Metadata-Version: 2.1
Name: Kami
Version: 0.4.2
Summary: Forecast sales with Entity Embedding LSTM
Home-page: https://github.com/MacarielAerial
Author: Yifei Yu
Author-email: yyu.mam2020@london.edu
License: MIT
Download-URL: https://github.com/MacarielAerial/AM18_SPR20_LondonLAB/archive/V_0.4.2.tar.gz
Description: # AM18_SPR20_LondonLAB
        
        ## Packagr Description
        
        The package contains one object **Kami** with four methods in the order of execution:  
        1. **Preprocess()**
        2. **Analyse(n_sample)**
        3. **Vis()**
        4. **Forecast(store_list, product_list, start, end)**
        
        To initiate the object **Kami**, the user is required to supply at least these three arguments:  
        1. Path to the grouped product sales input data (***input_f_path***)
        2. Path to an intermediary folder to store intermediary data (***cache_dir_path***)
        3. Path to an output folder to store final predictions (***output_dir_path***)
        
        While **Preprocess** and **Vis** methods are executed without any argument, **Analyse** method can be supplied with an optional argument *n_sample* which is the number of random samples drawn from the predefined training data.
        
        **Forecast** method is required to be supplied with four arguments including:  
        1. A list of stores whose sales are predicted (***store_list***)
        2. A list of products whose sales are predicted (***product_list***)
        3. The start date of the forecast (***start***)
        4. The end date of the forecast (***end***)
        
        ## Typical Use Case
        
        ***
        	from Kami import Kami
        
        	obj = Kami(input_f_path = 'PATH_TO_SALES_DATA/SALES_DATA.csv',
        			output_dir_path = 'OUTPUT_FOLDER/',
        			cache_dir_path = 'CACHE_FOLDER/')
        	obj.Preprocess()
        	obj.Analyse()
        	obj.Vis()
        	obj.Forecast(store_list = ['STORE_A', 'STORE_B'],
        			product_list = ['PRODUCT_A', 'PRODUCT_B'],
        			start = 'MM/DD/YYYY',
        			end = 'MM/DD/YYYY')
        ***
        
Keywords: SALES,FORECAST,LSTM,EMBEDDING
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Requires-Python: >= 3.6
Description-Content-Type: text/markdown
