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266 lines
12 KiB
266 lines
12 KiB
import json
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import os
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import numpy as np
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import pandas as pd
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import sklearn
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from matplotlib import pyplot as plt
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from pipeline import (
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load_dataset,
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filter_data,
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filter_test_data,
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prepare_user_data,
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train_models,
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evaluate_models,
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prepare_data_for_model, model_type_gru, model_type_lstm, model_type_bilstm, train_models_v2
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)
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year_str = 'Year'
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month_str = 'Month'
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user_str = 'user'
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split_str = 'split type'
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data_split_str = 'data percentages'
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month_split_str = 'month percentages'
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threshold_str = 'threshold used'
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with_threshold_str = 'WITH'
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without_threshold_str = 'WITHOUT'
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timespan_str = 'time used'
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hour_timespan_str = '1HR'
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min_timespan_str = '15MIN'
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sequence_length_str = 'sequence length'
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precision_str = 'precision'
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recall_str = 'recall'
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f1_string = 'f1 score'
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model_type_str = 'model type'
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weak_column_names = ['DayOfWeek_'+day for day in
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['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]]
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figure_path = 'figures/'
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# === Configurable Parameters ===
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dataset_path = './Datasets/'
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DATA_PATH = dataset_path +'ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx'
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OUTPUT_EXCEL_PATH = './working/evaluation_results.xlsx'
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result_filename_v1 = './working/evaluation_results.json'
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result_filename_v2 = './working/evaluation_results_v2.json'
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SEQUENCE_LENGTHS = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
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TRAINING_SCENARIO = [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]
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VALIDATION_SCENARIO = [(2019, [10, 11, 12])]
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TEST_SCENARIO = [(2020, [1, 2])] # Jan–Feb 2020 only
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# === Optional display only ===
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predefined_training_scenarios = {
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"Scenario 1": {"years_months": [(2018, list(range(1, 13))), (2019, list(range(1, 10)))]},
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"Scenario 2": {"years_months": [(2017, list(range(1, 13))), (2018, list(range(1, 13))), (2019, list(range(1, 10)))]}
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}
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predefined_validation_scenarios = {
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"Scenario A": {"years_months": [(2019, [10, 11, 12])]}
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}
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def remove_covid_data(df):
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df = df[~((df[year_str]==2020) & (df[month_str]>2))]
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return df
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def split_data_by_month_percentage(df, percentages):
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train_p, valid_p, test_p = percentages
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ids = df[[year_str, month_str]].drop_duplicates().sort_values([year_str, month_str])
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tr, va, te = np.split(ids, [int((train_p/100) * len(ids)), int(((train_p + valid_p)/100) * len(ids))])
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return df.merge(tr, on=[year_str, month_str], how='inner'), df.merge(va, on=[year_str, month_str], how='inner'), df.merge(te, on=[year_str, month_str], how='inner')
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def split_data_by_userdata_percentage(df, percentages):
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train_p, valid_p, test_p = percentages
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tr, va, te = pd.DataFrame(), pd.DataFrame(), pd.DataFrame()
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for user_id in df[user_str].unique():
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user_data = df[df[user_str]==user_id].sort_values([year_str, month_str])
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u_tr, u_va, u_te = np.split(user_data, [int((train_p/100)*len(user_data)), int(((train_p+valid_p)/100)*len(user_data))])
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tr = pd.concat([tr, u_tr], ignore_index=True)
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va = pd.concat([va, u_va], ignore_index=True)
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te = pd.concat([te, u_te], ignore_index=True)
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return tr, va, te
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def main():
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# print("=== Training Scenario Setup ===")
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# display_warning_about_2020_data()
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# display_warnings_for_scenarios("training", predefined_training_scenarios, predefined_validation_scenarios)
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# print("\n=== Validation Scenario Setup ===")
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# display_warning_about_2020_data()
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# display_warnings_for_scenarios("validation", predefined_training_scenarios, predefined_validation_scenarios)
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# === Load and preprocess ===
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df = load_dataset(DATA_PATH)
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = False
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if('ALLUSERS32_15MIN_WITHOUTTHREHOLD.xlsx' in DATA_PATH):
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ALLUSERS32_15MIN_WITHOUTTHREHOLD = True
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training_data = filter_data(df, TRAINING_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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validation_data = filter_data(df, VALIDATION_SCENARIO, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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user_data_train = prepare_user_data(training_data)
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user_data_val = prepare_user_data(validation_data)
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# === Train models ===
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best_models = train_models(user_data_train, user_data_val, sequence_lengths=SEQUENCE_LENGTHS)
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# === Load and evaluate test ===
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test_df = filter_test_data(df, TEST_SCENARIO)
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evaluate_models(best_models, test_df, SEQUENCE_LENGTHS, OUTPUT_EXCEL_PATH, ALLUSERS32_15MIN_WITHOUTTHREHOLD)
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print(f"\n✅ All evaluations completed. Results saved to: {OUTPUT_EXCEL_PATH}")
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def reduce_columns(df, filename):
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if min_timespan_str in filename:
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek']+weak_column_names, errors='ignore')
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else:
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return df.drop(columns=['Month', 'Year', 'date', 'DayOfWeek'], errors='ignore')
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def load_previous_results(filename):
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results = pd.DataFrame()
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if os.path.exists(filename):
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results = pd.DataFrame(json.load(open(filename)))
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return results
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def main_two_v2(model_type):
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seq_length = range(20,31, 10)
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for sequence_length in seq_length:
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for data_filename in os.listdir(dataset_path):
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timespan_id = hour_timespan_str
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threshold_id = with_threshold_str
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if min_timespan_str in data_filename:
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timespan_id = min_timespan_str
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if without_threshold_str in data_filename:
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threshold_id = without_threshold_str
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results = load_previous_results(result_filename_v2)
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if len(results) > 0:
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if len(results[(results[timespan_str]==timespan_id) &
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(results[threshold_str]==threshold_id) &
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(results[sequence_length_str]==sequence_length) &
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(results[model_type_str]==model_type)]) > 0:
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continue
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file_path = os.path.join(dataset_path, data_filename)
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df = load_dataset(file_path)
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df = remove_covid_data(df)
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tr,val,te = split_data_by_userdata_percentage(df, percentages=(80,10,10))
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tr = reduce_columns(tr, data_filename)
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val = reduce_columns(val, data_filename)
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te = reduce_columns(te, data_filename)
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user_data_train = prepare_user_data(tr)
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user_data_val = prepare_user_data(val)
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best_models = train_models_v2(user_data_train, user_data_val,
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sequence_length=sequence_length,
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model_type=model_type)
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results = load_previous_results(result_filename_v2)
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results = pd.concat([results,
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evaluate_model_on_test_data(model=best_models[sequence_length]['model'],
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test_df=te,
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sequence_length=sequence_length,
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time_span_id=timespan_id,
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threshold_id=threshold_id,
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model_type=model_type,
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split_id=data_split_str)],
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ignore_index=True)
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results.to_json(result_filename_v2)
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def main_two_v1():
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seq_length = [30, 25, 20, 15, 10, 5] # You can add more: [20, 25, 30]
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results = pd.DataFrame()
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if os.path.exists(result_filename_v1):
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results = pd.DataFrame(json.load(open(result_filename_v1)))
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for sequence_length in seq_length:
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for data_filename in os.listdir(dataset_path):
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for split_id, split_method in [(data_split_str, split_data_by_userdata_percentage),(month_split_str, split_data_by_month_percentage)]:
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for model_type in [model_type_lstm, model_type_bilstm, model_type_gru]:
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timespan_id = hour_timespan_str
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threshold_id = with_threshold_str
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if min_timespan_str in data_filename:
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timespan_id = min_timespan_str
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if without_threshold_str in data_filename:
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threshold_id = without_threshold_str
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if len(results) > 0:
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if len(results[(results[split_str]==split_id) &
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(results[timespan_str]==timespan_id) &
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(results[threshold_str]==threshold_id) &
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(results[sequence_length_str]==sequence_length) &
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(results[model_type_str]==model_type)]) > 0:
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continue
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file_path = os.path.join(dataset_path, data_filename)
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df = load_dataset(file_path)
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df = remove_covid_data(df)
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tr,val,te = split_method(df, percentages=(80,10,10))
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tr = reduce_columns(tr, data_filename)
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val = reduce_columns(val, data_filename)
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te = reduce_columns(te, data_filename)
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user_data_train = prepare_user_data(tr)
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user_data_val = prepare_user_data(val)
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best_models = train_models(user_data_train, user_data_val, sequence_lengths=[sequence_length], model_type=model_type)
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results = pd.concat([results,
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evaluate_model_on_test_data(model=best_models[sequence_length]['model'],
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test_df=te, split_id=split_id,
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sequence_length=sequence_length,
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time_span_id=timespan_id,
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threshold_id=threshold_id,
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model_type=model_type)], ignore_index=True)
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results.to_json(result_filename_v1)
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# === Evaluation ===
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def evaluate_model_on_test_data(model, test_df,sequence_length, split_id, threshold_id, time_span_id, model_type):
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user_data = prepare_user_data(test_df)
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x, y = prepare_data_for_model(user_data=user_data, sequence_length=sequence_length)
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y_pred = model.predict(x, verbose=0)
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y_pred_classes = np.argmax(y_pred, axis=1)
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recall = sklearn.metrics.recall_score(y, y_pred_classes, average='weighted')
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precision = sklearn.metrics.precision_score(y, y_pred_classes, average='weighted')
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f1_score = sklearn.metrics.f1_score(y, y_pred_classes, average='weighted')
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return pd.DataFrame({split_str:[split_id], threshold_str:[threshold_id], timespan_str:[time_span_id],
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sequence_length_str:[sequence_length],
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model_type_str:[model_type], recall_str:[recall],
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precision_str:[precision], f1_string:[f1_score]})
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def visualise_results_v1():
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results = pd.DataFrame(json.load(open(result_filename_v1)))
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# Month split ist immer schlechter
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results = results[results[split_str] == data_split_str]
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with_threshold = results[results[threshold_str] == with_threshold_str]
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without_threshold = results[results[threshold_str] == without_threshold_str]
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fig, axes = plt.subplots(2, 3)
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ax_col_id = 0
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ax_row_id = -1
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for timespan in [hour_timespan_str,min_timespan_str]:
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ax_row_id +=1
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for model in [model_type_lstm, model_type_bilstm, model_type_gru]:
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with_sub = with_threshold[(with_threshold[timespan_str] == timespan) & (with_threshold[model_type_str] == model)]
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without_sub = without_threshold[(without_threshold[timespan_str] == timespan) & (without_threshold[model_type_str] == model)]
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ax = axes[ax_row_id, ax_col_id]
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ax.set_title(model+' '+timespan)
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ax.plot(with_sub[sequence_length_str], with_sub[f1_string], label=with_threshold_str)
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ax.plot(without_sub[sequence_length_str], without_sub[f1_string], label=without_threshold_str)
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ax.legend()
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ax_col_id +=1
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ax_col_id %= 3
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fig.tight_layout()
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fig.savefig(figure_path+'v1_results.svg')
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# Fazit: keine eindeutig besseren Versionen erkennbar
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if __name__ == "__main__":
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# main_two_v1()
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# visualise_results_v1()
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main_two_v2(model_type=model_type_gru)
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print('Done')
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