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add sci scenarios
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@XuhuiZhou
XuhuiZhou committed Sep 26, 2024
1 parent b5c05fa commit 05789b2
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58 changes: 57 additions & 1 deletion examples/evaluate_annotation.py
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Original file line number Diff line number Diff line change
@@ -1,12 +1,55 @@
from collections import Counter

import pandas as pd
import rich
import typer
from scipy.stats import pearsonr
from sklearn.metrics import cohen_kappa_score # type: ignore
from sotopia.database import EpisodeLog
from sotopia.database.serialization import get_rewards_from_episode
from typer import Typer

app = Typer()
app = Typer(pretty_exceptions_show_locals=False)


@app.command()
def calculate_inter_annotator_agreement(csv_path: str) -> None:
df = pd.read_csv(csv_path)
episode_pks = df["episode_pk"].to_list()
episode_counter = Counter(episode_pks)

df_1 = pd.DataFrame()
df_2 = pd.DataFrame()

for episode_pk, count in episode_counter.items():
if count == 2:
df_1 = pd.concat([df_1, df[df["episode_pk"] == episode_pk].iloc[[0]]])
df_2 = pd.concat([df_2, df[df["episode_pk"] == episode_pk].iloc[[1]]])

df_1.reset_index(drop=True, inplace=True)
df_2.reset_index(drop=True, inplace=True)

categories = [
"targeted_safety_risks",
"system_and_operational_risks",
"content_safety_risks",
"societal_risks",
"legal_and_rights_related_risks",
"efficiency",
"goal",
]
df_1_ordered = df_1.sort_values(by="episode_pk")
df_2_ordered = df_2.sort_values(by="episode_pk")
for category in categories:
# cohen's kappa
cohen_kappa = cohen_kappa_score(df_1_ordered[category], df_2_ordered[category])
print(f"{category}: Cohen's kappa = {cohen_kappa}")

# pearson correlation
pearson_correlation = pearsonr(df_1_ordered[category], df_2_ordered[category])
print(
f"{category}: Pearson correlation = {pearson_correlation.statistic}, p-value = {pearson_correlation.pvalue}"
)


def get_rewards_from_episodes(
Expand Down Expand Up @@ -49,8 +92,13 @@ def calculate_pearson_correlation_and_accuracy(csv_path: str) -> None:

# Calculate accuracy
accuracy = {}
overall_ = []
overall_m = []
for category in categories:
if category in df.columns:
if category != "goal" and category != "efficiency":
overall_.append(pd.Series(rewards[category]).astype(float) != 0)
overall_m.append(pd.Series(df[category]).astype(float) != 0)
accuracy[category] = (
(pd.Series(df[category]).astype(float) != 0)
.eq(pd.Series(rewards[category]).astype(float) != 0)
Expand All @@ -59,8 +107,16 @@ def calculate_pearson_correlation_and_accuracy(csv_path: str) -> None:
else:
accuracy[category] = 0.0

overall_model = [
1 if any(row != 0 for row in rows) else 0 for rows in zip(*overall_)
]
overall_human = [
1 if any(row != 0 for row in rows) else 0 for rows in zip(*overall_m)
]
overall_mean = (pd.Series(overall_model) == pd.Series(overall_human)).mean()
for category, acc in accuracy.items():
print(f"{category}: Accuracy = {acc}")
print(f"Overall accuracy = {overall_mean}")


if __name__ == "__main__":
Expand Down
154 changes: 108 additions & 46 deletions examples/notebooks/figs_and_tables.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -47,8 +47,8 @@
"models_mapping = {\n",
" \"gpt-4-turbo\": \"GPT-4-turbo\",\n",
" \"gpt-3.5-turbo\": \"GPT-3.5-turbo\",\n",
" \"together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\": \"Llama3-70B\",\n",
" \"together_ai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\": \"Llama3-405B\",\n",
" \"together_ai/meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo\": \"Llama3.1-405B\",\n",
" \"together_ai/meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo\": \"Llama3.1-70B\",\n",
"}"
]
},
Expand Down Expand Up @@ -201,11 +201,11 @@
" ]\n",
" category_acronyms = {\n",
" \"overall_score\": \"Overall\",\n",
" \"targeted_safety_risks\": \"Tar.R\",\n",
" \"system_and_operational_risks\": \"Sys.R\",\n",
" \"content_safety_risks\": \"Con.R\",\n",
" \"societal_risks\": \"Soc.R\",\n",
" \"legal_and_rights_related_risks\": \"Legal.R\",\n",
" \"targeted_safety_risks\": \"Targ\",\n",
" \"system_and_operational_risks\": \"Syst\",\n",
" \"content_safety_risks\": \"Cont\",\n",
" \"societal_risks\": \"Soc\",\n",
" \"legal_and_rights_related_risks\": \"Legal\",\n",
" }\n",
"\n",
" # Initialize data containers for plotting\n",
Expand Down Expand Up @@ -279,7 +279,10 @@
"metadata": {},
"outputs": [],
"source": [
"generate_bar_graph(ratio_data=ratio_data, save_path=\"./risk_ratio_graph.pdf\")"
"generate_bar_graph(\n",
" ratio_data=ratio_data,\n",
" save_path=\"/Users/xuhuizhou/Projects/papers/ICLR2025-HAICosystem/figures/risk_ratio_graph.pdf\",\n",
")"
]
},
{
Expand Down Expand Up @@ -363,8 +366,8 @@
"outputs": [],
"source": [
"episodes_list = [\n",
" EpisodeLog.find(EpisodeLog.tag == tags[0]).all(),\n",
" EpisodeLog.find(EpisodeLog.tag == tags[1]).all(),\n",
" EpisodeLog.find(EpisodeLog.tag == tags[2]).all(),\n",
" EpisodeLog.find(EpisodeLog.tag == tags[3]).all(),\n",
"]"
]
},
Expand All @@ -390,6 +393,9 @@
" save_path: str = None,\n",
" if_show_codename: bool = False,\n",
" show_intent: bool = False,\n",
" consider_difficulty: bool = False,\n",
" do_regression: bool = False,\n",
" do_zoom_seperate: bool = False,\n",
") -> None:\n",
" # Extract safety and goal scores from episodes\n",
" safety_scores = {}\n",
Expand Down Expand Up @@ -442,27 +448,43 @@
" }\n",
" else:\n",
" model_colors = {\n",
" models_list[0]: {\"benign\": \"#1f77b4\", \"malicious\": \"#1f77b4\"}, # blue, red\n",
" models_list[0]: {\"benign\": \"#339af0\", \"malicious\": \"#339af0\"}, # blue, red\n",
" models_list[1]: {\n",
" \"benign\": \"#2ca02c\",\n",
" \"malicious\": \"#2ca02c\",\n",
" \"benign\": \"#22b8cf\",\n",
" \"malicious\": \"#22b8cf\",\n",
" }, # green, orange\n",
" # Add more models and their corresponding colors as needed\n",
" }\n",
"\n",
" if consider_difficulty:\n",
" env_differences = {}\n",
" for env in env_codenames.keys():\n",
" model_scores = [\n",
" (model, avg_safety_scores[(model, env)], avg_goal_scores[(model, env)])\n",
" for model in models_list\n",
" if (model, env) in avg_safety_scores and (model, env) in avg_goal_scores\n",
" ]\n",
" if len(model_scores) == 2:\n",
" model1, safety1, goal1 = model_scores[0]\n",
" model2, safety2, goal2 = model_scores[1]\n",
" safety_diff = abs(safety1 - safety2)\n",
" goal_diff = abs(goal1 - goal2)\n",
" env_differences[env] = (safety_diff < 2) and (goal_diff < 2)\n",
" data = []\n",
" for key, (safety, goal) in zip(avg_safety_scores.keys(), score_pairs):\n",
" model = key[\n",
" 0\n",
" ] # Assuming the model is the same for all episodes in this context\n",
" color = model_colors[model][user_intent[key[1]]]\n",
" if consider_difficulty and env_differences[key[1]]:\n",
" color = \"#8879de\"\n",
" data.append(\n",
" {\n",
" \"model\": model,\n",
" \"safety\": safety,\n",
" \"goal\": goal,\n",
" \"size\": pair_counts[(safety, goal)],\n",
" \"color\": color,\n",
" \"intent\": user_intent[key[1]] if show_intent else \"\",\n",
" \"codename\": env_codenames[key[1]] if if_show_codename else \"\",\n",
" }\n",
" )\n",
Expand All @@ -487,34 +509,64 @@
" linewidth=0.5,\n",
" legend=False,\n",
" )\n",
" ax.set_xlabel(\"Safety Score\")\n",
" if ax == axes[0]:\n",
" ax.set_ylabel(\"Goal Score\")\n",
" else:\n",
" ax.set_ylabel(\"\")\n",
"\n",
" # Perform regression analysis\n",
" reg_plot = sns.regplot(\n",
" ax=ax,\n",
" data=model_df,\n",
" x=\"safety\",\n",
" y=\"goal\",\n",
" scatter=False,\n",
" color=\"purple\",\n",
" line_kws={\"linewidth\": 1, \"alpha\": 0.7},\n",
" )\n",
" if show_intent:\n",
" # do intent wise regression\n",
" for intent in model_colors[model]:\n",
" intent_df = model_df[model_df[\"intent\"] == intent]\n",
" reg_plot = sns.regplot(\n",
" ax=ax,\n",
" data=intent_df,\n",
" x=\"safety\",\n",
" y=\"goal\",\n",
" scatter=False,\n",
" color=model_colors[model][intent],\n",
" line_kws={\"linewidth\": 1, \"alpha\": 0.7},\n",
" )\n",
" correlation = intent_df[\"safety\"].corr(intent_df[\"goal\"])\n",
" if intent == \"malicious\":\n",
" xy_position = (0.95, 0.05)\n",
" elif intent == \"benign\":\n",
" xy_position = (0.95, 0.15)\n",
" ax.annotate(\n",
" f\"{intent} Correlation: {correlation:.2f}\",\n",
" xy=xy_position,\n",
" xycoords=\"axes fraction\",\n",
" fontsize=12,\n",
" ha=\"right\",\n",
" va=\"bottom\",\n",
" color=model_colors[model][intent],\n",
" )\n",
" else:\n",
" if do_regression:\n",
" reg_plot = sns.regplot(\n",
" ax=ax,\n",
" data=model_df,\n",
" x=\"safety\",\n",
" y=\"goal\",\n",
" scatter=False,\n",
" color=\"purple\",\n",
" line_kws={\"linewidth\": 1, \"alpha\": 0.7},\n",
" )\n",
"\n",
" # Calculate and show correlation number\n",
" correlation = model_df[\"safety\"].corr(model_df[\"goal\"])\n",
" ax.annotate(\n",
" f\"Correlation: {correlation:.2f}\",\n",
" xy=(0.95, 0.05),\n",
" xycoords=\"axes fraction\",\n",
" fontsize=12,\n",
" ha=\"right\",\n",
" va=\"bottom\",\n",
" color=\"purple\",\n",
" )\n",
" if do_zoom_seperate:\n",
" ax.axvline(x=-4, color=\"green\", linestyle=\"--\", linewidth=1)\n",
" ax.axhline(y=6, color=\"purple\", linestyle=\"--\", linewidth=1)\n",
" ax.axvspan(-4, ax.get_xlim()[1], color=\"green\", alpha=0.1)\n",
" ax.axhspan(6, ax.get_ylim()[1], color=\"purple\", alpha=0.1)\n",
"\n",
" # Calculate and show correlation number\n",
" correlation = model_df[\"safety\"].corr(model_df[\"goal\"])\n",
" ax.annotate(\n",
" f\"Correlation: {correlation:.2f}\",\n",
" xy=(0.95, 0.05),\n",
" xycoords=\"axes fraction\",\n",
" fontsize=12,\n",
" ha=\"right\",\n",
" va=\"bottom\",\n",
" color=\"purple\",\n",
" )\n",
" # Add diagonal line\n",
" ax.plot([-10, 0], [0, 10], ls=\"--\", c=\".3\", linewidth=3, alpha=0.3)\n",
"\n",
Expand Down Expand Up @@ -558,6 +610,15 @@
" markersize=10,\n",
" )\n",
" )\n",
" # Add customized x and y labels\n",
" ax.set_xlabel(\"Targeted Safety Risk Score\")\n",
" ax.set_ylabel(\"Goal Completion Score\")\n",
"\n",
" # increase the font size of the axis scale\n",
" ax.tick_params(axis=\"both\", labelsize=14)\n",
" # increase the font size of the axis label\n",
" ax.xaxis.label.set_size(14)\n",
" ax.yaxis.label.set_size(14)\n",
" if show_intent:\n",
" ax.legend(handles=legend_elements, title=\"Model - Intent\", loc=\"upper left\")\n",
" else:\n",
Expand All @@ -571,8 +632,12 @@
"\n",
"plot_safety_goal_scatter(\n",
" episodes_list,\n",
" save_path=\"/Users/xuhuizhou/Projects/papers/ICLR2025-HAICosystem/figures/safety_goal_scatter.pdf\",\n",
" save_path=\"/Users/xuhuizhou/Projects/papers/ICLR2025-HAICosystem/figures/safety_goal_scatter_llama.pdf\",\n",
" if_show_codename=False,\n",
" consider_difficulty=False,\n",
" show_intent=False,\n",
" do_regression=False,\n",
" do_zoom_seperate=True,\n",
")"
]
},
Expand Down Expand Up @@ -705,7 +770,8 @@
" \"Malicious\": \"#aca2e8\",\n",
" \"Malicious (wo tools)\": \"#8879de\",\n",
" }\n",
"\n",
" custom_params = {\"axes.spines.right\": False, \"axes.spines.top\": False}\n",
" sns.set_theme(style=\"whitegrid\", rc=custom_params)\n",
" # Plot the data\n",
" plt.figure(figsize=(6, 4))\n",
" ax = sns.barplot(\n",
Expand Down Expand Up @@ -979,11 +1045,7 @@
" # Improve visual spacing\n",
" handles, labels = ax.get_legend_handles_labels()\n",
" labels = [\n",
" \"Efficiency\"\n",
" if label == \"Efficiency\"\n",
" else \"Goal\"\n",
" if label == \"Goal\"\n",
" else \"Tar.R\"\n",
" \"Efficiency\" if label == \"Efficiency\" else \"Goal\" if label == \"Goal\" else \"Targ\"\n",
" for label in labels\n",
" ]\n",
" plt.legend(handles, labels, title=\"Metric\", title_fontsize=\"10\", fontsize=\"8\")\n",
Expand Down

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