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support isolates in visualization

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Nate E TeBlunthuis 2021-05-13 22:26:58 -07:00
parent 582cf263ea
commit 0b95bea30e
4 changed files with 32 additions and 27 deletions
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16
ngrams/tf_comments.py
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@ -13,10 +13,7 @@ from nltk.corpus import stopwords
from nltk.util import ngrams from nltk.util import ngrams
import string import string
from random import random from random import random
from redditcleaner import clean
# remove urls
# taken from https://stackoverflow.com/questions/3809401/what-is-a-good-regular-expression-to-match-a-url
urlregex = re.compile(r"[-a-zA-Z0-9@:%._\+~#=]{1,256}\.[a-zA-Z0-9()]{1,6}\b([-a-zA-Z0-9()@:%_\+.~#?&//=]*)")
# compute term frequencies for comments in each subreddit by week # compute term frequencies for comments in each subreddit by week
def weekly_tf(partition, mwe_pass = 'first'): def weekly_tf(partition, mwe_pass = 'first'):
@ -95,8 +92,8 @@ def weekly_tf(partition, mwe_pass = 'first'):
# lowercase # lowercase
text = text.lower() text = text.lower()
# remove urls # redditcleaner removes reddit markdown(newlines, quotes, bullet points, links, strikethrough, spoiler, code, superscript, table, headings)
text = urlregex.sub("", text) text = clean(text)
# sentence tokenize # sentence tokenize
sentences = sent_tokenize(text) sentences = sent_tokenize(text)
@ -107,14 +104,13 @@ def weekly_tf(partition, mwe_pass = 'first'):
# remove punctuation # remove punctuation
sentences = map(remove_punct, sentences) sentences = map(remove_punct, sentences)
# remove sentences with less than 2 words
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
# datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase. # datta et al. select relatively common phrases from the reddit corpus, but they don't really explain how. We'll try that in a second phase.
# they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms # they say that the extract 1-4 grams from 10% of the sentences and then find phrases that appear often relative to the original terms
# here we take a 10 percent sample of sentences # here we take a 10 percent sample of sentences
if mwe_pass == 'first': if mwe_pass == 'first':
# remove sentences with less than 2 words
sentences = filter(lambda sentence: len(sentence) > 2, sentences)
sentences = list(sentences) sentences = list(sentences)
for sentence in sentences: for sentence in sentences:
if random() <= 0.1: if random() <= 0.1:

4
similarities/Makefile
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@ -1,7 +1,7 @@
#all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet #all: /gscratch/comdata/output/reddit_similarity/tfidf/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf/comment_authors_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms_130k.parquet /gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_authors_130k.parquet
srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh srun_singularity=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity.sh
srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh srun_singularity_huge=source /gscratch/comdata/users/nathante/cdsc_reddit/bin/activate && srun_singularity_huge.sh
base_data=/gscratch/comdata/output/ base_data=/gscratch/comdata/output
similarity_data=${base_data}/reddit_similarity similarity_data=${base_data}/reddit_similarity
tfidf_data=${similarity_data}/tfidf tfidf_data=${similarity_data}/tfidf
tfidf_weekly_data=${similarity_data}/tfidf_weekly tfidf_weekly_data=${similarity_data}/tfidf_weekly
@ -97,7 +97,7 @@ ${tfidf_data}/tfidf_weekly/comment_authors_100k.parquet: /gscratch/comdata/outpu
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=100000 --outpath=${tfidf_weekly_data}/comment_authors_100k.parquet
${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_terms_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
start_spark_and_run.sh 4 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet start_spark_and_run.sh 2 tfidf.py terms_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet
${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv ${tfidf_weekly_data}/comment_authors_30k.parquet: /gscratch/comdata/output/reddit_ngrams/comment_terms.parquet ${similarity_data}/subreddits_by_num_comments.csv
start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet start_spark_and_run.sh 4 tfidf.py authors_weekly --topN=30000 --outpath=${tfidf_weekly_data}/comment_authors_30k.parquet

7
similarities/similarities_helper.py
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@ -23,9 +23,6 @@ class tf_weight(Enum):
infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet" infile = "/gscratch/comdata/output/reddit_similarity/tfidf_weekly/comment_terms.parquet"
cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet" cache_file = "/gscratch/comdata/users/nathante/cdsc_reddit/similarities/term_tfidf_entries_bak.parquet"
def termauthor_tfidf(term_tfidf_callable, author_tfidf_callable):
# subreddits missing after this step don't have any terms that have a high enough idf # subreddits missing after this step don't have any terms that have a high enough idf
# try rewriting without merges # try rewriting without merges
def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF): def reindex_tfidf(infile, term_colname, min_df=None, max_df=None, included_subreddits=None, topN=500, week=None, from_date=None, to_date=None, rescale_idf=True, tf_family=tf_weight.MaxTF):
@ -283,7 +280,7 @@ def build_weekly_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weig
df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf) df = df.withColumn("tf_idf", (0.5 + 0.5 * df.relative_tf) * df.idf)
df = df.repartition(400,'subreddit','week') df = df.repartition(400,'subreddit','week')
dfwriter = df.write.partitionBy("week").sortBy("subreddit") dfwriter = df.write.partitionBy("week")
return dfwriter return dfwriter
def _calc_tfidf(df, term_colname, tf_family): def _calc_tfidf(df, term_colname, tf_family):
@ -339,7 +336,7 @@ def build_tfidf_dataset(df, include_subs, term_colname, tf_family=tf_weight.Norm
df = _calc_tfidf(df, term_colname, tf_family) df = _calc_tfidf(df, term_colname, tf_family)
df = df.repartition('subreddit') df = df.repartition('subreddit')
dfwriter = df.write.sortBy("subreddit","tf") dfwriter = df.write
return dfwriter return dfwriter
def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"): def select_topN_subreddits(topN, path="/gscratch/comdata/output/reddit_similarity/subreddits_by_num_comments_nonsfw.csv"):

32
visualization/tsne_vis.py
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@ -22,8 +22,12 @@ def base_plot(plot_data):
# #
# subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click') # subreddit_select = alt.selection_single(on='click',fields=['subreddit'],bind=subreddit_dropdown,name='subreddit_click')
base_scale = alt.Scale(scheme={"name":'category10',
"extent":[0,100],
"count":10})
color = alt.condition(cluster_click_select , color = alt.condition(cluster_click_select ,
alt.Color(field='color',type='nominal',scale=alt.Scale(scheme='category10')), alt.Color(field='color',type='nominal',scale=base_scale),
alt.value("lightgray")) alt.value("lightgray"))
@ -84,6 +88,11 @@ def viewport_plot(plot_data):
return chart return chart
def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4): def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
isolate_color = 101
cluster_sizes = clusters.groupby('cluster').count()
singletons = set(cluster_sizes.loc[cluster_sizes.subreddit == 1].reset_index().cluster)
tsne_data = tsne_data.merge(clusters,on='subreddit') tsne_data = tsne_data.merge(clusters,on='subreddit')
centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean}) centroids = tsne_data.groupby('cluster').agg({'x':np.mean,'y':np.mean})
@ -120,15 +129,17 @@ def assign_cluster_colors(tsne_data, clusters, n_colors, n_neighbors = 4):
color_assignments = np.repeat(-1,len(centroids)) color_assignments = np.repeat(-1,len(centroids))
for i in range(len(centroids)): for i in range(len(centroids)):
knn = indices[i] if (centroids.iloc[i].name == -1) or (i in singletons):
knn_colors = color_assignments[knn] color_assignments[i] = isolate_color
available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
if(len(available_colors) > 0):
color_assignments[i] = available_colors[0]
else: else:
raise Exception("Can't color this many neighbors with this many colors") knn = indices[i]
knn_colors = color_assignments[knn]
available_colors = color_ids[list(set(color_ids) - set(knn_colors))]
if(len(available_colors) > 0):
color_assignments[i] = available_colors[0]
else:
raise Exception("Can't color this many neighbors with this many colors")
centroids = centroids.reset_index() centroids = centroids.reset_index()
colors = centroids.loc[:,['cluster']] colors = centroids.loc[:,['cluster']]
@ -143,12 +154,13 @@ def build_visualization(tsne_data, clusters, output):
# clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather" # clusters = "/gscratch/comdata/output/reddit_clustering/subreddit_author_tf_similarities_10000.feather"
tsne_data = pd.read_feather(tsne_data) tsne_data = pd.read_feather(tsne_data)
tsne_data = tsne_data.rename(columns={'_subreddit':'subreddit'})
clusters = pd.read_feather(clusters) clusters = pd.read_feather(clusters)
tsne_data = assign_cluster_colors(tsne_data,clusters,10,8) tsne_data = assign_cluster_colors(tsne_data,clusters,10,8)
# sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index() sr_per_cluster = tsne_data.groupby('cluster').subreddit.count().reset_index()
# sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'}) sr_per_cluster = sr_per_cluster.rename(columns={'subreddit':'cluster_size'})
tsne_data = tsne_data.merge(sr_per_cluster,on='cluster') tsne_data = tsne_data.merge(sr_per_cluster,on='cluster')