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import pandas as pd |
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import ast |
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import numpy as np |
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import os |
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from pathlib import Path |
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import argparse |
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claim_mapping = { |
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"0": "No claim", |
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"1": "Global warming is not happening", |
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"2": "Human greenhouse gases are not causing climate change", |
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"3": "Climate impacts/global warming is beneficial/not bad", |
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"4": "Climate solutions won’t work", |
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"5": "Climate movement/science is unreliable" |
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} |
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subclaim_mapping = { |
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"0_0": "No claim", |
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"1_1": "Ice/permafrost/snow cover isn’t melting", |
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"1_2": "We’re heading into an ice age/global cooling", |
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"1_3": "Weather is cold/snowing", |
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"1_4": "Climate hasn’t warmed/changed over the last (few) decade(s)", |
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"1_6": "Sea level rise is exaggerated/not accelerating", |
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"1_7": "Extreme weather isn’t increasing/has happened before/isn’t linked to climate change", |
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"2_1": "It’s natural cycles/variation", |
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"2_3": "There’s no evidence for greenhouse effect/carbon dioxide driving climate change", |
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"3_1": "Climate sensitivity is low/negative feedbacks reduce warming", |
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"3_2": "Species/plants/reefs aren’t showing climate impacts/are benefiting from climate change", |
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"3_3": "CO2 is beneficial/not a pollutant", |
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"4_1": "Climate policies (mitigation or adaptation) are harmful", |
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"4_2": "Climate policies are ineffective/flawed", |
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"4_4": "Clean energy technology/biofuels won’t work", |
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"4_5": "People need energy (e.g. from fossil fuels/nuclear)", |
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"5_1": "Climate-related science is unreliable/uncertain/unsound (data, methods & models)", |
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"5_2": "Climate movement is unreliable/alarmist/corrupt" |
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} |
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def parse_args(): |
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parser = argparse.ArgumentParser(description='run binary classifer') |
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parser.add_argument("--path", default="data/training", help="path to exeter training dir. containing train, test, validation splits") |
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parser.add_argument("--out", default="climaEval/exeter/", help="output directory path") |
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return parser.parse_args() |
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if __name__ == '__main__': |
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args = parse_args() |
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for filename in os.listdir(args.path): |
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print(filename) |
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f = os.path.join(args.path, filename) |
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df = pd.read_csv(f, header=0) |
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df.rename(columns={"claim": "sub_claim_code"}, inplace=True) |
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df["claim_code"] = df["sub_claim_code"].str.split("_").str[0] |
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df["claim"] = df["claim_code"].map(claim_mapping) |
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df["sub_claim"] = df["sub_claim_code"].map(subclaim_mapping) |
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claim_df = df[['text', 'claim_code', 'claim']] |
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subclaim_df = df[['text', 'sub_claim_code', 'sub_claim']] |
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claim_df = claim_df.dropna() |
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Path(f"{args.out}/claim/").mkdir(parents=True, exist_ok=True) |
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Path(f"{args.out}/sub_claim/").mkdir(parents=True, exist_ok=True) |
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claim_df.to_csv(f"{args.out}/claim/{filename}", index=False) |
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subclaim_df.to_csv(f"{args.out}/sub_claim/{filename}", index=False) |
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print("---") |
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print(claim_df.claim_code.value_counts()) |
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print(sorted(list(subclaim_df["sub_claim_code"].unique()))) |
