New_Demo_with_plotting_added (#4)

- update metric and plot (87700ed2378d8a333275feb2f1c95d05b7029509)

.env

@@ -0,0 +1,2 @@
+# .env
+PASSWORD=88888888

.gitignore

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+ .gitignore
+ .env
+test.py

.idea/.gitignore

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+# Default ignored files
+/shelf/
+/workspace.xml

.idea/LLM-Open-Generation-Bias.iml

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+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <excludeFolder url="file://$MODULE_DIR$/venv" />
+    </content>
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,4 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (LLM-Open-Generation-Bias)" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/LLM-Open-Generation-Bias.iml" filepath="$PROJECT_DIR$/.idea/LLM-Open-Generation-Bias.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

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+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

pages/2_new_Demo_1.py

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+import streamlit as st
+import pandas as pd
+from datasets import load_dataset, Dataset
+from random import sample
+from utils.metric import Regard
+from utils.model import gpt2
+import matplotlib.pyplot as plt
+import os
+
+# Set up the Streamlit interface
+st.title('Gender Bias Analysis in Text Generation')
+
+
+def check_password():
+    def password_entered():
+        if password_input == os.getenv('PASSWORD'):
+        # if password_input == "  ":
+            st.session_state['password_correct'] = True
+        else:
+            st.error("Incorrect Password, please try again.")
+
+    password_input = st.text_input("Enter Password:", type="password")
+    submit_button = st.button("Submit", on_click=password_entered)
+
+    if submit_button and not st.session_state.get('password_correct', False):
+        st.error("Please enter a valid password to access the demo.")
+
+
+if not st.session_state.get('password_correct', False):
+    check_password()
+else:
+    st.sidebar.success("Password Verified. Proceed with the demo.")
+
+    if 'data_size' not in st.session_state:
+        st.session_state['data_size'] = 10
+    if 'bold' not in st.session_state:
+        bold = pd.DataFrame({})
+        bold_raw = pd.DataFrame(load_dataset("AlexaAI/bold", split="train"))
+        for index, row in bold_raw.iterrows():
+            bold_raw_prompts = list(row['prompts'])
+            bold_raw_wikipedia = list(row['wikipedia'])
+            bold_expansion = zip(bold_raw_prompts, bold_raw_wikipedia)
+            for bold_prompt, bold_wikipedia in bold_expansion:
+                bold = bold._append(
+                    {'domain': row['domain'], 'name': row['name'], 'category': row['category'], 'prompts': bold_prompt,
+                     'wikipedia': bold_wikipedia}, ignore_index=True)
+        st.session_state['bold'] = Dataset.from_pandas(bold)
+    if 'female_bold' not in st.session_state:
+        st.session_state['female_bold'] = []
+    if 'male_bold' not in st.session_state:
+        st.session_state['male_bold'] = []
+
+    st.subheader('Step 1: Set Data Size')
+    data_size = st.slider('Select number of samples per category:', min_value=1, max_value=50,
+                          value=st.session_state['data_size'])
+    st.session_state['data_size'] = data_size
+
+    if st.button('Show Data'):
+        st.session_state['female_bold'] = sample(
+            [p for p in st.session_state['bold'] if p['category'] == 'American_actresses'], data_size)
+        st.session_state['male_bold'] = sample(
+            [p for p in st.session_state['bold'] if p['category'] == 'American_actors'], data_size)
+
+        st.write(f'Sampled {data_size} female and male American actors.')
+        st.write('**Female Samples:**', pd.DataFrame(st.session_state['female_bold']))
+        st.write('**Male Samples:**', pd.DataFrame(st.session_state['male_bold']))
+
+    if st.session_state['female_bold'] and st.session_state['male_bold']:
+        st.subheader('Step 2: Generate Text')
+
+        if st.button('Generate Text'):
+            GPT2 = gpt2()
+            st.session_state['male_prompts'] = [p['prompts'] for p in st.session_state['male_bold']]
+            st.session_state['female_prompts'] = [p['prompts'] for p in st.session_state['female_bold']]
+            st.session_state['male_wiki_continuation'] = [p['wikipedia'].replace(p['prompts'], '') for p in
+                                                          st.session_state['male_bold']]
+            st.session_state['female_wiki_continuation'] = [p['wikipedia'].replace(p['prompts'], '') for p in
+                                                            st.session_state['female_bold']]
+
+            progress_bar = st.progress(0)
+
+            st.write('Generating text for male prompts...')
+            male_generation = GPT2.text_generation(st.session_state['male_prompts'], pad_token_id=50256, max_length=50,
+                                                   do_sample=False, truncation=True)
+            st.session_state['male_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
+                                                      zip(male_generation, st.session_state['male_prompts'])]
+
+            progress_bar.progress(50)
+
+            st.write('Generating text for female prompts...')
+            female_generation = GPT2.text_generation(st.session_state['female_prompts'], pad_token_id=50256,
+                                                     max_length=50, do_sample=False, truncation=True)
+            st.session_state['female_continuations'] = [gen[0]['generated_text'].replace(prompt, '') for gen, prompt in
+                                                        zip(female_generation, st.session_state['female_prompts'])]
+
+            progress_bar.progress(100)
+            st.write('Text generation completed.')
+
+    if st.session_state.get('male_continuations') and st.session_state.get('female_continuations'):
+        st.subheader('Step 3: Sample Generated Texts')
+
+        st.write("Male Data Samples:")
+        samples_df = pd.DataFrame({
+            'Male Prompt': st.session_state['male_prompts'],
+            'Male Continuation': st.session_state['male_continuations'],
+            'Male Wiki Continuation': st.session_state['male_wiki_continuation'],
+        })
+        st.write(samples_df)
+
+        st.write("Female Data Samples:")
+        samples_df = pd.DataFrame({
+            'Female Prompt': st.session_state['female_prompts'],
+            'Female Continuation': st.session_state['female_continuations'],
+            'Female Wiki Continuation': st.session_state['female_wiki_continuation'],
+        })
+        st.write(samples_df)
+
+        if st.button('Evaluate'):
+            st.subheader('Step 4: Regard Results')
+            regard = Regard("inner_compare")
+            st.write('Computing regard results to compare male and female continuations...')
+
+            with st.spinner('Computing regard results...'):
+                regard_male_results = regard.compute(data=st.session_state['male_continuations'],
+                                                     references=st.session_state['male_wiki_continuation'])
+                st.write('**Raw Regard Results:**')
+                st.json(regard_male_results)
+                st.session_state['rmr'] = regard_male_results
+
+                regard_female_results = regard.compute(data=st.session_state['female_continuations'],
+                                                       references=st.session_state['female_wiki_continuation'])
+                st.write('**Average Regard Results:**')
+                st.json(regard_female_results)
+                st.session_state['rfr'] = regard_female_results
+
+        if st.button('Plot'):
+            st.subheader('Step 5: Regard Results Plotting')
+            categories = ['GPT2', 'Wiki']
+
+            mp_gpt = st.session_state['rmr']['no_ref_diff_mean']['positive']
+            mn_gpt = st.session_state['rmr']['no_ref_diff_mean']['negative']
+            mo_gpt = 1 - (mp_gpt + mn_gpt)
+
+            mp_wiki = mp_gpt - st.session_state['rmr']['ref_diff_mean']['positive']
+            mn_wiki = mn_gpt -st.session_state['rmr']['ref_diff_mean']['negative']
+            mo_wiki = 1 - (mn_wiki + mp_wiki)
+
+            fp_gpt = st.session_state['rfr']['no_ref_diff_mean']['positive']
+            fn_gpt = st.session_state['rfr']['no_ref_diff_mean']['negative']
+            fo_gpt = 1 - (fp_gpt + fn_gpt)
+
+            fp_wiki = fp_gpt - st.session_state['rfr']['ref_diff_mean']['positive']
+            fn_wiki = fn_gpt - st.session_state['rfr']['ref_diff_mean']['negative']
+            fo_wiki = 1 - (fn_wiki + fp_wiki)
+
+            positive_m = [mp_gpt, mp_wiki]
+            other_m = [mo_gpt, mo_wiki]
+            negative_m = [mn_gpt, mn_wiki]
+
+            positive_f = [fp_gpt, fp_wiki]
+            other_f = [fo_gpt, fo_wiki]
+            negative_f = [fn_gpt, fn_wiki]
+
+            # Plotting
+            fig_a, ax_a = plt.subplots()
+            ax_a.bar(categories, negative_m, label='Negative', color='blue')
+            ax_a.bar(categories, other_m, bottom=negative_m, label='Other', color='orange')
+            ax_a.bar(categories, positive_m, bottom=[negative_m[i] + other_m[i] for i in range(len(negative_m))],
+                     label='Positive', color='green')
+
+            plt.xlabel('Categories')
+            plt.ylabel('Proportion')
+            plt.title('GPT vs Wiki on male regard')
+            plt.legend()
+
+            st.pyplot(fig_a)
+
+            fig_b, ax_b = plt.subplots()
+            ax_b.bar(categories, negative_f, label='Negative', color='blue')
+            ax_b.bar(categories, other_f, bottom=negative_f, label='Other', color='orange')
+            ax_b.bar(categories, positive_f, bottom=[negative_f[i] + other_f[i] for i in range(len(negative_f))],
+                     label='Positive', color='green')
+
+            plt.xlabel('Categories')
+            plt.ylabel('Proportion')
+            plt.title('GPT vs Wiki on female regard')
+            plt.legend()
+            st.pyplot(fig_b)
+
+            m_increase = mp_gpt - mn_gpt
+            m_relative_increase = mp_gpt - mp_wiki - (mn_gpt - mn_wiki)
+            f_increase = fp_gpt - fn_gpt
+            f_relative_increase = fp_gpt - fp_wiki - (fn_gpt - fn_wiki)
+
+            absolute_difference = [m_increase, f_increase]
+            relative_difference = [m_relative_increase, f_relative_increase]
+
+            new_categories = ['Male', 'Female']
+
+            fig_c, ax_c = plt.subplots()
+            ax_c.bar(new_categories, absolute_difference, label='Positive - Negative', color='#40E0D0')
+
+            plt.xlabel('Categories')
+            plt.ylabel('Proportion')
+            plt.title('Difference of positive and negative: Male vs Female')
+            plt.legend()
+            st.pyplot(fig_c)
+
+            fig_d, ax_d = plt.subplots()
+            ax_d.bar(new_categories, relative_difference, label='Positive - Negative', color='#40E0D0')
+
+            plt.xlabel('Categories')
+            plt.ylabel('Proportion')
+            plt.title('Difference of positive and negative (relative to Wiki): Male vs Female')
+            plt.legend()
+            st.pyplot(fig_d)
+

requirements.txt

@@ -1,3 +1,4 @@
 openai
 transformers
-torch==2.0.1
+torch==2.0.1
+matplotlib

utils/metric.py

@@ -43,13 +43,27 @@ class Regard:
                 return {"average_data_regard": pred_mean, "average_references_regard": ref_mean}
             else:
                 return {"regard_difference": {key: pred_mean[key] - ref_mean.get(key, 0) for key in pred_mean}}
-        else:
+        elif self.config_name == "inner_compare":
             pred_scores, pred_regard = self.regard(data)
-            pred_mean = {k: mean(v) for k, v in pred_regard.items()}
-            pred_max = {k: max(v) for k, v in pred_regard.items()}
-            if aggregation == "maximum":
-                return {"max_regard": pred_max}
-            elif aggregation == "average":
-                return {"average_regard": pred_mean}
-            else:
-                return {"regard": pred_scores}
+            ref_scores, ref_regard = self.regard(references)
+
+            postive_pred_regard = pred_regard['positive']
+            positive_ref_regard = ref_regard['positive']
+            postive_diff_regard = list(range(len(postive_pred_regard)))
+            for score_index in range(len(postive_pred_regard)):
+                postive_diff_regard[score_index] = postive_pred_regard[score_index] - positive_ref_regard[score_index]
+
+            negative_pred_regard = pred_regard['negative']
+            negative_ref_regard = ref_regard['negative']
+            negative_diff_regard = list(range(len(negative_pred_regard)))
+            for score_index in range(len(negative_pred_regard)):
+                negative_diff_regard[score_index] = negative_pred_regard[score_index] - negative_ref_regard[score_index]
+
+            ref_diff_regard = {'positive': postive_diff_regard, 'negative': negative_diff_regard}
+            ref_diff_mean = {k: mean(v) for k, v in ref_diff_regard.items()}
+            no_ref_diff_regard = {'positive': postive_pred_regard, 'negative': negative_pred_regard}
+            no_ref_diff_mean = {k: mean(v) for k, v in no_ref_diff_regard.items()}
+
+            return {"ref_diff_mean": ref_diff_mean,
+                    'no_ref_diff_mean': no_ref_diff_mean}
+