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deepair-encoder-0.0.9
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مشاهده بیشترتوضیحات
This is a sub modular package for encoding
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepair-encoder-0.0.9 |
| نام | deepair-encoder |
| نسخه کتابخانه | 0.0.9 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | DeepAir Dev |
| ایمیل نویسنده | naman@deepair.io |
| آدرس صفحه اصلی | https://bitbucket.org/deepair/ |
| آدرس اینترنتی | https://pypi.org/project/deepair-encoder/ |
| مجوز | - |
## Deep Air Encoder
This package is used for encoding data fields for machine compliant dataframes.
## Package structure
`deepair_encoder`
├── encoder.py
├── __init__.py
└── utils
├── encoder_tools.py
├── __init__.py
├── logger.py
└── splitters.py
1 directory, 6 files
## Dependencies
**Note**: The following python3 packages are necessary for this package to run:
* numpy
* scipy
* pandas
* sklearn
* tabulate
* tqdm
## Function Declarations
Here are the signatures for the functions in the package that can be used for deepair-dev.
### encoder.py
Below are the functions that can be accessed by importing this module as `from deepair_encoder.encoder import <function_name>`.
`encode_username`:
```
def encode_username(df, drop_field=True):
'''
Username one-hot encoder.
inputs:
df: Dataframe which have username column (pandas df series)
drop_field: a flag if the usename column should be dropped or not after encoding (bool)
return:
df: Dataframe which have only username indicator [0/1] (pandas df series)
'''
```
`encode_discounts`:
```
def encode_discounts(df, drop_field=True):
'''
Discounts encoder.
inputs:
df: dataframe which has discounts column (pandas df series)
drop_field: a flag if the discounts column should be dropped or not after encoding (bool)
return:
df: a dataframe with 3 new columns 'PROMOCODE', 'RES', 'LFG' and discounts droped if drop_field = True
'''
```
`minmax_score`:
```
def minmax_score(df, fields, keyval=['requestid', 'direction_onward'], drop_field=True, verbose=True):
'''
scorer function to normalize from 0-1 inversely to the value.
inputs:
df: Dataframe which contains fields (pandas df series)
fields: Dataframe columns on which the score is calculated (list)
keyval: Group the data by this key values (list)
drop_field: Indicator to replace fields by new scored fields (bool)
verbose: Progress bar indicator (bool)
return:
field: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup (pandas df series)
'''
```
`minmax_normalize`:
```
def minmax_normalize(df, fields, keyval=['requestid', 'direction_onward'], drop_field=True, verbose=True):
'''
normalizer function to normalize from 0-1 proportional to the value.
inputs:
df: Dataframe which contains fields (pandas df series)
fields: Dataframe columns on which the normalication is calculated (list)
keyval: Group the data by this key values (list)
drop_field: Indicator to replace fields by normalized new fields (bool)
verbose: Progress bar indicator (bool)
return:
field: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup (pandas df series)
'''
```
`encode_totalprice`:
```
def encode_totalprice(df):
'''
total price encoder.
inputs:
df: Dataframe which have totalprice and totaltaxes column (pandas df series)
return:
df: Dataframe with a score for totalprice relative to whole requestid-direction, by index, by faregroup (pandas df series)
'''
```
`encode_bookingid`:
```
def encode_bookingid(df):
'''
Bookingid flag one-hot encoder.
inputs:
df: Dataframe which have Bookingid column (pandas df series)
return:
df: Dataframe which have only Bookingid indicator [0/1] (pandas df series)
'''
```
`encode_faregroup`:
```
def encode_faregroup(raw_file, df, verbose=False):
'''
Faregroup encoder.
inputs:
raw_file: raw_file location where the faregroup_definition is available
df: Dataframe which have faregroup column (pandas df series)
verbose: Optional, if more detailed log is needed
return:
field: Dataframe with extra fields with faregroup attributes [-1/0/1] (pandas df series)
Note: -1 = not available, 0 = available for a fee, 1 = available for free (at no charge)
'''
```
`encode_datetime`:
```
def encode_datetime(df, field, verbose=False):
'''
Datetime encoder.
inputs:
df: Dataframe which have 'field' column (pandas df series)
field: the field that has to be encoded
return:
df: Dataframe with 6 ecoded values capturing TOD, DOW and WOY
'''
```
`encode_advanced_purchase`:
```
def encode_advanced_purchase(df, dptr_field='departuredate', sales_field='utctimestamp'):
'''
advanced purchase encoder.
inputs:
df: Dataframe which have dptr_field and sales_field column (pandas df series)
return:
df: Dataframe advanced purchase column
'''
```
`encode_los`:
```
def encode_los(df, verbose=False):
'''
los and trip_type encoder.
inputs:
df: Dataframe which have departuredate, requestid and direction column (pandas df series)
return:
df: Dataframe los and trip_type encodes column
'''
```
`encode_airports`:
```
def encode_airports(df, fields, reference_file_path, verbose=True):
'''
encode the airports.
inputs:
df : Dataframe which have airports (pandas df series)
fields : list of fields that you want to run this
function on (list)
reference_file_path : path to the codes file (string)
verbose : Indicator for progress bar (bool)
return:
df: Dataframe encoded with airports values
'''
```
`encode_city`:
```
def encode_city(df, fields, reference_file_path, verbose=True):
'''
encode the airports.
inputs:
df : Dataframe which have city (pandas df series)
fields : list of fields that you want to run this
function on (list)
reference_file_path : path to the codes file (string)
verbose : Indicator for progress bar (bool)
return:
df: Dataframe encoded with airports values
'''
```
### utils
This subpackage contains tools a level lower than `encoder` module i.e. those modules that are used by encoder.
#### encoder_tools
Below are the functions that can be accessed by importing this module as `from deepair_encoder.utils.encoder_tools import <function_name>`.
`one_hot_encoder`:
```
def one_hot_encoder(df, fields, fields_drop=True, verbose=True, classes=None):
'''
Converts the fields into one hot encoding.
inputs:
df: Dataframe containing all those fields (pandas df)
fields: Dataframe columns that you want to convert to one hot (string)
verbose: Indicator for progress bar (bool)
classes: Dictionary for the desired columns (dict)
return:
df: updated dataframe (pandas df)
'''
```
`integer_encoder`:
```
def integer_encoder(df, fields, verbose=True):
'''
Converts the fields into integer encoding.
inputs:
df: Dataframe containing all those fields (pandas df)
fields: Dataframe columns that you want to convert to one hot (string)
verbose: Indicator for progress bar (bool)
returns:
df: updated dataframe (pandas df)
'''
```
`get_wom`:
```
def get_wom(field):
'''
Function for week of the month.
inputs:
field: Dataframe column which have timestamp (pandas df series)
return:
field: Dataframe column which have wom (pandas df series)
'''
```
`get_dow`:
```
def get_dow(field):
'''
Function for day of the week.
inputs:
field: Dataframe column which have timestamp (pandas df series)
return:
field: Dataframe column which have dow (pandas df series)
'''
```
`get_month`:
```
def get_month(field):
'''
Function for month.
inputs:
field: Dataframe column which have timestamp (pandas df series)
return:
field: Dataframe column which have only month (pandas df series)
'''
```
`get_year`:
```
def get_year(field):
'''
Function for year.
inputs:
field: Dataframe column which have timestamp (pandas df series)
return:
field: Dataframe column which have only year (pandas df series)
'''
```
`obj2num`:
```
def obj2num(df, fields, verbose=True):
'''
Converts the fields into numeric from obj data type.
inputs:
df: Dataframe containing all those fields (pandas df)
fields: Dataframe columns that you want to convert to numeric (string)
verbose: Indicator for progress bar (bool)
returns:
df: updated dataframe (pandas df)
'''
```
#### logger
Below are the functions that can be accessed by importing this module as `from deepair_encoder.utils.logger import <function_name>`.
`unique_stats`:
```
def unique_stats(df):
```
`_log_with_timestamp`:
```
def _log_with_timestamp(message):
'''
prints message on console
input :
message : msg to print (string)
'''
```
#### splitters
Below are the functions that can be accessed by importing this module as `from deepair_encoder.utils.splitters import <function_name>`.
`type_splitter`:
```
def type_splitter(data, keys=['passengertypes'], newheadings=[['ADT', 'CHD', 'INF']]):
'''
Split the column (need to make it more generic).
inputs:
data: dataframe which contain this column (pd dataframe)
keys: list of column fields to split(list)
newheadings: list of list for header columns in keys (list)
returns:
data: updated dataframe (pandas df)
'''
```
`split_datewise`:
```
def split_datewise(df, directory='', postfix='', mode='w'):
'''
Split a Dataframe according to dates in UTC-Timestamp:
input :-
DF : dataframe which contain this column (pd dataframe)
directory : path to the target directory (string)
postfix : any postfix you want to add (string)
mode : writing mode ('w':writing, 'a': append)
'''
```
نحوه نصب
نصب پکیج whl deepair-encoder-0.0.9:
pip install deepair-encoder-0.0.9.whl
نصب پکیج tar.gz deepair-encoder-0.0.9:
pip install deepair-encoder-0.0.9.tar.gz