164 lines
4.7 KiB
Python
164 lines
4.7 KiB
Python
"""
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file_name: derive
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project_name: DBM
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created: 2020-20-07
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"""
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import pandas as pd
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import numpy as np
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import glob
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import os
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import logging
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from datetime import datetime
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logging.basicConfig(level=logging.INFO)
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logger=logging.getLogger()
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def dict_to_df(feature_dict, file):
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"""
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Converting ditionary to dataframe
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"""
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final_dict = {k: v for d in feature_dict for k, v in d.items()}
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feature_df = pd.DataFrame([final_dict])
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feature_df['Filename'] = file
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return feature_df
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def save_derive_output(df_list, out_loc):
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"""
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Saving derive variable output
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"""
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try:
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if len(df_list)>0:
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df = df_list[0]
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file_name = os.path.join(out_loc, 'derived_output.csv')
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if not os.path.exists(out_loc):
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os.makedirs(out_loc)
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df.to_csv(file_name, index=False)
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except Exception as e:
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logger.error('Failed to save derived variable csv')
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def feature_output(df_fea, exp_var, cal_type):
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"""
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Computing mean value of dataframe columns
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"""
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exp_val = np.nan
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try:
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df_ = df_fea[exp_var].astype(float).copy()
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df_ = df_.dropna().reset_index(drop=True)
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if len(df_)>0:
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if cal_type == 'mean':
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exp_val = df_.mean(axis = 0, skipna = True)
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elif cal_type == 'std':
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exp_val = df_.std(axis = 0, skipna = True)
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elif cal_type == 'count':#use case for eye blink
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exp_var = 'mov_blink'
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exp_val = (len(df_)/df_[0])*60
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elif cal_type == 'pct':
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if len(df_)>0:
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exp_val = len(df_[df_ > 0])/len(df_)
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elif cal_type == 'range':
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exp_val = max(df_) - min(df_)
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except Exception as e:
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logger.error('Failed to compute calculation: {}'.format(e))
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pass
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var_name = exp_var + '_' + cal_type
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exp_val = float("{0:.4f}".format(exp_val))
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var_val = (var_name, exp_val)
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return var_val
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def cal_type_dict(var_df, raw_df, d_cfg_Obj, r_cfg_Obj):
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var_name = str(var_df['var_id'])
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#fetching key based on variable name from raw config
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var_key = list(r_cfg_Obj.keys())[list(r_cfg_Obj.values()).index(var_name)]
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cal_type = d_cfg_Obj[var_key] # calculation type from config
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var_val = [feature_output(raw_df, var_name, cal) for cal in cal_type]
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var_val_dict = dict(var_val)
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return var_val_dict
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def compute_feature(raw_df, var_cols, d_cfg_Obj, r_cfg_Obj):
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"""
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Computing features
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"""
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#Variable data frame for each feature group
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var_df = pd.DataFrame(var_cols,columns=['var_id'])
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feature_dict = {}
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if len(raw_df)>0:
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feature_dict = var_df.apply(cal_type_dict, args=(raw_df, d_cfg_Obj, r_cfg_Obj, ), axis=1)
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return feature_dict
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def calc_derive(input_file, input_dir, r_cfg_Obj, d_cfg_Obj, feature):
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"""
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Calculating derived variable
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"""
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df_list = []
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df = pd.DataFrame()
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for file in input_file:
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file_name, _ = os.path.splitext(os.path.basename(file))
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input_loc = os.path.join(input_dir, file_name)
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var_cols = [r_cfg_Obj[x] for x in d_cfg_Obj[feature]]
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fea_loc = d_cfg_Obj[feature + '_LOC']
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fea_res = glob.glob(os.path.join(input_loc, '*/*/*' + fea_loc + '.csv'))
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if len(fea_res)>0:
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raw_df = pd.read_csv(fea_res[0])
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feature_dict = compute_feature(raw_df, var_cols, d_cfg_Obj, r_cfg_Obj)
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if len(feature_dict)>0:
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feature_df = dict_to_df(feature_dict, file)
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df_list.append(feature_df)
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if len(df_list)>0:
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df = pd.concat(df_list, ignore_index=True)
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return df
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def run_derive(input_file, input_dir, output_dir, r_config, d_config):
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"""
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Processing derived variable
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"""
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d_cfg_Obj = d_config.base_derive['derive_feature']
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r_cfg_Obj = r_config.base_raw['raw_feature']
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feature_group = d_cfg_Obj['FEATURE_GROUP']
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#Iterating over feature group
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df_list = []
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for feature in feature_group:
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try:
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df_fea = calc_derive(input_file, input_dir, r_cfg_Obj, d_cfg_Obj, feature)
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if len(df_fea)>0:
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if len(df_list) == 0:
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df_list.append(df_fea)
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else:
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result = pd.merge(df_list[0], df_fea, how='outer', on=['Filename'])
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df_list = [result]
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except Exception as e:
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logger.error('Failed to process derived variables {}'.format(feature))
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logger.info("Saving derived variable output...")
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save_derive_output(df_list, output_dir) |