from __future__ import absolute_import, division, print_function, unicode_literals
import os
import pandas as pd


class ModelRanking(object):
    """
    Generates model ranking based on the ranking factor
    """

    def __init__(self, config, preds):
        self.config = config
        self.preds = preds
        self.cdrs_ml = pd.read_csv(os.path.join(self.config.data_dir, self.
            config.cdrs_ml_fname), sep=' ', dtype={'gvkey': str})
        self.cdrs_src = pd.read_csv(os.path.join(self.config.data_dir, self
            .config.cdrs_src_fname), sep=' ', dtype={'gvkey': str})

    def generate_ranking(self):
        self.preds = self.preds.merge(self.cdrs_ml[['date', 'gvkey',
            'entval', 'tic', 'active']], how='left', left_on=['gvkey',
            'date'], right_on=['gvkey', 'date'])
        self.preds = self.preds.merge(self.cdrs_src[['gvkey', 'sectorcd']].
            drop_duplicates(), how='left', left_on=['gvkey'], right_on=[
            'gvkey'])
        self.preds['pred_var_entval'] = self.preds.apply(self.ey_pred_var,
            axis=1)
        self.preds['pred_entval'] = self.preds.apply(self.ey_pred, axis=1)
        self.preds = self.preds.sort_values(by=self.config.
            model_ranking_factor, ascending=False)
        self.preds.to_csv(self.config.model_ranking_fname, sep=' ', index=False
            )

    @staticmethod
    def ey_pred_var(row):
        try:
            factor = min(max(-1.0, row['pred_var_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

    @staticmethod
    def ey_pred(row):
        try:
            factor = min(max(-1.0, row['preds_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

4445ddaa94cbc6ef7df510872143ca63468568b0
<mask token>


class ModelRanking(object):
    <mask token>
    <mask token>

    def generate_ranking(self):
        self.preds = self.preds.merge(self.cdrs_ml[['date', 'gvkey',
            'entval', 'tic', 'active']], how='left', left_on=['gvkey',
            'date'], right_on=['gvkey', 'date'])
        self.preds = self.preds.merge(self.cdrs_src[['gvkey', 'sectorcd']].
            drop_duplicates(), how='left', left_on=['gvkey'], right_on=[
            'gvkey'])
        self.preds['pred_var_entval'] = self.preds.apply(self.ey_pred_var,
            axis=1)
        self.preds['pred_entval'] = self.preds.apply(self.ey_pred, axis=1)
        self.preds = self.preds.sort_values(by=self.config.
            model_ranking_factor, ascending=False)
        self.preds.to_csv(self.config.model_ranking_fname, sep=' ', index=False
            )
    <mask token>

    @staticmethod
    def ey_pred(row):
        try:
            factor = min(max(-1.0, row['preds_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

<mask token>


class ModelRanking(object):
    <mask token>

    def __init__(self, config, preds):
        self.config = config
        self.preds = preds
        self.cdrs_ml = pd.read_csv(os.path.join(self.config.data_dir, self.
            config.cdrs_ml_fname), sep=' ', dtype={'gvkey': str})
        self.cdrs_src = pd.read_csv(os.path.join(self.config.data_dir, self
            .config.cdrs_src_fname), sep=' ', dtype={'gvkey': str})

    def generate_ranking(self):
        self.preds = self.preds.merge(self.cdrs_ml[['date', 'gvkey',
            'entval', 'tic', 'active']], how='left', left_on=['gvkey',
            'date'], right_on=['gvkey', 'date'])
        self.preds = self.preds.merge(self.cdrs_src[['gvkey', 'sectorcd']].
            drop_duplicates(), how='left', left_on=['gvkey'], right_on=[
            'gvkey'])
        self.preds['pred_var_entval'] = self.preds.apply(self.ey_pred_var,
            axis=1)
        self.preds['pred_entval'] = self.preds.apply(self.ey_pred, axis=1)
        self.preds = self.preds.sort_values(by=self.config.
            model_ranking_factor, ascending=False)
        self.preds.to_csv(self.config.model_ranking_fname, sep=' ', index=False
            )

    @staticmethod
    def ey_pred_var(row):
        try:
            factor = min(max(-1.0, row['pred_var_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

    @staticmethod
    def ey_pred(row):
        try:
            factor = min(max(-1.0, row['preds_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

<mask token>


class ModelRanking(object):
    """
    Generates model ranking based on the ranking factor
    """

    def __init__(self, config, preds):
        self.config = config
        self.preds = preds
        self.cdrs_ml = pd.read_csv(os.path.join(self.config.data_dir, self.
            config.cdrs_ml_fname), sep=' ', dtype={'gvkey': str})
        self.cdrs_src = pd.read_csv(os.path.join(self.config.data_dir, self
            .config.cdrs_src_fname), sep=' ', dtype={'gvkey': str})

    def generate_ranking(self):
        self.preds = self.preds.merge(self.cdrs_ml[['date', 'gvkey',
            'entval', 'tic', 'active']], how='left', left_on=['gvkey',
            'date'], right_on=['gvkey', 'date'])
        self.preds = self.preds.merge(self.cdrs_src[['gvkey', 'sectorcd']].
            drop_duplicates(), how='left', left_on=['gvkey'], right_on=[
            'gvkey'])
        self.preds['pred_var_entval'] = self.preds.apply(self.ey_pred_var,
            axis=1)
        self.preds['pred_entval'] = self.preds.apply(self.ey_pred, axis=1)
        self.preds = self.preds.sort_values(by=self.config.
            model_ranking_factor, ascending=False)
        self.preds.to_csv(self.config.model_ranking_fname, sep=' ', index=False
            )

    @staticmethod
    def ey_pred_var(row):
        try:
            factor = min(max(-1.0, row['pred_var_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

    @staticmethod
    def ey_pred(row):
        try:
            factor = min(max(-1.0, row['preds_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

from __future__ import absolute_import, division, print_function, unicode_literals
import os
import pandas as pd


class ModelRanking(object):
    """
    Generates model ranking based on the ranking factor
    """

    def __init__(self, config, preds):
        self.config = config
        self.preds = preds
        self.cdrs_ml = pd.read_csv(os.path.join(self.config.data_dir, self.
            config.cdrs_ml_fname), sep=' ', dtype={'gvkey': str})
        self.cdrs_src = pd.read_csv(os.path.join(self.config.data_dir, self
            .config.cdrs_src_fname), sep=' ', dtype={'gvkey': str})

    def generate_ranking(self):
        self.preds = self.preds.merge(self.cdrs_ml[['date', 'gvkey',
            'entval', 'tic', 'active']], how='left', left_on=['gvkey',
            'date'], right_on=['gvkey', 'date'])
        self.preds = self.preds.merge(self.cdrs_src[['gvkey', 'sectorcd']].
            drop_duplicates(), how='left', left_on=['gvkey'], right_on=[
            'gvkey'])
        self.preds['pred_var_entval'] = self.preds.apply(self.ey_pred_var,
            axis=1)
        self.preds['pred_entval'] = self.preds.apply(self.ey_pred, axis=1)
        self.preds = self.preds.sort_values(by=self.config.
            model_ranking_factor, ascending=False)
        self.preds.to_csv(self.config.model_ranking_fname, sep=' ', index=False
            )

    @staticmethod
    def ey_pred_var(row):
        try:
            factor = min(max(-1.0, row['pred_var_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor

    @staticmethod
    def ey_pred(row):
        try:
            factor = min(max(-1.0, row['preds_1'] / row['entval']), 1.0)
        except ZeroDivisionError:
            factor = -1000
        return factor
