Any categorical dtype object's .unique() changes categories
#### Code Sample, a copy-pastable example if possible

```python
In [2]: pd.Categorical([1,3,2,4], categories=[4,2,3,1,5]).unique()
Out[2]: 
[1, 3, 2, 4]
Categories (4, int64): [1, 3, 2, 4]

```

same happens with ``CategoricalIndex`` and ``Series`` of ``dtype=category`` (as long as ``ordered=False``).

#### Problem description

The general pandas approach to categoricals seems to change the ``.categories`` field as seldom as possible. So I see no reason why ``unique()``, which _by definition_ does not affect the set of distinct values, should change the categories (reordering them based on appearance, and dropping unused ones).

#### Expected Output

```python
In [2]: pd.Categorical([1,3,2,4], categories=[4,2,3,1,5]).unique()
Out[2]: 
[1, 3, 2, 4]
Categories (4, int64): [4, 2, 3, 1, 5]

```
#### Output of ``pd.show_versions()``

<details>

In [3]: pd.show_versions()

INSTALLED VERSIONS
------------------
commit: 63e8527d32aaf6afe1cd4b2a7b3bfadb088c9a72
python: 3.5.3.final.0
python-bits: 64
OS: Linux
OS-release: 4.9.0-3-amd64
machine: x86_64
processor: 
byteorder: little
LC_ALL: None
LANG: it_IT.UTF-8
LOCALE: it_IT.UTF-8

pandas: 0.22.0.dev0+131.g63e8527d3
pytest: 3.2.3
pip: 9.0.1
setuptools: 36.7.0
Cython: 0.25.2
numpy: 1.12.1
scipy: 0.19.0
pyarrow: None
xarray: None
IPython: 6.2.1
sphinx: 1.5.6
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: 1.2.0dev
tables: 3.3.0
numexpr: 2.6.1
feather: 0.3.1
matplotlib: 2.0.0
openpyxl: None
xlrd: 1.0.0
xlwt: 1.1.2
xlsxwriter: 0.9.6
lxml: None
bs4: 4.5.3
html5lib: 0.999999999
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.10
s3fs: None
fastparquet: None
pandas_gbq: None
pandas_datareader: 0.2.1


</details>
