getting a bit further with the frontend and demoing the analysis to matti
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itsamejms
+15
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@@ -1,236 +1,20 @@
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# %% [markdown]
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# # Visualize Most Prepared Users
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# This workflow loads user embeddings, generates a prompt embedding, computes similarity, and visualizes the most prepared users.
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import logging
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import uvicorn
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import os
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from api import app
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# %%
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import pandas as pd
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import numpy as np
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from sklearn.metrics.pairwise import cosine_similarity
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from sklearn.decomposition import PCA
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from sklearn.manifold import TSNE
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import plotly.express as px
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import matplotlib.pyplot as plt
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import seaborn as sns
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import umap
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import umap.plot
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from utils import get_ollama_embedding
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# %% [markdown]
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# ## Read in the file with user vectors
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# Update the path/format as needed.
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# %%
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df = (
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pd.read_csv("./data/eurobarometer_preparedness_model_data_v3.csv")
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.reset_index()
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.assign(
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**{
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"user_id": lambda x: x["index"].astype(str)
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+ "_"
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+ x["country_code_iso_3166"]
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+ "_"
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+ x["age_recoded_6_categories"].astype(str)
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+ "_"
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+ x["gender"].astype(str)
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}
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)
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# select only relevant countries for visualization
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.loc[
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lambda x: x["country_code_iso_3166"].isin(
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["FI", "DE-E", "DE-W", "FR", "ES", "PT"]
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)
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]
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)
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df.shape
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# relabel None of the above/ Non binary/ do not recognize yourself in above categories/Prefer not to say to other
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df["gender"] = df["gender"].replace({
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"None of the above/ Non binary/ do not recognize yourself in above categories/Prefer not to say": "Other",
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})
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# %% [markdown]
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# ## Convert string embeddings to lists if needed
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# %%
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def parse_embedding(x):
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if isinstance(x, str):
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return [float(i) for i in x.strip("[]").split(",")]
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return x
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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df["embedding"] = df["embedding"].apply(parse_embedding)
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def run_api() -> None:
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"""Run the FastAPI application."""
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if "james" in os.environ.get("USER", ""):
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logger.info("Running in James's environment")
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uvicorn.run("api:app", host="0.0.0.0", port=8080, reload=True)
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else:
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uvicorn.run(app, host="0.0.0.0", port=8080)
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# %%
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# Select best user:
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# disaster_measures_in_hh_* == 1, and how_many_days_meet_* == (4 | 5)
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# Following relabeling in data_preparation_raw.py:
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# disaster_measures_in_hh_* != "Not mentioned"
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# how_many_days_meet_* == "More than 7 days"
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disaster_measures_in_hh_columns = [
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col for col in df.columns if col.startswith("disaster_measures_in_hh_")
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]
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how_many_days_meet_columns = [
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col for col in df.columns if col.startswith("how_many_days_meet_")
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]
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best_users = df[
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(df[disaster_measures_in_hh_columns] != "Not mentioned").all(axis=1)
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& (df[how_many_days_meet_columns] == "More than 7 days").all(axis=1)
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]
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print(f"Number of best users: {len(best_users)}")
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best_users.head(5)[["user_id", "user_text"]].to_dict(orient="records")
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# %% [markdown]
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# ## Write your prompt and generate its embedding
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# %%
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# prompt = "The user is highly prepared for disasters, with emergency supplies and a clear plan."
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prompt = best_users.head(1)["user_text"].values[
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0
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] # Example: use the first user's text as the prompt
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prompt_embedding = get_ollama_embedding(prompt)
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# %% [markdown]
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# ## Compute similarity between each user and the prompt
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# %%
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user_embeddings = np.vstack(df["embedding"].values)
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prompt_vec = np.array(prompt_embedding).reshape(1, -1)
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similarities = cosine_similarity(user_embeddings, prompt_vec).flatten()
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df["similarity"] = similarities
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# %% [markdown]
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# ## Visualize the users by similarity
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# %%
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_ = plt.figure(figsize=(5, 7))
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_ = sns.violinplot(
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x="similarity", y="country_code_iso_3166", hue="country_code_iso_3166", data=df
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)
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_ = sns.stripplot(
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x="similarity",
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y="country_code_iso_3166",
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data=df,
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hue="country_code_iso_3166",
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alpha=0.8,
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jitter=True,
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linewidth=0.5,
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edgecolor="white",
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)
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_ = plt.title("Distribution of User Similarities to Preparedness Prompt")
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_ = plt.xlabel("Cosine Similarity")
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# %%
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# np.savetxt("./data/embeddings.tsv", np.vstack(df["embedding"].values), delimiter="\t")
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# # %%
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# df[["user_id"]].to_csv(
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# "./data/metadata.tsv", sep="\t", index=False
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# )
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# %%
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# Reduce embeddings to 3D with PCA
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pca = PCA(n_components=3)
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embeddings_3d = pca.fit_transform(np.vstack(df["embedding"].values))
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# Add PCA components to dataframe
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df["pca1"] = embeddings_3d[:, 0]
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df["pca2"] = embeddings_3d[:, 1]
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df["pca3"] = embeddings_3d[:, 2]
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# %%
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# Interactive 3D scatter plot
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fig = px.scatter_3d(
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df,
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x="pca1",
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y="pca2",
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z="pca3",
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color="age_recoded_6_categories",
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hover_data=["user_id", "gender", "age_recoded_6_categories"],
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title="User Embeddings (PCA 3D)"
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)
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fig.write_html("./output/user_embeddings_pca_3d.html")
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# %%
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# Reduce embeddings to 3D with t-SNE
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tsne = TSNE(n_components=3, random_state=42, perplexity=30)
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embeddings_3d = tsne.fit_transform(np.vstack(df["embedding"].values))
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# Add t-SNE components to dataframe
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df["tsne1"] = embeddings_3d[:, 0]
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df["tsne2"] = embeddings_3d[:, 1]
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df["tsne3"] = embeddings_3d[:, 2]
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# Interactive 3D scatter plot
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fig = px.scatter_3d(
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df,
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x="tsne1",
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y="tsne2",
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z="tsne3",
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color="age_recoded_6_categories",
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hover_data=["user_id", "gender", "country_code_iso_3166"],
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title="User Embeddings (t-SNE 3D)"
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)
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fig.write_html("./output/user_embeddings_tsne_3d.html")
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# %%
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# example comparison based on t-sne projection
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index_1 = 9561
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index_2 = 5096
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df.loc[[index_1, index_2]][["user_id", "user_text", "similarity"]].to_dict(orient="records")
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# %%
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row1 = df.loc[index_1].drop(labels=["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3"])
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row2 = df.loc[index_2].drop(labels=["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3"])
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# column_list = df.columns.difference(["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3"])
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column_list = disaster_measures_in_hh_columns + how_many_days_meet_columns
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diffs = {}
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for col in column_list:
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val1 = row1[col]
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val2 = row2[col]
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if pd.isnull(val1) and pd.isnull(val2):
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continue
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if val1 != val2:
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diffs[col] = (val1, val2)
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# Print or display the differing columns and their values
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for col, (v1, v2) in diffs.items():
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print(f"{col}: {v1} | {v2}")
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# %%
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# Reduce embeddings to 3D with UMAP
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umap_3d = umap.UMAP(n_components=3, random_state=42, metric="cosine").fit_transform(np.vstack(df["embedding"].values))
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# Add UMAP components to dataframe
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df["umap1"] = umap_3d[:, 0]
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df["umap2"] = umap_3d[:, 1]
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df["umap3"] = umap_3d[:, 2]
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# Interactive 3D scatter plot
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fig = px.scatter_3d(
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df,
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x="umap1",
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y="umap2",
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z="umap3",
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color="country_code_iso_3166",
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hover_data=["user_id", "gender", "country_code_iso_3166"],
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title="User Embeddings (UMAP 3D)"
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)
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fig.write_html("./output/user_embeddings_umap_3d.html")
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# %%
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# example comparison based on UMAP projection
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index_1 = 21396
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index_2 = 10444
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df.loc[[index_1, index_2]][["user_id", "user_text", "similarity"]].to_dict(orient="records")
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# %%
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row1 = df.loc[index_1].drop(labels=["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3", "umap1", "umap2", "umap3"])
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row2 = df.loc[index_2].drop(labels=["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3", "umap1", "umap2", "umap3"])
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# column_list = df.columns.difference(["user_id", "embedding", "user_text", "similarity", "pca1", "pca2", "pca3", "tsne1", "tsne2", "tsne3", "umap1", "umap2", "umap3"])
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column_list = disaster_measures_in_hh_columns + how_many_days_meet_columns
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diffs = {}
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for col in column_list:
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val1 = row1[col]
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val2 = row2[col]
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if pd.isnull(val1) and pd.isnull(val2):
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continue
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if val1 != val2:
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diffs[col] = (val1, val2)
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# Print or display the differing columns and their values
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for col, (v1, v2) in diffs.items():
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print(f"{col}: {v1} | {v2}")
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# %%
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if __name__ == "__main__":
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run_api()
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