#!/usr/bin/env python
# -*- coding: utf-8 -*--
# Copyright (c) 2021, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
"""
The module that represents an Ordinal feature type.
Classes:
Ordinal
The Ordinal feature type.
"""
import matplotlib.pyplot as plt
import pandas as pd
from ads.feature_engineering.feature_type.base import FeatureType
from ads.feature_engineering.utils import (
_count_unique_missing,
_set_seaborn_theme,
SchemeTeal,
)
from ads.feature_engineering import schema
from ads.common.decorator.runtime_dependency import (
runtime_dependency,
OptionalDependency,
)
[docs]
class Ordinal(FeatureType):
"""Type representing ordered values.
Attributes
----------
description: str
The feature type description.
name: str
The feature type name.
warning: FeatureWarning
Provides functionality to register warnings and invoke them.
validator
Provides functionality to register validators and invoke them.
Methods
--------
feature_stat(x: pd.Series) -> pd.DataFrame
Generates feature statistics.
feature_plot(x: pd.Series) -> plt.Axes
Shows the counts of observations in each categorical bin using bar chart.
"""
description = "Type representing ordered values."
[docs]
@staticmethod
def feature_stat(x: pd.Series) -> pd.DataFrame:
"""Generates feature statistics.
Feature statistics include (total)count, unique(count),
and missing(count) if there is any.
Examples
--------
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal')
>>> x.ads.feature_type = ['ordinal']
>>> x.ads.feature_stat()
Metric Value
0 count 10
1 unique 9
2 missing 1
Returns
-------
:class:`pandas.DataFrame`
Summary statistics of the Series or Dataframe provided.
"""
return _count_unique_missing(x)
[docs]
@staticmethod
@runtime_dependency(module="seaborn", install_from=OptionalDependency.VIZ)
def feature_plot(x: pd.Series) -> plt.Axes:
"""
Shows the counts of observations in each categorical bin using bar chart.
Examples
--------
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal')
>>> x.ads.feature_type = ['ordinal']
>>> x.ads.feature_plot()
Returns
-------
matplotlib.axes._subplots.AxesSubplot
The bart chart plot object for the series based on the Continuous feature type.
"""
col_name = x.name if x.name else "ordinal"
df = x.to_frame(name=col_name)
df = df.dropna()
if len(df.index):
_set_seaborn_theme()
ax = seaborn.countplot(y=col_name, data=df, color=SchemeTeal.AREA_DARK)
ax.set(xlabel="Count")
return ax
[docs]
@classmethod
def feature_domain(cls, x: pd.Series) -> schema.Domain:
"""
Generate the domain of the data of this feature type.
Examples
--------
>>> x = pd.Series([1, 2, 3, 4, 5, 6, 7, 8, 9, np.nan], name='ordinal')
>>> x.ads.feature_type = ['ordinal']
>>> x.ads.feature_domain()
constraints:
- expression: $x in [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0]
language: python
stats:
count: 10
missing: 1
unique: 9
values: Ordinal
Returns
-------
ads.feature_engineering.schema.Domain
Domain based on the Ordinal feature type.
"""
return schema.Domain(
cls.__name__,
cls.feature_stat(x).to_dict()[x.name],
[schema.Expression(f"$x in {x.dropna().unique().tolist()}")],
)