin")
        yield "trained_model_path_ms1ms2", os.path.join(self.prefix + "_ms1ms2_model.bin")
        yield "trained_model_path_ms2", os.path.join(self.prefix + "_ms2_model.bin")
        yield "trained_model_path_transition", os.path.join(self.prefix + "_transition_model.bin")
        yield "report_path", os.path.join(self.prefix + "_report.pdf")


class PyProphetWeightApplier(PyProphetRunner):

    def __init__(self, infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, apply_weights, ss_score_filter, color_palette, main_score_selection_report):
        super(PyProphetWeightApplier, self).__init__(infile, outfile, classifier, xgb_hyperparams, xgb_params, xgb_params_space, xeval_fraction, xeval_num_iter, ss_initial_fdr, ss_iteration_fdr, ss_num_iter, ss_main_score, group_id, parametric, pfdr, pi0_lambda, pi0_method, pi0_smooth_df, pi0_smooth_log_pi0, lfdr_truncate, lfdr_monotone, lfdr_transformation, lfdr_adj, lfdr_eps, level, ipf_max_peakgroup_rank, ipf_max_peakgroup_pep, ipf_max_transition_isotope_overlap, ipf_min_transition_sn, tric_chromprob, threads, test, ss_score_filter, color_palette, main_score_selection_report)
        if not os.path.exists(apply_weights):
            raise click.ClickException("Weights file %s does not exist." % apply_weights)
        if self.mode == "tsv":
            if self.classifier == "LDA":
                try:
                    self.persisted_weights = pd.read_csv(apply_weights, sep=",")
                    if self.level != self.persisted_weights['level'].unique()[0]:
                        raise click.ClickException("Weights file has wrong level.")
                except Exception:
                    import traceback
                    traceback.print_exc()
                    raise
            elif self.classifier == "XGBoost":
                with open(apply_weights, 'rb') as file:
                    self.persisted_weights = pickle.load(file)
        elif self.mode == "osw":
            if self.classifier == "LDA":
                try:
                    con = sqlite3.connect(apply_weights)

                    if not check_sqlite_table(con, "PYPROPHET_WEIGHTS"):
                        raise click.ClickException("PYPROPHET_WEIGHTS table is not present in file, cannot apply weights for LDA classifier! Make sure you have run the scoring on a subset of the data first, or that you supplied the right `--classifier` parameter.")
                    data = pd.read_sql_query("SELECT * FROM PYPROPHET_WEIGHTS WHERE LEVEL=='%s'" % self.level, con)
                    data.columns = [col.lower() for col in data.columns]
                    con.close()
                    self.persisted_weights = data
                    if self.level != self.persisted_weights['level'].unique()[0]:
                        raise click.ClickException("Weights file has wrong level.")
                except Exception:
                    import traceback
                    traceback.print_exc()
                    raise
            elif self.classifier == "XGBoost":
                try:
                    con = sqlite3.connect(apply_weights)

                    if not check_sqlite_table(con, "PYPROPHET_XGB"):
                        raise click.ClickException("PYPROPHET_XGB table is not present in file, cannot apply weights for XGBoost classifier! Make sure you have run the scoring on a subset of the data first, or that you supplied the right `--classifier` parameter.")
                    data = con.execute("SELECT xgb FROM PYPROPHET_XGB WHERE LEVEL=='%s'" % self.level).fetchone()
                    con.close()
                    self.persisted_weights = pickle.loads(data[0])
                except Exception:
                    import traceback
                    traceback.print_exc()
                    raise
                
    def run_algo(self):
        (result, scorer, weights) = PyProphet(self.classifier, self.xgb_hyperparams, self.xgb_params, self.xgb_params_space, self.xeval_fraction, self.xeval_num_iter, self.ss_initial_fdr, self.ss_iteration_fdr, self.ss_num_iter, self.group_id, self.parametric, self.pfdr, self.pi0_lambda, self.pi0_method, self.pi0_smooth_df, self.pi0_smooth_log_pi0, self.lfdr_truncate, self.lfdr_monotone, self.lfdr_transformation, self.lfdr_adj, self.lfdr_eps, self.tric_chromprob, self.threads, self.test, self.ss_score_filter, self.color_palette, self.main_score_selection_report, self.outfile, self.level, self.ss_use_dynamic_main_score).apply_weights(self.table, self.persisted_weights)
        return (result, scorer, weights)

    def extra_writes(self):
        yield "output_path", os.path.join(self.prefix + "_scored.tsv")
        yield "summ_stat_path", os.path.join(self.prefix + "_summary_stat.csv")
        yield "full_stat_path", os.path.join(self.prefix + "_full_stat.csv")
        yield "report_path", os.path.join(self.prefix + "_report.pdf")
