getting started on the preparedness data

.gitignore

@@ -138,3 +138,5 @@ kp_determinants2
 
 *.Rproj
 .Rhistory
+
+scratch*

preparedness/data_preparation.py

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+# %%
+import pandas as pd
+import numpy as np
+from utils import get_ollama_embedding
+
+# %%[markdown]
+#### Data Preparation Steps
+
+# - Select relevant columns from the cleaned dataset, including a range and specific variables.
+# - Exclude columns containing substrings like "2nd", "spont", "other", and some specific variables.
+# - Add columns starting with "region_" and "education_level_".
+# - Convert character columns to categorical type.
+# - Generate a `subregion` variable by coalescing region columns, then drop the originals.
+# - Generate an `education_level` variable by coalescing education columns, replacing "Not mentioned" with NA, then drop the originals.
+# - Define core preparedness items and their human-readable labels.
+# - Map country codes to country names.
+
+# %%
+# Load your data
+data_cleaned = pd.read_csv('./data/eurobarometer_data_cleaned_csv.csv')
+
+print(data_cleaned.shape)
+
+# Select columns by name and range
+cols_to_select = [
+    'country_code_iso_3166',
+    'risks_cntry_most_exposed_to_firstly',
+    'risks_pers_most_exposed_to_firstly',
+    'risks_pers_most_exposed_to_number_of_mentioned_risks',
+    'pot_info_sources_to_learn_about_disaster_risks_firstly',
+    *data_cleaned.columns[404:448],  # Python is 0-indexed
+    'occupation_of_respondent',
+    'age_recoded_6_categories',
+    'size_of_community',
+    'social_class_self_assessment_5_cat',
+    'direction_things_are_going_life_personally',
+    'political_discussion_local_matters',
+    'political_discussion_national_matters',
+    'left_right_placement_recoded_5_cat',
+    'internet_use_total',
+    'gender',
+    'age_education',
+    'standard_of_living_last_5yrs_in_light_of_crises',
+    'personal_living_conditions_in_one_years_time',
+    'standard_of_living_next_5yrs'
+]
+
+# Remove columns containing certain substrings
+exclude_patterns = ['2nd', 'spont', 'other']
+cols_to_exclude = [col for col in data_cleaned.columns if any(p in col for p in exclude_patterns)]
+cols_to_exclude += [
+    'disaster_measures_in_hh_number_of_measures',
+    'disaster_pers_experienced_past_10yrs_none',
+    'pot_info_sources_to_learn_about_disaster_risks_interested_in_at_least_one_source'
+]
+
+# Add region_ and education_level_ columns
+cols_to_select += [col for col in data_cleaned.columns if col.startswith('region_')]
+cols_to_select += [col for col in data_cleaned.columns if col.startswith('education_level_')]
+
+final_cols = [col for col in cols_to_select if col not in cols_to_exclude]
+df_model = data_cleaned[final_cols].copy()
+
+# %% [markdown]
+# #### Combine all columns into a single string per user
+# This step creates a text representation of each user, which can be sent to an embedding model.
+
+def row_to_string(row):
+    return ' | '.join(f'{col}: {row[col]}' for col in row.index)
+
+# Combine all columns into a single string per user (except country_code_iso_3166)
+df_model['user_text'] = df_model.drop(columns=['country_code_iso_3166']).apply(row_to_string, axis=1)
+
+df_model.head()
+
+# %%
+# Convert character columns to category BEFORE adding embedding column
+for col in df_model.select_dtypes(include='object').columns:
+    if col != 'user_text':
+        df_model[col] = df_model[col].astype('category')
+
+# Generate embeddings for each user using Ollama
+df_model['embedding'] = df_model['user_text'].apply(get_ollama_embedding)
+
+# Add new columns to final_cols
+final_cols.extend(["user_text", "embedding"])
+
+# Check the lengths of all embeddings
+embedding_lengths = df_model['embedding'].apply(lambda x: len(x) if isinstance(x, list) else None)
+print('Embedding lengths:', embedding_lengths.tolist())
+df_model = df_model[final_cols].copy()
+
+# Generate subregion variable
+region_cols = [col for col in df_model.columns if col.startswith('region_')]
+df_model['subregion'] = df_model[region_cols].bfill(axis=1).iloc[:, 0]
+df_model.drop(columns=region_cols, inplace=True)
+
+# Generate education_level variable
+edu_cols = [col for col in df_model.columns if col.startswith('education_level_')]
+for col in edu_cols:
+    df_model[col] = df_model[col].replace('Not mentioned', np.nan)
+df_model['education_level'] = df_model[edu_cols].bfill(axis=1).iloc[:, 0]
+df_model['education_level'] = df_model['education_level'].astype('category')
+df_model.drop(columns=edu_cols, inplace=True)
+
+# Core preparedness items
+CORE_ITEMS_MAPPED = [
+    "disaster_measures_in_hh_emergency_supply_drinks_food",
+    "disaster_measures_in_hh_emergency_supply_water_cooking_hygiene", 
+    "disaster_measures_in_hh_agreed_with_friends_family_to_contact",
+    "disaster_measures_in_hh_discussed_common_prot_measures_in_neighbourhood",
+    "disaster_measures_in_hh_battery_powered_radio"
+]
+
+CORE_ITEM_LABELS = {
+    "disaster_measures_in_hh_emergency_supply_drinks_food": "Emergency supply of drinks, food",
+    "disaster_measures_in_hh_emergency_supply_water_cooking_hygiene": "Emergency supply of cooking and hygiene water",
+    "disaster_measures_in_hh_agreed_with_friends_family_to_contact": "Agreed with family, friends on how to contact in an emergency",
+    "disaster_measures_in_hh_discussed_common_prot_measures_in_neighbourhood": "Discussed precautions in neighbourhood",
+    "disaster_measures_in_hh_battery_powered_radio": "Battery-powered radio accessible"
+}
+
+COUNTRY_NAME_MAP = {
+    "FI": "Finland",
+    "DE-E": "East Germany",
+    "DE-W": "West Germany",
+    "FR": "France",
+    "ES": "Spain",
+    "PT": "Portugal",
+    # "EE": "Estonia",
+    # "DK": "Denmark",
+    # "SE": "Sweden",
+    # "NL": "Netherlands"
+}
+
+# %%
+df_model.head()
+
+# %%
+df_model.head().to_dict(orient='records')
+# %%
+df_model.to_csv('./data/eurobarometer_preparedness_model_data_v2.csv', index=False)

preparedness/data_preparation_raw.py

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+# %%
+import pyreadstat
+import json
+import re
+import datetime
+
+# %%
+df_sav, meta = pyreadstat.read_sav('./data/ZA8841_v1-0-0.sav')
+print(df_sav.shape)
+
+# %%
+df_sav.head(1).to_dict(orient='records')
+
+# %%
+def safe_json(obj):
+    if isinstance(obj, (datetime.datetime, datetime.date)):
+        return obj.isoformat()
+    if isinstance(obj, set):
+        return list(obj)
+    if hasattr(obj, '__dict__'):
+        return str(obj)
+    return obj
+
+meta_dict = vars(meta)
+meta_json = json.dumps(meta_dict, default=safe_json, indent=2)
+
+# %%
+with open('./data/za8841_meta.json', 'w') as f:
+    f.write(meta_json)
+
+# %%
+# Load your mapping dictionary (from JSON file or directly)
+with open("./data/za8841_meta.json") as f:
+    meta = json.load(f)
+value_labels = meta["variable_value_labels"]
+
+# %%
+# Assume df_sav is your loaded SPSS dataframe
+# For each column in the mapping, map values if the column exists in df_sav
+for col, mapping in value_labels.items():
+    if col in df_sav.columns:
+        # Convert keys to float if needed (SPSS values often are float)
+        mapping_float = {float(k): v for k, v in mapping.items()}
+        df_sav[col] = df_sav[col].map(mapping_float).fillna(df_sav[col])
+
+# Now all mapped columns have human-readable values
+print(df_sav.head())
+
+# %%
+labels_map = meta["column_names_to_labels"]
+def make_pandas_friendly(col):
+    col = labels_map.get(col, col)
+    col = re.sub(r'[.\s]+', '_', col)
+    col = re.sub(r'[^0-9a-zA-Z_]', '', col)
+    col = col.lower()
+    col = re.sub(r'__+', '_', col)      # Replace double (or more) underscores with single
+    col = col.strip('_')                # Remove leading/trailing underscores
+    return col
+
+df_sav.columns = [make_pandas_friendly(col) for col in df_sav.columns]
+
+# %%
+df_sav.head(1).to_dict(orient='records')
+
+# %%

preparedness/main.py

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+# %% [markdown]
+# # Visualize Most Prepared Users
+# This workflow loads user embeddings, generates a prompt embedding, computes similarity, and visualizes the most prepared users.
+
+# %%
+import pandas as pd
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+import matplotlib.pyplot as plt
+import seaborn as sns
+from utils import get_ollama_embedding
+
+# %% [markdown]
+# ## Read in the file with user vectors
+# Update the path/format as needed.
+# %%
+df = (
+    pd.read_csv("./data/eurobarometer_preparedness_model_data_v2.csv").assign(
+        **{
+            "user_id": lambda x: x["country_code_iso_3166"]
+            + "_"
+            + x["age_recoded_6_categories"].astype(str)
+            + "_"
+            + x["gender"].astype(str)
+        }
+    )
+    # select only relevant countries for visualization
+    .loc[
+        lambda x: x["country_code_iso_3166"].isin(
+            ["FI", "DE-E", "DE-W", "FR", "ES", "PT"]
+        )
+    ]
+)
+df.shape
+
+
+# %% [markdown]
+# ## Convert string embeddings to lists if needed
+# %%
+def parse_embedding(x):
+    if isinstance(x, str):
+        return [float(i) for i in x.strip("[]").split(",")]
+    return x
+
+
+df["embedding"] = df["embedding"].apply(parse_embedding)
+
+# %%
+# Select best user:
+# disaster_measures_in_hh_* == 1, and how_many_days_meet_* == (4 | 5)
+disaster_measures_in_hh_columns = [
+    col for col in df.columns if col.startswith("disaster_measures_in_hh_")
+]
+how_many_days_meet_columns = [
+    col for col in df.columns if col.startswith("how_many_days_meet_")
+]
+best_users = df[
+    (df[disaster_measures_in_hh_columns] == 1).all(axis=1)
+    & (df[how_many_days_meet_columns].isin([4, 5]).all(axis=1))
+]
+print(f"Number of best users: {len(best_users)}")
+
+# %% [markdown]
+# ## Write your prompt and generate its embedding
+# %%
+# prompt = "The user is highly prepared for disasters, with emergency supplies and a clear plan."
+prompt = best_users.head(1)["user_text"].values[
+    0
+]  # Example: use the first user's text as the prompt
+prompt_embedding = get_ollama_embedding(prompt)
+
+# %% [markdown]
+# ## Compute similarity between each user and the prompt
+# %%
+user_embeddings = np.vstack(df["embedding"].values)
+prompt_vec = np.array(prompt_embedding).reshape(1, -1)
+similarities = cosine_similarity(user_embeddings, prompt_vec).flatten()
+df["similarity"] = similarities
+
+# %% [markdown]
+# ## Visualize the users by similarity
+# %%
+_ = plt.figure(figsize=(5, 7))
+_ = sns.violinplot(
+    x="similarity", y="country_code_iso_3166", hue="country_code_iso_3166", data=df
+)
+_ = sns.stripplot(
+    x="similarity",
+    y="country_code_iso_3166",
+    data=df,
+    hue="country_code_iso_3166",
+    alpha=0.8,
+    jitter=True,
+    linewidth=0.5,
+    edgecolor="white",
+)
+_ = plt.title("Distribution of User Similarities to Preparedness Prompt")
+_ = plt.xlabel("Cosine Similarity")
+
+# %%

preparedness/requirements.txt

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+pandas
+ipykernel
+requests
+scikit-learn
+matplotlib
+seaborn
+pyreadstat

preparedness/utils.py

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+import requests
+
+def get_ollama_summary(text, model="gpt-oss:20b"):
+    url = "http://localhost:11434/v1/chat/completions"
+    payload = {
+        "model": model,
+        "messages": [
+            {"role": "system", "content": "You are a helpful assistant that summarizes text."},
+            {"role": "user", "content": text}
+        ]
+    }
+    response = requests.post(url, json=payload)
+    response.raise_for_status()
+    return response.json()["choices"][0]["message"]["content"]
+
+def get_ollama_embedding(text, model="nomic-embed-text"):
+    url = "http://localhost:11434/api/embeddings"
+    payload = {
+        "model": model,
+        "prompt": text
+    }
+    response = requests.post(url, json=payload)
+    response.raise_for_status()
+    return response.json()["embedding"]