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generate_models.py
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generate_models.py
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from data_import import data_import as di
from pc_analysis import pc_analysis
from nn import nn
from save_output import save_output
import os
import pandas as pd
def generate_models():
'''
This function generates 26 neural network models; 13 for each type of wine:
red and white. 11 of those models utilize PCA data, 1 is trained on all raw
feature data, and 1 is trained on 4 sensory features to equal 13 per type.
'''
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
filepath_red = './raw_data/winequality-red.csv'
filepath_white = './raw_data/winequality-white.csv'
red_wine_df = di(filepath_red)
red_pc_dict = pc_analysis(red_wine_df)
red_pc_model_list = nn(red_pc_dict)
red_raw_quality = pd.DataFrame(red_wine_df['quality'])
red_raw_quality.columns = ['quality']
del red_wine_df['quality']
red_raw_data_dict = {'11 Raw Data - Red':
[None, red_wine_df, red_raw_quality]}
red_raw_data_model = nn(red_raw_data_dict)
red_4_columns = pd.DataFrame((red_wine_df['fixed acidity'],
red_wine_df['citric acid'],
red_wine_df['residual sugar'],
red_wine_df['alcohol'])).transpose()
red_4_columns_dict = {'4 Columns Raw Data - Red':
[None, red_4_columns, red_raw_quality]}
red_4_columns_model = nn(red_4_columns_dict)
red_model_list = (red_pc_model_list
+ red_raw_data_model
+ red_4_columns_model)
save_output(red_model_list, 'red_performance.txt', red_pc_dict)
white_wine_df = di(filepath_white)
white_pc_dict = pc_analysis(white_wine_df)
white_pc_model_list = nn(white_pc_dict)
white_raw_quality = pd.DataFrame(white_wine_df['quality'])
white_raw_quality.columns = ['quality']
del white_wine_df['quality']
white_raw_data_dict = {'11 Raw Data - White':
[None, white_wine_df, white_raw_quality]}
white_raw_data_model = nn(white_raw_data_dict)
white_4_columns = pd.DataFrame((white_wine_df['fixed acidity'],
white_wine_df['citric acid'],
white_wine_df['residual sugar'],
white_wine_df['alcohol'])).transpose()
white_4_columns_dict = {'4 Columns Raw Data - White':
[None, white_4_columns, white_raw_quality]}
white_4_columns_model = nn(white_4_columns_dict)
white_model_list = (white_pc_model_list
+ white_raw_data_model
+ white_4_columns_model)
save_output(white_model_list, 'white_performance.txt', white_pc_dict)
if __name__ == '__main__':
generate_models()