Hamlet Batista hamletbatista

## uncompress_and_move_models.sh
!unzip /content/PreSumm/models/bertext_cnndm_transformer.zip
!unzip /content/PreSumm/models/bertsumextabs_cnndm_final_model.zip
!unzip /content/PreSumm/models/bertsumextabs_xsum_final_model.zip

!mkdir /content/PreSumm/models/CNN_DailyMail_Extractive
!mkdir /content/PreSumm/models/CNN_DailyMail_Abstractive
!mkdir /content/PreSumm/models/XSUM_OneSentence

!mv /content/PreSumm/models/bertext_cnndm_transformer.pt /content/PreSumm/models/CNN_DailyMail_Extractive
!mv /content/PreSumm/models/model_step_148000.pt /content/PreSumm/models/CNN_DailyMail_Abstractive

## download_presum_dependencies.sh
%cd /content/PreSumm/models

#CNN/DM Extractive bertext_cnndm_transformer.pt
!gdown https://drive.google.com/uc?id=1kKWoV0QCbeIuFt85beQgJ4v0lujaXobJ&export=download

#CNN/DM Abstractive model_step_148000.pt
!gdown https://drive.google.com/uc?id=1-IKVCtc4Q-BdZpjXc4s70_fRsWnjtYLr&export=download

#XSUM (One Sentence Summary) model_step_30000.pt
!gdown https://drive.google.com/uc?id=1H50fClyTkNprWJNh10HWdGEdDdQIkzsI&export=download

## summarizer.patch
--- /content/PreSumm/src/summarizer.py	2019-10-29 02:12:01.951535276 +0000
+++ /content/PreSumm/src/summarizer2.py	2019-10-29 03:47:19.168619951 +0000
@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 """
-    Inference entrance
+    Main training workflow
 """
 from __future__ import division


## create_file_colab.py
#For example, let's create a YAML file to feed Ludwig
contents="""
input_features:
    -
        name: text
        type: text
        level: word
        encoder: parallel_cnn

output_features:

## ranksense-tags.json
[
  {
    "id": 26,
    "name": "Account Pages",
    "type": "AFFECTED"
  },
  {
    "id": 55,
    "name": "Add Correct Canonicals",
    "type": "SOLUTION"

## visualize_final_reframe.py
data = []
ages = ["Pre-K", "Grade School", "Teens", "Young Adults", "Over 30", "Seniors"]

top_5 = []
for i in range(5):
    scores = []
    for age in ages:
        scores.append(tuple(rides.sort_values(age, ascending=False)[[age, "Ride"]].iloc[i].values))
    top_5.append(scores)

## get_disney_ride_ratings.py
# Get Disney Attractiveness Ratings
import requests
from bs4 import BeautifulSoup

r = requests.get("https://touringplans.com/disneyland/attractions")
soup = BeautifulSoup(r.text)

rows = []
table = soup.find("table")
for idx, tr in enumerate(table.findAll("tr")):

## enjoyment_score.py
# Data taken from here: https://touringplans.com/disneyland/attractions
# Ties in attraction ratings were resolved by selecting the age group closest to the next-highest score

df['Age Group'] = ["Pre-K", "Grade School", "Seniors",
                   "Pre-K", "Seniors", "Seniors",
                   "Pre-K", "Grade School", "Over 30",
                   "Seniors", "Pre-K", "Pre-K",
                   "Teens", "Teens", "Seniors",
                   "Young Adults", "Young Adults", "Teens",
                   "Pre-K", "Pre-K"]

## visualize_ridetimes.py
#!pip install plotly-express

import pandas as pd
from urllib.parse import urlparse
from collections import Counter
import plotly.express as px
import plotly
import plotly.graph_objects as go

fig = px.scatter(df, x="Average Wait Time", y="Ride Duration", color="Ride", symbol="Ride", height=900, width=1200)

## avgwaittimeandduration.py
average_wait=pd.Series(df.mean(), name="Average Wait Time")
print(average_wait)

#example output
#Astro Orbitor                                     12.453125
#Buzz Lightyear Astro Blasters                     17.953125

duration = pd.Series(ride_durations, name="Ride Duration")
print(duration)