#!/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 a String feature type.
Classes:
String
The feature type that represents string values.
"""
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,
random_color_func,
SchemeNeutral,
)
from ads.feature_engineering import schema
from ads.common import utils, logger
from ads.common.decorator.runtime_dependency import (
runtime_dependency,
OptionalDependency,
)
[docs]
def default_handler(data: pd.Series, *args, **kwargs) -> pd.Series:
"""Processes given data and indicates if the data matches requirements.
Parameters
----------
data: pd.Series
The data to process.
Returns
-------
pd.Series: The logical list indicating if the data matches requirements.
"""
return data.apply(lambda x: isinstance(x, str))
[docs]
class String(FeatureType):
"""
Type representing string 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 distributions of datasets using wordcloud.
Example
-------
>>> from ads.feature_engineering.feature_type.string import String
>>> import pandas as pd
>>> s = pd.Series(["Hello", "world", None], name='string')
>>> String.validator.is_string(s)
0 True
1 True
2 False
Name: string, dtype: bool
"""
description = "Type representing string 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
--------
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S',
'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string')
>>> string.ads.feature_type = ['string']
>>> string.ads.feature_stat()
Metric Value
0 count 22
1 unique 3
2 missing 3
Returns
-------
Pandas Dataframe
Summary statistics of the Series or Dataframe provided.
"""
return _count_unique_missing(x)
[docs]
@staticmethod
@runtime_dependency(module="wordcloud", install_from=OptionalDependency.TEXT)
def feature_plot(x: pd.Series) -> plt.Axes:
"""
Shows distributions of datasets using wordcloud.
Examples
--------
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S',
'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string')
>>> string.ads.feature_type = ['string']
>>> string.ads.feature_plot()
Returns
-------
matplotlib.axes._subplots.AxesSubplot
Plot object for the series based on the String feature type.
"""
col_name = x.name if x.name else "text"
df = x.to_frame(col_name)
df["validation"] = default_handler(x)
df = df[df["validation"] == True]
words = " ".join(df[col_name].dropna().to_list())
if not words:
return
from wordcloud import WordCloud
wc = WordCloud(
background_color=SchemeNeutral.BACKGROUND_LIGHT,
color_func=random_color_func,
).generate(words)
_, ax = plt.subplots(facecolor=SchemeNeutral.BACKGROUND_LIGHT)
ax.imshow(wc)
plt.axis("off")
return ax
[docs]
@classmethod
def feature_domain(cls, x: pd.Series) -> schema.Domain:
"""
Generate the domain of the data of this feature type.
Examples
--------
>>> string = pd.Series(['S', 'C', 'S', 'S', 'S', 'Q', 'S', 'S', 'S', 'C', 'S', 'S', 'S',
'S', 'S', 'S', 'Q', 'S', 'S', '', np.NaN, None], name='string')
>>> string.ads.feature_type = ['string']
>>> string.ads.feature_domain()
constraints: []
stats:
count: 22
missing: 3
unique: 3
values: String
Returns
-------
ads.feature_engineering.schema.Domain
Domain based on the String feature type.
"""
return schema.Domain(cls.__name__, cls.feature_stat(x).to_dict()[x.name], [])
String.validator.register("is_string", default_handler)