Spaces:
Sleeping
Sleeping
File size: 3,228 Bytes
9a3e23b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.manifold import TSNE
from sklearn.preprocessing import MinMaxScaler
import os
INPUT_FILE = "wallet_dataset_labeled.csv"
OUTPUT_DIR = "docs"
RADAR_CHART_FILE = os.path.join(OUTPUT_DIR, "persona_radar_chart.png")
TSNE_PLOT_FILE = os.path.join(OUTPUT_DIR, "clusters_tsne.png")
os.makedirs(OUTPUT_DIR, exist_ok=True)
def load_data():
if not os.path.exists(INPUT_FILE):
print(f"Error: {INPUT_FILE} not found.")
return None
return pd.read_csv(INPUT_FILE)
def plot_radar_chart(df):
print("Generating Radar Chart")
features = [
'tx_count', 'active_days', 'total_gas_spent',
'total_nft_volume_usd', 'dex_trades', 'total_traded_usd'
]
scaler = MinMaxScaler()
df_scaled = df.copy()
df_scaled[features] = scaler.fit_transform(df[features])
persona_means = df_scaled.groupby('Persona')[features].mean()
labels=np.array(features)
num_vars = len(labels)
angles = np.linspace(0, 2 * np.pi, num_vars, endpoint=False).tolist()
angles += angles[:1]
fig, ax = plt.subplots(figsize=(10, 10), subplot_kw=dict(polar=True))
colors = sns.color_palette("husl", len(persona_means))
for idx, (persona, row) in enumerate(persona_means.iterrows()):
values = row.tolist()
values += values[:1]
ax.plot(angles, values, color=colors[idx], linewidth=2, label=persona)
ax.fill(angles, values, color=colors[idx], alpha=0.1)
ax.set_theta_offset(np.pi / 2)
ax.set_theta_direction(-1)
ax.set_xticks(angles[:-1])
ax.set_xticklabels(labels)
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
plt.title("Persona Behavioral Fingerprints (Normalized)", y=1.08)
plt.savefig(RADAR_CHART_FILE, bbox_inches='tight', dpi=300)
print(f"Saved radar chart to {RADAR_CHART_FILE}")
plt.close()
def plot_tsne(df):
print("Generating t-SNE Plot (this may take a moment)...")
feature_cols = [
'tx_count', 'active_days', 'avg_tx_per_day', 'total_gas_spent',
'total_nft_buys', 'total_nft_sells', 'total_nft_volume_usd',
'unique_nfts_owned', 'dex_trades', 'avg_trade_size_usd',
'total_traded_usd', 'erc20_receive_usd', 'erc20_send_usd',
'native_balance_delta'
]
X = df[feature_cols].fillna(0)
tsne = TSNE(n_components=2, random_state=42, init='random', learning_rate='auto')
X_embedded = tsne.fit_transform(X)
plt.figure(figsize=(12, 8))
sns.scatterplot(
x=X_embedded[:, 0],
y=X_embedded[:, 1],
hue=df['Persona'],
palette='husl',
alpha=0.7
)
plt.title("t-SNE Projection of Wallet Clusters")
plt.xlabel("Dimension 1")
plt.ylabel("Dimension 2")
plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.savefig(TSNE_PLOT_FILE, dpi=300)
print(f"Saved t-SNE plot to {TSNE_PLOT_FILE}")
plt.close()
if __name__ == "__main__":
df = load_data()
if df is not None:
plot_radar_chart(df)
plot_tsne(df)
print("Visualization complete.")
|