126 twitter data (#620)

* Added a script file to process json archive files into more unified parquet files focused on tweet reply rows for further processing.

* Added README file for Twitter data collection.

* Re did code for processing json into standardized parquet files.

* Added file to process parquet files into a conversation tree jsonl file.

* Added requirements and ran pre-commit.

scripts/data-collection/twitter/README.md

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+# Twitter data collection for Open Assistant
+
+Conversations on Twitter can be an interesting and useful source of data for our
+model to learn from. Certain twitter threads may contain helpful prompts and
+replies, in a similar fashion to how we want our model to be able to respond to
+prompts in a useful way.
+
+Thus, these scripts are intended to process twitter data from a variety of
+sources, process them into cleaner and more useful formats, and then combine the
+various outputs into a unified training set that can be fed to our model as a
+conversation, or at least as a prompt with replies.
+
+**Note: Based on issue #126**
+
+## Possible Data Paths
+
+- Twitterstream archive: https://archive.org/details/twitterstream These are
+  large .tar files with compressed json files inside. However, some data points
+  such as reply counts seem to always be 0 due to limitations when scraping the
+  Twitter API.
+- Alternative APIs such as snscrape, twint, etc. These alternative APIs often
+  are harder to use than the official Twitter API but can often bypass API
+  limits which can make it useful for larger scale data collection. The downside
+  is potentially slower speed, and less features.
+- The official Twitter API
+
+## Currently Completed Items
+
+- Downloaded various archive files (both are .tar, but each have a different
+  format of json compression. One used .gz, and the other.bz2). Each json file
+  is roughly 2000 rows of tweets. There are thousands of these compressed json
+  files. Managing the IO of opening lots of small files is one of the
+  challenges, which is why future steps will consolidate data into larger easier
+  to process files.
+- Wrote script that can loop through the compressed json files, cleans them up a
+  bit by removing truncated tweets or tweets that aren't replies. The script
+  then standardizes the columns, and exports the polars dataframes into parquet
+  files for future processing. Note: Using polars instead of pandas due to
+  performance reasons.
+- Wrote scripts that process the large dump of tweets into conversation threads
+  using the tree and node architecture. This results in aroun 17K conversation
+  threads bassed on a dump of 90M tweets.
+- Script can output the conversation threads into a jsonl file for further
+  filtering or use in models.
+
+## Main Issue
+
+- The issue is that we can easily scrape replies, but there is no guarantee the
+  original tweet is in the archive file. Furthermore, the archives are large so
+  they would need to be kept completely in-memory or in a db to reference. We
+  still need to decide if we want to try to mine the archive to piece together
+  the conversations, or we can take the list of replied tweets and loop through
+  those and use alternative apis to fetch the original tweet text, and then
+  match it with the confirmed replies already in our archive to generate the
+  prompt/replies data. Currently, my script can extract conversations based on
+  the dump, but it is a small percentage of the overall dump, and there is no
+  guarantee of the quality of the tweets.
+- The tweet quality is the other major issue. We can get conversations through
+  the currently made scripts, but they most likely don't match a useful
+  instruction -> fulfilment. We are trying to filter the tweets through various
+  means such as matching useful hashtags, or by using cosine similarity against
+  known instructions.
+- The modern Twitter API has conversation_id as a field which can be a way to
+  gather all tweets in a thread sort of automatically although there is
+  pagination limits. The main issue with this is it seems hard to search for it
+  using alternative APIs.
+
+## TODO
+
+- Write scripts to filter existing conversations into useful instructions ->
+  fulfilment with hashtags or cosine similarity.
+- Train model to detect if text is a suitable instruction. This could then be
+  run through the conversations (or full tweet dump) to simplify the process.
+  Related to issue #143.
+- Write script that matches the original tweets and their text with the archive
+  data to create the prompt/reply dataset. (Optional)
+- Decide on final output format and storage options for the dataset. Currently
+  in JSONL with tree / node architecture as python dicts which is acceptable I
+  believe.
+- Alternatively: Store processed tweets into DB or alternative option.(Optional)

scripts/data-collection/twitter/requirements.txt

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+numpy==1.21.5
+polars==0.15.14
+tqdm==4.64.0

scripts/data-collection/twitter/twitter_create_convs.py

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+import json
+from pathlib import Path
+
+import polars as pl
+from tqdm import tqdm
+
+# Sets up paths
+# TODO: Source paths from env file
+path_string = "PUT THE PATH HERE TO WHERE YOU STORED THE PARQUET FILES"
+folder_path = Path(path_string)
+processed_folder_path = folder_path / "processed"
+output_path = folder_path / "twitter-conv-trees.jsonl"
+
+# Get parq files
+parq_files = sorted(processed_folder_path.rglob("*.parquet"))
+
+wanted_cols = [
+    "timestamp_ms",
+    "id",
+    "text",
+    "truncated",
+    "in_reply_to_status_id",
+    "in_reply_to_user_id",
+    "is_quote_status",
+    "quote_count",
+    "reply_count",
+    "retweet_count",
+    "favorite_count",
+    "filter_level",
+    "lang",
+    "possibly_sensitive",
+    "hashtags",
+    "user_id",
+    "user_verified",
+    "user_followers_count",
+    "user_statuses_count",
+]
+
+# Load parqs into list. Using Polars for performance reasons.
+df_list = []
+for p in parq_files:
+    df_list.append(pl.read_parquet(p, columns=wanted_cols))
+
+# Create major dataframe.
+# This can be done incrementally if RAM is constrained by modifying the above code.
+p_df = pl.concat(df_list)
+
+# Clean up the reference just in case to help with memory if needed.
+del df_list
+
+# Get tweets that are replies to other tweets
+p_df_replies_only = p_df.filter(pl.col("in_reply_to_status_id").is_null().is_not())
+
+# Group by replied to status id to see the most replied to statuses. This can take some time.
+p_df_group_reply_to_status = p_df_replies_only.groupby("in_reply_to_status_id").count().sort("count", reverse=True)
+
+# Save output of grouping the top replied to statuses
+group_reply_parq = folder_path / "group_reply_parq.parquet"
+p_df_group_reply_to_status.write_parquet(group_reply_parq)
+
+# Join the main dataframe with the top replies to find tweets that have replies.
+p_join = p_df.join(p_df_group_reply_to_status, left_on="id", right_on="in_reply_to_status_id", how="inner")
+
+# Save output of tweets that have replies
+tweets_that_have_replies_path = folder_path / "tweets_that_have_replies.parquet"
+p_join.write_parquet(tweets_that_have_replies_path)
+
+# Save output of tweets that are replies to other tweets
+tweets_that_are_replies_path = folder_path / "tweets_that_are_replies.parquet"
+p_df_replies_only.write_parquet(tweets_that_are_replies_path)
+
+# Filter the tweets that have replies to ones that aren't replies to others.
+# Also filter for only english for now.
+# This gives the root tweets that have replies but are the start of a conversation.
+origin_tweets = p_join.filter((pl.col("in_reply_to_status_id").is_null()) & (pl.col("lang") == "en"))
+
+
+# Helper functions and classes below for the next steps
+
+
+def role_decide(user_id, prompt_user):
+    if user_id == prompt_user:
+        return "prompter"
+    else:
+        return "assistant"
+
+
+class ConversationTreeNode:
+    def __init__(self, tweet_id, prompt_user, from_df, children_df, metadata=None):
+
+        if metadata:
+            self.metadata = metadata
+        else:
+            self.metadata = from_df.filter(pl.col("id") == tweet_id).to_dicts()[0]
+
+        self.metadata["prompt_user"] = prompt_user
+        self.role = role_decide(self.metadata["user_id"], prompt_user)
+        self.children = None
+        self.text = self.metadata["text"]
+        del self.metadata["text"]
+        self.get_children(tweet_id=tweet_id, children_df=children_df)
+
+    def get_children(self, tweet_id, children_df):
+        children_dicts = children_df.filter(pl.col("in_reply_to_status_id") == tweet_id).to_dicts()
+        if len(children_dicts) > 0:
+            children = [
+                ConversationTreeNode(
+                    tweet_id=c["id"],
+                    prompt_user=self.metadata["prompt_user"],
+                    from_df=children_df,
+                    children_df=children_df,
+                    metadata=c,
+                )
+                for c in children_dicts
+            ]
+            self.children = children
+
+
+class ConversationTree:
+    def __init__(self, tweet_id, prompt_user, from_df, children_df, r_metadata=None):
+
+        self.root = ConversationTreeNode(
+            tweet_id=tweet_id, prompt_user=prompt_user, from_df=from_df, children_df=children_df, metadata=r_metadata
+        )
+        self.metadata = None
+
+
+# Create conversation trees
+conv_tree_list = [
+    ConversationTree(
+        tweet_id=r["id"], prompt_user=r["user_id"], from_df=origin_tweets, children_df=p_df_replies_only, r_metadata=r
+    )
+    for r in tqdm(origin_tweets.to_dicts())
+]
+
+# Write conversation trees to jsonl file.
+# Might need to clean up the last newline.
+with open(output_path, "w") as output:
+    for t in tqdm(conv_tree_list):
+        json.dump(obj=t, fp=output, default=lambda x: x.__dict__)
+        output.write("\n")