first draft

app.py

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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))
+