getting started on the preparedness data

2025-09-13 17:27:46 +02:00
parent a86232a039
commit 8a2e7fdb6e
6 changed files with 341 additions and 1 deletions
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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)