max/model-training
1
0
Fork 0
mirror of https://git.gfz-potsdam.de/naaice/model-training.git synced 2025-12-16 08:08:22 +01:00
Code Issues Packages Projects Releases Wiki Activity

update experiments

Browse Source
This commit is contained in:
Hannes Signer 2025-03-28 21:10:54 +01:00
parent d678c0bfde
commit 69355a1e4e
1 changed files with 39 additions and 27 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

66
src/gpu_experiments.py
View File

@ -4,6 +4,7 @@ import matplotlib.pyplot as plt
import matplotlib.colors as mcolors import matplotlib.colors as mcolors
import matplotlib.patches as mpatches import matplotlib.patches as mpatches
import pickle import pickle
import csv
###### Experimental parameters ###### Experimental parameters
@ -27,6 +28,8 @@ df_results = pd.DataFrame(
) )
data_file.close() data_file.close()
# remove charge as species
df_design.drop("Charge", axis=1, inplace=True, errors="ignore") df_design.drop("Charge", axis=1, inplace=True, errors="ignore")
df_results.drop("Charge", axis=1, inplace=True, errors="ignore") df_results.drop("Charge", axis=1, inplace=True, errors="ignore")
@ -122,36 +125,11 @@ model.compile(
###### train model ###### train model
epochs = 200 epochs = 3
history = model_training(model, epochs=epochs) history = model_training(model, epochs=epochs)
###### evaluate model
results = mass_balance_evaluation(model, X_test, preprocess)
mass_balance_ratio(results, threshold=1e-5)
def test_model(model, X_test, y_test):
X_test.reset_index(inplace=True, drop=True)
y_test.reset_index(inplace=True, drop=True)
all = model.evaluate(X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])
class_0 = model.evaluate(X_test[X_test["Class"] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test["Class"] == 0].iloc[:, y_test.columns != "Class"])
class_1 = model.evaluate(
X_test[X_test["Class"] == 1].iloc[:, :-1], y_test[X_test["Class"] == 1].iloc[:, :-1])
print("metric all data: ", all)
print("metric class 0: ", class_0)
print("metric class 1: ", class_1)
test_model(model, X_test, y_test)
###### save model and history ###### save model and history
delimiter = "_" delimiter = "_"
@ -160,4 +138,38 @@ file_name = "history_" + idx_string
with open('../results/'+file_name, 'wb') as file_pi: with open('../results/'+file_name, 'wb') as file_pi:
pickle.dump(history.history, file_pi) pickle.dump(history.history, file_pi)
model.save_weights("../results/models/model_"+idx_string + ".weights.h5") model.save_weights("../results/models/model_"+idx_string + ".weights.h5")
###### evaluate model
results = mass_balance_evaluation(model, X_test, preprocess)
proportion = mass_balance_ratio(results, threshold=1e-5)
X_test.reset_index(inplace=True, drop=True)
y_test.reset_index(inplace=True, drop=True)
all_classes = model.evaluate(X_test.loc[:, X_test.columns != "Class"], y_test.loc[:, y_test.columns != "Class"])
class_0 = model.evaluate(X_test[X_test["Class"] == 0].iloc[:, X_test.columns != "Class"], y_test[X_test["Class"] == 0].iloc[:, y_test.columns != "Class"])
class_1 = model.evaluate(X_test[X_test["Class"] == 1].iloc[:, :-1], y_test[X_test["Class"] == 1].iloc[:, :-1])
print("metric all data: ", all_classes)
print("metric class 0: ", class_0)
print("metric class 1: ", class_1)
# Save evaluation results to a file
results_file_name = "../results/evaluation_" + idx_string + ".csv"
with open(results_file_name, mode="w", newline="") as results_file:
writer = csv.writer(results_file)
writer.writerow(["Metric", "Value"])
writer.writerow(["Mass balance fulfilled (all classes)", proportion["overall"]])
writer.writerow(["Mass balance fulfilled (class 0)", proportion["class_0"]])
writer.writerow(["Mass balance fulfilled (class 1)", proportion["class_1"]])
writer.writerow(["Metrics (all classes)", all_classes])
writer.writerow(["Metrics (class 0)", class_0])
writer.writerow(["Metrics (class 1)", class_1])
results_file.close()