code: from .imports import *
  
  from sklearn_pandas import DataFrameMapper
  from sklearn.preprocessing import LabelEncoder, Imputer, StandardScaler
  from pandas.api.types import is_string_dtype, is_numeric_dtype
  from sklearn.ensemble import forest
  from sklearn.tree import export_graphviz
  
  
  def set_plot_sizes(sml, med, big):
      plt.rc('font', size=sml)          # controls default text sizes
      plt.rc('axes', titlesize=sml)     # fontsize of the axes title
      plt.rc('axes', labelsize=med)    # fontsize of the x and y labels
      plt.rc('xtick', labelsize=sml)    # fontsize of the tick labels
      plt.rc('ytick', labelsize=sml)    # fontsize of the tick labels
      plt.rc('legend', fontsize=sml)    # legend fontsize
      plt.rc('figure', titlesize=big)  # fontsize of the figure title
  
  def parallel_trees(m, fn, n_jobs=8):
          return list(ProcessPoolExecutor(n_jobs).map(fn, m.estimators_))
  
  def draw_tree(t, df, size=10, ratio=0.6, precision=0):
      """ Draws a representation of a random forest in IPython.
  
      Parameters:
      -----------
      t: The tree you wish to draw
      df: The data used to train the tree. This is used to get the names of the features.
      """
      s=export_graphviz(t, out_file=None, feature_names=df.columns, filled=True,
                        special_characters=True, rotate=True, precision=precision)
      IPython.display.display(graphviz.Source(re.sub('Tree {',
         f'Tree {{ size={size}; ratio={ratio}', s)))
  
  def combine_date(years, months=1, days=1, weeks=None, hours=None, minutes=None,
                seconds=None, milliseconds=None, microseconds=None, nanoseconds=None):
      years = np.asarray(years) - 1970
      months = np.asarray(months) - 1
      days = np.asarray(days) - 1
      types = ('<M8[Y]', '<m8[M]', '<m8[D]', '<m8[W]', '<m8[h]',
               '<m8[m]', '<m8[s]', '<m8[ms]', '<m8[us]', '<m8[ns]')
      vals = (years, months, days, weeks, hours, minutes, seconds,
              milliseconds, microseconds, nanoseconds)
      return sum(np.asarray(v, dtype=t) for t, v in zip(types, vals)
                 if v is not None)
  
  def get_sample(df,n):
      """ Gets a random sample of n rows from df, without replacement.
  
      Parameters:
      -----------
      df: A pandas data frame, that you wish to sample from.
      n: The number of rows you wish to sample.
  
      Returns:
      --------
      return value: A random sample of n rows of df.
  
      Examples:
      ---------
      >>> df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['a', 'b', 'a']})
      >>> df
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      >>> get_sample(df, 2)
         col1 col2
      1     2    b
      2     3    a
      """
      idxs = sorted(np.random.permutation(len(df))[:n])
      return df.iloc[idxs].copy()
  
  def add_datepart(df, fldname, drop=True, time=False, errors="raise"):	
      """add_datepart converts a column of df from a datetime64 to many columns containing
      the information from the date. This applies changes inplace.
  
      Parameters:
      -----------
      df: A pandas data frame. df gain several new columns.
      fldname: A string that is the name of the date column you wish to expand.
          If it is not a datetime64 series, it will be converted to one with pd.to_datetime.
      drop: If true then the original date column will be removed.
      time: If true time features: Hour, Minute, Second will be added.
  
      Examples:
      ---------
  
      >>> df = pd.DataFrame({ 'A' : pd.to_datetime(['3/11/2000', '3/12/2000', '3/13/2000'], infer_datetime_format=False) })
      >>> df
  
          A
      0   2000-03-11
      1   2000-03-12
      2   2000-03-13
  
      >>> add_datepart(df, 'A')
      >>> df
  
          AYear AMonth AWeek ADay ADayofweek ADayofyear AIs_month_end AIs_month_start AIs_quarter_end AIs_quarter_start AIs_year_end AIs_year_start AElapsed
      0   2000  3      10    11   5          71         False         False           False           False             False        False          952732800
      1   2000  3      10    12   6          72         False         False           False           False             False        False          952819200
      2   2000  3      11    13   0          73         False         False           False           False             False        False          952905600
      """
      fld = df[fldname]
      fld_dtype = fld.dtype
      if isinstance(fld_dtype, pd.core.dtypes.dtypes.DatetimeTZDtype):
          fld_dtype = np.datetime64
  
      if not np.issubdtype(fld_dtype, np.datetime64):
          df[fldname] = fld = pd.to_datetime(fld, infer_datetime_format=True, errors=errors)
      targ_pre = re.sub('[Dd]ate$', '', fldname)
      attr = ['Year', 'Month', 'Week', 'Day', 'Dayofweek', 'Dayofyear',
              'Is_month_end', 'Is_month_start', 'Is_quarter_end', 'Is_quarter_start', 'Is_year_end', 'Is_year_start']
      if time: attr = attr + ['Hour', 'Minute', 'Second']
      for n in attr: df[targ_pre + n] = getattr(fld.dt, n.lower())
      df[targ_pre + 'Elapsed'] = fld.astype(np.int64) // 10 ** 9
      if drop: df.drop(fldname, axis=1, inplace=True)
  
  def is_date(x): return np.issubdtype(x.dtype, np.datetime64)
  
  def train_cats(df):
      """Change any columns of strings in a panda's dataframe to a column of
      categorical values. This applies the changes inplace.
  
      Parameters:
      -----------
      df: A pandas dataframe. Any columns of strings will be changed to
          categorical values.
  
      Examples:
      ---------
  
      >>> df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['a', 'b', 'a']})
      >>> df
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      note the type of col2 is string
  
      >>> train_cats(df)
      >>> df
  
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      now the type of col2 is category
      """
      for n,c in df.items():
          if is_string_dtype(c): df[n] = c.astype('category').cat.as_ordered()
  
  def apply_cats(df, trn):
      """Changes any columns of strings in df into categorical variables using trn as
      a template for the category codes.
  
      Parameters:
      -----------
      df: A pandas dataframe. Any columns of strings will be changed to
          categorical values. The category codes are determined by trn.
  
      trn: A pandas dataframe. When creating a category for df, it looks up the
          what the category's code were in trn and makes those the category codes
          for df.
  
      Examples:
      ---------
      >>> df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['a', 'b', 'a']})
      >>> df
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      note the type of col2 is string
  
      >>> train_cats(df)
      >>> df
  
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      now the type of col2 is category {a : 1, b : 2}
  
      >>> df2 = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['b', 'a', 'a']})
      >>> apply_cats(df2, df)
  
             col1 col2
          0     1    b
          1     2    a
          2     3    a
  
      now the type of col is category {a : 1, b : 2}
      """
      for n,c in df.items():
          if (n in trn.columns) and (trn[n].dtype.name=='category'):
              df[n] = c.astype('category').cat.as_ordered()
              df[n].cat.set_categories(trn[n].cat.categories, ordered=True, inplace=True)
  
  def fix_missing(df, col, name, na_dict):
      """ Fill missing data in a column of df with the median, and add a {name}_na column
      which specifies if the data was missing.
  
      Parameters:
      -----------
      df: The data frame that will be changed.
  
      col: The column of data to fix by filling in missing data.
  
      name: The name of the new filled column in df.
  
      na_dict: A dictionary of values to create na's of and the value to insert. If
          name is not a key of na_dict the median will fill any missing data. Also
          if name is not a key of na_dict and there is no missing data in col, then
          no {name}_na column is not created.
  
  
      Examples:
      ---------
      >>> df = pd.DataFrame({'col1' : [1, np.NaN, 3], 'col2' : [5, 2, 2]})
      >>> df
         col1 col2
      0     1    5
      1   nan    2
      2     3    2
  
      >>> fix_missing(df, df['col1'], 'col1', {})
      >>> df
         col1 col2 col1_na
      0     1    5   False
      1     2    2    True
      2     3    2   False
  
  
      >>> df = pd.DataFrame({'col1' : [1, np.NaN, 3], 'col2' : [5, 2, 2]})
      >>> df
         col1 col2
      0     1    5
      1   nan    2
      2     3    2
  
      >>> fix_missing(df, df['col2'], 'col2', {})
      >>> df
         col1 col2
      0     1    5
      1   nan    2
      2     3    2
  
  
      >>> df = pd.DataFrame({'col1' : [1, np.NaN, 3], 'col2' : [5, 2, 2]})
      >>> df
         col1 col2
      0     1    5
      1   nan    2
      2     3    2
  
      >>> fix_missing(df, df['col1'], 'col1', {'col1' : 500})
      >>> df
         col1 col2 col1_na
      0     1    5   False
      1   500    2    True
      2     3    2   False
      """
      if is_numeric_dtype(col):
          if pd.isnull(col).sum() or (name in na_dict):
              df[name+'_na'] = pd.isnull(col)
              filler = na_dict[name] if name in na_dict else col.median()
              df[name] = col.fillna(filler)
              na_dict[name] = filler
      return na_dict
  
  def numericalize(df, col, name, max_n_cat):
      """ Changes the column col from a categorical type to it's integer codes.
  
      Parameters:
      -----------
      df: A pandas dataframe. df[name] will be filled with the integer codes from
          col.
  
      col: The column you wish to change into the categories.
      name: The column name you wish to insert into df. This column will hold the
          integer codes.
  
      max_n_cat: If col has more categories than max_n_cat it will not change the
          it to its integer codes. If max_n_cat is None, then col will always be
          converted.
  
      Examples:
      ---------
      >>> df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['a', 'b', 'a']})
      >>> df
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      note the type of col2 is string
  
      >>> train_cats(df)
      >>> df
  
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      now the type of col2 is category { a : 1, b : 2}
  
      >>> numericalize(df, df['col2'], 'col3', None)
  
         col1 col2 col3
      0     1    a    1
      1     2    b    2
      2     3    a    1
      """
      if not is_numeric_dtype(col) and ( max_n_cat is None or len(col.cat.categories)>max_n_cat):
          df[name] = col.cat.codes+1
  
  def scale_vars(df, mapper):
      warnings.filterwarnings('ignore', category=sklearn.exceptions.DataConversionWarning)
      if mapper is None:
          map_f = [([n],StandardScaler()) for n in df.columns if is_numeric_dtype(df[n])]
          mapper = DataFrameMapper(map_f).fit(df)
      df[mapper.transformed_names_] = mapper.transform(df)
      return mapper
  
  def proc_df(df, y_fld=None, skip_flds=None, ignore_flds=None, do_scale=False, na_dict=None,
              preproc_fn=None, max_n_cat=None, subset=None, mapper=None):
      """ proc_df takes a data frame df and splits off the response variable, and
      changes the df into an entirely numeric dataframe. For each column of df 
      which is not in skip_flds nor in ignore_flds, na values are replaced by the
      median value of the column.
  
      Parameters:
      -----------
      df: The data frame you wish to process.
  
      y_fld: The name of the response variable
  
      skip_flds: A list of fields that dropped from df.
  
      ignore_flds: A list of fields that are ignored during processing.
  
      do_scale: Standardizes each column in df. Takes Boolean Values(True,False)
  
      na_dict: a dictionary of na columns to add. Na columns are also added if there
          are any missing values.
  
      preproc_fn: A function that gets applied to df.
  
      max_n_cat: The maximum number of categories to break into dummy values, instead
          of integer codes.
  
      subset: Takes a random subset of size subset from df.
  
      mapper: If do_scale is set as True, the mapper variable
          calculates the values used for scaling of variables during training time (mean and standard deviation).
  
      Returns:
      --------
      [x, y, nas, mapper(optional)]:
  
          x: x is the transformed version of df. x will not have the response variable
              and is entirely numeric.
  
          y: y is the response variable
  
          nas: returns a dictionary of which nas it created, and the associated median.
  
          mapper: A DataFrameMapper which stores the mean and standard deviation of the corresponding continuous
          variables which is then used for scaling of during test-time.
  
      Examples:
      ---------
      >>> df = pd.DataFrame({'col1' : [1, 2, 3], 'col2' : ['a', 'b', 'a']})
      >>> df
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      note the type of col2 is string
  
      >>> train_cats(df)
      >>> df
  
         col1 col2
      0     1    a
      1     2    b
      2     3    a
  
      now the type of col2 is category { a : 1, b : 2}
  
      >>> x, y, nas = proc_df(df, 'col1')
      >>> x
  
         col2
      0     1
      1     2
      2     1
  
      >>> data = DataFrame(pet=["cat", "dog", "dog", "fish", "cat", "dog", "cat", "fish"],
                   children=[4., 6, 3, 3, 2, 3, 5, 4],
                   salary=[90, 24, 44, 27, 32, 59, 36, 27])
  
      >>> mapper = DataFrameMapper([(:pet, LabelBinarizer()),
                            ([:children], StandardScaler())])
  
      >>>round(fit_transform!(mapper, copy(data)), 2)
  
      8x4 Array{Float64,2}:
      1.0  0.0  0.0   0.21
      0.0  1.0  0.0   1.88
      0.0  1.0  0.0  -0.63
      0.0  0.0  1.0  -0.63
      1.0  0.0  0.0  -1.46
      0.0  1.0  0.0  -0.63
      1.0  0.0  0.0   1.04
      0.0  0.0  1.0   0.21
      """
      if not ignore_flds: ignore_flds=[]
      if not skip_flds: skip_flds=[]
      if subset: df = get_sample(df,subset)
      else: df = df.copy()
      ignored_flds = df.loc[:, ignore_flds]
      df.drop(ignore_flds, axis=1, inplace=True)
      if preproc_fn: preproc_fn(df)
      if y_fld is None: y = None
      else:
          if not is_numeric_dtype(df[y_fld]): df[y_fld] = df[y_fld].cat.codes
          y = df[y_fld].values
          skip_flds += [y_fld]
      df.drop(skip_flds, axis=1, inplace=True)
  
      if na_dict is None: na_dict = {}
      else: na_dict = na_dict.copy()
      na_dict_initial = na_dict.copy()
      for n,c in df.items(): na_dict = fix_missing(df, c, n, na_dict)
      if len(na_dict_initial.keys()) > 0:
          df.drop([a + '_na' for a in list(set(na_dict.keys()) - set(na_dict_initial.keys()))], axis=1, inplace=True)
      if do_scale: mapper = scale_vars(df, mapper)
      for n,c in df.items(): numericalize(df, c, n, max_n_cat)
      df = pd.get_dummies(df, dummy_na=True)
      df = pd.concat([ignored_flds, df], axis=1)
      res = [df, y, na_dict]
      if do_scale: res = res + [mapper]
      return res
  
  def rf_feat_importance(m, df):
      return pd.DataFrame({'cols':df.columns, 'imp':m.feature_importances_}
                         ).sort_values('imp', ascending=False)
  
  def set_rf_samples(n):
      """ Changes Scikit learn's random forests to give each tree a random sample of
      n random rows.
      """
      forest._generate_sample_indices = (lambda rs, n_samples:
          forest.check_random_state(rs).randint(0, n_samples, n))
  
  def reset_rf_samples():
      """ Undoes the changes produced by set_rf_samples.
      """
      forest._generate_sample_indices = (lambda rs, n_samples:
          forest.check_random_state(rs).randint(0, n_samples, n_samples))
  
  def get_nn_mappers(df, cat_vars, contin_vars):
      # Replace nulls with 0 for continuous, "" for categorical.
      for v in contin_vars: df[v] = df[v].fillna(df[v].max()+100,)
      for v in cat_vars: df[v].fillna('#NA#', inplace=True)
  
      # list of tuples, containing variable and instance of a transformer for that variable
      # for categoricals, use LabelEncoder to map to integers. For continuous, standardize
      cat_maps = [(o, LabelEncoder()) for o in cat_vars]
      contin_maps = [([o], StandardScaler()) for o in contin_vars]
      return DataFrameMapper(cat_maps).fit(df), DataFrameMapper(contin_maps).fit(df)
  
apis: pandas.api.types.is_string_dtype pandas.api.types.is_numeric_dtype sklearn.preprocessing.LabelEncoder sklearn.ensemble.forest.check_random_state sklearn.preprocessing.StandardScaler sklearn.tree.export_graphviz