Delete data_modeling/evaluation

data_modeling/evaluation/amazon-employee-access-challenge_eval.py

@@ -1,37 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="ACTION")
-
-args = parser.parse_args()
-
-
-
-# 定义 RMSLE 计算函数
-def rmsle(y_true, y_pred):
-    return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_true)) ** 2))
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-performance = roc_auc_score(actual[args.value], submission[args.value])
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/bike-sharing-demand_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-# 计算RMSLE
-def rmsle(predicted, actual):
-    sum_log_diff = np.sum((np.log(predicted + 1) - np.log(actual + 1)) ** 2)
-    mean_log_diff = sum_log_diff / len(predicted)
-    return np.sqrt(mean_log_diff)
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = rmsle(predictions[args.value], answers[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/cat-in-the-dat-ii_eval.py

@@ -1,25 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/cat-in-the-dat_eval.py

@@ -1,25 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/commonlitreadabilityprize_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-def rmse(targets, predictions):
-    return np.sqrt(((predictions - targets) ** 2).mean())
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = rmse(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/conways-reverse-game-of-life-2020_eval.py

@@ -1,34 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="generated")
-
-args = parser.parse_args()
-
-actual = pd.read_csv( args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-# 移除id列，剩下的是矩阵的值
-submission_values = submission.drop(columns=['id']).values
-actual_values = actual.drop(columns=['id']).values
-
-# 计算平均绝对误差
-performance = np.mean(np.abs(submission_values - actual_values))
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/covid19-global-forecasting-week-1_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-def rmsle(predictions, actuals):
-    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
-    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
-    return (rmsle_confirmed + rmsle_fatalities) / 2
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = rmsle(predictions, answers)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/covid19-global-forecasting-week-2_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-def rmsle(predictions, actuals):
-    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
-    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
-    return (rmsle_confirmed + rmsle_fatalities) / 2
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-predictions = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-performance = rmsle(predictions, answers)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/covid19-global-forecasting-week-3_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-def rmsle(predictions, actuals):
-    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
-    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
-    return (rmsle_confirmed + rmsle_fatalities) / 2
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-predictions = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-performance = rmsle(predictions, answers)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/covid19-global-forecasting-week-4_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-def rmsle(predictions, actuals):
-    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
-    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
-    return (rmsle_confirmed + rmsle_fatalities) / 2
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-predictions = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-performance = rmsle(predictions, answers)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/covid19-global-forecasting-week-5_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-def rmsle(predictions, actuals):
-    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
-    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
-    return (rmsle_confirmed + rmsle_fatalities) / 2
-
-parser = argparse.ArgumentParser()
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="count")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-predictions = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-performance = rmsle(predictions, answers)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/demand-forecasting-kernels-only_eval.py

@@ -1,30 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-# 定义 SMAPE 计算函数
-def smape(y_true, y_pred):
-    return 100/len(y_true) * np.sum(2 * np.abs(y_pred - y_true) / (np.abs(y_true) + np.abs(y_pred)))
-
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="sales")
-
-args = parser.parse_args()
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = smape(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/dont-overfit-ii_eval.py

@@ -1,31 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-actual = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-submission = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-
-# 计算平均绝对误差
-performance = roc_auc_score(actual[args.value], submission[args.value])
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/feedback-prize-english-language-learning_eval.py

@@ -1,37 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-parser.add_argument('--value', type=str, default="place_id")
-
-args = parser.parse_args()
-
-actual = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-submission = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-
-def mcrmse(y_true, y_pred):
-    """
-    计算Mean Columnwise Root Mean Squared Error (MCRMSE)
-    """
-    assert y_true.shape == y_pred.shape, "The shapes of true and predicted values do not match"
-    columnwise_rmse = np.sqrt(((y_true - y_pred) ** 2).mean(axis=0))
-    return columnwise_rmse.mean()
-
-# 提取实际标签和预测结果
-actual_values = actual.iloc[:, 1:].values  # 假设实际标签文件中第一列是text_id，后面是实际标签值
-predicted_values = submission.iloc[:, 1:].values  # 假设提交文件中第一列是text_id，后面是预测标签值
-
-# 计算MAP@3
-performance = mcrmse(actual_values, predicted_values)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/google-quest-challenge_eval.py

@@ -1,45 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from scipy.stats import spearmanr
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="place_id")
-
-args = parser.parse_args()
-
-actual = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
-submission = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))
-def mean_spearmanr(y_true, y_pred):
-    """
-    计算每列的Spearman's rank correlation coefficient，并取平均值
-    """
-    assert y_true.shape == y_pred.shape, "The shapes of true and predicted values do not match"
-    correlations = []
-    for col in range(y_true.shape[1]):
-        corr, _ = spearmanr(y_true[:, col], y_pred[:, col])
-        correlations.append(corr)
-    return sum(correlations) / len(correlations)
-
-
-# 提取实际标签和预测结果
-actual_values = actual.iloc[:, 1:].values  # 假设实际标签文件中第一列是qa_id，后面是实际标签值
-predicted_values = submission.iloc[:, 1:].values  # 假设提交文件中第一列是qa_id，后面是预测标签值
-# 计算MAP@3
-performance = mean_spearmanr(actual_values, predicted_values)
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/instant-gratification_eval.py

@@ -1,43 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-y_true = answers[args.value].values
-y_pred = predictions[args.value].values
-
-
-
-
-performance = roc_auc_score(y_true, y_pred)
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))
-
-
-

data_modeling/evaluation/learning-agency-lab-automated-essay-scoring-2_eval.py

@@ -1,45 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import cohen_kappa_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="score")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-answers = answers.sort_values('essay_id')
-predictions = predictions.sort_values('essay_id')
-
-
-y_true = answers[args.value].values
-y_pred = predictions[args.value].values
-
-
-
-
-performance = cohen_kappa_score(y_true, y_pred, weights='quadratic')
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/liverpool-ion-switching_eval.py

@@ -1,46 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import cohen_kappa_score
-from sklearn.metrics import f1_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="open_channels")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-answers = answers.sort_values('time')
-predictions = predictions.sort_values('time')
-
-
-y_true = answers[args.value].values
-y_pred = predictions[args.value].values
-
-
-
-
-performance = f1_score(y_true, y_pred, average='macro')
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/lmsys-chatbot-arena_eval.py

@@ -1,42 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import log_loss
-
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import cohen_kappa_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="score")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-actual = pd.read_csv( args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-# 提取实际值和预测值
-actual_values = actual[['winner_model_a', 'winner_model_b', 'winner_tie']].values
-predicted_values = submission[['winner_model_a', 'winner_model_b', 'winner_tie']].values
-
-
-
-performance = log_loss(actual_values, predicted_values)
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/microsoft-malware-prediction_eval.py

@@ -1,44 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="HasDetections")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-answers.sort_values(by=["MachineIdentifier"])
-predictions.sort_values(by=['MachineIdentifier'])
-y_true = answers[args.value].values
-y_pred = predictions[args.value].values
-
-
-
-
-performance = roc_auc_score(y_true, y_pred)
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))
-
-
-

data_modeling/evaluation/nlp-getting-started_eval.py

@@ -1,37 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import f1_score
-
-
-# 计算多类对数损失
-def multiclass_logloss(actuals, predictions):
-    epsilon = 1e-15  # 避免对数运算中的数值问题
-    predictions = np.clip(predictions, epsilon, 1 - epsilon)  # 限制预测概率的范围，防止对数为无穷
-    predictions /= predictions.sum(axis=1)[:, np.newaxis]  # 归一化确保总和为1
-    log_pred = np.log(predictions)
-    loss = -np.sum(actuals * log_pred) / len(actuals)
-    return loss
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = f1_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e10_eval.py

@@ -1,32 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import log_loss
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Class")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-answers.sort_values(by=['id'])
-predictions.sort_values(by=['id'])
-if "Strength" in predictions:
-    performance = log_loss(answers[args.value], predictions["Strength"])
-else:
-    performance = log_loss(answers[args.value], predictions[args.value])
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e11_eval.py

@@ -1,27 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="cost")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = np.sqrt(mean_squared_log_error(answers[args.value], predictions[args.value]))
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e12_eval.py

@@ -1,35 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="target")
-
-args = parser.parse_args()
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-actual.sort_values(by=['id'])
-submission.sort_values(by=['id'])
-
-# 计算平均错误率
-performance = roc_auc_score(actual[args.value], submission[args.value])
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e13_eval.py

@@ -1,57 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', default='', type=str, required=False)
-parser.add_argument('--name', default='',  type=str, required=False)
-parser.add_argument('--answer_file', default='/Users/tencentintern/PycharmProjects/DSBench/kaggle_data/data_filted_csv/answers/playground-series-s3e13/test_answer.csv', type=str, required=False)
-parser.add_argument('--predict_file', default='/Users/tencentintern/PycharmProjects/DSBench/kaggle_data/data_filted_csv/answers/playground-series-s3e13/test_answer.csv', type=str, required=False)
-
-parser.add_argument('--value', type=str, default="prognosis")
-
-args = parser.parse_args()
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-actual.sort_values(by=['id'])
-submission.sort_values(by=['id'])
-
-
-def mpa_at_3(actual, predictions):
-    """
-    Calculate Mean Percentage Agreement at 3 (MPA@3).
-
-    Parameters:
-    actual (list): List of actual prognosis values.
-    predictions (list of lists): List of lists containing up to 3 predicted prognosis values.
-
-    Returns:
-    float: The MPA@3 score.
-    """
-    total = len(actual)
-    score = 0.0
-
-    for act, preds in zip(actual, predictions):
-        preds = preds.split()
-        if act in preds[:3]:
-            score += 1
-
-    return score / total
-
-# 计算平均错误率
-performance = mpa_at_3(actual[args.value], submission[args.value])
-print(performance)
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e14_eval.py

@@ -1,30 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="yield")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = mean_absolute_error(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e16_eval.py

@@ -1,33 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="yield")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-answers.sort_values(by=['id'])
-predictions.sort_values(by=['id'])
-if 'Age' in predictions:
-    performance = mean_absolute_error(answers['Age'], predictions['Age'])
-else:
-    performance = mean_absolute_error(answers['Age'], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e17_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Machine failure")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e18_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Machine failure")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = (roc_auc_score(answers['EC1'], predictions['EC1']) + roc_auc_score(answers['EC2'], predictions['EC2'])) / 2
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e19_eval.py

@@ -1,34 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Machine failure")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-# 提取预测值和实际标签
-predicted_values = predictions['num_sold'].values
-actual_values = answers['num_sold'].values  # 修改列名为answers
-
-
-smape = np.mean(2 * np.abs(predicted_values - actual_values) / (np.abs(actual_values) + np.abs(predicted_values)))
-performance = smape
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e1_eval.py

@@ -1,38 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import mean_squared_error
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="MedHouseVal")
-
-args = parser.parse_args()
-
-
-
-# 定义 RMSLE 计算函数
-def rmsle(y_true, y_pred):
-    return np.sqrt(np.mean((np.log1p(y_pred) - np.log1p(y_true)) ** 2))
-
-
-actual = pd.read_csv( args.answer_file)
-submission = pd.read_csv( args.predict_file)
-
-performance = np.sqrt(mean_squared_error(actual[args.value], submission[args.value]))
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e20_eval.py

@@ -1,36 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-from math import sqrt
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Machine failure")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-answers.sort_values(by=['ID_LAT_LON_YEAR_WEEK'])
-predictions.sort_values(by=['ID_LAT_LON_YEAR_WEEK'])
-# 提取预测值和实际标签
-predicted_values = predictions['emission'].values
-actual_values = answers['emission'].values  # 修改列名为answers
-
-
-smape = sqrt(mean_squared_error(actual_values, predicted_values))
-performance = smape
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e22_eval.py

@@ -1,45 +0,0 @@
-from sklearn.metrics import f1_score
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="outcome")
-
-args = parser.parse_args()
-
-# Compute MAE
-def mean_absolute_error(y_true, y_pred):
-    return np.mean(np.abs(y_pred - y_true))
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-answers.sort_values(by=["id"])
-predictions.sort_values(by=['id'])
-y_true = answers[args.value].values
-y_pred = predictions[args.value].values
-
-
-
-
-performance = f1_score(y_true, y_pred, average='micro')
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))
-
-
-

data_modeling/evaluation/playground-series-s3e23_eval.py

@@ -1,37 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="defects")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-answers.sort_values(by=['id'])
-predictions.sort_values(by=['id'])
-# 提取预测值和实际标签
-predicted_values = predictions['defects'].values
-actual_values = answers['defects'].values  # 修改列名为answers
-
-# 计算RMSE
-rmse = roc_auc_score(actual_values, predicted_values)
-
-performance = rmse
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e24_eval.py

@@ -1,32 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="smoking")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-# 提取预测概率和实际标签
-predicted_probabilities = predictions['smoking'].values
-actual_labels = answers['smoking'].values  #
-
-performance = roc_auc_score(actual_labels, predicted_probabilities)
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e25_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-from sklearn.metrics import median_absolute_error
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Hardness")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = median_absolute_error(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))
-
-

data_modeling/evaluation/playground-series-s3e2_eval.py

@@ -1,26 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="stroke")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e3_eval.py

@@ -1,35 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Attrition")
-
-args = parser.parse_args()
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-actual.sort_values(by=actual.columns[0])
-submission.sort_values(by=submission.columns[0])
-
-# 计算平均错误率
-performance = roc_auc_score(actual[args.value], submission[args.value])
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e4_eval.py

@@ -1,26 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import roc_auc_score
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Class")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e5_eval.py

@@ -1,61 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="quality")
-
-args = parser.parse_args()
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-actual.sort_values(by=['Id'])
-submission.sort_values(by=['Id'])
-
-def quadratic_weighted_kappa(actual, predicted, N):
-    O = np.zeros((N, N), dtype=int)
-    for a, p in zip(actual, predicted):
-        O[a][p] += 1
-
-    w = np.zeros((N, N))
-    for i in range(N):
-        for j in range(N):
-            w[i][j] = ((i - j) ** 2) / ((N - 1) ** 2)
-
-    actual_hist = np.zeros(N)
-    for a in actual:
-        actual_hist[a] += 1
-
-    pred_hist = np.zeros(N)
-    for p in predicted:
-        pred_hist[p] += 1
-
-    E = np.outer(actual_hist, pred_hist)
-    E = E / E.sum() * O.sum()
-
-    num = (w * O).sum()
-    den = (w * E).sum()
-
-    return 1 - num / den
-
-# 计算平均错误率
-performance = quadratic_weighted_kappa(actual[args.value], submission[args.value], 10)
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e6_eval.py

@@ -1,28 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="price")
-
-args = parser.parse_args()
-
-def rmse(targets, predictions):
-    return np.sqrt(((predictions - targets) ** 2).mean())
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = rmse(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e7_eval.py

@@ -1,30 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="booking_status")
-
-args = parser.parse_args()
-
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e8_eval.py

@@ -1,26 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="price")
-
-args = parser.parse_args()
-
-answers = pd.read_csv( args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = np.sqrt(mean_squared_error(answers[args.value], predictions[args.value]))
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s3e9_eval.py

@@ -1,42 +0,0 @@
-
-
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Strength")
-
-args = parser.parse_args()
-
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv(args.predict_file)
-
-actual.sort_values(by=['id'])
-submission.sort_values(by=['id'])
-
-def calculate_rmse(actual, predicted):
-    actual = np.array(actual)
-    predicted = np.array(predicted)
-    mse = np.mean((actual - predicted) ** 2)
-    rmse = np.sqrt(mse)
-    return rmse
-
-# 计算平均错误率
-performance = calculate_rmse(actual[args.value], submission[args.value])
-
-
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s4e1_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Exited")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = roc_auc_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s4e2_eval.py

@@ -1,37 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import accuracy_score
-
-
-# 计算多类对数损失
-def multiclass_logloss(actuals, predictions):
-    epsilon = 1e-15  # 避免对数运算中的数值问题
-    predictions = np.clip(predictions, epsilon, 1 - epsilon)  # 限制预测概率的范围，防止对数为无穷
-    predictions /= predictions.sum(axis=1)[:, np.newaxis]  # 归一化确保总和为1
-    log_pred = np.log(predictions)
-    loss = -np.sum(actuals * log_pred) / len(actuals)
-    return loss
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="NObeyesdad")
-
-args = parser.parse_args()
-
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = accuracy_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s4e3_eval.py

@@ -1,50 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import roc_auc_score
-
-
-# 计算多类对数损失
-def multiclass_logloss(actuals, predictions):
-    epsilon = 1e-15  # 避免对数运算中的数值问题
-    predictions = np.clip(predictions, epsilon, 1 - epsilon)  # 限制预测概率的范围，防止对数为无穷
-    predictions /= predictions.sum(axis=1)[:, np.newaxis]  # 归一化确保总和为1
-    log_pred = np.log(predictions)
-    loss = -np.sum(actuals * log_pred) / len(actuals)
-    return loss
-
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="NObeyesdad")
-
-args = parser.parse_args()
-
-actual = pd.read_csv(args.answer_file)
-submission = pd.read_csv( args.predict_file)
-
-# 定义要计算的类别
-categories = ['Pastry', 'Z_Scratch', 'K_Scatch', 'Stains', 'Dirtiness', 'Bumps', 'Other_Faults']
-
-# 提取数据并计算每个类别的 ROC AUC 分数
-auc_scores = {}
-for category in categories:
-    y_true = actual[category].values
-    y_pred = submission[category].values
-    auc_scores[category] = roc_auc_score(y_true, y_pred)
-
-# 计算平均 AUC 分数
-performance = sum(auc_scores.values()) / len(auc_scores)
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))
-
-

data_modeling/evaluation/playground-series-s4e4_eval.py

@@ -1,29 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Rings")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = np.sqrt(mean_squared_log_error(answers[args.value], predictions[args.value]))
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s4e5_eval.py

@@ -1,30 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-from sklearn.metrics import r2_score
-
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="FloodProbability")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv( args.predict_file)
-
-performance = r2_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))

data_modeling/evaluation/playground-series-s4e6_eval.py

@@ -1,31 +0,0 @@
-import os.path
-
-import numpy as np
-import pandas as pd
-import argparse
-from sklearn.metrics import mean_squared_error
-from sklearn.metrics import mean_squared_log_error
-from sklearn.metrics import mean_absolute_error
-from sklearn.metrics import r2_score
-
-from sklearn.metrics import accuracy_score
-from sklearn.metrics import roc_auc_score
-
-parser = argparse.ArgumentParser()
-
-parser.add_argument('--path', type=str, required=True)
-parser.add_argument('--name', type=str, required=True)
-parser.add_argument('--answer_file', type=str, required=True)
-parser.add_argument('--predict_file', type=str, required=True)
-
-parser.add_argument('--value', type=str, default="Target")
-
-args = parser.parse_args()
-
-answers = pd.read_csv(args.answer_file)
-predictions = pd.read_csv(args.predict_file)
-
-performance = accuracy_score(answers[args.value], predictions[args.value])
-
-with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
-    f.write(str(performance))