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Comparison_experiment_3.py
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from river import metrics, preprocessing, tree, forest
from Functions.Result_extractor import result_extractor
from LoanDataset.Create_loandataset import create_loandataset
from Functions.Create_Plots import create_plots
from Functions.Data_plot import data_plot
from Simple_pipeline.Simple_pipeline_use import simple_pipeline
from Functions.Evaluation import evaluation
from river import linear_model
from AML4S.AML4S_Usage import use_AML4S
import time
"""
An example of how to use all the functions of the project
"""
if __name__ == "__main__":
seed = 30
# create dataset
data = create_loandataset(4, datalimit=20000,
conceptdriftpoints={2500: "growth", 4000: "crisis",
6000: "growth", 10000: "normal",
13000: "growth", 16000: "normal",
18000: "crisis"},
datadriftpoints={2000: "crisis", 6000: "growth",
8000: "normal", 9000: "crisis",
12000: "growth", 14000: "crisis",
16000: "normal"},
seed=seed)
# set the target name
target = 'y'
# create the lists to save the results
y_real = []
y_predicted = []
data_drifts = []
concept_drifts = []
# pipeline 1
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(tree.HoeffdingAdaptiveTreeClassifier(seed = seed), None, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 1: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 1, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 2
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(tree.HoeffdingAdaptiveTreeClassifier(seed = seed), preprocessing.StandardScaler, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 2: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 2, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 3
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(tree.HoeffdingTreeClassifier(), None, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 3: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 3, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 4
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(tree.HoeffdingTreeClassifier(), preprocessing.StandardScaler, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 4: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 4, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 5
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(linear_model.LogisticRegression(), None, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 5: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 5, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 6
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(linear_model.LogisticRegression(), preprocessing.StandardScaler, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 6: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 6, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 7
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(forest.AMFClassifier(seed = seed), None, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 7: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 7, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 8
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
simple_pipeline(forest.AMFClassifier(seed = seed), preprocessing.StandardScaler, None, data, target))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of the pipeline 8: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 8, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
print("-----------------------------------------------------------------------------------------------------------")
# pipeline 9
start_time = time.time()
y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp = (
use_AML4S(data, target, True, True, seed = seed))
finish_time = time.time()
total_time = finish_time - start_time
print("Time of pipeline 9: ", total_time)
# add the results from the pipeline in the lists
y_real.append(y_real_temp)
y_predicted.append(y_predicted_temp)
data_drifts.append(data_drifts_temp)
concept_drifts.append(concept_drifts_temp)
result_extractor(3, 9, y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp)
# Delete the temporal variables
del y_real_temp, y_predicted_temp, data_drifts_temp, concept_drifts_temp
# evaluation of pipelines
evaluates = evaluation(y_real, y_predicted, metrics.Accuracy())
# plots for metrics and drifts of the pipelines
create_plots(evaluates, data_drifts, concept_drifts)
# plots for data
data_plot(data, 50)