def _get_part_dep(self):
    '\n        Makes a datafreme with partial dependencies for every pair of columns in fields\n        '
    fields = self.fields
    learn = self.learn
    cache_path = self.cache_path
    dep_name = self._get_dep_var()
    is_continue = self.is_continue
    l2k = self._list_to_key
    result = []
    to_save = {}
    from_saved = {}
    if (is_continue == True):
        if Path((cache_path / 'pd_interm.pkl')).exists():
            from_saved = ld_var(name='pd_interm', path=cache_path)
        else:
            is_continue = False
    elapsed = []
    left = []
    if (is_continue == True):
        for field in fields:
            if (l2k(field) in from_saved):
                elapsed.append(field)
                new_df = from_saved[l2k(field)]
                result.append(new_df)
                to_save[l2k(field)] = new_df
    for field in fields:
        if (l2k(field) not in from_saved):
            left.append(field)
    pbar = master_bar(left)
    cache_path.mkdir(parents=True, exist_ok=True)
    sv_var(var=to_save, name='pd_interm', path=cache_path)
    for field in pbar:
        new_df = self._get_part_dep_one(fields=field, masterbar=pbar)
        new_df['feature'] = self._list_to_key(field)
        if is_listy(field):
            new_df['value'] = new_df[field].values.tolist()
            new_df.drop(columns=field, inplace=True)
        else:
            new_df = new_df.rename(index=str, columns={str(field): 'value'})
        result.append(new_df)
        to_save[l2k(field)] = new_df
        sv_var(var=to_save, name='pd_interm', path=cache_path)
    clear_output()
    if Path((cache_path / 'pd_interm.pkl')).exists():
        Path((cache_path / 'pd_interm.pkl')).unlink()
    result = pd.concat(result, ignore_index=True, sort=True)
    result = result[['feature', 'value', dep_name, 'times']]
    clear_output()
    self.part_dep_df = result
Makes a datafreme with partial dependencies for every pair of columns in fields
def _get_part_dep(self):
    '\n        \n        '
    fields = self.fields
    learn = self.learn
    cache_path = self.cache_path
    dep_name = self._get_dep_var()
    is_continue = self.is_continue
    l2k = self._list_to_key
    result = []
    to_save = {}
    from_saved = {}
    if (is_continue == True):
        if Path((cache_path / 'pd_interm.pkl')).exists():
            from_saved = ld_var(name='pd_interm', path=cache_path)
        else:
            is_continue = False
    elapsed = []
    left = []
    if (is_continue == True):
        for field in fields:
            if (l2k(field) in from_saved):
                elapsed.append(field)
                new_df = from_saved[l2k(field)]
                result.append(new_df)
                to_save[l2k(field)] = new_df
    for field in fields:
        if (l2k(field) not in from_saved):
            left.append(field)
    pbar = master_bar(left)
    cache_path.mkdir(parents=True, exist_ok=True)
    sv_var(var=to_save, name='pd_interm', path=cache_path)
    for field in pbar:
        new_df = self._get_part_dep_one(fields=field, masterbar=pbar)
        new_df['feature'] = self._list_to_key(field)
        if is_listy(field):
            new_df['value'] = new_df[field].values.tolist()
            new_df.drop(columns=field, inplace=True)
        else:
            new_df = new_df.rename(index=str, columns={str(field): 'value'})
        result.append(new_df)
        to_save[l2k(field)] = new_df
        sv_var(var=to_save, name='pd_interm', path=cache_path)
    clear_output()
    if Path((cache_path / 'pd_interm.pkl')).exists():
        Path((cache_path / 'pd_interm.pkl')).unlink()
    result = pd.concat(result, ignore_index=True, sort=True)
    result = result[['feature', 'value', dep_name, 'times']]
    clear_output()
    self.part_dep_df = result