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| import os | |
| # stop tensorflow from printing novels to stdout | |
| os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' | |
| import pickle | |
| import numpy as np | |
| import pandas as pd | |
| import plotly.express as px | |
| import streamlit as st | |
| import tensorflow as tf | |
| import tensorflow_hub as hub | |
| from sklearn.cluster import DBSCAN | |
| def read_stops(p: str): | |
| """ | |
| DOCSTRING | |
| """ | |
| return pd.read_csv(p) | |
| def read_encodings(p: str) -> tf.Tensor: | |
| """ | |
| Unpickle the Universal Sentence Encoder v4 encodings | |
| and return them | |
| This function doesn't make any attempt to patch the security holes in `pickle`. | |
| :param p: Path to the encodings | |
| :returns: A Tensor of the encodings with shape (number of sentences, 512) | |
| """ | |
| with open(p, 'rb') as f: | |
| encodings = pickle.load(f) | |
| return encodings | |
| def cluster_encodings(encodings: tf.Tensor) -> np.ndarray: | |
| """ | |
| DOCSTRING | |
| """ | |
| # I know the hyperparams I want from the EDA I did in the notebook | |
| clusterer = DBSCAN(eps=0.7, min_samples=100).fit(encodings) | |
| return clusterer.labels_ | |
| def cluster_lat_lon(df: pd.DataFrame) -> np.ndarray: | |
| """ | |
| DOCSTRING | |
| """ | |
| # I know the hyperparams I want from the EDA I did in the notebook | |
| clusterer = DBSCAN(eps=0.025, min_samples=100).fit(df[['latitude', 'longitude']]) | |
| return clusterer.labels_ | |
| def plot_example(df: pd.DataFrame, labels: np.ndarray) -> px.Figure: | |
| """ | |
| DOCSTRING | |
| """ | |
| plot_size = 800 | |
| labels = labels.astype('str') | |
| fig = px.scatter(df, x='longitude', y='latitude', | |
| hover_name='display_name', | |
| color=labels, | |
| opacity=0.5, | |
| color_discrete_sequence=px.colors.qualitative.Safe, | |
| template='presentation', | |
| width=plot_size, | |
| height=plot_size) | |
| # fig.show() | |
| return fig | |
| def plot_venice_blvd(df: pd.DataFrame, labels: np.ndarray) -> px.Figure: | |
| """ | |
| DOCSTRING | |
| """ | |
| px.set_mapbox_access_token(st.secrets['mapbox_token']) | |
| venice_blvd = {'lat': 34.008350, | |
| 'lon': -118.425362} | |
| labels = labels.astype('str') | |
| fig = px.scatter_mapbox(df, lat='latitude', lon='longitude', | |
| color=labels, | |
| hover_name='display_name', | |
| center=venice_blvd, | |
| zoom=12, | |
| color_discrete_sequence=px.colors.qualitative.Dark24) | |
| # fig.show() | |
| return fig | |
| def main(data_path: str, enc_path: str): | |
| df = read_stops(data_path) | |
| # Cluster based on lat/lon | |
| example_labels = cluster_lat_lon(df) | |
| example_fig = plot_example(df, example_labels) | |
| # Cluster based on the name of the stop | |
| encodings = read_encodings(enc_path) | |
| encoding_labels = cluster_encodings(encodings) | |
| venice_fig = plot_venice_blvd(df, encoding_labels) | |
| # Display the plots with Streamlit | |
| st.write('# Example of what DBSCAN does') | |
| st.plotly_chart(example_fig, use_container_width=True) | |
| st.write('# Venice Blvd') | |
| st.plotly_chart(example_fig, use_container_width=True) | |
| if __name__ == '__main__': | |
| import argparse | |
| p = argparse.ArgumentParser() | |
| p.add_argument('--data_path', | |
| nargs='?', | |
| default='data/stops.csv', | |
| help="Path to the dataset of LA Metro stops. Defaults to 'data/stops.csv'") | |
| p.add_argument('--enc_path', | |
| nargs='?', | |
| default='data/encodings.pkl', | |
| help="Path to the pickled encodings. Defaults to 'data/encodings.pkl'") | |
| args = p.parse_args() | |
| main(**vars(args)) | |