def main(args):
args = get_arguments()
if args.command == "report":
get_report(args)
elif args.command == "buy":
buy_product(args)
elif args.command == "sell":
sell_product(args)
_input_fn = lambda: input_fn(
data_dir, batch_size, is_training=True, params=params)
model.train(input_fn=_input_fn, steps=train_steps)
print("Training complete")
# evaluate
if 'eval' in args.mode:
print("\nEvaluating...")
_eval_input_fn = lambda: input_fn(
data_dir, batch_size, is_training=False, params=params)
eval_result = model.evaluate(input_fn=_eval_input_fn)
print("global step:%7d" % eval_result['global_step'])
print("accuracy: %7.2f" % round(eval_result['accuracy'] * 100.0, 2))
print("loss: %7.2f" % round(eval_result['loss'], 2))
print("Evaluation complete")
if 'compile_only' in args.mode or 'validate_only' in args.mode:
print("\CS-1 preprocessing...")
validate_only = 'validate_only' in args.mode
_eval_input_fn = lambda: input_fn(
data_dir, batch_size, is_training=False, params=params)
model.compile(input_fn=_eval_input_fn)
print("\CS-1 preprocessing complete")
##______________________________________________________________________________
if __name__ == '__main__':
arguments = get_arguments()
main(arguments)
if d_analysis_option == 's':
plt.savefig('Images/' + file_path[-5] + str(nr_crt) + '_plot.png')
nr_crt += 1
if d_analysis_option == 'd':
plt.show()
if d_analysis_option == 'sd':
plt.savefig('Images/' + file_path[-5] + str(nr_crt) + '_plot.png')
nr_crt += 1
plt.show()
#######################################################################################################################
# actual code
#######################################################################################################################
# define the class objects
data_analysis = DataAnalysisModule.DataAnalysisClass()
nr_ind_start, nr_generations, genes, environment, \
nat_selection, d_analysis, d_analysis_option, file_path = get_arguments()
#######################################################################################################################
# run the natural selection algorithm from the function below
if nat_selection:
natural_selection_algorithm(nr_ind_start, nr_generations, environment,
file_path)
#######################################################################################################################
if d_analysis:
visual_analysis_df(file_path, d_analysis_option)
data_frame['TotalSF'] = data_frame['GrLivArea'] + data_frame['TotalBsmtSF']
data_frame['TotalPorchSF'] = data_frame['OpenPorchSF'] + data_frame['EnclosedPorch'] + \
data_frame['3SsnPorch'] + data_frame['ScreenPorch']
data_frame['TotalBath'] = data_frame['FullBath'] + data_frame['BsmtFullBath'] + \
0.5 * (data_frame['BsmtHalfBath'] + data_frame['HalfBath'])
return data_frame
automatic = True
display_plot = False
save_plots = False
machine_learning = False
input_file = ''
if __name__ == '__main__':
automatic, display_plot, save_plots, machine_learning, input_file = get_arguments(
automatic, display_plot, save_plots, machine_learning, input_file)
# counter represents the number of the figure in the /Images folder
counter = 1
train_df = pd.read_csv(input_file)
aux_df = train_df # save the data frame for later use
########################################################################################
########################################################################################
# General information and data analysis
print('#' * 100)
print('#' * 100)
data_analysis_module = DataAnalysisModule.DataAnalysisClass()
plt = data_analysis_module.general_information_plotting(train_df, 'SalePrice')