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import pandas as pd
import os
impact_columns = [
"Infrastructural impact",
"Political impact",
"Economic impact",
"Ecological impact",
"Agricultural impact",
"Human health impact"
]
groupby=["Date","Time_Period"]
gold_data = pd.read_csv("the_path_to_gold_data.csv")
gold_data.columns = [x.capitalize() for x in gold_data.columns]
def eval_row_wise_acc(data, output_file):
data.columns = [x.capitalize() for x in data.columns]
models = data['Model_type'].unique()
gold_grouped = gold_data.groupby(groupby)[impact_columns].max()
results = []
for model in models:
model_data = data[data['Model_type'] == model]
grouped = model_data.groupby(groupby)[impact_columns].max()
merged = grouped.join(gold_grouped, how='inner', lsuffix='_model', rsuffix='_gold')
all_correct = (merged[[f"{col}_model" for col in impact_columns]].values ==
merged[[f"{col}_gold" for col in impact_columns]].values).all(axis=1)
accuracy = all_correct.sum() / len(all_correct) if len(all_correct) > 0 else 0
results.append({
"Model_Type": model,
"Row-Wise-Accuracy": round(accuracy, 4)
})
df_result = pd.DataFrame(results)
if not os.path.isfile(output_file):
df_result.to_csv(output_file, index=False)
else:
df_result.to_csv(output_file, mode='a', header=False, index=False)
def eval_metrics(data, output_file):
data.columns = [x.capitalize() for x in data.columns]
models = data["Model_type"].unique()
gold_grouped = gold_data.groupby(groupby)[impact_columns].max()
results = []
for model in models:
model_data = data[data["Model_type"] == model]
grouped = model_data.groupby(groupby)[impact_columns].max()
merged = grouped.join(gold_grouped, how="inner", lsuffix="_model", rsuffix="_gold")
for metric_name in ["Precision", "Recall", "F1", "Accuracy"]:
metrics = {"Model_Type": model, "Metric": metric_name}
for col in impact_columns:
tp = ((merged[f"{col}_model"] == 1) & (merged[f"{col}_gold"] == 1)).sum()
tn = ((merged[f"{col}_model"] == 0) & (merged[f"{col}_gold"] == 0)).sum()
fp = ((merged[f"{col}_model"] == 1) & (merged[f"{col}_gold"] == 0)).sum()
fn = ((merged[f"{col}_model"] == 0) & (merged[f"{col}_gold"] == 1)).sum()
if metric_name == "Precision":
value = tp / (tp + fp) if (tp + fp) > 0 else 0
elif metric_name == "Recall":
value = tp / (tp + fn) if (tp + fn) > 0 else 0
elif metric_name == "F1":
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
value = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
elif metric_name == "Accuracy":
value = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0
metrics[col] = round(value, 4)
results.append(metrics)
df_result = pd.DataFrame(results)
print(df_result)
if not os.path.isfile(output_file):
df_result.to_csv(output_file, index=False)
else:
df_result.to_csv(output_file, mode="a", header=False, index=False)
data = pd.read_csv("/content/output_gpt.csv")
eval_metrics(data, "accuracy_results.csv") |