Metadata-Version: 1.1
Name: seq2ftr
Version: 0.1.7
Summary: A Nice and Convenient Feature Engineering Tool on Sequential Data
Home-page: https://github.com/smoothnlp/seq2ftr
Author: Ruinan(Victor) Zhang， YinJun(YJ)
Author-email: ruinan.zhang@icloud.com, yjun1989@gmail.com
License: UNKNOWN
Description: 
        This project handles series data
        
        
        Support Series data Feature Calculation.
        
        
        Installation
        ==============
        
        You need Python installed on your system to able to use seq2ftr.
        
        This package contains many feature extraction methods.
        
        Support different type (continues/class) features calculation.
        
        >>> $ pip install seq2ftr
        
        Install Requirements
         * numpy
         * pandas
         * sklearn
        
        
        Feature Calculation
        ^^^^^^^^^^^^^^^^^^^^^^^^
         Support Function
        
         - mean
         - max
         - min
         - freq_of_max
         - freq_of_min
         - median
         - median_mean_distance
         - percentage_below_mean
         - var
         - std
         - uniqueCount
         - ...
        
        Support Type
        ^^^^^^^^^^^^^^^^^
         - 0 - boolean
         - 1 - numericla
         - 2 - categorical
        
        
        Example
        ^^^^^^^^^^^^^
        To start , we load data to python
        
        >>>
        import pandas as pd
        df = pd.DataFrame([[1,200,"1"],[1,500,"2"],[2,300,"2"],[2,600,"2"]],columns=['id','stock_price',"type"])
        df = df.set_index("id")
        
        >>>
        from seq2ftr import SequenceTransformer
        st_num = SequenceTransformer()
        st_num.transformer(df['stock_price']）
        # output all features
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
